Genetic Algorithm based Sensor Deployment with ... - Semantic Scholar

Genetic Algorithm based Sensor Deployment with Area Priority Tahir Emre Kalaycı1, Aybars Uğur2 1

Celal Bayar University, Dept. of Computer Engineering, Manisa, Türkiye 2

Ege University, Dept. of Computer Engineering, Izmir, Türkiye

Abstract. We are introducing a new design goal called area priority for finding optimal sensor node distribution. Environment that wireless sensor network (WSN) will be placed is divided into parts and priorities are attached to these parts. Priorities make the deployment problem adaptable to non-homogeneous environments such as forests having regions that have different importance levels. Various tree/animal types and densities, residential in the forest can be modeled by the area priority concept that we proposed. We also developed a genetic algorithm based method to optimize the total importance in a fully connected WSN. Experimental results obtained for different priorities are presented and discussed.

1 Introduction With the proliferation in Micro-Electro-Mechanical Systems (MEMS) technology which has facilitated the development of smart sensors, wireless sensor networks (WSNs) have gained worldwide attention in recent years. Sensors that comprise these networks are small, with limited processing and computing resources, and they are inexpensive compared to traditional sensors. Sensor nodes can sense, measure, and gather information from the environment and, based on some local

1 Tahir Emre Kalayci is supported by the Ege University through DPT ÖYP (“Öğretim Üyesi Yetiştirme Programı”), ÖYP thesis project 05-DPT-003/05 and TÜBİTAK 2211 Yurt İçi Doktora scholarship.

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decision processes, they can transmit the sensed data to the user (Yick, 2008). A WSN typically has little or no infrastructure. It consists of a number of sensor nodes (few tens to thousands) working together to monitor a region to obtain data about the environment. Careful and accurate node positioning can be a very effective optimization means for achieving application requirements and design goals (Younis, 2008). WSNs have great potential for many applications such as military target tracking and surveillance, natural disaster relief, biomedical health monitoring, etc. However, wireless sensors have several constraints such as restricted sensing and communication range as well as limited battery capacity (Ghosh, 2008). These limitations bring issues such as coverage, connectivity, network lifetime, scheduling and data aggregation (Nor, 2009). Coverage characterizes the monitoring quality provided by a sensor network on a designated region and reflects how well a sensor network is monitored or tracked by sensors (Huang, 2003). Some applications may require that some areas in the network are more important than other areas and need to be covered by more sensors, these important regions are called hot spots (Huang, 2003). Consequently, coverage can be considered a measure of quality of the service of a sensor network (Yildirim, 2008). The coverage requirement on hot spot regions also depends on the number of faults that must be tolerated. Providing connectivity between sensor nodes is another important issue in wireless sensor networks. Without connectivity, nodes may not be able to coordinate effectively or transmit data back to base stations. Thus, combination of connectivity and coverage is an important concept in sensor networks (Wang, 2003). Genetic algorithms (GA), the most widely used form of evolutionary computation, has proven to be a very successful meta-heuristic technique for many NP-complete optimization problems (Ugur, 2008). GA are stochastic search methods that have been successfully applied in many search, timetabling, scheduling, and machine learning problems and have been especially used in engineering, biology, and medicine (Ugur, 2008). GA have also been used in a variety of hybrid intelligent systems (such as evolutionary neural networks, fuzzy evolutionary systems, or genetic-neural architectures) to solve

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many other real world applications (Ugur, 2008). There are no examples of area priority based sensor deployment optimization research in the literature. In this study, we are using GA with novel fitness function to obtain a good sensor deployment based on priority of zones with preserving connectivity and covering some important regions with at least k sensors. Area priority concept in sensor network deployment used to classify the parts of the environment to be monitored. Environment that wireless sensor network placed may not be homogeneous. We split environment into small fragments called cells, and we assign a value to the every cell that indicates priority (importance) of the zone specified by that cell. For example, in a forest there are many areas with different priorities. We can distinguish these areas based on tree density and types, residential, animal types and density, etc. And we can give different priority values based on these parameters. We can use “area priority” notion not only in forests, also transporter parks to distinguish most valuable parts of it, national parks, residential areas, fish farms, historical buildings, etc.

2 Related Work Zorbas et al. (Zorbas, 2010) propose various coverage algorithms to achieve power efficient monitoring of targets by sensor networks. They present a novel and efficient coverage algorithm that can produce both disjoint cover sets as well as non-disjoint cover sets. Through simulations, they show that the proposed algorithm outperforms similar heuristic algorithms found in the literature. Nor et al. (Nor, 2009) aims to review the common strategies used in solving coverage problem in WSN. The strategies studied are used during deployment phase where the coverage is calculated based on the placement of the sensors on the region of interest (ROI). The strategies reviewed are categorized into three groups based on the approaches used, namely; force based, grid based or computational geometry based approach.

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Yildirim et al. (Yildirim, 2008) are proposed a GA based solution to find an optimal sensor node distribution to use as an initial deployment strategy that maximizes the coverage area of wireless sensor network while preserving connectivity between nodes provided that all given hot spot regions are covered by at least k sensors. Wu et al. (Wu, 2007) propose a centralized and deterministic sensor deployment method, DT-Score (Delaunay Triangulation-Score), aims to maximize the coverage of a given sensing area with obstacles. According to the simulation results, DT-Score can reach higher coverage than grid-based and random deployment methods with the increasing of deployable sensors. Chen et al. (Chen, 2007) survey five recent research approaches on coverage of wireless sensor networks and present in some detail the algorithms, assumptions, and results. A comprehensive comparison among these approaches is given from the perspective of design objectives, assumptions, algorithm attributes, and related results. Xu et al. (Xu, 2006) identify two deployment errors, namely, misalignment and random errors. They derive the minimum number of sensors required by a robust grid-based sensor deployment assuming that the errors are bounded. Bai et al. (Bai, 2006) propose an optimal deployment pattern to achieve both full coverage and 2connectivity, and prove its optimality for all values of r c÷r s (rc is the communication radius, rs is the sensing radius). They also prove the optimality of a previously proposed deployment pattern for achieving both full coverage and 1- connectivity, when r c÷r s < √ 3 . Shen et al. (Shen, 2006) describe a basic coverage issue, and propose a scheme named Grid Scan that is applied to calculate the basic coverage rate with arbitrary sensing radius of each node. The results of simulation experiments support that Grid Scan based re-deployment is more effective to cover monitored area than random spread.

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Megerian et al. (Megerian, 2005) briefly discuss the definition of the coverage problem from several points of view and formally define the worst and best-case coverage in a sensor network. They establish the main highlight of the paper, an optimal polynomial time worst and average case algorithm for coverage calculation for homogeneous isotropic sensors by combining computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms. For a comprehensive list of node placement strategies and techniques, Younis and Akkaya’s survey (Younis, 2008), which report current state of the research, can be checked.

3 Problem Definition Before giving the definition, we should introduce the parameters required for the problem. We are working on a 2D area that is defined by A(width, height). Together with the dimensions of the area we have sensor count N, sensing radius rs, communication radius - which is calculated using rc = 2 * rs formula -, h hot spot areas and a k value for minimal sensor count needed for hot spot areas. In addition to these parameters we need cell size cs, which will be used to divide area to cell fragments, and priority value for every cell cpi = (rs, re, cs, ce) (s starting cell index, e ending cell index). These cell parameters can also be entered using graphical user interface (GUI). The problem we are struggling based on some assumptions listed in Lemma 1 and tries to fulfill objectives presented in Proposition 2. And it is formalized as Problem 3. Lemma 1. Area is obstacle free, the sensing and communication ranges of all sensors are identical, sensors and hot spot areas are unit disk shaped and they all have identical radius. Proposition 2. All sensors should communicate with each other (connectivity), h hot spot areas must be covered by at least k sensors (k-covered), and total covered area priority of sensor network should

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be maximized. Problem 3. Using the given parameters A, N, rs, k, cs, cpi; try to increase covered area priority of the sensor network satisfying that all hot spot areas are k-covered and all sensors are connected.

4 Solving Problem Using Genetic Algorithm We are using a traditional GA with optimizations to solve the problems. Optimizations are based on the problem and area information and serve for faster improvement of solutions. Developed GA uses parameters that are required for every traditional GA: Pc, Pm, T, G2. After loading the problem from a XML file and entering the parameters, before the GA loop, cells and cell groups of the area are calculated based on the cell size, area dimensions and sensor radiu s. Cell groups are an important part of our method which are used to improve sensor locations in the run time. They are calculated using cell edge size and sensor radius. After calculation cell groups, a random population is initialized. An important optimization when initializing the population is connecting unconnected sensor blocks to achieve connectivity before GA run. With this optimization, GA operates on already connected sensor network and this means that it operates on feasible solutions in terms of the connectivity. Connecting unconnected sensor blocks is one of the novel contributions of this study. Finally, after population initialization, GA loop starts. It continues to improve population until number of the iterations exceeds the predetermined termination criteria, generation count parameter (G). At every iteration a new population are generated from the individuals (solutions, chromosomes, individuals are used interchangeably in this paper) of the old population. Selected individuals are crossed over and mutated based on GA parameters using GA operators. Outline of the GA follows:

2 crossover probability, mutation probability, population size, generation count 6

• Start: Initialize a random population • Loop: Repeat until generation count is reached (G) • Crossover selected results from population (T * Pc chromosomes are crossed over) • Mutate selected results from population (T * Pm chromosomes are mutated) • Select the best result as elite from generated population for this iteration • End: Return elite (optimal) solution At the end of the run, optimal solution of GA is returned to the invoker of the algorithm.

4.1 Chromosome Encoding Choosing or designing suitable chromosomes for the problem are crucial for the success of the GA. Our problem deals with placement of sensors, so we are using 2D coordinate representation for the sensors that resides on a 2D plane ( A(width,height)) in the form:

S ( x,y ) : { 0≤ x≤width, 0≤ y≤height } (1) Chromosomes has N genes and every gene (g) represents coordinates for every sensor. In the GA, genes can be produced in four different ways: randomly, by crossover, by mutation or by optimization. Population consist of T chromosomes. Formulations of population and chromosomes (C) are listed in equations two and three respectively.

C i ={ g ij :0