Freezing of gaits (FOG) is a very disabling symptom of Parkinson’s Disease (PD), affecting about 50% of PD patients and 80% of advanced PD patients. Studies have shown that FOG is related to a complex interplay between motor, cognitive and affective factors. A full characterization of FOG is crucial for FOG detection/prediction and prompt intervention. A protocol has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a length of 3 hours and 42 minutes of valid data including 2 hours and 14 minutes of normal gait and 1 hour and 28 minutes of freezing of gait. The FOG episodes were labeled by two qualified physicians. The multimodal data have been validated by a FOG detection task.

Exploring data connection information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications using advanced machine learning approaches, an intelligent transportation system (ITS) can provide better safety services to mitigate the risk of road accidents and improve traffic efficiency. In this work, we propose an end-edge-cloud architecture to deploy machine learning-driven approaches at network edges to predict vehicles’ future trajectories, which is further utilized to provide an effective safety message dissemination scheme. With our approach, the traffic safety message will only be disseminated to relevant vehicles that are predicted to pass by accident areas, which can significantly reduce the network data transmission overhead and avoid unnecessary interference. Depending on the vehicle connectivity, our system adaptively chooses vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications to disseminate safety messages. We evaluate the system by using a real-world VANET mobility dataset, and experimental results show that our system outperforms other mechanisms without considering any predicted vehicle trajectory density information.

A novel multichannel multiple access protocol, namely channel selection collision avoidance (CSCA) multiple access protocol, is presented for efficient channel sharing in highly dynamic air-based self-organizing networks. It flexibly employs request-to-send and clear-to-send (RTS/CTS) dialogue on a common channel and selects conflict-free traffic channel to accomplish the transmission of data packet. The acknowledgment (ACK) packet for the data packet transmission is replied to the sender over another common channel, which completely eliminates the influence of exposed terminal problem. The influence of hidden terminal problem is also greatly reduced because most of possible packet collisions on a single channel are avoided due to traffic load balance on multiple channels. In addition, any communication pair within locality can take full advantage of multiple TCHs without collisions and spatial reuse of same channel are extended to other communication pair which is at least 2 hops away from them. Finally, simulation results validate the effectiveness of the proposed protocol.

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Based on the concept of contention reservation MAC (M\ulticode MAC), CSMI\/LCA,