An Adaptive Low-Power Listening Protocol for Wireless Sensor Networks in Noisy Environments

Dinh, Ngoc-Thanh; Kim, Young-Han; Gu, Tao; Vasilakos, Athanasios V.

doi:10.1109/jsyst.2017.2720781

Cited by 29 publications

(51 citation statements)

References 28 publications

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“…Therefore, in addition to the usage of the optimal consolidated sensing interval determined by the aggregator above, a physical sensor also needs to adapt its wakeup schedule based on the updates of the consolidated sensing interval. We assume physical sensors running with a low power listening protocol (LPL) [ 45 , 46 , 47 ], which is one of the most popular energy-efficient protocols deployed for WSNs. In this section, we propose a sensing update request-based adaptive low power listening protocol (SLPL) for sensor nodes.…”

Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

“…In this section, we propose a sensing update request-based adaptive low power listening protocol (SLPL) for sensor nodes. A part of the protocol is based on our previous work [ 46 ].…”

Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

“…Low power listening (LPL) [ 45 , 46 , 47 ] is a common mechanism that has been greatly explored in designing energy-efficient MAC protocols. Although there are several different LPL implementations, their basic design is quite similar.…”

Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

“…When a new set of values

and

) is discovered by a node, the node then adjusts its LPL temporal parameters accordingly to optimize its energy consumption. The detailed operations for parameter calculation and exchange are discussed in [ 46 ].…”

Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

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An Efficient Interactive Model for On-Demand Sensing-As-A-Servicesof Sensor-Cloud

Dinh

Kim

2016

Sensors

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This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud.

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Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

“…In this section, we propose a sensing update request-based adaptive low power listening protocol (SLPL) for sensor nodes. A part of the protocol is based on our previous work [ 46 ].…”

Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

“…When a new set of values

and

Section: A Sensing Update Request-based Adaptive Low Power Listenimentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Interactive Model for On-Demand Sensing-As-A-Servicesof Sensor-Cloud

Dinh

Kim

2016

Sensors

Self Cite

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“…In real world deployment, energy efficient MAC protocols lead to a great difference in forms of nodes power utilization. Energy efficient MAC protocols were introduced to WSNs area with the aim to decrease the waste of node's energy in idle listening state which increase the network's lifetime and this mechanism is known as sleep wake system [4]. In some books it is also referred as Duty Cycling mechanisms and commonly classified as synchronous and asynchronous schemes.…”

Section: Introductionmentioning

confidence: 99%

Efficient Node Monitoring Mechanism in WSN using Contikimac Protocol

Ashraf¹,

Gao²,

Chen³

et al. 2017

ijacsa

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Abstract-Wireless Sensor Network is monitored withContikiMAC Cooja flavor to diagnose the energy utilization ratio by nodes and the fault detection process in distributed approach; adopted the Low power Listening (LPL) mechanism with ContikiMAC to prolong the network's lifetime. LPL locate the root cause of communication issue, get rid of the interruption problems, and get back normal communication state. The LPL mechanism reduces the energy utilization in centralized and distribute approaches. Even more, the distributed approach is best suited for network monitoring when energy utilization is main objective in the presence of LPL. It is also important how soon the faulty node can be detected. In this case, latency has vital contributions in monitoring mechanism and latency is achieved by developing the efficient faulty node detection methodology.

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