Jie Jia

Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.

Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius

In this paper, the problem of maintaining sensing coverage by keeping a small number of active sensor nodes and a small amount of energy consumption in a wireless sensor network is studied. As opposed to the uniform sensing model previously, we consider a large number of sensors with adjustable sensing radius that are randomly deployed to monitor a target area. A novel coverage control scheme based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed in a heterogeneous sensor network. By devising a cluster-based architecture, the algorithm is applied in a distributed way. Furthermore, an ameliorated binary coding is addressed to represent both sensing radius adjustment and sensor selection. Numerical and simulation results validate that the procedure to find the optimal balance point among the maximum coverage rate, the least energy consumption, as well as the minimum number of active nodes is fast and effective.

Coverage Optimization based on Improved NSGA-II in Wireless Sensor Network

Wireless sensor networks (WSN) constitute the platform of a wide application related to military, remote monitoring, inhospitable physical environment, and national security. Reducing energy consumption to extend network lifetime is one of the most important requirements in designing wireless sensor networks. Keeping only a minimal number of sensors active and putting others into low-power sleep mode is one promising approach to conserve system energy, in which the active sensors can maintain the communication connectivity and cover the target region completely. However, the problem of sorting such minimal active sensor set is NP-complete. In this paper, elitist non-dominated sorting genetic algorithm (NSGA-II), a new multi-objective genetic algorithm, is applied to coverage problem in wireless sensor networks. The novel scheme maximizes the coverage using a relative small quantity of sensor nodes in a given target area. Simulation results show that the algorithm is fast and effective, which gives strong support to the selection of optimal node set.

Efficient Cover Set Selection in Wireless Sensor Networks

Abstract The effectiveness of a cluster-based distributed sensor network, to a large extent, depends on the coverage provided by the sensor nodes. To activate only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy. However, this is an NP-complete problem because of the high-density deployment of wireless sensor networks. In this paper, a novel searching algorithm based on improved NSGA-II (elitist nondominated sorting genetic algorithm) is proposed to select an optimal cover set. In contrast to the binary detection model used in the previous work, a probabilistic detection model is adopted in combination with the detection error range and coverage threshold. With the full network coverage being guaranteed, a number of nodes are made into dormancy mode to save energy. The circulated combination and delete operators are proposed to enhance the search capability. Extensive simulation results are presented to demonstrate the effectiveness of our approach.

A genetic approach to channel assignment for multi-radio multi-channel wireless mesh networks

Multi-channel communication in a Wireless Mesh Network with routers having multiple radio interfaces significantly enhances the network capacity. Efficient channel assignment is critical for realization of optimal throughput in such networks. In this paper, we investigate the problem of finding the largest number of links that can be connected with the overall network interference is minimized. Since the number of radios on any node can be less than the number of available channels, the channel assignment must obey the constraint that the number of different channels assigned to the links incident on any node is at most the number of radio interfaces on that node. The above optimization problem is known to be NP-hard. By presenting the theoretical model, the above task is formulated as a multi-objective problem, and then a novel channel assignment based on improved NSGA-II is proposed. Extensive empirical evaluations represent that the novel algorithm proposed in this paper can implement network connectivity with little interference rapidly and efficiently. To meet the actual demand in wireless mesh network, ns-2 simulations are used to demonstrate the performance potential of our channel assignment algorithms in 802.11-based multi-radio mesh networks.

Modeling and extending lifetime of wireless sensor networks using genetic algorithm

To extend the lifetime of the sensor networks as far as possible while maintaining the quality of network coverage is a major concern in the research of coverage control. A systematical analysis on the relationship between the network lifetime and cover sets alternation is given, and by introducing the concept of time weight factor, the network lifetime maximization model is presented. Through the introduction of the solution granularity T, the network lifetime optimization problem is transformed into the maximization of cover sets. A solution based on NSGA-II is proposed. Compared with the previous method, which has the additional requirement that the cover sets being disjoint and results in a large number of unused nodes, our algorithm allows the sensors to participate in multiple cover sets, and thus makes fuller use of the whole sensor nodes to further increase the network lifetime. Simulation results are presented to verify these approaches.

Maximization for wireless sensor network lifetime with power efficient cover set alternation

One of the most critical issues in wireless sensor network is represented by the limited availability of energy on sensor nodes. In this paper, we propose the power efficient organization of k-separate cover sets as a backbone to maximum network lifetime. All the sensors are divided into K-separate sets on the basis of multi-objective genetic algorithm, guaranteeing each cover set is pareto optimal. By alternating subsets of sensor nodes and using only one at each round, redundancy elimination can contribute to reduce energy consumption. The algorithm is evaluated via a simulation study. Clearly, by solving the optimization model, a network designer can gain useful insights into the possible gains in term of network lifetime.

Research on hierarchical routing protocol based on optimal clustering head for wireless sensor networks

Developing an energy-efficient clustering protocol is one of the major issues in wireless sensor networks. In order to reduce overall energy consumption of wireless sensor networks, prolong network lifetime, the shortcomings of LEACH algorithm are studied. The clustering algorithm is composed of two phases, the cluster building phase and stable data communication phase. In this paper, the cluster head selection of the algorithm is improved. According to the residual energy of sensor nodes, communication energy consumption of neighbour nodes and the distance between the nodes and the sink nodes, a hierarchical routing protocol based on optimal clustering head for wireless sensor networks is proposed. On condition that the residual energy of the node exceeds a certain threshold, the probability of being clustering head is affected by the nodes’ network parameters. Simulation results show that the proposed algorithm can balance the energy consumption and prolong the network lifetime.

Efficient Traffic Aware Multipath Routing Algorithm in Cognitive Networks

Cognitive networks embody a sense of dynamic responsiveness as actions are typically taken in response to changing circumstances and changing resource availability, which use prior and current knowledge gained from the network to take actions with respect to the end-to-end goals of the whole network. According to the cognitive network framework, a multi-path routing algorithm based on traffic prediction model, Efficient Traffic Aware Multi-path Routing (ETAMR) is proposed in cognitive networks. Traffic prediction routing scheme has been investigated with ATPRA [1] that is proposed in previous works. ETAMR considers traffic distribution and traffic load to build a multi-path routing, depending on the prediction model-MMSE to construct the prediction matrix and select the primary route with the shortest delay and lowest traffic load, meanwhile according to the real time traffic load it dynamically triggers the backup paths to avoid congestion and balance the traffic load of the network. Further more, ETAMR is able to adaptively build a multi-path routing scheme of the lowest aggregated traffic load by learning and reasoning scheme. Comparing with current routing algorithms, ETAMR has good performances at load balancing and lower transmission delay, which is validated by the simulation.

A Load Balanced Routing Protocol Based on Ant Colony Algorithm for Wireless Mesh Networks

Routing algorithm as a research core in the wireless mesh network, the design is good or bad is very important to network performance. In the field of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques that find applications in computer networking. Unbalanced traffic may lead to more delay, packet dropping, and decreasing packet delivery ratio (PDR). Therefore, this paper proposes a routing algorithm based on the ant colony algorithm. The proposed algorithm improves rules of pheromone update. The link which has the smaller number of hops, lower congestion level and higher bandwidth should have a larger probability. Experimental results show that the proposed algorithm reduces the average end-to-end delay and control overhead, and increases the successful transfer rate of WMN. So the network throughput and stability can be improved significantly by the proposed algorithm.