Wireless Communications and Mobile Computing

As one of the key focuses in 6G research, the space–air–ground integrated network incorporates a variety of technological frameworks. Network function virtualization allows network functions to be deployed on general servers in the form of software and creates a service function chain (SFC) according to user service requirements. In recent years, the deployment of SFC has become popular research due to the increasing demand for low delay in network application scenarios. Low delay is a crucial indicator of the quality of service, especially for delay-sensitive applications. To address this issue, we propose a method for the deployment of delay-sensitive SFC based on parallelization and the improved cuckoo search (ICS) algorithm (DDSSFC-PICS). This method optimizes the composition and deployment of SFC jointly. First, the serial structure of the SFC is transformed into a parallel structure by determining the dependency of virtual network functions, which reduces the length of the SFC and thereby reduces delay. Second, with the optimization goal of minimizing network delay, a parallel SFC deployment model is established under constraints including packet loss rate and resource availability. Finally, the ICS algorithm is applied for optimization, where delay is used as the fitness measure. By improving the Lévy flight step size and drawing inspiration from the whale algorithm, the performance of the cuckoo search (CS) algorithm is enhanced, leading to a further reduction in delay. The simulation results show that using the same CS deployment method, parallelized SFC has a significantly lower delay compared to serial SFC. Furthermore, the DDSSFC-PICS reduces the delay by 22.58% and 19.02%, respectively, compared with the CS deployment and particle swarm optimization SFC deployment methods.

Hybrid communication systems, where millimeter-wave (mmWave) links coexist with microwave links, have been an essential component in the fifth-generation (5G) wireless networks. Nevertheless, the open feature of the wireless medium makes hybrid systems vulnerable to eavesdropping attacks. Eavesdroppers in hybrid communication systems can enhance their attack performance by opportunistically eavesdropping on mmWave or microwave links. This paper, therefore, aims to answer a natural question: in which region do eavesdroppers prefer the mmWave links? To this end, we first formulate this question as an eavesdropping region characterization problem from the physical layer security perspective, where eavesdroppers select the link to eavesdrop based on the ratio between the security performances of the mmWave and microwave links. To model the security performances of both the mmWave and microwave links, we derive closed-form expressions for the secrecy outage probabilities and lower bounds/exact expressions for the secrecy rates of both links. Finally, we provide numerical results to validate our theoretical analysis and also illustrate the mmWave eavesdropping region under various network parameter settings.

Synthetic impulse and aperture radar (SIAR) is a multi-input multi-output orthogonal multicarrier frequency radar, kind of frequency diverse array (FDA). This radar has omnidirectional emission in the transmitter and each receiver or receiving array can form a beam. The 4D matched filter of this radar’s circular arrays can reveal the range, elevation angle, azimuth angle, and Doppler. The existence of a high sidelobe in the range-Doppler is one of the significant challenges of this radar, and pulse-to-pulse frequency code non-agile (PPFCNA) and pulse-to-pulse frequency code agile (PPFCA) are often used to reduce it. Weighting is one of the available methods for reducing sidelobes in array radars, but weighting in the matched filter is more effective in SIAR radars due to their ability to transmit signals with different and orthogonal frequencies. This paper proposes the use of amplitude weighting in the submatched filter, which is made possible by increasing the degree of freedom in the SIAR process. In this method, each receiver’s signal is independently processed and weighted with a submatched filter. Then, a synthetic pulse is formed by combining the data from multiple channels. The output of the simulation with weighting in the matched filter for the PPFCNA indicates an 8.7908 dB reduction in sidelobes, while the output for the PPFCA indicates a 3.3779 dB reduction in sidelobes.

Reasonable planning of travel routes can keep people away from crowded areas and reduce the probability of contracting the COVID-19. In view of the characteristics related to virus infection and human flow density, it can overcome the shortcomings of using the same pheromone initial value and slow initial convergence in route planning of ant colony optimization (ACO) algorithm. In this paper, the decision tree algorithm is used to divide the human flow density into three levels: high risk, medium risk, and low risk; and different pheromone volatility coefficients are set to change the distribution of pheromone concentration. The experimental results show that the improved ACO algorithm could help to reduce the likehood of passing through the medium-risk areas and the high-risk areas, which is reduced to less than 1%. This scheme provides an efficient route planning method for epidemic prevention and control that can be applied in the daily prevention of COVID-19 in universities.

In many edge computing applications (e.g., wireless sensor networks, WSNs) where nodes are mostly battery-powered, queries’ energy consumption, and response time are two of the most important metrics, representing the network’s sustainability and performance, respectively. Conventional techniques used to focus on only one of the two metrics and did not attempt to optimize both in a coordinated manner. This work aims to achieve both high sustainability and high performance of these queries at the same time. To that end, a new mechanism is proposed to construct the topology of a three-tier WSN. The proposed mechanism eliminates the conventional stateful routing tables and employs a stateless and efficient addressing scheme inspired by the Chinese remainder theorem (CRT). The CRT-based topology allows for query parallelism, an unprecedented feature in the WSNs. On top of the new topology encoded by CRT, a new protocol is designed to parallelly preprocess collected data on sensor nodes by effectively aggregating and deduplicating data in a cluster of neighborhood nodes. At the same time, the hibernating mechanism is proposed to prolong the network life cycle. Moreover, a new algorithm is devised to allow the queries and results to be transmitted through low-power and fault-tolerant paths using recursive elections over a subset of the entire power range. With all these new techniques taken together, the system presented in this work outperforms approximate algorithms from various perspectives: (i) the query response is improved by up to 21.6%; (ii) the energy consumption is reduced by up to 16.8%; and (iii) the reliability is increased by up to 18.3%.

How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network on which the migration depends is stable. However, in practice, these networks often fluctuate greatly due to the fault of edge devices, resulting in unexpected service interruptions during the migration process. Besides, most of the existing solutions do not consider the migration cost and path selection in the event of edge network fault. Aiming at the above problems, we propose a service migration approach based on network fault prediction (SMNFP) for mobile edge environment. The SMNFP method first introduces the software-defined network as a global controller, which is used to monitor and collect the changing of the edge network and schedule the migration tasks. Second, a network fault prediction model based on Wide&Deep model is proposed to predict the upcoming faults in the network according to the alarm information of network equipment. Finally, the service migration problem is constructed as a Markov decision process, and a fault penalty function is introduced to avoid faulty nodes, together with the deep Q-learning method to solve the migration strategy. Simulation experiments are conducted on the public metro network fault dataset, and results show the proposed method can effectively predict network faults and complete service migration.