Multi-objective Node Placement Considering Non-uniform Event Pattern

Ease of use, high flexibility and variety of applications have made wireless sensor networks very popular. Node placement in a sensor network is very critical since it affects important network attributes such as coverage, lifetime, and reliability. Therefore, controlled node placement is necessary for achieving specific network features with minimum number of nodes. Since node placement is an NP-hard problem, many placement algorithms have been proposed based on heuristic and meta-heuristic methods. Most of those algorithms assume a uniform event pattern (UEP) throughout the area under investigation. However, in practice some networks deal with non-uniform event pattern (NEP). Optimization of one attribute of the network usually results in degradation of other attributes. That is why in recent studies multi-objective optimization methods have been used which usually provide a set of best answers while the final answer is selected by a decision maker applying a trade-off between all attributes. In this paper a method for controlled node placement is proposed based on multi-objective optimization (MOO) algorithms considering a non-uniform event pattern for the network (NPNEP). In that sense this method is an extension of the previous methods that use uniform event pattern. Because of the good performance of multi-objective optimization evolutionary algorithm based on decomposition (MOEA/D), it is utilized as the optimization tool in node placement. In order to use MOEA/D, a few objective functions are defined which can optimize important attributes like coverage, power consumption, delay, reliability and lifetime. In order to achieve optimal node utilization, load balance, and increased lifetime, a cost function for routing is proposed as well as a data gathering and reporting method which both help increase the lifetime of a network. The proposed method can be used for wireless sensor networks with heterogeneous nodes. This algorithm can be used in initial deployment phase as well as operation phase or when problems like fragmentation or loss of coverage occurs. The result of NPNEP algorithm is the initial position of nodes or position of the new nodes which provide the best answers in MOO algorithm. Performance of the proposed algorithm and the effect of NEP as opposed to UEP have been verified by simulations.