The application of unmanned aerial vehicles (UAVs) in disaster response missions is rapidly expanding. Reliable path planning is critical for flight safety and mission execution efficiency. To address this issue, this paper proposes an improved particle swarm optimization (PSO) algorithm, termed LCPSO, which incorporates a similarity-based crossover and an elite list strategy. In LCPSO, a local search mechanism is introduced into the personal best update process using the similarity-based crossover strategy, which enhances search capability while maintaining the stability of personal best solutions through a similarity threshold. Furthermore, an elite list strategy is adopted during the global best update, allowing more particles to guide the swarm evolution. This helps the algorithm escape local optima and improves the stability of the solution process. Simulation results demonstrate that LCPSO is capable of generating high-quality flight paths in mountainous terrain and outperforms SDPSO, GPSO, CPSO, and PSO in terms of both optimization effectiveness and solution robustness.

Nowadays, the application of a UAV swarm is becoming more and more widespread in the military field, and more and more attention is paid to the security of mission resource allocation. However, the relay node forwarding in the wireless transmission process brings greater risks to data leakage, and the computing power and energy of the UAV consumption is limited, so a lighter solution is required. This paper proposes a mechanism for the safe allocation of UAV swarm mission resources based on random labels. Each task has a random label to solve the problem of database security and wireless transmission security in the process of UAV task assignment. Furthermore, a lightweight stream cipher encryption scheme is illustrated to ensure the security of the UAV database. The irreversible hash function SHA‐256 and the lightweight foam structure hash function SPONGENT‐128 are used to generate random labels and then allocate task resources. In the case of energy consumption, it reduces the possibility of the enemy successfully obtaining private data. The simulation results show that the scheme has good performance in terms of security and has better performance than existing methods in terms of throughput and delay, without increasing too much energy consumption.

Recently, Unmanned Aerial Vehicle (UAVs) have been widely adopted to collect data in dangerous or inaccessible areas due to their unique characteristics, including on-demand deployment, flexibility, low-cost, and mobility. However, limited power supply severely restricts the transmission range of a UAV, thus the sensed data needs to be transmitted to the ground control station (GCS) with the help of multiple UAVs in a multi-hop manner. This paper firstly describes the general process of the UAV relay network deployment, and analyzes the number of relay UAVs needed by two types of straightforward static model, i.e. track-based dynamic routing model with planned path and the infrastructure-based dynamic routing model with uncertain path. Deploying the relay UAVs in a static way as current works do cannot fulfill the requirements for an ideal UAV relay network, such as high reliability, low-cost and guaranteed quality of service. Thus, we detail our prediction-based dynamic relay network deployment model, and prove that our proposal is feasible and requires less number of relay UAVs while providing higher communication quality compared with state-of-the-art methods. Based on this model, an Elastic Relay Network Construction (ERNetC) algorithm is proposed. Finally, a series of simulation experiments are conducted on OMNeT++ simulation platform to evaluate the performance of ERNetC algorithm. Simulation results show that ERNetC algorithm outperforms existing algorithms in total interruption time as well as goodput.

Indoor localization is of great significance to a wide range of applications in the era of mobile computing. The maturity of the computer vision techniques and the ubiquity of embedded sensors in commercial off-the-shelf (COTS) smartphones shed the light on the submeter localization services for indoor environment. The state-of-the-art indoor localization works suffer from high-cost deployment and inaccurate results due to the coarse readings from internal measurement units (IMUs) sensors in the smartphones. In this article, we mainly innovate in introducing the WiFi-sensing technology to extract the distance information in a low-cost and device-free manner. Along with the computer vision technology, we model and implement an accurate and easy-to-deploy system for indoor localization. This system enhances indoor localization with multimodal sensing via two images, IMU sensors reading and CSI of WiFi signal. Specifically, we first model and design camera-based, sensor and WiFi-assisted indoor localization and propose several algorithms in this model. We then implement a prototype with smartphones and commercial WiFi devices and evaluate it in several distinct indoor environments. The experimental results show that 92-percentile error is within 0.2 m for indoor targets which sheds light on submeter indoor localization.

One of the major challenges in indoor localization is the matching difficulty and prediction accuracy of anchor points. In this work, we innovate in proposing a camera-based, sensor- and WiFi-assisted, and easy-to-deploy system for localization. The proposed method is based on muliti-modal sensing to enhancing localization measurement. We implement a prototype with smartphones and commercial WiFi devices and evaluate it in distinct indoor environments. Experimental results show that the 85-percentile error is within 0.21m for indoor POIs that sheds light on sub-meter level localization.

Self-organizing networks (SON) aim to offer high quality services while reducing both capital expenditure (CAPEX) and operational expenditure (OPEX). SON consists of three main functions: self- configuration, self-optimization, and self-healing. Comparing with self-configuration and self- optimization, there exits only few studies on self- healing. However, it plays an important role in maintaining network operation. Note that self- healing mainly includes fault detection, fault diagnosis, and fault compensation. In this paper, we focus on fault diagnosis and propose an ensemble learning based fault diagnosis system for a self- organizing cellular network. Specifically, in the proposed ensemble learning framework, the base learner is strengthened in each iteration and the final diagnosis result is obtained from the combination of all base classifications. Moreover, traditional classification algorithms are designed considering the premise of balanced data set. However, the classification accuracy of minority classes is not satisfactory. To deal with imbalanced training data sets, we applied the synthetic minority over- sampling technique (SMOTE) in the proposed system, which could also alleviate the difficulties caused by insufficient fault data. Simulation results show that the proposed system can achieve a high diagnosis accuracy, which can be further improved with the increase of training samples. In addition, the diagnosis accuracy of minority fault classes can be significantly improved with the application of SMOTE.

Wireless virtualization which enables resource sharing among different mobile virtual network operators (MVNOs) has become an important enabling technique in 5G cellular networks for increasing resource utilization and lowering the cost per bit. A main challenge for virtualization is efficient resource allocation while keeping isolation among different parties. In this paper, we consider a multi-dimensional resource market among multiple MVNOs and users. A combinatorial double auction (CDA) model is proposed, based on which a truthful and efficient resource allocation framework is provided. Specifically, for maximizing the social welfare, a winner determination problem (WDP) is formulated considering different QoS requirements of users, and a computationally tractable algorithm is proposed to solve the WDP. Also, a pricing scheme is designed such that several desirable properties (e.g., incentive compatibility, individual rationality, and budget balance) can be achieved in the proposed CDA framework. Numerical results show the effectiveness of the proposed scheme.

Internet service providers should deploy effective active queue management (AQM) strategies to provide high-bandwidth low-latency access links to customers. However, they do not, and customers see high latencies, which are problematic for latency-sensitive applications (e.g., VoIP). As a result, customers have to deploy token buckets, but they are hard to configure. In this paper, we evaluate different token bucket configurations and find that the anecdotal evidence regarding token bucket configuration is not optimal. We analyze different configurations using the ns-2 simulator and find a formula to derive optimal parameters depending on the link bandwidth, which brings about much lower latency and higher throughput.