With the advancement of technology and time, people have always sought to solve problems in the most efficient and quickest way possible. Since the introduction of the cloud computing environment along with many different sub-substructures such as task schedulers, resource allocators, resource monitors, and others, various algorithms have been proposed to improve the performance of the individual unit or structure used in the cloud environment. The cloud is a vast virtual environment with the capability to solve any task provided by the user. Therefore, new algorithms are introduced with the aim to improve the process and consume less time to evaluate the process. One of the most important sections of cloud computing is that of the task scheduler, which is responsible for scheduling tasks to each of the virtual machines in such a way that the time taken to execute the process is less and the efficiency of the execution is high. Thus, this paper plans to propose an ideal and optimal task scheduling algorithm that is tested and compared with other existing algorithms in terms of efficiency, makespan, and cost parameters, that is, this paper tries to explain and solves the scheduling problem using an improved meta-heuristic algorithm called the Hybrid Weighted Ant Colony Optimization (HWACO) algorithm, which is an advanced form of the already present Ant Colony Optimization Algorithm. The outcomes found by using the proposed HWACO has more benefits, that is, the objective for reaching the convergence in a short period of time was accomplished; thus, the projected model outdid the other orthodox algorithms such as Ant Colony Optimization (ACO), Quantum-Based Avian Navigation Optimizer Algorithm (QANA), Modified-Transfer-Function-Based Binary Particle Swarm Optimization (MTF-BPSO), MIN-MIN Algorithm (MM), and First-Come-First-Serve (FCFS), making the proposed algorithm an optimal task scheduling algorithm.

Internet of Health Things plays a vital role in day-to-day life by providing electronic healthcare services and has the capacity to increase the quality of patient care. Internet of Health Things (IoHT) devices and applications have been growing rapidly in recent years, becoming extensively vulnerable to cyber-attacks since the devices are small and heterogeneous. In addition, it is doubly significant when IoHT involves devices used in healthcare domain. Consequently, it is essential to develop a resilient cyber-attack detection system in the Internet of Health Things environment for mitigating the security risks and preventing Internet of Health Things devices from becoming exposed to cyber-attacks. Artificial intelligence plays a primary role in anomaly detection. In this paper, a deep neural network-based cyber-attack detection system is built by employing artificial intelligence on latest ECU-IoHT dataset to uncover cyber-attacks in Internet of Health Things environment. The proposed deep neural network system achieves average higher performance accuracy of 99.85%, an average area under receiver operator characteristic curve 0.99 and the false positive rate is 0.01. It is evident from the experimental result that the proposed system attains higher detection rate than the existing methods.

Jamming attack is a serious security threat in wireless sensor networks. Therefore, it is important to frame a mechanism to protect wireless sensor networks from various jamming attacks. Jammer intrusion detection and jamming detection are two separate issues. In this paper, a novel jammer detection framework to detect the intrusion of jammer and the presence of jamming in a cluster-based wireless sensor network is proposed. The proposed framework is novel in three aspects: whenever the cluster head receives a packet, it first verifies whether the source node is a legitimate, new node, or a jammer node. Second, when the source node is declared as a new one in the first step, then the framework validates whether the new node is legitimate node in the previous cluster or a jammer node by using cluster head code. Third, the framework observes the behavior of the newly joined node and the existing nodes to identify whether the nodes in the cluster is jammed or not. Additionally, it also classifies the types of jamming, if the presence of jamming is detected. Simulation result shows that the proposed framework performs extremely well and achieves jamming detection rate as high as 99.88 %.