Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions

RESOURCE ALLOCATION AND EFFICIENT ROUTING IN WIRELESS NETWORKS

In wireless networks, devices (nodes) are connected by wireless links. An important issue is to set up high quality (high bandwidth) and efficient routing paths when one node wants to send packets to other nodes. Resource allocation is the foundation to guarantee high quality connections. In addition, it is critical to handle void areas in order to set up detour-free paths. Moreover, fast message broadcasting is essential in mobile wireless networks. Thus, my research includes dynamic channel allocation in wireless mesh networks, geographic routing in Ad Hoc networks, and message broadcasting in vehicular networks. The quality of connections in a wireless mesh network can be improved by equip- ping mesh nodes with multi-radios capable of tuning to non-overlapping channels. The essential problem is how to allocate channels to these multi-radio nodes. We develop a new bipartite-graph based channel allocation algorithm, which can improve bandwidth utilization and lower the possibility of starvation. Geographic routing in Ad Hoc networks is scalable and normally loop-free. However, traditional routing protocols often result in long detour paths when holes exist. We propose a routing protocol-Intermediate Target based Geographic Routing (ITGR) to solve this problem. The novelty is that a single forwarding path can be used to reduce the lengths of many future routing paths. We also develop a protocol called Hole Detection and Adaptive Geographic Routing, which identifies the holes efficiently by comparing the length of a routing path with the Euclidean distance between a pair of nodes. We then set up the shortest path based on it. Vehicles play an important role in our daily life. During inter-vehicle communication, it is essential that emergency information can be broadcast to surrounding vehicles quickly. We devise an approach that can find the best re-broadcasting node and propagate the message as fast as possible

Leveraging a Publish/Subscribe Fog System to Provide Collision Warnings in Vehicular Networks

[EN] Fog computing, an extension of the Cloud Computing paradigm where routers themselves may provide the virtualisation infrastructure, aims at achieving fluidity when distributing in-network functions, in addition to allowing fast and scalable processing, and exchange of information. In this paper we present a fog computing architecture based on a content island which interconnects sets of things to exchange and process data among themselves or with other content islands. We then present a use case that focuses on a smartphone-based forward collision warning application for a connected vehicle scenario. This application makes use of the optical sensor of smartphones to estimate the distance between the device itself and other vehicles in its field of view. The vehicle travelling directly ahead is identified relying on the information from the GPS, camera, and inter-island communication. Warnings are generated at both content islands, if the driver does not maintain a predefined safe distance towards the vehicle ahead. Experiments performed with the application show that with the developed method, we are able to estimate the distance between vehicles, and the inter-island communication has a very low overhead, resulting in improved performance. On comparing our proposed solution based on edge/fog computing with a cloud-based api, it was observed that our solution outperformed the cloud-based api, thus making us optimistic of the utility of the proposed architectureThis work was partially funding by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00Patra, S.; Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Zamora-Mero, WJ.; Cano, J. (2019). Leveraging a Publish/Subscribe Fog System to Provide Collision Warnings in Vehicular Networks. Sensors. 19(18):1-22. https://doi.org/10.3390/s19183852S1221918Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your Way in the Fog. ACM SIGCOMM Computer Communication Review, 44(5), 27-32. doi:10.1145/2677046.2677052MQTT Version 3.1.1 http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.docSultana, T., & Wahid, K. A. (2019). Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things. IEEE Access, 7, 41607-41624. doi:10.1109/access.2019.2907525Mehmood, F., Ullah, I., Ahmad, S., & Kim, D. (2019). Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT. Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-019-01272-8https://tools.ietf.org/html/rfc2616https://tools.ietf.org/html/rfc7252Volvo Official Website https://www.volvocars.com/Chang, B. R., Tsai, H. F., & Young, C.-P. (2010). Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing. Expert Systems with Applications, 37(3), 2439-2450. doi:10.1016/j.eswa.2009.07.036Tan, H.-S., & Huang, J. (2006). DGPS-Based Vehicle-to-Vehicle Cooperative Collision Warning: Engineering Feasibility Viewpoints. IEEE Transactions on Intelligent Transportation Systems, 7(4), 415-428. doi:10.1109/tits.2006.883938Gelernter, D. (1985). Generative communication in Linda. ACM Transactions on Programming Languages and Systems, 7(1), 80-112. doi:10.1145/2363.2433Raspberry Pi Official Website https://www.raspberrypi.org/https://tools.ietf.org/html/rfc768Wallace, G. K. (1991). The JPEG still picture compression standard. Communications of the ACM, 34(4), 30-44. doi:10.1145/103085.103089Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33(2), 225-236. doi:10.1016/s0031-3203(99)00055-2Road Safety Authority of Ireland Suggest the Use of Two Second Rule http://www.rotr.ie/Rules_of_the_road.pdfOpenALPR Cloud-API Website https://www.openalpr.com/cloud-api.htmlPatra, S., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2015). An ITS solution providing real-time visual overtaking assistance using smartphones. 2015 IEEE 40th Conference on Local Computer Networks (LCN). doi:10.1109/lcn.2015.736632