A Min–Max Model Predictive Control Approach to Robust Power Management in Ambulatory Wireless Sensor Networks

Witheephanich, Kritchai; Escaño, Juan Manuel; Peña, David Muñoz de la; Hayes, Martin J.

doi:10.1109/jsyst.2013.2271388

Cited by 4 publications

(7 citation statements)

References 30 publications

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“…The length of the horizon determines the trade-off between complexity and accuracy. Longer horizons need further look ahead and more complex prediction but in turn result in a more foresighted control action [159]. Reducing the horizon reduces the complexity while resulting in a more myopic action.…”

Section: B Model Predictive Controlmentioning

confidence: 99%

“…This trade-off is examined in [158] by proposing an algorithm that adaptively adjusts the horizon length. In general, the prediction horizon is kept to a fairly low number (1 step in [159] and 6 steps in [100]) to avoid high computation overhead.…”

Section: B Model Predictive Controlmentioning

confidence: 99%

“…Probabilistic: [12], [16]- [21], [23]- [26], [30], [50], [53], [60], [61], [93], [102], [116], [136], [157] Bayesian: [33], [37], [58], [126], [127], [129], [130], [132], [135], [158], [159] 2) Bayesian inference: This approach allows to make statements about what is unknown, by conditioning on what is known. Bayesian prediction can be summarized in the following steps: 1) define a model that expresses qualitative aspects of our knowledge but has unknown parameters, 2) specify a prior probability distribution for the unknown parameters, 3) compute the posterior probability distribution for the parameters, given the observed data, and 4) make predictions by averaging over the posterior distribution.…”

Section: Referencesmentioning

confidence: 99%

“…This approach has been adopted for on-line prediction and optimization of wireless networks [100], [158]. Since the process model (for the prediction of future states and outputs) is available in this kind of systems, autoregressive methods can be used along with Kalman filtering [100], or max-min MPC formulation [159]. In [158], Kalman filtering is compared to other methods such as mean and median value estimation, Markov chains, and exponential averaging filters.…”

Section: B Model Predictive Controlmentioning

confidence: 99%

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A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

220

137

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A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

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Section: B Model Predictive Controlmentioning

confidence: 99%

Section: B Model Predictive Controlmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

Section: B Model Predictive Controlmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

220

137

View full text Add to dashboard Cite

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“…Because of the extensive use of the WSNs, such as healthcare applications [10], most of the previous studies on WSN's security are focused on data leakage prevention and error detection [11]. Ren et al [12] proposed the LEDS framework to overcome the vulnerabilities of hop-by-hop security framework.…”

Section: Introductionmentioning

confidence: 99%

Attack-resistant power management scheme for wireless sensor network

Tsai

et al. 2015

2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS)

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Wireless sensor networks (WSNs) which powered by batteries are widely used in military, healthcare, and scientific environments. One important challenge for setting WSN is the energy problem. Power management is one of efficient solutions for such problem and must be taken into account at all levels of the WSN. However, the power management may be out of action when the WSN suffers malicious attacks. In this paper, an Attack-Resistant Power Management (A-RPM for short) scheme is proposed for WSN to deal with the energy problem and prolong the operating time of sensor nodes as well as coordinators. Most importantly, the A-RPM is still effectual when WSN under malicious attacks such as denial-of-service attack. The A-RPM efficiently turns WSN nodes into sleep mode, which consumes less energy, but requires higher latency to awaken, when these nodes are idle and wake them up when necessary. A token based operating mode transition policy is developed to control the power of WSN nodes. Our simulation result shows that the A-RPM effectively reduces power consumption up to 16.35% when the network suffer malicious attacks compared with that of the standard power management scheme.

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