Use of multiple mobile sinks in wireless sensor networks for large‐scale areas

Al-Behadili, Hasanain Abbas Hasan; Alwane, Saddam K.; Al‐Yasir, Yasir I. A.; Parchin, Naser Ojaroudi; Olley, Peter M.; Abd‐Alhameed, Raed A.

doi:10.1049/iet-wss.2019.0208

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…These types of attacks are considered packet dropping attacks because they attract communicated packets by advertising attractive routing paths and then prevent packets from reaching intended destinations. The efficient energy consumption of WSNs and MWSNs such as in [4] [5] [6], network connection and delay such as in [7], and fault tolerance such as in [8] are the most researched field for routing protocols in WSNs and MWSN whereas security is not considered in most of the available network routing protocols mostly because security operations can consume valuable resources of the mobile nodes in MWSNs. Many protection methods depend on the protection provided in higher Open Systems Interconnection (OSI) layers.…”

Section: Introductionmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Al-Naser

Al-Junaid

Almesaeed

2022

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Section: Introductionmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Al-Naser

Al-Junaid

Almesaeed

2022

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“…Sensor nodes are usually powered by limited-capacity batteries; therefore, energy is a crucial problem in WSN. Many works to prolong the lifetime of WSN have been based on balancing power consumption [3], mobile sensors [4,5], multiple routes [6], mobile sinks [7,8], and multiple sinks [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Charging Scheduling of Hybrid Vehicles in Wireless Rechargeable Sensor Networks

Chen

2022

Energies

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Wireless rechargeable sensor networks (WRSN) are utilized in environmental monitoring, traffic video surveillance, medical services, etc. In most existing schemes, WRSNs provide sustainable energy for sensor nodes by employing one or more wireless charging vehicles (WCVs). However, two essential drawbacks, regional limitations and traveling speed limitations, constrain these schemes when applied in hostile and large-scale environments. On the other hand, benefiting from the intrinsic flexibility, high flight speed, low cost, and small size of drones, some works have used drones to charge sensor nodes. However, suffering from limited battery capacities, it is also hard to only use drones in large-scale WRSNs. To overcome the drawbacks of WCVs and drones, we proposed a novelty wireless charging system that deploys WCV, WCV-carried drones, and wireless charging pads (pads) in a large-scale wireless sensor network. Based on this new wireless charging system, we first formulated a pad deployment problem for minimizing the total number of pads subject to each sensor in the pad region that only can be charged by drones. In this work, three near-optimal algorithms, i.e., greedy, K-mean, and static, for the pad deployment problem are proposed. Then, to form a sustainable WRSN, we elucidated the collaborative charging scheduling problem with the deadlines of sensors. To guarantee the maximum number of sensors to be charged before the deadlines, we also presented an approximation algorithm to find the collaborative charging scheduling of WCV and WCV-carried drones with the help of pads based on the three deployment pad schemes. Through extensive simulations, we demonstrate the effectiveness of the proposed deployment pad schemes. and that the number of pads obtained by the greedy and K-mean scheme was generally lower than that of the static scheme with respect to network density, WCV region, and flight range. Then, we also examined the proposed collaborative charging scheduling scheme by extensive simulations. The results were compared and showed the effectiveness of the proposed schemes in terms of lifetime, the percentage of nodes being charged in time, the average move time of drones, the percentage of nodes being charged late by the drones, and the charge efficiency of all vehicles under different traffic loads. Related statistical analyses showed that the percentage of nodes being charged in time and the percentage of nodes being charged late based on the greedy and K-mean schemes were slightly better than those of the static scheme, but the charge efficiency of drones of the static scheme was significantly superior to that of the K-mean scheme under a busy network.

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“…1 [8], with the help of the sensing unit, microcontroller and the memory connected with it, the sensor node can process the collected data and store it until the node sends the data to the base station or sink node. The sink node can be fixed, and it can also move naturally through any natural reasons such as water flow.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Method of Sink Node Path Planning Guided by the Hamiltonian of Quantum Annealing Algorithm

Huang

Zhang

et al. 2021

IEEE Access

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In order to solve the NP-hard problem of mobile sink path planning in wireless sensor networks (WSN) where the communication range is modeled as a circular area and overlaps with each other, this paper proposes a sink node path planning method guided by the Hamiltonian of quantum annealing algorithm (EMGH) to balance the energy consumption of wireless sensor networks, improve the network life and solve the energy hole problem. First of all, this paper analyzes the problem in theory, and transforms the characteristics of the problem into a mathematical model. The mathematical model considers that the sensor network itself is a travelling salesman problem (TSP), but also requires the shortest path of the mobile sink node. Then, the path of each node in the sensor network is iterated by quantum annealing algorithm, and an optimal TSP path is obtained by using quantum tunneling effect and quantum circuit to achieve parallelism. Finally, in the case of compressing the solution space, the moving path of the mobile sink node is quantum coded, and the Hamiltonian in the quantum annealing algorithm is taken as the guiding factor, and then the individual dimensions are moved, which improves the accuracy of the algorithm and speeds up the convergence speed of the algorithm. Finally, the feasibility of EMGH is verified by simulation experiments and comparison with other algorithms, which provides a reference for the optimization and improvement of path planning method.

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Use of multiple mobile sinks in wireless sensor networks for large‐scale areas

Cited by 6 publications

References 16 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

Collaborative Charging Scheduling of Hybrid Vehicles in Wireless Rechargeable Sensor Networks

Evolutionary Method of Sink Node Path Planning Guided by the Hamiltonian of Quantum Annealing Algorithm

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