Dynamic power control with energy constraint for Multimedia Wireless Sensor Networks

Kobbane, Abdellatif; Koulali, Mohammed-Amine; Tembiné, Hamidou; Koutbi, Mohammed El; Ben‐Othman, Jalel

doi:10.1109/icc.2012.6363971

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…The authors show how the computational time varies by increasing the number of states and actions. Other works presented in the survey [ 32 ] use MDP [ 34 , 35 , 36 , 41 ]. An industrial application is studied by Gatsis et al, where the state information of one plant is transmitted by a sensor node to a controller [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Self-Learning Power Control in Wireless Sensor Networks

Chincoli

Liotta

2018

Sensors

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Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

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Section: Related Workmentioning

confidence: 99%

Self-Learning Power Control in Wireless Sensor Networks

Chincoli

Liotta

2018

Sensors

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“…Thus, each node can utilize channel information to increase its transmission probability during the channel idle state. Kobbane et al [3] built an energy configuration model using an MDP. This centralized scheme is to manage the node transmission behavior to maximize the network's lifetime, while ensuring the network connectivity and operations.…”

Section: B Opportunistic Transmission Strategy and Neighbor Discoverymentioning

confidence: 99%

“…Static decision commands may lead to inefficient energy usage. For example, a node sending data at fixed transmit power without considering the channel conditions will drain its energy faster than the one that adaptively manages its transmit power [2], [3]. Therefore, using MDPs for dynamically optimizing the network operations to fit the physical conditions results in significantly improved resource utilization.…”

Section: Introductionmentioning

confidence: 99%

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Alsheikh

Hoang

Niyato

et al. 2015

IEEE Commun. Surv. Tutorials

182

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Abstract-Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

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“…This reduces average power dissipation and latency caused by PM but needs an additional control unit to make power efficient PM and peak power analysis needs to be provided. An automated system for energy-aware server (ACES) is modelled using finite state Markov model to reduce the unmet demand of server at the cost of with power and reliability cost reduction for host clusters is presented [19]. The simulation results shows that automated system for energyaware server reduces energy consumption within 96% and the greedy (always on) policy reduces energy consumption within 84%.…”

Section: A Dpm Using Stochastic Control Modelmentioning

confidence: 99%

A survey on dynamic power management approach in wireless sensor networks

Pughat

Sharma

2014

2014 6th IEEE Power India International Conference (PIICON)

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The wireless sensor network (WSN) tends to an exciting and promising area in wireless, communication and computing research which has the capability of interaction with physical environment at low cost. The limiting factor is that; wireless sensor nodes are energy constrained and recharging or replacing the batteries is costly and complex process after deployment. The limited work has been found in power management strategies to make energy-efficient wireless sensor node. This paper surveys and classifies the main dynamic power management approaches that can be used individually or together and dynamically manage wireless sensor node operations in order to minimize its power consumption after deployment. This paper is expected to be helpful for the researchers engaged in industrial or academic research in the field of power aware wireless sensor network.

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Dynamic power control with energy constraint for Multimedia Wireless Sensor Networks

Cited by 8 publications

References 9 publications

Self-Learning Power Control in Wireless Sensor Networks

Self-Learning Power Control in Wireless Sensor Networks

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

A survey on dynamic power management approach in wireless sensor networks

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