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

Optimal Resource Allocation for Network Protection Against Spreading Processes

We study the problem of containing spreading processes in arbitrary directed networks by distributing protection resources throughout the nodes of the network. We consider two types of protection resources are available: (i) Preventive resources able to defend nodes against the spreading (such as vaccines in a viral infection process), and (ii) corrective resources able to neutralize the spreading after it has reached a node (such as antidotes). We assume that both preventive and corrective resources have an associated cost and study the problem of finding the cost-optimal distribution of resources throughout the nodes of the network. We analyze these questions in the context of viral spreading processes in directed networks. We study the following two problems: (i) Given a fixed budget, find the optimal allocation of preventive and corrective resources in the network to achieve the highest level of containment, and (ii) when a budget is not specified, find the minimum budget required to control the spreading process. We show that both resource allocation problems can be solved in polynomial time using Geometric Programming (GP) for arbitrary directed graphs of nonidentical nodes and a wide class of cost functions. Furthermore, our approach allows to optimize simultaneously over both preventive and corrective resources, even in the case of cost functions being node-dependent. We illustrate our approach by designing optimal protection strategies to contain an epidemic outbreak that propagates through an air transportation network

South Johor Economic Region (SJER) comprehensive development plan

South Johor Economic Region (SJER) is located in the southern part of Peninsular Malaysia and northern of Singapore; its area coverage is 221,634.1 hectare or 2,217 sq. km and population 1.4 Million. SJER includes District of Johor Bahru, Mukim of Jeram Batu, Mukim of Sg. Karang and Mukim of Serkat, Pulau Kukup (in Mukim Ayer Masin). SJER is 2.5 times size of Singapore, 48 times size of Putrajaya. Johor Bahru the capital of Johor state is located in the SJER area. Projected South Johor Authority (SJA) population by 2025 is 3 million and area of SJER covered PBT Pasir Gudang, Majlis Bandaraya Johor Bahru, Majlis Perbandaran Kulai and Majlis Daerah Pontian. It’s Local Authorities include are Majlis Bandaraya Johor Bahru, Majlis Perbandaran Johor Bahru Tengah, Majlis Perbandaran Kulai and Pasir Gudang Local Authority

Engineering at San Jose State University, Winter 2014

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