Abstract
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
Highlights
Optimization plays a key role in wireless sensor networks
For example in [39], the authors investigated the effect of various parameters of energy consumption in nanosensor networks and proposed a multi-objective optimization formulation to achieve a balance between the energy consumption, delay and bit error rate
In [170], the authors have used genetic algorithm to solve the multi-objective optimization formulation used to achieve optimal deployment of sensor nodes at the port of entry for inspecting the containers in order to detect the presence of illegal cargo
Summary
Optimization plays a key role in wireless sensor networks. The optimization in WSNs can be broadly categorized into single and multi-objective optimization problem. In [25], the authors have categorized various WSNs applications and reviewed different energy conservation schemes their impact on the overall performance of the specific application They surveyed some existing techniques based on evolutionary algorithm to achieve various trade-offs between multiple conflicting requirements for prolonging the lifetime of the WSNs. Metaheuristic algorithms are getting popular due to their better performance in terms of convergence to the optimality and avoidance from being trapped in local optima [28]. The aforementioned surveys are either objective function specific or they are centered about some specific algorithms to tackle the problems relating to multi-objective optimization in WSNs. Multi-objective deployment of wireless sensor nodes has been surveyed in [31] to achieve pareto optimal front while considering multiple conflicting objectives namely, coverage, energy efficiency, lifetime and the number of sensors.
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