PSO-ELPM: PSO with elite learning, enhanced parameter updating, and exponential mutation operator

With the maturity of 5G and Intelligent Transportation Systems (ITS) technologies and the prospect of Beyond 5G (B5G) and 6G technologies, the limited lifetime and computing of mobile devices pose significant challenges to Quality of Service (QoS). In addition, the problem of inefficient use of computing, storage, communication, and other resources still exists in communication systems. In response to the above issues, Multi-tier Computing Networks (MTCNs) migrate computationally intensive tasks to the cloud, fog, or edge with sufficient resources, thereby realizing energy-efficient collaborative computing and multi-dimensional resource sharing. However, in the MTCN environment with complex heterogeneity, and high-intensity dynamics, how to provide sustainable solutions for resource scheduling strategies is a meaningful issue. Inspired by Virtual Network Embedding (VNE) to decouple physical network configuration, we propose a multi-target-aware dynamic resource scheduling algorithm for MTCN to improve resource flexibility, which is the first attempt in this direction. Specifically, we consider differentiated QoS requirements like computing, storage, bandwidth, delay, etc., and establish multi-target-aware embedded constraints. Additionally, we present a Deep Reinforcement Learning (DRL)-based scheduling network that can interact scientifically and efficiently with the MTCN environment. It extracts environmental information as state input to better focus on dynamic characteristics as well as calculates candidate nodes and links using a three-layer network architecture and related constraints. Furthermore, the learning process is optimized through the combination of the reward mechanism and the gradient descent mechanism. Finally, comparison experiments on three widely used evaluation indicators (long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate) verify that the proposed algorithm has made an average improvement of 19.042%, 2.563%, and 3.932% respectively compared with all baselines.

Multi-Target-Aware Dynamic Resource Scheduling for Cloud-Fog-Edge Multi-Tier Computing Network

In the technologically changing world, the demand for ultra-reliable, faster, low power, and secure communication has significantly risen in recent years. Researchers have shown immense interest in emerging quantum computing (QC) due to its potentials of solving the computing complexity in the robust and efficient manner. It is envisioned that QC can act as critical enablers and strong catalysts to considerably reduce the computing complexities and boost the future of sixth generation (6G) and beyond communication systems in terms of their security. In this study, the fundamentals of QC, the evolution of quantum communication that encompasses a wide spectrum of technologies and applications and quantum key distribution, which is one of the most promising applications of quantum security, have been presented. Furthermore, various parameters and important techniques are also investigated to optimise the performance of 6G communication in terms of their security, computing, and communication efficiency. Towards the end, potential challenges that QC and quantum communication may face in 6G have been highlighted along with future directions. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/qtc2.12060

Quantum for 6G communication: A perspective

Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors' residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH's number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime. 

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> and acceleration coefficients <inline-formula> <tex-math notation="LaTeX">$c_{1},c_{2}$ </tex-math></inline-formula>, and introduces a controlling factor <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles cannot only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09732987.pdf

CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

Based on quantum mechanics, quantum communications have clock synchronization properties and unbreakable security. Recently, related technologies of quantum communications have made breakthroughs one after another, and it has also realized functions and applications that are difficult to achieve in traditional networks. In this era of the rise of quantum networks similar to the eve of the birth of the classical Internet "Arpanet", researchers are full of longing for the blueprint of the future quantum network. In this work, we summarize, analyze and look forward to quantum communications and networking compared with the traditional Internet. Specifically, the related fundamentals, related technological developments, and existing challenges of quantum networks are firstly analyzed. On this basis, we look ahead and propose a prototype of the future quantum network architectures. Finally, we sort out and analyze the paradigm, principle, and properties of quantum communications. To the best of our knowledge, this is the first review and vision work on quantum communications and networking. 

Future Quantum Communications and Networking: A Review and Vision

With the rapid development of information technology, hacker invasion, Internet fraud and privacy disclosure and other events frequently occur, therefore information security issues become the focus of attention. Protecting the secure transmission of information has become a hot topic in today's research. As the carrier of information, image has the characteristics of vivid image and large amount of information. It has become an indispensable part of people's communication. In this paper, we proposed the key simulation analysis research based on M-J set. The research uses a complex iterative mapping to construct M set. On the basis of the constructed M set, the constructed Julia set is used to form the encryption key. The experimental results show that the generalized M-set has the characteristics of chaotic characteristic and initial value sensitivity, and the complex mapping greatly exaggerates the key space. The research on the key space based on the generalized M-J set is helpful to improve the effect of image encryption.

Research on Image Encryption based on Generalized M-J Set

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CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

Future Quantum Communications and Networking: A Review and Vision

Research on Image Encryption based on Generalized M-J Set