A traffic-aware electric vehicle charging management system for smart cities

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The expected increase in the number of electric vehicles (EVs) in the coming years will contribute to reducing CO2 pollution in our cities. Currently, EVs' users may suffer from distress due to long charging service times and overloaded charging stations (CSs). Critical traffic conditions (e.g., traffic jams) affect EVs' trip time (TT) towards CSs and thus influence the total trip duration. With this concern, Intelligent transport systems (ITS) and more specifically connected vehicle technologies, can leverage an efficient real-time EV charging service by jointly considering CSs status and traffic conditions in the city. In this work, we propose a scheme to manage EVs' charging planning, focusing on the selection of a CS for the energy-requiring EV. The proposed scheme considers anticipated charging slots reservations performed through a vehicular ad hoc network (VANET), which has been regarded as a cost-efficient communication framework. In specific, we consider two aspects: 1) the EV's total trip time towards its destination considering an intermediate charging at each candidate CS, and 2) the communication delay of the VANET routing protocol. First, in order to estimate the EV's total trip time, our CS selection scheme takes into account the average road speed, traffic lights, and route distance, along the path of the EV. The optimal CS that produces the minimum total charging service time (including the TT) is suggested to that energy-requiring EV. Then, we introduce two communication modes based on geographical routing protocols for VANETs to attain an anticipated charging slot reservation. Simulation results show that with our charging scheme EVs' charging service time is reduced and more EVs are successfully charged.Peer ReviewedPostprint (author's final draft

A novel cross-layer framework for large scale emergency communications

This paper explores the problem of improving coverage and capacity of large-scale communication networks in disaster-struck areas. We propose a novel integrated dynamic cross-layer distributed energy aware emergency framework, E3F, that spans large geographical areas and variable time ranges. E3F enables adaptive storage, dynamic packet scheduling and utility driven forwarding that avoids congestion and energy depletion. Our extensive experiments with realistic traces show significantly improved energy efficiency and low overheads while maintaining high success ratios for both data dissemination and query answering

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Socio-economic aware data forwarding in mobile sensing networks and systems

The vision for smart sustainable cities is one whereby urban sensing is core to optimising city operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned to become pervasive form of data collection and analysis for smart cities but deployment of millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the data using cellular communication or short range opportunistic communication. The largest challenge here is the efficient transmission of potentially huge volumes of sensor data over sometimes meagre or faulty communications networks in a cost-effective way. This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed algorithms are developed for efficient network performance including data routing and forwarding, sensing rate control and and pricing. This thesis also considered realistic urban sensing issues such as economic incentivisation and demonstrates how social network and mobility awareness improves data transmission. Through simulations and real testbed experiments, it is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces