Optimization of Electronic Power Consumption in Wireless Sensor Nodes

Jayapal, S.; Huang, Ronghua; Ramachandran, Shyamala; Bhutada, R.; Manoli, Yiannos

doi:10.1109/dsd.2005.60

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Jayapal et al [5] investigated multifarious possibilities of power reduction at system, architectural and circuit levels of a sensor node design and explored hardware and software co-design approach to reduce power consumption. A dynamic energy scanning technology was proposed to reduce voltage supply according to system requirements.…”

Section: Related Workmentioning

confidence: 99%

Energy awareness workflow model for wireless sensor nodes

Zhu

Oyadiji

2012

Wireless Communications

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Before the development of a large‐scale wireless sensor network (WSN) infrastructure, it is necessary to create a model to evaluate the lifespan of the infrastructure, the system performance and the cost so that the best design solution can be obtained. Energy consumption is an important factor that influences the lifespan of WSNs. One of the ways to extend the lifespan of WSNs is to design wireless sensor nodes with low power consumption. This involves component selection and the optimisation of hardware architecture, monitoring software system and protocols to satisfy the requirements of the particular applications. This paper proposes a comprehensive model to describe the workflow of a wireless sensor node. Parameter setup and energy consumption calculation are demonstrated through the model simulation. It provides a mathematical approach to dynamically evaluate the energy consumption of a sensor node. This will benefit the development of wireless sensor nodes based on microprocessors with limited computational capability. Therefore, the model can be applied in dynamic power management systems for wireless sensor nodes or in wireless communication protocols with energy awareness, in particular, for WSNs with self‐organisation. More importantly, the generalisation of the model may be employed as a standard paradigm for the development of wireless sensor node with energy awareness. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Energy awareness workflow model for wireless sensor nodes

Zhu

Oyadiji

2012

Wireless Communications

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“…Using transmit‐only sensors has a direct impact on the network lifetime since the receiver chain consumes considerable amount of power. Power consumption on the receiver chain could be comparable with, and in some cases, higher than that of the transmitter chain . A trivial solution to the transmit power allocation in this case is to s et all nodes for constant transmit power before field deployment.…”

Section: Introductionmentioning

confidence: 99%

“…Power consumption on the receiver chain could be comparable with, and in some cases, higher than that of the transmitter chain. 3 A trivial solution to the transmit power allocation in this case is to s et all nodes for constant transmit power before field deployment. However, this will lead to the well-known near-far effect due to unequal node distances to the access point.…”

Section: Introductionmentioning

confidence: 99%

Transmit power randomization for wireless ad hoc green networks

Tarhuni

2020

Int J Communication

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Summary Future green technologies require the reduction of power consumptions, increasing energy efficiencies, and managing the electromagnetic interference footprints. In this paper, we investigate the use of randomized transmit power strategy in which the nodes transmit with random power in a single‐hop ad hoc wireless network. We assume Nakagami‐m channel with very slow feedback channel for power update commands. Hence, by using the characteristic function of the received power, we develop a new mathematical formulation for the outage probability. Further, we test the developed formulation on the known constant transmit power case and randomized uniform distribution with constant maximum case. We show that the performance in terms of outage probabilities and total power consumption of all nodes is generally poor. Therefore, we extend our results to the case of optimized constant power and propose the use of randomized uniform distribution with optimized maximum power. We show that the proposed power distribution outperforms the previous ones and reduces the overall power consumption by many orders of magnitude.

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“…Ultimately, energy is consumed by the node hardware -and this sets a lower limit on the energy consumption of any algorithm. Design of progressively more efficient hardware has been a characteristic feature of research into WSNs [8]. But the energy consumed also depends on the system software that runs on a node: the operating system, device drivers, and networking stack.…”

Section: Introductionmentioning

confidence: 99%

An energy benchmark for software updates on wireless sensor nodes

Brown

Sreenan²

2013

IET International Conference on Information and Communications Technologies (IETICT 2013)

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Energy consumption is arguably the key factor in the design and operation of Wireless Sensor Networks (WSNs). This holds both for normal operation and maintenance operationssuch as software updates. Whereas software updates will probably be infrequent, they must still not consume a significant fraction of a WSN's energy reserve; also, the required consumption must be known before triggering an update, in order to ensure that it can complete. Software updates are an expensive operation: there can be a significant volume of data, guaranteed delivery is required, and normal data fusion algorithms cannot be used to reduce the communication load. In this paper we present a node-level energy consumption model for the evaluating the efficiency of software updates on wireless sensor nodes. This model is then used to derive a novel minimum energy benchmark equation. This paper also presents some new power measurements, and uses these, along with published data, to interpret the benchmark in quantitative terms for some specific hardware platforms. This benchmark provides a standardized and quantitative figure to use in comparing software update algorithms. The methodology is also applicable to establishing energy benchmarks for other tasks in the WSN domain.

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Optimization of Electronic Power Consumption in Wireless Sensor Nodes

Cited by 8 publications

References 14 publications

Energy awareness workflow model for wireless sensor nodes

Energy awareness workflow model for wireless sensor nodes

Transmit power randomization for wireless ad hoc green networks

An energy benchmark for software updates on wireless sensor nodes

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