Design Considerations for Energy-Efficient Radios in Wireless Microsensor Networks

Shih, Eugene; Cho, Seonghwan; Lee, Fred S.; Calhoun, Benton H.; Chandrakasan, Anantha P.

doi:10.1023/b:vlsi.0000017004.57230.91

Cited by 75 publications

(53 citation statements)

References 26 publications

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“…One of the main findings in [11] was that in highly contentious networks, "transmitting large payloads is more energy efficient". On the other hand, receive and idle mode operation of the radio is seen to consume as much energy as the transmit mode, if not more [12]. Thus, the number of packets (sent and received) appear to be a better measure to predict energy consumption than the number of bits.…”

Section: Distributed Computation Karp Et Almentioning

confidence: 99%

Distributed Optimal Estimation from Relative Measurements for Localization and Time Synchronization

Barooah

Silva

Hespanha

2006

Distributed Computing in Sensor Systems

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Abstract.We consider the problem of estimating vector-valued variables from noisy "relative" measurements. The measurement model can be expressed in terms of a graph, whose nodes correspond to the variables being estimated and the edges to noisy measurements of the difference between the two variables. This type of measurement model appears in several sensor network problems, such as sensor localization and time synchronization. We consider the optimal estimate for the unknown variables obtained by applying the classical Best Linear Unbiased Estimator, which achieves the minimum variance among all linear unbiased estimators.We propose a new algorithm to compute the optimal estimate in an iterative manner, the Overlapping Subgraph Estimator algorithm. The algorithm is distributed, asynchronous, robust to temporary communication failures, and is guaranteed to converges to the optimal estimate even with temporary communication failures. Simulations for a realistic example show that the algorithm can reduce energy consumption by a factor of two compared to previous algorithms, while achieving the same accuracy.

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Section: Distributed Computation Karp Et Almentioning

confidence: 99%

Distributed Optimal Estimation from Relative Measurements for Localization and Time Synchronization

Barooah

Silva

Hespanha

2006

Distributed Computing in Sensor Systems

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“…Using this framework, the authors have explored various modulations schemes and observed that using modulations saves energy as compared to unmodulated data transmission. E. Shih, S. Cho, F. S. Lee [5] analyze both transmission time and constellation size optimization for MQAM and MFSK (both coded and uncoded), considering both transmission and circuit energy consumption. For both MQAM and MFSK, it has been shown that optimizing transmission time or, equivalently, constellation size, minimizes the total energy used for transmission of information.…”

Section: Related Work On Wsnmentioning

confidence: 99%

Energy Efficiency of the IEEE 802. 15. 4 Standard in Wireless Sensor Networks: Modeling and Improvement Perspectives

Sharma¹,

Sachan²,

Imam³

2012

IJCA

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The IEEE focuses on the physical and MAC layer of Wireless Sensor Networks (WSN) and the Internet Engineering Task Force (IETF) works on Network layer and higher in the seven layer OSI reference model for networking. The IEEE 802.15.4 standard, defines the physical layer (PHY) and medium access control (MAC) sub layer specifications for low-data-rate wireless connectivity with fixed, portable, and moving devices with no battery or very limited battery consumption requirements. In this paper, the energy consumption of a practical Wireless Sensor Network scenario is modeled at the bottom three layers of the traditional networking stack-the physical layer, data link layer and the MAC layer. Using these models, the optimization of energy consumption of WSN nodes is achieved using different modulation schemes to maximize the network lifetime. The approach is to design and simulate a wireless sensor network scenario consisting of 12 nodes in 500x500 m area and optimize the energy consumption (radio energy + circuit energy) of each node using suitable modulation techniques and reduce the Energy Consumption up to 80% in a home automation scenario.

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“…On the other hand, the transitional energy that occurs when a sensor node switches from one mode to another, for example, from idle mode to busy mode, has a significant effect on the power consumption [13]. To conserve energy, the sensor node turns off its radio transceiver whenever the buffer system becomes empty.…”

Section: Introductionmentioning

confidence: 99%

“…In idle mode, the sensor node itself generates packets and cannot relay packets toward other nodes; in busy mode, the sensor node both generates and relays packets. According to [13], the transitional energy when switching from idle mode to busy mode has a significant effect on the power consumption. Compared with other types of energy wastes, mode alternations incur much more energy waste.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Model for Maximizing the Lifetime of Wireless Sensor Networks

Lee¹,

Yang²

2012

Journal of the Korean Operations Research and Management Scienc

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Reduction of power consumption has been a major issue and an interesting challenge to maximize the lifetime of wireless sensor networks. We investigate the practical meaning of N-policy in queues as a power saving technique in a WSN. We consider the N-policy of a finite M/M/1 queue. We formulate the optimization problem of power consumption considering the packet loss probability. We analyze the trade-off between power consumption and the packet loss probability and demonstrate the operational characteristics of N-policy as a power saving technique in a WSN with various numerical examples.

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Design Considerations for Energy-Efficient Radios in Wireless Microsensor Networks

Cited by 75 publications

References 26 publications

Distributed Optimal Estimation from Relative Measurements for Localization and Time Synchronization

Distributed Optimal Estimation from Relative Measurements for Localization and Time Synchronization

Energy Efficiency of the IEEE 802. 15. 4 Standard in Wireless Sensor Networks: Modeling and Improvement Perspectives

A Stochastic Model for Maximizing the Lifetime of Wireless Sensor Networks

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