DRE2: Achieving Data Resilience in Wireless Sensor Networks: A Quadratic Programming Approach

Hsu, Shang‐Lin; Yu, Yuning; Tang, Bin

doi:10.1109/mass50613.2020.00019

Cited by 6 publications

(12 citation statements)

References 32 publications

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“…After aggregation, data is then offloaded from data nodes to storage nodes with minimum energy consumption. Our previous work [26,49] has shown this can be modeled as a minimum cost flow problem [1], which can be solved optimally and efficiently. One optimal solution in Fig.…”

Section: G(v E)mentioning

confidence: 99%

“…It has a total aggregation cost of 5. After aggregation, the sizes of overflow data at B, E, D, G, and I are 0, 3/4, 3/4, 3/4, and 7/4, respectively, which is a total of 4 units thus can be offloaded to storage nodes using techniques in [26,49]. Note that 7/4 units of data at I now include 3/4 units of I's own aggregated overflow data and one unit of initiator B's overflow data.…”

Section: Algorithmic Solutions For Daomentioning

confidence: 99%

“…Comparing B-Walk with LP-Walk. Next, we compare the aggregation costs in B-Walk and LP-Walk in the whole range of p ∈ [26,49] with varied ρ. Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…To avoid data loss, our previous works have designed a suite of techniques to offload overflow data from storage-depleted sensor nodes to nearby sensor nodes with available storage [25,26,48,49,58]. However, if these offloaded data cannot be collected and uploaded timely by data mules or satellite links, they could soon overflow the available storage in the entire network.…”

Section: Introductionmentioning

confidence: 99%

“…After being aggregated to the size accommodable by the network, the overflow data can then be stored into sensor nodes with available storage using techniques proposed in [25,26,49,58] (see Example 1 in Section II). Note that we do not consider how to upload data from sensor nodes to the base station, which has been studied extensively using data mules or robots [20,27,46,56,59].…”

Section: Introductionmentioning

confidence: 99%

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DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Tang¹

2022

Preprint

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Many emerging sensor network applications operate in challenging environments wherein the base station is not available. Data generated from such intermittently connected sensor networks (ICSNs) therefore must be stored inside the network for some unpredictable period of time before uploading opportunities become available. Consequently, sensory data could overflow the limited storage capacity available in the entire network, making discarding valuable data inevitable. To overcome such overall storage overflow in ICSNs, we propose and study a new algorithmic framework called data aggregation for overall storage overflow (DAO2). Utilizing spatial data correlation that commonly exists among sensory data, DAO2 employs data aggregation techniques to reduce the overflow data size while minimizing the total energy consumption in data aggregation. At the core of our framework are two new graph theoretical problems that have not been studied. We refer to them as traveling salesmen placement problem (TSP2) and quota traveling salesmen placement problem (Q-TSP2). Different from the well-known multiple traveling salesman problem (mTSP) and its variants, which mainly focus on the routing of multiple salesmen initially located at fixed locations, TSP2 and Q-TSP2 must decide the placement as well as the routing of the traveling salesmen. We prove that both problems are NP-hard and design approximation, heuristic, and distributed algorithms. Our algorithms outperform the state-of-the-art data aggregation work with base stations by up to 71.8% in terms of energy consumption. The building blocks of our framework are two novel graph structures called aggregation network and minimum q-edge forest that accurately capture the information needed for energy-efficient data aggregation in ICSNs.

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Section: G(v E)mentioning

confidence: 99%

Section: Algorithmic Solutions For Daomentioning

confidence: 99%

“…Comparing B-Walk with LP-Walk. Next, we compare the aggregation costs in B-Walk and LP-Walk in the whole range of p ∈ [26,49] with varied ρ. Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%