Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method

Load-balancing rendezvous approach for mobility-enabled adaptive energy-efficient data collection in WSNs

Copyright © 2020 KSII The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs

Time Efficient Data Collection with Mobile Sink and vMIMO Technique in Wireless Sensor Networks

Data collection is a fundamental yet challenging task of Wireless Sensor Networks (WSN) to support a variety of applications, due to the inherent distinguish characteristics for sensor networks, such as limited energy supply, self-organizing deployment and QoS requirements for different applications. Mobile sink and virtual MIMO (vMIMO) techniques can be jointly considered to achieve both time efficient and energy efficient for data collection. In this paper, we aim to minimize the overall data collection latency including both sink moving time and sensor data uploading time. We formulate the problem and propose a multihop weighted revenue (MWR) algorithm to approximate the optimal solution. To achieve the trade-off between full utilization of concurrent uploading of vMIMO and the shortest moving tour of mobile sink, the proposed algorithm combines the amount of concurrent uploaded data, the number of neighbours, and the moving tour length of sink in one metric for polling point selection. The simulation results show that the proposed MWR effectively reduces total data collection latency in different network scenarios with less overall network energy consumption

Time Efficient Data Collection with Mobile Sink and vMIMO Technique in Wireless Sensor Networks

Data collection is a fundamental yet challenging task of Wireless Sensor Networks (WSN) to support a variety of applications, due to the inherent distinguish characteristics for sensor networks, such as limited energy supply, self-organizing deployment and QoS requirements for different applications. Mobile sink and virtual MIMO (vMIMO) techniques can be jointly considered to achieve both time efficient and energy efficient for data collection. In this paper, we aim to minimize the overall data collection latency including both sink moving time and sensor data uploading time. We formulate the problem and propose a multihop weighted revenue (MWR) algorithm to approximate the optimal solution. To achieve the trade-off between full utilization of concurrent uploading of vMIMO and the shortest moving tour of mobile sink, the proposed algorithm combines the amount of concurrent uploaded data, the number of neighbours, and the moving tour length of sink in one metric for polling point selection. The simulation results show that the proposed MWR effectively reduces total data collection latency in different network scenarios with less overall network energy consumption