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Energy Efficiency and Quality of Data Reconstruction Through Data-Coupled Clustering for Self-Organized Large-Scale WSNs
Abstract
Energy efficiency has been a leading issue inWireless Sensor Networks (WSNs) and has produced a vast amount of research. Although the classic tradeoff has been between quality of gathered data versus lifetime of the network, most works gave preference to an increased network lifetime at the expense of the data quality. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where representative nodes simply transmit or average measurements inside the cluster. In this work, we explore the joint use of innetwork processing techniques and clustering algorithms. This approach seeks both high data quality with a controlled number of transmissions using an aggregation function and an energy efficient network partition, respectively. The aim of this combination is to increase energy efficiency without sacrificing the data quality. We compare the performance of the Second-Order Data-Coupled Clustering (SODCC) and Compressive-Projections Principal Component Analysis (CPPCA) algorithm combination, in terms of both energy consumption and quality of the data reconstruction, to other combinations of state of the art clustering algorithms and in-network processing techniques. Among all the considered cases, the SODCC+CPPCA combination revealed a perfect balance between data quality, energy expenditure and ease of network management. The main conclusion of this paper is that the design of WSN algorithms must be processing-oriented rather than transmission-oriented, i.e., investing energy on both clustering and in-network processing algorithms ensures both energy efficiency and data quality.
... Unsupervised learning approach used as classifying the set of similar patterns into clusters, dimensionality reduction, and anomaly detection from the data. The major contributions of unsupervised learning in WSNs are to tackle various issues such as connectivity problem [110] , anomaly detection [111] , routing [112][113][114][115] , and data aggregation [116][117][118][119][120][121][122][123][124][125] . Unsupervised learning further categorized into clustering ( k -means, hierarchical and fuzzy-c -means) and dimensionality reduction (PCA, ICA and SVD). ...
... It reduce the buffer overflows at the sensor nodes or cluster heads in event-driven applications, which avoids the congestion problem. Several algorithms of WSNs such as localization [141] , fault detection [100,101] , data aggregation [117][118][119][120][121][122][123][124] , and target tracking [142] have adopted PCA. ...
... This approach dynamically determines the eigenvectors without any explicit constructions. A controlled number of transmissions for producing quality data using Compressive-Projections PCA has been developed in [122] to reconstruct data. This approach provides an energy efficient quality data reconstruction. ...
Wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effective and easily deployable nature. Due to some external or internal factors, WSN may change dynamically and therefore it requires depreciating dispensable redesign of the network. The traditional WSN approaches have been explicitly programmed which make the networks hard to respond dynamically. To overcome such scenarios, machine learning (ML) techniques can be applied to react accordingly. ML is the process of self-learning from the experiences and acts without human intervention or re-program. The survey of the ML techniques for WSNs is presented in [1], covering period of 2002 – 2013. In this survey, we present various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018. In addition, we also discuss ML algorithms for synchronization, congestion control, mobile sink scheduling and energy harvesting. Finally, we present a statistical analysis of the survey, the reasons for selection of a particular ML techniques to address a issue in WSNs followed by some discussion on the open issues.
... Although LPWA aims to achieve 10 years battery life, energy efficient mechanism are still needed for IoT devices as the efficiency is highly dependent on the node usage [122]. There exist several algorithms that have been developed for energy efficiency (EE) in WSN such as clustering and in-network processing algorithms [122]- [124]. ...
... Although LPWA aims to achieve 10 years battery life, energy efficient mechanism are still needed for IoT devices as the efficiency is highly dependent on the node usage [122]. There exist several algorithms that have been developed for energy efficiency (EE) in WSN such as clustering and in-network processing algorithms [122]- [124]. Other energy efficient schemes for IoT such as ability to predict the sleep interval of IoT devices based upon their remaining battery level, their previous usage history, and quality of information required for a particular application as proposed in [125] can be further studied. ...
The surge in global population is compelling a shift towards smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of internet of things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensors network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies such as WSN, radio frequency identification, cloud computing, middleware systems and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business and marketability.
... In this case, the energy of the CH may become depleted very sharply, which may limit the lifetime of the IoTs network. Therefore, a dynamic CH selection method for energy balancing is required to balance the energy among IoT devices in the IoTs network [26][27][28][29][30][31]. The authors in [27][28][29] presents a clustering mechanism for preserving the energy of WSNs, but they assume a fixed CH. ...
... The authors in [27][28][29] presents a clustering mechanism for preserving the energy of WSNs, but they assume a fixed CH. Although [30,31] present a CH selection strategy for balancing the energy among nodes in WSNs, they do not consider that the sensors are harvesting energy from RF sources. Therefore, their work is not suitable for the energy-harvested IoTs network due to the dynamic energy variations in the residual energy of the IoT devices. ...
This paper highlights three critical aspects of the internet of things (IoTs), namely (1) energy efficiency, (2) energy balancing and (3) quality of service (QoS) and presents three novel schemes for addressing these aspects. For energy efficiency, a novel radio frequency (RF) energy-harvesting scheme is presented in which each IoT device is associated with the best possible RF source in order to maximize the overall energy that the IoT devices harvest. For energy balancing, the IoT devices in close proximity are clustered together and then an IoT device with the highest residual energy is selected as a cluster head (CH) on a rotational basis. Once the CH is selected, it assigns channels to the IoT devices to report their data using a novel integer linear program (ILP)-based channel allocation scheme by satisfying their desired QoS. To evaluate the presented schemes, exhaustive simulations are carried out by varying different parameters, including the number of IoT devices, the number of harvesting sources, the distance between RF sources and IoT devices and the primary user (PU) activity of different channels. The simulation results demonstrate that our proposed schemes perform better than the existing ones.
... Here, Q1 is an identity matrix (all 1s in diagonal) which corresponds to a stable stage (the data queue has not changed). Here, defines as a matrix combination using Kronecker product [18]. Similarly, however, denotes a transition matrix of the energy decrease but with the possible change of data in queue. ...
Energy harvesting has recently been discussed and researched in a context of prolonging network lifetime, in particular, Inter-net of Things where a sensor device has a limited energy source (via battery). In this paper, we investigate a model of how to achieve an optimal use of energy-transfer from a mobile charger (MC) to sensor nodes (SN) harvested towards radio frequency. We adopt a constrained Markov Decision Process (CMDP) as a simplified stochastic optimization to determine an energy policy with respects to energy transfer in both energy and data queues. We also propose a modified CMDP adopting linear programming approach (CMDP-Linear) to derive the optimal use to balance those factors. To validate our model, we evaluated the schemes with analysis and simulation for model justification purposes: first, in terms on energy, recharging, charging, and loss probabilities for MC; and second, the possibility of loss for SNs due to lacking of recharging from MC.
... Apart from classification and regression, which use labelled datasets, there are some approaches which can work on unlabeled datasets too like in [150][151][152][153][154][155][156][157][158][159] some clustering algorithms like k-means and Fuzzy c-means are used to focus on clustering and faulty node detection related issues. Clustering, data compression and outlier detection can best be solved by principal component analysis technique mentioned in [160][161][162][163]. The WSN nodes are battery powered so energy is the main constraint for any sensor network. ...
Abstract – Since the last decade, wireless sensor network (WSN) and Internet of Things (IoT) has proved itself a versatile technology in many real-time applications. The scalability, cost -effectiveness, and self-configuring nature of WSN make it the fittest technology for many network designs and scenarios. The traditional WSN algorithms are programmed for fixed parameters without any touch of Artificial Intelligence as well as the optimization technique. So, they suffer from a trade-off between various QoS parameters like network lifetime, energy efficiency, and others. To conquer the limitations of traditional
WSN algorithms, machine learning has been introduced in wireless technology. But machine learning approaches also cannot solve all the problems in WSN solely. Some of the applications like target tracking, congestion control, and many more, do not give desired results even after applying the machine learning techniques. So, there is a need to introduce optimization in such cases. The paper gives an extensive survey on various optimization methods employed to solve many WSN
issues from 2005 till 2020. It also gives a brief description of the usage of various machine learning techniques in WSNs from 2002 till 2020. The paper discusses the advantages, limitations, effects of these methods on various WSN techniques like topology, coverage, localization, network and node connectivity, routing, clustering, cluster head selection, cross-layer issues, intrusion detection, etc. This paper gives a lucid comparison of many state-of-the-art optimization algorithms and descriptive and statistical analysis for discussed issues and algorithms associated with them. It also elucidates some open issues for WSNs/IoT networks that can be solved using these approaches.
... The clustered communication, divides the sensor nodes into small groups called clusters and then the information transformation takes place [14,15]. This type of communication is mostly a two-phased communication involving a setup phase where clustering is generated and a steady state phase, in which data transmission is performed [16,17]. For each cluster, a head is selected that performs compression and aggregation of data by removing data redundancy if any within a cluster and eventually transmits the information to the sink. ...
Energy conservation is one of the major concerns in mobile cloud sensor environment, where mobile sensing devices are capable of acquiring the sensing information and send the collaborated data to cloud hosted applications. However, they are constrained by the limited computing power and the short battery life. Hence, sensor based applications need an effective scheme to collect the sensed information and offload it to the base station in an efficient manner. In this paper, an approach named Modified Partitioning Around Medoids clustering with Threshold based Cluster Head Replacement (MPAM-TCHR), is proposed that aims for better clustering and change of cluster heads for every round of data offload to reduce the energy utilization of the sensor nodes. The proposed work divides the process into four phases namely, initialization, clustering, Cluster Head (CH) formation and transmission phases. Initially, node initialization is performed, and then the modified PAM clustering algorithm accurately estimates the medoid points based on which optimal cluster set partitioning is done. Next, the four parameters namely residual energy, signal to noise ratio (SNR) of the sensors, average path loss between the node and the other nodes and the path loss between the sensor and the base station influence the CH selection. This involves a threshold based modulation measure to identify probability effective node to become CH, iteratively changing the CH at different time intervals. Ultimately, the CH offloads the collected information to the base station (BS) with low communication cost possible during a particular time interval. Experimental results show better performances in terms of metrics like network lifetime, network utilization, and the average number of alive nodes. Thus, the proposed method performs data transmission with low energy consumption and thereby able to prolong the network lifespan.
... The temperature, humidity, and cluster head data are sent from the sensor node to the BS and collected environmental information such as classification failures and aggregation. A combination of clustering (SODCC) and compressed predictive principal component analysis (CPPCA) algorithms (Chidean et al. 2016) are combined to compare the performance of quadratic data In terms of quality of reconstruction of the energy consumption. Advanced clustering algorithms and the network data processing techniques, combinations, there are two aspects. ...
In wireless sensor networks, sensor nodes are distributed in different geographically dispersed areas, sink nodes are used for sensing data and sink nodes are used for data collected from different sensor nodes. Therefore, data collection is a key issue in wireless sensor networks. In addition to computational overhead, many challenging security issues have been introduced to in-network data processing. Data collection and communication methods are also proposed based on the Optimized Leibler Distance Matrix-based data aggregation (OLDMDA) algorithm. These sensor nodes belong to the category forming the OLDMDA plane data aggregation technology, which includes homogeneous nodes and data collection protocols. Leibler Distance Matrix for Node Location to find the location and multi sink node selection is based on node location distance using a Leibler matrix. In this distance matrix to collect the information of node energy, transmission rate to identify the higher performance node on the network. The Optimized Johnson's shortest path algorithm to solve the path problem source to destination. In this path selection method proposed to find the all the possible multi path randomly generate the graph. The performance of the new technology has been implemented using the network emulator (NS2). Simulation results of the proposed method OLDMDA higher performance with respect to the average packet transfer rate and metrics, as compared with the delay and network lifetime.
... It also helps in taking the appropriate decision that will better for agriculture activity [5]. 4 based on a low power wide area (LPWA). Lora, Sigfox, and NB-IoT are examples of long-range communication standards [12][13][14]. ...
The Internet of Things (IoT) is empowering the field of agriculture. The IoT application helps the farmer and makes him aware about all the latest information which is related to agriculture. In this study, authors have found that the implementation of IoT is very important in the field of agriculture that makes farming smarter. IoT applications make farmers smarter with the latest information about the crop and weather. By using the IoT application the farmer can prepare a plan for next season’s crop. With the help of IoT based agriculture application the farmers already know the information about the weather, soil, wind speed and direction, relative humidity, and temperature. The farmers know about the disease of crops and then consult the expert and get an appropriate solution to the disease. The experts may advise the farmer how farmers can protect the crop from the disease. The IoT agriculture application helps to increase the production of crops and reduce the loss of crops from the disease. The IoT based agriculture application is like a friend of a farmer because the IoT based agriculture applications are helping the farmer at every moment of agriculture activities. India has set a food grain production target of 298.3 million tonnes for the current fiscal year 2020-21 which is 2.5% higher compared to previous fiscal year’s targets. Agriculture sector contribution to Indian economy is 17%. To empower Indian farmers and in turn to empower Indian economy, IoT based agriculture applications are essential for the farmers.
... In addition, there are some strategies combining the mathematics method to improve the EE. For instance, by means of decoupling the whole network into clusters with the help of the SCoReI [245] framework that is integrated with Principal Component Analysis (PCA), the data collection is conducted in CS data acquisition mode in each cluster [246]. An Unbalanced Expander Graph-Based Compressed Data Gathering algorithm (UEGCD) that theoretically indicates that a proper sparse measurement matrix is equivalent to an adjacency matrix of an unbalanced expansion graph was proposed [247]. ...
A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out.
... The thresholds of cluster formation are used for even distribution of clusters. Chidean et al. [7] used Data-coupled clustering (DCC) to enhance the efficiency of WSNs. In this, the combination of clustering and in-network processing techniques are utilized. ...
With the advancements in sensor applications, wireless sensor networks (WSNs) become significant area of research. WSNs compose various tiny sensor nodes to sense an environment, depends upon the given application. However, these nodes are battery constrained (i.e., may become dead after passing certain iterations). Therefore, number of energy efficient protocols have been implemented in literature. However, selecting an optimal path between base station and sensor nodes is defined as an ill-posed problem. To overcome this issue, a Deep Q-routing based inter-cluster data aggregation is considered to improve the inter-cluster communication in WSNs. In every epoch, Recurrent neural network is considered to compute shortest path between cluster heads and sink. We have trained the network in such a way that it considers various features of WSNs and able to decide which sensor node will be selected as next-hop to establish a shortest path between elected cluster heads and sink. The non-cluster head nodes may also be considered while selecting a shortest path. Extensive experimental results show that the proposed technique outperforms the competitive energy efficient protocols.
... This scheme provides reliable data delivery and energy efficient routing in WSNs [9]. Energy Efficiency and Quality of Data Reconstruction was used to increase energy efficiency without sacrificing the data quality [10]. Enhanced Developed Distributed Energy Efficient Clustering (EDDEEC) employs an energy aware clustering protocol in WSNs and provides good scalability [11]. ...
The economic part of farming to India’s Gross Domestic Product is steady refusing with the nation broad-based economical development. Therefore, developing agriculture field is the important factor. To solve this problem we propose precision farming solution using wireless sensor network (WSN) to gain farming production in India. We use WSN as a placement of Camera Sensors in crop field that can be used to monitor the environmental condition, detect the pest, and send the pest details to the corresponding former. The use of pesticides in crop growing is necessary to assert the quality of large-scale product. However, the attacker inserts the fake sensor for destruct the crop field from pests. In WSN, the sensors are equipped with irreplaceable batteries and qualified by limited computing ability. Thus, reducing the sensor energy depletion is an important factor. To overcome these problems we propose fake sensor detection and secure data transmission based on predictive parser in WSNs (FSD-PP). The main goal is the operation of a WSN for early snail pest detection to diminish the use of pesticides in crops and gain more productivity in the cultivation. Here, the predictive parser method is used to check the sensor authentication. Elliptical curve cryptography algorithm is used to remove the eaves dropping attack in the network. The sleep/awake scheduling algorithm is used to save the sensor energy. The simulation result demonstrates that the proposed scheme will enhance the network life span and secure data transmission from sensor to base station in WSN.
... Practically, these smart devices can be deployed over an area in large numbers and networked through wireless links, providing unprecedented opportunities for data gathering and controlling the remote environments forming what is known as wireless sensor networks (WSNs) [10][11][12]. Since its innovation, WSNs have gained an increasing importance for their broad variety of civil and military applications [13][14][15]. Currently, many WSN applications fall under the categories of commercial, human-centric, military, or environmental monitoring [16][17][18]. ...
Through the past years, wireless sensor networks (WSNs) have witnessed great efforts to improve its performance and efficiency in terms of energy consumption and network throughput. Among all efficiency aspects, the energy awareness has captured the significant attention of current researchers. In this paper, we propose an adaptive energy-aware fixed clustering data dissemination protocol (AEA-FCP) for WSNs that mainly aims at minimizing and balancing the energy consumption among all nodes that are participating in such networks. In particular, this work proposes multiple novel mechanisms to achieve this goal. Firstly, a new approach is presented for the construction of clusters with balanced size and even distribution during the initial cluster head selection. Secondly, a novel scheme is introduced for distributing the cluster head task, dependent upon a node’s energy and location information within each cluster. Lastly, a multi-hop routing paradigm is employed to minimize the communication distances and save the nodes’ energy. The results of the simulation showed that our protocol’s performance surpasses other directly connected works in both continuous data and event-based applications.
... The network lifetime is utilized by the following factors such as: node deployment, cluster leader selection, cluster formulation and optimal path selection [2]. Wireless sensor networks (WSNs) are progressively used in the fields such as military, medicinal services, natural, organic, basic health observation, and state built forecasting [3][4][5][6]. ...
The energy efficiency and lifetime of Wireless Sensor Networks (WSNs) are the more focusing points. The WSNs are faced many challenged during the data transmission. Node deployment, leader selection and optimal route selection are challenges that affect the energy level and lifetime of WSNs. Many existing techniques have been proposed to node deployment, cluster leader and optimal route selection. But, all existing techniques have not given satisfactory results in the network energy optimization. Therefore, this paper presents Hybrid Artificial Grasshopper Optimization Algorithm (HAGOA). It is inherited behavior of Artificial Grasshopper Optimization and Artificial Bee Colony Variance. The proposed algorithm will place sensor nodes using Artificial Grasshopper Optimization technique. These sensor nodes may be static or dynamic that depends on the network scenario. The cluster head selection and optimal route selection will perform using Artificial Bee Colony Variance. It also performs balancing between exploration and exploitation phases in the given search space. This algorithm is combination of two families: Artificial Grasshopper and ABC Variance respectively. It compares with existing classical and swarm intelligence (SI) protocols in the terms of remaining energy, sensor node lifetime, consumed energy, end to end delay and maximum number of rounds.
... Sink node is only a base station which assumes a crucial part in remote condition and it acts like an appropriated controller. Base station is essential for the accompanying reasons: sensor nodes are inclined to down, better accumulation of information and gives the backup if the main node is not properly working or failure [1][2][3][4][5][6]. In this paper, Hybrid Artificial Bee Colony with Salp (HABCS) meta-heuristic algorithm is proposed to enhance WSNs lifetime based on optimal cluster selection, cluster formulation and optimal route selection. ...
Energy efficiency is critical issue in wireless sensor networks (WSNs). To improve lifetime of WSNs, energy utilization is important factor. Many existing classical routing and Swarm Intelligence (SI) based protocols have implemented in WSNs to solve optimization problem of energy efficiency. But all existing techniques suffer with poor exploitation in given search space and more energy consumption during cluster head selection and optimal route selection. This paper proposes Hybrid Artificial Bee Colony with Salp (HABCS) meta-heuristic algorithm to improve the exploitation phase in given search space and It is also capable of balancing in exploitation and exploration in given search space. Therefore, the proposed algorithm will select the cluster head selection, cluster formulation and select the path with optimal energy consumption. The proposed algorithm will implement using network simulator-2 (ns-2).The simulation results show that proposed algorithm is more energy efficient than other existing classical and SI protocols in the term of remaining energy, consumed energy, end to end delay and network lifetime.
... According to the analysis of the above WSNs location algorithm performance indicators, in general, a better location algorithm should have higher location accuracy, lower reference node dependency, strong fault tolerance and robustness, and lower energy consumption and the cost of realize and other characteristics [6][7][8][9]. These performance indicators are not only the performance indicators for evaluating WSNs' selflocalization algorithms, but also the optimal targets for the research and design of location algorithms. ...
span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">This paper aims to create a desirable positioning method for nodes in wireless sensor networks (WSNs). For this purpose, a source node positioning algorithm was developed based on time-of-arrival (TOA), in view of the nonlinear correlation between the measured values and unknown parameters in the observation equation of TOA source position. Several experiments were carried out to evaluate the performance of the proposed algorithm in terms of time measurement error, computing complexity, location error and Cramér–Rao lower bound (CRLB). The results show that the CRLB acquired by this algorithm can be used for WSN node positioning, provided that the independent zero mean Gauss measurement error is sufficiently small. The research findings lay a solid technical basis for optimal management, load balance, efficient routing, and automatic topology control of WSNs.</span
... Normally, IoT systems are resource-constrained, while a significant proportion of network resources are consumed by data transmissions [7]. Hence, a well-designed data aggregation algorithm could reduce the amount of data transmissions while ensure the data accuracy [8], [9]. Since data outlier detection and data aggregation are among the most critical requirements of IoT data analysis [10], [11], there are increasing research interests on this area, which are summarized in Section II. ...
... One of the broadly utilized and energy-efficient algorithms that have been lately used as a part of diverse applications is the cluster-based or hierarchical routing protocols (HRPs) [49][50][51][52][53][54][55][56][57]. HRPs are based on dividing the network into smaller subnetworks called clusters where one node acts as cluster head and the rest of the nodes behave as member nodes. ...
Advances introduced to electronics and electromagnetics leverage the production of low-cost and small wireless sensors. Wireless sensor networks (WSNs) consist of large amount of sensors equipped with radio frequency capabilities. In WSNs, data routing algorithms can be classified based on the network architecture into flat, direct, and hierarchal algorithms. In hierarchal (clustering) protocols, network is divided into sub-networks in which a node acts as a cluster head, while the rest behave as member nodes. It is worth mentioning that the sensor nodes have limited processing, storage, bandwidth, and energy capabilities. Hence, providing energy-efficient clustering protocol is a substantial research subject for many researchers. Among proposed cluster-based protocols, low-energy adaptive clustering hierarchy (LEACH) and threshold LEACH (T-LEACH), as well as modified threshold-based cluster head replacement (MT-CHR) protocols are of a great interest as of being energy optimized. In this article, we propose two protocols to cluster a WSN through taking advantage of the shortcomings of these protocols (i.e., LEACH, T-LEACH, and MT-CHR), namely centralized density- and threshold-based cluster head replacement (C-DTB-CHR) and C-DTB-CHR with adaptive data distribution (C-DTB-CHR-ADD) protocols that mainly aim at optimizing energy through minimizing the number of re-clustering operations, precluding cluster heads nodes premature death, deactivating some nodes located at dense areas from cluster’s participation, as well as reducing long-distance communications. In particular, in C-DTB-CHR protocol, some nodes belong to dense clusters are put in the sleeping mode based on a certain node active probability, thereby reducing the communications with the cluster heads and consequently prolonging the network lifetime. Moreover, the base station is concerned about setting up the required clusters and accordingly informing sensor nodes along with their corresponding active probability. C-DTB-CHR-ADD protocol provides more energy optimization through adaptive data distribution where direct and multi-hoping communications are possible. Interestingly, our simulation results show impressive improvements over what are closely related in the literature in relation to network lifetime, utilization, and network performance degradation period.
... Though few constraints in WSNs, like developing multi-hop communication and autonomous aerial vehicle technology, routing in dense and difficult terrain, monitoring of resource limited systems, data management, collection and analysis, and optimization of energy consumption etc. need to be resolved [1][2][3]. Amongst these, energy consumption optimization is one of the key areas of research in WSN [11][12][13][14][15]. Moreover, the data communication in WSN consumes more energy than the sensing, data processing [16][17][18][19][20]. Consequently, effective energy optimization techniques are required to minimize the energy consumption in the communication process in WSN. ...
Wireless sensor networks (WSNs) are used for several commercial and military applications, by collecting, processing and distributing a wide range of data. Maximizing the battery life of WSNs is crucial in improving the performance of WSN. In the present study, different variations of genetic algorithm (GA) method have been implemented independently on energy models for data communication of WSNs with the objective to find out the optimal energy \(\hbox {(E)}\) consumption conditions. Each of the GA methods results in an optimal set of parameters for minimum energy consumption in WSN related to the type of selected energy model for data communication, while the best performance of the GA method [energy consumption \((\hbox {E}=3.49\times 10^{-4}\,\hbox {J})\)] is obtained in WSN for communication distance (d) \({\ge }87\,\hbox {m}\) in between the sensor cluster head and a base station.
... Environmental military applications. On extend these WUSN's can be used for border patrol by deploying wireless pressure sensors along length of the border could be used for the detection of intruder or an object and information will be transmitted to responsible authority [4]. ...
The paper mainly focuses on the methods for improving accuracy of energy prediction using piggybacking technique. Generally, the sensor nodes send their energy/location information to the Base Station (BS). BS then applies suitable clustering technique to group the nodes in clusters and declares the list of CGs and CMs. Afterwards, the CHs and CMs go through the steady state phase data transmission. The reclustering process is called iteratively at every round. The centralized clustering protocol suffers from the excess energy usage during location or energy information transmission to BS. It severely affects the network lifetime. In proposed scheme an energy usage estimation technique (LCEFCM), which employs the fuzzy C-means clustering for creating, clusters in the wireless sensor networks. In this scheme, energy estimation based centralization clustering protocol is used. LCEFCM reduces the energy consumption considerably compared to other clustering methods like simulated annealing and K-means clustering. It applies the dynamic clustering mechanism combined with balances clustering method. LCEFCM outperforms LEACHC, LEACHC Estimate (LCE) AND LCWKM for various performance measuring factors like network lifetime, data received, alive nodes etc. a method is contributed to improve accuracy of energy prediction using piggybacking technique.
... The design of our MAC scheme revolves around the performance metric of distortion. Other works in the literature consider joint source coding and transmission policies and study the tradeoff between energy efficiency and data quality [29], [30]. In [31], an online joint compression and transmission optimization strategy is investigated for sensors with EH capabilities that generate correlated information, but how to schedule transmissions in a time slot is not treated. ...
The Internet of Things paradigm envisages the presence of many battery-powered sensors and this entails the design of energy-aware protocols. Source coding techniques allow to save some energy by compressing the packets sent over the network, but at the cost of a poorer accuracy in the representation of the data. This paper addresses the problem of designing efficient policies to jointly perform processing and transmission tasks. In particular, we aim at defining an optimal scheduling strategy with the twofold ultimate goal of extending the network lifetime and guaranteeing a low overall distortion of the transmitted data. We propose a Time Division Multiple Access (TDMA)-based access scheme that optimally allocates resources to heterogeneous nodes. We use realistic rate-distortion curves to quantify the impact of compression on the data quality and propose a complete energy model that includes the energy spent for processing and transmitting the data, as well as the circuitry costs. Both full knowledge and statistical knowledge of the wireless channels are considered, and optimal policies are derived for both cases. The overall problem is structured in a modular fashion and solved through convex and alternate programming techniques. Finally, we thoroughly evaluate the proposed algorithms and the influence of the design variables on the system performance adopting parameters of real sensors.
Video transmission over wireless sensor networks is an on-demand research due to the advantage of low power consumption and low implementation cost. Video transmission has deployed in many of the WMSN trends, which includes multimedia, video surveillance, health-care monitoring, military, environmental and industrial applications. In this paper, an energy efficient video transmission using hierarchical LEACH based routing protocol has been conducted through MATLAB simulation, based on H.264 video coding technique. In this investigation of video transmission over LEACH based routing protocols, our proposed modified micro genetic algorithm consumes less energy when compared with other LEACH variants.
Cyber-physical systems (CPSs) are integrated systems engineered to combine computational control algorithms and physical components such as sensors and actuators, effectively using an embedded communication core. Smart cities can be viewed as large-scale, heterogeneous CPSs that utilise technologies like the Internet of Things (IoT), surveillance, social media, and others to make informed decisions and drive the innovations of automation in urban areas. Such systems incorporate multiple layers and complex structure of hardware, software, analytical algorithms, business knowledge and communication networks, and operate under noisy and dynamic conditions. Thus, large-scale CPSs are vulnerable to enormous technical and operational challenges that may compromise the quality of data of their applications and accordingly reduce the quality of their services. This paper presents a systematic literature review to investigate data quality challenges in smart-cities large-scale CPSs and to identify the most common techniques used to address these challenges. This systematic literature review showed that significant work had been conducted to address data quality management challenges in smart cities, large-scale CPS applications. However, still, more is required to provide a practical, comprehensive data quality management solution to detect errors in sensor nodes’ measurements associated with the main data quality dimensions of accuracy, timeliness, completeness, and consistency. No systematic or generic approach was demonstrated for detecting sensor nodes and sensor node networks failures in large-scale CPS applications. Moreover, further research is required to address the challenges of ensuring the quality of the spatial and temporal contextual attributes of sensor nodes’ observations.
Purpose
In the past recent years, wireless sensor network (WSN) has progressively grown as an emerging technology. Various research efforts have been made in the literature to address the problem associated with WSN security. Based on the review analysis, it is found that the existing methods are mostly associated with complex security operations that are not suitable for resource constraint sensor nodes. The proposed paper has presented cost-effective modeling of the security framework that addresses the problem of security and energy in WSN.
Design/methodology/approach
The proposed security framework implements two different protocols to attain maximum security services and optimizes the security operation of the proposed security models to achieve higher energy efficiency and privacy preservation against a majority of the lethal attacks. The first security model introduces a novel cost-efficient pairwise key-based authentication mechanism to identify the availability of optimal routes under the presence of adversary in the network. The second security model introduces an integrated part of the first security model that optimizes security operation to perform secure communication using a lightweight encryption mechanism.
Findings
Based on the experimental outcome and analysis, the proposed system attains a 60% performance improvement in terms of security and computational efficiency compared to the existing Sec-LEACH. The second security model has achieved a 50% improvement in terms of overall aspects like reduction in transmission delay, packet delivery ratio, remaining energy and communication performance.
Originality/value
The proposed study has presented a computationally efficient model that provides lightweight security operations based on secure hash function. It also focuses on the security associations between WSN nodes and the selection of reliable routes for secure data transmission. The design of the proposed security model is best suited for homogeneous and heterogeneous sensor networks, which will be robust to any attacking scenario.
A wireless sensor network (WSN) contains sensor nodes that can forecast the ambience and operate accordingly to transmit the information in adverse surroundings. Though, inadequate energy as well as protection effects limits better transactions in the network. Conventional energy-based schemes utilizing cluster heads (CHs) as well as security is rendered using encryption. Yet, cluster method using encryption security is difficult due to increases the routing complexity as well as routing overhead. So, it degrades the performance of the network. To resolve these problems, Rabin-Karp Algorithm based Malevolent Node Detection and Energy-Efficient Data Gathering approach (RAMD) in WSN is proposed. In this scheme, the Rabin-Karp Algorithm thereby isolates the malevolent sensor from the network. It also removes the eavesdropping attack, and verifies the sensor authentication in the WSN. The sensor nodes forward the supervise data to CH. This CH is elected by cooperativeness rank, data transmission rank and remaining energy. The Mobile sink (MS) is used to gather the sensing data from the CH via intermediate CHs. Thus it reduces both the delay and the additional energy utilization in the network. This approach is mainly used for military or other security application. The simulation outcomes demonstrate that the RAMD is increasing the remaining energy and packet forwarding rate as well as diminishes the delay in the network.
Data collection through the mobile sink in wireless sensor networks (WSNs) is an effective solution to the hot-spot or sink-hole problem caused by multi-hop routings using the static sink. However, most of the existing research focuses on energy balance of the global network, but ignores the impact of local energy imbalance on the network lifetime. Therefore, this paper proposes an energy-efficient data collection algorithm to extend the network lifetime by balancing inter-cluster and inner-cluster energy (BIIE). In the proposed BIIE, we design an improved hierarchical clustering algorithm to reduce communication costs. To balance the energy between clusters, we design an efficient mechanism to select the optimal rendezvous node (RN) for each cluster and construct the traveling path of the mobile sink to access all RNs by particle swarm optimization (PSO). To balance the energy of the inner cluster, a local re-clustering mechanism is designed according to the residual energy level of sensor nodes. We also conducted simulation tests which confirm that the proposed BIIE can increase the network lifetime by approximately 46% and shorten the path length of the mobile sink by approximately 7% in comparison with other commonly-used algorithms (i.e., WRP and EAPC).
A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out.
This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed lGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.
This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.
In the construction of a smart marine, marine big data mining has a significant impact on the growing maritime industry in the Beibu Gulf. Clustering is the key technology of marine big data mining, but the conventional clustering algorithm cannot achieve the efficient clustering of marine data. According to the characteristics of marine big data, a marine big data clustering scheme based on self‐organizing neural network (SOM) algorithm is proposed. First, the working principle of SOM algorithm is analyzed, and the algorithm's two‐dimensional network model, similarity model and competitive learning model are focused. Secondly, combining with the working principle of algorithm, the marine big data clustering process and algorithm achievement based on SOM algorithm are developed; finally, experiments show that all vectors in marine big data clustering are stable, and the neurons in the output layer of clustering result have obvious consistency with the data itself, which shows the effectiveness of SOM algorithm in marine big data clustering.
Till date, the research work in Wireless Sensor Network is mainly inclined towards rectifying the problem associated with the nodes and protocol associated with it, e.g., energy problems, clustering issue, security loopholes, uncertain traffic, etc. However, there is less emphasis towards the user’s demand, i.e., data quality. As wireless nodes undergo various forms of adverse wireless condition in order to carry out data aggregation, it is quite inevitable that an aggregated data forwarded may not have a good data quality. Therefore, we present a novel clustering technique that concentrates on achieving the lowest possible error. With an aid of analytical modeling, a novel clustering technique is formulated using probability theory that targets the node with higher retention of redundant information so that it can be mitigated effectively. The study outcome shows better data quality of the proposed system.
The presence of many battery-powered sensors in the Internet of Things paradigm calls for the design of energy-aware protocols. Source coding techniques make it possible to save some energy by compressing the packets sent over the network, but at the cost of a poorer accuracy in the representation of the data. This paper addresses the problem of designing efficient policies to jointly perform processing and transmission tasks. In particular, we aim at defining a scheduling strategy with the twofold goal of extending the network lifetime and guaranteeing a low overall distortion of the transmitted data. We propose a Time Division Multiple Access (TDMA)-based access scheme that efficiently allocates resources to heterogeneous nodes. We use realistic rate-distortion curves to quantify the impact of compression on the data quality and propose a complete energy model that includes the energy spent for processing and transmitting the data, as well as the circuitry energy costs. We consider both full and statistical knowledge of the wireless channels, and derive communication policies for the two cases. The overall problem is structured in modules and solved through convex and alternate programming techniques. Finally, we thoroughly evaluate the proposed algorithms and the influence of the design variables on the system performance adopting parameters taken from real sensors.
Energy efficiency in wireless sensor networks (WSNs) is a crucial factor that affects the performance of the networks, as long-time and unceasing energy supply is impossible. Recent studies demonstrated that the previously proposed scheme of joint adaptive network-channel coding (JANCC) and adaptive dynamic energy consumption (ADEC) obtained excellent performance in terms of energy efficiency. Based on the joint network channel coding, we propose an enhanced adaptive dynamic energy consumption optimization scheme, which integrates the essence of both JANCC and ADEC, and overcomes the shortcomings of them. In the scheme an identifier vector is inserted into the feedback information. The relay node is able to acquire the specific signs of the channel codes received in error based on the identifier vector and transmit the network coded packets only related to those channel codes received in error. Through this method, it can achieve adjustments for the current round of transmission. Performance analyses and simulation results show that the proposed optimization scheme is more energy-efficient than any other schemes of interest under the condition of meeting the requirement of transmission reliability.
Self-Organizing Algorithms (SOAs) for Wireless Sensor Networks (WSNs) usually seek to increase the lifetime, to minimize unnecessary transmissions or to maximize the transport capacity. The goal left out in the design of this type of algorithms is the capability of the WSN to ensure an accurate reconstruction of the sensed field while maintaining the selforganization. In this work, we formulate a general framework where the data from the resulting clusters ensures the wellposedness of the signal processing problem in the cluster. We develop the Second-Order Data-Coupled Clustering (SODCC) algorithm and the Distributed Compressive-Projections Principal Component Analysis (D-CPPCA) algorithm, that use secondorder statistics. The condition to form a cluster is that D-CPCCA does not fail to resolve the Principal Components in any given cluster. We show that SODCC is scalable and has similar or better message complexity than other well-known SOAs. We validate these results with extensive computer simulations using an actual LS-WSN. We also show that the performance of SODCC is, comparative to other state-of-the-art SOAs, better at any compression rate and needs no prior adjustment of any parameter. Finally, we show that SODCC compares well to other energy efficient clustering algorithms in terms of energy consumption while excelling in data reconstruction Average SNR.
The optimal configuration for a Large Scale Wireless Sensor Networks (LS-WSN) is the one that minimizes the sampling rate, the CPU time and the channel accesses (thus maximizing the network lifetime), with a controlled distortion in the recovered data. Initial deployments of LS-WSN are usually not able to adapt to changing environments and rarely take into account either the spatial or temporal nature of the sensed variables, both techniques that optimize the network operation. In this work we propose the use of Self-Organized Distributed Compressive Projection (SODCP) in order to let the nodes to form clusters in a distributed and data-driven way, exploiting the spatial correlation of the sensed data. We compare the performance of this innovative technique, using actual data from the LUCE LS-WSN, with two different baselines: Centralized Compressive Projection (CCP) and Distributed Compressive Projection (DCP). The former uses no clustering, whereas the latter makes use of an a priori clustering that favors proximity and balances the number of nodes in each cluster. We show that SODCP outperforms DCP (in terms of Signal-to-Noise vs. Compression Rate). We also show that the performance of SODCP converges to that of CCP for relatively high compression rates of 55%.
The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions.
Data collection is a fundamental issue in wireless sensor networks. In many application scenarios for sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with given error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform a global approximate data collection on the sink according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of several subsets of sensor nodes, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and use a greedy heuristic algorithm to find an approximate solution. We also propose a monitoring algorithm to adjust these subsets according to the changes of sensor readings. Our trace-driving simulation results show that our data collection approach ADC can notably reduce the communication cost with given error bounds.
In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.
Regionalization is a classification procedure applied to spatial objects with an areal representation, which groups them into homogeneous contiguous regions. This paper presents an efficient method for regionalization. The first step creates a connectivity graph that captures the neighbourhood relationship between the spatial objects. The cost of each edge in the graph is inversely proportional to the similarity between the regions it joins. We summarize the neighbourhood structure by a minimum spanning tree (MST), which is a connected tree with no circuits. We partition the MST by successive removal of edges that link dissimilar regions. The result is the division of the spatial objects into connected regions that have maximum internal homogeneity. Since the MST partitioning problem is NP‐hard, we propose a heuristic to speed up the tree partitioning significantly. Our results show that our proposed method combines performance and quality, and it is a good alternative to other regionalization methods found in the literature.
For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult-it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. We propose an energy-efficient framework for clustering-based data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme. Our framework is clustering based. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, we present adaptive scheme to control prediction used in our framework, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of adaptive scheme to enable/disable prediction operations. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical algorithm for data aggregation: it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme for clustering-based continuous data collection in sensor networks. Our proposed models, analysis, and framework are validated via simulation and comparison with competing - echniques.
Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed.
While wireless sensor networks have been extensively studied in the past few years, most results are of theoretical nature and were obtained outside of a practical context. This can be problematic for real applications, especially in the area of environmental monitoring where many factors, such as harsh weather conditions, can greatly influence the performance of such a network, while reliable delivery and high-quality measurements are required. SensorScope is an interdisciplinary project, elaborated by environmental and networking researchers, that aims at narrowing the gap between theory and practice. Several successful real-world deployments have already been undertaken in rugged environments. In this paper, we analyze the particular requirements of environmental monitoring and how these requirements have been met in the SensorScope project. We also present an application example of a deployment, undertaken in a harsh mountain environment.
A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.
A new method for low-complexity multiuser detection (MUD) based in
the fast subspace decomposition (FSD) is proposed. The use of FSD allows
the estimation of the number of users along with the multiuser detection
on line. This leads to a fast multiuser estimation-detection scheme with
ultra-low complexity. Furthermore, the method is proved to be strongly
consistent and blind. This is applied here to the MMSE. Results included
show the MMSE performance at a fraction of the computational cost
reported until now. The use for UMTS-TDD receivers is also
proposed
Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.
In this paper, we explore the significance of second- and higher-order statistics learning in communication systems. The final goal in spread-spectrum communication systems is to receive a signal of interest completely free from interference caused by other concurrent signals. To achieve this end, we exploit the structure of the interference by designing second-order statistics detectors, such as the minimum square error, in conjunction with higher-order statistics (HOS) techniques, such as the blind source separation (BSS). This hybrid higher-order statistics (HyHOS) approach enables us to alleviate BSS algorithms of one of their main problems, that is, their sensitiveness to high levels of noise. In addition, we benefit from remarkable properties of BSS in learning such as fast learning (superefficiency) and independence of the initial settings of the problem (equivariance). We successfully applied the results of this approach to the design of multiuser detectors in code-division multiple access channels.
A new approach is presented to the problem of detecting the number of signals in a multichannel time-series, based on the application of the information theoretic criteria for model selection introduced by Akaike (AIC) and by Schwartz and Rissanen (MDL). Unlike the conventional hypothesis testing based approach, the new approach does not requite any subjective threshold settings; the number of signals is obtained merely by minimizing the AIC or the MDL criteria. Simulation results that illustrate the performance of the new method for the detection of the number of signals received by a sensor array are presented.
We present the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments. TAG allows users to express simple, declarative queries and have them distributed and executed efficiently in networks of low-power, wireless sensors. We discuss various generic properties of aggregates, and show how those properties affect the performance of our in network approach. We include a performance study demonstrating the advantages of our approach over traditional centralized, out-of-network methods, and discuss a variety of optimizations for improving the performance and fault tolerance of the basic solution.
How much traffic can wireless networks carry? Consider n nodes located in a disk of area A sq. meters, each capable of transmitting at a data rate of W bits/sec. Under a protocol based model for successful receptions, the total network can carry only Θ (W√An) bit-meters/sec, where 1 bit carried a distance of 1 meter is counted as 1 bit-meter. This is the best possible even assuming the nodes locations, traffic patterns, and the range/power of each transmission, are all optimally chosen. If the node locations and their destinations are randomly chosen, and all transmissions employ the same power/range, then each node only obtains a throughput of Θ (W√nlogn) bits/sec, if the network is optimally operated. Similar results hold for a physical SIR based model
To conserve energy, sensor nodes with similar readings can be grouped such that readings from only the representative nodes within the groups need to be reported. However, efficiently identifying sensor groups and their representative nodes is a very challenging task. In this paper, we propose a centralized algorithm to determine a set of representative nodes with high energy levels and wide data coverage ranges. Here, the data coverage range of a sensor node is considered to be the set of sensor nodes that have reading behaviors very close to the particular sensor node. To further reduce the extra cost incurred in messages for selection of representative nodes, a distributed algorithm is developed. Furthermore, maintenance mechanisms are proposed to dynamically select alternative representative nodes when the original representative nodes run low on energy, or cannot capture spatial correlation within their respective data coverage ranges. Using experimental studies on both synthesis and real data sets, our proposed algorithms are shown to effectively and efficiently provide approximate data collection while prolonging the network lifetime.
An abstract is not available.
We present the Tiny AGgregation (TAG) ap-proach to distributing declarative SQL-style ag-gregate queries with grouping over networks of low-power, wireless sensors. We discuss vari-ous generic properties of aggregates, and show how those properties affect the performance of our in-network approach. We include a per-formance study demonstrating the advantages of our approach over traditional centralized, out-of-network methods, and discuss a variety of op-timizations for improving the performance and fault-tolerance of the basic solution.
Principal component analysis (PCA) is often central to dimensionality reduction and compression in many applications, yet its data-dependent nature as a transform computed via expensive eigendecomposition often hinders its use in severely resource-constrained settings such as satellite-borne sensors. A process is presented that effectively shifts the computational burden of PCA from the resource-constrained encoder to a presumably more capable base-station decoder. The proposed approach, compressive-projection PCA (CPPCA), is driven by projections at the sensor onto lower-dimensional subspaces chosen at random, while the CPPCA decoder, given only these random projections, recovers not only the coefficients associated with the PCA transform, but also an approximation to the PCA transform basis itself. An analysis is presented that extends existing Rayleigh-Ritz theory to the special case of highly eccentric distributions; this analysis in turn motivates a reconstruction process at the CPPCA decoder that consists of a novel eigenvector reconstruction based on a convex-set optimization driven by Ritz vectors within the projected subspaces. As such, CPPCA constitutes a fundamental departure from traditional PCA in that it permits its excellent dimensionality-reduction and compression performance to be realized in an light-encoder/heavy-decoder system architecture. In experimental results, CPPCA outperforms a multiple-vector variant of compressed sensing for the reconstruction of hyperspectral data.
In wireless sensor networks, it is already noted that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the research of in-network data aggregation. In this paper, an α -local spatial clustering algorithm for sensor networks is proposed. By measuring the spatial correlation between data sampled by different sensors, the algorithm constructs a dominating set as the sensor network backbone used to realize the data aggregation based on the information description/summarization performance of the dominators. In order to evaluate the performance of the algorithm a pattern recognition scenario over environmental data is presented. The evaluation shows that the resulting network achieved by our algorithm can provide environmental information at higher accuracy compared to other algorithms.
Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. These spatial and temporal correlations along with the collaborative nature of the WSN bring significant potential advantages for the development of efficient communication protocols well-suited for the WSN paradigm. In this paper, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to model the spatial and temporal correlations in WSN. The objective of this framework is to enable the development of efficient communication protocols which exploit these advantageous intrinsic features of the WSN paradigm. Based on this framework, possible approaches are discussed to exploit spatial and temporal correlation for efficient medium access and reliable event transport in WSN, respectively.
Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local “growth budgets” to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time. However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.
The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. To support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. In this paper, we present a taxonomy and general classification of published clustering schemes. We survey different clustering algorithms for WSNs; highlighting their objectives, features, complexity, etc. We also compare of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.
In this article, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network.
We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but nonexhaustive) centralized approximation algorithm that returns a solution within O (log n ) of the optimal size. Based on the approximation algorithm, we design a class of centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique—even in dynamic conditions.
We present the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments. TAG allows users to express simple, declarative queries and have them distributed and executed efficiently in networks of low-power, wireless sensors. We discuss various generic properties of aggregates, and show how those properties affect the performance of our in network approach. We include a performance study demonstrating the advantages of our approach over traditional centralized, out-of-network methods, and discuss a variety of optimizations for improving the performance and fault-tolerance of the basic solution.
The physical phenomena monitored by sensor networks, e.g. forest temperature, water contamination, usually yield sensed data that are strongly correlated in space. With this in mind, researchers have designed a large number of sensor network protocols and algorithms that attempt to exploit such correlations. To carefully study the performance of these algorithms, there is an increasing need to synthetically generate large traces of spatially correlated data representing a wide range of conditions. Further, a mathematical model for generating synthetic traces would provide guidelines for designing more efficient algorithms. These reasons motivate us to obtain a simple and accurate model of spatially correlated sensor network data. The proposed model is Markovian in nature and can capture correlation in data irrespective of the node density, the number of source nodes or the topology. We describe a rigorous mathematical procedure and a simple practical method to extract the model parameters from real traces. We also show how to efficiently generate synthetic traces on a given topology using these parameters. The correctness of the model is verified by statistically comparing synthetic and real data. Further, the model is validated by comparing the performance of algorithms whose behavior depends on the degree of spatial correlation in data, under real and synthetic traces. The real traces are obtained from remote sensing data, publicly available sensor data, and sensor networks that we deploy. We show that the proposed model is more general and accurate than the commonly used jointly Gaussian model. Finally, we create tools that can be easily used by researchers to synthetically generate traces of any size and degree of correlation.
Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.
In this letter, we present an extension of the PASTd algorithm to both rank and signal subspace tracking. It has a low computational complexity O(nr), where n is the input vector length, and r denotes the signal subspace dimension. Its performance in tracking time-varying direction of arrival is comparable with that of the expensive eigenvalue decomposition and more robust than the O(n/sup 2/) rank revealing URV updating algorithm proposed by Stewart.< >
Many recent signal processing techniques in various areas, e.g.,
array signal processing, system identification, and spectrum estimation,
require a step of extracting the low-dimensional subspace from a large
space. This step is usually called subspace decomposition, which is
conventionally accomplished by an eigendecomposition (ED). Since an ED
requires O(M<sup>3</sup>) flops for an M×M matrix, it may
represent a barrier to the real-time implementation of these algorithms.
The authors present a fast algorithm for signal subspace decomposition,
which is termed FSD, that exploits the special matrix structure
(low-rank plus identity) associated with signal subspace algorithms and
requires only O(M<sup>2</sup>d) flops, where d(often≪M) denotes the
signal subspace dimension. Unlike most of alternatives, the dimension of
the signal subspace is not assumed known apriori and is estimated as
part of the procedure. Moreover, theoretical analysis has been
conducted, and its results show the strong consistency of the new
detection scheme and the asymptotic equivalence between FSD and ED in
estimating the signal subspace. Their new approach can also exploit
other matrix structure common in signal processing areas, e.g., Toeplitz
or Hankel, and thus achieve another order of computational reduction.
Moreover, it can be easily implemented in parallel to reduce further the
computation time to as little as O(Md) (using O(M) simple processors)
One of the most prominent and comprehensive ways of data collection in sensor networks is to periodically extract raw sensor readings. This way of data collection enables complex analysis of data, which may not be possible with in-network aggregation or query processing. However, this flexibility in data analysis comes at the cost of power consumption. In this paper, we develop ASAP, which is an adaptive sampling approach to energy-efficient periodic data collection in sensor networks. The main idea behind ASAP is to use a dynamically changing subset of the nodes as samplers such that the sensor readings of the sampler nodes are directly collected, whereas the values of the nonsampler nodes are predicted through the use of probabilistic models that are locally and periodically constructed. ASAP can be effectively used to increase the network lifetime while keeping the quality of the collected data high in scenarios where either the spatial density of the network deployment is superfluous, which is relative to the required spatial resolution for data analysis, or certain amount of data quality can be traded off in order to decrease the power consumption of the network. The ASAP approach consists of three main mechanisms: First, sensing-driven cluster construction is used to create clusters within the network such that nodes with close sensor readings are assigned to the same clusters. Second, correlation-based sampler selection and model derivation are used to determine the sampler nodes and to calculate the parameters of the probabilistic models that capture the spatial and temporal correlations among the sensor readings. Last, adaptive data collection and model-based prediction are used to minimize the number of messages used to extract data from the network. A unique feature of ASAP is the use of in-network schemes, as opposed to the protocols requiring centralized control, to select and dynamically refine the subset of the sensor nodes serving as samplers and to - adjust the value prediction models used for nonsampler nodes. Such runtime adaptations create a data collection schedule, which is self-optimizing in response to the changes in the energy levels of the nodes and environmental dynamics. We present simulation-based experimental results and study the effectiveness of ASAP under different system settings.
Limited energy supply is one of the major constraints in wireless sensor networks. A feasible strategy is to aggressively reduce the spatial sampling rate of sensors, that is, the density of the measure points in a field. By properly scheduling, we want to retain the high fidelity of data collection. In this paper, we propose a data collection method that is based on a careful analysis of the surveillance data reported by the sensors. By exploring the spatial correlation of sensing data, we dynamically partition the sensor nodes into clusters so that the sensors in the same cluster have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. Furthermore, during a short-time period, a sensor may report similar readings. Such a correlation in the data reported from the same sensor is called temporal correlation, which can be explored to further save energy. We develop a generic framework to address several important technical challenges, including how to partition the sensors into clusters, how to dynamically maintain the clusters in response to environmental changes, how to schedule the sensors in a cluster, how to explore temporal correlation, and how to restore the data in the sink with high fidelity. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic data set.
We consider dense wireless sensor networks deployed to observe arbitrary random fields. The requirement is to reconstruct an estimate of the random field at a certain collector node. This creates a many-to-one data gathering wireless channel. In this note, we first characterize the transport capacity of many-to-one dense wireless networks subject to a constraint on the total average power. In particular, we show that the transport capacity scales as Theta(log(N)) when the number of sensors N grows to infinity and the total average power remains fixed. We then use this result along with some information-theoretic tools to derive sufficient and necessary conditions that characterize the set of observable random fields by dense sensor networks. In particular, for random fields that can be modeled as discrete random sequences, we derive a certain form of source/channel coding separation theorem. We further show that one can achieve any desired nonzero mean-square estimation error for continuous, Gaussian, and spatially bandlimited fields through a scheme composed of single-dimensional quantization, distributed Slepian-Wolf source coding, and the proposed antenna sharing strategy. Based on our results, we revisit earlier conclusions about the feasibility of data gathering applications using dense sensor networks.
When n identical randomly located nodes, each capable of
transmitting at W bits per second and using a fixed range, form a
wireless network, the throughput λ(n) obtainable by each node for
a randomly chosen destination is Θ(W/√(nlogn)) bits per
second under a noninterference protocol. If the nodes are optimally
placed in a disk of unit area, traffic patterns are optimally assigned,
and each transmission's range is optimally chosen, the bit-distance
product that can be transported by the network per second is
Θ(W√An) bit-meters per second. Thus even under optimal
circumstances, the throughput is only Θ(W/√n) bits per
second for each node for a destination nonvanishingly far away. Similar
results also hold under an alternate physical model where a required
signal-to-interference ratio is specified for successful receptions.
Fundamentally, it is the need for every node all over the domain to
share whatever portion of the channel it is utilizing with nodes in its
local neighborhood that is the reason for the constriction in capacity.
Splitting the channel into several subchannels does not change any of
the results. Some implications may be worth considering by designers.
Since the throughput furnished to each user diminishes to zero as the
number of users is increased, perhaps networks connecting smaller
numbers of users, or featuring connections mostly with nearby neighbors,
may be more likely to be find acceptance
Adaptive Approximate Data Collection for Wireless Sensor Networks
- C Wang
- H Ma
- Y He
- S Xiong
C. Wang, H. Ma, Y. He, and S. Xiong, "Adaptive Approximate Data
Collection for Wireless Sensor Networks," IEEE T Parallel Distrib Syst,
vol. 23, no. 6, pp. 1004-1016, 2012.
Wireless Sensor Networks for Environmental Monitoring: The SensorScope Experience
- G Barrenetxea
- F Ingelrest
- G Schaefer
- M Vetterli
G. Barrenetxea, F. Ingelrest, G. Schaefer, and M. Vetterli, "Wireless
Sensor Networks for Environmental Monitoring: The SensorScope
Experience," IEEE Intl Zurich Seminar on Commun, pp. 98-101, 2008.