Performance evaluation of efficient and reliable routing protocols for fixed-power sensor networks

Loh, Peter K. K.; Pan, Yi; Jing, Hsu Wen

doi:10.1109/twc.2009.060772

Cited by 30 publications

(12 citation statements)

References 19 publications

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“…The routing protocols included QoS [9], Speed [9], GBR [7], LAR [10], LBAR [10], AODVjr [10], BVR [8], DD [11], and EAR [7]. Denoting the node count in a WSN as n nodes , analysis of empirical data shows that p delivery decreases when the value of n nodes increases.…”

Section: Reduced Complexity Simulator Modelmentioning

confidence: 99%

Empirical Model Development for Message Delay and Drop in Wireless Sensor Networks

Serpen

Gao

2014

Procedia Computer Science

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Section: Reduced Complexity Simulator Modelmentioning

confidence: 99%

Empirical Model Development for Message Delay and Drop in Wireless Sensor Networks

Serpen

Gao

2014

Procedia Computer Science

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“…Packet Latency (PL) is the time taken by a data packet to travel from a source sensor node to the sink [16] is given by Eq. (13).…”

Section: Calculation Of Packet Latencymentioning

confidence: 99%

Energy Modeling of ML-MAC Wireless Sensor Network Protocol

Khurana

Thalore

Raina

et al. 2014

Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies

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It is well recognized that a proper energy consumption model is a foundation for developing and evaluating a power management scheme in the wireless sensor networks (WSN). Duty Cycle is a prominent mechanism for achieving energy efficiency in wireless sensor networks (WSNs). For this purpose, current WSN MAC (Medium Access Control) protocols use duty cycle schemes, where they consciously switch a node's radio between active and sleep modes.The contribution of this paper is twofold: First, a theoretical energy model based on simple finite automata is presented. This model can be used for simulation and generation of ML-MAC with duty cycle variation. Second, the proposed theoretical model is backed up by simulation measurements using QualNet 6.1 Thereafter; output of the theoretical model and simulated results are compared and validated. Categories and Subject Descriptors C.2.1 [Sensor Networks]:A network consisting of hundreds or thousands of wireless sensor nodes. Collect information from their surrounding environment and send to remote control centre. Each sensor performs: sensing + processing + communication.

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“…Thus, we relieve the difficulties in performance analysis by considering specific cases and imposing practical assumptions in order to observe the effects of the number of hops on the estimation performance. At first, we consider a fixed transmission power, that is, [20][21][22]. Moreover, we restrict our concerns to the particular cases where the noise variance is the same, that is, 2 [11][12][13][14][15].…”

Section: Analysis Of Mse Performancementioning

confidence: 99%

Performance analysis of channel estimation in amplify‐and‐forward multi‐hop direct forwarding wireless networks

Choi

Baek²,

Han

et al. 2014

Wireless Communications

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In this paper, we study the performance of a training-based least square (LS) and linear minimum mean-square-error (LMMSE) channel estimation for both hop-by-hop and multi-hop direct forwarding wireless sensor networks over frequency-selective fading channels. Specifically, to investigate the properties of the channel estimation, we accomplish a theoretical analysis of MSE in terms of various link parameters. From the performance evaluation, we analytically present the effects of the number of hops on the MSE performance for channel estimations in both multi-hop networks. Interesting observations of MSE behaviors under various conditions are discussed, and the receiver complexity and channel equalization performance are also analyzed. Finally, through the computer simulations, the analytical results and detection performance are demonstrated.

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Performance evaluation of efficient and reliable routing protocols for fixed-power sensor networks

Cited by 30 publications

References 19 publications

Empirical Model Development for Message Delay and Drop in Wireless Sensor Networks

Empirical Model Development for Message Delay and Drop in Wireless Sensor Networks

Energy Modeling of ML-MAC Wireless Sensor Network Protocol

Performance analysis of channel estimation in amplify‐and‐forward multi‐hop direct forwarding wireless networks

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