Extending Lifetime with Differential Duty Cycles in Wireless Sensor Networks

Medidi, Muralidhar; Zhou, Yuanyuan

doi:10.1109/glocom.2007.199

Cited by 16 publications

(16 citation statements)

References 11 publications

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“…Another adaptive duty cycle approach, ALPL, adjusts a node's duty cycle according to the node's neighbors' duty cycles in order to support the data flows it receives [7]. The work in [8] proposes a differential duty cycle assignment approach to extend the network lifetime without sacrificing the delay and throughput performance for convergecast. However, none of these approaches optimize the duty cycle for convergecast data patterns under receiver-based routing.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Duty Cycle Assignment for Receiver-Based Convergecast in Wireless Sensor Networks

Zhang

Feng

Demirkol

et al. 2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Abstract-Duty cycling is often used to reduce the energy consumption caused by idle listening in Wireless Sensor Networks (WSNs). Most studies on WSN protocols define a common duty cycle value throughout the network to achieve synchronization among the nodes. On the other hand, a few studies propose adaptation of the duty cycle according to uniform traffic conditions, which is beneficial assuming one-to-one traffic patterns that result in evenly distributed packet traffic. In this work, we consider the convergecast communication pattern commonly observed in WSNs. In convergecast communication, the packet traffic observed around the sink node is much higher than the traffic observed far from the sink, i.e., nodes with different distances to the sink node receive and must relay different amounts of traffic. Additionally, we utilize receiver-based protocols, which enable nodes to communicate with no synchronization or neighbor information, and hence do not require all nodes in the network to have the same duty cycle. In this paper, we model the expected energy consumption of nodes utilizing receiver-based protocols as a function of their duty cycle and their distance to the sink node. Using this analysis, we derive a closed-form formula for the duty cycle that minimizes the expected energy consumption at a given distance. Moreover, we propose an adaptation method for the derived distance-based duty cycle based on local observed traffic. Performance evaluations of the two proposed duty cycle assignment methods show that they greatly improve the energy efficiency without sacrificing packet delivery ratio or delay significantly.Index Terms-Receiver-based protocols, convergecast traffic, adaptive duty cycle, energy-efficiency, wireless sensor networks.

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Section: Introductionmentioning

confidence: 99%

Energy-Efficient Duty Cycle Assignment for Receiver-Based Convergecast in Wireless Sensor Networks

Zhang

Feng

Demirkol

et al. 2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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“…Only if the state of roads (nodes) is in the active mode, then vehicles move. However, a major energy of nodes (roads) is usually used by listening among the communication processes, instead of packet reception and transmission [6]. Reducing the time spent in listening to each road is the most effective way to extend the lifetime of the roads.…”

Section: Analysis For Necessity Of Proposed Schemementioning

confidence: 99%

“…The major energy consumption of wireless sensor networks, however, is caused by the idle listening state but not by packet reception and transmission in a dense network or under light traffic [6] and reducing idle listening time is the most effective way to extend the lifetime. In sleep/wakeup protocols [7,8], nodes follow a periodic cycle of sleep/active mode without considering the connectivity of the network.…”

Section: Introductionmentioning

confidence: 99%

“…(1) for all ℎ ∈ do / * hi: header nodes of each group, : set of header nodes * / (2) broadcast own group size within 2 hop (3) end for (4) for all ℎ ∈ do (5) calculate average group size ( ) using information received from the other headers (6) calculate standard deviation ( ) using information received from the other headers (7) estimate maximum group size using and (8) determine own duty cycle by comparing own group size with the maximum group size (9) end for Algorithm 1: Adaptive duty-cycling algorithm. group sizes known to the head node will follow the normal distribution, in accordance with the central limit theorem.…”

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confidence: 99%

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Adaptive Duty-Cycling to Enhance Topology Control Schemes in Wireless Sensor Networks

Cha

Kim

et al. 2014

International Journal of Distributed Sensor Networks

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To prolong the network lifetime, various scheduling approaches that schedule wireless devices of nodes to switch between active and sleep states have been studied. Topology control schemes are one of the scheduling approaches that can extend the network lifetime and reduce the additional communication delays at the same time. However, they do not guarantee that all nodes have the same lifetime. They reduce the network coverage and prevent seamless communications. This paper proposes an adaptive dutycycling (ADC) scheme to solve the unbalanced energy consumption generated from the conventional topology control schemes. Our scheme can be applied as a subprocess of them and enable well-balanced energy consumption among all nodes by applying a different duty-cycle to each group based on group size. Therefore, ADC scheme reduces the coverage reduction and maintains the communication delay as a constant throughout the network lifetime. Simulation results show that our scheme extends the network lifetime by at least 25%. This paper also proposes t-ADC scheme. It can be more effectively applied to various environments by adjusting the duty-cycle determined by the ADC scheme which is based on the network traffic amount. We show that t-ADC scheme prolongs the lifetime up to 17% compared to ADC scheme in a low traffic network.

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“…Duty cycle is defined as the fraction of time nodes which are active during their lifetime. Data driven approaches can be used to improve the energy efficiency even more that will be described in detail in the following sections [18].…”

Section: General Approaches To Energy Savingmentioning

confidence: 99%