Asynchronous Neighbor Discovery on Duty-Cycled Mobile Devices: Models and Schedules

Chen, Sixia; Morillo, Reynaldo; Qin, Yanyuan; Russell, Alexander; Jin, Ruofan; Wang, Bing; Vasudevan, Subramanyan

doi:10.1109/twc.2020.2990764

“…Many asynchronous neighbor discovery algorithms have emerged in recent years. Based on whether the discovery latency is finite or not, existing pairwise asynchronous protocols are divided into the probabilistic algorithms [ 12 , 19 , 20 ] and deterministic ones [ 8 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

“…The later researchers find that employing unequal-sized slots can obtain good discovery performance. Searchlight-Striped [ 7 ], Non-integer [ 25 ] and Lightning [ 26 ] are included. There are other related methods, i.e., block-based methods [ 8 , 27 ] and Nihao [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

A Fast Neighbor Discovery Algorithm in WSNs

Wei

¹

,

Sun

²

,

Chen

³

et al. 2018

Sensors

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With the quick development of Internet of Things (IoT), one of its important supporting technologies, i.e., wireless sensor networks (WSNs), gets much more attention. Neighbor discovery is an indispensable procedure in WSNs. The existing deterministic neighbor discovery algorithms in WSNs ensure that successful discovery can be obtained within a given period of time, but the average discovery delay is long. It is difficult to meet the need for rapid discovery in mobile low duty cycle environments. In addition, with the rapid development of IoT, the node densities of many WSNs greatly increase. In such scenarios, existing neighbor discovery methods fail to satisfy the requirement in terms of discovery latency under the condition of the same energy consumption. This paper proposes a group-based fast neighbor discovery algorithm (GBFA) to address the issues. By carrying neighbor information in beacon packet, the node knows in advance some potential neighbors. It selects more energy efficient potential neighbors and proactively makes nodes wake up to verify whether these potential neighbors are true neighbors, thereby speeding up neighbor discovery, improving energy utilization efficiency and decreasing network communication load. The evaluation results indicate that, compared with other methods, GBFA decreases the average discovery latency up to 10.58% at the same energy budget.

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“…Many asynchronous neighbor discovery algorithms have emerged in recent years. Based on whether the discovery latency is finite or not, existing pairwise asynchronous protocols are divided into the probabilistic algorithms [12,19,20] and deterministic ones [8,[21][22][23][24][25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

“…The later researchers find that employing unequal-sized slots can obtain good discovery performance. Searchlight-Striped [7], Non-integer [25] and Lightning [26] are included. There are other related methods, i.e., block-based methods [8,27] and Nihao [28].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Fast Neighbor Discovery Algorithm in WSNs

Wei¹,

Sun²,

Chen³

et al. 2018

Computer Supported Cooperative Work and Social Computing

1

0

View full text Add to dashboard Cite

With the quick development of Internet of Things (IoT), one of its important supporting technologies, i.e., wireless sensor networks (WSNs), gets much more attention. Neighbor discovery is an indispensable procedure in WSNs. The existing deterministic neighbor discovery algorithms in WSNs ensure that successful discovery can be obtained within a given period of time, but the average discovery delay is long. It is difficult to meet the need for rapid discovery in mobile low duty cycle environments. In addition, with the rapid development of IoT, the node densities of many WSNs greatly increase. In such scenarios, existing neighbor discovery methods fail to satisfy the requirement in terms of discovery latency under the condition of the same energy consumption. This paper proposes a group-based fast neighbor discovery algorithm (GBFA) to address the issues. By carrying neighbor information in beacon packet, the node knows in advance some potential neighbors. It selects more energy efficient potential neighbors and proactively makes nodes wake up to verify whether these potential neighbors are true neighbors, thereby speeding up neighbor discovery, improving energy utilization efficiency and decreasing network communication load. The evaluation results indicate that, compared with other methods, GBFA decreases the average discovery latency up to 10.58% at the same energy budget.

show abstract

Distributed low‐latency broadcast scheduling for multi‐channel duty‐cycled wireless IoT networks

Long,

Wu,

Chen

et al. 2024

Concurrency and Computation

1

0

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SummaryData broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict‐free broadcast schedule has not been extensively explored in duty‐cycled networks. The existing works either work in a centralized scheme or rely on a fixed tree for broadcasting. Additionally, they all employ a strict premise that each node can only utilize one channel for both transmitting and receiving messages. Thus, to address the issues mentioned above, we examine the first distributed broadcasting algorithm in multi‐channel duty‐cycled wireless IoT networks, without relying on a predetermined tree. First, the MLBS problem in such networks is defined and proved to be NP‐hard. Then, in order to avoid transmission conflicts between different links locally, two efficient data structures are designed to help compute the earliest time and channel of receiving messages without conflicts. Based on the above data structures, we introduce an efficient distributed broadcasting algorithm, which can generate a latency‐sensitive broadcast tree while calculating a collision‐free broadcast schedule, simultaneously. Finally, the theoretical analysis and simulations demonstrate the efficiency of the proposed algorithm.

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Asynchronous Neighbor Discovery on Duty-Cycled Mobile Devices: Models and Schedules

Cited by 22 publications

References 41 publications

A Fast Neighbor Discovery Algorithm in WSNs

A Fast Neighbor Discovery Algorithm in WSNs

A Fast Neighbor Discovery Algorithm in WSNs

Distributed low‐latency broadcast scheduling for multi‐channel duty‐cycled wireless IoT networks

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