Efficient data collection from wireless nodes under the two-ring communication model

Tekdas, Onur; Bhadauria, Deepak; Isler, Volkan

doi:10.1177/0278364912439429

Cited by 17 publications

(13 citation statements)

References 31 publications

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“…To provide more realistic communication modeling, two-ring communication models have been explored [9], and methods that optimize based on expected network latency have been proposed [10]. Similar approaches have also been applied to improve the placement of sensors to maximize communication efficiency [11].…”

Section: Related Workmentioning

confidence: 99%

Squared error distortion metrics for motion planning in robotic sensor networks

Hollinger

Choudhuri

Mitra

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

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We examine the problem of planning the trajectory of a robotic vehicle to gather data from a deployment of stationary sensors monitoring a set of dynamic source signals. The robotic vehicle and the sensors are equipped with wireless modems (e.g., radio in terrestrial environments or acoustic in underwater environments), which provide noisy communication across limited distances. In such scenarios, the robotic vehicle can improve its efficiency by planning an informed data gathering trajectory. We propose a novel performance metric for data gathering in robotic sensor networks based on the concept of squared error distortion. We analyze the formal properties of the distortion function, and we propose a sampling-based motion planning algorithm for optimizing data gathering tours for minimal distortion. The proposed algorithms are compared in simulation, and the results show that distortion metrics provide substantial improvements in data gathering efficiency.

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Section: Related Workmentioning

confidence: 99%

Squared error distortion metrics for motion planning in robotic sensor networks

Hollinger

Choudhuri

Mitra

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

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“…Most of the common heuristic optimization algorithms have been used to find TSP solutions. [19][20][21] TSP can be classified into two different classes based on the form of distances between cities: asymmetric and symmetric. In asymmetric TSP, ≠ , i.e.…”

Section: Travelling Salesperson Problemmentioning

confidence: 99%

“…In [80], the authors first establish a TSP tour using any existing TSP solver to generate minimum tour, and later reduce the tour . Adapted from [20].…”

Section: Tspn-like Data Collectionmentioning

confidence: 99%

“…by calculating a new waypoint within the radio range. This new waypoint is referred to as a data collection site or stop point or visiting point, which simply means a single point in the sensor node's communication range for the MS to visit for data collection.Collecting data in minimum time is formulated as the Two-Ring Tour (TRT) problem in[20]. Tekdas et al developed experimental models for sensor network deployments with a two ring communication model, with a faster inner ring and a slower outer ring.…”

mentioning

confidence: 99%

“…The difference between Tekdas's work and the work of He, Pan and Xu is how the data collection is conducted. In MR-CSS, the MS collects data while travelling, while in[20] the MS stops at the stop point to collect the data.MR-CSS schemes allow the communication to happen over a longer distance at lower data rate or shorter distance for higher data rate. The schemes allow adjustment of communication ranges of the sensor nodes to ensure the data collection jobs can be accomplished in a feasible time.…”

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confidence: 99%

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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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Learning a Spatial Field with Gaussian Process Regression in Minimum Time

Suryan

Tokekar

2020

Springer Proceedings in Advanced Robotics

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Efficient data collection from wireless nodes under the two-ring communication model

Cited by 17 publications

References 31 publications

Squared error distortion metrics for motion planning in robotic sensor networks

Squared error distortion metrics for motion planning in robotic sensor networks

Mobile sink for data collection in wireless sensor networks

Learning a Spatial Field with Gaussian Process Regression in Minimum Time

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