Predicting-Scheduling-Tracking: Charging Nodes With Non-Deterministic Mobility

Li, Yuanhao; Zhong, Lina; Lin, Feng

doi:10.1109/access.2020.3046857

Cited by 11 publications

(10 citation statements)

References 29 publications

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“…While employing the DKEF, the global estimates are formed by forwarding the measurement data of all sensors to the information union center. The numerical appearance of the target dynamic is illustrated in Equation (8).…”

Section: Distributed Extended Kalman Filteringmentioning

confidence: 99%

“…The principal sensor used by the PET is in an active state, and the auxiliary sensors are also functioning properly. 8 The framework greatly reduced the number of messages that needed to be sent between beacons, sensors, and trackers. 9 To increase the network endurance of target tracking applications, a proposed approach employs a mobile sink.…”

Section: Introductionmentioning

confidence: 99%

“…The cooperative tracking protocol's key features included early assessment of the target's velocity, direction, and position, which is possible with several sensors. The principal sensor used by the PET is in an active state, and the auxiliary sensors are also functioning properly 8 . The framework greatly reduced the number of messages that needed to be sent between beacons, sensors, and trackers 9 .…”

Section: Introductionmentioning

confidence: 99%

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Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Ramadevi,

Subramanyam,

Bindu

2024

Int J Communication

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SummaryIn wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization of WSN, the attention on target tracking is greatly increased. The estimated optimal value derived from the earlier moment is rarely taken into consideration in traditional target‐tracking algorithms. One of the most crucial uses of WSNs is mobile target tracking, and it is especially used for spying. Precision surveillance is heavily dependent on localization or distance estimation, and extensive study has been done in this area. This research aims to develop a new target‐tracking network‐assisted target movement prediction model in WSN with reduced energy consumption. The major phases involved in the proposed target movement prediction in WSN are (a) mobility target tracking and (b) target movement prediction. Initially, the mobility target tracking is done with the help of adaptive distributed extended Kalman filtering (ADEKF). The mobility target tracking performance is improved by optimally tuning the parameters from ADEKF with the support of the improved squid game optimizer (ISGO). Then, the target movement prediction phase is executed with the help of input parameters like “Angle of Arrival (AoA) and Received Signal Strength (RSS),” and the optimal progress of the mobile node is predicted. The implementation outcome of the proposed tracking network‐assisted target movement prediction model is validated concerning various performance metrics. Overall performance analysis shows that the developed model offers 2.5% in terms of RMSE measures. The developed model shows better analysis while validating with existing approaches.

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Section: Distributed Extended Kalman Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Ramadevi,

Subramanyam,

Bindu

2024

Int J Communication

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“…Charger types may include charging vehicles, unmanned aerial vehicles (UAVs), or a combination of both. Previous research on wireless charging for wireless rechargeable sensor networks (WRSNs) has predominantly focused on utilizing WCVs [20][21][22][23][24] or UAVs [6,11,16,[25][26][27][28].…”

Section: Charging Approachesmentioning

confidence: 99%

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

Cheng,

Yu,

Zhang

2024

Algorithms

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The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.

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“…The WSN has different application scenarios such as ecological surveying, smart city, and wildlife surveillance because of its characteristics of low cost & strong self-organization. The capacity of node batteries also limits the operating duration of nodes, affecting WSN lifespan and efficiency, which has become a critical issue limiting WSN's growth [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

Mohiddin

Kohli

Dutt

et al. 2021

Wireless Communications and Mobile Computing

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In wireless sensor networks, due to the restricted battery capabilities of sensor nodes, the energy issue plays a critical role in network efficiency and lifespan. In our work, an upgraded long short-term memory is executed by the base station to frequently predict the forecast positions of the node with the help of load-adaptive beaconing scheduling algorithm. In recent years, new technologies for wireless charging have offered a feasible technique in overcoming the WSN energy dilemma. Researchers are deploying rechargeable wireless sensor networks that introduce high-capacity smartphone chargers for sensor nodes for charging. Nearly all R-WSN research has focused on charging static nodes with relativistic routes or mobile nodes. In this work, it is analysed how to charge nondeterministic mobility nodes in this work. In this scenario, a new mechanism is recommended, called predicting-based scheduling algorithm, to implement charging activities. In the suggested technique, it directs them to pursue the mobile charger and recharge the sensor, which is unique for the present work. The mobile charger will then choose a suitable node, utilizing a scheduling algorithm, as the charging object. A tracking algorithm based on the Kalman filter is preferred during energy transfer to determine the distance needed for charging between the destination node & mobile charger. Here, the collecting & processing of data are performed through the big data collection in WSNs. The R-WSN charging operations of nondeterministic mobility nodes will be accomplished using the proposed charging strategy.

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Predicting-Scheduling-Tracking: Charging Nodes With Non-Deterministic Mobility

Cited by 11 publications

References 29 publications

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

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