Optimal Stop Points for Data Gathering in Sensor Networks with Mobile Sinks

Park, Jun-Young; Moon, Kyoung-jin; Yoo, Sungjoo; Lee, Sunggu

doi:10.4236/wsn.2012.41002

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…In [19], the MS moves in a fixed path and stops on a set of stop points to collect data from the sensor nodes via multihop. The authors used the heuristic algorithm-based Tabu search algorithm to select the optimal number of set stop points in which the minimal hop count from sensor nodes and the MS is achieved.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Path Generation Algorithm Using Principle Component Analysis for Mobile Sinks in Wireless Sensor Networks

Banimelhem

Taqieddin

Shatnawi

2021

JSAN

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Recently, the data collection problem in wireless sensor networks (WSNs) using mobile sinks has received much attention. The main challenge in such problems is constructing the path that the mobile sink (MS) will use to collect the data. In this paper, an efficient path generation algorithm for the mobile sink based on principal component analysis (PCA) is proposed. The proposed approach was evaluated using two data collection modes—direct and multihop—and it was compared with another approach called the mobile-sink-based energy-efficient clustering algorithm for wireless sensor networks (MECA). When compared with MECA, simulation results have shown that the proposed approach improves the performance of WSN in terms of the number of live nodes and average remaining energy.

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Section: Related Workmentioning

confidence: 99%

An Efficient Path Generation Algorithm Using Principle Component Analysis for Mobile Sinks in Wireless Sensor Networks

Banimelhem

Taqieddin

Shatnawi

2021

JSAN

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“…[19]. Several works advocate data muling to minimize node energy consumption by gathering data with a mobile sink [13][14][15]. The resulting communication range based rendezvous are defined by a successful communication link and are thus binary in contrast to sensing range based rendezvous.…”

Section: Related Workmentioning

confidence: 99%

“…Past research efforts on exploiting rendezvous in mobile networks focused on data communication [12][13][14][15] and network-wide time synchronization [16]. In both cases, a rendezvous depends on the node's communication range and is assessed in terms of successful communication between two nodes.…”

Section: Introductionmentioning

confidence: 99%

On Rendezvous in Mobile Sensing Networks

Saukh

Hasenfratz

Walser

et al. 2013

Lecture Notes in Electrical Engineering

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A rendezvous is a temporal and spatial vicinity of two sensors. In this chapter, we investigate rendezvous in the context of mobile sensing systems. We use an air quality dataset obtained with the OpenSense monitoring network to explore rendezvous properties for carbon monoxide, ozone, temperature, and humidity processes. Temporal and spatial locality of a physical process impacts the number of rendezvous between sensors, their duration, and their frequency. We introduce a rendezvous connection graph and explore the trade-off between locality of a process and the amount of time needed for the graph to be connected. Rendezvous graph connectivity has many potential use cases, such as sensor fault detection. We successfully apply the proposed concepts to track down faulty sensors and to improve sensor calibration in our deployment.

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“…The MBS will pause at stopping locations where the MBS stays for a period called the sojourn time and collects data from nearby sensor via multi-hop communications [69,70]. The selection of stop point is important to minimizing energy consumption [71]. In MBS-based solutions, the main objective is to balance and reduce the energy consumption among the sensor nodes [66,67,72].…”

Section: Data Collection Techniques Using Mobile Elementsmentioning

confidence: 99%

Mobile sink for data collection in wireless sensor networks

Annuar¹

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The work in this thesis investigates the problem of designing a path for a mobile sink, like an Unpiloted Aerial Vehicle (UAV), to traverse a sensing field and collect data from a network of wireless sensor nodes. This problem is investigated as an optimization problem with a range of different network architectures, different network constraints and different objective functions. The thesis develops a new multi-level optimization framework which provides excellent solutions across a range of different scenarios. This framework starts with a Travelling Salesperson Problem (TSP) tour, generated with a simulated annealing framework. It then uses a gradient-descent style optimization framework to test for new waypoint positions which optimize the objective function, which initially is minimum tour time. Since tour time consists of the travel time (determined by the TSP tour of waypoints) plus additional stopping time for data collection, an inner optimization loop, based on Linear Programming, is used to find a communications schedule for a specific trial tour which minimizes stopping time, and hence minimizes tour time. Unique amongst previously published solutions, this algorithm is able to model radio ranges with data rates that vary with range in arbitrary increments. In particular, three different research questions are posed relating to several different scenarios. High quality, minimum tour time data collection paths are generated for known data loads at sensor nodes, using an algorithm variant called TSP-DC. In comparison with previously published solutions, it is shown to provide superior tour times. Next, a second variant, called TSP-DA is developed to deal with the situation where data loads are unknown when the tour is first developed, and the algorithm needs to recalculate paths when new nodes are encountered. Again results are better than a pre-planned worst case tour, and in most cases are close to the tour time with known loads. The extra cost of re-evaluating the tour is investigated, and if cloud computing support is available, then the recalculation time cost is small compared to the tour time saved. The computational complexity of the algorithms are investigated. Theoretical and experimental analyses show that TSP-DC has O(n 2) complexity and TSP-DA has O(n 3) complexity. Another algorithm variant, TSP-EM, is developed which minimizes energy consumption of the mobile sink, which gives good results, and is able to automatically adapt to different scenarios, such as the difference between ground robot and UAV energy costs. iii The final algorithm variant, TSP-ST, was able to optimize the tour path for network architectures where data could be forwarded from leaf nodes to relay nodes to cluster heads. In this case, an added constraint was the amount of energy at each sensor node, which could be different for every node. Again, the algorithm could generate good paths, across a broader range of scenarios than previously published algorithms. For example, with low node energy or low radio range, the si...

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Optimal Stop Points for Data Gathering in Sensor Networks with Mobile Sinks

Cited by 6 publications

References 14 publications

An Efficient Path Generation Algorithm Using Principle Component Analysis for Mobile Sinks in Wireless Sensor Networks

An Efficient Path Generation Algorithm Using Principle Component Analysis for Mobile Sinks in Wireless Sensor Networks

On Rendezvous in Mobile Sensing Networks

Mobile sink for data collection in wireless sensor networks

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