Coverage Repair Strategies for Wireless Sensor Networks Using Mobile Actor Based on Evolutionary Computing

A standard traveling salesman problem(TSP) under dual-objective strategy constrained is proposed in this paper, characterized by the fact that the demand of both as many as possible the numbers of nodes be visited in time and minimum trajectory distance. The motivation for this TSP problem under dual-objective strategy constrain stems from the coverage repair strategies for wireless sensor networks using mobile actor based on energy analysis, wherein a mobile robot replenishes sensors energy when it reaches the sensor node location. The Evolutionary Algorithm (EA) meta-heuristic elegantly solves this problem by the reasonable designed operators of crossover, mutation and local search strategy,which can accelerate convergence of the optimal solution. The global convergence of the proposed algorithm is proved, and the simulation results show the effectiveness of the proposed algorithm

Coverage Repair Strategies for Wireless Sensor Networks using Mobile Actor Based on Evolutionary Computing

A standard traveling salesman problem(TSP) under dual-objective strategy constrained is proposed in this paper, characterized by the fact that the demand of both as many as possible the numbers of nodes be visited in time and minimum trajectory distance. The motivation for this TSP problem under dual-objective strategy constrain stems from the coverage repair strategies for wireless sensor networks using mobile actor based on energy analysis, wherein a mobile robot replenishes sensors energy when it reaches the sensor node location. The Evolutionary Algorithm (EA) meta-heuristic elegantly solves this problem by the reasonable designed operators of crossover, mutation and local search strategy,which can accelerate convergence of the optimal solution. The global convergence of the proposed algorithm is proved, and the simulation results show the effectiveness of the proposed algorithm

A Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) for Energy and Network Lifetime Maximization under Coverage Constrained Problems in Heterogeneous Wireless Sensor Networks

Network lifetime maximization of Wireless Heterogeneous Wireless Sensor Networks (HWSNs) is a difficult problem. Though many methods have been introduced and developed in the recent works to solve network lifetime maximization. However, in HWSNs, the energy efficiency of sensor nodes becomes also a very difficult issue. On the other hand target coverage problem have been also becoming most important and difficult problem. In this paper, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent HWSNs with each vertex representing the assignment of a sensor nodes in a subset. At the same time, Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K-Coverage (KC) known as TFMGA-MDCCKC. Based on gene and chromosome information from the TFMGA, the gene seeks an optimal path on the construction graph model that maximizes the MDCCKC. In TFMGA gene thus focuses on finding one more connected covers and avoids creating subsets particularly. A local search procedure is designed to TFMGA thus increases the search efficiency. The proposed TFMGA-MDCCKC approach has been applied to a variety of HWSNs. The results show that the TFMGA-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that proposed TFMGA-MDCCKC approach performs better than Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method and the performance of the TFMGA-MDCCKC approach is closer to the energy-conserving strategy

Improvement of non-uniform node deployment mechanism for corona-based wireless sensor networks

The promising technology of Wireless Sensor Networks (WSNs), lots of applications have been developed for monitoring and tracking in military, commercial, and educational environments. Imbalance energy of sensors causes significant reduction in the lifetime of the network. In corona-based Wireless Sensor Networks (WSNs), nodes that are positioned in coronas near the sink drain their energy faster than others as they are burdened with relaying traffic come from distant coronas forming energy holes in the network. This situation shows significant effects on the network efficiency in terms of lifetime and energy consumption. The network may stop operation prematurely even though there is much energy left unused at the distant nodes. In this thesis, non-uniform node deployments and energy provisioning strategies are proposed to mitigate energy holes problem. These strategies concerns the optimal number of sensors required in each corona in order to balance the energy consumption and to meet the coverage and connectivity requirements in the network. In order to achieve this aim, the number of sensors should be optimized to create sub-balanced coronas in the sense of energy consumption. The energy provisioning technique is proposed for harmonizing the energy consumption among coronas by computing the extra needed energy in every corona. In the proposed mechanism, the energy required in each corona for balanced energy consumption is computed by determining the initial energy in each node with respect to its corona, and according to the corona load while satisfying the network coverage and connectivity requirements. The theoretical design and modeling of the proposed sensors placement strategy promise a considerable improvement in the lifetime of corona-based networks. The proposed technique could improve the network lifetime noticeably via fair balancing of energy consumption ratio among coronas about 9.4 times more than other work. This is confirmed by the evaluation results that have been showed that the proposed solution offers efficient energy distribution that can enhance the lifetime about 40% compared to previous research works

Prolonging network lifetime under probabilistic target coverage in wireless mobile sensor networks

International audienceOne of the main operations in wireless sensor networks is the surveillance of a set of events (targets) that occur in the field. In practice, a node monitors an event accurately when it is located closer to it, while the opposite happens when the node is moving away from the target. This detection accuracy can be represented by a probabilistic distribution. Since the network nodes are usually randomly deployed, some of the events are monitored by a few nodes and others by many nodes. In applications where there is a need of a full coverage and of a minimum allowed detection accuracy, a single node may not be able to sufficiently cover an event by itself. In this case, two or more nodes are needed to collaborate and to cover a single target. Moreover, all the nodes must be connected with a base station that collects the monitoring data. In this paper we describe the problem of the minimum sampling quality, where an event must be sufficiently detected by the maximum possible amount of time. Since the probability of detecting a single target using randomly deployed static nodes is quite low, we present a localized algorithm based on mobile nodes. Our algorithm sacrifices a part of the energy of the nodes by moving them to a new location in order to satisfy the desired detection accuracy. It divides the monitoring process in rounds to extend the network lifetime, while it ensures connectivity with the base station. Furthermore, since the network lifetime is strongly related to the number of rounds, we propose two redeployment schemes that enhance the performance of our approach by balancing the number of sensors between densely covered areas and areas that are poorly covered. Finally, our evaluation results show an over 10 times improvement on the network lifetime compared to the case where the sensors are static. Our approaches, also, outperform a virtual forces algorithm when connectivity with the base station is required. The redeployment schemes present a good balance between network lifetime and convergence time

Prolonging network lifetime under probabilistic target coverage in wireless mobile sensor networks

International audienceOne of the main operations in wireless sensor networks is the surveillance of a set of events (targets) that occur in the field. In practice, a node monitors an event accurately when it is located closer to it, while the opposite happens when the node is moving away from the target. This detection accuracy can be represented by a probabilistic distribution. Since the network nodes are usually randomly deployed, some of the events are monitored by a few nodes and others by many nodes. In applications where there is a need of a full coverage and of a minimum allowed detection accuracy, a single node may not be able to sufficiently cover an event by itself. In this case, two or more nodes are needed to collaborate and to cover a single target. Moreover, all the nodes must be connected with a base station that collects the monitoring data. In this paper we describe the problem of the minimum sampling quality, where an event must be sufficiently detected by the maximum possible amount of time. Since the probability of detecting a single target using randomly deployed static nodes is quite low, we present a localized algorithm based on mobile nodes. Our algorithm sacrifices a part of the energy of the nodes by moving them to a new location in order to satisfy the desired detection accuracy. It divides the monitoring process in rounds to extend the network lifetime, while it ensures connectivity with the base station. Furthermore, since the network lifetime is strongly related to the number of rounds, we propose two redeployment schemes that enhance the performance of our approach by balancing the number of sensors between densely covered areas and areas that are poorly covered. Finally, our evaluation results show an over 10 times improvement on the network lifetime compared to the case where the sensors are static. Our approaches, also, outperform a virtual forces algorithm when connectivity with the base station is required. The redeployment schemes present a good balance between network lifetime and convergence time