Critical-path aware broadcast scheduling in duty-cycled Wireless Sensor Networks

Im, Giyeol; Le, Duc-Tai; Choo, Hyunseung; Kim, Dongsoo S.

doi:10.1109/icoin.2015.7057929

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…The works in References 1,17–22 have studied the MLBS problem in duty‐cycled wireless IoT networks. The work in Reference 17 first proved that such a problem is NP‐hard and introduced two approximate algorithms based on D2 coloring technique with approximation ratios of

O\left({\Delta}^2+1\right)

and

24\mid T\mid +1

respectively, where

\Delta

denotes the maximum degree of nodes in the network and

\mid T\mid

represents the total amount of active time slots in a working cycle.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the work in Reference 20 proposed a latency‐aware broadcast algorithm relying on the geometrical unrelated set. The research in Reference 21 offered a low‐latency broadcast scheduling method that made use of the critical path nodes' priority. The author presented a new collision‐tolerant scheduling technique in Reference 22, which permits non‐critical nodes to experience conflicts in order to expedite the broadcast process of critical nodes.…”

Section: Related Workmentioning

confidence: 99%

“…The broadcast latency is determined by the final time slot in which the message is successfully received by one of the leaf nodes. To reduce the broadcast latency, the works in References 17–22 have investigated the MLBS problem in duty‐cycled networks. These studies involve a separated consideration between the scheduling of nodes and the design of the broadcast tree.…”

Section: Introductionmentioning

confidence: 99%

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

Long,

Wu,

Chen

et al. 2024

Concurrency and Computation

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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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O\left({\Delta}^2+1\right)

and

24\mid T\mid +1

respectively, where

\Delta

denotes the maximum degree of nodes in the network and

\mid T\mid

represents the total amount of active time slots in a working cycle.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Long,

Wu,

Chen

et al. 2024

Concurrency and Computation

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“…The intuition behind this is that broadcast latency may increase if a delay occurs along critical paths of the network. In [37], such critical paths are determined on an arbitrary Shortest Path Tree (SPT) constructed by a one-toall shortest path algorithm, such as Dijkstra's algorithm [38]. Based on the critical-path awareness, we have sketched a collision-free schedule [37] and a collision-tolerant schedule [39] and presented preliminary simulation results to show the advantage of critical-path aware scheduling in reducing broadcast latency.…”

Section: Related Workmentioning

confidence: 99%

Critical‐Path Aware Scheduling for Latency Efficient Broadcast in Duty‐Cycled Wireless Sensor Networks

Duc

et al. 2018

Wireless Communications and Mobile Computing

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Minimum latency scheduling has arisen as one of the most crucial problems for broadcasting in duty-cycled Wireless Sensor Networks (WSNs). Typical solutions for the broadcast scheduling iteratively search for nodes able to transmit a message simultaneously. Other nodes are prevented from transmissions to ensure that there is no collision in a network. Such collision-preventions result in extra delays for a broadcast and may increase overall latency if the delays occur along critical paths of the network. To facilitate the broadcast latency minimization, we propose a novel approach, critical-path aware scheduling (CAS), which schedules transmissions with a preference of nodes in critical paths of a duty-cycled WSN. This paper presents two schemes employing CAS which produce collision-free and collision-tolerant broadcast schedules, respectively. The collision-free CAS scheme guarantees an approximation ratio of (Δ-1)T in terms of latency, where Δ denotes the maximum node degree in a network. By allowing collision at noncritical nodes, the collision-tolerant CAS scheme reduces up to 10.2 percent broadcast latency compared with the collision-free ones while requiring additional transmissions for the noncritical nodes experiencing collisions. Simulation results show that broadcast latencies of the two proposed schemes are significantly shorter than those of the existing methods.

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“…An On-demand Scheduling Cooperative MAC protocol (OSC-MAC) is proposed in [8] to solve the energy hole problem in multi-hop WSNs by using pipelined and orthogonal schedule embedded in the MAC. Reducing the broadcast latency by finding first critical-paths in a duty-cycled WSN and scheduling transmissions with a preference of nodes in the critical-path is proposed in [9].…”

Section: Introductionmentioning

confidence: 99%

Brain Storm Optimization for Sensors’ Duty Cycle in Wireless Sensor Network

Khedr¹

2017

International Journal of Advanced Research in Computer and Comm

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Wireless Sensor Networks (WSNs) became one of the emergent networks that are used in many real life applications. They are also used in many of the critical applications such as battle field monitoring and health care. Therefore, sensor nodes are required to survive for long time. However, such nodes suffer from low memory, limited storage, limiting sensing and communication ranges, limited energy sources, and limited processing capabilities. One of the effective methods to save the nodes' energy is the duty cycling where some of the nodes can go to sleep while others can perform the required monitored field tasks. In this paper, we propose a new algorithm that utilizes the concept of the people thinking which the Brain Storming Optimization (BSO) for sensor nodes duty cycle management. Through extensive set of experiments using homogenous and heterogeneous WSNs, BSO seems to outperform the Non-duty cycle algorithm.

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Critical-path aware broadcast scheduling in duty-cycled Wireless Sensor Networks

Cited by 8 publications

References 3 publications

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

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

Critical‐Path Aware Scheduling for Latency Efficient Broadcast in Duty‐Cycled Wireless Sensor Networks

Brain Storm Optimization for Sensors’ Duty Cycle in Wireless Sensor Network

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