Energy Packet Networks With Energy Harvesting

Gelenbe, Erol; Ceran, Elif Tuğçe

doi:10.1109/access.2016.2545340

Cited by 78 publications

(89 citation statements)

References 32 publications

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“…Noting that a battery operates very much like a queue -energy chunks being the "customers" in the queue, see [55,56] -we focus on a single energy-harvesting sensor node in an WSN as depicted in Figure 1. The energy harvesting sensor node is modelled as a queueing system with two queues, one finite-capacity queue for the battery and one infinite-capacity queue for the data buffer while service is only available when the mobile sink is in range.…”

Section: Model Descriptionmentioning

confidence: 99%

A queueing model of an energy harvesting sensor node with data buffering

2017

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Battery lifetime is a key impediment to long-lasting low power sensor nodes and networks thereof. Energy harvesting -conversion of ambient energy into electrical energy -has emerged as a viable alternative to battery power. Indeed, the harvested energy mitigates the dependency on battery power and can be used to transmit data. However, unfair data delivery delay and energy expenditure among sensors remain important issues for such networks. We study performance of sensor networks with mobile sinks: a mobile sink moves towards the transmission range of the different static sensor nodes to collect their data. We propose and analyse a Markovian queueing system to study the impact of uncertainty in energy harvesting, energy expenditure, data acquisition and data transmission. In particular, the energy harvesting sensor node is described by a system with two queues, one queue corresponding to the battery and the other to the data buffer. We illustrate our approach by numerical examples which show that energy harvesting correlation considerably affects performance measures like the mean data delay and the effective data collection rate.

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Section: Model Descriptionmentioning

confidence: 99%

A queueing model of an energy harvesting sensor node with data buffering

2017

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“…Recent work has considered how an electricity dispatching system can be designed to deal with different computer architectures (Gelenbe and Ceran [76]), while in (Gelenbe and Ceran [77]) an approach using G-Networks was developed to select the proportion of different sources of electricity (such as wind and photovoltaic) to different consumers (e.g., security sensors, computational resources, communication devices) so as to minimize a composite utility function that includes the relative importance of the consumers and the availability of the different sources of energy. In (Ceran and Gelenbe [17]) an approximate computational algorithm for such problems is discussed.…”

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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“…The EPN model uses a discretised representation of energy, in terms of Energy Packets (EPs), and of voice or other traffic that is being carried in digital form as Data Packets (DPs) [33,34,36]. In the EPN framework, batteries are represented as discrete energy buffers, and renewable energy flows into the batteries in the form of random processes of EP arrivals.…”

Section: Contributions Of the Papermentioning

confidence: 99%

“…Using the approach in [36], the ratio of the energy arrival rate to the energy consumption rate at N i yields the probability π i that the node's battery contains at least one EP:…”

Section: Modelling the Energy Storage Systemmentioning

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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Energy Packet Networks With Energy Harvesting

Cited by 78 publications

References 32 publications

A queueing model of an energy harvesting sensor node with data buffering

A queueing model of an energy harvesting sensor node with data buffering

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

An Energy Packet Network model for mobile networks with energy harvesting

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