Power Optimization for Wireless Sensor Networks

Pinto, Alex Sandro Roschildt; Poehls, Leticia Bolzani; Montez, Carlos; Vargas, F.

doi:10.5772/50603

Cited by 8 publications

(10 citation statements)

References 48 publications

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“…Two important aspects must not be forgotten in this modeling: the radio-frequency energy transmission (or other energy harvesting scheme) and the rate capacity effect [18], which is related to the discharge rate of the battery: drawing higher current than the rated value significantly reduces the battery lifetime and, thus, reduces the network lifetime. Graphene-based batteries [46] are promising devices that can dramatically influence these models and considerably push the network lifetime to unprecedented values.…”

Section: Discussionmentioning

confidence: 99%

“…Approaches that act mainly on the energy spent during the task of communication among sensor nodes or between sensor nodes and sink nodes can be found in [5,17,18,19,20,21]. For example, transmission power control techniques aiming at managing energy consumption during communications are discussed in [18].…”

Section: Introductionmentioning

confidence: 99%

“…For example, transmission power control techniques aiming at managing energy consumption during communications are discussed in [18]. Some of these techniques use a single transmission power for the whole network; others apply local power management algorithms that do not involve MAC protocols.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these techniques use a single transmission power for the whole network; others apply local power management algorithms that do not involve MAC protocols. Additionally, time-division multiple access (TDMA) with collision avoidance and duty cycle strategies are also discussed in [18]. In [5], the authors propose a technique to mitigate the interference between WBANs using convex optimization in order to optimize the overall transmission power.…”

Section: Introductionmentioning

confidence: 99%

“…It presents a novel convex optimization-based procedure (or method) that controls what we define as the communication activity levels of the sensor nodes, by dynamically assigning a number of time or time-frequency slots for communication with the sink nodes within a frame. The optimization concentrates on the communication task, since this is the most energy-consuming process of a sensor node [18]. Autonomous sensor node mobility is not considered here, since in this specific case large energy consumption would result from the mechanisms that produce mobility, as for instance in robots.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity

Guimarães

Sakai

Alberti

2016

Sensors

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In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication activity levels of the sensor nodes to save energy without sacrificing the data throughput. The activity levels are defined to represent portions of time or time-frequency slots in a frame, during which the sensor nodes are scheduled to communicate with the sink node to report sensory measurements. Besides node mobility, it is considered that sensors’ batteries may be recharged via a wireless power transmission or equivalent energy harvesting scheme, bringing to the optimization problem an even more dynamic character. We report large increased lifetimes over the non-optimized network and comparable or even larger lifetime improvements with respect to an idealized greedy algorithm that uses both the real-time channel state and the residual energy information.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity

Guimarães

Sakai

Alberti

2016

Sensors

View full text Add to dashboard Cite

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Impacts of Computational Techniques for Wireless Sensor Networks

Shiva¹,

Vedik²,

Kumar³

et al. 2021

Springer Tracts in Nature-Inspired Computing

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Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Fimbombaya

Mvungi

Ndyetabura

et al. 2018

Modelling and Simulation in Engineering

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Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

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Power Optimization for Wireless Sensor Networks

Cited by 8 publications

References 48 publications

Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity

Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity

Impacts of Computational Techniques for Wireless Sensor Networks

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

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