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

Abstract

Abstract-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.

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

“…(4) New uses and integrations of MWSNs, such as in the multiple-input multiple-output technologies (MIMO) [33], machine-to-machine (M2M) communications [34], and industrial Internet of things (IIoT) technologies, have been introduced with a motivation of supporting more intelligent decision-making and autonomous control [35].…”

Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs

“…With n increasing constantly, the following equality is satisfied mathematically. lim ( 1) ( ) 0 t t n f n f n →+∞ + − = (17) In reality, the number of RNs is very finite in a cluster. Generally, as long as we guarantee the marginal increment between ( 1) t f n + and ( ) t f n is minimum, that is, min( ( 1) ( )) t t f n f n + − (18) the number of RNs can be illustrated arg min( ( 1) ( )) t t n f n f n + − (19)…”

“…In this case, introducing MEs has been proved that it is not noly an effective approach for addressing the energy optimization issue to prolong the lifetime of WSNs but also an effective manner for organizing WSNs to achieve energy balance [15,16]. Sequentially, the participation of ME inevitably causes the problem of time delay [17], which comes to be dealt with when ME is employed as an assisted tool to improve the performance of WSNs. For example, cluster-based techniques supporting ME are more efficient for data collection [18].…”

Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment

“…In reference [31], the authors presented a data collection schema that adopts sink mobility technology called MWR. In MWR, the compatible sensor pairs are elected to function as multiple antennas to simulate virtual multiple input and output (vMIMO).…”

“…Figure 3 describes the rendezvous-based schema with a mobile data collector [31,32,33,34,35,36,37]. The rendezvous points are chosen in advance, and the mobile collector moves along a scheduled path to traverse all the rendezvous points.…”