Power‐Saving Algorithms on IEEE 802.15.4 for Wireless Sensor Networks

Park, Tae Rim; Lee, Myung J.

doi:10.1002/9780470403686.ch18

Cited by 6 publications

(8 citation statements)

References 6 publications

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“…In this section, we adopt the notion of the active ratio, which is defined as the average active period per unit time [25]. 2 Although the active ratio does not distinguish transmission, reception, and idle listening, it is still a reasonable measure of energy consumption because of the following reason: In many devices, energy consumption for transmission is quite similar to that for reception (including idle listening and channel sensing), both of which are much larger than that for the standby mode [26]- [28].…”

Section: Derivation Of Active Ratiomentioning

confidence: 99%

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Design and analysis of asynchronous wakeup for wireless sensor networks

Park

Lee

2009

IEEE Trans. Wireless Commun.

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In wireless sensor networks, scheduling the sleep duration of each node is one of the key elements for controlling critical performance metrics such as energy consumption and latency. Since the wakeup interval is a primary parameter for determining the sleeping schedule, how to tune the wakeup interval is crucial for the overall network performance. In this paper, we present an effective framework for tuning asynchronous wakeup intervals of IEEE 802.15.4 sensor networks from the energy consumption viewpoint. First, we derive an energy consumption model of each node as an explicit function of the wakeup interval, and empirically validate the derived model. Second, based on the proposed model, we formulate the problem of tuning the wakeup interval with the following two objectives: to minimize total energy consumption and to maximize network lifetime. We show that these two problems can be optimally solved by an iterative algorithm with global information by virtue of the convexity of the problem structure. Finally, as practical solutions, we further propose heuristic optimization algorithms that only exploit local information. In order to develop heuristic algorithms, we propose two broadcasting schemes, which are entitled as maximum wakeup interval broadcasting and efficient local maximum broadcasting. These broadcasting algorithms enable nodes in the network to have heterogeneous wakeup intervals.Index Terms-Sensor networks, energy saving MAC, IEEE 802.15.4 MAC, distributed optimization.

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Section: Derivation Of Active Ratiomentioning

confidence: 99%

“…Note that we refine the concept of the active ratio in[25] and further provide its closed-form expression for both, unicast and broadcast scenarios 3. For example, in cc2420, the current ratio among transmission, reception, and idle modes is 17.4:19.7:0.4 4.…”

mentioning

confidence: 99%

Design and analysis of asynchronous wakeup for wireless sensor networks

Park

Lee

2009

IEEE Trans. Wireless Commun.

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“…The authors in [4] investigated the IEEE 802.15.4 and represented six algorithms on both the beacon enabled and nonbeacon enabled operation modes. These algorithms are investigated analytically and experimentally.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work done in WSN at the MAC level [2,4,5] is based on both contention and TDMA. IEEE 802.15.4 is the only available standard for low power and low rate wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Adaptive MAC protocols for forest fire detection using Wireless Sensor Networks

Al-Habashneh

Ahmed

Husain

2009

2009 Canadian Conference on Electrical and Computer Engineering

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Power consumption, latency, and complexity are considered to be a benchmark for comparing Medium Access Control (MAC) protocols in Wireless Sensor Networks (WSN). However, the importance of these elements varies according to the application. In this paper, three MAC protocols are proposed for forest fire detection. Basically, two of these protocols are based on the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol with some modification to suit the forest fire detection application. These protocols are called Persistent CSMA (P-CSMA) and Per Hope Synchronization CSMA (PHS-CSMA). The third one is a Time Division Multiple Access (TDMA)-based protocol, called Sensor TDMA (S-TDMA). These three protocols are investigated and analyzed by simulation. Results show that there is no superior protocol which outperforms others in terms of power consumption, delay and complexity. However, a trade-off does exist. In terms of power consumption, S-TDMA outperforms other protocols but it has the worst performance in terms of delay. On the other hand, considering the design complexity, S-TDMA is the most complex as it needs time synchronization over the entire cluster while PCSMA and PHS-CSMA don't require time synchronization between nodes.

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“…and node design considerations. Hardware [17] and firmware [18] design of the sensing node is crucial and it is usually done in a superficial way, just looking at the power requirements of the different hardware blocks and optimizing firmware [19]. This paper analyses energy issues associated with the different design alternatives.…”

Section: Introductionmentioning

confidence: 99%

Hardware Architecture Design for WSN Runtime Extension

Asensio

Blasco

Marco

et al. 2013

International Journal of Distributed Sensor Networks

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Internet of Things imposes demanding requirements on wireless sensor networks as key players in context awareness procurement. Temporal and spatial ubiquities are one of the essential features that meet technology boundaries in terms of energy management. Limited energy availability makes anywhere and anytime sensing a challenging task that forces sensor nodes to wisely use every bit of available power. One of the earliest and most determining decisions in the electronic design stage is the choice of the silicon building blocks that will conform hardware architecture. Designers have to choose between dual architectures (based on a lowpower microcontroller controlling a radio module) and single architectures (based on a system on chip). This decision, together with finite state machine design and application firmware, is crucial to minimize power consumption while maintaining expected sensor node performance. This paper provides keys for energy analysis of wireless sensor node architecture according to the specific requirements of any application. It thoroughly analyzes pros and cons of dual and single architectures providing designers with the basis to select the most efficient for each application. It also provides helpful considerations for optimal sensing-system design, analyzing how different strategies for sensor measuring and data exchanging affect node energy consumption.

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Power‐Saving Algorithms on IEEE 802.15.4 for Wireless Sensor Networks

Cited by 6 publications

References 6 publications

Design and analysis of asynchronous wakeup for wireless sensor networks

Design and analysis of asynchronous wakeup for wireless sensor networks

Adaptive MAC protocols for forest fire detection using Wireless Sensor Networks

Hardware Architecture Design for WSN Runtime Extension

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