Joint Data Collection and Fusion Using Mobile Sink in Heterogeneous Wireless Sensor Networks

Lin, Zhang; Keh, Huan‐Chao; Wu, Runze; Roy, Diptendu Sinha

doi:10.1109/jsen.2020.3019372

Cited by 33 publications

(11 citation statements)

References 19 publications

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“…Lin et al [30] aimed at prolonging the network lifespan of heterogeneous WSN by adopting mobile sink. They introduced DDCF as a data collection mechanism that primarily comprised of two phases: data collection points and tree topology construction that were executed during each simulation round.…”

Section: A Optimized Mobile Sink Path Without Clusteringmentioning

confidence: 99%

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

et al. 2021

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Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors' data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energyhole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks' trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks' sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

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Section: A Optimized Mobile Sink Path Without Clusteringmentioning

confidence: 99%

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

et al. 2021

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“…Compared with the other three algorithms, the life cycle has been extended by 27.7%, 19.4%, and 11.1%. (1) Step 1: initialization of clustered data collection (2) Step 2: select the cluster head (3) net � net(X(n), M)//Create SOM, X(n) is the input vector, M is the number of neurons (4) ISSA-SOM(ω 1 i (n), ω 2 kl , n, N )//Set parameters and variables, ω 1 i (n), ω 2 kl are the weight, n is the number of training times, N is the number of network training times (5) for i, j in(M, n)//Set variable (6) {if j �� 0//initialization…”

Section: Number Of Surviving Sensor Nodesmentioning

confidence: 99%

“…Over the years, the technology of heterogeneous wireless sensor networks (HWSNs) has been developed by leaps and bounds. Using its own advantages, it has been widely used in environmental detection, smart home, public transportation, and other fields [ 1 ]. However, due to its own software and hardware conditions, the storage, computing, and communication capabilities are weak; especially, the energy is limited and cannot be recharged in real time.…”

Section: Introductionmentioning

confidence: 99%

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

Cao

Yue

Zhang

2021

Computational Intelligence and Neuroscience

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In the clustering routing protocol, prolonging the lifetime of the sensor network depends to a large extent on the rationality of the cluster head node selection. The selection of cluster heads for heterogeneous wireless sensor networks (HWSNs) does not consider the remaining energy of the current nodes and the distribution of nodes, which leads to an imbalance of network energy consumption. A strategy for selecting cluster heads of HWSNs based on the improved sparrow search algorithm- (ISSA-) optimized self-organizing maps (SOM) is proposed. In the stage of cluster head selection, the proposed algorithm establishes a competitive neural network model at the base station and takes the nodes of the competing cluster heads as the input vector. Each input vector includes three elements: the remaining energy of the node, the distance from the node to the base station, and the number of neighbor nodes of the node. The best cluster head is selected through the adaptive learning of the improved competitive neural network. When selecting the cluster head node, comprehensively consider the remaining energy, the distance, and the number of times the node becomes a cluster head and optimize the cluster head node selection strategy to extend the network life cycle. Simulation experiments show that the new algorithm can reduce the energy consumption of the network more effectively than the basic competitive neural network and other algorithms, balance the energy consumption of the network, and further prolong the lifetime of the sensor network.

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“…It has been validated that this protocol can enhance the performance of the data fusion system and improve the training speed of the BPNN. Lin et al introduced a data collection and fusion mechanism that uses a mobile SN to collect the data from collection points [44]. The collection points are selected periodically and the collection points are the places where the data fusion is performed, which is capable of reducing the energy consumption and extending the network lifetime.…”

Section: The Data Fusion Techniquementioning

confidence: 99%

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

2020

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Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.

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Joint Data Collection and Fusion Using Mobile Sink in Heterogeneous Wireless Sensor Networks

Cited by 33 publications

References 19 publications

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

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