A Speed Control-Based Big Data Collection Algorithm (SCBDCA) Using Clusters and Portable Sink WSNs

WSNs collects a huge amount of heterogeneous information and have been widely applied in various applications. Most of the information collecting techniques for WSNs can't keep away from the hotspot trouble. This problem affects the connectivity of the community and reduces the life of the entire community. Hence, an efficient speed control based data collection algorithm (SCBDCA) using mobile sink in WSNs is proposed. A tree construction technique is introduced based on weight such that the origin nodes of the built timber are described as rendezvous points (RPs). Also, other unique type of nodes named sub or additional rendezvous points (ARPs or SRPs) is chosen in accordance to their traffic consignment and hops to origin nodes in the network. Mobile sink gathers the information from the cluster head (CH) and is controlled by speed control mechanism. Simulation results among different existing procedures exhibit that the SCBDCA can extensively stabilize the consignment of the communication network, decrease the energy consumption, and extend the lifetime in the communication network.

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Energy-Efficient Cluster-Based Data Collection by a UAV with a Limited-Capacity Battery in Robotic Wireless Sensor Networks

Sensors ◽

10.3390/s20205865 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5865

Author(s):

Omer Melih Gul ◽

Aydan Muserref Erkmen

Keyword(s):

Data Collection ◽

Total Energy ◽

Energy Efficient ◽

Cost Optimization ◽

Cluster Head ◽

Minimum Cost ◽

Mobile Sink ◽

Total Energy Consumption ◽

Battery Capacity ◽

Optimal Approach

In this work, our motivation focuses on an energy-efficient data collection problem by a mobile sink, an unmanned aerial vehicle (UAV) with limited battery capacity, in a robot network divided into several robot clusters. In each cluster, a cluster head (CH) robot allocates tasks to the remaining robots and collects data from them. Our contribution is to minimize the UAV total energy consumption coupled to minimum cost data collection from CH robots by visiting optimally a portion of the CH robots. The UAV decides the subset of CH robots to visit by considering not only the locations of all CH robots but also its battery capacity. If the UAV cannot visit all CH robots, then the CH robots not visited by the UAV transmit their data to another CH robot to forward it. The decision of transmission paths of transmitting robots is included in the cost optimization. Our contribution passes beyond the existing paradigms in the literature by considering the constant battery capacity for the UAV. We derive the optimal approach analytically for this problem. For various numbers of clusters, the performance of our strategy is compared with the approach in the close literature in terms of total energy consumed by CH robots, which affects network lifetime. Numerical results demonstrate that our strategy outperforms the approach in the close literature.

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A Data Collecting Strategy for Farmland WSNs using a Mobile Sink

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2020.6.4024 ◽

2020 ◽

Vol 15 (6) ◽

Author(s):

Yaqiong Zhang ◽

Jiyan Lin ◽

Hui Zhang

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Collection ◽

Time Allocation ◽

Large Scale ◽

Cluster Head ◽

Mobile Sink ◽

Sensor Nodes ◽

Wireless Sensor ◽

Allocation Mechanism

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks.

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Optimization-Based Artificial Bee Colony Algorithm for Data Collection in Large-Scale Mobile Wireless Sensor Networks

Journal of Sensors ◽

10.1155/2016/7057490 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 19

Author(s):

Yinggao Yue ◽

Jianqing Li ◽

Hehong Fan ◽

Qin Qin

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Collection ◽

Large Scale ◽

Cluster Head ◽

Mobile Sink ◽

Wireless Sensor ◽

Mobile Wireless ◽

Mobile Wireless Sensor Networks ◽

Mobile Wireless Sensor

Data collection is a fundamental operation in various mobile wireless sensor networks (MWSN) applications. The energy of nodes around the Sink can be untimely depleted owing to the fact that sensor nodes must transmit vast amounts of data, readily forming a bottleneck in energy consumption; mobile wireless sensor networks have been designed to address this issue. In this study, we focused on a large-scale and intensive MWSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. We also derived a corresponding formula to represent the MWSN and proved that it represents an NP-hard problem. Traditional data collection methods only focus on increasing the amount data collection or reducing the overall network energy consumption, which is why we designed the proposed heuristic algorithm to jointly consider cluster head selection, the routing path from ordinary nodes to the cluster head node, and mobile Sink path planning optimization. The proposed data collection algorithm for mobile Sinks is, in effect, based on artificial bee colony. Simulation results show that, in comparison with other algorithms, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime.

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