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Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center
Author(s) -
Choubin Morteza,
Taherpour Abbas
Publication year - 2020
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3848
Subject(s) - fusion center , wireless sensor network , computer science , antenna diversity , antenna (radio) , optimization problem , energy (signal processing) , sensor fusion , function (biology) , wireless , mathematical optimization , relaxation (psychology) , energy harvesting , convex optimization , distributed computing , real time computing , algorithm , regular polygon , computer network , mathematics , cognitive radio , telecommunications , artificial intelligence , psychology , social psychology , statistics , geometry , evolutionary biology , biology
This paper addresses the detection improvement using multiple antenna fusion center (FC) and optimal collaboration among sensors with energy harvesting (EH) capability in WSNs. Using multiple antennas at FC, we benefit from the available fundamental spatial diversity in distributed WSN. We formulate to maximize the probability of collaborative detection based on parameters of collaboration among sensors and the energy of each sensor for the different scenarios as the optimization problem. In particular, we consider several practical constraints for EH and battery energy storage to maintain network connectivity. The objective function is the nonconvex function so the optimization problem is obtained as the nonconvex problem. To resolve this issue, the objective function is converted to a convex function using a relaxation method, and an optimum solution is obtained for this problem using local optimum values. The numerical results show the impact of the different parameters on the performance of the collaborative decision. These results also demonstrate that using distributed spatial diversity at FC by multiple antennas leads to a better decision in terms of error probability, and distributed wireless sensor‐enabled with EH capability results to more robust network connectivity in practice.