The Experts below are selected from a list of 20469 Experts worldwide ranked by ideXlab platform

Anh Vu Dinh Duc - One of the best experts on this subject based on the ideXlab platform.

  • toward a global Power Manager in energy harvesting wireless sensor networks
    International Conference on Advanced Computing, 2016
    Co-Authors: Anh Vu Dinh Duc
    Abstract:

    Wireless Sensor Networks (WSNs) bring a great attention in recent years due to their potential monitoring applications in remote areas, where wire connections are impractical to deploy. Moreover, energy harvesting capability is considered as a promising solution to extend the lifetime of the network. As ambient energy can be scavenged as long as desired, the lifetime of the network can be theoretically infinite. To achieve that, a Power Manager (PM) in a WSN node is required to balance between the consumed energy and the harvested energy to reach the Energy Neutral Operation (ENO) state. However, due to the limited resources in the WSN nodes, the localized PMs must be low complexity and thus less intelligent. In this paper, we propose a Global Power Manager (GPM) that is implemented at a base station to control adaptations of the WSN nodes' Power in the network. This solution inaugurates a new paradigm to design an efficient PM as the base station has less constrained on memory, computational capability and particularly energy resources than the WSN nodes. Moreover, a survey of state-of-the-art localized PM techniques is presented. It provides fundamental operations of these techniques to achieve satisfactory performances within a given energy budget.

Alain Pegatoquet - One of the best experts on this subject based on the ideXlab platform.

  • Energy-Efficient Power Manager and MAC Protocol for Multi-Hop Wireless Sensor Networks Powered by Periodic Energy Harvesting Sources
    IEEE Sensors Journal, 2015
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys
    Abstract:

    To overcome the limited energy in battery-Powered wireless sensor networks (WSNs), harvested energy is considered as a potential solution to achieve autonomous systems. A Power Manager (PM) is usually embedded in wireless nodes to adapt the computation load by changing their wake-up interval according to the harvested energy. In order to prolong the network lifetime, the PM must ensure that every node satisfies the energy neutral operation (ENO) condition. However, when a multi-hop network is considered, changing the wake-up interval regularly may cripple the synchronization among nodes and, therefore, degrade the global system quality of service. In this paper, a wake-up variation reduction PM is proposed to solve this issue. This PM is applied for wireless nodes Powered by a periodic energy source (e.g., light energy in an office) over a constant cycle of 24 h. Our PM not only follows the ENO condition, but also reduces the wake-up interval variations of WSN nodes. Based on this PM, an energy-efficient protocol, named synchronized wake-up interval MAC, is also proposed. OMNET++ simulation results using three different harvested profiles show that the data rate of a WSN node can be increased up to 65% and the latency reduced down to 57% compared with state-of-the-art PMs. Validations on a real WSN platform have also been performed and confirmed the efficiency of our approach.

  • A Power Manager with Balanced Quality of Service for Energy-Harvesting Wireless Sensor Nodes
    2014
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys
    Abstract:

    Future Internet of Things is paving the way for the proliferation of Wireless Sensor Networks (WSNs). To overcome the limited energy in batteries, WSN nodes are relying on everlasting environmental energy. Moreover, a Power Manager (PM) is also embedded in each WSN node to guarantees that the total consumed energy is equal to the harvested energy for a long period, leading to Energy Neutral Operation (ENO) with a theoretically infinite lifetime. In this paper, a new PM for WSN nodes Powered by periodic sources (e.g. ambient energy is not available during the full harvesting cycle) is proposed. Not only respecting the ENO condition, our PM is able to balance the Quality of Service (QoS) during the whole cycle to provide regular data tracking, which is essential for WSN applications like monitoring. Simulations on OMNET++ show that our PM can improve the QoS during the absence of energy by a factor up to 84% compared to state-of-the-art PMs, while guaranteeing the same global QoS.

  • Duty-Cycle Power Manager for Thermal-Powered Wireless Sensor Networks
    2013
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys, Cécile Belleudy
    Abstract:

    Exploiting energy from the environment to extend the system lifetime of Wireless Sensor Network (WSN), especially thermal energy, is considered as a promising approach. When considering self-Powered systems, the Power Manager (PM) plays an important role in energy harvesting WSNs. Instead of minimizing the consumed energy as in the case of battery-Powered systems, it causes the harvesting node to converge to Energy Neutral Operation (ENO) in order to achieve a theoretically infinite lifetime. In this paper, a low complexity PM for a thermal-Powered WSN is presented. Our PM adapts the duty cycle of the node according to the estimation of harvested energy and the consumed energy provided by a simple energy monitor for a super capacitor based WSN to achieve the ENO. Experiments are performed on a real WSN platform where harvested energy is extracted from the wasted heat of a PC adapter by two thermoelectric generators.

  • Power Manager with PID controller in Energy Harvesting Wireless Sensor Networks
    2012
    Co-Authors: Olivier Sentieys, Olivier Berder, Alain Pegatoquet, Cécile Belleudy
    Abstract:

    System lifetime is the crucial problem of Wireless Sensor Networks (WSNs), and exploiting environmental energy provides a potential solution for this problem. When considering self-Powered systems, the Power Manager (PM) plays an important role in energy harvesting WSNs. Instead of minimizing the consumption energy as in the case of battery Powered systems, it makes the harvesting node converge to Energy Neutral Operation (ENO) to achieve a theoretically infinite lifetime and maximize the system performance. In this paper, a low complexity PM with a Proportional Integral Derivative (PID) controller is introduced. This PM monitors the buffered energy in the storage device and performs adaptation by changing the wake-up period of the wireless node. This shows the interest of our approach since the impractical monitoring harvested energy as well as consumed energy is not required as it is the case in other previously proposed techniques. Experimental results are performed on a real WSN platform with two solar cells in an indoor environment. The PID controller provides a practical strategy for long-term operations of the node in various environmental conditions.

  • An Open-Loop Energy Neutral Power Manager for Solar Harvesting WSN
    2012
    Co-Authors: A. Castagnetti, Alain Pegatoquet, Cécile Belleudy, Michel Auguin
    Abstract:

    Energy harvesting Wireless Sensor Networks are receiving increasing interest due to their potential to extend system lifetime. Because environmental energy availability is highly variable, an efficient Power management is required. An energy harvesting Power Manager must adapt to different situations that depends on the energy that can be harvested from the environment. In this paper we propose a generic model for solar energy harvesting wireless sensor node, that we validate on real hardware. A novel Power management architecture is then proposed. Simulation results show that up to 30% performance improvement can be achieved compared to a state of the art Power management algorithm.

Olivier Sentieys - One of the best experts on this subject based on the ideXlab platform.

  • Energy-Efficient Power Manager and MAC Protocol for Multi-Hop Wireless Sensor Networks Powered by Periodic Energy Harvesting Sources
    IEEE Sensors Journal, 2015
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys
    Abstract:

    To overcome the limited energy in battery-Powered wireless sensor networks (WSNs), harvested energy is considered as a potential solution to achieve autonomous systems. A Power Manager (PM) is usually embedded in wireless nodes to adapt the computation load by changing their wake-up interval according to the harvested energy. In order to prolong the network lifetime, the PM must ensure that every node satisfies the energy neutral operation (ENO) condition. However, when a multi-hop network is considered, changing the wake-up interval regularly may cripple the synchronization among nodes and, therefore, degrade the global system quality of service. In this paper, a wake-up variation reduction PM is proposed to solve this issue. This PM is applied for wireless nodes Powered by a periodic energy source (e.g., light energy in an office) over a constant cycle of 24 h. Our PM not only follows the ENO condition, but also reduces the wake-up interval variations of WSN nodes. Based on this PM, an energy-efficient protocol, named synchronized wake-up interval MAC, is also proposed. OMNET++ simulation results using three different harvested profiles show that the data rate of a WSN node can be increased up to 65% and the latency reduced down to 57% compared with state-of-the-art PMs. Validations on a real WSN platform have also been performed and confirmed the efficiency of our approach.

  • A Power Manager with Balanced Quality of Service for Energy-Harvesting Wireless Sensor Nodes
    2014
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys
    Abstract:

    Future Internet of Things is paving the way for the proliferation of Wireless Sensor Networks (WSNs). To overcome the limited energy in batteries, WSN nodes are relying on everlasting environmental energy. Moreover, a Power Manager (PM) is also embedded in each WSN node to guarantees that the total consumed energy is equal to the harvested energy for a long period, leading to Energy Neutral Operation (ENO) with a theoretically infinite lifetime. In this paper, a new PM for WSN nodes Powered by periodic sources (e.g. ambient energy is not available during the full harvesting cycle) is proposed. Not only respecting the ENO condition, our PM is able to balance the Quality of Service (QoS) during the whole cycle to provide regular data tracking, which is essential for WSN applications like monitoring. Simulations on OMNET++ show that our PM can improve the QoS during the absence of energy by a factor up to 84% compared to state-of-the-art PMs, while guaranteeing the same global QoS.

  • Duty-Cycle Power Manager for Thermal-Powered Wireless Sensor Networks
    2013
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys, Cécile Belleudy
    Abstract:

    Exploiting energy from the environment to extend the system lifetime of Wireless Sensor Network (WSN), especially thermal energy, is considered as a promising approach. When considering self-Powered systems, the Power Manager (PM) plays an important role in energy harvesting WSNs. Instead of minimizing the consumed energy as in the case of battery-Powered systems, it causes the harvesting node to converge to Energy Neutral Operation (ENO) in order to achieve a theoretically infinite lifetime. In this paper, a low complexity PM for a thermal-Powered WSN is presented. Our PM adapts the duty cycle of the node according to the estimation of harvested energy and the consumed energy provided by a simple energy monitor for a super capacitor based WSN to achieve the ENO. Experiments are performed on a real WSN platform where harvested energy is extracted from the wasted heat of a PC adapter by two thermoelectric generators.

  • Power Manager with PID controller in Energy Harvesting Wireless Sensor Networks
    2012
    Co-Authors: Olivier Sentieys, Olivier Berder, Alain Pegatoquet, Cécile Belleudy
    Abstract:

    System lifetime is the crucial problem of Wireless Sensor Networks (WSNs), and exploiting environmental energy provides a potential solution for this problem. When considering self-Powered systems, the Power Manager (PM) plays an important role in energy harvesting WSNs. Instead of minimizing the consumption energy as in the case of battery Powered systems, it makes the harvesting node converge to Energy Neutral Operation (ENO) to achieve a theoretically infinite lifetime and maximize the system performance. In this paper, a low complexity PM with a Proportional Integral Derivative (PID) controller is introduced. This PM monitors the buffered energy in the storage device and performs adaptation by changing the wake-up period of the wireless node. This shows the interest of our approach since the impractical monitoring harvested energy as well as consumed energy is not required as it is the case in other previously proposed techniques. Experimental results are performed on a real WSN platform with two solar cells in an indoor environment. The PID controller provides a practical strategy for long-term operations of the node in various environmental conditions.

Olivier Berder - One of the best experts on this subject based on the ideXlab platform.

  • GRAPMAN: Gradual Power Manager for Consistent Throughput of Energy Harvesting Wireless Sensor Nodes
    2015
    Co-Authors: Fayçal Ait Aoudia, Matthieu Gautier, Olivier Berder
    Abstract:

    In this work, Wireless Sensor Network (WSN) applications that require long-term sustainability are considered. Energy harvesting forms a promising technology to address this challenge, by allowing each node to be entirely Powered by energy harvested from its environment. To be sustainable, each node must dynamically adapt its Quality of Service (QoS), regarding the harvested energy using a Power management strategy. This strategy is implemented on each node by the Power Manager (PM). In this paper, GRAPMAN (GRAdual Power Manager) is proposed, a novel PM for Energy-Harvesting WSN (EH-WSN) Powered by pseudo-periodic energy sources. Unlike most state of the art PMs, GRAPMAN aims to achieve high average throughput while maintaining consistent QoS, i.e. with low fluctuations with respect to time, by looking for the highest throughput that can be supplied by the node over a finite time horizon while remaining sustainable. We show through extensive trace-driven network simulations that GRAPMAN outperforms state of the art PMs in both average throughput and throughput consistency.

  • Energy-Efficient Power Manager and MAC Protocol for Multi-Hop Wireless Sensor Networks Powered by Periodic Energy Harvesting Sources
    IEEE Sensors Journal, 2015
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys
    Abstract:

    To overcome the limited energy in battery-Powered wireless sensor networks (WSNs), harvested energy is considered as a potential solution to achieve autonomous systems. A Power Manager (PM) is usually embedded in wireless nodes to adapt the computation load by changing their wake-up interval according to the harvested energy. In order to prolong the network lifetime, the PM must ensure that every node satisfies the energy neutral operation (ENO) condition. However, when a multi-hop network is considered, changing the wake-up interval regularly may cripple the synchronization among nodes and, therefore, degrade the global system quality of service. In this paper, a wake-up variation reduction PM is proposed to solve this issue. This PM is applied for wireless nodes Powered by a periodic energy source (e.g., light energy in an office) over a constant cycle of 24 h. Our PM not only follows the ENO condition, but also reduces the wake-up interval variations of WSN nodes. Based on this PM, an energy-efficient protocol, named synchronized wake-up interval MAC, is also proposed. OMNET++ simulation results using three different harvested profiles show that the data rate of a WSN node can be increased up to 65% and the latency reduced down to 57% compared with state-of-the-art PMs. Validations on a real WSN platform have also been performed and confirmed the efficiency of our approach.

  • A Power Manager with Balanced Quality of Service for Energy-Harvesting Wireless Sensor Nodes
    2014
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys
    Abstract:

    Future Internet of Things is paving the way for the proliferation of Wireless Sensor Networks (WSNs). To overcome the limited energy in batteries, WSN nodes are relying on everlasting environmental energy. Moreover, a Power Manager (PM) is also embedded in each WSN node to guarantees that the total consumed energy is equal to the harvested energy for a long period, leading to Energy Neutral Operation (ENO) with a theoretically infinite lifetime. In this paper, a new PM for WSN nodes Powered by periodic sources (e.g. ambient energy is not available during the full harvesting cycle) is proposed. Not only respecting the ENO condition, our PM is able to balance the Quality of Service (QoS) during the whole cycle to provide regular data tracking, which is essential for WSN applications like monitoring. Simulations on OMNET++ show that our PM can improve the QoS during the absence of energy by a factor up to 84% compared to state-of-the-art PMs, while guaranteeing the same global QoS.

  • Duty-Cycle Power Manager for Thermal-Powered Wireless Sensor Networks
    2013
    Co-Authors: Alain Pegatoquet, Olivier Berder, Olivier Sentieys, Cécile Belleudy
    Abstract:

    Exploiting energy from the environment to extend the system lifetime of Wireless Sensor Network (WSN), especially thermal energy, is considered as a promising approach. When considering self-Powered systems, the Power Manager (PM) plays an important role in energy harvesting WSNs. Instead of minimizing the consumed energy as in the case of battery-Powered systems, it causes the harvesting node to converge to Energy Neutral Operation (ENO) in order to achieve a theoretically infinite lifetime. In this paper, a low complexity PM for a thermal-Powered WSN is presented. Our PM adapts the duty cycle of the node according to the estimation of harvested energy and the consumed energy provided by a simple energy monitor for a super capacitor based WSN to achieve the ENO. Experiments are performed on a real WSN platform where harvested energy is extracted from the wasted heat of a PC adapter by two thermoelectric generators.

  • Power Manager with PID controller in Energy Harvesting Wireless Sensor Networks
    2012
    Co-Authors: Olivier Sentieys, Olivier Berder, Alain Pegatoquet, Cécile Belleudy
    Abstract:

    System lifetime is the crucial problem of Wireless Sensor Networks (WSNs), and exploiting environmental energy provides a potential solution for this problem. When considering self-Powered systems, the Power Manager (PM) plays an important role in energy harvesting WSNs. Instead of minimizing the consumption energy as in the case of battery Powered systems, it makes the harvesting node converge to Energy Neutral Operation (ENO) to achieve a theoretically infinite lifetime and maximize the system performance. In this paper, a low complexity PM with a Proportional Integral Derivative (PID) controller is introduced. This PM monitors the buffered energy in the storage device and performs adaptation by changing the wake-up period of the wireless node. This shows the interest of our approach since the impractical monitoring harvested energy as well as consumed energy is not required as it is the case in other previously proposed techniques. Experimental results are performed on a real WSN platform with two solar cells in an indoor environment. The PID controller provides a practical strategy for long-term operations of the node in various environmental conditions.

Yanzhi Wang - One of the best experts on this subject based on the ideXlab platform.

  • Reinforcement Learning-Based Dynamic Power Management of a Battery-Powered System Supplying Multiple Active Modes
    2020
    Co-Authors: Maryam Triki, Ahmed C Ammari, Yanzhi Wang, Massoud Pedram
    Abstract:

    Abstract-This paper addresses the problem of extending battery service lifetime in a portable electronic system while maintaining an acceptable performance degradation level. The proposed dynamic Power management (DPM) framework is based on model-free reinforcement learning (RL) technique. In this DPM framework, the Power Manager (PM) adapts the system operating mode to the actual battery state of charge. It uses RL technique to accurately define the optimal battery voltage threshold value and use it to specify the system active mode. In addition, the PM automatically adjusts the Power management policy by learning the optimal timeout value. Moreover, the SoC and latency tradeoffs can be precisely controlled based on a userdefined parameter. Experiments show that the proposed method outperforms existing methods by 35% in terms of saving battery service lifetime. Keywords-Dynamic Power management; reinforcement learning, extending battery lifetime; battery-Powered system design

  • a hierarchical framework of cloud resource allocation and Power management using deep reinforcement learning
    International Conference on Distributed Computing Systems, 2017
    Co-Authors: Ning Liu, Sheng Lin, Qinru Qiu, Jian Tang, Yanzhi Wang
    Abstract:

    Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloudcomputing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high Power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic Power management (DPM) policy should minimize Power consumption while maintaining performance degradationwithin an acceptable level. Thus, a joint virtual machine (VM) resource allocation and Power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework forsolving the overall resource allocation and Power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed Power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server Power managements comprises an LSTM based workload predictor and a model-free RL based Power Manager, operating in a distributed manner. Experiment results using actual Google cluster traces showthat our proposed hierarchical framework significantly savesthe Power consumption and energy usage than the baselinewhile achieving no severe latency degradation. Meanwhile, the proposed framework can achieve the best trade-off between latency and Power/energy consumption in a server cluster.