ABSTRACT Background The fast growth of Internet of Things (IoT) ecosystems has created huge amounts of heterogeneous big data, imposing significant load on latency of network, energy usage of devices, and computation resources. The conventional cloud‐centric architecture cannot support the high responsiveness needed by the current IoT applications owing to the high transmission latency and resource coordination inefficiency. Objective To overcome such constraints, this paper suggests a smart task offloading system that relies on a Deep Reinforcement Learning (DRL) system, which is implemented on an Advantage Actor‐Critic (A2C) framework. Methods The offloading issue is modelled as a Markov Decision Process (MDP) and state variables that describe the characteristics of the tasks and the device battery, the channel and server usage. The A2C agent learns dynamically the best policies of splitting computation among device and edge layers, fog and cloud layers and jointly optimizes the latency, energy usage, and resource utilization. The system is tested with the help of a realistic simulation environment, including heterogeneous IoT devices, mobility, and stochastic wireless conditions. Results Experimental outcomes show that the proposed framework can reduce the average latency by up to 76 percent, and the energy consumption of the device level by 74 percent in comparison with the baseline strategies, such as, local execution, greedy offloading and DQN‐based learning. The framework also enhances task completion rates on time constraints and the workload allocation across computing layers, which highly increase scalability, a robust system, and overall Quality of Service (QoS) in the large‐scale deployment of IoT application. Conclusion The findings indicate the promise of the use of actor‐critic DRL to assist the next‐generation IoT applications across different fields including smart cities, healthcare monitoring, and industrial automation.

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha ($\alpha$) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay, 30-40% less latency rate, 20-40% less propagation delay, and 20-30% better prediction accuracy, and outperforms Fuzzy, Logistic Regression and Decision Tree methods.

Summary Wireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes.

Abstract With advancements in science and technology, cloud computing is the next big thing in the industry. Cloud cryptography is a technique that uses encryption algorithms to secure data. The significant advantage of cloud storage is no difficulty to get to, diminished equipment, low protection, and fixing cost so every association is working with the cloud. Encryption is the process of encoding information to prevent unauthorized access. Nowadays, we desire to secure the information that is to be stored in our computer or transmitted utilizing the internet against attacks. The cryptographic method depends on their response time, confidentiality, bandwidth, and integrity. Furthermore, security is a significant factor in cloud computing for ensuring client data is placed on the safe mode in the cloud. Our research paper compares the efficiency, usage, and utility of available cryptography algorithms. Evaluation results suggest which algorithm is better for which type of data and environment.

Nowaday, emails are used in almost every field, from business to education. Emails have two subcategories, i.e., ham and spam. Email spam, also called junk emails or unwanted emails, is a type of email that can be used to harm any user by wasting his/her time, computing resources, and stealing valuable information. The ratio of spam emails is increasing rapidly day by day. Spam detection and filtration are significant and enormous problems for email and IoT service providers nowadays. Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches. Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest. This paper surveys the machine learning techniques used for spam filtering techniques used in email and IoT platforms by classifying them into suitable categories. A comprehensive comparison of these techniques is also made based on accuracy, precision, recall, etc. In the end, comprehensive insights and future research directions are also discussed.