Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

Abstract

a b s t r a c tThe K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing the network coverage and lifetime objectives under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem-specific knowledge for increasing its efficiency. In a constrained Multi-objective Optimization Problem (such as K-connected DPAP), the search space is divided into feasible and infeasible regions. Therefore, problem-specific operators are designed for MOEA/D to direct the search into optimal, feasible regions of the space. Namely, a DPAP-specific population initialization that seeds the initial solutions into promising regions, problem-specific genetic operators (i.e. M-tournament selection, adaptive crossover and mutation) for generating good, feasible solutions and a DPAP-specific Repair Heuristic (RH) that transforms an infeasible solution into a feasible one and maintains the MOEA/D's efficiency simultaneously. Simulation results have shown the importance of each proposed operator and their interrelation, as well as the superiority of the DPAP-specific MOEA/D against the popular constrained NSGA-II in several WSN instances.

Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks

“…Hu et al [22] assumed a GA to maximise the network lifetime by splitting the WSNs as Cardei and Du [15]. Konstantinidis and Yang [23] improved the work done in [19] ensuring a certain connectivity. Le Berre et al [24] applied some MultiObjective Evolutionary Algorithms (MOEAs) to optimise lifetime, coverage, and financial cost.…”

“…Other authors assumed that both energy cost and coverage were conflicting objectives in WSNs [19] [23,24,54,55], where [23,54] also tackled the connectivity issue. With the purpose of showing that our definition of the RNPP is an MO problem, we analyse the trade-off among the fitness functions in Table 3, where Increase implies that the value of the fitness function is increased and Decrease otherwise.…”

“…The sensor coordinates obtained are detailed in [14]. Table 4 describes the data set considered, where the general parameters are obtained from [23,25]. Being as adding routers …”

Analysing wireless sensor network deployment performance using connectivity

“…To do this, all the routes of the network must be discovered and then the PRR for each hop of the route will be used to calculate APRR. The solutions based on evolutionary algorithms [10][11][12][13] can use this metric to evaluate the resulting deployment in each step of their algorithms. The solutions may also be modified by using this metric to improve the evolution of the result set in the iterations of the algorithms.…”

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

“…Several of these aspects, such as optimal location and radio characteristics of the devices, have a certain effect on the energy levels of the APs and other communication elements. For example, there are methods for reducing power consumption in communication terminals by adjusting the radio transmission range [3] and for minimising the energy consumption in wireless networks [4].…”