Vehicular energy network (VEN) is a vehicular network which can transport energy over a large geographical area by means of electric vehicles (EVs). In the near future, an abundance of EVs, plentiful generation of the renewables, and mature wireless energy transfer and vehicular communication technologies will expedite the realization of VEN. To transmit energy from a source to a destination, we need to establish energy paths, which are composed of segments of vehicular routes, while satisfying various design objectives. In this paper, we develop a method to construct all energy paths for a particular energy source-destination pair, followed by some analytical results of the method. We describe how to utilize the energy paths to develop optimization models for different design goals and propose two solutions. We also develop a heuristic for the power loss minimization problem. We compare the performance of the three solution methods with artificial and real-world traffic networks and provide a comprehensive comparison in terms of solution quality, computation time, solvable problem size, and applicability. This paper lays the foundations of VEN routing.
VEN is specially designed for conveying energy while VANET aims to disseminate information. Yet they both utilize the vehicular network to provide additional services over geographical areas other than transportation of passengers or goods. They share many similarities on the underlying routing principle making use of the opportunistic contacts of vehicles for energy or data exchanges. @cite_15 proposed an opportunistic routing protocol for VANET by exploiting vehicular mobility patterns and geographical information provided in navigation systems. @cite_4 focused on position-based routing with topological knowledge for VANET in a city environment. @cite_13 proposed an opportunistic forwarding scheme, which utilizes velocity information to make forwarding decisions. However, routing algorithms developed for VANET may not be applicable to VEN as data and energy are different in nature. Data packets are different from one another, i.e., we are dealing with a multi-commodity routing problem, although they can be replicated to increase the chance of transmission success. However, energy packets'' are indistinguishable, i.e., we have a a single commodity routing problem, and we cannot replicate energy.
@cite_13 @cite_15 @cite_4
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When highly mobile nodes are interconnected via wireless links, the resulting network can be used as a transit network to connect other disjoint ad-hoc networks. In this paper, we compare five different opportunistic forwarding schemes, which vary in their overhead, their success rate, and the amount of knowledge about neighboring nodes that they require. In particular, we present the MoVe algorithm, which uses velocity information to make intelligent opportunistic forwarding decisions. Using auxiliary information to make forwarding decisions provides a reasonable trade-off between resource overhead and performance. Vehicular networks can be seen as an example of hybrid delay tolerant network where a mixture of infostations and vehicles can be used to geographically route the information messages to the right location. In this paper we present a forwarding protocol which exploits both the opportunistic nature and the inherent characteristics of the vehicular network in terms of mobility patterns and encounters, and the geographical information present in navigator systems of vehicles. We also report about our evaluation of the protocol over a simulator using realistic vehicular traces and in comparison with other geographical routing protocols. Routing of data in a vehicular ad hoc network is a challenging task due to the high dynamics of such a network. Recently, it was shown for the case of highway traffic that position-based routing approaches can very well deal with the high mobility of network nodes. However, baseline position-based routing has difficulties to handle two-dimensional scenarios with obstacles (buildings) and voids as it is the case for city scenarios. In this paper we analyze a position-based routing approach that makes use of the navigational systems of vehicles. By means of simulation we compare this approach with non-position-based ad hoc routing strategies (dynamic source routing and ad-hoc on-demand distance vector routing). The simulation makes use of highly realistic vehicle movement patterns derived from Daimler-Chrysler's Videlio traffic simulator. While DSR's performance is limited due to problems with scalability and handling mobility, both AODV and the position-based approach show good performances with the position-based approach outperforming AODV.