Distributed Low-Latency Data Aggregation for Duty-Cycle Wireless Sensor Networks

Chen, Quan; Gao, Hong; Cai, Zhipeng; Cheng, Lianglun; Li, Jianzhong

doi:10.1109/tnet.2018.2868943

Cited by 42 publications

(18 citation statements)

References 26 publications

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“…In the second scenario, each sensor node has to establish and update the table with routing information, which is not a too effective way to deliver information to a fusion center over mobile networks and networks with limited energy sources such as WSNs [9]. Therefore, the distributed schemes are more frequently applied nowadays [10][11][12][13]. In these schemes, the adjacent sensor nodes communicate with each other and update their states according to the collected data [14].…”

Section: • Distributedmentioning

confidence: 99%

“…Therefore, the distributed schemes are more frequently applied nowadays [10][11][12][13]. In these schemes, the adjacent sensor nodes communicate with each other and update their states according to the collected data [14]. Thus, a fusion center is not necessary for data aggregation as well as the sensor nodes know only local information whereby data aggregation is significantly simplified.…”

Section: • Distributedmentioning

confidence: 99%

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Distributed Linear Summing in Wireless Sensor Networks with Implemented Stopping Criteria

Kenyeres¹,

Kenyeres²

2020

Adv. sci. technol. eng. syst. j.

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Many real-life applications based on the wireless sensor networks are equipped with data aggregation mechanisms for suppressing or even overcoming negative environmental effects and data redundancy. In this paper, we present an extended analysis of the linear average consensus algorithm for distributed summing with bounded execution over wireless sensor networks. We compare a centralized and a fully-distributed stopping criterion proposed for the wireless sensor networks with a varied initial configuration over random geometric graphs with 200 vertices in order to identify the optimal initial configuration of both analyzed stopping criteria and the algorithm as well as finding out which stopping criterion ensures higher performance of the examined algorithm in terms of the estimation precision and the convergence rate expressed as the iteration number for the consensus.

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Section: • Distributedmentioning

confidence: 99%

Section: • Distributedmentioning

confidence: 99%

Distributed Linear Summing in Wireless Sensor Networks with Implemented Stopping Criteria

Kenyeres¹,

Kenyeres²

2020

Adv. sci. technol. eng. syst. j.

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“…This continues until the total prize of all the combined fragments reaches Q. Finally, a traveling salesman is placed at the starting node (with the maximum mode value) in each fragment, which visits all other nodes in the fragment to collect prizes by traversing each edge at most twice, and returns the total collected prizes and total traveling cost (lines [11][12][13][14][15]. The time complexity of Algo.…”

Section: B Approximation Algorithm For Q-tspmentioning

confidence: 99%

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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“…In [18], another problem discussed by Hawbani et al for low-duty-cycle WSNs, they proposed a protocol based on the candidate zone and prioritisation of each node in any zone. Chen et al [19] identified the problem of the minimum latency aggregation schedule and proposed a solution distributed approximate aggregation algorithm. He et al targeted the problem of transmission delay and to overcome the problem proposes an algorithm minimise transmission delay routing (MTDR).…”

Section: Related Workmentioning

confidence: 99%

Algorithm for energy consumption minimisation in wireless sensor network

2020

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Distributed Low-Latency Data Aggregation for Duty-Cycle Wireless Sensor Networks

Cited by 42 publications

References 26 publications

Distributed Linear Summing in Wireless Sensor Networks with Implemented Stopping Criteria

Distributed Linear Summing in Wireless Sensor Networks with Implemented Stopping Criteria

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Algorithm for energy consumption minimisation in wireless sensor network

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