ABSTRACT In wireless rechargeable sensor networks (WRSN), wireless energy charging (WEC) is a potential approach to extend sensor lifetime. To continuously provide electric charge to sensors, WEC uses a mobile charger (MC). All things considered, creating a charging plan that works for the MC is difficult because it depends on various aspects such the amount of energy left, the location of the limitations, and the time of day. The purpose of this work is to offer a novel and efficient charging process to extend the life of sensors in WRSN. According to this algorithm, the sensors periodically transmit to the service station (SS) the energy spending rate and their remaining energy. The SS estimates how long the sensors will last, and if it falls below a predetermined level, that sensor is taken into consideration for charging and is placed in a serving queue. After that, the SS schedules MC using a suggested priority function. Comparing the suggested method to baseline charging techniques, simulation experiments show that it performs better in terms of charging, especially in terms of prolonging the lifetime of sensors. The experimental results demonstrate that the suggested method outperforms the state‐of‐the‐art approaches, in achieving a superior average dead period for sensors.

To perform large-scale monitoring of sensitive events, energy harvesting wireless sensor network is considered where a mobile data sink$MS$collects data while travelling on a fixed path$P_{ms}$. The sensor nodes sense environmental data continuously at a pre-specified rate. The sensors close to$P_{ms}$are referred as gateways. Sensors forward their data to the$MS$through the gateways. In practice, the usage of$MS$is not suitable for time-sensitive applications due to its long data gathering delay. Time-bound data gathering for path constrained environment is not accounted in literature. We aim at finding energy-efficient maximum data gathering sub-path for the MS for a given data gathering period$T$. To deal with the problem a novel optimal deterministic data collection sub-path finding algorithm is proposed which is based on the geometric properties of the sensors’ communication disks and the data gathering path. It maximizes the data collection and reduces the energy consumption by jointly optimizing the data gathering sub-path selection and the data forwarding path optimization. The performance of the proposed algorithm is compared with an existing baseline algorithm DDGA and a heuristic algorithm H-DGSPF. The simulation results show that our proposed algorithm outperforms DDGA and H-DGSPF in terms of data collection, data delivery success ratio, and energy consumption

Wireless sensor networks have drawn considerable attention among researchers for providing a low-cost framework for Intelligent Transport Systems. Coverage problem in wireless sensor networks measures quality a region or parts of it is sensed by the sensors. Definition of coverage metric depends on the applications for which sensors are deployed. In this paper, we introduce a new quality control metric/measure called road coverage to partially cover road networks. It can be used for measuring the efficiency of a sensor network, which is deployed for tracking moving/mobile objects in a road network. First, we propose different definitions of road coverage metrics. It is shown that the problem of deploying minimum number of sensors to achieve road coverage is NP-hard. It motivates to design centralized approximate solution. For axis-parallel road segments constant factor approximation algorithms are proposed for finding proper sensors positions. Experimental performance analyses of our algorithms are carried out through extensive simulations.