The evolution of information and communication technology, besides the proliferation of mobile devices, pushes the horizon of the Internet of Things. The main challenges for mobile devices are limited battery power and insufficient computational resources. Multi-access edge computing is an emerging paradigm that can provide task offloading services in the proximity of mobile devices to alleviate their load. In addition, mobile devices cannot work in emergency scenarios (e.g., post-earthquake, post-flood, post-hurricane) or areas without infrastructure. Tethered unmanned aerial vehicles have received widespread attention as an alternative base station because of their line-of-sight solid links status, flexible deployment, and sufficient energy supply. With this in mind, we propose a tethered-unmanned-aerial-vehicle-based aerial multi-access edge computing network to provide communication and task offloading services in areas without infrastructure. In addition, an energy-efficient task scheduling framework is proposed to achieve more energy-efficient task scheduling. First, we propose a geometry-based placement algorithm to generate optimal placement positions for the unmanned aerial vehicle placement problem. Then, for the non-convex task scheduling and resource allocation problem, we propose a low complexity divide and conquer scheme, which decomposes the original problem into three sub-problems and solves them separately. Extensive simulations demonstrate the better energy efficiency of the proposed framework.

With the increasing demand for location-based services in indoor environments, research on indoor localization technology has become an urgent need. In complex indoor environments, strong multipath effects can greatly interfere with the propagation of wireless signals, which may seriously degrade localization accuracy. To solve the problem, this paper proposes an indoor localization system DuLoc based on fine-grained subcarriers and a dual-channel convolutional neural network (CNN). In this system, abnormal sampling points are removed by the Isolation Forest method to deal with environmental noise. In order to select more stable and effective signals, the number of clusters of each subcarrier is analyzed by the Mean Shift clustering method, and the fingerprint database is divided into two sub-databases according to the threshold. The dual-channel CNN model is constructed to optimize the classification results with the data characteristics. Those two types of subcarrier data construct the input feature images to train the dual-channel CNN model. Then the final location coordinate is calculated by the probability weighted centroid method. Experimental results are presented to demonstrate that DuLoc has higher localization accuracy in complex indoor environments compared with the other existing methods.

Video fall detection is one of the most significant challenges in computer vision domain, and it usually involves the recognition of events that do not conform to expected falls. Recently, a majority of unsupervised models are popular to address issues that call for substantial manual labeled training data in supervised learning. However, almost all existing unsupervised methods usually minimize reconstruction errors, which may lead to insufficient reconstruction errors between fall and non-fall video frames because of the powerful representation ability of the neural network. In this paper, we propose a novel efficient fall detection method based on future frame prediction. Specifically, attention U-Net with flexible global aggregation blocks that can achieve better performance is regarded as a frame prediction network, achieving that several video frames predict the next future frame. In the training phase, commonly used appearance constraints on intensity and gradient and motion constraint are combined to further generate higher quality frames. Such constraints promote the performance of the prediction network, which can enlarge the difference between the predicted fall frame and the real fall frame. In the testing phase, the fall score based on the error between the predicted frame and the real frame can be computed to distinguish the fall event. Exhaustive experiments have been conducted on UR fall dataset, multiple cameras fall dataset and high quality fall simulation dataset, and the results verify the effectiveness of the proposed method and outperform other existing state-of-the-art methods.

In cellular networks, device-to-device communications can increase the spectrum efficiency, but some conventional schemes only consider uplink or downlink resource allocation. In this paper, we propose the joint uplink and downlink resource allocation scheme which maximizes the system capacity and guarantees the signal-to-noise-and-interference ratio of both cellular users and device-to-device pairs. The optimization problem is formulated as a mixed integer nonlinear problem that is usually NP hard. To achieve the reasonable resource allocation, the optimization problem is divided into two sub-problems including power allocation and channel assignment. It is proved that the objective function of power control is a convex function, in which the optimal transmission power can be obtained. The Hungarian algorithm is developed to achieve joint uplink and downlink channel assignment. The proposed scheme can improve the system capacity performance and increase the spectrum efficiency. Numerical results reveal that the performance of the proposed scheme of jointly uplink and downlink is better than that of the schemes for independent allocation.

The widespread application of wireless mobile services and requirements of ubiquitous access have resulted in drastic growth of the mobile traffic and huge energy consumption in ultradense networks (UDNs). Therefore, energy-efficient design is very important and is becoming an inevitable trend. To improve the energy efficiency (EE) of UDNs, we present a joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs. The problem is formulated as a nonconvex nonlinear programming problem and is then decomposed into two subproblems: user association and SBS on/off strategies. In the user association strategy, users associate with base stations (BSs) according to their movement speeds and utility function values, under the constraints of the signal-to-interference ratio (SINR) and load balancing. In particular, taking care of user mobility, users are associated if their speed exceeds a certain threshold. The macrocell base station (MBS) considers user mobility, which prevents frequent switching between users and SBSs. In the SBS on/off strategy, SBSs are turned off according to their loads and the amount of time required for mobile users to arrive at a given SBS to further improve network energy efficiency. By turning off SBSs, negative impacts on user associations can be reduced. The simulation results show that relative to conventional algorithms, the proposed scheme achieves energy efficiency performance enhancements.

In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.