Vehicular ad-hoc network (VANET) is highly dynamic due to the high speed and sparse distribution of vehicles on the road. This creates major challenges (e.g., network fragmentation, packet routing) for the researchers to enable robust, reliable, and scalable communication, especially in a highly dense network. Clustering in VANET is one of the remedies to address the scalability issue. However, it is observed in the literature, that existing clustering techniques produce a high number of clusters for the vehicular environment. Consequently, it increases the consumption of scarce resources in a wireless network. Furthermore, it also increases the communication overhead as well as the number of hops for data routing. As a result communication latency also increases and the reliability of communication protocol decreases. So it is highly desirable to find out the optimal clusters for a given vehicular environment. As finding optimal clusters is a multi-objective combinatorial optimization problem, therefore by employing nature-inspired meta-heuristic algorithms we can optimize the multi-objective problem. To this end, we proposed a novel clustering algorithm based on the Harris Hawks Optimization (HHO) algorithm for VANET (HHOCNET). HHO algorithm is a nature-inspired meta-heuristic algorithm inspired by the foraging maneuver of hawks called surprise pounce. The proposed framework imitates the cooperative foraging maneuver of hawks (i.e., surprise pounce for creating optimized vehicular clusters). The stochastic operators of the HHO algorithm and proper maintenance of the equilibrium state between the operations of exploration and exploitation enable the proposed algorithm to escape from the local optima and provide a globally optimal solution (i.e., the optimal number of vehicular clusters). Simulations are performed in MATLAB and the results are compared with the state-of-art schemes (i.e., Gray Wolf optimization-based clustering algorithm (GWOCNET), Multi-objective Particle Swarm Optimization (MO, PSO), and Comprehensive Learning Particle Swarm Optimization (CLPSO)) using different performance metrics. The results demonstrate that the proposed approach is an effective approach for clustering in VANET and outer performs the other benchmark algorithms in terms of optimizing the multi-objective clustering problem. HHOCNET algorithm selects 36.04% of nodes as cluster heads while the existing state-of-the-art schemes are providing 50.42%, 56.7%, and 60.89% for GWOCNET, CLPSO, and Multi-objective Particle Swarm Optimization (MOPSO). The proposed HHOCNET algorithm enhances the performance of the vehicular network by up to 15%. Consequently, it increases network efficiency by reducing the consumption of the required wireless resources. It also reduces the number of hops for packet routing. Hence it achieves a minimum end-to-end communication latency.

The cloud-fog computing paradigm is a novel hybrid computing model that delivers computational services to Fog nodes situated near data sources. This paradigm features a volatile and dynamic network topology, comprising heterogeneous IoT devices with varying computational capabilities, alongside a large number of diverse end-user requests. These complexities present significant challenges for researchers in establishing a robust, energy-efficient, and reliable communication environment. Efficient and optimal task scheduling is among these challenges, as it involves finding appropriate computing resources for processing tasks. Assigning tasks to fog nodes reduces delay but increases energy consumption, while routing tasks to cloud servers conserves energy but prolongs transmission delay. Therefore, it is essential to develop an optimal task scheduling algorithm for a reliable, delay-efficient, and energy-efficient communication environment. To address this, we propose a multiobjective Harris hawks optimization (HHO)-based task scheduling algorithm (MoHHOTS) for cloud-fog computing networks, aiming to optimize task scheduling with the objectives of minimizing delay and energy consumption. MoHHOTS is implemented in MATLAB and evaluated against state-of-the-art benchmark algorithms, including MOGWO and the cloud-fog cooperation algorithm. Leveraging the high convergence and stochastic operators of the HHO algorithm, alongside a balanced approach to iteration between diversification and intensification, the proposed algorithm provides a set of tradeoff solutions via the Pareto-optimal Front. Simulation results demonstrate the efficacy of the proposed solution, achieving improvements of up to 25% over a similar scheduling algorithm in terms of optimizing transmission delay and energy consumption.

The exponential increase in the Internet of Things (IoT) has affected the cloud computing with increase transmission latency and network overhead for real-time applications. Cloud-fog computing paradigm tackle these limitations by moving computational services closer to the network edge i.e., fog nodes, enhancing the speed of real-time applications. This architecture, with its dynamic computing environment and diverse IoT devices and tasks, demands a reliable and energy-efficient communication network. Joint optimization of computation offloading and task scheduling is a primary challenge, as it involves offloading tasks to optimal computational resources and scheduling them in an efficient order for operational efficacy. While offloading tasks to fog nodes reduces delay but raises energy utilization, offloading them to cloud servers reduces energy usage but raises computational costs and latency. Additionally, inefficient order of task execution (executing lower priority jobs before higher priority tasks) can disrupt system stability and reliability. Therefore, an effective joint optimal computation offloading and task scheduling strategy is essential. To this end, we propose a Multi-objective Arithmetic Optimization-based joint computation offloading and task scheduling algorithm, aiming to minimize energy consumption and transmission latency. Extensive simulations in MATLAB demonstrate the efficacy of the proposed algorithm in terms of designated optimization objectives.

Vehicular Adhoc Network (VANET) suffers from the loss of perilous data packets and disruption of links due to the fast movement of vehicles and dynamic network topology. Moreover, the reliability of the vehicular network is also threatened by malicious vehicles and messages. The malicious vehicle can promulgate fake messages to the node to misguide it, which may result in the loss of precious lives. In this situation, maintaining efficient, reliable, and secure communication among automobiles is of extreme importance, especially for a densely populated network. One of the remedies is vehicular clustering, which can effectively perform in a high-density network. However, secure cluster formation and cluster optimization are important factors to consider during the clustering process because non-optimal clusters may incur high end-to-end communication delays and produce overhead on the network. In addition, malicious nodes and packets reduce passenger and driver safety, increase road accidents, and waste passenger and driver time. To this end, we employ Arithmetic Optimization Algorithm (AOA) to design a secure intelligent clustering named AOACNET. AOA is used to achieve optimality of vehicular clusters. During cluster formation, the algorithm prevents unauthentic nodes from becoming cluster members by taking into consideration the performance value of each automobile. The vehicle’s performance value is based on the record of data transmission. If a vehicle transmits a fake message, it will receive a penalty of (-1), and in the case of transmitting a legitimate message, a reward of (+1) will be assigned to the vehicle. Initially, all the vehicles have equal performance value which either increase or decrease based on communication with their peers. The vehicles will become cluster members only if their performance value is greater than the threshold value (0). AOACNET is tested in MATLAB using various evaluation metrics (i.e., number of clusters, load balancing, computational time, network overhead and delay). The simulation results show that the proposed algorithm performs up to 25% better than the similar contenders in terms of designated optimization objectives.

The Internet of Medical Things (IoMT) plays a crucial role in smart healthcare by leveraging medical devices that generate vast amounts of data for monitoring and diagnosis. IoMT networks are prone to challenges like dynamic topology, computational limitations, and power constraints that make it difficult to process data. Offloading computationally intensive operations to high-capacity computational nodes is necessary. However, medical devices provide nonindependent and identically distributed (non-i.i.d.) data in different formats, making it more complex. Conventional rule-based offloading schemes are inefficient in enhancing network performance and hence have high latency and energy consumption. This article introduces a secure computational offloading framework using deep reinforcement learning. With deep deterministic policy gradient (DDPG) and federated learning as the basis, the framework: 1) observes the network; 2) learns offloading decisions; and 3) safeguards data privacy. It reduces energy and latency with a security guarantee. A~cloud-fog architecture is implemented with a DDPG-based local model, which is executed at the fog broker, and a global model that is executed at the cloud server. We use advance encryption standard (AES) + encryption for secure transmission of data (ECC). MATLAB simulations are conducted for encrypted and unencrypted offloading and exhibit up to 12% improvement in encrypted cases and 13% in unencrypted cases compared to existing solutions.