An extended ACO-based mobile sink path determination in wireless sensor networks

Donta, Praveen Kumar; Amgoth, Tarachand; Annavarapu, Chandra Sekhara Rao

doi:10.1007/s12652-020-02595-7

Cited by 54 publications

(31 citation statements)

References 39 publications

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“…In Wang et al, 18 an improved particle swarm optimization combined with mutation operator is introduced to search the parking positions and the genetic algorithm is adopted to schedule the moving trajectory for mobile sinks. Donta et al 19 propose an extended ant colony optimization-based mobile sink path construction, with RPs re-selection and virtual RPs to improve WSNs' performance. The unsupervised learning-based hierarchical agglomerative clustering method is used in Donta et al 20 to solve the problem in three-dimensional (3D) WSNs with mobile sink.…”

Section: Related Workmentioning

confidence: 99%

A mobile sink–integrated framework for the collection of farmland wireless sensor network information based on a virtual potential field

Yang

Miao

2021

International Journal of Distributed Sensor Networks

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To overcome the limitations of traditional data collection methods in large-scale farmland wireless sensor network, in this study, we introduce a mobile sink and propose a virtual potential field-based strategy for mobile sink path planning. Virtual potential field-based strategy constructs a virtual field based on the residual energy, data generation rate, location information and cache urgency of nodes in the monitoring area. The stronger the virtual field, the more attractive it will be to mobile sink, which consequently affects the mobile path of sink node. Rendezvous points are selected in accordance with the maximum-farthest criterion, and the shortest path connecting all rendezvous points is taken as the mobile path of sink. Furthermore, the monitoring nodes employ the distance probability transmission strategy to have the transmission moment selected and the energy consumption optimized with reference to the path control information sent by the sink. The virtual potential fields and the rendezvous points are recalculated periodically according to the dynamic changes of both the node residual energy and the real-time cache. The simulation results showed that excellent transmission efficiency and network lifetime, and the combination of virtual potential field-based strategy and distance probability transmission strategy can have the fairness and real time of nodes guaranteed, thus it may meet the needs of large-scale farmland data collection.

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Section: Related Workmentioning

confidence: 99%

A mobile sink–integrated framework for the collection of farmland wireless sensor network information based on a virtual potential field

Yang

Miao

2021

International Journal of Distributed Sensor Networks

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“…The bufer utilization (BU) [45] is defned as the amount of the bufer used during the unit time. The BU is always directly proportional to the data gathering process.…”

Section: G Bufer Utilizationmentioning

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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“…In this, the ACO technique is used for both RPs selection and path planning. The same authors extended this work in [12] by modifying the ACO probability function to minimize the complexity and number of iterations. In this, the virtual RPs mechanism added to minimize the transmissions between SNs and RPs, so that the energy consumption will be reduced.…”

Section: Related Workmentioning

confidence: 99%

“…Because, prolonging the memory size will take a long time to transfer the data to the MS, so the MS needs to spend more time at a particular node or RP. The BU of a network is considered using Equation ( 12 (12) where BO i indicates the buffer occupancy or number of packets allocated by node i at the time t. In general BO i is always less than B (i.e. BO i < B).…”

Section: Buffer Utilizationmentioning

confidence: 99%

See 1 more Smart Citation

Energy-efficient Dynamic Mobile Sink Path Planning for Data Acquisition for Wireless Sensor Networks

2021

IJATCSE

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Wireless sensor networks (WSNs) are widely used in various applications such as defense, forest fire, healthcare,structural health monitoring, etc., because of itsflexibility, low cost and tiny. In WSNs, the sensor nodes are scattered over the target area to acquire the data from the environment and transmit it to the base station via single or multi-hop communication. Due to the sensor nodes' constrained battery, the sensor nodes near the base station are more involved in data transmissions. These relay nodes drain more energy and die soon, leading to a hotspot/energy-hole problem. Several algorithms have been proposed in the literature to address the hotspot problem using the mobile sink. However, most of the existing approaches are highlycomputational and also provide a static solutiononly. In this context, we proposed an energy-efficient dynamic mobile sink path construction with low computational complexity for data acquisition in WSNs. We use the minimum spanning tree-based clustering for selecting the data collection points and a computational geometry-based method to identify the visiting order of the data collection points by the mobile sink. Our proposed work is better than the existing approaches in terms of average energy consumption, network lifetime, fairness index, buffer utilization, etc.

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An extended ACO-based mobile sink path determination in wireless sensor networks

Cited by 54 publications

References 39 publications

A mobile sink–integrated framework for the collection of farmland wireless sensor network information based on a virtual potential field

A mobile sink–integrated framework for the collection of farmland wireless sensor network information based on a virtual potential field

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

Energy-efficient Dynamic Mobile Sink Path Planning for Data Acquisition for Wireless Sensor Networks

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