A light weight, economic, and high–energy density Zn/MnO 2 fiber battery was integrated with a textile body area network.

No abstract

The Internet of Things (IoT) platforms bring significant convenience for increased home automation. Especially, these platforms provide many new features for managing multiple IoT devices to control their physical surroundings. However, these features also bring new safety and security challenges. For example, an attacker can manipulate IoT devices to launch attacks through unexpected physical interactions. Unfortunately, very few existing research investigates the physical interactions among IoT devices and their impacts on IoT safety and security. In this paper, we propose a novel dynamic safety and security policy enforcement system called IOTSAFE, which can capture and manage real physical interactions considering contextual features on smart home platforms. To identify real physical interactions of IoT devices, we present a runtime physical interaction discovery approach, which employs both static analysis and dynamic testing techniques to identify runtime physical interactions among IoT devices. In addition, IOTSAFE constructs physical models for temporal physical interactions, which can predict incoming risky situations and block unsafe device states accordingly. We implement a prototype of IOTSAFE on the SmartThings platform. Our extensive evaluations demonstrate that IOTSAFE effectively identifies 39 real physical interactions among 130 potential interactions in our experimental environment. IOTSAFE also successfully predicts risky situations related to temporal physical interactions with nearly 96% accuracy and prevents highly risky conditions.

Vibration Sensors In article number 2201132, Jiyu Wang, Wenbo Ding, and co‐workers design a highly sensitive self‐powered vibration sensor based on the triboelectric nanogenerator (VS‐TENG) for machinery condition monitoring. The VS‐TENG can detect vibrations with frequencies ranging from 1 to 2000 Hz and can detect vibrations of low amplitude (≈sub‐μm). It has been successfully used to monitor the operating conditions of mechanical gear systems, reaching a recognition accuracy of 99.78%.