The inherent limitations of existing 5G networks have catalyzed the evolution of Beyond-5G (B5G) technologies to address the growing demands of intelligent and data-intensive applications across diverse sectors. Concurrently, global environmental and climatic challenges have amplified the urgency for sustainable technological solutions. Such solutions are critical for reshaping the development trajectory of highly digitized and industrialized societies, particularly in addressing pressing issues related to energy efficiency and environmental sustainability. These concerns are further intensified by the challenges associated with recycling and managing the increasing volumes of electronic equipment and Internet of Things (IoT) devices. Consequently, there is a compelling need to design and implement sustainable solutions tailored to B5G-enabled applications in order to mitigate anticipated future challenges. These sustainability driven solutions are expected to accommodate the evolving service requirements of heterogeneous computing paradigms, including cloud, edge, and fog computing environments. Achieving this objective necessitates the seamless integration of communication, computing, and caching functionalities, commonly referred to as 3C convergence. This integration presents significant opportunities for innovation across application domains such as industrial systems, healthcare, education, and intelligent transportation. The advanced capabilities inherent in B5G technologies are poised to substantially enhance the efficiency, scalability, and responsiveness of Cloud/ Edge/Fog (CEF) networks, thereby enabling improved service delivery. In this context, the present work aims to provide a comprehensive review of existing research and literature on B5G-aware CEF networks. It systematically examines the associated opportunities and challenges while exploring potential pathways for the effective integration and deployment of these advanced network architectures across a wide range of application domains.

The rapid growth of the Internet of Things (IoT) has transformed domains such as smart homes, healthcare, and industrial automation, while increasing concerns about security, privacy, and trust. Due to their decentralized structure and large-scale data generation, IoT systems are highly vulnerable to cyber threats, making transparency essential. This survey highlights Federated Learning (FL) and Blockchain as effective solutions. FL enables collaborative model training without sharing raw data, preserving privacy by exchanging only aggregated updates. Blockchain provides a distributed and immutable ledger that ensures transparent, verifiable, and tamper-resistant records. Together, they create a complementary framework where FL supports privacy-preserving intelligence and Blockchain ensures trust. The survey reviews their integration in IoT applications, examines existing research, and identifies key challenges such as scalability, computational overhead, and regulatory issues. It also outlines future research directions to support the development of secure, efficient, and trustworthy IoT systems. Furthermore, the work also surveys the system models, and experimental studies.

Digital Twin (DT) technology has emerged as a transformative innovation, enabling the creation of precise digital replicas of real-world systems, processes, and objects. By establishing a seamless connection between the physical and digital realms, DTs facilitate real-time data integration, advanced simulations, and optimization processes. This capability significantly enhances decision-making, predictive maintenance, and operational efficiency across diverse industries. The adoption of DT technology has accelerated rapidly, with sectors such as agriculture, healthcare, energy, and transportation leading its implementation. The present study employs scientometric analysis to evaluate the impact and efficacy of DT models in advancing the United Nations’ Sustainable Development Goals (SDGs). Utilizing cutting-edge tools such as CiteSpace and VOSviewer for network analysis, the research leverages data sourced from the Scopus database, encompassing publications from 2018 to 2024. The analysis examines key dimensions, including publication trends, citation dynamics, keyword co-occurrence networks, document co-citation patterns, country-level collaboration, and author co-citation networks. The study identifies influential publications, prominent researchers, and leading nations contributing to the evolution of DT technology, highlighting critical innovations and contributions. These insights not only provide a comprehensive understanding of the current state of DT technology in the context of sustainable development but also reveal emerging research directions and trends. Furthermore, the findings underscore the potential for interdisciplinary collaboration to advance the role of DT technology in achieving the SDGs, paving the way for future advancements in the domain.

Child safety is of paramount importance as it directly impacts the development, well-being, and future of the children. Ensuring their safety is a collective responsibility shared by parents, caregivers, communities, and society as a whole. Smart wearable devices for child safety can play a significant role in enhancing the security and well-being of children. In market, several child tracking and monitoring devices are available but have some limitations such as unreliable communication, low accuracy, unavailability of real-time data processing and analysis, etc. To address these limitations, an Edge-Fog-Cloud enabled smart IoT wearable device for improved child safety is proposed in this paper. The architecture of the proposed system is composed of two modules, namely child module and parent module. The child module consists of four submodules for GPS, hearbeat, voice and image sensing. The parent module has a smart-phone application submodule. Further, the methodology and process workflow of the proposed architecture are explained in this paper. The authors are working on the implementation of the proposed architecture and it is the future work of this paper.

A digital twin (DT) is a representation of anything in the real-world that gets updates from both its physical and environmental equivalents. As a result, it is seen as the cornerstone of Industry 4.0 and the wave of innovation to come since it connects virtual cyberspace with actual physical entities. Virtualization of machinery control, use of data analysis in decision-making, and information flow through the Internet are some trends in Industrial Digital Twins (IDTs). IDT is one of the possible digital technologies that are currently being developed to facilitate digital transformation and decision-making in many industrial sectors. Even after more than two decades, the concept of digital twin is continuously developing, taking new forms like IDT as it permeates industries and applications. This has led to an ever-growing diversity of definitions, which poses a danger to the concept’s clarity and might result in the technology’s use ineffectively. To define what qualifies an IDT and what does not, there is a requirement for a comprehensive description with well-defined criteria. A digital representation of a physical process enables safe management, remote control, and simulations of the process. This paper offers recommendations for creating a Digital Twin architecture that incorporates the Industrial Internet of Things (IIoT) and modern technology, integrating aforementioned features in an experimental application. Additionally, the article concentrates on the IDT characteristics, major technological practices behind IDT, related sustainable solutions, and major use-cases. Furthermore, the existing challenges are explored with a specific focus on the open issues and related future opportunities.

Routing involves transferring data from one point to another within a network of interconnected devices. During the transmission of data, there is at least one middle nodethat is encountered in the network. Essentially, this concept includes two main tasks: findingthebest routing paths and sending packets across a network. The process of moving packets across an internetwork is known as packet switching, and while it is direct, determiningthepath can be quite complicated. Routing protocols utilize various metrics as a standardtodetermine the best path for routing packets to their destination, which may includethenumber of hops used by the routing algorithm to find the most efficient path to the packet'sdestination. Routing algorithms are responsible for discovering and managing routingtables, which store all the necessary route details for the packet in the path determination process. The details of the route differ depending on the specific routing algorithm. The routingtablescontain entries that include the IP address prefix and the corresponding next hop. Therearetwo main classifications of routing: static routing and dynamic routing. Static routingis whenthe routing scheme is set manually in the router, rather than dynamically. Static routinginvolves the creation of a routing table typically established by a network administrator. Dynamic routing is when an interior or exterior routing protocol is learning the routingstrategy. The routing is mainly determined by the network's condition, meaning the routingtable is influenced by how active the destination is.

Inefficient management of Internet of Things (IoT) network traffic poses the risk of load imbalance and unauthorized access, resulting in a notable decline in network performance. This challenge can be effectively addressed by improving the network performance through the edge and fog computing implementation. The primary objective is to offload certain computing tasks to the network edge and fog layers, thereby facilitating effective network maintenance and well distribution of the overall network load. Additionally, leveraging cloud services can further refine this process, fostering green networking by optimizing resource utilization through efficient layered resource management. Despite existing literature exploring load balancing and access control in IoT, a comprehensive event-based resource management solution that addresses mobility issues in IoT-Edge-Fog-Cloud networks is notably scarce. In response, this work proposes the development of an Event-Driven and Mobility-Aware Resource Management (EDMA-RM) framework tailored for green IoT-Edge-Fog-Cloud networks. The framework is evaluated through multiple test cases, including NLBRM, LBRM, RBAC-LBRM, and EDMA-RM, considering performance indicators such as CPU Usage, Memory Usage, Delay, and Jitter. The assessment reveals notable improvement rates for EDMA-RM in comparison to alternative techniques, with average enhancement percentages of 13.84% for CPU, 12.40% for Memory, 10.18% for Delay, and 21.23% for Jitter. These outcomes highlight the efficacy of the proposed EDMA-RM approach.

Load balancing techniques are vital for the efficient allocation of workloads across nodes in cloud infrastructures, playing an indispensable role in optimizing overall performance. By ensuring that load factors are managed effectively, these techniques significantly enhance system efficiency. This is particularly relevant in data centers, which serve as fundamental elements within cloud computing frameworks. Effective utilization of data centers can lead to marked improvements in system performance. A key aspect of successful load balancing is the precise computation of workloads among nodes. To address this, numerous load balancing models have been developed, particularly tailored for public cloud environments. These models often employ partitioning strategies to optimize performance. This paper delves into existing partitioning concepts and load balancing models within the realm of public clouds. Furthermore, it introduces a comprehensive generalized model designed to adeptly handle a variety of network load scenarios, thereby advancing the field of load balancing in cloud computing.

There have been a number of new technologies and applications that can track various events and activities in a variety of environments. Software Defined Networking (SDN) is one of these technologies that has the ability to bring the unanticipated changes in the networking space. SDN aids in the implementation and management of new networks, lowering associated expenses. SDN's unique characteristics make it ideal for monitoring the harsh environmental conditions. Agriculture, Military, Industry, Natural disasters, Health monitoring, Crisis response, and Emergency management are just few examples of common applications. In the last few decades, some of these applications have been of critical relevance and the subject of ongoing research. SDN becomes increasingly popular owing to its perfect benefits, which include aid in various surveillance scenarios. Thus, this survey covers several areas of surveillance and assists the readers to have a better grasp of SDN-based surveillance. Furthermore, various open research problems and the related surveillance challenges have also been explored.

A variety of issues with traditional IoT design, including complicated networking, data gathering, and quick response, are addressed by the combination of Internet of Things (IoT) and Software Defined Networking (SDN). Similarly, Cloud computing has been a major technology and Cloud based IoT (Cloud-IoT) networks are augmented with SDN to improve its functionality. However, SDN assisted Cloud-IoT (SDN-Cloud-IoT) networks also expose the SDN controller to a variety of threats. Attackers can use SDN’s unique capabilities to launch severe Distributed Denial of Service (DDoS) attacks. There are a number of methods in the literature for defending against common DDoS flooding attacks in SDN-Cloud-IoT networks. However, attackers are constantly developing new attack techniques to evade traditional detection systems like Low-Rate DDoS (LR-DDoS) attack. This article introduces discusses a method that not only identifies and counters LR-DDoS attack but also traces its source in SDN-Cloud-IoT environment.

Wireless networks have been in focus since the last few decades due to their indispensable role in the future generation networks like the Internet of Things (IoT). However, the associated challenges in wireless network implementation such as distance, line-of-sight, interference, weather, power issues, etc., affect the performance adversely. Software Defined Networking (SDN) is a future generation networking technology and has been proven to alleviate the performance challenges in the existing wireless IoT networks. It helps to evolve the wireless IoT domain in the form of Software Defined Wireless Network based IoT (SDWN-IoT). Traffic Engineering (TE) has been part of traditional network designs since long back, to improve the performance of the communication networks. However, its more optimized forms and their usefulness in SDWN-IoT networks have been under active investigation. This work explores the existing literature related to the major types of SDWN-IoT networks namely, Software Defined Wireless Sensor Network based IoT (SDWSN-IoT) and Software Defined Wireless Mesh Network based IoT (SDWMN-IoT). Additionally, the article also draws some useful inferences, and compares respective contributions and shortcomings. Finally, various research opportunities and challenges have been discussed with respect to the SDWSN-IoT and SDWMN-IoT networks.

Cloud computing is changing the traditional ways of computing using its different forms and architectural types such as Edge and Fog computing. These computing frameworks are the extensions of the basic cloud computing model and promise to offer improved network performance. The trio of these technologies forms a new domain called Edge–Fog–Cloud environment and is consistently being explored by researchers across the world. Numerous industrial applications use cloud resources and its services to avail the related benefits. Most of these industrial applications use a large number of power-sensitive devices that generate a huge volume of data. This data is termed as Industrial IoT (IIoT) data, and includes multi-dimensional information in its pool. This voluminous data pool need to be analyzed carefully to offer deep rooted insights that may help rejuvenate the system and boost its performance. This paper provides detailed coverage of the Edge–Fog–Cloud-based architectural frameworks, compares their pros and cons, and explores the scientific side of the multi-dimensional IIoT data. In addition to this, the current state-of-the-art and respective implementation challenges are also highlighted.

Software Defined Networking (SDN) expands the networking capabilities using abstraction, open-source protocols, energy efficiency, and programmable features for controlling the forwarding devices at the network edges and intensifying the network performance. Despite all the unprecedented features, SDN still might get exploited by an attacker to launch Distributed Denial of Service (DDoS) attacks at SDN planes i.e. Application, Control, and Data planes. Substantially, the DDoS attacks have been implemented by sending volumetric malicious traffic to exhaust the targeted resources. Such attacks can be easily observed and detected due to their high packet rates. Thus, now attackers are fascinated by the Low-Rate DDoS (LR-DDoS) attacks. In recent years, many efforts have been devoted to defending against the DDoS attacks in SDN. As the attackers benefit from the programmable nature of SDN, an in-detail review of various DDoS attacks and their corresponding defense approaches are essential. Initially, this paper presents a conceptual architecture of SDN and discusses the vulnerable locations in each plane that are exploited by the attacker for launching the DDoS attacks. Secondly, the work offers a detailed classification of DDoS attacks (HR-DDoS and LR-DDoS) concerning the SDN planes and the corresponding defense solutions. The convergence point of this research work is to discover the related security issues and stimulate the network researchers to counter these issues by employing the respective SDN DDoS defense solutions efficiently. Finally, the work gets concluded with a focus on the respective future challenges.

Smart transportation systems have been the focus of research due to the development of smart cities. However, existing vehicular networks are not sufficient enough to fulfill the vision of futuristic smart cities due to limited flexibility, scalability, poor connection, and insufficient intelligence. These technological hurdles make the role of Software-Defined Networking (SDN) very important to improve the overall performance of the existing vehicular networks considering the unique properties of SDN such as Decoupling of network planes and Real-time network programming. This leads to the development of Software-Defined Vehicular Networks (SDVNs). SDVNs help to realize the development of smart transportation systems which further helps to optimize the vision of truly smart cities. However, the security remains a consistent concern due to the increased mobility, larger attack surface, and improvised future attack vector. This work includes the different design components, and offers a detailed survey to understand different security issues including the architectural and functional ones. Additionally, multiple security solutions are discussed including Service-based, Infrastructure-based, and Application-based solutions. Furthermore, the work also covers the possible challenges in the development of SDVNs based on Improved Architectural Development, Holistic Integration, Effective Orchestration, Environmental Volatility Handling, Global Network Management, Efficient Components/Technologies Integration, Diverse Security Offerings, and Design Issues’ Maintenance. Lastly, the work highlights the resultant opportunities based on Application, Open Research, Network Management, Device Configuration, Traffic Management, QoS, and Efficient Routing.

Improving the network lifetime is a major concern in Wireless Sensor Networks (WSNs) due to the limited network resources. As the sensor nodes are usually deployed in a random fashion across the network area, network-wide energy optimization becomes a challenge. An energy-optimized WSN offers improved fault tolerance, and this can be further enhanced with the help of Software Defined Networking (SDN). Hence, a Software Defined WSN (SDWSN) based energy efficient approach is proposed in this paper to improve the performance of the network. The proposed approach discusses an Energy Optimized Multi-Constrained Sustainable Routing (EOMCSR) model. This model formulates a Mixed Integer Linear Programming (MILP) problem to optimize the network resource based energy consumption in SDWSN. The simulation results are compared with the existing SDWSN and traditional WSN approaches with respect to the performance metrics for different numbers of rounds. The experimental results verify that EOMCSR achieves an efficiency of around 8% and 48% for average energy per node in comparison to the SDWSN approach (MES) and traditional approach (E-TORA) respectively, after 100 rounds for 200 nodes. Similarly, an efficiency of around 36% and 60% is achieved for the number of dead nodes. In addition to this, the proposed approach is also tested under different network scenarios w.r.t. multiple network performance metrics, and substantial improvements have been obtained w.r.t. each performance metric.

The inefficient handling of Internet of Things (IoT) network traffic may result in load-imbalance and unauthorized access, causing network performance degradation. The performance can be improved by off-loading some of the tasks to the network edge and fog layers. The existing works have discussed the issues of load balancing and access control in IoT using edge and fog environments; however, the design of an efficient resource management framework for an IoT-edge-fog environment is still under development. This work presents a role-based access control (RBAC)-based load balancing-assisted efficient resource management framework for IoT-edge-fog network named RBAC-LBRM. The overall average improvements of 29.85, 16.82, 13.63, and 16.55% have been achieved by RBAC-LBRM over other approaches w.r.t. CPU usage, memory usage, delay, and jitter metrics, respectively.

Considering the exceptional growth of Cyber Physical Systems (CPSs), multiple and potentially grave security challenges have emerged in this field. Different vulnerabilities and attacks are present in front of new generation CPSs, such as Industrial CPS (I-CPS). The underlying non-uniform standards, device heterogeneity, network complexity, etc., make it difficult to offer a systematized coverage on CPS security in an industrial environment. This work considers the security perspective of I-CPSs, and offers a decade-wide survey including different vulnerabilities, attacks, CPS components, and various other aspects. The comparative year-wise analysis of the existing works w.r.t objective, approach referred, testbed used and derived inference, is also presented over a decade. Additionally, the work details different security issues and research challenges present in I-CPS. This work attempts to offer a concise and precise literature study focused on the state-of-the-art I-CPS security. This work also encourages the young researchers to explore the wide possibilities present in this emerging field.