With the rapid development of the Internet of Things, cloud computing, and big data, more comprehensive and powerful applications become available. Meanwhile, people pay more attention to higher QoE and QoS in a “terminal- cloud” integrated system. Specifically, both advanced terminal technologies (e.g., smart clothing) and advanced cloud technologies (e.g., big data analytics and cognitive computing in clouds) are expected to provide people with more reliable and intelligent services. Therefore, in this article we propose a Wearable 2.0 healthcare system to improve QoE and QoS of the next generation healthcare system. In the proposed system, washable smart clothing, which consists of sensors, electrodes, and wires, is the critical component to collect users' physiological data and receive the analysis results of users' health and emotional status provided by cloud-based machine intelligence.

The world is facing problems, such as uneven distribution of medical resources, the growing chronic diseases, and the increasing medical expenses. Blending the latest information technology into the healthcare system will greatly mitigate the problems. This paper presents the big health application system based on the health Internet of Things and big data. The system architecture, key technologies, and typical applications of big health system are introduced in detail.

With increasing popularity of related applications of mobile crowdsensing, especially in the field of Internet of Vehicles (IoV), task allocation has attracted wide attention. How to select appropriate participants is a key problem in vehicle-based crowdsensing networks. Some traditional methods choose participants based on minimizing distance, which requires participants to submit their current locations. In this case, participants' location privacy is violated, which influences disclosure of participants' sensitive information. Many privacy preserving task allocation mechanisms have been proposed to encourage users to participate in mobile crowdsensing. However, most of them assume that different participants' task completion quality is the same, which is not reasonable in reality. In this paper, we propose an optimal location privacy preserving and service quality guaranteed task allocation in vehicle-based crowdsensing networks. Specifically, we utilize differential privacy to preserve participants' location privacy, where every participant can submit the obfuscated location to the platform instead of the real one. Based on the obfuscated locations, we design an optimal problem to minimize the moving distance and maximize the task completion quality simultaneously. In order to solve this problem, we decompose it into two linear optimization problems. We conduct extensive experiments to demonstrate the effectiveness of our proposed mechanism.

Multipath routing in wireless multimedia sensor network makes it possible to transfer data simultaneously so as to reduce delay and congestion and it is worth researching. However, the current multipath routing strategy may cause problem that the node energy near sink becomes obviously higher than other nodes which makes the network invalid and dead. It also has serious impact on the performance of wireless multimedia sensor network (WMSN). In this paper, we propose a pair-wise directional geographical routing (PWDGR) strategy to solve the energy bottleneck problem. First, the source node can send the data to the pair-wise node around the sink node in accordance with certain algorithm and then it will send the data to the sink node. These pair-wise nodes are equally selected in 360° scope around sink according to a certain algorithm. Therefore, it can effectively relieve the serious energy burden around Sink and also make a balance between energy consumption and end-to-end delay. Theoretical analysis and a lot of simulation experiments on PWDGR have been done and the results indicate that PWDGR is superior to the proposed strategies of the similar strategies both in the view of the theory and the results of those simulation experiments. With respect to the strategies of the same kind, PWDGR is able to prolong 70% network life. The delay time is also measured and it is only increased by 8.1% compared with the similar strategies.