Lifetime Optimization Algorithm with Mobile Sink Nodes for Wireless Sensor Networks Based on Location Information

Chen, Yourong; Wang, Zhangquan; Ren, Tiaojuan; Lv, Hexin

doi:10.1155/2015/857673

Cited by 17 publications

(11 citation statements)

References 19 publications

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“…, where γ ∈ [2,4] denotes the loss coefficient of data transmission, and ε fs i is related to the characteristics of the node hardware. Then, the energy consumption constraint of the nodes [21] is given by…”

Section: B Data Collection Of the Sink Nodementioning

confidence: 99%

“…In summary, when the artificial intelligence algorithm determines the food source of the bee according to the position of the sink node on this food source, it can solve the optimization model (Eq. 18) of the sink node, and obtain the current communication scheme [21]. After determining the communication schemes at all positions, we can obtain the optimal data collection scheme with the known sink node's path, and calculate the fitness value of the food source by Eq.…”

Section: A Fitness Value Calculation Of Food Sourcementioning

confidence: 99%

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Data Collection Algorithm of a 3D Wireless Sensor Network That Weighs Node Coverage Rate and Lifetime

et al. 2020

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Considering the movement of a sink node in this study to solve the data transmission problem of sensor nodes in a 3D network environment, we propose a Data Collection Algorithm of a 3D wireless sensor network (DCA_3D) that weighs node coverage rate and lifetime. DCA_3D establishes a data collection optimization model that weighs node coverage rate and lifetime with the constraints of the sink node's moving path selection, data flow, energy consumption, and link transmission. Subsequently, DCA_3D calculates the fitness value of the sink node's moving path by solving the data collection optimization model with the known sink node's moving path. Then it uses a modified artificial bee colony algorithm to solve the moving path selection problem of the sink node, and finally obtains the optimal scheme. In the scheme, sink node can find the optimal moving path, whereas the sensor node can find the optimal data communication path. The simulation results show that regardless of the moving path length of the sink node, the maximum data collection hops of the sink node and the number of static sensor nodes change, DCA_3D can find the optimal moving path of the sink node. It can improve the coverage rate of the sensor nodes, the network lifetime and average data transmission amount, and reduce the average energy consumption variance and the average packet loss rate. DCA_3D outperforms the state-of-arts such as RAND, GREED, EDG_3D, and ANT. INDEX TERMS 3D Wireless Sensor Networks, Data Collection, Network Lifetime, Packet Loss Rate I. INTRODUCTION W IRELESS Sensor Networks(WSNs), including one or several kinds of sensors, such as visual, temperature, sound, infrared, radar, and seismic sensors, have been widely studied and applied in recent decades [1], [2]. Sensor nodes work together for network monitoring tasks in dangerous environments (such as volcanoes, radiation, and toxic chemicals) and can be applied in disaster rescue, military action, underwater detection, and other application fields. WSNs are static in most applications, and their nodes report data periodically and do not move. However, static WSNs have two prominent problems. First, as the nodes are distributed in harsh environments, users can hardly reach the designated monitoring areas, and manual deployment is unsuitable for node deployment. Only random deployment methods, such as airdrop and scattering, can be used. However, random deployment can easily lead to the uneven distribution of nodes. Second, owing to the numerous communication tasks that involve data transmitting and data transporting of other nodes away from the sink node, the nodes near the sink node tends to run out of energy. Non-uniform node distribution and unbalanced energy consumption easily cause energy hole problems in monitoring areas, leading to network division and lifetime declination [3]-[5]. Thus, the WSN technology should be improved to balance node energy and prolong network lifetime. Many scholars presently focus on the moving path selection, data collection of sink nodes and the...

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Section: B Data Collection Of the Sink Nodementioning

confidence: 99%

Section: A Fitness Value Calculation Of Food Sourcementioning

confidence: 99%

Data Collection Algorithm of a 3D Wireless Sensor Network That Weighs Node Coverage Rate and Lifetime

et al. 2020

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“…Nodes (LOA-MSN) [50] was designed to overcome the energy hole problem in WSNs. LOA-MSN uses a hybrid positioning algorithm of satellite positioning and RSS positioning to obtain locations of all sensor nodes in the network with minimum energy consumption.…”

Section: Lifetime Optimization Algorithm With Mobile Sink Nodes a LImentioning

confidence: 99%

A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

Sabor

Sasaki

Abo‐Zahhad

et al. 2017

Wireless Communications and Mobile Computing

126

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Introducing mobility to Wireless Sensor Networks (WSNs) puts new challenges particularly in designing of routing protocols. Mobility can be applied to the sensor nodes and/or the sink node in the network. Many routing protocols have been developed to support the mobility of WSNs. These protocols are divided depending on the routing structure into hierarchical-based, flat-based, and location-based routing protocols. However, the hierarchical-based routing protocols outperform the other routing types in saving energy, scalability, and extending lifetime of Mobile WSNs (MWSNs). Selecting an appropriate hierarchical routing protocol for specific applications is an important and difficult task. Therefore, this paper focuses on reviewing some of the recently hierarchical-based routing protocols that are developed in the last five years for MWSNs. This survey divides the hierarchical-based routing protocols into two broad groups, namely, classical-based and optimized-based routing protocols. Also, we present a detailed classification of the reviewed protocols according to the routing approach, control manner, mobile element, mobility pattern, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated in this survey depending on delay, network size, energy-efficiency, and scalability while mentioning the advantages and drawbacks of each protocol. Finally, we summarize and conclude the paper with future directions.

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“…Several nodes can route packets to the mobile sink. In our network mode, individual node only owns the local information, such as its unique ID, residual energy level, delay and the distances to its neighbors, which can be estimated based on the received signal strength [ 24 , 25 ]. Our model does not need any extra positioning system such as GSP.…”

Section: System Mode and Terminologiesmentioning

confidence: 99%

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

Jian-hui

Liu

Cao

et al. 2016

Sensors

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Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

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Lifetime Optimization Algorithm with Mobile Sink Nodes for Wireless Sensor Networks Based on Location Information

Cited by 17 publications

References 19 publications

Data Collection Algorithm of a 3D Wireless Sensor Network That Weighs Node Coverage Rate and Lifetime

Data Collection Algorithm of a 3D Wireless Sensor Network That Weighs Node Coverage Rate and Lifetime

A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

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