Schedule unifying algorithm extending network lifetime in S-MAC-based wireless sensor networks

Lee, Woonsik; Nguyen, Minh Tuan; Verma, Akansha; Lee, Hwang

doi:10.1109/twc.2009.070370

Cited by 21 publications

(14 citation statements)

References 9 publications

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“…To solve the problem of high energy consumption of border nodes in SMAC, [51] proposes a schedule unifying algorithm for network-wide synchronisation of the sleep schedules. In SMAC, a node either follows a sleep schedule received from a neighbour or broadcasts its own sleep schedule.…”

Section: H Schedule Unifying Algorithm (Sua) For Smacmentioning

confidence: 99%

Application of reinforcement learning to medium access control for wireless sensor networks

Chu

Koşunalp

Mitchell

et al. 2015

Engineering Applications of Artificial Intelligence

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This thesis investigates the application of Reinforcement Learning (RL) on Medium Access Control (MAC) for Wireless Sensor Networks (WSNs). RL is applied as an intelligent slot selection strategy to Framed ALOHA, along with analytical and experimental performance evaluation. Informed Receiving (IR) and ping packets are applied to multi-hop WSNs to avoid idle listening and overhearing, thereby further improving the energy efficiency.The low computational complexity and signalling overheads of the ALOHA schemes meet the design requirement of energy constraint WSNs, but suffer collisions from the random access strategy. RL is applied to solve this problem and to achieve perfect scheduling. Results show that the RL scheme achieves over 0.9 Erlangs maximum throughput in single-hop networks. For multi-hop WSNs, IR and ping packets are applied to appropriately switch the relay nodes between active and sleep state, to reserve as much energy as possible while ensuring no information loss.The RL algorithms require certain time to converge to steady state to achieve the optimum performance. The convergence behaviour is investigated in this thesis. A Markov model is proposed to describe a learning process, and the model produces the proof of the convergence of the learning process and the estimated convergence time. The channel performance before convergence is also evaluated.Contents

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Section: H Schedule Unifying Algorithm (Sua) For Smacmentioning

confidence: 99%

Application of reinforcement learning to medium access control for wireless sensor networks

Chu

Koşunalp

Mitchell

et al. 2015

Engineering Applications of Artificial Intelligence

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“…All the nodes can select their own listening/dormancy timetable freely, however, to decrease and save expenses, S-MAC is more likely to make mutual synchronization among neighbouring nodes [6]. In this way, they are listening at the same time and dormant at the same time.…”

Section: Periodic Monitoring and Dormancymentioning

confidence: 99%

Improvement Research on S-MAC Protocol in Wireless Sensor Network

Shi¹,

Jin²,

Cai³

2016

IJFGCN

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“…Therefore, after some time, a sensor network covering a wide area and consisting of tens of thousands of sensor nodes is divided into several isolated virtual clusters due to the death of these nodes. To minimize energy consumption due to multiple listen and sleep schedules, UDDC-MAC is proposed in which Schedule Unifying Algorithm (SUA) [5] is integrated with DDC-MAC so that multiple listen and sleep schedules into a single unified sub-optimal schedule. The convergence time analysis for SUA is discussed in [5] and it is shown to be proportional to order of where N is total number of nodes in the network.…”

Section: Fig 1: Periodic Listen and Sleepmentioning

confidence: 99%

“…To minimize energy consumption due to multiple listen and sleep schedules, UDDC-MAC is proposed in which Schedule Unifying Algorithm (SUA) [5] is integrated with DDC-MAC so that multiple listen and sleep schedules into a single unified sub-optimal schedule. The convergence time analysis for SUA is discussed in [5] and it is shown to be proportional to order of where N is total number of nodes in the network. So, SUA has acceptable scalability to the network size.…”

Section: Fig 1: Periodic Listen and Sleepmentioning

confidence: 99%

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Energy Efficient Traffic Aware MAC Protocols for Delay-Sensitive Wireless Sensor networks

Awasthi¹,

Sharma²

2013

IJCA

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Wireless sensor network (WSN) consists of large number of distributed sensor nodes, which are generally battery-powered and may not recharge easily. Consequently, how to maximize the lifetime of the nodes is an important issue while designing a MAC protocol. However, lowering the energy consumption may result in higher latency. To address such a tradeoff, this paper proposes DDC-MAC and UDDC-MAC. For delaysensitive WSNs, DDC-MAC minimizes latency with energy consumption at a reasonable level. In DDC-MAC, each sensor node chooses a duty cycle according to traffic and average latency experienced by that node. Each node follows multiple listen and sleep schedules. Therefore, they switch into the listen state frequently and reduce the network lifetime. To minimize energy consumption due to multiple listen and sleep schedules, UDDC-MAC is proposed in which Schedule Unifying Algorithm (SUA) is integrated with DDC-MAC so that multiple listen and sleep schedules are unified into a single unified sub-optimal schedule. We study the proposed protocols by simulating these protocols using NS-2 and compare their performance with DS-MAC, S-MAC (Full), S-MAC (10%) in terms of average energy consumption of a node and average end to end latency experienced by the packet. The simulation results show that UDDC-MAC achieves improved latency performance and is more energy efficient than DDC-MAC, S-MAC and DS-MAC.

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Schedule unifying algorithm extending network lifetime in S-MAC-based wireless sensor networks

Cited by 21 publications

References 9 publications

Application of reinforcement learning to medium access control for wireless sensor networks

Application of reinforcement learning to medium access control for wireless sensor networks

Improvement Research on S-MAC Protocol in Wireless Sensor Network

Energy Efficient Traffic Aware MAC Protocols for Delay-Sensitive Wireless Sensor networks

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