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EDDEEC: Enhanced Developed Distributed Energy-Efficient Clustering for Heterogeneous Wireless Sensor Networks
Abstract
Wireless Sensor Networks (WSNs) consist of large number of randomly
deployed energy constrained sensor nodes. Sensor nodes have ability to
sense and send sensed data to Base Station (BS). Sensing as well as
transmitting data towards BS require high energy. In WSNs, saving energy
and extending network lifetime are great challenges. Clustering is a key
technique used to optimize energy consumption in WSNs. In this paper, we
propose a novel clustering based routing technique: Enhanced Developed
Distributed Energy Efficient Clustering scheme (EDDEEC) for
heterogeneous WSNs. Our technique is based on changing dynamically and
with more efficiency the Cluster Head (CH) election probability.
Simulation results show that our proposed protocol achieves longer
lifetime, stability period and more effective messages to BS than
Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC)
and Enhanced DEEC (EDEEC) in heterogeneous environments.
... This reduces the consistent penalty on the nodes with high residual energy, but DDEEC only considers two levels of heterogeneity as shown in Table 2, thus ignoring multilevel heterogeneity. Enhanced DDEEC (EDDEEC) [119] enhances DDEEC to three levels of heterogeneity but does not use multi-hop communication between (CH)s. On the other hand, in [120] and [121], two new protocols, LEACH-Energy Association (LEACH-EA) and LEACH-Load Balancing (LEACH-EC), are proposed to improve the energy consumption and thus extend the network lifetime. ...
... In this review existing energy hole mitigation methods have been evaluated and classified in terms of equal or unequal sized clusters. It can be seen that [93]- [98], [100], [105], [116]- [119], [126]- [133] use equal clustering and [66], [113]- [115], [122]- [125], [134]- [136] use unequal clustering whereas [120] proposes two techniques LEACH-EA that uses unequal clustering and LEACH-EC that uses equal clustering. Furthermore, both direct and multi-hop communication methods are used for inter-cluster communication. ...
... On the contrary a heterogeneous energy network can further be classified based on number of different levels considered for the initial energies of nodes. SEP [116], DDEEC [118], and GWO [130] use two levels of initial energies for nodes whereas DEEC [117], EDDEEC [119], UCR-H [135], ETASA and TEAR [139] use multi-levels of heterogeneous energies for the network nodes. Due to various levels of energy, it is also important to distinguish methods that consistently penalize high energy nodes by selecting them excessively for relaying operations. ...
A huge increase in the percentage of the world’s urban population poses resource management especially energy management challenges in smart cities. In this paper, the growing challenges of energy management in smart cities have been explored and significance of elimination of energy holes in converge cast communication has been discussed. The impact of mitigation of energy holes on the network lifetime and energy efficiency has been thoroughly covered. The particular focus of this work has been on energy-efficient practices in two major key enablers of smart cities namely, the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). In addition, this paper presents a robust survey of state-of-the-art energy-efficient routing and clustering methods in WSNs. A niche energy efficiency issue in WSNs routing has been identified as energy holes and a detailed survey and evaluation of various techniques that mitigate the formation of energy holes and achieve balanced energy-efficient routing has been covered.
... -is scalable to any desired level of energy heterogeneity so that the scheme's performance is not affected due to the varying levels of energy heterogeneity • Performance comparison of the EEHCT with the existing schemes, like [11], [17], [18], [20], [21], and [22] with respect to the parameters-network lifetime and energy consumption. • Stability and statistical analysis of the simulation results. ...
... Clustering has not only proved its significance in the traditional WSNs but also in the IoT-based HWSN to achieve energy efficiency. Many works have already been reported in this context, like [14]- [18], [20]- [22], [37]- [62]. The schemes such as SEP (Stable Election Protocol) [14], DEEC (Distributed Energy Efficient Clustering) [15], D-DEEC (Developed Distributed Energy Efficient Clustering) [16], E-DEEC (Enhanced Distributed Energy Efficient Clustering) [17], ED-DEEC (Enhanced Developed Distributed Energy Efficient Clustering) [18], DRE-SEP (Distance-Based Residual Energy-Efficient SEP) [20], DARE-SEP (Distance Aware Residual Energy-Efficient SEP) [21], and DE-SEP (Distance and Energy Aware SEP) [22] have been proposed following the philosophy of LEACH [11]. ...
... Many works have already been reported in this context, like [14]- [18], [20]- [22], [37]- [62]. The schemes such as SEP (Stable Election Protocol) [14], DEEC (Distributed Energy Efficient Clustering) [15], D-DEEC (Developed Distributed Energy Efficient Clustering) [16], E-DEEC (Enhanced Distributed Energy Efficient Clustering) [17], ED-DEEC (Enhanced Developed Distributed Energy Efficient Clustering) [18], DRE-SEP (Distance-Based Residual Energy-Efficient SEP) [20], DARE-SEP (Distance Aware Residual Energy-Efficient SEP) [21], and DE-SEP (Distance and Energy Aware SEP) [22] have been proposed following the philosophy of LEACH [11]. Like LEACH, such schemes implement strategies like the randomized rotation of cluster heads (ensuring the load distribution), data aggregation (to lower the energy consumption), and localized coordination (to assure scalability and robustness in dealing with the dynamic networks) too. ...
Internet-of-Things (IoT)-based Heterogeneous Wireless Sensor Network (HWSN) has emerged as a prevalent technology that plays a significant role in developing various human-centric applications. Like in a wireless sensor network (WSN), energy is also the most crucial resource in IoT-based HWSN. The researchers have proposed many works to achieve energy-efficient network operations by minimizing energy usage. A vast proportion of these works emphasize using the clustering approach, which has proved its worth to a great extent. However, most schemes require the repeated formation of clusters incurring a significant amount of nodes’ energy in the clustering process. The protocol design of such schemes also varies with the changing levels of heterogeneity. In this work, a hybrid clustering scheme- An Energy-Efficient Hybrid Clustering Technique (EEHCT) has been proposed for IoT-based HWSN that minimizes the energy consumption in clusters’ formation and distributes the network load evenly irrespective of the heterogeneity level to prolong network lifetime. It appropriately utilizes dynamic and static clustering strategies to formulate the load-balanced clusters in the network. EEHCT establishes its supremacy over state-of-the-art schemes via an extensive set of simulations and experimentation in terms of multiple network performance metrics like stability, throughput, and network lifetime. Like, it achieves a gain up to 90.27% with respect to network lifetime over its peers in the standard operating conditions and under varying network configurations. In addition to quantitative analysis, a statistical analysis has also been provided to demonstrate the formation of energy-balanced clusters through the proposed scheme.
... The DEEC approach has a different time to become the CHs than the SEP; CHs are selected using a probability based on each SN's ratio of remaining energy to network average energy. The Enhanced Developed Distributed Energy Efficient Clustering (EDDEEC) (Javaid et al., 2013) is a novel clustering approach for HWSNs. This approach is designed to change the CH election possibility more dynamically and efficiently. ...
... The distinction between DEEC (Singh et al., 2017), EDDEEC (Javaid et al., 2013), E-DEEC (Saini & Sharma, 2010a), and developed DEEC (DDEEC) (Elbhiri et al., 2010) is extensive, indicating the likelihood of becoming CH in the current round. These approaches aim to dispense energy consumption across the network as efficiently as possible, SEP (Smaragdakis et al., 2004) In this heterogeneous WSN network is introduced to prolong the network lifetime. ...
... EDDEEC (Javaid et al., 2013) The Enhanced Developed Distributed Energy Efficient Clustering (EDDEEC) is a novel clustering approach for HWSNs. This approach is designed to change the CH election possibility more dynamically and efficiently. ...
The energy efficiency in wireless sensor networks (WSNs) is a significant challenge. A clustering-based approach
is an energy-saving approach in WSN. The practical clustering approach divides the network into various clusters
that directly affect the total energy consumption and reduce the network lifetime of the WSN. As a result, this
paper introduces novel energy-efficient clustering approaches for cluster head (CH) selection and cluster
formulation. The cluster head (CH) selection is designed based on the threshold-based advanced LEACH (ADVLEACH2)
approach. The cluster formulation (sensor node distribution) among the cluster heads is done with
modified fuzzy c-mean (MFCM) approaches. The clusters are formed using the MFCM approach, and cluster
heads (CHs) are selected using ADV-LEACH2. The cluster formulation and head selection are selected at the
beginning of each round. The MFCM approach forms balanced clusters and balances sensor node distribution
among the cluster heads. The proposed approach maintains the cluster head energy. The experimental simulation
outcome justifies that the proposed approach, TEEECH surpasses the existing clustering approach by an average
of 13%. The comparison with DEC, SEP-E, EBCS, MEDDEEC, and EDDEEC presents the TEEECH approach to
maintain the CH energy and prolong the network lifetime. The proposed approach, TEEECH, also significantly
improves the network stability and lifetime based on Half node dead (HND).
... However, if the base station is remote from the sensor node, the node will soon die due to excessive energy consumption for delivering data. In order to solve this problem, some of the clustering algorithms aimed at saving energy have been proposed like leach (low-energy adaptive clustering [1,4,14,21]. hierarchically it is based on the randomized rotation of the cluster head to distribute the energy load among the sensor nodes evenly distribute in the entire network. ...
... This study further explains more about routing methods of Enhanced Developed Distributed Energy-Efficient Clustering scheme (EDDEEC) for heterogeneous Wireless sensor networks [21]. The algorithm uses the techniques of changing dynamically Cluster Head (CH) election probability. ...
... Although the fast dying of nodes once first node died is still a challenge. Javaid et al. [34] created an enhanced distributed EE clustering EDEEC protocol. They presented a brand-new clustering-based routing strategy in this study for heterogeneous WSNs. ...
... They presented a brand-new clustering-based routing strategy in this study for heterogeneous WSNs. The process dynamically alters the chance of choosing a cluster head, making it more effective [34]. Sharma et al. [35] have designed the hybrid data aggregation and DC based routing protocol. ...
The size of the wireless sensor network (WSN) is extending continually with use of IOT networks. The main difficulty for design wide area WSN is to maintain the higher stability period and energy efficiency (EE) for the routing protocols. The creation of clustering-based routing protocols was applied to the optimization of overall network energy. But, traditional clustering methods were unable to produce improved node heterogeneity, and extended network lifetime. Distributed clustering-based routing protocols are specially designed for enhancing the EE of the networks. In addition, EE can be improved by enhancing the heterogeneity of the node distribution. This paper aims to design the extended distributed clustering-based EE routing protocol. The heterogeneity of nodes is improved by introducing the additional intermediate advanced nodes layer in the network. Therefore, paper proposed to design the Multi-Level Heterogynous EDEEC rousing protocol called ML-HEDEEC by adapting optimum energy enhancing parameters. The notes are divided to normal, advance, advance-interdicted and supper nodes based on the energy allocated to them. The probability of nodes is modified for better clustering and cluster head election by introducing additional energy enhancement parameter. In addition, it is proposed to automatically adopt the network initial energy based on the scaling of network dimensions. This may lead to enhance EE of the network and may improve stability period. Finally, the results are evaluated for a case of WSN routing under the dynamic sink locations. Performance is compared for various distributed clustering protocols and other state of art protocols viz. LEACH, SEP, zonal- SEP, DEC considering the network scaling. Various performances of the network stability, packets sent to base station, and lifetime,.are defined for result evaluation. The network dimensions are scaled up to four times and proposed protocol is tested under scaling consideration. In addition, sink locations are also varied for dynamic sink locations performance evaluation. Overall paper efficiently designed and test heterogeneous improved routing protocol with extended lifetime and stability.
... It is preferable to have a few nodes with additional energy to extend the network life span in such applications. Numerous researchers have proposed distinctive clustering-based methodologies for increased energy efficiency and improved network lifetime (Heinzelman et al. 2000;Liao and Yang 2012;Qing et al. 2006;Elbhiri et al. 2010;Kumar et al. 2009;Javaid et al. 2013;Naranjo et al. 2017;Mittal et al. 2017aMittal et al. , 2017bNorouzi et al. 2011;Bhasker 2014;Bayraklı and Erdogan 2012;Kumar and Vishwas 2015;Dehghani et al. 2018;Ragavan and Ramasamy 2018;Yuan et al. 2017;Verma et al. 2019). The essential task in these protocols is to pick the CH proficiently. ...
... Instead, it considers the three levels of heterogeneity for deploying the nodes in the networks. Another protocol for the purpose is Enhanced DEEC (EDDEEC) (Javaid et al. 2013), which follows a similar approach as DDEEC (Elbhiri et al. 2010). ...
Internet of Things (IoT)-enabled Wireless Sensor Networks is not only an encouraging research domain but also a promising industrial trend which permits the development of various IoT-based applications, ranging from industry to education and military to agriculture. The IoT device plays a significant role in various IoT-based networks and working of such network depends upon the battery power. Once the devices have deployed in the hostile environments, replacement of batteries is not feasible. To address this, challenge a plethora of research has been conducted but IoT networks still suffer one or the other way. In this paper, a genetic algorithm is integrated with an efficient clustering for power grid application where monitoring and controlling process is proposed by using movable sinks in IoT-enabled HWSNs (OptiGeA) . The OptiGeA protocol is depicted for cluster heads election by joining the factor of density, distance, energy and heterogeneous node’s capacity for its created fitness function. The investigation analysis of OptiGeA is prepared to work with single sink, multiple static sinks and multiple movable sinks to have an unslanted comparative assessment. The novel deployment technique and multiple mobile sinks approaches are proposed to shorten transmission distance between the sink and CH during system operation and pact with the hotspot issue. It is evident from a simulation study that the proposed OptiGeA protocol outflank state-of-the-art protocols on determinations of particular execution measurements precisely stability period, system's residual energy, network lifetime, throughput, and number of clusters per round execution.
... The more networks are being used are bi-directional. TheWSN [6][7][8][9][10][11] is consisting of several hundred and thousands sensor nodes, these nodes are comprised of large number of low-cost,Multifunctional this processes the data by sensing which is then communicated through its transceiver.The costof sensor nodes is similarly variable and depends upon the complexity of sensor the.A WSN [12][13][14][15][16] consists of spatially distributed autonomoussensors and have the features such as: to monitor physical or ecological conditions, such as temperature, sound, vibration, pressure, motion or pollutants and agreeably send the data with the help of network to the base station [7]. The main characteristics of WSN [9]are :No wired-infrastructure, Ability to manage with node failures, Mobile Nodes, Dynamic network topology, Self-organization , Heterogeneity of nodes, Scalability to large scale of deployment, Performance as router by all nodes, Ease of deployment, Unattended operation, Low-power consumption, Ease of use etc.The wireless sensor network [13] is being used broadly with the applications [14]: Military, Environmental Sensing, Area monitoring, Air pollution monitoring, Health, Forest fire detection, Home, Space Exploration and Chemical Processing. ...
... The more networks are being used are bi-directional. TheWSN [6][7][8][9][10][11] is consisting of several hundred and thousands sensor nodes, these nodes are comprised of large number of low-cost,Multifunctional this processes the data by sensing which is then communicated through its transceiver.The costof sensor nodes is similarly variable and depends upon the complexity of sensor the.A WSN [12][13][14][15][16] consists of spatially distributed autonomoussensors and have the features such as: to monitor physical or ecological conditions, such as temperature, sound, vibration, pressure, motion or pollutants and agreeably send the data with the help of network to the base station [7]. The main characteristics of WSN [9]are :No wired-infrastructure, Ability to manage with node failures, Mobile Nodes, Dynamic network topology, Self-organization , Heterogeneity of nodes, Scalability to large scale of deployment, Performance as router by all nodes, Ease of deployment, Unattended operation, Low-power consumption, Ease of use etc.The wireless sensor network [13] is being used broadly with the applications [14]: Military, Environmental Sensing, Area monitoring, Air pollution monitoring, Health, Forest fire detection, Home, Space Exploration and Chemical Processing. ...
Alot of routing protocols have been developed for Wireless sensor networks on different parameters but few parameters are not considered yet for homogeneous network scenario where each node have same capability in terms of processing power, storage, energy etc. A sensor network worksuntil the nodes do not drain their batteries. Further, it is very difficult to replenish the batteries of the nodes, once they are being deployed to some hostile environment. In this paper, five well accepted WSN routing protocols namely DEEC, EESAA, M-GEAR, M-LEACH and Z-SEP have been benchmarked for their energy pattern in homogeneous scenario. All simulations are done in MATLAB. Different parameters are used for checking the efficiency of the considered routing protocol for WSN. Simulation results shows that there is no clear winner for all situations but most of the time DEEC and M-GEAR protocol provide better results in terms of packets transferred to base station and cluster heads. EESAA and Z-SEP protocols performs well in terms of clusters and cluster heads formation than other protocols, on the other hand all protocols perform well in terms total number of alive and dead nodes.
... This mechanism route the data to transfer as a chain from the node through intermediate nodes to sink is known as PEGASIS. Javaid et al. [8], proposed a distributed energy-efficient clustering model to overcome the energy issues. This technique is based on the dynamic changes in the probability of the cluster head selection. ...
... The matrix used to store the values of each cluster node8 CSSE, 2022 ...
Throughout the use of the small battery-operated sensor nodes encourage us to develop an energy-efficient routing protocol for wireless sensor networks (WSNs). The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN. Many routing protocols are available, but the issue is still alive. Clustering is one of the most important techniques in the existing routing protocols. In the clustering-based model, the important thing is the selection of the cluster heads. In this paper, we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster. Initially, the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance. The proposed scheme performs hierarchal routing and direct routing with some energy thresholds. The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance. Moreover, the simulations will be performed in two scenarios, gateway based and without gateway to achieve more energy-efficient results.
... Then, two algorithms have been presented based on Particle Swarm Optimization (PSO), developed with an efficient particle encoding scheme and multi-objective fitness function [23]. In [24] author proposed an EDDEEC, 1 3 which is an energy-aware protocol. It dynamically changes the possibility of nodes becoming a CH to distribute an equal amount of energy consumption between sensor nodes. ...
... Type of Paper Problem [13] Survey WSN configuration for mining environment [14] Clustering Routing path optimization [21] Clustering Path optimization [22] Path Optimization Path Optimization [16] Clustering Load balancing problem to prolog all the nodes [15] Clustering Network clustering [17] Clustering Load balancing [19] Clustering Regional energy and weight computation [20] Survey Clustering algorithm classification [18] Clustering Handling uncertain level decision [23] Clustering Load balancing [24] Clustering CH selection ...
Wireless Sensor Network (WSN) is the future of next-generation’s communication and computational technology. Now WSN is being used to fulfill various application requirements like medical, engineering, industries, agriculture, etc. It is a resource-constrained network. Additionally, mobility in WSN causes serious issues related to QoS (Quality of Service) requirements like energy efficiency. In order to deal with this issue, in this paper, an Energy Efficient Weighted Clustering Algorithm (EE-WCA) has been proposed. The main aim of EE-WCA is to create a clustered network, which minimizes energy consumption and creates efficient regional Cluster Heads (CH). For this, three phases in clustering are defined. First, evaluating QoS requirements (i.e., buffer length, node displacement, battery level, connectivity, and SNR (signal to noise ratio)). Second, minimize the computational overhead of nodes to save energy using the weighted computation-based technique. This technique helps to regulate the application’s QoS requirements for the selection of CH. Finally, to distribute the resource consumption uniformly over the entire region of WSN, the CH updation process has been described. The experimental setup is prepared on the NS-2.35 simulator and the results are measured using 10 different sizes of network. The experimental observations on different performance factors, i.e. energy consumption, E2E(End to End) delay, throughput, packet delivery ratio, and packet drop ratio, confirm the enhanced performance of network with respect to a state-of-art WCA algorithm.
... Moreover, decrease in mean square errors between the clusters, increases cluster's efficiency. In [39], cluster-based routing is proposed wherein the cluster heads are dynamically changing in heterogeneous WSN. ...
Localization in wireless sensor network (WSN) is an important issue since it helps to find the origin of the event. Many localization algorithms have been proposed and are efficient up to certain extent. Energy efficient and low cost 3D localization algorithm in wireless sensor networks is still a big challenge. Accuracy of location estimation with minimum computational complexity is the main objective of the localization algorithm. In this paper, we propose the clustering-based localization algorithm for 3D environment which is energy efficient and has less computational complexity. The sensor nodes are grouped in a cluster which is based on the received signal strength at the respective anchor nodes. The nearest neighbor clustering algorithm is formulated for forming the clusters. The anchor nodes act as the cluster heads. The distance information from received signal strength indicator (RSSI) along with the angle of arrival (AoA) information is combined to estimate the location of the cluster members creating the local map. The energy dissipation in the proposed approach can be reduced by adopting the density control strategy. The simulation results show that the proposed approach performs better as compared to the PSO, BBO, and FA in terms of localization error.
... The results showed that DEEC achieved a longer network lifetime. Further, Javaid et al. [8] worked on an enhanced developed distributed energy-efficient clustering scheme (EDDEEC) for heterogeneous WSNs. Results achieved longer lifetime and stability period. ...
In this paper, we consider a remote environment with randomly deployed sensor nodes, with an initial energy of E0 (J) and a solar panel. A hierarchical clustering technique is implemented. At each round, the normal nodes send the sensed data to the nearest cluster head (CH) which is chosen on the probability value. Data after aggregation at CHs is sent to the base station (BS). CH requires more energy than normal nodes. Here, we energize only CHs if their energy is less than 5% of its initial value with the use of solar energy. We evaluate parameters like energy consumption, the lifetime of the network, and data packets sent to CH and BS. The obtained results are compared with existing techniques. The proposed protocol provides better energy efficiency and network lifetime. The results show increased stability with delayed death of the first node. The network lifetime of the proposed protocol is compared to the multi-level hybrid energy efficient distributed (MLHEED) technique and low-energy adaptive clustering hierarchy (LEACH) variants. Network lifetime is enhanced by 13.35%. Energy consumption is reduced with respect to MLHEED-4, 5, and 6 by 7.15%, 12.10%, and 14.975% respectively. The no. of packets transferred to the BS is greater than the MLHEED protocol by 39.03%.
... EEHC (energy-efficient heterogeneous cluster scheme) [47] provides the election probability weights that are directly related to the residual energy of the sensor node, whereas BEENISH [48] (balanced energy-efficient network-integrated super heterogeneous) protocol is also a clustering algorithm that assigns one of the four energy levels to the sensor node and uses this energy level for selecting the cluster heads. Enhanced developed distributed energyefficient clustering for heterogeneous network (EDDEEC) [49] classifies nodes as normal nodes and advanced nodes and then changes the probability of becoming cluster heads. ...
In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH’s selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs.
... Based on multi-hops communication, [34] uses the public key between the sensors node and the BS to secure data transmission. In contrast, in [35], public keys cryptographic scheme is used to secure single hop communications in hierarchical clustering WSN. In [36], the number of computations in each round is reduced by using the concept of the private key algorithm. ...
Reducing the amount of data transmitted and protecting devices from the adversary environment are the major challenges faced in the development of the Internet of Things (IoT) network. Compressive sensing (CS) offers the major advantage of performing lightweight encryption and compression at the same time, making it energy efficient. Like any private key algorithm, the CS encryption method has problems with key distribution and management. Public key algorithms, on the other hand, do not have these problems and provide a high level of security. However, public key algorithms increase the cost of communication that leads to a decrease in network lifetime. In this paper, we propose an efficient technique that achieves a high level of security with minimum energy consumption. The proposed technique utilizes the advantage of public-key algorithms to provide a high level of security and reduces communication costs by using the CS method. The simulation results indicate that the proposed security approach is effective in achieving better performance in security with minimal communication cost, along with prolonging network lifetime. In addition, the proposed technique makes it possible to use public-key algorithms for securing the IoT network efficiently.KeywordsSecurityAsymmetric CryptographyCompressive SensingWireless Sensor NetworksNetwork lifetimeCommunication cost
... [57] EEHS (energy-efficient heterogeneous cluster scheme) used election probability proportional to residual energy [58]. BEENISH Balanced Energy-Efficient Network-Integrated Super Heterogeneous algorithm for WSNs which assigns one of the four energy levels to the node for selecting the cluster heads (CHs).EDDEEC protocol [59] was developed for the heterogeneous network which classifies nodes as normal nodes, advanced nodes, or supernodes and selecting as cluster heads (CHs). PEACH [60] is another heuristic algorithm used for multiple clustering which used location unaware and location-aware in WSNs. ...
State-of-the-art technologies are upcoming due to innovation and its implications in various domains. Wireless sensor networks playing vital role and energy consumption is considered as an important issues in this domain. All the IoT based applications needs to be multi-objective and optimized for improved performance, we can’t deny the fact that WSN plays an important role in every technologies as sensors were involved for collecting and sensing environment. Many solutions were given regarding the power utilization. Clustering is one such important solutions for efficient energy utilization. The extensive evolution of clustering algorithms leads to increase network lifetime, efficient energy consumption but because of many conflicting attributes that affects the efficiency of clustering that needs to be optimized such as residual energy, BS connectivity, distance of BS-CH’s, etc. and it required proper co-ordination. Our work proposes Multi-Attribute Decision Making (MADM) based Cluster Heads (CH’s) selection for enhancing network lifetime and efficient energy consumptions. We have considered a total 20 attributes, and co-ordination among these attributes has been done by using MADM approaches which selects optimal CHs for increasing network lifetime of WSN. Our results validate that our proposed algorithms performs better than EECH, LEACH-C, and LEACH in rapports of energy efficiency and optimal load-balanced among the sensor nodes. Also enhancement in network lifetime due to efficient energy consumption in proposed work.
... EDDEEC [11] method is used for heterogeneous WSNs. It is three level heterogeneous WSNs. ...
Wireless communication technologies continue to undergo rapid advancement. In recent years, there has been a steep growth in research in the area of wireless sensor networks (WSNs). Sensor nodes are small devices with sensing, computing and wireless communication capabilities. Such devices form a WSNs which can be used for many applications like gathering of environmental data and even monitoring enemy activity on a battlefield. In order to achieve this efficient routing protocols must be used. Such routing protocols are of prime research interest. There exist many different approaches most of which are studied through simulation only. At the same time real hardware platforms for this research become widely available and affordable. In this paper we will investigate the performance of distributed heterogeneous WSNs protocol: DDEEC, EDEEC and EDDEEC. I have analysed these protocol in terms of lifetime of the network and data transferred through each and every nodes of the network. The performance of these routing protocols is then measured and compared through simulation environment deployment of the WSNs.
... The sensor node energy is not taken into account by EEHC in the CH selection approach but during the placement of nodes in networks, 3-levels of heterogeneity are considered. Enhanced DEEC (EDDEEC) [8], which uses another protocol for this purpose followed by the same approach as DDEEC [6]. In terms of energy heterogeneity, Qureshi et al. proposed another procedure termed BEENISH [9], which has four levels of heterogeneity. ...
The outlook of the World toward health infrastructure has drastically changed due to COVID-19 which created the need for the development of emerging technologies where interactions between the patients and the health workers can be minimized. Consequently, a secure and energy-efficient internet of medical things (IoMT) enabled wireless sensor network (WSN) is proposed for communicable infectious diseases that utilizes genetic algorithm. The proposed system makes use of movable sinks in IoT-enabled WSNs for healthcare called OptiGeA. The OptiGeA protocol is depicted for cluster heads (CHs) election by joining the factor of energy, density, distance, and heterogeneous node's capacity for fitness function. Additionally, a novel deployment technique and multiple mobile sink approaches are proposed to reduce transmission distance between sink and CH during system operation which mitigates hotspot issues. It is evident from the simulations that the OptiGeA protocol outflanks state-of-the-art protocols in terms of different performance measurements.
... This mechanism route the data to transfer as a chain from the node through intermediate nodes to sink is known as PEGASIS. Javaid et al. [8], proposed a distributed energy-efficient clustering model to overcome the energy issues. This technique is based on the dynamic changes in the probability of the cluster head selection. ...
Throughout the use of the small battery-operated sensor nodes encourage us to develop an energy-efficient routing protocol for wireless sensor networks (WSNs). The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN. Many routing protocols are available, but the issue is still alive. Clustering is one of the most important techniques in the existing routing protocols. In the clustering-based model, the important thing is the selection of the cluster heads. In this paper, we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster. Initially, the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance. The proposed scheme performs hierarchal routing and direct routing with some energy thresholds. The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance. Moreover, the simulations will be performed in two scenarios, gateway based and without gateway to achieve more energy-efficient results.
... The new threshold of TDEEC guarantees that only nodes with higher residual energy should be chosen as cluster heads. Javaid et al. [39] introduced an Enhanced Developed Distributed Energy-Efficient Clustering (EDDEEC) scheme where the cluster head election probability is dynamically changed according to an absolute energy level threshold. to avoid selecting only the advanced and super sensors as cluster heads in all rounds, which depletes their energy quickly, the energy level threshold guarantees that all nodes have the same CH selection probability when they own almost the same residual energy. ...
In this work, an improved version of the gateway based multi-hop routing protocol was studied. The MGEAR protocol is mainly used for prolonging network lifetime in homogeneous wireless sensor network. Herein, the proposed approach aims to prolong network lifetime and enhance the throughput of this protocol in the case of heterogeneous wireless sensor networks (HWSNs). In the MGEAR, the network is divided into several fields; sensor nodes in the first field communicate directly with the base station. Sensors in the center of the network send their data to the gateway which perform data fusion and spread to the base station. The rest of nodes are divided into two equal regions, in each region sensor nodes are grouped into clusters with a leading node as cluster-head. The central point of our approach is the selection of cluster-heads which is based on a ratio between the residual energy of each sensor node and the average energy of the region which it belongs. In order to equalize the load and prolong the lifetime of sensors, the cluster-head election probability is computed in each round according to the residual energy of each sensor node. Finally, the simulation results showed that this model had higher throughput and increased the lifetime by 130%, 151%, 167%, 171%, and 215% compared to HCR, ERP, ModLEACH, D-MSEP and DDEEC protocols respectively in the case of 2-level heterogeneity. In case of 3-level heterogeneity, the network lifetime is increased by 123%, 150%, 163% and 218% compared to HCP, ModLEACH, hetSEP and hetDEEC.
... Qing et al. [36] and Javaid et al. [37] developed the DEEC (Developed Distributive Energy-Efficient Clustering) and EDDEEC (Enhanced Developed Distributive Energy-Efficient Clustering) protocols, respectively. In DEEC, the average network energy and node initial energy-based probability function is formulated. ...
A wireless sensor network consists of numerous low-power microsensor devices that can be deployed in a geographical area for remote sensing, surveillance, control, and monitoring applications. The advancements of wireless devices in terms of user-friendly interface, size, and deployment cost have given rise to many smart applications of wireless sensor networks (WSNs). However, certain issues like energy efficiency, long lifetime, and communication reliability restrict their large scale utilization. In WSNs, the cluster-based routing protocols assist nodes to collect, aggregate, and forward sensed data from event regions towards the sink node through minimum cost links. A clustering method helps to improve data transmission efficiency by dividing the sensor nodes into small groups. However, improper cluster head (CH) selection may affect the network lifetime, average network energy, and other quality of service (QoS) parameters. In this paper, a multiobjective clustering strategy is proposed to optimize the energy consumption, network lifetime, network throughput, and network delay. A fitness function has been formulated for heterogenous and homogenous wireless sensor networks. This fitness function is utilized to select an optimum CH for energy minimization and load balancing of cluster heads. A new hybrid clustered routing protocol is proposed based on fitness function. The simulation results conclude that the proposed protocol achieves better efficiency in increasing the network lifetime by 63%, 26%, and 10% compared with three well-known heterogeneous protocols: DEEC, EDDEEC, and ATEER, respectively. The proposed strategy also attains better network stability than a homogenous LEACH protocol.
... All nodes had different levels of energy due to random allocation. But this two-level heterogeneous network model has a limitation that each node has different energy level, and therefore, the deployed sensor nodes with higher energy levels may not be practically feasible [41,50,51]. Therefore, an advanced algorithm i.e. effective data gathering algorithm (EDGA) proposed for heterogeneous WSNs [41] which considers three levels of heterogeneity viz. ...
The wireless sensor networks (WSNs) with high sensing, computing and communication capabilities had wide applications in different fields. Till date, the WSNs are characterized by several resource constraints viz. energy, processing power, storage and transmission range etc. Therefore, the present research work adopted the hybrid protocol integrating homogeneous and heterogeneous clustering protocols viz. Low Energy Adaptive Clustering Hierarchy (LEACH)-Distributed Energy Efficient Clustering (DEEC) (LEACH-DEEC protocol) in comparison to LEACH-Centralized (LEACH-C) and LEACH-Centralized Sleeping (LEACH-CS) protocols to increase the energy efficiency and network lifetime of WSNs. For LEACH-C protocol, the network lifetime varied between 1058-1454 rounds, as compared with 1202-1987 rounds for LEACH-CS protocol, and 1389-2472 rounds for LEACH-DEEC protocol. Average across the number of alive nodes, the network lifetime for LEACH-CS and LEACH-DEEC protocol was improved by ~27.0 and 52.3%, respectively. The hybrid protocol resulted in ~158.1 and 121.7% increase in networks' lifetime, compared with LEACH-C and LEACH-CS protocols, respectively. The total initial energy was 63.7 Joules (J) for LEACH-C, 75.7 J for LEACH-CS and 97.9 J for LEACH-DEEC protocol. The number of data packets transferred to the BS with respect to the number of rounds by LEACH-C, LEACH-CS and LEACH-DEEC protocols was 2.44 x 10 4 , 3.24 x 10 4 and 4.24 x 10 4 packets, respectively. The reliability of LEACH-DEEC protocol was considerably higher by ~28.8 and 14.6% as compared to LEACH-C and LEACH-CS protocols, respectively. These results revealed that the number of rounds at half of the active nodes was ~14.9 and 29.6% higher with hybrid protocol, compared with LEACH-C and LEACH-CS protocols, respectively. These results highlight that newly proposed hybrid protocol was more energy efficient and had prolonged network lifetime.
... EEHC 38 presented an algorithm that made use of three types of heterogeneous nodes for the first time, but still, its network suffered from the penalizing the high-energy nodes. Enhanced developed DEEC (EDDEEC) 39 handled this matter of penalization of the high-energy nodes by considering the energy threshold concept which was done at only two levels in case of DDEEC. With the gradual advancements in the development of routing protocols, balanced energy efficient network integrated super heterogeneous (BEENISH) 40 and improved BEENISH 41 exploited four levels of energy heterogeneity in their algorithms. ...
The proliferation in the sensing technology in wireless sensor network has left an everlasting impact to revolutionize every sector of human's lives. The limited battery of sensor nodes (SNs) has generated a tremendous challenge for the researchers to ameliorate the network's life span. To pacify this concern, in this paper, we have proposed a particle swarm optimization (PSO)‐based dual sink mobility (PSODSM) technique to reduce the energy expenditure of the SNs. PSODSM has the paramount focus on the cluster head (CH) selection based on integration of crucial factors: “ratio of remaining energy to the initial energy,” node degree, node centrality, separation factor between the SN and the sink, CH number, and energy consumption rate. As soon as the CHs are selected, two sinks placed opposite to each other are made to move towards the selected CHs for data collection. The most prominent fact is the movement of sinks which is done outside the periphery of the network to collect data rather than the reported conventional research works that employ sink mobility inside the network. Extensive simulations are performed to examine the empirical evaluation of PSODSM based on the benchmark of different performance metrics. The performance validation of PSODSM is done against the other meta‐heuristic algorithms, and findings show that the PSODSM outperforms the competitive algorithms and is also found to be scalable pertaining to real time implementation. Particle swarm optimization (PSO)‐based clustering is performed that selects cluster head (CH) based on six parameters, (a) residual energy, (b) distance factor, (c) node centrality, (d) node degree, (e) CH count, and (f) energy consumption rate. The dual sinks are made to move opposite to the each other in such a way that they collectively cover four sides of the square‐shaped network. The simulation results demonstrate the proposed protocol outperforms the competitive algorithms.
... In Priyadarshi et al. (2020), A review on clustering protocols with energy heterogeneity in wireless sensor networks is presented. Well known probabilistic clustering protocols for homogeneous network being LEACH, Hybrid Energy Efficient Distributed Clustering (HEED) (Younis & Fahmy, 2004) etc. and well known protocols for heterogeneous network are Distributed Energy Efficient Clustering (DEEC) (Qureshi, Javaid, Malik, Qasim, & Khan, 2012), Enhanced DEEC(EDEEC) (Javaid et al., 2013), Energy efficient heterogeneous clustered scheme for wireless sensor networks(EEHC) (Kumar, Aseri, & Patel, 2009). In these protocols, CHs are selected based on a probability value defined by a threshold. ...
Wireless Sensor Networks is one of the most significant area of research where large number of sensor nodes that are distributed in a geographical area operate on limited battery power. As these networks, depending on the application, are sometimes deployed in in-hostile environment, which makes energy conservation one of the major challenge faced in WSN. To reduce energy consumption, clustering is considered to be the most efficient technique. This work proposes a new clustering approach that decreases energy consumption and results in prolonged network lifetime which is an important requirement for networks operating in inaccessible areas. In the proposed approach, heterogeneity is also implemented to increase the stability and energy efficiency of random networks. We have evaluated the efficiency of the proposed protocol through simulations and comparison is performed with well-known existing distributed protocols. Proposed approach shows efficient results in terms of the stability period for different network configurations and Base Station positions. Also, the results are found better in terms of number of alive nodes and network lifetime.
We report a new improved energy competentand optimizeddata packet flowprotocol with Hierarchical Clustering utilized in Wireless camera Sensor Network.Theexisting Extensive Zonal Stable Election Protocol has been modified along with the threshold parameters amplification and residual energy. It incorporates dynamic hybrid method withfinite number of Member Sensor nodes (MSN) in proximity with the base station share their data directly , while the rest of the farther nodes form a cluster for data transference using Cluster Head. The performance metrics accompanied by heterogeneity, longer network survival and better throughput have been improved. The network field was divided into 4 zones with a gateway for defined region 2, 3, and 4. The criterion for zone division remainedon the energy status (residual) of the MSNs and distance from the BS and the formulated field characteristics in the simulation were kept unknown. The obtained results demonstrate that our proposed modified version of EZSE protocol considerably performs better thanexisting EZ-SEP, Z-SEP,SEP, LEACH, Mod-Leach protocols during entire stabilitytimeframe. The notable achievement is also reported in throughput as the same isenhancedmore than by ~ 39%, 43%, 49% ,56%, 53% while total packets communicatedwithbase station has been increasedmore than by ~ 127%, 131%, 147%, 151%, 148% stability of the network is also improved more than by ~ 37%, 42%, 45%, 49%, 51% with the corresponding increase in the heterogeneity of networks.
Energy awareness is a key concern of recent advances in the Internet of Things (IoT) enabled wireless sensor networks (WSNs), and many optimization approaches to reduce energy consumption have been proposed. The most widely used routing technique for achieving energy efficiency in WSNs is the clustering hierarchy. Data transmission over long distances has a negative impact on network efficiency in terms of stability period, network lifetime, and QoS due to the selection of inadequate Cluster Heads (CHs). This paper describes a new Evolutionary Gateway-based Load-Balanced Routing (E-GLBR) algorithm for efficiently selecting appropriate CHs. The proposed algorithm is based on the genetic algorithm optimization method with a new fitness function that takes into account four major parameters to achieve the following goals: improving the CH selection process, reducing energy transmission range, increasing network stability and lifetime, and improving WSN coverage. A comparison simulation with the most recent related methods in MATLAB simulator is performed to evaluate the performance of our proposed algorithm. The simulation findings demonstrate that applying the developed evolutionary approach reduces the network’s energy consumption rate and increases the wireless network throughput. In various network scenarios, our suggested approach surpasses all other examined methods, extending the network coverage and prolonging the stability periods of Evolutionary Routing Protocol (ERP), Energy-Efficient Weighted Clustering (EEWC), Distance Incorporated Modified Stable Election Protocol (D-MSEP), and Energy dependent cluster formation (EDCF) by 55%, 43%, 26%, and 12% respectively.
In this study, an angular interrogation technique has been used for modeling a highly sensitive surface plasmon resonance (SPR)-based biosensor. The large surface area of graphene layer facilitates the biomolecules absorption. A five-layer Kretschmann configuration of the SPR biosensor containing the BP/MoS2 heterostructure with a gold layer is proposed. Compared to the traditional gold film-based SPR biosensors, the sensitivity of the proposed SPR biosensor has been significantly improved. By optimizing the proposed structure with a 50-nm-thick gold layer and a monolayer of graphene and heterostructure BP/MoS2 with a thickness of 0.34 and 0.75 nm, respectively, enhanced sensitivity 229.12°/RIU has been achieved. Moreover, the proposed SPR sensor design offers extremely small FWHM, high detection accuracy (DA), and high-quality factor (QF) parameters. The highest sensitivity of 251.6°/RIU was found with two layers of graphene with fixed monolayer of heterostructure BP/MoS2 configuration. It is also noted that the proposed SPR biosensor shows better results as compared to previously recorded SPR sensor parameters.KeywordsSurface plasmon resonanceHeterostructureBiosensorKretschmann configurationSensitivity
With the wide variety of applications involving different types of sensor nodes, heterogeneous wireless sensor Network (HWSN) has attained a lot of popularity. However, alike wireless sensor network (WSN), performance of the nodes in HWSN also succumbs due to scarcity of power. And hence, the researchers are consistently aiming to achieve energy-efficient network operations. And in this attempt, clustering has been found as a significant tool. In this work, two popular routing schemes for homogeneous WSN- energy-efficient protocol with static clustering (EEPSC) and an enhanced energy-efficient protocol with static clustering (E\({}^{3}\)PSC) have been deployed in the heterogeneous environment in order to figure out their performance; then, a new clustering-based scheme—heterogeneous energy-efficient clustering protocol for wireless sensor network (HEECP) has been proposed. The suitability of the HEECP is established through an extensive set of experiments conducted in MATLAB with respect to the network parameters—network lifetime and network throughput.KeywordsClusteringEnergy efficiencyEnergy heterogeneityNetwork lifetime
Background
The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge.
Methods
This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node.
Results
Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.
In this paper, an optimized cluster head (CH) selection method based on genetic algorithm (NCOGA) is proposed which uses the adaptive crossover and binary tournament selection methods to prolong the lifetime of a heterogeneous wireless sensor network (WSN). The novelty of the proposed algorithms is the integration of multiple parameters for the CH selection in a heterogeneous WSN. NCOGA formulates fitness parameters by integrating multiple parameters like the residual energy, initial energy, distance to the sink, number of neighbors surrounded by a node, load balancing factor, and communicating mode decider (CMD). The parameters for load balancing and CMD are utilized to discover out the best candidate to be selected as a relay CH and for deciding the mode of communication (single or multi-hop) of CH. Further, these parameters are useful in avoiding hot-spot problem in the network. The working of the NCOGA starts based on the criteria “consider only those nodes which have energy higher than the pre-defined threshold energy”. This criterion of nodes selection makes the NCOGA more efficient and quickly convergent. Extensive computer simulations are conducted to determine the effectiveness of the NCOGA. Simulation results reveal that the proposed NCOGA outperforms the state-of-the-art optimization algorithms based on GA in terms of several performance metrics, specifically, stability period, residual energy, network lifetime, and throughput.
WSN consist of tiny sensors which are distributed over the entire network having capabilities of sensing the data, processing it and convey it from one node to another node. The purpose of the study is to minimize the power utilization per round and elevate the network lifespan. In the present case, the nature inspired mechanisms are used to minimize the power utilization of the network. In the proposed study Butterfly Optimization Mechanism is used to choose the optimum quantity of CH from the dense nodes. The parameter is to be considered for the choosing of the CH is the remaining power of the node, interspace from the other nodes in the network, interspace from the BS, node centrality and node degree. The PSO is used to form the CH by choosing certain parameters such as interspace from the CH and the BS. The path is choosing by means of the Ant Colony Optimization (ACO) Mechanism. The route is optimized by the interspace, node degree and the choose remaining power. The inclusive performance of the projected protocol is measured in terms of stability period, quantity of active nodes, data acknowledged by the BS and the overall power utilization of the network. The results of the put redirect methodology are correlated with the extant mechanisms such as LEACH, DEEC, DDEEC, EDEEC [50–53] and also correlated with the swarm mechanisms such as CRHS, BERA, FUCHAR, ALOC, CPSO, FLION. This review will help investigators to discover the applications in this arena.
Tremendous growth in technology in last decade resulted in deployment of fifth generation (5G) of
wireless mobile networks by 2020. These networks will be leveraged with wide variety of applications
including device to device communication (D2D), machine to machine communication (M2M) and
internet of things (IOT). Development of these next generation wireless networks will be supported by
backbone semiconductor materials and devices. Traditional semiconductor material silicon has
approached its limits and will not be able to cope up with requirement of new applications. So, new
materials need to be investigated. Gallium Nitride presents a promising solution to satisfy needs of
future applications. This paper presents a brief overview of Gallium Nitride and its applicability in 5G
wireless networks
Currently, the world is witnessing a rapid development in the area of wireless sensor networks. It holds a potential to transform many features of our economy and life, starting from bio diversity mapping to industry automation, transportation, healthcare monitoring. These networks primarily aim to develop protocols to utilize the sensor node energy efficiently and thereby maximize the lifetime of the network. In this research paper, we compare the energy efficient clustering-based protocols used in wireless sensor networks. The work involves implementation of homogeneous protocols, namely low-energy adaptive clustering hierarchy (LEACH), energy aware multi-hop multi-path hierarchical (EAMMH). Next, the heterogeneous protocols such as enhanced distributed energy efficient clustering (EDEEC) and stable election protocol (SEP) are executed. The analysis is carried out in terms of number of nodes and probability of election for cluster head (CH). The observations and results obtained for these protocols are presented.KeywordsWireless sensor networksLow-energy adaptive clustering hierarchyEnergy aware multi-hop multi-path hierarchicalEnhanced distributed energy efficient clusteringStable election protocolAverage energyDead nodesEnergy efficiencyCluster head
With the explosion of the Internet of Things (IoT) devices, the advent of the edge computing paradigm, and the rise of intelligent applications for smart infrastructure surveillance, in-network data management is gaining a lot of research attention these days. The challenge lies in accommodating multiple application microservices with varying Quality of Service (QoS) requirements to share the sensing infrastructure for better resource utilization. In this work, we propose a novel data collection framework, CaDGen (Context-aware Data Generation) for such a shared IoT infrastructure that enables integrated data filtration and forwarding towards minimizing the resource consumption footprint for the IoT infrastructure. The proposed filtration mechanism utilizes the contextual information associated with the running application for determining the relevance of the data. Whereas the proposed forwarding policy aims to satisfy the diverse QoS requirements for the running applications by selecting the suitable next-hop forwarder based on the microservices distribution across different edge devices. A thorough performance evaluation of CaDGen through a testbed implementation as well as a simulation study for diverse setups reveals promising results concerning network resource utilization, scalability, energy conservation, and distribution of computation for optimal service provisioning. It is observed that the CaDGen can achieve nearly 35% reduction in the generated data for a moderately dynamic scenario without compromising on the data quality.
Nowadays, humanity lives in the digital era; the machine age. Information is shared between humans and machines ubiquitously. Several researchers focus on data routing in wireless sensor networks (WSN). Among the major challenges to make the network more and more efficient are the conservation of energy, which can extend the life of the network, and the speed of routing of information, which allows the network to respond quickly. The network security is another important aspect in the WSNs. These factors are the fundamental design issues in sensor networks. A WSN protocol can be either homogeneous or heterogeneous. In the homogeneous WSN, the sensor nodes are supplied with an equal amount of initial energy while heterogeneous networks have uneven initial energy distribution. One may consider multi-level initial energy distribution. In a two-level distribution, advanced nodes have higher energy in comparison to normal nodes. Distributed Energy Efficient Clustering (DEEC) and Enhanced Distributed Energy Efficient Clustering (EDEEC) heterogeneous wireless network protocols are presented in this paper.KeywordsWSNsClusteringHeterogeneous protocols Wireless sensor networksDEECEDEEC
The tag antenna exhibiting operation in European and North American regions covering major UHF RFID bands resonating at 866 MHz and 915 MHz, respectively, has been designed in this paper. The tag antenna operating in single UHF RFID region is converted to operate in dual UHF RFID region band tag antenna by modifying its geometry and optimizing the final geometry to obtain resonance at the required resonant frequencies. The tag antenna proposed in this paper comprises a meandered line element with extended lower stub to obtain additional band at European Region. The designed tag employs Alien Higgs-4 RFID chip having capacitive reactance. The designed tag utilizes inductive spiral loop to obtain conjugate impedance to match the capacitive RFID IC. Further, the designed modified tag antenna is simulated and its performance is analyzed based on different parameters such as its resistance, reactance, radiation efficiency, realized gain, etc. Also, it has been seen that the designed dual band antenna shows bidirectional and omnidirectional radiation pattern in E-plane and H-plane, respectively
In Wireless Sensor Networks (WSNs), issues including energy management, topology management, bandwidth estimation, packet loss calculation, etc. are dealt. Only a few works have paid attention to mitigating congestion while estimating delay and bandwidth for transmitting data packets in WSNs. Several works on queue management and congestion control have incorporated Soft Computing (SC) techniques to solve some of the problems in WSNs. In this paper, an Active Queue Management (AQM) model to estimate congestion using Random Early Detection (RED) and mitigate congestion is proposed. It is found that the results obtained outperform the existing methods in terms of delay between intermediate nodes, end-to-end delay, packet loss ratio, packet loss probability, queue size and energy consumption.
Due to high sensing, computing and communication capabilities, the wireless sensor networks (WSNs) are widelyused in different sectors, although with numerous resource constraints such as energy, processing power, storageand transmission range etc. The present study adopted the hybrid protocol integrating homogeneous and heterogeneous clustering protocols viz. Distributed Energy Efficient Clustering-maximum threshold (DEEC-MT) as compared with the basic DEEC, balanced and centralized DEEC (BC-DEEC) protocols. These three protocols were to enhance the energy efficiency and network lifetime of WSNs at different number of nodes (100 and 200) and packet size (3000 and 4000). The implementation of DEEC-MT protocol (number of nodes=200 and packet size=4000) resulted in increased lifetime of WSN by ~19.8% over the BC-DEEC protocol. However, the corresponding increase was ~37.6% over basic DEEC protocol. At 10000 rounds, the numbers of packets sent to the base station (BS) were 9.8 x 10 5 packets for DEEC-MT, while less than 1.7 x 10 5 packets for BC-DEEC and 2.3 x 10 5 packets for DEEC protocol.The reliability of DEEC-MT protocol was considerably higher by ~24.7 and 13.8% as compared to basic DEEC and BC-DEEC protocols, respectively. The implementation of DEEC-MT protocol decreased the end-to-end delay by ~23.7%, compared with BC-DEEC protocol. These results highlight that newly proposed DEEC-MT protocol was more energy efficient and had prolonged network lifetime.
The wireless sensor networks (WSNs) operate on resource constrained environment, therefore, changing or recharging the batteries is difficult and unmanageable task. Over the years, several routing algorithms have been studied in WSNs to enhance their lifespan with varying degree of success. The present review compared the energy efficiency of various homogeneous and heterogeneous routing algorithms for scheduling the distribution of cluster heads (CHs) in WSNs. The Low Energy Adaptive Clustering Hierarchy (LEACH), Power Efficient Gathering in Sensor Information Systems (PEGASIS) and Hybrid Energy-Efficient Distributed Clustering (HEED) are intended for homogenous WSNs, while the Stable Election Protocol (SEP), Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC) are important in WSNs which adequately operate within heterogeneous regions. The homogeneous WSNs had nodes with less energy expire sooner, compared with the nodes with high energy. The homogenous clustering-based algorithms are incompetent to delight every node with respect to energy. The HEED protocol has low overhead in terms of processing cycles and message exchanged and does not assume any distribution of the nodes or location awareness. The LEACH has been the most accepted distributed cluster-based routing algorithms in WSNs, which has been highly effective and efficient approach to prolong the network lifetime with increased energy efficiency. The improvement over LEACH is that HEED can evenly distribute the cluster heads in the sensing area by local competition. The Threshold sensitive energy efficient sensor network (TEEN) protocol has not been considered good for applications where the periodic reports are generated because some users may not get any data at all if the thresholds are not reached. The PEGASIS protocol is nearly optimal in terms of energy cost for data gathering applications.
Typically, a wireless sensor network contains an important number of inexpensive power constrained sensors, which collect data from the environment and transmit them towards the base station in a cooperative way. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. Clustering techniques are largely used for these purposes. In this paper, we propose and evaluate a clustering technique called a Developed Distributed Energy-Efficient Clustering scheme for heterogeneous wireless sensor networks. This technique is based on changing dynamically and with more efficiency the cluster head election probability. Simulation results show that our protocol performs better than the Stable Election Protocol (SEP) by about 30% and than the Distributed Energy-Efficient Clustering (DEEC) by about 15% in terms of network lifetime and first node dies.
Wireless Sensor Networks (WSNs), with growing applications in the environment
which are not within human reach have been addressed tremendously in the recent
past. For optimized working of network many routing algorithms have been
proposed, mainly focusing energy efficiency, network lifetime, clustering
processes. Considering homogeneity of network, we proposed Energy Efficient
Sleep Awake Aware (EESAA) intelligent routing protocol for WSNs. In our
proposed technique we evaluate and enhance certain issues like network
stability, network lifetime and cluster head selection process. Utilizing the
concept of characteristical pairing among sensor nodes energy utilization is
optimized. Simulation results show that our proposed protocolnificantly
improved the
We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.
A surveillance area is to be monitored using a grid network of heterogeneous sensor nodes. There are two types of nodes; type 0 nodes which perform sensing and relaying of data within a cluster, and type 1 nodes which act as cluster heads or fusion points. A surveillance aircraft visits the area periodically, and gathers information about the activity in the area. During each data gathering cycle, the sensor nodes use multi-hopping to communicate with their respective cluster heads, while the cluster heads perform data fusion, and transmit the aggregated data directly to the aircraft. We formulate and solve a cost based optimization problem to determine the optimum number of sensor nodes (n
0), cluster head nodes (n
1) and the battery energy in each type of nodes (E
0 and E
1 respectively) to ensure at least T data gathering cycles. We observe that the number of cluster heads required, n
1, scales approximately as \({n_0}{1-\frac{k}{4}}\) where k is the propagation loss exponent.
Typically, a wireless sensor network contains an important number of inexpensive power constrained sensors which collect data from the environment and transmit them towards the base station in a cooperative way. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. Many new protocols are specifically designed for these raisons where energy awareness is an essential consideration. The clustering techniques are largely used for these purposes.In this paper, we present and evaluate a Stochastic and Balanced Developed Distributed Energy-Efficient Clustering (SBDEEC) scheme for heterogeneous wireless sensor networks. This protocol is based on dividing the network into dynamic clusters. The cluster’s nodes communicate with an elected node called cluster head, and then the cluster heads communicate the information to the base station. SBDEEC introduces a balanced and dynamic method where the cluster head election probability is more efficient. Moreover, it uses a stochastic scheme detection to extend the network lifetime. Simulation results show that our protocol performs better than the Stable Election Protocol (SEP) and than the Distributed Energy-Efficient Clustering (DEEC) in terms of network lifetime. In the proposed protocol the first node death occurs over 90% times longer than the first node death in DEEC protocol and by about 130% than SEP. Postprint (published version)
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.
The design and analysis of routing protocols is an important issue in dynamic networks such as packet radio and ad-hoc wireless networks. Most conventional protocols exhibit their least desirable behavior for highly dynamic interconnection topologies. We propose a new methodology for routing and topology information maintenance in dynamic networks. The basic ideabehind the protocol is to divide the graph into a number of overlapping clusters. A change in the network topology corresponds to a change in cluster membership. We present algorithms for creation of clusters, as well as algorithms to maintain them in the presence of various network events. Compared to existing and conventional routing protocols, the proposed cluster-based approach incurs lower overhead during topology updates and also has quicker reconvergence. The e#ectiveness of this approach also lies in the fact that existing routing protocols can be directly applied to the network # replacing the nodes by clusters. 1 Int...
In order to prolong the network lifetime, energy-efficient protocols should be designed to adapt the characteristic of wireless sensor networks. Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. This paper studies the performance of clustering algorithm in saving energy for heterogeneous wireless sensor networks. A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks is proposed and evaluated. In the new clustering scheme, cluster-heads are elected by a probability based on the ratio between residual energy of node and the average energy of network. The high initial and residual energy nodes will have more chances to be the cluster-heads than the low energy nodes. Simulational results show that the clustering scheme provides longer lifetime and higher throughput than the current important clustering protocols in heterogeneous environments.
Many routing protocols on clustering structure have been proposed in recent years. In recent advances, achieving the energy efficiency, lifetime, deployment of nodes, fault tolerance, latency, in short high reliability and robustness have become the main research goals of wireless sensor network. Many routing protocols on clustering structure have been proposed in recent years based on heterogeneity. We propose EDEEC for three types of nodes in prolonging the lifetime and stability of the network. Hence, it increases the heterogeneity and energy level of the network. Simulation results show that EDEEC performs better than SEP with more stability and effective messages.
Wireless Sensor Networks (WSNs) contain numerous sensor nodes having limited
power resource, which report sensed data to the Base Station (BS) that requires
high energy usage. Many routing protocols have been proposed in this regard
achieving energy efficiency in heterogeneous scenarios. However, every protocol
is not suitable for heterogeneous WSNs. Efficiency of protocol degrades while
changing the heterogeneity parameters. In this paper, we first test Distributed
Energy- Efficient Clustering (DEEC), Developed DEEC (DDEEC), Enhanced DEEC
(EDEEC) and Threshold DEEC (TDEEC) under several different scenarios containing
high level heterogeneity to low level heterogeneity. We observe thoroughly
regarding the performance based on stability period, network life time and
throughput. EDEEC and TDEEC perform better in all heterogeneous scenarios
containing variable heterogeneity in terms of life time, however TDEEC is best
of all for the stability period of the network. However, the performance of
DEEC and DDEEC is highly effected by changing the heterogeneity parameters of
the network.
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.
The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.
The popularity of Wireless Sensor Networks have increased tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since these devices rely on battery power and may be placed in hostile environments replacing them becomes a tedious task. Thus, improving the energy of these networks becomes important.The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency. It presents a comparison between the different methods on the basis of the network lifetime . It proposes a modified approach for cluster head selection with good performance and reduced computational complexity .In addition it also proposes BFO as an algorithm for clustering of WSN which would result improved performance with faster convergence.
Sensor webs consisting of nodes with limited battery power and wireless communications are deployed to collect useful information from the field. Gathering sensed information in an energy efficient manner is critical to operate the sensor network for a long period of time. In W. Heinzelman et al. (Proc. Hawaii Conf. on System Sci., 2000), a data collection problem is defined where, in a round of communication, each sensor node has a packet to be sent to the distant base station. If each node transmits its sensed data directly to the base station then it will deplete its power quickly. The LEACH protocol presented by W. Heinzelman et al. is an elegant solution where clusters are formed to fuse data before transmitting to the base station. By randomizing the cluster heads chosen to transmit to the base station, LEACH achieves a factor of 8 improvement compared to direct transmissions, as measured in terms of when nodes die. In this paper, we propose PEGASIS (power-efficient gathering in sensor information systems), a near optimal chain-based protocol that is an improvement over LEACH. In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. Simulation results show that PEGASIS performs better than LEACH by about 100 to 300% when 1%, 20%, 50%, and 100% of nodes die for different network sizes and topologies.
Routing in ad-hoc networks is a difficult challenge that involves a tradeoff between efficiency and responsiveness. An ad-hoc network routing algorithm must adapt rapidly enough to topology changes to meet the performance demands of users, without over-utilizing network resources. This paper presents the (α,t) cluster framework which utilizes a distributed dynamic clustering strategy to organize nodes into clusters in which the probability of path failure due to node movement can be bounded over time. The objective of the clustering strategy is to achieve scalability and support robust, efficient routing subject to a wide range of mobility rates. Based on the (α,t) cluster scheme, routes within clusters are maintained on a proactive basis, whereas hierarchical routing between clusters is managed on a demand-basis. Simulation results show that the cluster organization can be effectively adapted to node mobility and that routing is both more robust and efficient than routing in fully proactive, reactive or fixed-hybrid schemes
Topology control in a sensor network balances load on sensor nodes, and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad-hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intra-cluster and inter-cluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.
PEGASIS: “power efficient gathering in sensor information systems
- S Lindsey
- C S Raghavenda