In this paper, we present our fast-paced development experience of a Smart Campus IoT platform built upon Commercial-Off-The-Shelf devices and Microsoft Azure cloud services. Different from a traditional, long development cycle of IoT platform, the design, implementation, and deployment of this Smart Campus IoT platform was completed within three months of time, with only four junior undergraduate students who had no prior knowledge about IoT. In addition to the fast development cycle, this platform is also made generic to enable the aggregation and open distribution of campus IoT data pertaining to student-life and academic activities. Two communities of campus data users, the data contributors who interact and control the IoT network to generate meaningful data and the application developers who utilize collected data to create innovative applications, can collaborate via such a virtual marketplace to harness the true power of IoT - the Open IoT Data.

This paper proposes a new-type prosthesis model and control mechanism, represents an ongoing investigation of voluntary and natural control of lower limb prosthesis using the myoelectric signals. First of all, myoelectric signals, which are sampled from the lower limb during the subjects walked normally on the three kinds terrains, such as flat, slop and stair, are extracted the motion features with wavelet packet decomposition. And then these features are sent into LVQ neural network to classify. In the end sending the new signals into the network, the high identification ratio was obtained. The greatest characteristic of the robot-prosthesis is that it can be controlled at will, when the active control should offered corresponding parameter to the execution part for different terrain

Targeting mobile vehicle communications, Dedicated Short Range Communications (DSRC) wireless technology has been widely used in Vehicular Ad-hoc Networks (VANETs). Though DSRC can provide reliable and low-latency communications for various VANET applications, the multi-channel operations standardized by IEEE 802.11p suffer from high contention on control channel, especially in dense traffic scenarios. Meanwhile, as a conventional wireless technology for VANET communications, WiFi is often underutilized when a vehicle is on road. In this paper, we propose an innovative approach that can effectively offload control frames from DSRC to WiFi when the control traffic is dense, resulting in significantly improved throughput of control traffic (about 28.7% under moderate traffic density in simulation).

The future Internet of Things (IoT) will enable Internet connectivity for a vast amount of battery-powered devices, which usually need to communicate with each other or to some remote gateways through multihop communications. Although ZigBee has become a widely used communication technology in IoT, Wi-Fi, on the other hand, has its unique advantages such as high throughput and native IP compatibility, despite its potentially higher energy consumption. With the development of IoT, more and more IoT devices are equipped with multiple radio interfaces, such as both Wi-Fi and ZigBee. Inspired by this, we propose a dual-interface dual-pipeline scheduling (DIPS) scheme, which leverages an activation pipeline mainly constructed by low-power ZigBee interfaces to wake up a data pipeline constructed by high-power Wi-Fi interfaces on demand, toward enabling multihop data delivery in IoT. The objective is to minimize network energy consumption while satisfying certain end-to-end delay requirements. Extensive simulations and prototype-based experiments have been conducted. The results show that the energy consumption of DIPS is 96.5% and 92.8% lower than that of the IEEE 802.11's standard power saving scheme and a state-of-the-art pipeline-based scheme in moderate traffic scenarios, respectively.