A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

Optimal Node Scheduling for Desired Percentage of Coverage in Wireless Sensor Networks

Recent developments in wireless communication and embedded computing technologies have led to the advent of wireless sensor network technology. Hundreds of thousands of these micro sensors can be deployed in many areas including health, environment and battlefield in order to monitor the domain with desired level of accuracy. When wireless sensors are deployed in an area, the lifetime of the network should last as long as possible according to the original amount of energy. Therefore, reducing energy consumption in WSNs is of primary concern. We have proposed a node scheduling solution that solves the coverage and connectivity problem in sensor networks in an integrated manner. In this way we will divide network life time to finite number of rounds and in each round we will generate a coverage bitmap of sensors of the domain and based on this bitmap it will decided which sensors remain active or go to sleep. We will check the connection of the sensor network by using Laplacian of adjancy graph of active nodes in each round. Also the network will be capable of producing desired percentage of coverage by using coverage bitmap. We will define the connected coverage problem as an optimization problem and we will seek a solution for the problem by using Genetic Algorithm optimization method.

An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks

A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.

Analysis of coverage problem under border effects in wireless sensor networks

Wireless sensor networks can be used to monitor the interested region by deploying dense sensor nodes. Coverage is a primary metric to evaluate the capacity of monitoring. In this paper, we focus on the coverage problem under border effects, where the sensor nodes are distributed in a circle-shaped region randomly. Under this scenario, we derive the expected coverage of the sensor node and the total network coverage provided by n sensor nodes accurately by probability. These findings are useful to determine the related parameters （sensing range, number of sensor nodes and radius of monitored region） for a specific network coverage ratio. Simulation results demonstrate that our analysis is correct and effective.

On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach

One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of WSNs.Layout optimization is an NP-hard combinatorial problem,which requires optimization of multiple competing objectives like cost,coverage,connectivity,lifetime,load balancing,and energy consumption of sensor nodes.In the last decade,several meta-heuristic optimization techniques have been proposed to solve this problem,such as genetic algorithms(GA)and particle swarm optimization(PSO).However,these approaches either provided computationally expensive solutions or covered a limited number of objectives,which are combinations of area coverage,the number of sensor nodes,energy consumption,and lifetime.In this study,a meta-heuristic multi-objective firefly algorithm(MOFA)is presented to solve the layout optimization problem.Here,the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs,which includes coverage,connectivity,lifetime,energy consumption and the number of sensor nodes.Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions,in comparison to multi-objective genetic algorithms(IBEA,NSGA-II)and particle swarm optimizers(OMOPSO,SMOPSO).Therefore,MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring.

Energy Proficient Reduced Coverage Set with Particle Swarm Optimization for Distributed Sensor Network

Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective node.In this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space.PSO is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting nodes.Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole network.The proposed algorithm has experimented with a different number of nodes.Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,which leads to coverage holes in WSNs.Thus,coverage control plays an important role in WSNs.To alleviate unnecessary energy wastage and improve network performance,we consider both energy efficiency and coverage rate for WSNs.In this paper,we present a novel coverage control algorithm based on Particle Swarm Optimization(PSO).Firstly,the sensor nodes are randomly deployed in a target area and remain static after deployment.Then,the whole network is partitioned into grids,and we calculate each grid’s coverage rate and energy consumption.Finally,each sensor nodes’sensing radius is adjusted according to the coverage rate and energy consumption of each grid.Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.

A Power- and Coverage-aware Clustering Scheme for Wireless Sensor Networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

Energy-Efficient Area Coverage in Heterogeneous Energy Wireless Sensor Networks

Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large extent on the sensing coverage, and its lifetime is determined by its energy consumption. In this paper, an energy-efficient Area Coverage protocol for Heterogeneous Energy sensor networks (ACHE) is proposed. ACHE can achieve a good performance in terms of sensing area coverage, lifetime by minimizing energy consumption for control overhead, and balancing the energy load among all nodes. Adopting the hierarchical clustering idea, ACHE selects the active nodes based on the average residual energy of neighboring nodes and its own residual energy parameters. Our simulation demonstrates that ACHE not only provide the high quality of sensing coverage, but also has the good performance in the energy efficiency. In addition, ACHE can better adapt the applications with the great heterogeneous energy capacities in the sensor networks, as well as effectively reduce the control overhead.

Dynamic Coverage with Wireless Sensor and Actor Networks in Underwater Environment

This paper studies the problem of dynamic coverage with wireless sensor and actor networks (WSANs) in underwater environment. Different from most existing works, the WSANs consist of two kinds of nodes, i.e., sensor nodes (SNs) which cannot move autonomously and actor nodes (ANs) which can move autonomously according to the performance requirement. The problem of how to coordinate two kinds of nodes to facilitate dynamic coverage in underwater environment is challenging due to their heterogeneous capabilities. To reduce redundancy of communication links and improve connectivity between ANs and SNs in underwater WSANs, a min-weighted rigid graph based topology optimization scheme is first developed, such that the underwater communication energy consumption can be saved. With the optimized topology, a dynamic coverage strategy is proposed to improve the coverage among SNs and ANs for underwater WSAN where underwater fluid motions are considered. Furthermore, it is proved that the network coverage area is connected by using the min-weighted rigid graph. Finally, simulation results are presented to show the effectiveness of the main results. © 2014 Chinese Association of Automation.

Localized Coverage Connectivity Based on Shape and Area Using Mobile Sensor Robots in Wireless Sensor Networks

A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.

Radio Coverage Mapping in Wireless Sensor Networks

Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The existing approaches (often RSSI measurement-based), however, suffer from heavy measurement cost and are not well suitable for the large-scale densely deployed WSNs. NRC-Map, a novel algorithm is put forward for sensor nodes radio coverage mapping. The algorithm is based on the RSSI values collected by the neighbor nodes. According to the spatial relationship, neighbor nodes are mapping to several overlapped sectors. By use of the least squares fitting method, a log-distance path loss model is established for each sector. Then, the max radius of each sector is computed according to the path loss model and the given signal attenuation threshold. Finally, all the sectors are overlapped to estimate the node radio coverage. Experimental results show that the method is simple and effectively improve the prediction accuracy of the sensor node radio coverage.

An Algorithm for Hybrid Nodes Barrier Coverage Based on Voronoi in Wireless Sensor Networks

In order to make up for the deficiencies and insufficiencies that In order to make up for the deficiencies and insufficiencies that wireless sensor network is constituted absolutely by static or dynamic sensor nodes. So a deployment mechanism for hybrid nodes barrier coverage (HNBC)is proposed in wireless sensor network, which collaboratively consists of static and dynamic sensor nodes. We introduced the Voronoi diagram to divide the whole deployment area. According to the principle of least square method, and the static nodes are used to construct the reference barrier line (RBL). And we implemented effectively barrier coverage by monitoring whether there is a coverage hole in the deployment area, and then to determine whether dynamic nodes need limited mobility to redeploy the monitoring area. The simulation results show that the proposed algorithm improved the coverage quality, and completed the barrier coverage with less node moving distance and lower energy consumption, and achieved the expected coverage requirements

Cooperative algorithm for improving network connectivity in clustered wireless sensor networks

In order to improve network connectivity in clustered wireless sensor networks,a node cooperative algorithm based on virtual antenna arrays is proposed.All the nodes in the network are assumed to be clustered via Poisson Voronoi tessellation（PVT）.The activation of the node cooperative algorithm is determined by the cluster heads（CHs） according to communication links.When the cooperative algorithm is activated,the CH selects cooperative nodes（CNs） from its members to form a virtual antenna array.With the cooperation,nodes can extend the inter-cluster communication range to directly contact with further nodes after a coverage hole is detected,or compensate for channel gains while inter-cluster transmission fails due to deep channel fading.Simulation results show that the proposed algorithm achieves better network connectivity and energy efficiency.It can reduce outage probability,sustain network connectivity and maintain operations as long as possible,which prolongs network operation time.

Optimization method of linear barrier coverage deployment for multistatic radar

To address the problem of building linear barrier coverage with the location restriction, an optimization method for deploying multistatic radars is proposed, where the location restriction splits the deployment line into two segments. By proving the characteristics of deployment patterns, an optimal deployment sequence consisting of multiple deployment patterns is proposed and exploited to cover each segment. The types and numbers of deployment patterns are determined by an algorithm that combines the integer linear programming(ILP)and exhaustive method(EM). In addition, to reduce the computation amount, a formula is introduced to calculate the upper threshold of receivers’ number in a deployment pattern. Furthermore, since the objective function is non-convex and non-analytic, the overall model is divided into two layers concerning two suboptimization problems. Subsequently, another algorithm that integrates the segments and layers is proposed to determine the deployment parameters, such as the minimum cost, parameters of the optimal deployment sequence, and the location of the split point. Simulation results demonstrate that the proposed method can effectively determine the optimal deployment parameters under the location restriction.

Power-efficient topologies for wireless sensor networks with fixed communication range

The expectations for sensor networks are growing. The performance of wireless sensor networks （WSNs） is greatly influenced by their network topology. In this paper, we consider four patterned topologies that best support connectivity among these deployed sensor nodes in two-tiered WSNs. The theoretical and simulation results show that the triangle-based topology has smaller cell number, shorter maximum hop length, less total energy consumption, and better performance than other topologies. The analysis carried out in this paper could provide the guidelines for network deployment and protocol design in the future applications.

Genetic Algorithm Based Node Deployment in Hybrid Wireless Sensor Networks

In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.