A Review on Sensor Node Placement Techniques in Wireless Sensor Networks

One way to provide Wireless Sensor Network (WSN) with maximum coverage, maximum connectivity, minimum deployment cost and minimum energy consumption is through an effective planning mechanism in arranging an optimum number of sensor nodes. Proper planning will provide a cost-effective deployment by having optimal placements for the sensor nodes. Sensor node placement schemes are needed to accommodate the balance of coverage and energy consumption since closer sensor nodes not only reduces the energy consumption but will result in the network coverage becoming smaller. This paper critically reviews the research and development work done in sensor node placement. Based on the review, the design objectives that need to be considered are identified. Most of the work reviewed focused on two or three design objectives

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions

Evolutionary Algorithms (EA) consist of several heuristics which are able to solve optimisation tasks by imitating some aspects of natural evolution. Two widely-used EAs, namely Harmony Search (HS) and Imperialist Competitive Algorithm (ICA), are considered for improving single objective EA and Multi Objective EA (MOEA), respectively. HS is popular because of its speed and ICA has the ability for escaping local optima, which is an important criterion for a MOEA. In contrast, both algorithms have suffered some shortages. The HS algorithm could be trapped in local optima if its parameters are not tuned properly. This shortage causes low convergence rate and high computational time. In ICA, there is big obstacle that impedes ICA from becoming MOEA. ICA cannot be matched with crowded distance method which produces qualitative value for MOEAs, while ICA needs quantitative value to determine power of each solution. This research proposes a learnable EA, named learning automata harmony search (LAHS). The EA employs a learning automata (LA) based approach to ensure that HS parameters are learnable. This research also proposes a new MOEA based on ICA and Sigma method, named Sigma Imperialist Competitive Algorithm (SICA). Sigma method provides a mechanism to measure the solutions power based on their quantity value. The proposed LAHS and SICA algorithms are tested on wellknown single objective and multi objective benchmark, respectively. Both LAHS and MOICA show improvements in convergence rate and computational time in comparison to the well-known single EAs and MOEAs

Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have attracted a great deal of attention. One significant problem linked with DSNs is target coverage, which primarily operate based on simultaneously observing a group of targets occurring in a set area, hence maximizing the network lifetime. As there are limitations to the directional sensors’ sensing angle and energy resource, designing new techniques for effectively managing the energy consumption of the sensors is crucial. In this study, two problems were addressed. First, a new learning automata-based algorithm is proposed to solve the target coverage problem, in cases where sensors have multiple power levels (i.e., sensors have multiple sensing ranges), by selecting a subset of sensor directions that is able to monitor all the targets. In real applications, targets may have different coverage quality requirements, which leads to the second; the priority-based target coverage problem, which has not yet been investigated in the field of study. In this problem, two newly developed algorithms based on learning automata and greedy are proposed to select a subset of sensor directions in a way that different coverage quality requirements of all the targets could be satisfied. All of the proposed algorithms were assessed for their performances via a number of experiments. In addition, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. All algorithms are successful in solving the problems; however, the learning automata-based algorithms are proven to be superior by up to 18% comparing with the greedy-based algorithms, when considering extending the network lifetime

A Deterministic Algorithm for the Deployment of Wireless Sensor Networks

Wireless sensor networks are made up by communicating sensor nodes that gather and elaborate information from real world in a distributed and coordinated way in order to deliver an intelligent support to human activities. They are used in many fields such as national security, surveillance, health care, biological detection, and environmental monitoring. However, sensor nodes are characterized by limited wireless communication and computing capabilities as well as reduced on-board battery power. Therefore, they have to be carefully deployed in order to cover the areas to be monitored without impairing network lifetime. This paper presents a new deterministic algorithm to solve the coverage problem of well-known areas by means of wireless sensor networks. The proposed algorithm depends on a small set of parameters and can control sensor deployment within areas even in the presence of obstacles. Moreover, the algorithm makes it possible to control the redundancy degree that can be obtained in covering a region of interest so as to achieve a network deployment characterized by a minimum number of wireless sensor nodes

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Potential games form a class of non-cooperative games where unilateral improvement dynamics are guaranteed to converge in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on Communications, vol. E98-B, no. 9, Sept. 201