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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Applications of Artificial Intelligence, Volume 26, Issue 1, January 2013, Pages 405–416
The increased demand of Wireless Sensor Networks (WSNs) in different areas of application have intensified studies dedicated to the deployment of sensor nodes in recent past. For deployment of sensor nodes some of the key objectives that need to be satisfied are coverage of the area to be monitored, net energy consumed by the WSN, lifetime of the network, and connectivity and number of deployed sensors. In this article the sensor node deployment task has been formulated as a constrained multi-objective optimization (MO) problem where the aim is to find a deployed sensor node arrangement to maximize the area of coverage, minimize the net energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes while maintaining connectivity between each sensor node and the sink node for proper data transmission. We assume a tree structure between the deployed nodes and the sink node for data transmission. Our method employs a recently developed and very competitive multi-objective evolutionary algorithm (MOEA) known as MOEA/D-DE that uses a decomposition approach for converting the problem of approximation of the Pareto fronts (PF) into a number of single-objective optimization problems. This algorithm employs differential evolution (DE), one of the most powerful real parameter optimizers in current use, as its search method. The original MOEA/D has been modified by introducing a new fuzzy dominance based decomposition technique. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. We have compared the performance of the resulting algorithm, called MOEA/DFD, with the original MOEA/D-DE and another very popular MOEA called Non-dominated Sorting Genetic Algorithm (NSGA-II). The best trade-off solutions from MOEA/DFD based node deployment scheme have also been compared with a few single-objective node deployment schemes based on the original DE, an adaptive DE-variant (JADE), original particle swarm optimization (PSO), and a state-of-the art variant of PSO (Comprehensive Learning PSO). In all the test instances, MOEA/DFD performs better than all other algorithms. Also the proposed multi-objective formulation of the problem adds more flexibility to the decision maker for choosing the necessary threshold of the objectives to be satisfied.
An ad-hoc Wireless Sensor Network (WSN) consists of a number of sensors spread across a geographical area. Each sensor has wireless communication capability and some level of intelligence for signal processing and networking of the data. The development of WSNs was originally motivated by military applications such as battlefield surveillance. However, they are currently being employed in many industrial and civilian application areas including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control (Callaway, 2003, Zhao and Guibas, 2004 and Bulusu and Jha, 2005). A few excellent surveys on the present state-of-the-art research on sensor networks can be traced in Al-Karaki and Kamal (2004), Bojkovic and Bakmaz (2008) and Yick et al. (2008). An important problem of any sensor node design is the deployment of the sensor nodes in the area to be monitored. The number of sensor nodes that can be deployed in an area is a limitation to the designers. Again, there are always some limitations to the payload of the sensor nodes that are generally carried and deployed by an aircraft apart from the cost limitation (Liu and Mohaparta, 2007). Sensor nodes usually have limited energy storage and low processing and communication capabilities (Park et al., 2001). So the energy consumed in them must be small enough so that all the deployed nodes can function till a certain time interval. The impossibility of recharging or replacing the node batteries, especially in networks installed in regions of difficult access, imposes a serious constraint for the designers: each node in the network has a limited lifetime, which cannot be extended. The main motive behind the sensor node deployment is the monitoring of the area concerned. Monitoring of the area may be based on uniform event detection or differentiated event detection, where the probability of the appearance of the event in the area concerned varies both geographically and with time. And last but not the least maintaining connectivity among the nodes so that the data collected by any individual sensor node can flow through other nodes to the sink node. Deployment of the sensor nodes satisfying all such objectives together is a challenging problem.
نتیجه گیری انگلیسی
This paper proposes a new fuzzy dominance concept along with the decomposition type of multi-objective evolutionary algorithm. This fuzzy dominance falls under a category of newly defined relaxation dominance concept. MOEA/DFD happens to tackle the multi-objective sensor node deployment problem better than other contemporary state-of-the-art. There are lots of challenges in developing new algorithm based on alternative definition of dominance like relaxation dominance which plays an essential role in the evolution of the algorithm. The problem of deployment of sensor nodes to optimize the coverage, energy consumption, lifetime and number of nodes maintaining connectivity can be modified and applied for different fields of application. The modeling of the objective function is to be changed as per the requirement of the problem. This problem can also be applied in areas where probabilistic event detection is necessary rather than distributed event detection. In recent times density control of a largely deployed sensor node through sleep scheduling is an important area of research. Tracking of a single particle or a dynamic probabilistic distribution is also a challenging problem. Sensor network is a large research area having lots of challenging problems to tackle other than deployment. Localization, tracking, Routing and Scheduling, Data fusion, Security etc. are the promising research areas where multi-objective algorithms and evolutionary computations can be applied. There are scopes of developing dynamic multi-objective optimization to solve the deployment problem more efficiently. The evolutionary multi-objective optimization approach can also be extended to those areas for better flexibility and higher accuracy.