D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems

The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Prediction-enhanced Routing in Disruption-tolerant Satellite Networks

This thesis introduces a framework for enhancing DTN (Delay-/Disruption-Tolerant Networking) routing in dynamic LEO satellite constellations based on the prediction of contacts. The solution is developed with a clear focus on the requirements imposed by the 'Ring Road' use case, mandating a concept for dynamic contact prediction and its integration into a state-of-the-art routing approach. The resulting system does not restrict possible applications to the 'Ring Road,' but allows for flexible adaptation to further use cases. A thorough evaluation shows that employing proactive routing in concert with a prediction mechanism offers significantly improved performance when compared to alternative opportunistic routing techniques

Self-stabilizing cluster routing in Manet using link-cluster architecture

We design a self-stabilizing cluster routing algorithm based on the link-cluster architecture of wireless ad hoc networks. The network is divided into clusters. Each cluster has a single special node, called a clusterhead that contains the routing information about inter and intra-cluster communication. A cluster is comprised of all nodes that choose the corresponding clusterhead as their leader. The algorithm consists of two main tasks. First, the set of special nodes (clusterheads) is elected such that it models the link-cluster architecture: any node belongs to a single cluster, it is within two hops of the clusterhead, it knows the direct neighbor on the shortest path towards the clusterhead, and there exist no two adjacent clusterheads. Second, the routing tables are maintained by the clusterheads to store information about nodes both within and outside the cluster. There are two advantages of maintaining routing tables only in the clusterheads. First, as no two neighboring nodes are clusterheads (as per the link-cluster architecture), there is no need to check the consistency of the routing tables. Second, since all other nodes have significantly less work (they only forward messages), they use much less power than the clusterheads. Therefore, if a clusterhead runs out of power, a neighboring node (that is not a clusterhead) can accept the role of a clusterhead. (Abstract shortened by UMI.)