Target coverage problem in wireless sensor networks: A column generation based approach

Wireless Sensor Network is useful in broad range of applications such as natural disaster relief, military, environmental and health monitoring. Coverage is one of the fundamental problem and an active research area in wireless sensor network. WSN is an emerging field due to its large contribution in dealing with coverage. It consist of low cost, low power, small size and multifunction sensor nodes. The critical aspect with wireless sensor network is energy conservation. In power constrained WSN, scheduling of sensors to be done effectively and efficiently so as to maximize network lifetime. In this paper we give an introduction to WSN and its fundamental problems that is target coverage problem together with energy constraint. The target coverage problem is proven to be NP-Complete problem by many researchers. We propose a new energy-efficient heuristic for target coverage problem in wireless sensor network to maximize total network lifetime.

The target coverage problem in wireless sensor networks is concerned with maximizing the lifetime of the network while continuously monitoring a set of targets. We add a QoS requirement to the target coverage such that the targets are covered by more than one sensor at any time. Given an integer vector Q, where q i is the minimum number of sensors to simultaneously cover targets i, the problem becomes Q-coverage problem. In this paper, we study the target Q-coverage problem. The objective is to maximize sensor network lifetime satisfying Q-coverage requirement. The problem is shown to be NP-complete and there is no known practical algorithm. We propose a heuristic and show that the proposed algorithm yields solution very near to the optimal solution. Our algorithm has two important features. It uses a greedy heuristic to generate Q-covers by prioritizing sensors in terms of the residual battery life and the algorithm assigns a small constant of lifetime to Q-covers so generated. In this process, it allows the sensors to participate in many Q-covers. We observe that the smaller the constant, the closer is the solution to optimal solution. Through experiments on randomly generated problem instances, we show that the proposed algorithm yields near-optimal solution.KeywordsTarget Q-coverage problemWireless sensor networksenergy efficiencynetwork lifetimeQoS requirement

Sensor deployment is a fundamental issue in Wireless Sensor Networks (WSNs). Studies on the coverage problem are concentrated on deploying sensors to cover determined areas or a set of determined points (targets). On the other hand, the sensor network should have a lifetime long enough to fulfill the application requirements. Therefore, prolonging the network lifetime must be taken into account in designing a solution for a WSN that covers determined points. The problem of determining the location of sensor nodes such that the targets are covered and the network lifetime is maximized is called the sensor deployment problem. So far, various approaches, such as heuristics and approximation algorithms, have been used to solve the sensor deployment problem in WSNs. In this paper, we use two improved versions of the Particle Swarm Optimization (PSO) algorithm to solve the sensor deployment problem. The first one is cooperative PSO and the second one is improved cooperative PSO using fuzzy logic. The simulation results show that the algorithms proposed in this paper solve the target coverage problem with a longer network lifetime compared to the other similar algorithms.

Achieving optimal field coverage is a significant challenge in various sensor network applications. In some specific situations, the sensor field (target) may have coverage gaps due to the random deployment of sensors; hence, the optimized level of target coverage cannot be obtained. Given a set of sensors in the plane, the target coverage problem is to separate the sensor into different groups and provide them specific time intervals, so that the coverage lifetime can be maximized. Here, the constraint is that the network should be connected. Presently, target coverage problem is widely studied due to its lot of practical application in Wireless Sensor Network (WSN). This paper focuses on target coverage problem along with the minimum energy usage of the network so that the lifetime of the whole network can be increased. Since constructing a minimum connected target coverage problem is known to be NP-Complete, so several heuristics, as well as approximation algorithms, have been proposed. Here, we propose a heuristic for connected target coverage problem in WSN. We compare the performance of our heuristic with the existing heuristic, which states that our algorithm performs better than the existing algorithm for connected target coverage problem. Again, we have implemented the 2-connected target coverage properties for the network which provide fault tolerance as well as robustness to the network. So, we propose one algorithm which gives the target coverage along with 2-connectivity.

Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime.

Secure data collection is an important problem in wireless sensor networks. Different approaches have been proposed. One of them is overhearing. We investigate the problem of constructing a shortest path overhearing tree with the maximum lifetime. We propose three approaches. The first one is a polynomial-time heuristic. The second one uses ILP (Integer Linear Programming) to iteratively find a monitoring node and a parent for each sensor node. The last one optimally solves the problem by using MINLP (Mixed-Integer Non-Linear Programming). We have implemented the three approaches using MIDACO solver and MATLAB Intlinprog, and performed extensive simulations using NS2.35. The simulation results show that the average lifetime of all the network instances achieved by the heuristic approach is 85.69% of that achieved by the ILP-based approach and 81.05% of that obtained by the MINLP-based approach, and the performance of the ILP-based approach is almost equivalent to that of the MINLP-based approach.

Proposing a method to solve energy hole problem in wireless sensor networks

Coverage problems are fundamental and crucial in designing a wireless sensor networks. The target coverage problem is finding an optimal scheduling for sensors such that the time (called lifetime) to monitor every target can be as long as possible. Unfortunately, the target coverage problem has been proved to be NP-complete. Most of previous work only considers one or two factors exclusively and thus fails to prolong the lifetime to near the optimum. The main objective of this work is to design efficient scheduling algorithms to maximize the lifetime of a given whole wireless sensor network by considering adjusting sensing range, locations of target and sensors, residue battery power of sensor nodes, and assignment between sensors and targets simultaneously. A maximum weighted matching algorithm is devised by considering full coverage and the maximum total monitored duration for each target-sensor assignments. We also conduct simulations to demonstrate that the proposed algorithms can achieve very high network lifetime closed to the optimum.

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this article, we aim to study the multi-objective differential evolution and use the improved algorithm to solve a two-objective coverage problem of wireless sensor networks (WSNs). It is found that there are some redundant operations in the classic multi-objective evolution based on the Pareto sorting. Hence, we introduce a sorting method which only handles the highest rank individual in current population. The introduced method sorts the individuals meanwhile chooses some of them into next generation. When the next generation is achieved, the method will finish the evolution of the current population. We incorporate the proposed method into differential evolution to solve the two-objective coverage problem of WSN. We provide the optimization model of this two-objective coverage problem and offer the encoding style of chromosomes. Simulation result shows that the improved algorithm has better performance than the classic multi-objective algorithms.

A selection problem is among the basic communication primitives in networks. In this problem at most k participating stations have to broadcast successfully their messages. This problem is especially important in packet radio networks, where simultaneous transmissions of many neighbors result in interference among delivered messages. This work focuses on a single-hop radio networks with n-stations, also called a multiple access channel, and considers both static and dynamic versions of the selection problem. We construct a family of efficient oblivious deterministic protocols based on selectors, one of them with selection time cO(klog(n/k)), and the second explicit construction with selection time cO(k polylog n). The first construction matches the lower bound Ω(klog(n/k)) on deterministic oblivious selection, while the second one is the first known explicit construction better than Θ(k2). In the dynamic case we introduce the model of dynamic requests, called k-streams, which generalizes the static model and the dynamic requests with at most k participants. We prove that each oblivious deterministic protocol has latency Ω(k2/log k), and on the other hand we prove the existence of the oblivious deterministic protocol with latency cO(k2log n). In view of the existence of the randomized oblivious protocol with expected latency cO(klog(n/k)), this shows that randomization is substantially better than determinism for dynamic setting. Selection problem can be applied to implement other communication primitives --- we demonstrate it in the example of broadcast problem in multi-hop ad-hoc radio networks. In particular, we design an adaptive deterministic protocol broadcasting in time cO(nlog D) in every D-hop radio network of n-stations.

On the use of multiple sinks to extend the lifetime in connected wireless sensor networks

Energy consumption is a fundamental and critical issue in wireless sensor networks. Mobile sensors consume much more energy during the movement than that during the communication or sensing process. Thus how to schedule mobile sensors and minimize their moving distance has great significance to researchers. In this paper, we study the target coverage problem in mobile sensor networks. Our goal is to minimize the moving distance of sensors to cover all targets in the surveillance region. Here initially all the sensors are located at k base stations. Thus we define this problem as k-Sink Minimum Movement Target Coverage. To solve this problem, we propose a PTAS, named Energy Effective Movement Algorithm (EEMA). We can divide EEMA into two phases. In the first phase, we partition the surveillance region into some subareas. In the second phase, we select subareas and schedule sensors to the selected subareas. We also prove that the approximation ratio of EEMA is 1 + e and the time complexity is ηO(1/e2 Finally, we conduct experiments to validate the efficiency and effectiveness of EEMA.

Energy consumption is a fundamental and critical issue in wireless sensor networks. Mobile sensors consume much more energy during the movement than that during the communication or sensing process. Thus how to schedule mobile sensors and minimize their moving distance, while keeping the coverage requirement has great significance to researchers. In this paper, we study the target coverage problem in mobile sensor networks where all the targets need to be covered by sensors continuously. Our goal is to minimize the moving distance of sensors to cover all targets in the surveillance region, which is in Euclidean space. Here initially all the sensors are located at $k$ base stations. Thus, we define this problem as $k$ -Sink Minimum Movement Target Coverage . To solve this problem, we propose a polynomial-time approximation scheme, named Energy Effective Movement Algorithm (EEMA). We prove that the approximation ratio of EEMA is $1+\varepsilon $ and the time complexity is $n^{O(1/\varepsilon ^{2})}$ . We also propose a distributed version of EEMA (D-EEMA) for large-scale networks where EEMA is not efficient enough in practice. Finally, we conduct experiments to validate the efficiency and effectiveness of EEMA and D-EEMA.

Personal health status is becoming a matter of concern while pressure of work growing gradually in everyday life, in the meantime, the ageing population needs more attention. Thanks to many challenging problems of wireless sensor networks (WSNs) solved, exploring work of human body monitoring based on WSNs achieved great progress, we called it wireless body sensor networks(BSNs). In this paper, we propose a novel health-monitoring system based on BSNs; we named it SENS-U. It consists of wireless multi-parameters monitoring terminals and two medical care centers; with human body, they form a remote human in loop 4 vital signs and ECG monitoring system.

We consider the maximum lifetime routing problem in wireless sensor networks, which has been formulated as a linear programming problem in the literature (Chang and Tassiulas [1]). The optimal value and optimal solution of this problem provide optimal flows for the network and the corresponding predicted lifetime, respectively. We study the situation when there is uncertainty in various network parameters (available energy and energy depletion rates). We show that for specific, yet typical, network topologies the actual lifetime will reach the predicted value with a probability that converges to zero as the number of nodes grows large. We develop a series of alternative robust problem formulations, ranging from worst-case to optimistic. A set of parameters enable the tuning of the conservatism of the formulation to obtain network flows with a desirably high probability that the corresponding lifetime prediction will be achieved. We establish a number of properties for the robust network flows and provide an illustrative set of numerical results to highlight the trade-off between predicted lifetime and the probability it is achieved.