As more IoT devices are generating massive amounts of data today than ever before, there has been an outstanding need for solutions that can manage this data effectively. Cloud computing was a solution to this problem, but as new advancements were made in real-time data analysis and decision-making capabilities, the need for solutions with significantly reduced latency emerged. This requirement gave rise to fog computing, which introduces the challenge of placing application modules in such a way that the optimization can be maximized and latency can be further minimized to meet modern requirements. In this chapter, we propose to place these application modules utilizing the Particle Swarm Optimization (PSO) algorithm. Our work also compares the results with a few other module placement algorithms. PSO algorithms explore multiple solutions and look for efficient task allocation strategies that align with the principles of social swarm behavior. Moreover, our proposed system is designed to handle fluctuating levels of resource availability present in dynamic environments, such as those offered by large fog computing infrastructures. We evaluate the performance of our system in terms of energy consumption, cost of execution on the cloud, and total network usage by making simulations in iFogSim. We observe that the application module placement using our system leads to a significant optimization of these key parameters

Fog computing is a distributed computing paradigm that extends the capabilities of cloud computing to the edge of the network, closer to the data source or user. Resource management in fog computing is a complex task due to the heterogeneity of devices, dynamic workloads, limited resources, energy efficiency, task offloading, load balancing, quality of service (QoS) management, security, and privacy concerns. It plays a crucial role in optimizing the performance and efficiency of fog computing systems. The chapter delves into the challenges posed by the diverse nature of devices, dynamic workloads, and distributed architecture, emphasizing the need for adaptive resource allocation strategies. It provides a systematic and mathematical approach to resource management, including the formulation of optimization problems such as the Knapsack Problem, Traveling Salesman Problem, Transportation Problem, Vehicular Routing Problem, and N-Queens Problem. Furthermore, it underscores the significance of load balancing, task offloading, and resource provisioning as adaptive strategies to dynamically allocate resources, ensuring optimal utilization without causing underutilization. It offers valuable insights into the complexities of managing resources in fog computing and provides a holistic view of the challenges, strategies, and mathematical formulations involved in resource management across various contexts

As more IoT devices are generating massive amounts of data today than ever before, there has been an outstanding need for solutions that can manage this data effectively. Cloud computing was a solution to this problem, but as new advancements were made in real-time data analysis and decision-making capabilities, the need for solutions with significantly reduced latency emerged. This requirement gave rise to fog computing, which introduces the challenge of placing application modules in such a way that the optimization can be maximized and latency can be further minimized to meet modern requirements. In this chapter, we propose to place these application modules utilizing the Particle Swarm Optimization (PSO) algorithm. Our work also compares the results with a few other module placement algorithms. PSO algorithms explore multiple solutions and look for efficient task allocation strategies that align with the principles of social swarm behavior. Moreover, our proposed system is designed to handle fluctuating levels of resource availability present in dynamic environments, such as those offered by large fog computing infrastructures. We evaluate the performance of our system in terms of energy consumption, cost of execution on the cloud, and total network usage by making simulations in iFogSim. We observe that the application module placement using our system leads to a significant optimization of these key parameters

Vehicular Edge Computing (VEC) is a critical enabler for intelligent transportation systems (ITS). It provides low-latency and energy-efficient services by offloading computation to the network edge. Effective edge server placement is essential for optimizing system performance, particularly in dynamic vehicular environments characterized by mobility and variability. The Edge Server Placement Problem (ESPP) addresses the challenge of minimizing latency and energy consumption while ensuring scalability and adaptability in real-world scenarios. This paper proposes a framework to solve the ESPP using real-world vehicular mobility traces to simulate realistic conditions. To achieve optimal server placement, we evaluate the effectiveness of several advanced evolutionary algorithms. These include the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Teaching-Learning-Based Optimization (TLBO). Each algorithm is analyzed for its ability to optimize multiple objectives under varying network conditions. Our results show that ACO performs the best, producing well-distributed pareto-optimal solutions and balancing trade-offs effectively. GA and PSO exhibit faster convergence and better energy efficiency, making them suitable for scenarios requiring rapid decisions. The proposed framework is validated through extensive simulations and compared with state-of-the-art methods. It consistently outperforms them in reducing latency and energy consumption. This study provides actionable insights into algorithm selection and deployment strategies for VEC, addressing mobility, scalability, and resource optimization challenges. The findings contribute to the development of robust, scalable VEC infrastructures, enabling the efficient implementation of next-generation ITS applications

This book offers a comprehensive overview of the theory and practical applications of swarm intelligence in fog computing, beyond 5G networks, and information security. The introduction section provides a background on swarm intelligence and its applications in real-world scenarios. The subsequent chapters focus on the practical applications of swarm intelligence in fog-edge computing, beyond 5G networks, and information security. The book explores various techniques such as computation offloading, task scheduling, resource allocation, spectrum management, radio resource management, wireless caching, joint resource optimization, energy management, path planning, UAV placement, and intelligent routing. Additionally, the book discusses the applications of swarm intelligence in optimizing parameters for information transmission, data encryption, and secure transmission in edge networks, multi-cloud systems, and 6G networks. The book is suitable for researchers, academics, and professionals interested in swarm intelligence and its applications in fog computing, beyond 5G networks, and information security. The book concludes by summarizing the key takeaways from each chapter and highlighting future research directions in these areas.

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This paper tackles the facility location problem in 6G -enabled Vehicular Edge Computing (VEC) systems, focusing on the optimal placement of Roadside Units (RSUs) and Unmanned Aerial Vehicles (UAVs). The goal is to minimize Quality of Service (QoS) degradation by addressing challenges like dynamic vehicle mobility, traffic variations, and real-time task offloading. A mathematical optimization model is proposed, considering latency, energy consumption, packet loss, and handover costs. To solve this complex problem, heuristic algorithms such as Hill Climbing, Tabu Search, Simulated Annealing, and A* search are introduced. Extensive simulations evaluate their performance on energy efficiency and cumulative latency across various traffic and network conditions. The results reveal the strengths of each algorithm, offering valuable insights for their application in VEC scenarios. These findings contribute to scalable, energy-efficient solutions for 6G-aware VEC networks, particularly in dynamic vehicular environments, advancing research in edge computing and network optimization

Data centers have become an indispensable part of modern computing infrastructures. It becomes necessary to manage cloud resources efficiently to reduce those ever-increasing power demands of data centers. Dynamic consolidation of virtual machines (VMs) in a data center is an effective way to map workloads onto servers in a way that requires the least resources possible. It is an efficient way to improve resources utilization and reduce energy consumption in cloud data centers. Virtual machine (VM) consolidation involves host overload/underload detection, VM selection, and VM placement. If a server becomes overloaded, we need techniques to select the proper virtual machines to migrate. By considering the migration overhead and service level of agreement (SLA) violation, we investigate design methodologies to reduce the energy consumption for the whole data center. We propose a novel approach that optimally detects when a host is overloaded using known CPU utilization and a given state configuration. We design a VM selection policy, considering various resource utilization factors to select the VMs. In addition, we propose an improved version of the JAYA approach for VM placement that minimizes the energy consumption by optimally pacing the migrated VMs in a data center. We analyze the performance in terms of energy consumption, performance degradation, and migrations. Using CloudSim, we run simulations and observed that our approach has an average improvement of 24% compared to state-of-the-art approaches in terms of power consumption.

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

We explore the usage of char-RNN which is special type of recurrent neural network (RNN) in generating music pieces and propose an approach to do so. First, we train a model using existing music data. The generating model mimics the music patterns in such a way that we humans enjoy. The generated model does not replicate the training data but understands and creates patterns to generate new music. We generate honest quality music which should be good and melodious to hear. By tuning, the generated music can be beneficial for composers, film makers, artists in their tasks, and it can also be sold by companies or individuals. In our paper, we focus more on char ABC-notation because it is reliable to represent music using just sequence of characters. We use bidirectional long short-term memory (LSTM) which takes input as music sequences and observer that the proposed model has more accuracy compared with other models.

Blockchain technology finds widespread application across various fields due to its key features such as immutability, reduced costs, decentralization, and transparency. The security of blockchain relies on elements like hashing, digital signatures, and cryptography. However, the emergence of quantum computers and supporting algorithms poses a threat to blockchain security. These quantum algorithms pose a significant threat to both public-key cryptography and hash functions, compelling the redesign of blockchain architectures. This paper investigates the status quo of the post-quantum, quantum-safe, or quantum-resistant cryptosystems within the framework of blockchain. This study starts with a fundamental overview of both blockchain and quantum computing, examining their reciprocal influence and evolution. Subsequently, a comprehensive literature review is conducted focusing on Post-Quantum Distributed Ledger Technology (PQDLT). This research emphasizes the practical implementation of these protocols and algorithms providing extensive comparisons of characteristics and performance. This work will help to foster further research at the intersection of post-quantum cryptography and blockchain systems and give prospective directions for future PQDLT researchers and developers.

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