Smart devices have become increasingly popular. However, it is challenging to accurately and rapidly identify and control the desired device among a number of smart devices. We present AcouController, a motion-based device control approach that allows users to efficiently control the target smart device by performing a simple motion with their smartphones. Utilizing the microphone commonly built into smart devices, AcouController employs an acoustic technique to estimate the distance change as an indicator for target identification. To improve performance, we find the optimal motion pattern for target identification with the best spatial resolution. To mitigate errors in target identification, AcouController performs a joint phase analysis of signals with different frequencies and leverages the average distance during repeated motion patterns. AcouController provides multiple motion patterns for different control commands and recognizes the motion pattern with the Inertial Measurement Unit (IMU). The experimental results show that AcouController can achieve target identification with a resolution of 5° for up to 5 devices within 5 meters and an accuracy of 99% for the recognition of four different motion patterns.

Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.

Since multi-radio multi-channel wireless mesh networks are widely brought into use, the channel assignment scheme plays a critical role in determining the performance of wireless mesh networks. Nowadays many channel assignment schemes have been proposed to enhance the performance of the wireless networks. However, most of the existing schemes assume that all the interference links induce the same level of interference on the target link, which will lead to poor network performance as the assumption deviates from the actual situation. In this paper, we propose a novel clique-based clustering channel assignment (CCCA) scheme which takes different levels of interfering links into consideration. By locally calculating maximum cliques in the interfering graph, CCCA logically partitions the networks into clusters and executes channel assignment in three stages to minimize interference of links including both non-coordinated interference and coordinated interference. The simulation results demonstrate that CCCA can significantly increase the network throughput, improve the fairness of network capacity distribution and decrease the end-to-end delay, which enhances the performance of the wireless mesh networks effectively.

Coded cooperation (CC) is an excellent scheme for cooperative communication. However, the throughput of CC is not high enough. In this paper, we propose a novel cooperative scheme called hierarchically modulated coded cooperation (HMCC). This scheme takes the frame group as the input instead of a single frame in CC and adopts hierarchical modulation in source node according to the asymmetry between the links of source-relay and source-destination, expecting that some frames can be received by destination node directly and others are forwarded by relay node. In HMCC, a higher order modulation can be used by source node. Theoretical analysis and simulation show that HMCC achieves a better performance on throughput compared to other schemes. On the other hand, HMCC distributes burst errors to the frame group owing to the hierarchical modulation, which suggests that HMCC is able to struggle against the burst errors.

Femto is introduced to enhance the indoor coverage and the system capacity in LTE-Advanced system. Some methods, such as time domain interference coordination (TDIC) and frequency domain interference coordination (FDIC), have been discussed to deal with the interference between macro and femto cell when they are deployed in co- channel manner. However, TDIC operates well even though the system load is heavy but it cannot offer enough throughput in certain scenarios. On the contrary, FDIC could offer high throughput under light-load conditions at the cost of much more bandwidth. Combined the advantages of TDIC and FDIC, an approach of hybrid domain interference coordination (HDIC) is proposed and a greedy algorithm is designed in this paper. Moreover, different to the pure TDIC, TDIC used in HDIC is enhanced by allowing the user hopping to a better carrier to cooperate, namely carrier selective TDIC (CS-TDIC). Simulations show that HDIC could improve the average throughput of system in the case of light load and enable more users to meet their QoS demand of services when the load is heavy.

Measurement studies of YouTube show that there are some distinctive properties regarding to video sharing service, e.g., small clip size, suggestion links between related clips. This observation motivates us to explore the possibility to use peer-assisted approach to offload content providers. Specifically, we want to leverage the suggestion links between video clips to construct cooperative caching overlay. In this paper, taking advantage of this correlation between video clips, we design a distributed algorithm to adapt the overlay towards a loosely connected Small World Network (SWN), in which similar-interest peers are clustered together by short links to maximize streaming cooperation, and long links are introduced to reduce switching delay when one peer switches to an unrelated clip. The simulation results indicate that our algorithm can reduce server load significantly, and maintains the overlay as a SWN.