Abstract-The Internet of Things (IoT) technology with huge number power-constrained devices has been heralded to improve the operational efficiency of many industrial applications. It is vital to reduce the energy consumption of each device, however, this could also degrade the Quality of Service (QoS) provisioning. In this paper, we study the problem of how to achieve the tradeoff between the QoS provisioning and the energy efficiency for the industrial IoT systems. We first formulate the multi-objective optimization problem to achieve the objective of balancing the outage performance and the network lifetime. Then we propose to combine the Quantum Particle Swarm Optimization (QPSO) with the improved Non-dominated Sorting Genetic algorithm (NSGA-II) to obtain the Pareto optimal front. In particular, NSGA-II is applied to solve the formulated multi-objective optimization problem and QPSO algorithm is used to obtain the optimum cooperative coalition. The simulation results suggest that the proposed algorithm can achieve the tradeoff between the energy efficiency and QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance.

The Cooperative Multiple-input-multiple-output (CMIMO) scheme has been suggested to extend the lifetime of cluster heads (CHs) in cluster-based capillary networks in Internet of Things (IoT) systems. However, the CMIMO scheme introduces extra energy overhead to cooperative devices and further reduces the lifetime of these devices. In this paper, we first articulate the problem of cooperative coalition's selection for CMIMO scheme to extend the average battery capacity among the whole network, and then propose to apply the quantuminspired particle swarm optimization (QPSO) to select the optimum cooperative coalitions of each hop in the routing path. Simulation results proved that the proposed QPSO-based cooperative coalition's selection scheme could select the optimum cooperative sender and receiver devices in every hop dynamically and outperform the virtual MIMO scheme with a fixed number of cooperative devices.

Energy efficiency is a crucial challenge in clusterbased capillary networks for Internet of Things (IoT) systems, where the cluster heads (CHs) selection has great impact on the network performance. It is an optimization problem to find the optimum number of CHs as well as which devices are selected as CHs. In this paper, we formulate the clustering problem into the CHs selection procedure with the aim of maximizing the average network lifetime in every round. In particular, we propose a novel CHs selection scheme based on QPSO and investigate how effective it is to prolong network lifetime and reserve the overall battery capacity. The simulation results prove that the proposed QPSO outperforms other evolutionary algorithms and can improve the network lifetime by almost 10%.Index Terms-IoT systems, cluster, QPSO, energy efficiency, network lifetime, battery capacity.

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