Scheduling algorithms for extending directional sensor network lifetime

Recently, directional sensor networks that are composed of a large number of directional sensors have attracted a great deal of attention. The main issues associated with the directional sensors are limited battery power and restricted sensing angle. Therefore, monitoring all the targets in a given area and, at the same time, maximizing the network lifetime has remained a challenge. As sensors are often densely deployed, a promising approach to conserve the energy of directional sensors is developing efficient scheduling algorithms. These algorithms partition the sensor directions into multiple cover sets each of which is able to monitor all the targets. The problem of constructing the maximum number of cover sets has been modeled as the multiple directional cover sets (MDCS), which has been proved to be an NP-complete problem. In this study, we design two new scheduling algorithms, a greedy-based algorithm and a learning automata (LA)-based algorithm, in order to solve the MDCS problem. In order to evaluate the performance of the proposed algorithms, several experiments were conducted. The obtained results demonstrated the efficiency of both algorithms in terms of extending the network lifetime. Simulation results also revealed that the LA-based algorithm was more successful compared to the greedy-based one in terms of prolonging network lifetime.     

Solving Target Coverage Problem Using Cover Sets in Wireless Sensor Networks Based on Learning Automata

Recent years have witnessed a significant increase in employing wireless sensor networks (WSNs) for a variety of applications. Monitoring a set of discrete targets and, at the same time, extending the network lifetime is a critical issue in WSNs. One method to solve this problem is designing an efficient scheduling algorithm that is able to organize sensor nodes into several cover sets in such a way that each cover set could monitor all the targets. This study presents three learning automata-based scheduling algorithms to solve the problem. Moreover, several pruning rules are devised to avoid the selection of redundant sensors and manage critical sensors for extending the network lifetime. To evaluate the performance of proposed algorithms, we conducted several experiments, and the obtained results indicated that Algorithm 3 was more successful in terms of extending the network lifetime.     

An optimal algorithm for solving partial target coverage problem in wireless sensor networks

This paper deals with the partial target coverage problem in wireless sensor networks under a novel coverage model. The most commonly used method in previous literature on the target coverage problem is to divide continuous time into discrete slots of different lengths, each of which is dominated by a subset of sensors while setting all the other sensors into the sleep state to save energy. This method, however, suffers from shortcomings such as high computational complexity and no performance bound. We showed that the partial target coverage problem can be optimally solved in polynomial time. First, we built a linear programming formulation, which considers the total time that a sensor spends on covering targets, in order to obtain a lifetime upper bound. Based on the information derived in previous formulation, we developed a sensor assignment algorithm to seek an optimal schedule meeting the lifetime upper bound. A formal proof of optimality was provided. We compared the proposed algorithm with the well‐known column generation algorithm and showed that the proposed algorithm significantly improves performance in terms of computational time. Experiments were conducted to study the impact of different network parameters on the network lifetime, and their results led us to several interesting insights. Copyright © 2011 John Wiley & Sons, Ltd.     

Scheduling sensor activity for information coverage of discrete targets in sensor networks

In this paper, we study the problem of scheduling sensor activity to cover a set of targets with known locations such that all targets can be monitored all the time and the network can operate as long as possible. A solution to this scheduling problem is to partition all sensors into some sensor covers such that each cover can monitor all targets and the covers are activated sequentially. In this paper, we propose to provide information coverage instead of the conventional sensing disk coverage for target. The notion of information coverage is based on estimation theory to exploit the collaborative nature of geographically distributed sensors. Due to the use of information coverage, a target that is not within the sensing disk of any single sensor can still be considered to be monitored (information covered) by the cooperation of more than one sensor. This change of the problem settings complicates the solutions compared to that by using a disk coverage model. We first define the target information coverage (TIC) problem and prove its NP‐completeness. We then propose a heuristic to approximately solve our problem. Simulation results show that our heuristic is better than an existing algorithm and is close to the upper bound when only the sensing disk coverage model is used. Furthermore, simulation results also show that the network lifetime can be significantly improved by using the notion of information coverage compared with that by using the conventional definition of sensing disk coverage. Copyright © 2008 John Wiley & Sons, Ltd.     

Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.     

Polytype target coverage scheme for heterogeneous wireless sensor networks using linear programming

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy‐efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Lifetime Maximization for Connected Target Coverage in Wireless Sensor Networks

In this paper, we consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity among all the active sensors and the sink. We model the CTC problem as a maximum cover tree (MCT) problem and prove that the MCT problem is NP-Complete. We determine an upper bound on the network lifetime for the MCT problem and then develop a $(1+w)H(mathhat{M})$ approximation algorithm to solve it, where $w$ is an arbitrarily small number, $H(mathhat{M})=sum_{1leq ileqmathhat{M}}(1/i)$ and $mathhat{M}$ is the maximum number of targets in the sensing area of any sensor. As the protocol cost of the approximation algorithm may be high in practice, we develop a faster heuristic algorithm based on the approximation algorithm called Communication Weighted Greedy Cover (CWGC) algorithm and present a distributed implementation of the heuristic algorithm. We study the performance of the approximation algorithm and CWGC algorithm by comparing them with the lifetime upper bound and other basic algorithms that consider the coverage and connectivity problems independently. Simulation results show that the approximation algorithm and CWGC algorithm perform much better than others in terms of the network lifetime and the performance improvement can be up to 45% than the best-known basic algorithm. The lifetime obtained by our algorithms is close to the upper bound. Compared with the approximation algorithm, the CWGC algorithm can achieve a similar performance in terms of the network lifetime with a lower protocol cost.      

A Hybrid Approach to Maximize the Lifetime of Directional Sensor Networks with Smoothly Varying Sensing Ranges

The difficulty of maximizing the lifetime in directional sensor networks has gained increasing atten-tion recently. Most of the existing studies are focused on directional sensors with single or several predefined sensing ranges. In the present study, directional sensors can change sensing ranges smoothly. We address the problem of maxi-mizing the lifetime in directional sensor networks with such smoothly varying sensing ranges,and propose a hybrid ap-proach that combines a column generation method with an immune genetic algorithm. We search for attractive columns with the genetic algorithm, and optimize them by designing dynamic vaccines. Computational results demon-strate the performance of the proposed approach. Mean-while,the advantage of the mentioned sensors in terms of solution quality is also revealed.     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

QoS‐aware target coverage in wireless sensor networks

Wireless sensor networks have emerged recently as an effective way of monitoring remote or inhospitable physical targets, which usually have different quality of service (QoS) constraints, i.e., different targets may need different sensing quality in terms of the number of transducers, sampling rate, etc. In this paper, we address the problem of optimizing network lifetime while capturing those diversified QoS coverage constraints in such surveillance sensor networks. We show that this problem belongs to NP‐complete class. We define a subset of sensors meeting QoS requirements as a coverage pattern, and if the full set of coverage patterns is given, we can mathematically formulate the problem. Directly solving this formulation however is difficult since number of coverage patterns may be exponential to number of sensors and targets. Hence, a column generation (CG)‐based approach is proposed to decompose the original formulation into two subproblems and solve them iteratively. Here a column corresponds to a feasible coverage pattern, and the idea is to find a column with steepest ascent in lifetime, based on which we iteratively search for the maximum lifetime solution. An initial feasible set of patterns is generated through a novel random selection algorithm (RSA), in order to launch our approach. Experimental data demonstrate that the proposed CG‐based approach is an efficient solution, even in a harsh environment. Simulation results also reveal the impact of different network parameters on network lifetime, giving certain guidance on designing and maintaining such surveillance sensor networks. Copyright © 2009 John Wiley & Sons, Ltd.     

A genetic algorithm based exact approach for lifetime maximization of directional sensor networks

This paper addresses the problem of maximizing lifetime of directional wireless sensor networks, i.e., where sensors can monitor targets in an angular sector only and not all the targets around them. These sectors usually do not overlap, and each sensor can monitor at most one sector at a time. An exact method is proposed using a column generation scheme where a two level strategy, consisting of a genetic algorithm and an integer linear programming approach, is used to solve the auxiliary problem. The role of integer linear programming (ILP) approach is limited to either escaping from local optima or proving the optimality of the current solution. Computational results clearly show the advantage of the proposed approach over a column generation approach based on solving the auxiliary problem through ILP approach alone as the proposed approach is several times faster.     

Lifetime optimization of wireless sensor networks with sleep mode energy consumption of sensor nodes

The energy constraint is a major issue in wireless sensor networks since battery cells that supply sensor nodes have a limited amount of energy and are neither replaceable nor rechargeable in most cases. A common assumption in previous work is that the energy consumed by sensors in sleep mode is negligible. With this hypothesis, the usual approach is to iteratively consider subsets of nodes that cover all the targets. These subsets, also called cover sets, are then put in the active mode whereas the others are in the low-power or sleep mode. The scheduling of the appropriate cover sets in order to maximize the network lifetime is a challenging problem known to be NP-hard. The consideration of non-zero energy consumption of sensor nodes in sleep mode is more realistic but significantly increases the complexity of the problem. In this paper, we address this question by proposing a greedy algorithm that gives priority to sensors with lowest energy, and uses a blacklist to limit the number of sensors covering critical targets. Simulations show that this algorithm outperforms the previously published solutions. We then propose for regular arrays, an analytical approach which shows that, for any optimal solution, all sensors’ remaining energies are zero. This theoretical approach sheds a new light on ring connected arrays of odd size, that are known to be rather tricky when non-disjoint cover sets are considered.

     

Maximization of WSN Life Using Hybrid Evolutionary Programming

Of all the challenges faced by wireless sensor networks (WSN), extending the lifetime of the network has received the most attention from researchers. This issue is critically important, especially when sensors are deployed to areas where it is practically impossible to charge their batteries, which are their only sources of power. Besides the development and deployment of ultra low-power devices, one effective computational approach is to partition the collection of sensors into several disjoint covers, so that each cover includes all targets, and then, activate the sensors of each cover one at a time.. This maximizes the possible disjoint covers with an available number of sensors and can be treated as a set-K cover problem, which has been proven to be NP-complete. Evolutionary programming is a very powerful algorithm that uses mutation as the primary operator for evolution. Hence, mutation defines the quality and time consumed in the final solution computation. We have applied the self adaptive mutation strategy based on hybridization of Gaussian and Cauchy distributions to develop to develop a faster and better solution. One of the limitations associated with the evolutionary process is that it requires definition of the redundancy covers, and therefore, it is difficult to obtain the upper bound of a cover. To solve this problem, a redundancy removal operator that forces the evolution process to find a solution without redundancy is introduced. Through simulations, it is shown that the proposed method maximizes the lifespan of WSNs.     

An incremental deployment algorithm for wireless sensor networks using one or multiple autonomous agents

The paper studies the deployment problem of wireless sensor networks using one or multiple autonomous agents. An online incremental algorithm based on Voronoi partition is proposed to solve the problem, for which each agent deploys sensors one-at-a-time with the objective of using less number of sensors to cover an area and maintain communication connectivity. A probabilistic sensor sensing model is applied for area coverage evaluation. The shape of target area is assumed to be known by the agents, but how the environment affects the communication is unknown a priori. Therefore, the agents are desired to autonomously place every new sensor at an appropriate location based on deployed sensors to ensure connectivity and coverage specifications. Both simulations and experiments using our self-made wireless sensors are conducted to validate the algorithm.     

有向传感器网络覆盖增强问题的贪婪迭代算法

 在有向传感器网络中,可以通过调整节点的感知方向来增强目标区域的覆盖率.提出了有向传感器网络覆盖增强问题的一种贪婪迭代算法,在每次迭代中,调整那些使得覆盖率增加最大的节点的感知方向,重复此迭代过程直至通过调整任一节点的感知方向已不能再增加覆盖率.此外,还提出了一种通过计算几何求解该算法中区域计算问题的方法,以提高计算精度和减少计算时间.大量的仿真实验结果表明,该算法的执行时间较短,收敛速度较快,能够获得比现有算法更高的目标区域覆盖率.     

定向传感器网络中的最小化覆盖间隙和最大化网络生命时间问题的研究

本文研究了定向传感器网络中最小化覆盖间隙和最大化网络生命时间的问题.本文采用的定向感知天线模型,每个传感器有多个感应方向.在无线传感器网络中,最大化网络生命时间和最小化覆盖间隙是两个冲突的目标.为了在两者之间做出权衡,文章研究了在生命时间受约束的情况下最小化覆盖间隙问题(MCBLC)和在覆盖间隙受约束的条件下最大化网络生命时间问题(MLCBC).对于MCBLC问题,我们首先将它模型化为整数规划问题,并提出两个启发式算法(MCBLC-G和MCBLC-G-1).基于MCBLC-G(MCBLC-G-1)算法,利用二分搜索技术得到MLCBC问题的算法.最后,模拟验证了算法的性能.     

Multi-objective Binary Reinforced Cuckoo Search Algorithm for Solving Connected Coverage target based WSN with Critical Targets

In the stream of WSN, covering the targets using sensors and communication among the sensors to forward the data packets is a prime challenge due to the sparse target locations. Dedicated sensors lead more installation cost and significant amount of maintenance needs to be charged. Coverage of multiple targets by few sensors leads to network failure in case if any sensor runs out of power. Targets in sparse region also should be considered into account while sensing the environment. Hence in this paper, an effective multi-objective connected coverage target based WSN algorithm is proposed namely Multi-Objective Binary Cuckoo Search algorithm. The proposed model also handles the critical targets in the given sensing region. The algorithms hold the potentiality to handle minimized sensor deployment, maximized coverage and connectivity cost simultaneously. The proposed model is compared with the state of art algorithms to prove its significance. Two dedicated simulation region is developed in a large scale to examine the efficiency of the proposed algorithm. The results shows the significance of the proposed model over existing algorithms.

     

On coverage issues in directional sensor networks: A survey

The coverage optimization problem has been examined thoroughly for omni-directional sensor networks in the past decades. However, the coverage problem in directional sensor networks (DSN) has newly taken attraction, especially with the increasing number of wireless multimedia sensor network (WMSN) applications. Directional sensor nodes equipped with ultrasound, infrared, and video sensors differ from traditional omni-directional sensor nodes with their unique characteristics, such as angle of view, working direction, and line of sight (LoS) properties. Therefore, DSN applications require specific solutions and techniques for coverage enhancement. In this survey article, we mainly aim at categorizing available coverage optimization solutions and survey their problem definitions, assumptions, contributions, complexities and performance results. We categorize available studies about coverage enhancement into four categories. Target-based coverage enhancement, area-based coverage enhancement, coverage enhancement with guaranteed connectivity, and network lifetime prolonging. We define sensing models, design issues and challenges for directional sensor networks and describe their (dis)similarities to omni-directional sensor networks. We also give some information on the physical capabilities of directional sensors available on the market. Moreover, we specify the (dis)advantages of motility and mobility in terms of the coverage and network lifetime of DSNs.     

Flow-balanced routing for multi-hop clustered wireless sensor networks

Power efficiency and coverage preservation are two important performance metrics for a wireless sensor network. However, there is scarcely any protocol to consider them at the same time. In this paper, we propose a flow-balanced routing (FBR) protocol for multi-hop clustered wireless sensor networks that attempts to achieve both power efficiency and coverage preservation. The proposed protocol consists of four algorithms, one each for network clustering, multi-hop backbone construction, flow-balanced transmission, and rerouting. The proposed clustering algorithm groups several sensors into one cluster on the basis of overlapping degrees of sensors. The backbone construction algorithm constructs a novel multi-level backbone, which is not necessarily a tree, using the cluster heads and the sink. Furthermore, the flow-balanced routing algorithm assigns the transferred data over multiple paths from the sensors to the sink in order to equalize the power consumption of sensors. Lastly, the rerouting algorithm reconstructs the network topology only in a place where a head drops out from the backbone due to the head running out of its energy. Two metrics called the network lifetime and the coverage lifetime are used to evaluate the performance of FBR protocol in comparison with previous ones. The simulation results show that FBR yields both much longer lifetime and better coverage preservation than previous protocols. For example, FBR yields more than twice network lifetime and better coverage preservation than a previous efficient protocol, called the coverage-preserving clustering protocol (CPCP) [18], when the first sensor dies and the network coverage is kept at 100%, respectively.     

Maximizing Lifetime of Sensor Surveillance Systems

This paper addresses the maximal lifetime scheduling problem in sensor surveillance systems. Given a set of sensors and targets in an area, a sensor can watch only one target at a time, our task is to schedule sensors to watch targets and forward the sensed data to the base station, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration that all targets are watched and all active sensors are connected to the base station. We propose an optimal solution to find the target-watching schedule for sensors that achieves the maximal lifetime. Our solution consists of three steps: 1) computing the maximal lifetime of the surveillance system and a workload matrix by using the linear programming technique; 2) decomposing the workload matrix into a sequence of schedule matrices that can achieve the maximal lifetime; and 3) determining the sensor surveillance trees based on the above obtained schedule matrices, which specify the active sensors and the routes to pass sensed data to the base station. This is the first time in the literature that the problem of maximizing lifetime of sensor surveillance systems has been formulated and the optimal solution has been found