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By looking into the vehicle-infrastructure cooperation (VIC) which is oriented towards intelligent, networked and integrated development, this paper analyzes and proposes the essence and development direction of Intelligent Vehicle Infrastructure Cooperation Systems (I-VICS). With an in-depth analysis of technologies of core importance to VIC and influence factors that constrain VIC development as a whole, the paper comes up with a technological route for VIC, and identifies a direction for vehicle-infrastructure cooperative development that progresses from primary to intermediate cooperation, then to advanced cooperation, and finally to full-fledged cooperation. Policy recommendations aiming at strengthening top-level design, building an integrated vehicle-infrastructure-cloud platform, expediting independence of key techs, building robust standards and regulations for VIC, enhancing workforce development as well as greater efforts at market promotion are put forward.

In agriculture industry, increasing use of Vehicle Internet of Things (IoT), telematics and emerging technologies are resulting in smarter machines with connected solutions. Inter and Intra Communication with vehicle to vehicle and inside vehicle - Electronic Control Unit (ECU) to ECU or ECU (Electronic Control Unit) to sensor, requirement for flow of data increased in-turn resulting in increased need for secure communication. In this paper, we focus on functional verification and validation of secure Controller Area Network (CAN) for intra vehicular communication to establish confidentiality, integrity, authenticity, and freshness of data, supporting safety, advanced automation, protection of sensitive data and IP (Intellectual Property) protection. Network security algorithms and software security processes are the layers supporting to achieve our cause.

Day by day, airports adopt more IoT devices. However, airports are not exempt from possible failures due to malware’s proliferation that can abuse vulnerabilities. Computer criminals can access, corrupt, and extract information from individuals or companies. This paper explains the development of a propagation model, which started with a Delphi process. We discuss the preliminary implications for airports of the simulation model built from the Delphi recommendations.

Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.