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Wireless sensor networks (WSNs) found application in many diverse fields, starting from environment monitoring to machine health monitoring. The sensor in WSNs senses information. Sensing and transmitting this information consume most of the energy. Also, this information requires proper processing before final usages. This paper deals with minimising the redundant information sensed by the sensors in WSNs to reduce the unnecessary energy consumption and prolong the network lifetime. The redundant information is expressed in terms of the overlapping sensing area of the working sensors set. A mathematical model is proposed to find the redundant information in terms of the overlapping area. A combined meta-heuristic approach is used to achieve the optimal coverage, and the effect of the overlapping area is considered in the objective function to reduce the amount of redundant information sensed by the working sensors set. Improved genetic algorithm (IGA) and Binary ant colony algorithm (BACA) are used as heuristic tools to optimise the multi-objective function. The objective was to find the minimum number of sensors that cover a complete scenario with minimum overlapping sensing region. The results show that optimal coverage with the minimum working sensor set is achieved and then by incorporating the concept of overlapping area in the objective function, sensing of redundant information is further reduced.
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... Not all the bio-inspired algorithms are of potential use in WSNs. The algorithms for any specific problems in WSNs arena are selected based on the analogous parameters between the problem domain and the algorithm (e.g., Table 3) [37,38]. According to the previous studies (Table 1), only three algorithms (PSO, GA, and ACO) covers all the problem domains of WSNs (i.e. ...
... GA is proven to be good for random as well as deterministic deployment [25,38,[203][204][205][206]. It is also good at finding lesser number of data aggregation points while routing the data to the base station [40,[207][208][209]. ...
... They also reported a high coverage rate. Recently, Singh et al. [38] have used the same IGA-BACA and conventional GA for optimal coverage in WSNs with reduced sensing of redundant information. ...
Article
In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGA-BACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area.
... Not all the bio-inspired algorithms are of potential use in WSNs. The algorithms for any specific problems in WSNs arena are selected based on the analogous parameters between the problem domain and the algorithm (e.g., Table 3) [37,38]. According to the previous studies (Table 1) In the next section, we have tried to elaborate and give an insight into all these algorithms. ...
... GA is proven to be good for random as well as deterministic deployment [38,25,203,204,205,206]. ...
... Recently, Singh et al. [38] have used the same IGA-BACA and conventional GA for optimal coverage in WSNs with reduced sensing of redundant information. ...
Preprint
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In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery-driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGABACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area. Keywords: Optimal Coverage, Bio-inspired Algorithm, Lion Optimization, WSNs.
... A hybrid meta-heuristic approach using the Improved Genetic Algorithm (IGA) and Binary Ant Colony Algorithm (BACA) ensure optimal coverage, minimizes the sensing of redundant information, and optimizes the multi-objective function by determining the minimum number of sensors [202]. Artificial Bee Colony (ABC) prolongs the network's performance in WSNs and achieves better exploitation and exploration rate at the time of cluster head selection [203]. ...
... Rule-based approaches in machine learning techniques are mainly used to save the energy of sensor nodes, while transmission of information, thereby prolong the network lifetime. A hybrid meta-heuristic approach using the Improved Genetic Algorithm (IGA) and Binary Ant Colony Algorithm (BACA) ensures optimal coverage, minimizes the sensing of redundant information, and optimizes the multi-objective function by determining the minimum number of sensors [202]. Square grid deployment is simple to implement, achieves better coverage performance, and supports grid merging for clustering than hexagonal grid deployment [288]. ...
Article
Wireless Sensor Networks (WSNs) have attracted various academic researchers, engineers, science, and technology communities. This attraction is due to their broad research areas such as energy efficiency, data communication, coverage, connectivity, load balancing, security, reliability, scalability, and network lifetime. Researchers are looking towards cost-effective approaches to improve the existing solutions that reveal novel schemes, methods, concepts, protocols, and algorithms in the desired domain. Generally, review studies provide complete, easy access or solution to these concepts. Considering this as a driving force and the impact of clustering on the deterioration of energy consumption in wireless sensor networks, this review focus on clustering methods based on different aspects. This study’s significant contribution is to provide a brief review in the field of clustering in wireless sensor networks based on three different categories, such as classical, optimization, and machine learning techniques. For each of these categories, various performance metrics and parameters are provided, and a comparative assessment of the corresponding aspects like cluster head selection, routing protocols, reliability, security, and unequal clustering are discussed. Various advantages, limitations, applications of each method, research gaps, challenges, and research directions are considered in this study, motivating the researchers to carry out further research by providing relevant information in cluster-based wireless sensor networks.
... The popularity of these networks is due to the reason that they can be formed on the fly easily and operate in a decentralized fashion without requiring any fixed infrastructure such as base station 3) . As a result, they are being deployed for a large number of military and civilian applications such as border surveillance, enemy tracking andreconnaissance, precision agriculture, landslide monitoring, wildlife monitoring, seismic activity monitoring, industrial automation, ubiquitous and pervasive computing, health industry and so on 4,5,6,7,8,9) . ...
... The expected useful coverage area of an SN lying in a corner region is obtained by averaging over the entire circular region and is given by Eq. 9 = � − 29 24 � 2 (9) and the probability that an arbitrary SN will be lying in a corner region can be computed using Eq. 10. ...
Article
Coverage is one of the most critical performance metric of wireless sensor networks (WSNs) because it shows how well a region of interest (RoI) is being monitored by the deployed network. In general, the RoI is either circular or rectangular in shape which have boundary regions. Sensor nodes (SNs) deployed in these regions suffer boundary effects (BEs), i.e., the useful coverage area of an SN deployed near the boundary regions is less as compared to the SNs deployed in the middle of the RoI. It is imperative to consider these BEs while evaluating the performance of WSNs because analytical results derived for large networks are not valid for finite networks. In addition, SNs of a deployed WSN are prone to failure due to a large number of factors such as battery drainage, high temperature, and other environmental conditions. Earlier researchers have ignored the impact of BEs in the presence of sensor failure while evaluating the coverage performance of WSNs. In this work, we derive an analytical model by considering BEs and sensor failure to achieve a closed form expression for the k-coverage performance of a WSN deployed in a rectangular RoI. Further, we analyze the influence of various network parameters such as number of SNs, sensing range, and sensor failure rate on the k-coverage performance of the network. The results obtained using the proposed model show a good match with simulations outcomes with Root Mean Square Error (RMSE) no more than 0.03, thus, validating our model. For 𝑟𝑟𝑠𝑠 = 80 m and N = 100, 1-coverage probabilities are found to be 0.9874, 0.9313 and 0.8069 for k = 1, 2 and 3 respectively showing that the k-coverage probability deriorates with the increase in the value of k.
... Wireless sensor networks (WSNs) have drawn tremendous attention over the last few years in the field of research and industry. The key reason for this rapid development is their potential applications in various situations such as in border surveillance systems, military operations, space exploration, health care, agriculture, environmental monitoring, and public safety [1][2][3][4]. Such applications involve a vast number of deployed sensor nodes to monitor a particular Region of Interest (RoI). ...
Article
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Border surveillance is indeed one of the most pertinent applications of wireless sensor networks, primarily used for security purposes such as intrusion detection in border regions or protected areas. In order to detect unauthorized access or penetration through the region of interest, sensor nodes are deployed to form barriers, that acts as the performance metric of wireless sensor networks. In this paper, a Distributed Border Surveillance (DBS) system incorporating shadowing effects is proposed for a wireless sensor network deployed in a rectangular region of interest. The DBS system evaluates the number of required barriers to monitor the given region and conserves energy. Besides, a log-normal shadowing model is considered, which incorporates the asymmetry in sensing range along with the stochastic nature of wireless channels. The performance of the proposed DBS system is analyzed based on the number of barriers obtained. Then, the impact of various network and system parameters such as the number of nodes, sensing range of nodes, height and width of the network region on the number of barriers obtained in a rectangular region are analyzed. The same approach is extended for a circular region of interest in terms of sensing range of nodes. The proposed system is implemented in NS-2.35 simulator, and it is found that the performance of the proposed DBS system is 75% better than the existing binary sensing range model-based DBS system.
... To reduce redundant sensing in WSNs, mathematical modelling has been proposed in [19], which uses biologically inspired techniques to minimize the overlapped sensing area. These models define an objective function for an overlapping area and use genetic and ant colony algorithms as meta-heuristics to find optimal answers for objective function. ...
Article
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Wireless sensor networks can be used as cost-effective monitoring and automation platforms in smart manufacturing and Industry 4.0. Maximizing the covered area and increasing the network lifetime are two challenging tasks in wireless sensor networks. A feasible solution for maximizing the coverage area and network lifetime is detecting and relocating the covered redundant nodes. A covered redundant node is a node whose covered area is also covered by the other active nodes in the network. After identifying the covered redundant nodes, putting them in sleep mode can increase the network lifetime. In addition, moving the detected redundant nodes to the uncovered locations can improve the overall covered area by the sensor nodes. However, finding the redundant nodes is an NP-complete problem. In this paper, we propose a localized distributed algorithm for identifying the redundant nodes based on the 2-hop local neighborhood information of the nodes. The proposed algorithm uses the existing connections between the neighbors of each sensor node to decide the redundancy of the node. The algorithm is localized and does not need the entire topology of the network or the coordinates of the nodes.
... Ref. [101] reported using a hybrid technique based on an improved GA and a binary ACO to achieve optimal coverage, reduce data redundancy, and optimize the multiobjective function by determining the least number of sensors required. The role of the ABC was to extend the performance of the WSN and achieve better exploration and exploitation during CH selection [102]. ...
Article
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Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.
... Coverage is a crucial QoS parameter for WSNs and has been studied widely over tim [19][20][21][22][23]. It is defined as the percentage of the entire RoI being covered by the deployed network. ...
Article
Coverage is a crucial quality of service (QoS) parameter for wireless sensor networks (WSNs), which tells how effectively the deployed network monitors a given region. Analytical models available for the coverage analysis of finite WSNs are not scalable for large networks due to boundary effects (BEs).The effective coverage area (ECA) of a sensor node lying near the boundary regions is less than the ECA of a sensor node lying in the region's middle. Also, with the presence of obstacles in the transmission path and the radio irregularities, there are frequent changes in the wireless channel characteristics known as shadowing effects (SEs). Therefore, it becomes crucial to include BEs and SEs while investigating the coverage performance of WSNs. In this work, we analyze the-coverage performance of a WSN spread in a circular region of interest (RoI) by considering BEs and using a binary and a log-normal sensing range model.Furthermore, we also assess the effect of various network parameters viz.,the number of sensor nodes, maximum sensing range, and standard deviation of SEs on the-coverage of the WSN. Also, we compare the-coverage outcomes obtained by considering BEs with the results obtained by ignoring BEs. It is found that both BEs and SEs have a significant effect on the-coverage performance of the WSNs. The simulation outcomes substantiate analytical results and match upto a great extent.
... The results are promising. Recently, Singh et al. [42] proposed an approach that uses a hybrid meta-heuristic algorithm using Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGA-BACA) for optimal coverage with minimum redundant information. Still, the process has a high computational cost. ...
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Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5-0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.
... However, these sensor nodes have a restricted battery life, and the battery's replacement is not a feasible solution in hard to reach areas such as sensors implanted in the human body. Therefore, optimal network coverage [2], and energy harvesting (EH) could be a solution to create WSNs autonomous and provide widespread use of these systems in various applications such as military, health, environment, and security [3][4][5][6][7][8]. EH refers to harnessing and converting energy from the surroundings or alternative sources into electrical energy. ...
Article
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In this paper, the newly emerging wireless powered communication network is studied. In doing so, the performance of the global controller (GC) is evaluated, which coordinates the wireless energy transmissions between two sensor nodes. Both the sensors have the same harvested energy for uplink (UL) transmission of information through time-division-multiple-access. Afterwards, the information transmission time is optimised to maximise the common throughput of both the sensors with a total time constraint based on the user's UL channels along with the same harvested energy value. Further, due to the "doubly near-far" phenomenon, a remote sensor from the GC, which has poor channel conditions than a nearer user, has to transmit more time in the UL for maximum common throughput. To overcome this problem, the energy exchange (EEx) model is proposed where both sensors first harvest the same amount of wireless energy and then exchange energy to nullify the different channel conditions between sensors and GC to send their independent information in the UL. Simulation results demonstrate the EEx Model's effectiveness over without energy exchange (WEEx) model in eliminating the doubly near-far problem in wireless powered communication network but at the cost of maximum sum-throughput. The maximum sum-throughput of the proposed EEx model is 35% lower than the WEEx model. However, the average BER in the proposed EEx model is 74.6% lower than the WEEx model, which increases the reliability of the model.
... WSNs is a widely accepted and renowned technology because it is cheap, readily available, and can be installed on the fly in almost no time at any place [5,6]. In addition, WSNs consist of small and homogeneous sensors that work in a de-centralised fashion requiring no pre-installed foundation and communicating over wireless channels [7]. ...
Article
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The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms
... A WSN does not need any pre-installed base for support and operates in a self-structured and decentralised manner (Nagar, Chaturvedi, & Soh, 2020). Also, ease in deployment in remote/ inaccessible regions, hazardous environments and emergency conditions, have paved the path for their numerous military and civilian applications such as border surveillance, industrial monitoring and control, security, structural health monitoring, precision agriculture, healthcare, remote landslides monitoring and forest fire detection (Noel et al., 2017;Jawad, Nordin, Gharghan, Jawad, & Ismail, 2017;Dey, Ashour, Shi, Fong, & Sherratt, 2017;Kumar, Duttagupta, Rangan, & Ramesh, 2020;Aponte-Luis et al., 2018;Singh, Sharma, Singh, & Kumar, 2019). ...
Article
Sensors in Wireless Sensor Network (WSN) sense, process, and transmit information simultaneously. They mainly find applications in agriculture monitoring, environment monitoring, smart city development and defence. These applications demand high-end performance from the WSN. However, the performance of a WSN is highly vulnerable to various types of security threats. Any intrusion may reduce the performance of the WSN and result in fatal problems. Hence, fast intrusion detection and prevention is of great use. This paper aims towards fast detection and prevention of any intrusion using a machine learning approach based on Gaussian Process Regression (GPR) model. We proposed three methods (S-GPR, C-GPR and GPR) based on feature scaling for accurate prediction of k-barrier coverage probability. We have selected the number of nodes, sensing range, Sensor to Intruder Velocity Ratio (SIVR), Mobile to Static Node Ratio (MSNR), angle of the intrusion path and required k as the potential features. These features are extracted using an analytical approach. Simulation results demonstrate that the proposed method III accurately predicts the k-barrier coverage probability and outperforms the other two methods (I and II) with a correlation coefficient (R = 0.85) and Root Mean Square Error (RMSE = 0.095). Further, the proposed methods achieve a higher accuracy as compared to other benchmark schemes.
... As a part of future work, we can use intelligence algorithm to further optimize and improve the reuse factor [29]. ...
Presentation
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Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.
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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.
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Full-text available
Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.
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Chapter
In this chapter, an investigation is carried out to formulate theoretical results regarding the behavior of a genetic algorithm-based pattern classification methodology, for an infinitely large number of sample points n, in an N dimensional space RN. It is shown that for n → ∞, and for a sufficiently large number of iterations, the performance of this classifier approaches that of the Bayes classifier. Experimental results, for a triangular distribution of points, are also included that conform to this claim.
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Conference Paper
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Conference Paper
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Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)-based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.
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Every insect is considered, from the viewpoint of biological evolution, to be a neural cell that constitutes a neural network in a casual and loose way of joint. Through simulating the ant swarm intelligence on the basis of human neural network, this paper advances a linear binary network. The binary code expects a low intelligence of each ant, and each path corresponds to a comparatively small storage space, thus considerably improving the efficiency of computation. The test of function optimization and multi-dimensional 0/1 Knapsack proves that the computation has a good speed of convergence, a high stability and a perfect solution.
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The paper provides a new method for route optimization in wireless sensor network (WSN). WSN is an adaptive, self-organizing, jump communication network and is used widely in many fields. The proposed method consists of both ant colony algorithm and genetic algorithm. The improved ant colony algorithm is presented by overcoming its own many defects, such as the long initial population time, slow convergence speed and easy in local optimum and precocity. The genetic algorithm is absorbed into the improved ant colony algorithm, which reduces the complexity and enhances the efficiency greatly. And the multi-path route is added for transmitting data. The simulation result shows that the model is effective and improved algorithm has better performance than basic GA or improved AA.
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This paper introduces a new kind of simulated evolutionary al- gorithm, ant colony optimal algorithm (ACO). By analyzing the resemblance between this algorithm and the cluster problem, we proposes a mathematic model for cluster analysis based on ACO, put forward a new improved ACO based on rudimentary ACO, and apply it to diagnose operation state of diesel engine fuel system.The conclusion shows that this method is feasible.
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Form the view of the biological viewpoint; each ant of social insects is regarded as neuron. They compose a neural network by stochastic and loose connections with each other; Similar to the artificial neural network simulating the ant colony, the Ant colony algorithm of binary network is presented. As the binary coding is adapted, lower aptitude behave of single ant is requested, less storage is needed, the algorithm efficiency is enhanced largely. The test function and the multi 0/1 Knapsack problem show that the algorithm has better convergence speed and stability, the result is very excellent
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This paper concerns the design of user-oriented, interactive systems that combine knowledge-acquisition techniques with the basic techniques of database searching and word processing. The object of such a system is to create, for each individual user, a database and acquisition system relevant to his changing needs and purposes. Six criteria for an interactive knowledge-acquisition system employing learning are presented. Then a prototype, based on extant systems and using an adaptive algorithm as an inference procedure, is used to explore these criteria in detail.
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This paper addresses an integrated model that schedules multi-item replenishment with uncertain demand to determine delivery routes and truck loads, where the actual replenishment quantity only becomes known upon arrival at a demand location. This paper departs from the conventional ant colony optimization (ACO) algorithm, which minimizes total travel length, and incorporates the attraction of pheromone values that indicate the stockout costs on nodes. The contributions of the paper to the literature are made both in terms of modeling this combined multi-item inventory management with the vehicle-routing problem and in introducing a modified ACO for the inventory routing problem.
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Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. This paper surveys recent routing protocols for sensor networks and presents a classification for the various approaches pursued. The three main categories explored in this paper are data-centric, hierarchical and location-based. Each routing protocol is described and discussed under the appropriate category. Moreover, protocols using contemporary methodologies such as network flow and quality of service modeling are also discussed. The paper concludes with open research issues.
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A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application’s design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.
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This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node's dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme.
On the problem of k-coverage in mission-oriented mobile wireless sensor networks
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