Optimizing Energy Consumption and Provisioning for Wireless Charging and Data Collection in Large-Scale WRSNs With Mobile Elements

Abstract

Wireless Rechargeable Sensor Networks (WRSNs) have emerged with strong potentials to address the bottlenecks of energy/lifetime of a WSN. Recent techniques have shown the efficacy of multiple mobile elements (MMEs) in terms of energy consumption optimization. However, new challenges for energy efficient MMEs scheme have emerged due to the emergence of large-scale WRSNs with big sensor-based data systems. Thus, large-scale deployments are currently limited owing to the bottlenecks of energy/lifetime, and mode of deployments of the sensor nodes. This paper proposes a deadline-based multiple mobile elements (DB-MMEs) model exploiting the efficacies of MMEs scheme to optimize energy consumption and provisioning. The DB-MMEs scheme exploits multifunctional wireless Mobile Charging Vehicles (MCVs) for both wireless charging and data collection via a single-hop transmission. The scheme is specifically designed for delay-intolerant applications. None of the existing techniques have considered this approach to minimize latency and optimize energy consumption and provisioning for LS-WRSNs scenarios. The proposed scheme first organizes the sensors into several clusters for wireless charging and data collection. To optimize energy consumption and provisioning and address the challenges of energy/lifetime for LS-WRSNs scenarios, this paper proposes analytical-based approaches to address some critical trade-offs including: (1) determining the optimal amount of energy available for the MCVs; (2) finding the optimal number of MCVs deployed within a given deadline; and (3) finding the optimal number of data collection and charging points (DCCPs). Lastly, the performance of the proposed approach is evaluated through experimental simulations, and the results validate the efficacy of the analytical-based method.