Energy Packet Networks: ICT Based Energy Allocation and Storage

Gelenbe, Erol

doi:10.1007/978-3-642-33368-2_16

Cited by 46 publications

(33 citation statements)

References 28 publications

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“…Earlier work [12] studied the performance of an autonomous energy harvesting communication node as a function of the random flow of harvested energy using an "energy packet" model which discretises both the data flow and the energy flow in the sensor node [13] based on queueing networks [14]. Here we extend the work to study the ability of such a system to operate in an unattended and autonomous manner, in the presence of leakage from energy storage units such as batteries or capacitors.…”

Section: Introduction and Previous Workmentioning

confidence: 93%

Performance of an Autonomous Energy Harvesting Wireless Sensor

Gelenbe

Kadioglu

2015

Lecture Notes in Electrical Engineering

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Abstract-While complex autonomic self-organising systems have received much attention, simple autonomous systems are also needed for remote sensing applications, as well as for the the Internet of Things. Such autonomous stand-alone unattended devices may not have access to reliable sources of mains power, and will have to harvest energy locally from ambient sources such as vibrations, heat or light. However energy leakage will also be a problem. This paper proposes a mathematical model to analyse the performance of such systems in the presence of a random source of energy, as well as a random source of data. The equilibrium between random energy, random data and random leakage results in an interesting performance analysis of these small but ubiquitous systems as a whole. A discussion is also provided about the effect of transmission errors.

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Section: Introduction and Previous Workmentioning

confidence: 93%

Performance of an Autonomous Energy Harvesting Wireless Sensor

Gelenbe

Kadioglu

2015

Lecture Notes in Electrical Engineering

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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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“…Erol's first work published in 2015 analyzes the link between the random nature of harvested energy, and the random nature of the data collection activities of a wireless sensor [73], leading to an original analysis of "synchronization" between the two resources that in this case enable wireless communications: the data packets and the energy packets, first studied in a paper published in 2014 [70]. However in some earlier work he had introduced of a novel way to view energy as a "packet-based" resource that can be modelled in discrete units which he called Energy Packets [66,67].…”

Section: Intermittent Energy Sources For Computing and Communicationsmentioning

confidence: 99%

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Caglayan¹

2016

Prob. Eng. Inf. Sci.

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We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducing Energy Packet Networks which are a probabilistic representation of the flow, storage and consumption of electrical energy at the microscopic level (in electronic chips), and at the macroscopic level (e.g. in buildings or data centers) and for its routing and dynamic usage by consuming units (such as computer elements, chips or machines). We next discuss his work on designing computer and communication systems that parsimoniously use energy in order to achieve a satisfactory level of quality of service (QoS). Trade-offs between system QoS and energy consumption are also considered. Then we turn to Prof. Gelenbe's pioneering work on Autonomic Communications and the design and implementation of CPN, the Cognitive Packet Network, and we also briefly review his spiking random neural network that was used in CPN. This is followed by a brief review of work that he conducted since 1999 on human evacuation from dangerous or catastrophic environments, and the design of technology driven Emergency Management Systems. His research since the late 2000s on Gene Regulatory Networks is then covered together with its application to the detecting possible disease from microarray data. Finally, we briefly discuss some novel analytical models that he developed in this period with publications appearing in journals of physics and applied mathematics.

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Energy Packet Networks: ICT Based Energy Allocation and Storage

Cited by 46 publications

References 28 publications

Performance of an Autonomous Energy Harvesting Wireless Sensor

Performance of an Autonomous Energy Harvesting Wireless Sensor

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

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

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