Battery Management in a Green Fog-Computing Node: a Reinforcement-Learning Approach

Conti, S.; Faraci, G.; Nicolosi, R.; Rizzo, Santi Agatino; Schembra, Giovanni

doi:10.1109/access.2017.2755588

Cited by 36 publications

(15 citation statements)

References 25 publications

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“…A fuzzy-based RL algorithm was used to monitor PHD in real time. The characteristics of healthcare IoT requires RL to trace the patient background health state in minimum time [21]. The selection of data packets for computation in different fog nodes was performed using RL and a NN [44].…”

Section: Methodsmentioning

confidence: 99%

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An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

et al. 2019

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Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT–FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.

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Section: Methodsmentioning

confidence: 99%

“…The main goal of FC is to reduce the high latency between IoTs and the cloud. It has proximity to end-devices [21, 22].…”

Section: Introductionmentioning

confidence: 99%

An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

et al. 2019

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“…The BESS controller is an important part of the DRES and it has an enormous impact of the overall performance of the total system. Some of the most important aspects of this controller are to maintain the storage elements in good condition, control the state-of-charge and also source or sink power when requested by DRES [22,23]. The requested power from BESS occurs when some specific circumstances are present.…”

Section: Controller For the Battery Energy Storage Systemmentioning

confidence: 99%

Battery Storage Integration in Voltage Unbalance and Overvoltage Mitigation Control Strategies and Its Impact on the Power Quality

et al. 2019

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The increased utilisation of distributed renewable energy sources in low voltage grids leads to power quality problems such as overvoltages and voltage unbalance. This imposes challenges to the distribution system operators to maintain the power quality in their grids. To overcome these issues, energy storage systems could be integrated together with the distributed energy resources and the stored energy could be used when needed to better improve power quality and achieve better grid performance. However, integrating an energy storage system introduces additional cost, therefore, determining the right capacity is essential. In this article, an energy storage system is combined with the classical positive-sequence control strategy and the three-phase damping control strategy. The three-phase damping control strategy is able to mitigate the voltage unbalance by emulating a resistive behaviour towards the zero- and negative-sequence voltage components. This resistive behaviour can be set on different values such that the desired voltage unbalance mitigation is achieved. Hence, the three-phase damping control strategy, equipped with the energy storage system is investigated under different values of the resistive behaviour. Both control strategies are investigated under the same conditions and the impact of the different capacities of the energy storage systems is investigated.

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“…With this information, FN i computes L Ω i (a i , a Γ i (τ )), ∀a i ∈ F i locally by fixing the decisions of FNs in Γ i . Then, FN i samples a new a i (τ + 1) according to the probability distribution in (9). After the hosting decisions are updated, the chosen FNs send new decisions to the FNs in Γ i , which prepares for the next iteration.…”

Section: Distributed Algorithm Based On Cpgsmentioning

confidence: 99%

“…Third, FNs are deployed in a "drop-andplay" fashion to enable Fog computing on the existing infrastructure. In this scenario, FNs may not be powered by main electric grids but have to rely on batteries (or renewable energy sources) [9]. The battery energy constraints couple the fog configuration decisions over time, yet decisions have to be made without foreseeing the future system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fog Configuration for the Industrial Internet of Things

Chen

Zhou

Gao

et al. 2018

IEEE Trans. Ind. Inf.

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Industrial Fog computing deploys various industrial services, such as automatic monitoring/control and imminent failure detection, at the Fog Nodes (FNs) to improve the performance of industrial systems. Much effort has been made in the literature on the design of fog network architecture and computation offloading. This paper studies an equally important but much less investigated problem of service hosting where FNs are adaptively configured to host services for Sensor Nodes (SNs), thereby enabling corresponding tasks to be executed by the FNs. The problem of service hosting emerges because of the limited computational and storage resources at FNs, which limit the number of different types of services that can be hosted by an FN at the same time. Considering the variability of service demand in both temporal and spatial dimensions, when, where, and which services to host have to be judiciously decided to maximize the utility of the Fog computing network. Our proposed Fog configuration strategies are tailored to battery-powered FNs. The limited battery capacity of FNs creates a long-term energy budget constraint that significantly complicates the Fog configuration problem as it introduces temporal coupling of decision making across the timeline. To address all these challenges, we propose an online distributed algorithm, called Adaptive Fog Configuration (AFC), based on Lyapunov optimization and parallel Gibbs sampling. AFC jointly optimizes service hosting and task admission decisions, requiring only currently available system information while guaranteeing close-to-optimal performance compared to an oracle algorithm with full future information. Index Termsfog computing, industrial Internet of things, energy efficiency, distributed algorithms.

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Battery Management in a Green Fog-Computing Node: a Reinforcement-Learning Approach

Cited by 36 publications

References 25 publications

An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

Battery Storage Integration in Voltage Unbalance and Overvoltage Mitigation Control Strategies and Its Impact on the Power Quality

Adaptive Fog Configuration for the Industrial Internet of Things

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