The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for the correct actions and punished for incorrect actions. In an RL deployment setting, agents intend to get cumulative maximal rewards through the continuous learning process. These systems often operate in a highly decentralized environment and exhibit complex adaptive behavior. However, the agent’s actions on the imperfect nature of context may cause inconsistent reasoning behavior in terms of the agent’s reward policies. In this paper, we present a semantic knowledge-based Multi-agent Reinforcement Learning (MARL) formalism for a context-aware heterogeneous decision support system. This is a four-layered architecture to schedule user’s routine tasks where user’s data is acquired with limited or no human intervention and perform operations autonomously based on agent’s reward/punishment policies. For this, we develop a comprehensive case study considering three different domains’ ontologies; namely, Smart Home, Smart Shopping, and Smart Fridge Systems, with the prototypal implementation of the system and show the valid execution dynamics, correctness behavior, and verify the agent’s optimal reward policies.

Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based formalism for smart learning environments that facilitates students in managing their academic as well as extra-curricular activities autonomously with limited human intervention. This system consists of a three-tier architecture including the acquisition of the contextualized information from the environment autonomously, modeling the system using Web Ontology Rule Language (OWL 2 RL) and Semantic Web Rule Language (SWRL), and perform reasoning to infer the desired goals whenever and wherever needed. For contextual reasoning, we develop a non-monotonic reasoning based formalism to reason with contextual information using rule-based reasoning. The focus is on distributed problem solving, where context-aware agents exchange information using rule-based reasoning and specify constraints to accomplish desired goals. To formally model-check and simulate the system behavior, we model the case study of a smart learning environment in the UPPAAL model checker and verify the desired properties in the model, such as safety, liveness and robust properties to reflect the overall correctness behavior of the system with achieving the minimum analysis time of 0.002 s and 34,712 KB memory utilization.

Wireless Ad Hoc Networks consist of devices that are wirelessly connected. Mobile Ad Hoc Networks (MANETs), Internet of Things (IoT), and Vehicular Ad Hoc Networks (VANETs) are the main domains of wireless ad hoc network. Internet is used in wireless ad hoc network. Internet is based on Transmission Control Protocol (TCP)/Internet Protocol (IP) network where clients and servers interact with each other with the help of IP in a pre-defined environment. Internet fetches data from a fixed location. Data redundancy, mobility, and location dependency are the main issues of the IP network paradigm. All these factors result in poor performance of wireless ad hoc networks. The main disadvantage of IP is that, it does not provide in-network caching. Therefore, there is a need to move towards a new network that overcomes these limitations. Named Data Network (NDN) is a network that overcomes these limitations. NDN is a project of Information-centric Network (ICN). NDN provides in-network caching which helps in fast response to user queries. Implementing NDN in wireless ad hoc network provides many benefits such as caching, mobility, scalability, security, and privacy. By considering the certainty, in this survey paper, we present a comprehensive survey on Caching Strategies in NDN-based Wireless Ad Hoc Network. Various caching mechanism-based results are also described. In the last, we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

Cyber-physical systems (CPS) represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing, healthcare, and autonomous infrastructure. However, their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats, potentially leading to operational failures and data breaches. Furthermore, CPS faces significant threats related to unauthorized access, improper management, and tampering of the content it generates. In this paper, we propose an intrusion detection system (IDS) optimized for CPS environments using a hybrid approach by combining a nature-inspired feature selection scheme, such as Grey Wolf Optimization (GWO), in connection with the emerging Light Gradient Boosting Machine (LightGBM) classifier, named as GWO-LightGBM. While gradient boosting methods have been explored in prior IDS research, our novelty lies in proposing a hybrid approach targeting CPS-specific operational constraints, such as low-latency response and accurate detection of rare and critical attack types. We evaluate GWO-LightGBM against GWO-XGBoost, GWO-CatBoost, and an artificial neural network (ANN) baseline using the NSL-KDD and CIC-IDS-2017 benchmark datasets. The proposed models are assessed across multiple metrics, including accuracy, precision, recall, and F1-score, with an emphasis on class-wise performance and training efficiency. The proposed GWO-LightGBM model achieves the highest overall accuracy (99.73%) for NSL-KDD and (99.61%) for CIC-IDS-2017, demonstrating superior performance in detecting minority classes such as Remote-to-Local (R2L) and Other attacks—commonly overlooked by other classifiers. Moreover, the proposed model consumes lower training time, highlighting its practical feasibility and scalability for real-time CPS deployment.