Pushback: A hidden Markov model based scheme for energy efficient data transmission in sensor networks

Li, Sha; Srivastava, Richa; Köksal, C. Emre; Sinha, Prasun

doi:10.1016/j.adhoc.2008.09.001

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…The authors in [10] developed Markov chain model for the MAC layer of WPAN standard and found that the double carrier sensing mechanism does not give an optimal solution and proposed modifications to the protocol to improve throughput, delay and energy efficiency. The Hidden Markov model based scheme for energy efficient data transmission is proposed in [11]. They have developed an algorithm called Push-back algorithm.…”

Section: The Literature Survey In Related Workmentioning

confidence: 99%

Modeling and throughput analysis of randomly deployed IEEE 802.15.4 based Wireless Personal Area Networks

Vatti¹,

Gaikwad

2015

2015 International Conference on Pervasive Computing (ICPC)

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This paper presents Markov Chain modeling of the randomly deployed IEEE 802.15.4 based Wireless Personal Area Networks (WPAN) in which, the wireless sensor nodes communicate to the coordinator in a single hop. In Most of the previous works, the un-slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel access mechanism and the sensor nodes without buffer are considered for the sake of simplicity. In real time networks, every sensor node will have a finite amount of buffer. In this work, we considered nodes with finite buffer, beacon enabled data transfer with no acknowledgements and no re-transmissions. The influence of buffer size of the sensor node and packet size on the throughput is analyzed in separate experiments. The Network is simulated using ns-2. The experimental results are in close agreement with the theoretical values.

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Section: The Literature Survey In Related Workmentioning

confidence: 99%

Modeling and throughput analysis of randomly deployed IEEE 802.15.4 based Wireless Personal Area Networks

Vatti¹,

Gaikwad

2015

2015 International Conference on Pervasive Computing (ICPC)

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“…Prediction schemes are usually used to predict the arrival of PUs so that effective actions will be taken in a timely manner to avoid data loss or time-outs. In this respect, [ 78 ] introduces a channel-aware transmission mechanism based on CSMA to optimize energy efficiency. The pushback mechanism simply predicts the channel status to determine the optimal times to transmit and to refrain from transmission to conserve energy.…”

Section: Routing In Crsnsmentioning

confidence: 99%

Assessing Routing Strategies for Cognitive Radio Sensor Networks

Zubair

Fisal

Baguda

et al. 2013

Sensors

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Interest in the cognitive radio sensor network (CRSN) paradigm has gradually grown among researchers. This concept seeks to fuse the benefits of dynamic spectrum access into the sensor network, making it a potential player in the next generation (NextGen) network, which is characterized by ubiquity. Notwithstanding its massive potential, little research activity has been dedicated to the network layer. By contrast, we find recent research trends focusing on the physical layer, the link layer and the transport layers. The fact that the cross-layer approach is imperative, due to the resource-constrained nature of CRSNs, can make the design of unique solutions non-trivial in this respect. This paper seeks to explore possible design opportunities with wireless sensor networks (WSNs), cognitive radio ad-hoc networks (CRAHNs) and cross-layer considerations for implementing viable CRSN routing solutions. Additionally, a detailed performance evaluation of WSN routing strategies in a cognitive radio environment is performed to expose research gaps. With this work, we intend to lay a foundation for developing CRSN routing solutions and to establish a basis for future work in this area.

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“…This model itself could emanate from an underlying hidden Markov model (HMM). HMMs have been proposed earlier for interference modeling [8], [9], but to our knowledge only for pure scheduling purposes and not incorporating any spatial information. The estimation of such a model is outside the scope of the current paper, the focus here is rather to illustrate how it may be exploited in receive beamforming design and analyze the performance of the different designs.…”

Section: Introductionmentioning

confidence: 99%

Receive Beamforming Design Based on a Multiple-State Interference Model

Yang

Björnson

Bengtsson

2011

2011 IEEE International Conference on Communications (ICC)

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Abstract-In this paper, we propose a multiple-state interference model, which takes into account the interference variability and uncertainty created by scheduling and other fast resource allocation adaptivity. This situation is modeled by a set of states, each described by a spatial covariance matrix and a probability. In order to illustrate the usefulness of such a model, we study two receive beamforming design problems, one maximizing the average data rate and one maximizing the worst-case data rate. We compare the resulting performance with what can be obtained when the state information is not available. We show theoretically and numerically that exploring the multiple-state interference structure can improve the receive beamforming design efficiency, especially for cell-edge users in a interference-limited system.

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Pushback: A hidden Markov model based scheme for energy efficient data transmission in sensor networks

Cited by 11 publications

References 40 publications

Modeling and throughput analysis of randomly deployed IEEE 802.15.4 based Wireless Personal Area Networks

Modeling and throughput analysis of randomly deployed IEEE 802.15.4 based Wireless Personal Area Networks

Assessing Routing Strategies for Cognitive Radio Sensor Networks

Receive Beamforming Design Based on a Multiple-State Interference Model

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