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The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.
Pathak et al. (Mon,) studied this question.