This work aims at proposing a transfer learning (TL) based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts: metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.

Standard upper-limb motor function impairment assessments, such as the Fugl-Meyer Assessment (FMA), are a critical aspect of rehabilitation after neurological disorders. These assessments typically take a long time (about 30 min for the FMA) for a clinician to perform on a patient, which is a severe burden in a clinical environment. In this paper, we propose a framework for automating upper-limb motor assessments that uses low-cost sensors to collect movement data. The sensor data is then processed through a machine learning algorithm to determine a score for a patient’s upper-limb functionality. To demonstrate the feasibility of the proposed approach, we implemented a system based on the proposed framework that can automate most of the FMA. Our experiment shows that the system provides similar FMA scores to clinician scores, and reduces the time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing standard assessment methods.

With recent development of wireless communication, sensing and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Nevertheless, it is challenging to process time-critical tasks due to unique characteristics of IoV, including heterogeneous computation and communication capacities of network nodes, intermittent wireless connections, unevenly distributed workload, massive data transmission, intensive computation demands, and high mobility of vehicles. In this paper, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog and the mobile fog on processing time-critical tasks in IoV. Then, we give a motivational case study by implementing a prototype of a traffic abnormity detection and warning system, which demonstrates the necessity and urgency of developing adaptive task offloading mechanisms in such a scenario and gives insight into the problem formulation. Further, we formulate the offloading model, aiming at maximizing the completion ratio of time-critical tasks. On this basis, we propose an adaptive task offloading algorithm (ATOA). Specifically, it adaptively categorizes all tasks into four types of pending lists by considering the dynamic requirements and resources constraints, and then tasks in each list will be cooperatively offloaded to different nodes based on their features. Finally, we build the simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of ATOA.

This study investigates the synergy between centralized and decentralized (i.e., ad hoc) data scheduling in vehicular ad hoc networks (VANETs) for offloading and balancing the workloads of roadside units (RSU) in bidirectional road scenarios. In the centralized scheduling, an RSU schedules data dissemination using a hybrid of infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications. Specifically, RSUs cooperate with each other by transferring unserved requests, and each RSU schedules the data services based on its locally received requests and the transferred requests. In the decentralized scheduling, vehicles driving in opposite directions share the cached data items via V2V communication when out of the coverage of RSU. The ad hoc scheduling can be benefited from the cooperation among RSUs since the chance for V2V data sharing could be enhanced when considering transferred requests into scheduling at each RSU. We formulate a hybrid of centralized and ad hoc data scheduling (HCA) problem, aiming at best exploiting the synergistic effects of I2V and V2V communication based on centralized data broadcast and ad hoc data sharing. On this basis, we propose an RSU Cooperation-based Adaptive Scheduling (RCAS) algorithm that consists of three mechanisms, including a centralized scheduling mechanism at each RSU, an ad hoc scheduling mechanism for vehicles, and a cluster management mechanism. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution under a variety of circumstances.

Efficient data dissemination is critical for enabling emerging applications in vehicular ad hoc networks. As a typical traffic scenario, the bidirectional road scenario of highways bring unique challenges on well exploiting the benefit of vehicle-to-vehicle (V2V) communication for data sharing among vehicles driving in opposite directions. This paper is dedicated to investigating the characteristics of data services in such a scenario and exploring new opportunities for enhancing overall system performance. Specifically, we present a system architecture to enable the road-side unit assisted data scheduling via vehicle-to-infrastructure communication. Then, we give a theoretical analysis on the opportunity of successful data sharing among vehicles driving in opposite directions based on the analysis of signal-to-interference-noise-ratio of V2V communication. On this basis, we propose a clustering mechanism based on the design of a time division policy and the derivation of the optimal cluster length. In addition, a cluster association strategy is designed to enable vehicles to dynamically join or leave a cluster based on their real-time velocities. Furthermore, a two-phase backoff mechanism is designed for distributed data sharing based on V2V communication, and a cooperative scheduling algorithm is proposed for selecting sender vehicles as well as the corresponding data items for broadcasting. Finally, we build the simulation model and give a comprehensive simulation study, which demonstrates that the proposed solutions can effectively improve the overall system performance.

This paper embarks the first study on exploiting the synergy between vehicular caching and network coding for enhancing the bandwidth efficiency of data broadcasting in heterogeneous vehicular networks by presenting a service architecture that exercises the software defined network concept. In particular, we consider the scenario where vehicles request a set of information and they could be served via heterogeneous wireless interfaces, such as roadside units and base stations (BSs). We formulate a novel problem of coding-assisted broadcast scheduling (CBS), aiming at maximizing the broadcast efficiency for the limited BS bandwidth by exploring the synergistic effect between vehicular caching and network coding. We prove the NP-hardness of the CBS problem by constructing a polynomial-time reduction from the simultaneous matrix completion problem. To efficiently solve the CBS problem, we employ memetic computing, which is a nature inspired computational paradigm for tackling complex problems. Specifically, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement, and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.