Device-free hand gesture is one of the most natural ways to interact with everyday objects. However, existing WiFi-based gesture recognition solutions are typically restricted to indoor environments due to limited outdoor coverage. Furthermore, to achieve high sampling rates, they may interfere with normal data transmissions. In this paper, we aim to develop a robust dynamic gesture interaction system that can be ubiquitously deployed using Long-term Evolution (LTE) mobile terminals. Through both empirical studies and in-depth analysis using the Fresnel zone model, we reveal the key factors that contribute to the repeatability and discernibility of gestures. We show that the optimal location and orientation to perform gestures indeed exist and can be identified without prior knowledge of the position of LTE base stations (BSs) relative to a terminal. Guided by the design principles derived from Fresnel zone characteristics around a 4G terminal, we design highly repeatable and discernible gestures with salient received signal profiles. A gesture interaction system has been developed and implemented to achieve robust recognition with this careful design. Extensive experiments have been conducted in both indoor and outdoor environments, for different relative placements of mobile terminal and BS, and with different users. The proposed system can automatically identify the direction of BSs with a median error of less than 15 degrees and achieve gesture recognition accuracy as high as 98% in all scenarios without the need to acquire any training data.

With the recent adoption of edge computing, Internet of Things (IoT) devices collaborate at the network edge to facilitate edge-native applications. In this setting, IoT devices are typically encapsulated as IoT services to encode their functionalities, and their collaboration is achieved through IoT service composition. Due to the continuous resource occupancy, release, and consumption of IoT devices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certain IoT services may significantly downgrade in their Quality-of-Services (QoS). To guarantee the compatibility of compositions with QoS variations, this paper proposes an adaptive composition mechanism leveraging Computation Tree Logic (CTL) specifications. Specifically, we formalize the composition as a temporal task, and convert it to CTL formulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted by CTL semantics during the execution of compositions. Besides, we construct a QoS Dependency Graph (QoSDG) to capture QoS variations, and achieve adaptive composition with dynamic QoS satisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higher QoS dependencies ranging from 0.3$\%$ to 27.8$\%$.

Wireless sensor networks (WSNs) have been widely adopted in many domains like military and environment monitoring.In general, sensors in WSNs aim to sense and collect various types of information from environments. Sensors usually can be divided into two types as static or mobile sensors, where mobile sensors can move to collect the information from static sensors. How to make the best use of sensor's energy and prolong network lifetime is a challenge, which has attracted the attention of researchers in recent years. In this paper, we schedule mobile sensors by applying techniques derived from ant colony optimization and genetic algorithm in order to balance the energy consumption of mobile sensors. The network where static sensors are deployed are divided into relatively small regions, then we use our technique to allocate these regions to each mobile sensor, while ensuring that energy consumption of each mobile sensor is close to each other. Experimental evaluation shows that our technique can effectively balance the load of each mobile sensor and thus prolong the networks lifetime.