Soft Comput DOI 10.1007/s00500-014-1462-y METHODOLOGIES AND APPLICATION Multi-objective evolutionary routing protocol for efficient coverage in mobile sensor networks Bara’a A. Attea · Enan A. Khalil · Ahmet Cosar © Springer-Verlag Berlin Heidelberg 2014 Abstract Individual sensors in wireless mobile sensor net- works (MSNs) can move in search of coverage region for the sensing accuracy and for reaching the most efficient topol- ogy. Besides, sensors’ clustering is crucial for achieving an efficient network performance. Although MSNs have been an area of many research efforts in recent years, integrat- ing the coverage problem of MSNs with the efficient routing requirement that will maximize the network lifetime is still missing. In this paper, we consider the coverage optimization problem where the location of a given number of mobile sen- sors needs to be re-decided such that the sensed data from the detected targets can be routed more efficiently to the sink and thus increasing the network lifetime. We formulate this NP- complete problem as a multi-objective optimization (MOO) problem, with two conflicting and correlated objectives; aim- ing at high coverage as well as longevity of network lifetime. The Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) is utilized as a cluster-based routing protocol to tackle this MOO problem. Each round of the proposed NSGA-II based routing protocol creates a set of near-Pareto-optimal Communicated by V. Loia. B. A. Attea (B ) Department of the Computer Science, Baghdad University, Baghdad, Iraq e-mail: baraaali@scbaghdad.edu.iq; baraaali@yahoo.com E. A. Khalil Computer Engineering Department, Gazi University, Ankara, Turkey e-mail: enanameen@yahoo.com A. Cosar Computer Engineering Department, Middle East Technical University, Ankara, Turkey e-mail: cosar@metu.edu.tr solutions containing a number of non-dominated solutions, in which the sink can pick up and distribute the one with high coverage to form the clustered routes. Heuristic operators are also proposed to enhance the quality of the solutions. Simu- lation results are provided to illustrate the effectiveness and performance of the proposed evolutionary algorithm. Keywords Energy efficient clustering · Wireless mobile sensor network · MOEA/D · Multi-objective optimization · NSGA-II · Routing 1 Introduction Augmenting sensor nodes with locomotion facilities (i.e., mobile sensors) can greatly enhance our capabilities in many application scenarios (ranging from wildlife monitoring, environmental monitoring, surveillance to coordinated target detection). Mobile sensors can move around to self-deploy and reach the desired coverage level, relocate their physi- cal locations to compensate sensor failures, and aggregate themselves around a new event or interest to meet the issued sensing quality. An example of a mobile sensor network is Robomote (Sibley et al. 2002). Coverage problem in MSNs has received great attention in recent years, and most of these works (Wang et al. 2004a, b; Yang et al. 2007; Heo and Varshney 2005; Cortes et al. 2004; Aurenhammer 1991) define the problem of self-deploying sensors as an energy-efficient coverage problem, proposing deterministic protocols to meet the required coverage. The basic concept of these techniques depends on a virtual repul- sive and attractive forces being reflected off the sensors to control their movements. For example, Voronoi diagram- based algorithms in Wang et al. (2004a, b), Cortes et al. (2004) and Aurenhammer (1991) enable sensors to iden- 123