Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multi-tier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. QoS aware admission control, offloading and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multi-tier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.

This article provides a comprehensive overview of the scientific and technological advances that have the capability to shape future 6G vehicle-to-everything (6G-V2X) communications.

Video games, virtual reality, augmented reality, and smart appliances all call for a new way for users to interact and control them. This paper develops high-preCision Acoustic Tracker (CAT), which aims to replace a traditional mouse and let a user control various devices by moving a smartphone in the air. At its heart lies a distributed Frequency Modulated Continuous Waveform (FMCW) that can accurately estimate the distance between a transmitter and a receiver that are separate and unsynchronized. We further develop an optimization framework to combine FMCW estimation with Doppler shifts to enhance the accuracy. We implement CAT on a mobile phone. The performance evaluation and user study show that our system achieves high tracking accuracy and ease of use using existing hardware.

With 5G mobile communication systems been commercially rolled out, research discussions on next generation mobile systems, i.e., 6G, have started. On the other hand, vehicular technologies are also evolving rapidly, from connected vehicles as coined by V2X (vehicle to everything) to autonomous vehicles to the combination of the two, i.e., the networks of connected autonomous vehicles (CAV). How fast the evolution of these two areas will go head-in-head is of great importance, which is the focus of this paper. After a brief overview on technological evolution of V2X to CAV and 6G key technologies, this paper explores two complementary research directions, namely, 6G for CAVs versus CAVs for 6G. The former investigates how various 6G key enablers, such as THz, cell free communication and artificial intelligence (AI), can be utilized to provide CAV mission-critical services. The latter discusses how CAVs can facilitate effective deployment and operation of 6G systems. This paper attempts to investigate the interactions between the two technologies to spark more research efforts in these areas.

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