Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions.

The future communication technologies like 6G are capable to provide higher mobility and better quality-of-service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large-scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next-generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next-generation networks. In this article, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next-generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of the PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost and load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next-generation large-scale IoT networks.

The upcoming generation of communications can provide richer mobility, high data rate, reliable security, better quality of services, and supporting mobility requirements in the Internet of Things (IoT) environment. Integrating modern communication with IoT demands more secure, scalable, and resource-efficient mobility solutions for better business opportunities. In a massive 6G-enabled IoT environment, modern mobility solutions such as proxy mobile IPv6 (PMIPv6) have the potential to provide enhanced mobility and resource efficiency. For supporting richer mobility, a cost-effective and resource-efficient mobility solution is required in a massive 6G-enabled IoT environment. The main objective of the presented study is to provide a resource-friendly mobility solution for supporting the effective integration of future communication in the massive IoT domain. In that context, a location-based, resource-efficient PMIPv6 extension protocol is proposed to provide resource efficiency in terms of required signaling, packet loss, and handover latency. To compare and analyze the proposed model’s effectiveness, mathematical equations are derived for the existing as well as for the proposed solution, and such equations are implemented. Based on the comparison among existing and proposed solutions, the results show that the proposed location-based service-oriented proxy mobile IPv6 extension is resource efficient for supporting mobility in 6G-enabled IoT.

In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain‐wise. To address such aspects of learning styles, we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students’ domain‐wise learning style preferences for better content adaptation. We have developed a web‐based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style‐based model, and the experimental results show that personalization based on incorporating discipline‐wise learning style variations becomes more effective.