Aging Aware Random Channel Access for Battery-Powered Wireless Networks

Valentini, Roberto; Levorato, Marco; Santucci, F.

doi:10.1109/lwc.2016.2515079

Cited by 7 publications

(9 citation statements)

References 9 publications

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“…Our work represents a first attempt to introduce energy harvesting considerations in the context of distributed detection using established (but arguably simple) design metrics such as the Bhattacharyya distance. Our approach furthermore utilizes simplified models such as the energy arrival model taken from [36]- [39]. While the simplifying assumptions make the results more tractable and interpretable, they also naturally come with some strong limitations.…”

Section: Discussionmentioning

confidence: 99%

“…We further assume that only sending a message costs a packet of energy, and the energy of making the observation and processing is negligible. These assumptions, while rather simplistic, are repeatedly used in the literature (see [36]- [39] and references therein) for an energy harvesting system. We adopt this model for the sake of analytical tractability and insight.…”

Section: B Energy Harvesting Sensorsmentioning

confidence: 99%

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Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

Tarighati¹,

Gross

Jaldén

2017

IEEE Trans. Signal Process.

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We consider the problem of decentralized hypothesis testing in a network of energy harvesting sensors, where sensors make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center. The fusion center makes a decision about the present hypothesis using the aggregate received data during a time interval. We explicitly consider a scenario under which the messages are sent through parallel access channels towards the fusion center. To avoid limited lifetime issues, we assume each sensor is capable of harvesting all the energy it needs for the communication from the environment. Each sensor has an energy buffer (battery) to save its harvested energy for use in other time intervals. Our key contribution is to formulate the problem of decentralized detection in a sensor network with energy harvesting devices. Our analysis is based on a queuing-theoretic model for the battery and we propose a sensor decision design method by considering long term energy management at the sensors. We show how the performance of the system changes for different battery capacities. We then numerically show how our findings can be used in the design of sensor networks with energy harvesting sensors.

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Section: Discussionmentioning

confidence: 99%

Section: B Energy Harvesting Sensorsmentioning

confidence: 99%

Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

Tarighati¹,

Gross

Jaldén

2017

IEEE Trans. Signal Process.

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“…This is especially cumbersome in LPWAN systems with energy harvesting. In fact, in these systems, the state of each node s ∈ S specifies the state of charge of the rechargeable battery, which may even be difficult to estimate [8]- [10], as well as the state of the ambient energy source (e.g., "high" or "low" as in [11]). Therefore, network adaptation should be based on the state and dynamics of the energy storage element of each node, which can become unmanageable even for small networks [12].…”

Section: Introductionmentioning

confidence: 99%

Energy-based adaptive multiple access in LPWAN IoT systems with energy harvesting

Michelusi¹,

Levorato

2017

2017 IEEE International Symposium on Information Theory (ISIT)

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Abstract-This paper develops a control framework for a network of energy harvesting nodes connected to a Base Station (BS) over a multiple access channel. The objective is to adapt their transmission strategy to the state of the network, including the energy available to the individual nodes. In order to reduce the complexity of control, an optimization framework is proposed where energy storage dynamics are replaced by dynamic average power constraints induced by the time correlated energy supply, thus enabling lightweight and flexible network control. Specifically, the BS adapts the packet transmission probability of the "active" nodes (those currently under a favorable energy harvesting state) so as to maximize the average long-term throughput, under these dynamic average power constraints. The resulting policy takes the form of the packet transmission probability as a function of the energy harvesting state and number of active nodes. The structure of the throughput-optimal genie-aided policy, in which the number of active nodes is known non-causally at the BS, is proved. Inspired by the genie-aided policy, a Bayesian estimation approach is presented to address the case where the BS estimates the number of active nodes based on the observed network transmission pattern. It is shown that the proposed scheme outperforms by 20% a scheme in which the nodes operate based on local state information only, and performs well even when energy storage dynamics are taken into account.

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“…In [7], authors considered a network of nodes exchanging information over a shared channel. In order to optimize the battery work duration (i.e., to reduce battery degradation state), a random channel access scheme is proposed based on an aging-aware Binary Exponential Backoff algorithm, whose objective is to avoid excessive charges and discharges of the battery.…”

Section: Introductionmentioning

confidence: 99%

“…• minimization of transmitting completion time for a given number of data packets (in offline settings) [5] • minimization of battery health degradation [7] • maximization of channel usage [9] • minimization of energy overflow [8] • maximization of the battery lifetime [10] • maximization of the network sum rate [12] For the development of the optimal policy, different operating battery models are considered. State of the art articles usually focus on battery operation considering different kinds of imperfections and their combinations.…”

Section: Introductionmentioning

confidence: 99%

Towards self-control of service rate for battery management in energy harvesting devices

Gindullina¹,

Badia²

2017

2017 IEEE International Conference on Communications Workshops (ICC Workshops)

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We consider the operation of an energy harvesting wireless device (sensor node) powered by a rechargeable battery, taking non-idealities into account. In particular, we consider sudden decrease and increase of the battery level (leakage and charge recovery consequently) due to the inner diffusion processes in the battery. These processes are affecting the stability of the device operation. In particular, leakage accelerates the depletion of the battery, which results in inactive periods of the device and, thus, potential data loss. In this paper, we propose a simplified self-control management of a battery expressed by restrictions, which could be used for an efficient operational strategy of the device. To achieve this, we rely on the double-queue model which includes the imperfections of the battery operation and bi-dimensional battery value. This includes both apparent, i.e., available at the electrodes and true energy levels of a battery. These levels can be significantly different because of deep discharge events and can equalize thanks to charge recovery effect. We performed some simulation and observed that we can diminish the models variable number to predict possible unwanted events such as apparent discharge events (when the areas near electrodes are depleted while other areas of the battery still contain some energy) and data losses. This observation helps to achieve sufficiently effective self-control management by knowing and managing just few parameters, and therefore offers valuable directions for the development of autonomic and self-sustainable operation.

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Aging Aware Random Channel Access for Battery-Powered Wireless Networks

Cited by 7 publications

References 9 publications

Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

Energy-based adaptive multiple access in LPWAN IoT systems with energy harvesting

Towards self-control of service rate for battery management in energy harvesting devices

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