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.
@cite_18 discussed routing in the mobile electrical grid in the presence of traffic congestion by assuming every route capable of transmitting unlimited amount of energy. It constructed energy routes heuristically in terms of shortest paths. @cite_6 relaxed the above unlimited energy assumption and considered a simple flow model for multiple route construction. However, the shortest-path strategy may not be appropriate when the focus is not on energy loss. Even so, we will show that this strategy may not give the optimal results. In this paper, we provide the fundamentals of VEN routing which can be applied to problems of different system objectives.
@cite_18 @cite_6
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The recent interest in sensor networks has led to a number of routing schemes that use the limited resources available at sensor nodes more efficiently. These schemes typically try to find the minimum energy path to optimize energy usage at a node. In this paper we take the view that always using lowest energy paths may not be optimal from the point of view of network lifetime and long-term connectivity. To optimize these measures, we propose a new scheme called energy aware routing that uses sub-optimal paths occasionally to provide substantial gains. Simulation results are also presented that show increase in network lifetimes of up to 40 over comparable schemes like directed diffusion routing. Nodes also burn energy in a more equitable way across the network ensuring a more graceful degradation of service with time. Abstract The energy efficiency of wired networks has received considerable attention over the past decade due to its economic and environmental impacts. However, because of the vertical integration of the control and data planes in conventional networks, optimizing energy consumption in such networks is challenging. Software-defined networking (SDN) is an emerging networking paradigm that decouples the control plane from the data plane and introduces network programmability for the development of network applications. In this work, we propose an energy-aware integral flow-routing solution to improve the energy efficiency of the SDN routing application. We consider discreteness of link rates and pose the routing problem as a mixed integer linear programming (MILP) problem, which is known to be NP complete. The proposed solution is a heuristic implementation of the Benders decomposition method that routes additional single and multiple flows without resolving the routing problem. Performance evaluations demonstrate that the proposed solution achieves a close-to-optimal performance (within 3.27 error) compared to CPLEX on various topologies with less than 0.056 of CPLEX average computation time. Furthermore, our solution outperforms the shortest path algorithm by 24.12 to 54.35 in power savings. The inefficiency of energy usage on the Internet has become a critical problem with its rapid growth, as all network devices operate at full capacity in spite of the real traffic load. Existing studies try to develop energy efficient routings by aggregating traffic and switching underutilized devices into sleep mode. However, most existing approaches do not address the problem of routing convergence well. Since traffic changes frequently in a network, routing convergence may be triggered frequently for an energy efficient routing, which may induce routing loops and black holes, resulting in severe packet loss. In this paper, we present a fast rerouting-based (FRR-based) energy efficient routing scheme, namely GreenFRR, which leverages the technique of fast rerouting to reduce the convergence time. We first study typical fast rerouting techniques and address the challenge of guaranteeing loop-free routing. Then, we formalize the FRR-based energy efficient routing problem and prove that the problem is NP-hard. In order to solve this problem, we design heuristic algorithms to maximize the number of sleeping links. In particular, we consider link utilization ratio and path stretch in our algorithms. We evaluate our scheme by simulations on real and synthetic topologies with real and synthetic traffic traces. The results show that the power consumed by line cards achieves a saving of 40 and the convergence time can be reduced by 95 . In this paper, we develop an energy efficient routing scheme that takes into account the interference created by existing flows in the network. Unlike previous works, we explicitly study the impact of routing a new flow on the energy consumption of the network. Under certain assumptions on how links are scheduled, we can show that our proposed algorithm is asymptotically (in time) optimal in terms of minimizing the average energy consumption. We also develop a distributed version of the algorithm. Our algorithm automatically detours around a congested area in the network, which helps mitigate network congestion and improve overall network performance. Using simulations, we show that the routes chosen by our algorithm (centralized and distributed) are more energy efficient than the state of the art.