In this article, we present a work using a smartphone with an off-the-shelf WiFi router for human activity recognition with various scales. The router serves as a hotspot for transmitting WiFi packets. The smartphone is configured with customized firmware and developed software for capturing WiFi channel state information (CSI) data. We extract the features from the CSI data associated with specific human activities, and utilize the features to classify the activities using machine learning models. To evaluate the system performance, we test 20 types of human activities with different scales including seven small motions, four medium motions, and nine big motions. We recruit 60 participants and spend 140 hours for data collection at various experimental settings, and have 36 000 data points collected in total. Furthermore, for comparison, we adopt three distinct machine learning models, including convolutional neural networks (CNNs), decision tree, and long short-term memory. The results demonstrate that our system can predict these human activities with an overall accuracy of 97.25%. Specifically, our system achieves a mean accuracy of 97.57% for recognizing small-scale motions that are particularly useful for gesture recognition. We then consider the adaptability of the machine learning algorithms in classifying the motions, where CNN achieves the best predicting accuracy. As a result, our system enables human activity recognition in a more ubiquitous and mobile fashion that can potentially enhance a wide range of applications such as gesture control, sign language recognition, etc.

Lossy decode-and-forward (DF) relaying, also referred to as lossy forwarding (LF), can significantly enhance the transmission reliability and expand the communication coverage at the cost of a small increase in computational effort compared to its DF counterpart. Furthermore, it can further simplify the operations at the relay nodes by removing the error-detecting operation, e.g., cyclic redundancy check, which is used in the conventional DF systems. Due to these advantages, LF has been intensively investigated with the aim of its applications to various cooperative communication networks with different topologies. This paper offers a comprehensive literature review on the LF relaying strategy and makes comparisons between LF and DF. Five basic exemplifying scenarios are taken into consideration. These are the three-node network, the single-source multi-relay network with direct source-to-destination link, the multiple access relay channel, the two-way relay network, and the general multi-source multi-relay network. The paper includes not only theoretical performance limit analyses, but also performance evaluation by employing low-complexity accumulator aided turbo codes at the sources and relays as well as joint decoding at the destination. As expected, the performance enhancement in terms of outage probability, frame error rate, and -outage achievable rate by LF over DF is significant, which is demonstrated in all the exemplifying scenarios in the literatures. Hence LF has a great potential to be applied to future 5G wireless communication networks, e.g., device-to-device, vehicle-to-vehicle, and machine-type communications, which are composed of the aforementioned exemplifying scenarios.

The WiFi backscatter communications offer ultralow power and ubiquitous connections for IoT systems. Caused by the intermittent-nature of the WiFi traffics, state-of-the-art WiFi backscatter communications are not reliable for backscatter link or simple for the tag to do the adaptive transmission. In order to build reliable WiFi backscatter communications, we present GuardRider, a WiFi backscatter system that enables backscatter communications to improve the quality of service (QoS). The key contribution of GuardRider is an optimization algorithm of designing RS codes to follow the statistical knowledge of WiFi traffics and adjust backscatter transmission. With GuardRider, the reliable baskscatter link is guaranteed and a backscatter tag is able to adaptively transmit information without heavily listening to the excitation channel, by taking QoS into account. We built a hardware prototype of GuardRider using a customized tag with FPGA implementation. Both the simulations and field experiments verify that GuardRider could achieve notably gains in bit error rate and frame error rate, which are a hundredfold reduction in simulations and around 99% in filed experiments. Our system is able to achieve around 700 kbps throughput.

In Multi-access Edge Computing (MEC) systems, service migration has been extensively applied to ensure service quality by migrating services to follow mobile users. The existing migration methods mainly focus on optimizing service response latency and migration costs by predicting user’s movements. However, some malicious adversaries can learn auxiliary knowledge, i.e., users’ mobility model and service migration trajectory, and launch location inference attacks to infer user locations. This leads to serious personal security threats, like malvertising, fraud and kidnapping. In this paper, we propose a location privacy-aware service migration method to against adversaries’ location inference attacks in multi-user MEC systems. First, we adopt an entropy-based location privacy metric to accurately measure user’s location privacy leakage risk. Then, we formulate the service migration progress as a joint optimization problem that minimizes service response latency and location privacy leakage risk. To cope with inter-user interference, we developed a multi-agent soft actor-critic (MASAC) algorithm to help users collaboratively make service migration decisions. Finally, simulations based on real-world user movement trajectories were conducted to demonstrate the superiority of the proposed method. Evaluation and analysis results showed that our proposed method can effectively protect user location privacy while maintaining a low service response latency.

Parked vehicle edge computing (PVEC) utilizes both idle resources in parked vehicles (PVs) and roadside units (RSUs) as service providers (SPs) to improve the performance of vehicular internet of things (IoT). However, it is difficult to make optimal service migration decisions in PVEC networks due to the uncertain parking duration and resources heterogeneity of PVs. In this paper, we formulate the service migration of all the vehicles as an optimization problem with the objective of minimizing the average latency. We propose a two-stage service migration algorithm for PVEC networks, which divides the original problem into the service migration between SPs and the serving PV selection in parking lots. The service migration between SPs is transformed to an online problem based on Lyapunov optimization, where the expected parking duration of PVs is utilized. A modified Hungarian algorithm is proposed to select the PVs for migration. A series of simulation experiments based on the real-world vehicle traces are conducted to verify the superior performance of the proposed two-stage service migration (SEA) algorithm as compared with the state-of- art solutions.