Adaptive Scheduling and Routing Scheme for Delay Guarantee in Wireless Sensor Networks

Sun, Yan; Chen, Cong; Luo, Hong

doi:10.1155/2013/801758

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Yan et al [34] proposed an adaptive scheduling and routing scheme to guarantee dynamic end-to-end delay requirements while minimizing the energy consumption in wireless sensor networks (WSNs). In the case where the endto-end delay requirement of a region changes, they proposed an adaptive adjustment algorithm to locally adjust the wakeup schedule or routing table with the minimum energy cost while satisfying the new delay requirement.…”

Section: B Joint Routing and Tdma Link Schedulingmentioning

confidence: 99%

Deep Learning for MANET Routing

Danilchenko,

Azoulay,

Reches

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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Power control and scheduling are among the most well-known resource allocation challenges in wireless networks, and are often solved as optimization problems with constraints. However, solving these optimization challenges by using optimal algorithms often incurs a significant time complexity, which creates considerable discrepancies between the theoretical results and real-time processing required. In this study, we propose a novel machine learning-based perspective to address this issue. We propose a scheduling and power control deep neural network SPCDNet method and its modification SPCDNet R . SPCDNet solves the scheduling problem for point-to-point transmission requests while SPCDNet R solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and SPCDNet R are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and SPCDNet R can serve as a computationally inexpensive solution (regarding time complexity), compared with state-of-the-art schemes, while showing to be near-optimal approximation solutions to the time scheduling and power control challenges. Moreover, we found that both SPCDNet and SPCDNet R reach efficient solutions for large problem instances, even though they were trained on small problems.

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Section: B Joint Routing and Tdma Link Schedulingmentioning

confidence: 99%

Deep Learning for MANET Routing

Danilchenko,

Azoulay,

Reches

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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“…An adaptive scheduling and routing scheme in [8] utilizes an optimal sleeping scheduling algorithm to obtain wake-up schedules and routes and thus adaptively adjust wake-up of nodes. It does not consider remaining energy of nodes when the frequency increases so the energy consumption of the entire network is not balanced.…”

Section: Related Workmentioning

confidence: 99%

An optimization scheme in wireless temperature measuring sensor network

Jiang

Zhu

2015

2015 IEEE 12th International Conference on Networking, Sensing and Control

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In wireless sensor networks, power consumption is a critical issue. For our National Grid temperature measuring and monitoring project, energy harvesting is limited to provide sufficient power to temperature measuring nodes allocated throughout the entire grid substation site. To maintain longer lifetime of the entire network is especially important. Currently, various media access control (MAC) layer protocols use different energy scheduling methods to realize data transmission on wireless channel under noisy environment. Although they are efficient for data transfer, they have limitations on saving power consumption.This paper proposes a network design solution for efficiently managing the energy consumption of wireless sensor network, in which sleep scheduling and forwarding set choosing are improved. Network nodes choose their own sleep scheduling parameters adaptively. Nodes also select individual forwarding set according to a set of priority criteria. In addition, nodes have their backup nodes determined by a set of rules in order to handle critical events and in case of node failure. Overall, this scheme enhances MAC layer's ability to more efficiently control and lower network node energy consumption, leading to increased lifetime for the entire network. Omnet++ is used to run the network simulation. The results show advantages over other MAC protocols.

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“…Yan Sun et al [7] proposed a scheduling and routing scheme to guarantee delay adaptive dynamic requirements of end-to-end while minimizing energy consumption. They use a wake-up scheduling algorithm to get optimal schedule wake-up early and routes are designed with an adaptive adjustment algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network

Abror

Widodo

Rasyid

2018

emitter

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NO and CO. In WSN, node system is constrained to a limited power supply, so that the node system has a lifetime. To doing lifetime maximization, power management scheme is required and sensor nodes should use energy efficiently. This paper proposes dynamic sleep scheduling using Time Category-Fuzzy Logic (TimeFuzzy) Scheduling as a reference for calculating time interval for sleep and activated node system to support power management scheme. This research contributed in power management design to be applied to the WSN system to reduce energy expenditure. From the test result in real hardware node system, it can be seen that Time-Fuzzy Scheduling is better in terms of using the battery and it is better in terms of energy consumption too because it is more efficient 51.85% when it is compared with Fuzzy Scheduling, it is more efficient 68.81% when it is compared with Standard Scheduling and it is more efficient 85.03% when compared with No Scheduling.

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Adaptive Scheduling and Routing Scheme for Delay Guarantee in Wireless Sensor Networks

Cited by 4 publications

References 14 publications

Deep Learning for MANET Routing

Deep Learning for MANET Routing

An optimization scheme in wireless temperature measuring sensor network

Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network

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