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Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately-owned and commercial vehicles. Numerous high profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP) which divides and distributes the route planning task across multiple levels, and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points (endpoint location privacy) and the route itself (route privacy). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤20% in length from optimal shortest paths, with completion times within ∼ 25 seconds which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8). 

Vehicle mobility data is significant for many types of smart city applications such as smart transportation, logistics, urban planning, and carbon dioxide emissions reduction. Particularly, microscopic mobility data, in which the location, direction, and speed of each vehicle are included, is promising for cyber-physical systems services and applications. Nevertheless, there are only limited datasets available to the public due to the difficulty of collecting the data from each real vehicle due to cost, privacy, and many other reasons. To address the issue, this position paper introduces our challenge of generating microscopic mobility data using traffic simulator as well as publicly available statistical and measured traffic data in Japan. We hope this approach contributes to those researchers and service designers to move beyond the limitation that comes from the microscopic mobility dataset unavailability. 

Towards IoT-enabled world, the response time starting from data occurrence at the source until processed data delivery to the actuator is another QoS metric to be concerned. We call this requirement deadline. In coarse-grained stream processing, we could partially drop data in the streams with a specific drop rate to meet the deadline. This paper proposes an autonomic sampling method to decide the drop rate aiming at response time reduction oriented by the user-specified deadline. With consideration of processing and communicating time sharing among distributed worker nodes, we calculate a sampling number to satisfy the deadline requirement while preserving the maximum drop rate. The device will set a goal to maintain this sampling number for the next operating window. To evaluate the performance, we have implemented the proposed method on top of our previously-proposed stream processing engine called EdgeCEP. The results present that our proposed method can reduce almost 2-times latency and preserve a higher amount of request outputs compared to the fixed rate approach.