Abstract-In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems including these devices.The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions.Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics.Our tool has been developed using Matlab TM and is available under the GPL license at .
In this paper we present a novel framework for ns2 to facilitate the simulation and, in general, the design of beyond 3G networks. The set of libraries we wrote for this purpose is called Multi InteRfAce Cross Layer Extension for ns2 (MIRACLE). They enhance the functionalities offered by the Network Simulator ns2 by providing an efficient and embedded engine for handling cross-layer messages and, at the same time, enabling the coexistence of multiple modules within each layer of the protocol stack. For instance, multiple network, link, MAC or physical layers can be specified and used within the same node. The implications of this are manifold. First of all, the framework facilitates the implementation and the simulation of modern communication systems in ns2. Secondly, due to its modularity, the code will be portable, re-usable and extensible.As an example of the advantages offered by our architecture, we show how the MIRACLE framework can be used to quickly set up protocol architectures for Ambient Networks  and evaluate their performance in wireless and multi-technology environments. We stress that, even though the emphasis in the present paper is put on wireless systems, MIRACLE is a general framework which can be used for simulating wired networks as well as a mixture of wired and wireless scenarios. Throughout the paper we also discuss some of the downsides of existing ns2 extensions, which are often programmed in a rather ad hoc manner, according to specific needs or technologies and, as such, are often difficult to extend/re-use. In contrast, our effort aims at providing well defined interfaces and is based on a truly modular architectural design. Our work can be seen as a step toward the definition of a standard framework for the simulation of cross-layer, multi-technology and mobile systems in ns2.
In this article, we cover eco-friendly cellular networks, discussing the benefits that ambient energy harvesting offers in terms of energy consumption and profit. We advocate for future networks where energy harvesting will be massively employed to power network elements; even further, communication networks will seamlessly blend with future power grids. This vision entails the fact that future base stations may trade some of the excess energy they harvest so as to make a profit and provide ancillary services to the electricity grid. We start by discussing recent developments in the energy harvesting field, and then deliberate on the way future energy markets are expected to evolve and the new fundamental trade-offs that arise when energy can be traded. Performance estimates are given throughout to support our arguments, and open research issues in this emerging field are discussed.
Abstract-We consider a two-tier urban Heterogeneous Network where small cells powered with renewable energy are deployed in order to provide capacity extension and to offload macro base stations. We use reinforcement learning techniques to concoct an algorithm that autonomously learns energy inflow and traffic demand patterns. This algorithm is based on a decentralized multi-agent Q-learning technique that, by interacting with the environment, obtains optimal policies aimed at improving the system performance in terms of drop rate, throughput and energy efficiency. Simulation results show that our solution effectively adapts to changing environmental conditions and meets most of our performance objectives. At the end of the paper we identify areas for improvement.
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