Energy aware and delay-tolerant data gathering in sensor networks with a mobile sink

Bassam, Nizar Al; Jerew, Oday D.

doi:10.1109/icbdsc.2016.7460372

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…Each DC changes its location in such a way that the load (i.e., forwarded data) is balanced and distributed among sensor nodes. The authors in [31,[38][39][40] proposed a new model of mobile data collection that reduces data latency. This was achieved by combining the use of mobile data collectors and clustering.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

2019

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A recent trend in wireless sensor network (WSN) research is the deployment of a mobile element (ME) for transporting data from sensor nodes to the base station (BS). This helps to achieve significant energy savings as it minimizes the communications required among nodes. However, a major problem is the large data gathering latency. To address this issue, the ME (i.e., vehicle) should visit certain rendezvous points (i.e., nodes) to collect data before it returns to the BS to minimize the data gathering latency. In view of this, we propose a rendezvous-based approach where some certain nodes serve as rendezvous points (RPs). The RPs gather data using data compression techniques from nearby sources (i.e., affiliated nodes) and transfer them to a mobile element when the ME traverses their paths. This minimizes the number of nodes to be visited, thereby reducing data gathering latency. Furthermore, we propose a minimal constrained rendezvous point (MCRP) algorithm, which ensures the aggregated data are relayed to the RPs based on three constraints: (i) bounded relay hop, (ii) the number of affiliation nodes, and (iii) location of the RP. The algorithm is designed to consider the ME's tour length and the shortest path tree (SPT) jointly. The effectiveness of the algorithm is validated through extensive simulations against four existing algorithms. Results show that the MCRP algorithm outperforms the compared schemes in terms of the ME's tour length, data gathering latency, and the number of rendezvous nodes. MCRP exhibits a relatively close performance to other algorithms with respect to power algorithms.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

2019

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“…The multi-hop data-gathering approach can be further divided into the hop count-free approach, such as [4,8,9,10,11,12,13,20], and the hop count restricted approach, such as [14,15,16]. …”

Section: Related Workmentioning

confidence: 99%

“…In [15], Bassam et al tried to find the shortest travel route of the mobile sink to maximize the network lifetime in WSN-MS, where the number of hops between sensor nodes and the mobile sink is bounded. They proposed an energy-aware bounded hop count algorithm, which selected sensor nodes closest to the sink as cluster heads.…”

Section: Related Workmentioning

confidence: 99%

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An Adaptive Clustering Approach Based on Minimum Travel Route Planning for Wireless Sensor Networks with a Mobile Sink

Tang

Yang

Zhu

et al. 2017

Sensors

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In recent years, Wireless Sensor Networks with a Mobile Sink (WSN-MS) have been an active research topic due to the widespread use of mobile devices. However, how to get the balance between data delivery latency and energy consumption becomes a key issue of WSN-MS. In this paper, we study the clustering approach by jointly considering the Route planning for mobile sink and Clustering Problem (RCP) for static sensor nodes. We solve the RCP problem by using the minimum travel route clustering approach, which applies the minimum travel route of the mobile sink to guide the clustering process. We formulate the RCP problem as an Integer Non-Linear Programming (INLP) problem to shorten the travel route of the mobile sink under three constraints: the communication hops constraint, the travel route constraint and the loop avoidance constraint. We then propose an Imprecise Induction Algorithm (IIA) based on the property that the solution with a small hop count is more feasible than that with a large hop count. The IIA algorithm includes three processes: initializing travel route planning with a Traveling Salesman Problem (TSP) algorithm, transforming the cluster head to a cluster member and transforming the cluster member to a cluster head. Extensive experimental results show that the IIA algorithm could automatically adjust cluster heads according to the maximum hops parameter and plan a shorter travel route for the mobile sink. Compared with the Shortest Path Tree-based Data-Gathering Algorithm (SPT-DGA), the IIA algorithm has the characteristics of shorter route length, smaller cluster head count and faster convergence rate.

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“…By classifying the nodes inside the UAV's coverage area into different frames according to their locations, the method achieves the goal of a reduction in packet collisions and the packet loss originated from nodes in the rear-side of the UAV when the UAV moves in the forward direction. A heuristic algorithm is proposed for finding a trajectory of the sink for data gathering in [4]. The algorithm strikes the trade-off between the latency of packet relay and the tour length of the mobile sink.…”

Section: Introductionmentioning

confidence: 99%

A Multi-hop Data Gathering Method for Wireless Sensor Networks

Fan¹,

Sun²,

Fang³

et al. 2017

dtmse

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Abstract. In wireless sensor networks used in environmental monitoring, data collection, the data generated by the sensor nodes will be transmitted with many hops from the sources to the destinations. In order to transmit the important data quickly, as well as reduce the energy consumption and extend the lifetime of networks, a multi-hop data gathering method (MHDG) for wireless sensor networks is proposed. The method aggregated the data from sensor nodes and marked the priority for the data with the source nodes. Simulation results show that compared to existing traditional method, the proposed method achieves the goal of a longer life time of networks, a lower packet drop ratio and energy consumption. IntroductionThe wireless sensor networks (WSNs) are usually deployed among widely region for pollution monitoring, data collection and other fields. In WSNs, the data generated by the sensor nodes will be transmitted with many hops from the sources to their destinations. So designing an efficient data gathering methods is important for the WSNs [1].Many data gathering methods have been proposed in recent years. In [1], a data gathering method is proposed for mobile sink. In the method, the mobile sink visits the overlapping areas of communication ranges of sensors instead of sensors one by one. The method delivers good results in terms of the computational complexity, and reduction of the delay time of data gathering. In order to increase the efficiency of the data gathering in sensor networks using an unmanned aerial vehicle (UAV), a priority-based data gathering framework is proposed in [2]. In [3], a decentralized method for the compressive data gathering problem (DCDG) is proposed. By classifying the nodes inside the UAV's coverage area into different frames according to their locations, the method achieves the goal of a reduction in packet collisions and the packet loss originated from nodes in the rear-side of the UAV when the UAV moves in the forward direction. A heuristic algorithm is proposed for finding a trajectory of the sink for data gathering in [4]. The algorithm strikes the trade-off between the latency of packet relay and the tour length of the mobile sink. In [5], a stability-based and energy-efficient distributed data gathering algorithms for wireless mobile sensor networks is presented. In order to minimize the data loss rate over all sensor nodes, an optimal movement strategy among a class of strategies is used in [6]. The numerical simulations that the optimal movement strategy outperforms the standard random walk strategy under various settings of network topology, buffer size, and the number of mobile collectors. In [7], a cross-layer optimization method, namely DaGCM, for mobile data gathering for sink equipped with multiple antennas, is proposed. By considering dynamic wireless link capacity and power control jointly, the mobile sink remarkably improves the efficiency of data collection in terms of data gathering cost, latency and energy consumption.The existed data gathering methods fai...

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Energy aware and delay-tolerant data gathering in sensor networks with a mobile sink

Cited by 6 publications

References 12 publications

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

An Adaptive Clustering Approach Based on Minimum Travel Route Planning for Wireless Sensor Networks with a Mobile Sink

A Multi-hop Data Gathering Method for Wireless Sensor Networks

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