In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones. Thus, effectively filtering out these activities has become a significant problem. In this paper, a novel machine learning based segmentation scheme with a multi-probability threshold is proposed for HAR. Threshold segmentation (TS) and slope-area (SA) approaches are employed according to the characteristics of small fluctuation of static activity signals and typical peaks and troughs of periodic-like ones. In addition, a multi-label weighted probability (MLWP) model is proposed to estimate the probability of each activity. The HAR error can be significantly decreased, as the proposed model can solve the problem that the fixed window usually contains multiple kinds of activities, while the unknown activities can be accurately rejected to reduce their impacts. Compared with other existing schemes, computer simulation reveals that the proposed model maintains high performance using the UCI and PAMAP2 datasets. The average HAR accuracies are able to reach 97.71% and 95.93%, respectively.

As the extension of cloud computing, edge computing has witnessed remarkable progress in the last few years. In edge computing, task offloading from edge device to edge server is becoming a promising technique that substantially enhances the quality of service for edge devices. Nevertheless, appropriate edge server selection for task offloading remains a challenge if multiple edge servers are selectable nearby. In this paper, we propose a parameter estimation-aided edge server selection scheme for task offloading (PEESS) with multiple potential edge servers. The proposed scheme jointly leverages Baum-Welch algorithm and forward algorithm of hidden Markov model. That is, PEESS leverages Baum-Welch algorithm to estimate the parameters of potential edge servers based on their time-sequential incoming task records. Subsequently, the forward algorithm is utilized for the probability calculation of the observations based on the estimated parameters. Eventually, the probability calculation results are sorted in descending order, and the edge device selects the target edge server based on the sequence of sending requests to the available edge servers from optimal to suboptimal. Extensive simulation results demonstrate that PEESS outperforms other conventional and state-of-the-art edge server selection schemes in terms of time consumption and energy consumption.

Time-Sensitive Networking (TSN) is an advance Ethernet paradigm designed to provide low delay, low jitter, and deterministic transmission time. The Cycling Queuing and Forwarding (CQF) mechanism is introduced in TSN as a scheduler to achieve precise communication. Multi-CQF, as an extension of CQF, supports the transmission of various traffic types by assigning different cycle lengths to each queue group. In its original form, Multi-CQF-based scheduling algorithms do not account for flow sorting, leading to increased transmission delays and reduced network efficiency as a network dynamically changes. To enhance the performance of Multi-CQF, this paper initially utilizes queuing theory to analyze and manage traffic, providing foundation solutions. Subsequently, Mixed Integer Programming (MIP) and the Variable Neighborhood Search Genetic Algorithm (VNS-GA) are employed to optimize transmission delay in small- and large-traffic TSN networks, respectively. MIP quickly seeks out the optimal scheduling solution for small-traffic TSN networks using branch-and-bound and linear programming techniques, while the VNS-GA improves efficiency and performance for large-traffic ones by continuously adjusting the search neighborhood strategy. Comparing with other existing schemes, computer simulation reveals that MIP reduces delay by approximately 13% on average in small-traffic TSN networks, while the VNS-GA achieves an average delay reduction of 7% in large-traffic ones.