A Diffusion Model for Energy Harvesting Sensor Nodes

Abdelrahman, Omer H.; Gelenbe, Erol

doi:10.1109/mascots.2016.74

Cited by 24 publications

(26 citation statements)

References 22 publications

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“…Theorem 1 If a unique non-negative solution of the traffic equations given in (1) and (2) exists such that 0 < q i < 1 for all i = 1, … , N hold, then the following PFS:…”

Section: Random Neural Network and G-network With Positive And Negamentioning

confidence: 99%

“…These EPN paradigms, which are not based on G-networks, are associated with various stochastic models. Some EPN use diffusion approximation with continuous flows, instead of discrete EPs or jobs moving at random times [2]. A distinct approach to the discrete EPN in [38,44,45,84] was developed for the devices with "zero service time".…”

Section: Energy Packet Network Derived From G-networkmentioning

confidence: 99%

“…One EP is a fixed amount of energy stored in batteries or energy stores (ESs), each of which is modeled as a "queue of energy", while jobs or data packets are normal customers (also called positive customers) circulating at workstations (WSs), servers or sensor nodes. Some EPN models also use a diffusion model with continuous flows, e.g., diffusion approximation, instead of discrete EPs or jobs moving at random times [2], whereas the two main approaches to the EPN are both discrete. One main approach to the EPN was first initiated in [38] to consider one single wireless node that can collect energy through energy harvesting and reap data through sensing.…”

Section: Introductionmentioning

confidence: 99%

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G-Networks and the Performance of ICT with Renewable Energy

Zhang

2020

SN COMPUT. SCI.

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As a consequence of developing information and communication technology that is playing a significant role in our society and has changed our life dramatically, we witnessed a significant increase in energy consumption in computer systems and networks. Subsequently, energy harvesting technologies with renewable energy are of great interest in the field of computer systems and networks, and thus lead to abundant research which has been carried out to address energy harvesting from different aspects. However, the majority of them focuses on wireless or small-scale networks, which left wired networks with a general structure neglected. We first present a comprehensive systemic review of the trends of overall energy consumption, and energy and quality of service optimization in computer systems and networks. Then, this paper reviews the recent research progress in G-networks and energy packet networks with renewable and intermittent energy from both the system paradigms and the performance optimization and energy reduction algorithms for the wired networks.

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“…Theorem 1 If a unique non-negative solution of the traffic equations given in (1) and (2) exists such that 0 < q i < 1 for all i = 1, … , N hold, then the following PFS:…”

Section: Random Neural Network and G-network With Positive And Negamentioning

confidence: 99%

Section: Energy Packet Network Derived From G-networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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G-Networks and the Performance of ICT with Renewable Energy

Zhang

2020

SN COMPUT. SCI.

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“…In this context, mathematical models capturing the interactions between the information and energy planes have been proposed based on Markov processes and queueing theory [27,28,35,37], risk theory [30] and diffusion processes [31]. The models enable the study of networks and operational conditions that can balance the energy needs for sensing, communications and computing, with the availability of renewable and intermittent sources of energy, so as to achieve energy neutrality [32].…”

Section: Energy Harvesting For Wireless Communicationsmentioning

confidence: 99%

An Energy Packet Network model for mobile networks with energy harvesting

Gelenbe

Abdelrahman

2018

NOLTA

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Mobile communications are a powerful contributor to social and economic development worldwide, including in less developed or remote parts of the world. However they are large users of electricity through their base stations, backhaul networks and Cloud servers, so that they have a large environmental impact when they use the electric grid. On the other hand, they could operate with renewable energy sources and thus reduce their CO2 impact and be accessible even in areas where the electric grid is unavailable or unreliable. The counterpart is that intermittent sources of energy, such as photovoltaic and wind, can affect the quality of service (QoS) that is experienced by mobile users. Thus in this paper we model the performance of mobile telecommunications that use intermittent and renewable energy sources. In such cases to analyse the performance of such systems, both the energy supply and the network traffic, can be modeled as random processes, and we develop mathematical models using the Energy Packet Network paradigm, where both data and energy flows are discretised. QoS metrics for the users are computed based on the traffic intensity and the availability of energy.

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“…The network that will then contain at least two types of customers: for instance, energy packets (EP), and DP or tasks as first suggested in (Gelenbe [62,63]). A continuous space approximation for such models was discussed in (Abdelrahman and Gelenbe [7]). …”

Section: Energy Packet Networkmentioning

confidence: 99%

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

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A Diffusion Model for Energy Harvesting Sensor Nodes

Cited by 24 publications

References 22 publications

G-Networks and the Performance of ICT with Renewable Energy

G-Networks and the Performance of ICT with Renewable Energy

An Energy Packet Network model for mobile networks with energy harvesting

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

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