Optimization deployment of wireless sensor networks based on culture–ant colony algorithm

doi:10.1016/j.amc.2014.10.091

Applied Mathematics and Computation

Volume 250, 1 January 2015, Pages 58-70

https://doi.org/10.1016/j.amc.2014.10.091 Get rights and content

Highlights

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We propose an improved culture algorithm–ant colony algorithm to solve the problem of nodes deployment.
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The search for optimal solution in our algorithm becomes much better and more stable.
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A new convergence judging method is used to achieve the purpose of global optimization.
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Extensive simulation experiments have been conducted to verify the effectiveness of our CA–ACA algorithm.

Abstract

The optimization of nodes deployment is one of the most active research areas in wireless sensor networks. In this paper, we propose an improved culture algorithm–ant colony algorithm (CA–ACA) to solve the problem of nodes deployment. Double evolution mechanism of culture algorithm is integrated into the improved ant colony optimization algorithm within the population space as an evolutionary strategy, and then directs the search of population space through the elites of continuous evolution in belief space. The introduction of culture algorithm makes the search for optimization faster and better stability of CA–ACA than traditional ones. In addition, greedy strategy is introduced for the situation of sparsely monitored points, which makes CA–ACA be suitable for any environment. Furthermore, we also investigate the convergence judging method which makes CA–ACA avoid premature convergence so as to achieve the purpose of global optimization. A large number of simulation experiments have been conducted and the results not only demonstrate the validity of CA–ACA, but also verify that CA–ACA algorithm can optimize the number of sensors deployed in network under the conditions of guaranteed connectivity and coverage. Current results are of great significance to effectively design the optimal deployment of nodes in wireless and mobile sensor networks.

Introduction

With the rapid development of system on chip, wireless communication and embedded technology, wireless sensor networks (WSNs) [1] have triggered a revolution of information perception with the features of low energy consumption, low cost, distributed working and self-organization. WSNs are multi-hop self-organizing networks formed by a large number of low-cost sensor nodes deployed within a monitored area, and these sensor nodes communicate with each other in the way of wireless communication. In recent years, a growing list of applications have attracted extensive interests in WSNs. Nodes deployment problem [2] in WSNs is one of the basic problems for constructing wireless sensor network, and effective deployment algorithms can not only reduce cost (the number of sensors) and energy consumption, but also guarantee the longer lifetime of the network.

Nodes deployment problem can be dated back to two classical computational geometry problems [3], [4], the art gallery problem proposed by O’Rourke and the circumference coverage proposed by Williams. And these problems have important impacts on nodes deployment problem in WSNs. Nodes deployment in WSNs is to deploy sensor nodes inside a specific area through proper methods to satisfy the requirements. In addition, effective nodes deployment, which satisfies the connectivity and coverage [5], is also an important part of network control considering the attributes coverage and connectivity of WSNs.

In this paper, we propose a culture algorithm–ant colony algorithm (CA–ACA) to optimize the number of deployed sensor nodes while guaranteeing connectivity and coverage of network. CA–ACA introduces modified ant colony algorithm (M-ACA) into the framework of culture algorithm. It applies the two-layer evolutionary structure, i.e. population space based on M-ACA and belief space, to search for the optimal solution. Furthermore, CA–ACA takes full advantage of the evolution information carried by elite ants in belief space to guide the evolution of population space, which eventually makes ant colony algorithm converge to the global optimal solution under the conditions of guaranteed connectivity and coverage.

The rest of this paper is organized as follows. Section 2 introduces some related works on nodes deployment in WSNs, and some significant concepts and definitions are described in Section 3. Then, our CA–ACA algorithm is expounded in Section 4. After that, a large quantity of numerical simulations are given and in detail explained in Section 5. At last, Section 6 gives some concluding remarks and points out the further research directions.

Section snippets

Related works

At present, there are many different areas of researches on nodes deployment [6], among which one of the hottest topics is the optimal nodes deployment to guarantee the coverage and connectivity of network with the consideration of the costs. One way to solve this problem is the grid-based nodes deployment in which the designated area is partitioned into several grids (uniformly or not) and the sensors are placed on the grids. Partitioning grids can simplify the problem and limit the solution

Description and definition of nodes deployment problem

Nodes deployment problem in WSNs can be divided into two cases according to the modeling approach: models based on grid network and based on consecutive network. The solution obtained by solving nodes deployment problem in the consecutive network is more accurate, but it also needs much more computing resources in the aspects of the computation and optimization. Instead, the data is discrete in the grid network, in which the algorithm will reduce the complexity of the calculation and become

Nodes deployment based on CA–ACA

Solving the nodes deployment problem in the mesh networks is to find the optimal solution satisfying the connectivity and coverage within them. So-called optimal solution is to deploy the least sensors to get the wireless sensor network satisfying connectivity and coverage. The solution for nodes deployment problem satisfying connectivity and coverage, obtained by using ant colony algorithm, can be approximate but not be the optimal solution. Furthermore, there are some other drawbacks, such as

Simulated experiments and analysis

In order to verify the feasibility and validity of CA–ACA algorithm, the existing and classical ACO algorithms including Easidesign algorithm [9] and ACO–TCAT [10] are chosen to make the comparison. In our simulation, we use Java to develop some experiments and a grid space of $20 \times 20$ , each grid size is 24, and generate monitoring points by the method of pseudo random number. We will check the performance of CA–ACA from three aspects, including the ability of searching optimal solution, searching

Conclusions and discussions

Nodes deployment algorithm based on grid is one of the hot topics in the field of WSNs. In this paper, we propose a novel deployment method based on culture–ant colony algorithm. A large number of simulations have been conducted to verify that CA–ACA optimizes the number of sensors deployed in network under the conditions of guaranteed connectivity and coverage. The next step is to improve this algorithm at the functional level, for example, adding crossover and mutation mechanism to increase

Acknowledgments

This project is supported by the National Nature Science Foundation of China (No. 61173032), the Tianjin Higher Education Science and Technology Development Fund (No. 20100806) and the Tianjin Research Program of Application Foundation and Advanced Technology (No. 12JCYBJC31900).

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Optimization deployment of wireless sensor networks based on culture–ant colony algorithm

Highlights

Abstract

Introduction

Section snippets

Related works

Description and definition of nodes deployment problem

Nodes deployment based on CA–ACA

Simulated experiments and analysis

Conclusions and discussions

Acknowledgments

Expert Syst. Appl.

Comput. Networks

Knowl. Based Syst.

Front. Phys.

Appl. Math. Comput.

Physica A

A survey on sensor networks

IEEE Commun. Mag.

Deploying sensor networks with guaranteed fault tolerance

IEEE/ACM Trans. Networking

A survey of solutions to the coverage problems in wireless sensor networks

J. Int. Technol.