The fast growth of 6G-enabled Internet of Things (IoT) networks has transformed communication and made it possible for smart cities, driverless cars, healthcare, and industrial automation to all have seamless connectivity. However, the growing complexity and diversity of 6G-IoT infrastructures present serious cybersecurity issues, leaving these networks open to a range of attacks like malware dissemination, spoofing, and Distributed Denial-of-Service (DDoS). Because traditional intrusion detection systems (IDS) cannot adjust to changing attack patterns, they are unable to effectively combat these threats. Using Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost) to improve detection accuracy and robustness, this study proposed a novel Stacked-Hybrid Model for attack detection in 6G-IoT networks. The proposed model maximizes classification performance while reducing computational cost by utilizing feature selection and ensemble learning. Training and evaluation are conducted on the RT-IoT dataset, proving the effectiveness of the method. The experimental results demonstrate that the proposed Stacked-Hybrid Model outperforms individual machine learning (ML) models, achieving 99.90% detection accuracy. The other performance metrics of the proposed model have also been evaluated including precision, sensitivity, specificity and F1-score rates at 99.56%, 99.56%, 99.93% and 99.56% respectively. Significant advancements in identifying intricate and dynamic cyber threats in 6G-IoT contexts are also revealed by a comparison with other models.

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data. First, the traceability and tamper-proof of blockchain are used to realize the secure transaction of IoMT data, transforming the secure transaction of IoMT data into a two-stage Stackelberg game. The dual-chain architecture is used to ensure the security and privacy protection of the transaction. The main-chain manages regular IoMT data transactions, while the side-chain deals with data trading activities aimed at resale. Simultaneously, the perceptual hash technology is used to realize data rights confirmation, which maximally protects the rights and interests of each participant in the transaction. Subsequently, medical time-series data is modeled using bidirectional simple recurrent units to detect anomalies and cyberthreats accurately while overcoming vanishing gradients. Lastly, an adversarial sample generation method based on local interpretable model-agnostic explanations is provided to evaluate, secure, and improve the anomaly detection model, as well as to make it more explainable and resilient to possible adversarial attacks. Simulation results are provided to illustrate the high performance of the integrated secure data management framework leveraging blockchain and XAI, compared with the benchmarks.

The dynamic optimization of large-scale transportation networks presents significant challenges due to their complexity, stochasticity, and the need for real-time decision-making. In conventional methods, there is often a failure to employ fully the resources of big data present in cities, thus not being able to respond appropriately to fluctuations in traffic conditions. This paper introduces a novel enhanced big data-driven reinforcement learning (EBD-RL) algorithm for dynamic optimization of transportation networks, addressing these challenges by leveraging advanced machine learning techniques and heterogeneous data sources. We propose a hierarchical control framework that decomposes the global optimization problem into manageable sub-problems while maintaining network-wide coordination. The EBD-RL algorithm incorporates prioritized experience replay and adaptive exploration strategies to improve learning efficiency and stability in high-dimensional state spaces. Experiments on a realistic urban network show that our method is superior to six state-of-the-art methods. Results show that EBD-RL reduces total travel time by up to 30% compared to the best baseline under various traffic demands and connected autonomous vehicle penetration rates. Furthermore, the algorithm exhibits enhanced resilience to traffic incidents, achieving up to 33% faster network recovery time in severe disruption scenarios. These findings highlight the potential of big data-driven reinforcement learning approaches to significantly improve the efficiency, adaptability, and resilience of modern urban transportation systems.

Abstract With the emergence of fog computing, new paradigms for data processing and management for IoT devices have been established in the quickly changing world of teaching/learning. This study addresses the complex issues brought about by the infiltration of diverse data sources by investigating novel approaches to strengthen data security and enhance access control mechanisms in fog computing environments. The commonly used cryptographic technique known as CP‐ABE is renowned for providing accurate access control. Unfortunately, current multi‐authority CP‐ABE methods have difficulties when implemented on low‐resource IoT devices. These techniques are not appropriate for resource‐constrained IoT devices since the sizes of the secret key and ciphertext grow in proportion to the number of attributes. In this paper, a novel multi‐authority CP‐ABE approach, called MA‐based CP‐ABE, efficiently tackles these issues by optimizing the length of secret keys and ciphertext. Users' secret keys are always the same size, no matter how many attributes they own. Moreover, MA‐based CP‐ABE ensures that the size of the ciphertext scales linearly with the number of authorities rather than characteristics, which makes it a sensible option for devices with restricted resources. A Generative AI approach has also been integrated along with CP‐ABE to make sure that the IoT data is secure and privacy is maintained. As per the security and experimental analysis, the proposed approach is considered secure and suitable for IoT‐based applications.

The integration of the Internet of Things (IoT) in healthcare has revolutionized supply chain operations by enabling real-time monitoring, data-driven decision-making, and automation. This study proposes a secure and resilient IoT-enabled healthcare supply chain framework that enhances agility, integrity, and operational efficiency. The framework uses edge and cloud computing for real-time data aggregation, AI-driven predictive analytics for demand forecasting and anomaly detection, and blockchain technology for ensuring data security and traceability. Additionally, it employs renewable energy sources and low-power IoT protocols for sustainability in resource-constrained environments. The proposed framework uses renewable energy sources and low-power IoT protocols to guarantee viability in environments with limited resources. The results indicate substantial gains, including an 86.67% reduction in device authentication failures, a 91.67% decrease in data manipulation instances, an 88% decrease in illegal access attempts, and a 95% upgrade in data encryption security. Enhanced system resilience and availability metrics comprise a 7.31% increase in uptime and a 75% reduction in redundancy failures. The root mean square error and mean absolute error for the proposed framework are 0.28 and 0.03.

The integration of software-defined networking (SDN) and the Internet of Things (IoT) presents significant challenges in large-scale healthcare systems, particularly in terms of optimizing resource allocation, managing energy consumption (EC), and ensuring real-time data processing. This research introduces an AI-driven resource management framework designed to address these challenges. Using autonomous aerial vehicles (AAVs) for aerial computing, the framework optimizes energy usage, reduces network latency, and enhances anomaly detection through machine learning models. Key contributions include dynamic allocation of bandwidth and processing resources, adaptive power management, and real-time traffic prediction, ensuring high Quality of Service (QoS) even in resource-constrained environments. The simulation results demonstrate a 10%–15% reduction in EC, 15% decrease in latency, and improved real-time data processing, making the system ideal for critical healthcare applications such as telemedicine and remote monitoring. The framework offers a scalable solution to efficiently manage the growing number of IoT devices and AAVs, while also maintaining a low-latency secure service delivery.

Smart medical rescue vehicles (SMRVs) are crucial in providing timely and effective emergency medical services in urban environments. However, the efficiency and safety of SMRVs are often hindered by dynamic and complex traffic conditions, leading to longer response times and increased risk of accidents. To address these challenges, this article proposes a novel lane-changing strategy called AIoT-LC, which leverages the Augmented Intelligence of Things (AIoT) framework to enable efficient vehicle road cooperation for smart medical rescue. First, a deep Q-network is employed to process real-time data streaming from SMRVs and support their collaborative management with roads and pedestrians. Then, a cooperative lane-changing strategy is developed to ensure the safe and efficient navigation of SMRVs through dynamic traffic environments, considering factors, such as safety distance, lane-changing trajectory planning, and multivehicle coordination. Finally, a collision resolution and avoidance strategy is proposed for SMRVs at intersections, leveraging the vehicle-infrastructure cooperative environment and the AIoT framework to minimize the risk of collisions and optimize the intersection crossing process. The experimental results demonstrate that the proposed AIoT-LC method significantly outperforms existing approaches, reducing average travel time for SMRVs by up to 25%, improving average speed by 15%, and increasing the success rate of emergency responses by 20% points compared to the best performing baseline method. Additionally, the proposed method reduces fuel consumption by 11.7% and CO2 emissions by 11.7% while improving overall traffic flow efficiency by 8.9%.

Modern vehicles involve large number of sensors, cameras and communication systems for real-time traffic management, collision avoidance and vehicle health monitoring. As the Internet of Vehicles (IoV) ecosystem evolve with more number of connected vehicles, handling of the enormous data is a challenge. This is overcome with the promising distributed edge intelligence (DEI) approach in which the computational tasks are distributed among the intelligent road side units (RSUs) at the network edge. The edge servers cooperate among themselves so as not to overload the central cloud server. This article presents a cooperative vehicular communication network which exploits the existing 5G infrastructure in roadside building as the edge/relay nodes. To overcome the communication and energy overhead, network resource management is enabled through proposed edge node selection algorithm. Further, a joint edge node and antenna selection algorithm is proposed for enhanced energy efficiency (EE) and reduced outage. The closed-form expression for the outage probability of the proposed cooperative communication scheme is derived. Our analysis shows that the proposed selection approach achieves improved outage probability and energy-efficiency. In particular, the proposed edge node selection approach improves the EE by 10.46% at total transmit power to noise power ratio of 16 dB. Moreover, the overall system performance is further enhanced by proposing a joint selection scheme. Specifically, the analysis shows that the energy-efficiency improves by 27.87% with the joint selection scheme. In the end, a use case scenario of DEI empowered IoVs in emergency healthcare support is discussed along with the future research directions.

With the accelerated progression of future-generation technologies, the network is anticipated to achieve enhanced reliability, adaptability, and energy efficiency. High-frequency transmission is prone to coverage issues due to blockages, compromising the reliability of real-time healthcare data transfer, including patient monitoring and remote diagnostics. Additionally, inefficient resource management leads to delays and limits the scalability of IoT-based healthcare systems. This work proposes a novel RIS-assisted NOMA-based resource allocation framework tailored for healthcare IoT applications to address these challenges. RIS mitigates signal blockage by improving coverage, while NOMA enhances resource utilization, ensuring seamless data transmission in dense environments. The RIS deployment is done for the indoor scenario serving only blocked users/low signal strength users, while, the rest of the users form NOMA pairs. The results are analyzed in terms of throughput, coverage, and fairness index. Through numerical analysis, the proposed approach shows a considerable improvement in throughput of approximately 58%. A trade-off is observed among the downlink scenario’s throughput, coverage, and fairness.

There is an explosive growth of intelligent devices in the IoT ecosystem over the years. Owing to the massive multiple access at the network edge, there is increased latency and transmission overhead. Multiaccess edge computing (MEC) is a key technology used to offload the wireless devices from the computational tasks. But the wireless signal propagation is subject to fading, attenuation, obstructions, and other disturbances thereby affecting the performance of edge network. Reconfigurable intelligent surface (RIS) technology improves the quality of wireless propagation links through controlled reflection. This article presents an RIS-aided framework for a heterogenous edge network to offload the computation tasks of the resource constraint user equipment to the small access points (APs). A resource control algorithm is proposed which enables selection of an RIS-AP pair for each node in the edge network. The proposed algorithm selects the RIS-AP pair using maximum channel gain criteria such that the system sum throughput is maximized. Also, enabling reflection through the multiple RISs, the shortest path is selected using the graph theory to obtain the tradeoff between latency and reflection loss. It is observed that the proposed approach improves the achieved sum throughput of the system by 21.7% and the latency is reduced by 13.8%. The network performance is evaluated for varied RIS size and number of reflecting elements under different RIS phase shift design. It is shown that RIS with 1000 reflecting elements each of size${}({\lambda }/{2})\times {}({\lambda }/{2})$with equal phase shifts achieve sum throughput gain of 25.2% over randomly chosen phase shifts. Further, the comparison of intelligent reflecting the surface-aided MEC system with the conventional MEC system and the clustered MEC system is performed.

Abstract Neural architecture search (NAS) has emerged as a promising approach for automating deep learning model design. However, its application in sports analytics faces unique challenges due to the complex interplay between biomechanical patterns, physiological adaptations, and coaching expertise. Traditional NAS methods need help to effectively capture the multifaceted nature of athletic performance, often failing to integrate qualitative coaching insights with quantitative measurements. We introduce ChampionNet, a framework incorporating NAS and large language models to enhance accuracy in predicting athletic performance and tailoring training regimens. Our approach offers three primary contributions: integrating hyperdimensional embedding to capture fine-grained biomechanical features and physiological parameters with exceptional detail, a structure-preserving graph encoding leverages to maintain crucial spatiotemporal relationships in athletic movements, and the novel comprehensiveness of the training graph that models forward performance prediction and backward physiological adaptation pathways. Our experiments on various sports demonstrate that ChampionNet outperforms other models by 2.5% in accuracy and over 61.9% in computational cost. Further insights illustrate the framework's performance with complex patterns and multi-modal data, especially for sports with advanced biomechanical needs. These findings support ChampionNet's effectiveness as an integrative athletic performance optimization solution, highlighting the need for automated architecture search tailored to sports.

In the realm of medical data processing, particularly in the diagnosis and monitoring of cardiac diseases, the analysis of electrocardiogram (ECG) signals represents a critical challenge, especially with the burgeoning volume of ECG Big Data. Traditional methods and existing research often fall short in effectively analyzing this data, limited by their inability to fully capture the complex and nonlinear patterns inherent in ECG signals. Addressing these limitations, in this article, we introduce a novel deep neuro-fuzzy model augmented with multimodal feature fusion. Our method ingeniously combines the power of neuro-fuzzy systems with the robust feature extraction capabilities of deep learning, specifically leveraging a Transformer-based architecture, to analyze both ECG signals and their corresponding spectral images. This multimodal fusion not only enriches the model's input data, providing a comprehensive understanding of cardiac signals, but also enhances the adaptability and accuracy of cardiac arrhythmia detection. We rigorously validate our approach on the MIT-BIH arrhythmia database, conducting a series of experiments, including performance evaluations and ablation studies, to highlight the significant contributions of the multimodal feature fusion and neuro-fuzzy module. The results achieve significant improvements in classification metrics: an accuracy of 98.46% and an F1 score of 99.1%. Moreover, we benchmark the Transformer's feature extraction performance against other architectures, such as ResNet. The results unequivocally demonstrate our model's superiority and illustrate the potential of integrated neuro-fuzzy and deep learning approaches in overcoming the current limitations of ECG signal analysis.

In medical diagnostics, the accurate classification and analysis of biomedical signals play a crucial role, particularly in the diagnosis of neurological disorders such as epilepsy. Electroencephalogram (EEG) signals, which represent the electrical activity of the brain, are fundamental in identifying epileptic seizures. However, challenges such as data scarcity and imbalance significantly hinder the development of robust diagnostic models. Addressing these challenges, in this paper, we explore enhancing medical signal processing and diagnosis, with a focus on epilepsy classification through EEG signals, by harnessing AI-generated content techniques. We introduce a novel framework that utilizes generative adversarial networks for the generation of synthetic EEG signals to augment existing datasets, thereby mitigating issues of data scarcity and imbalance. Furthermore, we incorporate an attention-based temporal convolutional network model to efficiently process and classify EEG signals by emphasizing salient features crucial for accurate diagnosis. Our comprehensive evaluation, including rigorous ablation studies, is conducted on the widely recognized Bonn Epilepsy Data. The results achieves an accuracy of 98.89% and F1 score of 98.91%. The findings demonstrate substantial improvements in epilepsy classification accuracy, showcasing the potential of AI-generated content in advancing the field of medical signal processing and diagnosis.

Conventional security architectures and models are considered single-network architecture solutions, which assume that devices authenticated within the network are implicitly trusted. However, such an approach is unsuitable for next-generation networks (NGNs). Zero-trust security was introduced to overcome these challenges using context-aware, dynamic, and intelligent authentication schemes. This paper proposes a novel zero-trust blockchain-enabled framework for secure next-generation healthcare communication network (HCN). The proposed framework integrates zero-trust and blockchain to provide a decentralized, secure, and intelligent solution for healthcare communication in NGNs. The system model comprises three components: HCN user identity modeling, blockchain and risk assessment-based access control, and dynamic trust gateway. The user identity modeling component utilizes attribute-based user behavior trajectory features, while the access control component leverages smart contracts-based risk assessment. The dynamic trust gateway component employs a consensus mechanism to achieve dynamic gateway switching and enhance network resilience. Simulation results demonstrate that the proposed framework achieves 31% lower calculation delays, 3% higher trust values, and 3% better attack detection accuracy compared to best baseline methods. It also exhibits a 2% improvement in access control granularity and maintains 95% network throughput under various failure scenarios.

The rapid expansion of Internet of Things (IoT) devices has introduced a new phase of inter connectivity and convenience, while also presenting notable security obstacles. This research paper investigates novel methodologies for enhancing the security of IoT networks by using machine learning and deep learning methodologies. In order to accomplish this objective, two well acknowledged datasets, namely UNSW-NB15 and BoT-IoT, are used for the purpose of constructing and assessing resilient security models. The UNSW-NB15 dataset is well recognized for its extensive compilation of network traffic data, while the BoT-IoT dataset is especially designed to facilitate study on security issues related to the IoT. These datasets provide a wide array of attack scenarios and network traffic patterns, therefore enhancing the diversity of available resources for researchers in this field. By using these datasets, our research endeavours to examine many crucial facets of security inside IoT networks. Initially, machine learning techniques are used for the purpose of conducting intrusion detection on IoT networks. This stage encompasses the categorization of network traffic into two distinct categories: normal and malicious. This process facilitates the prompt detection and reaction to potential threats in real-time. Deep learning methodologies demonstrate exceptional proficiency in capturing complicated patterns and behaviours inherent in IoT network traffic, hence enhancing the capacity to detect intricate and dynamic security risks. In order to assure the practical usability of the presented models, we take into account variables such as computing efficiency and scalability. This is particularly important since IoT devices often have limited resources available. Furthermore, we investigate approaches aimed at enhancing the interpretability of models, therefore offering valuable insights into the underlying decision-making mechanisms, ultimately fostering trust and transparency.

The evolution of Internet-of-Vehicles (IoV) from IoT has revolutionized Smart cities, enabling vehicle communication for safety and traffic information dissemination. However, fulfilling time-sensitive applications like safety alerts via Cellular Vehicle-to-Everything (C-V2X) faces resource constraints. This study presents a Non-Orthogonal Multiple Access (NOMA) based resource allocation for C-V2X in Ultra-dense networks (UDN). This paper has also discussed the role of TinyML in unmanned aerial vehicle (UAV) and it is demonstrated with use case scenario. Additionally, a generalized expression for scheduling time fraction is derived for the proposed scheme. The proposed framework optimizes power allocation, accommodating high-speed users and UAV scenarios to improve performance in obstructed regions. Numerical analysis demonstrates an approximate 85% throughput increase over conventional schemes, affirming the efficiency of the NOMA-based approach for enhanced C-V2X performance.

Nowadays, there is global consensus on the threats to forests and their crucial role in mitigating global warming and its impact on Earth’s biodiversity. Both private and public entities, alongside governments, have engaged the most advanced technologies to safeguard and monitor forests against encroachment. This article examines the application of various drone technologies in the surveillance of forest areas. The system described herein employs drones to continuously survey forests, recording any changes, particularly in instances of encroachment or fire. The data captured are transmitted to a control unit for subsequent analysis. To circumvent the risk of task failure due to technical challenges, monitoring tasks within a predefined flight duration are allocated to the available drones. Given the critical nature of timing in the success of these tasks, this study addresses the forest monitoring challenge by seeking to minimize the maximum time required to complete all monitoring tasks. This challenge was addressed through the development of a suite of enhanced algorithms aimed at optimizing task efficiency. The primary goal of the proposed methodology is to afford the monitoring system additional time, thereby enabling the handling of an increased volume of tasks and providing support to firefighting teams in responding to forest fires. The system’s adaptability to new, unforeseen forest fire scenarios through the generation of novel solutions is also discussed. Extensive testing involving 1350 different scenarios has demonstrated the effectiveness of the proposed algorithms in reducing the maximum time needed for the completion of surveillance tasks by drones. The most effective algorithm was the two-group clustering algorithm (TGC), which achieved a success rate of 97.2%, with an average gap of less than 0.001 and an average computation time of 0.016 s. Furthermore, the application of this methodology to a case study of the Daintree Rainforest in Australia showcases the potential and real-world applicability of the proposed system, highlighting its performance and adaptability.

Internet-connected robotic systems today predominantly rely on isolated sensing, computing, and communication modules, limiting cross-layer optimizations. However, emerging applications like industrial automation and augmented reality necessitate tight coupling between complementary capabilities for versatility, precision, and autonomy improvements. To this end, this paper proposes an adaptive sensing algorithm for Internet of Robotic Things (IoT) platforms with integrated sensing, computing, and communication (as-ISCC-IoRT) capabilities. The framework leverages a model-driven methodology to dynamically harness the benefits of complementary techniques for improving localization accuracy and operational efficiency. First, three classical sensing algorithms are introduced to realize multi-target ranging and speed measurement, and the algorithms are analyzed in terms of sensing accuracy, communication performance, and computational complexity, which shows that any one of the algorithms alone cannot achieve the optimization of sensing accuracy, sensing capacity, and communication rate simultaneously. Then, combining the characteristics of different sensing algorithms, an adaptive sensing algorithm is proposed, and the receiver selects the appropriate sensing algorithm based on the ratio of the measured received signal to the interference plus noise. Extensive simulations under varying signal-to-interference-plus-noise ratio levels, number of sensors, and quality of service constraints validate the effectiveness of as-ISCC-IoRT -consistently showing the fastest convergence, lowest weighted MSE, highest communications rate gain, and minimum transmit power by adaptively switching between component algorithms. The consistent performance gains of the proposed as-ISCC-IoRT scheme across key metrics like accuracy, latency, and efficiency validate the benefits of integrating sensing, computing, and communication capabilities in Internet-connected robotic systems.

The expanding number of low cost sensors and smart devices drives the internet-of-things (IoT) ecosystem of the future. These sensing devices are connected to the internet for information exchange. The location and positioning of these nodes is very important information required in vast range of location based services like smart homes , smart healthcare , environmental monitoring, personal navigation and smart transportation. This paper presents an intelligent solution for node localization in a 6G enabled IoT network. An indoor communication network scenario is proposed in which reconfigurable intelligent surfaces (RISs) are installed to locate the sensor nodes operating in that network. The performance evaluation of the proposed scheme is carried out with optimum number of reflecting elements and optimum phase shifts. It is observed that optimized RISs with 100 reflecting elements improve the estimated localization error by 7.4% over non-optimum RISs. Also, the minimum gain of 6% in localization error is offered using equal phase shifts over random phase shifts. Further, the effect of channel conditions on the average estimation error in node locations is also elaborated. In the end, the explainable artificial intelligence (XAI) empowered indoor localization is discussed as a use case scenario and the performance comparison of the algorithms is evaluated.

Presently, trillions of Internet of Things (IoT) devices are in use, with many more projected to join IoT networks in the future. These IoT devices create a massive volume of data, which cannot be transmitted over the network without proper security and privacy. Furthermore, as the amount of information and variety of interconnected devices grows, problems, including excessive response time, bandwidth constraints, and scalability, emerge in proper network design. To solve the constraints of today’s smart cities for next-generation networks, an effective, secure, and scalable distributed framework must be designed bringing computing and storage resources nearer to endpoints. In this article, combining the strengths of software-defined networks (SDNs) and blockchain technology, an innovative adaptable network infrastructure for smart cities is developed. The network is divided into different domains in which SDN will detect potential attacks and transmit the secured data to the blockchain. Our in-depth experimental analysis on performance evaluation show that the proposed framework achieves 12.75% improvement over baseline methodologies.

The Information Centric Networking (ICN) is a future internet architecture to support efficient content distribution in a vehicular environment. In-network caching in ICN provides a realistic solution for vehicular communication due to storage of content replicas inside network vehicles. However, the challenge still exists while caching content replicas in resource constraint vehicles (such as limited power and cache capacity) to minimize the communication latency. To address the above mentioned challenge, this paper proposes EPC - an ICN based Energy efficient Placement of Content chunk that fits well in a vehicular environment. The proposed resource management strategy mainly aims to reduce the content fetching delay by caching content replicas towards the network edge router. The EPC strategy decides on placement of content chunks on each vehicle by jointly considering residual power of current vehicle, local popularity of content, and caching gain. The EPC supports efficient utilization of network available resources by allowing only vehicles with their residual power greater than threshold to perform chunk caching and hence, further offers reduced content duplication in the whole network. The effectiveness of the proposed scheme is evaluated in Icarus- an ICN simulator for analyzing the performance of ICN caching and routing strategies. The EPC outperforms various state of the art caching strategies approximately by 30% when gets evaluated in terms of offered cache hit ratio, content retrieval delay, and the average number of hops utilized for fetching the requested content.

Maritime search and rescue plays an important part in ensuring the safety of life at sea. When using wireless Sensor Network (WSN) technologies in maritime, nevertheless, it endures from situations where the measurement information is inadequate. In computing and networking for maritime applications, Wireless Sensor Network (WSNs) is a rising inflexion because of its amazing features. Simultaneously, there are some challenges faced by WSNs and node localization is one of them. Node localization is an important factor because until the location of reporting node is unknown, the data sensed by that node is totally useless. The main aim of this paper is towards gaining more improvement in localization by using swarm intelligence algorithm. To achieve this aim, a range-free and distributed method by using the application of salp swarm algorithm for moving target node in network for maritime rescue is proposed. The results are compared with existing algorithm Particle Swarm Optimization (PSO) and Butterfly Optimization Algorithm (BOA). The proposed method has approximately 10% less localization error as compared to PSO and BOA. The proposed algorithm is validated in terms of localization accuracy, localized nodes, localization errors and computing time.

The Internet of Things (IoT) has recently emerged as a new family of technologies that allow a great number of things to be connected over heterogeneous networks. Conventional networks, however, face a technological barrier in efficiently handling such a large number of devices. The approach based on software-defined networks (SDNs), with its speed and flexibility, has recently been introduced into IoT area to potentially achieve scalability and adaptability, resulting in the SDN-IoT, a unique IoT design. In this work, we describe a new multiple-path routing model for the SDN-IoT. This model consists of two components: 1) a congested source path discovery technique; 2) a multi-path selection method taking into account route similarity and priority. In this paper we first describe the SDN based Quality of Service and highlight the unique features of this routing technique, then report on the performance evaluation of such system, which achieves an approximate 17% decrease in packet loss ratio over the existing approaches.

Intelligent Transportation Systems (ITS), mainly Autonomous Vehicles (AV's), are susceptible to security and safety problems that risk the users' lives. Sophisticated threats can damage the security of AV's communications and computational capabilities, slowing down their integration into our daily lives. Cyber-attacks are getting more complex, posing greater hurdles in identifying intrusions effectively. Failing to prevent the intrusions could tarnish the security services' reliability, including data confidentiality, authenticity, and reliability. IDS is an overall prediction paradigm for detecting malicious network traffic in the ITS. This article studies the role of machine or deep learning in Software Defined-Intrusion Detection System (SD-IDS) in ITS; discusses the mathematical analysis of existing deep learning models and evaluates their performances on the basis of the various metrics (i.e., accuracy, precision, recall, f-measure) to observe which model gives the best results for the existing state of art. The results show that improved Recurrent Neural Networks (RNN) is best suited for the detection of SD-IDS attacks in the data plane and control plane.

In present day era use of emerging technologies has given a rise to the healthcare issues. Combination of sensors, the industrial Internet of Things (IIoT), and big data analytics to enhance patient care can lower the healthcare costs. This will enable the patients with more secure, affordable, and rising medical services. Besides problems, such as resource-constrained IoT stuff, identity theft attacks, and malicious insiders, there is a need to address smart healthcare in big data and artificial intelligence using edge computing services. To fix these concerns, we are proposing a software-defined networking (SDN)-based security compliance structure for smart healthcare load migration systems. Toward this end, the use of SDN-IIoT technology for effective and real-time protection against security attacks is being explored by researchers and professionals. In our proposed framework, there are three domains and each domain has one virtual machine and various OpenFlow virtual switches. This scenario helps in migrating the heavily loaded domain healthcare data to the lightly loaded domain to make the domain balanced and prevent the migration from happening any type of security attacks. The RYU SDN controller is used to test the simulations and effectiveness of the performance obtained in the mininet after capturing the OpenFlow packets in Wireshark. Secure data management is achieved through the proposed framework and proposed algorithm gives 80% accurate for all the fetched healthcare data packets.

The relationship between computing and healthcare has a long history, but adoption of telemedicine is gradual due to political resistance, lack of infrastructure development frameworks, and lack of resources. One of the most rapid technological advancements will be the Internet of Medical Things (IoMT), which is predicted to bring about the greatest technological delivery ever. Edge computing in conjunction with 5G speed is the solution to achieve the requirements of quality of service metrics metrics during the analysis of clinical data. Artificial intelligence with edge computing has made significant contributions to the smart healthcare system's network for ultra-reliable communication in the areas of less delay, widespread device connectivity, and enhanced speed of data transmission. Since the edge-enabled IoMT-based system in the healthcare system offers a number of extraordinary potential, this Special Issue explores those areas of applicability. The aim of Special Issue is to cover the research difficulties associated with the implementation of edge computing-based IoMT systems in the healthcare system and suggests a framework for such a system.

For the effective green communications amongst the vehicles, the energy-efficient routing protocol for intelligent transportation system (ITS) is essential. Due to the high speed and recurring topological variations of Vehicular sensor Networks, identifying a connected route with a sufficient latency is a difficult task with many constraints and obstacles. Therefore, to overcome this, we developed the statistical approach to theoretically determine the load congestion and consumption of energy during the lifetime of the sensor network for ITS. Hence, dynamic clustering green communication routing (DCGCR) protocol is proposed for vehicular communication. To manage energy consumption and enhance the lifetime of the network deployed on the roadside units (RSU), we analyze the evolution of energy holes and apply our analytical conclusions for ITS with WSN routing. The proposed routing protocol considers various metrics: i) energy consumption of vehicular sensor nodes,ii) network stability iii) reliability and iv) amount of data exchange among vehicles. The efficiency of the proposed computational model in calculating the lifetime of the vehicular network and energy hole evolution process is demonstrated through extensive computation results. DCGCR approach is compared with the various energy-aware routing algorithms namely, Dynamic Energy Balanced Routing (DEBR), Geographic Greedy Routing (GGR), double cost function-based routing (DCFR) and found that proposed approach achieves more accuracy with 7% less failure rate.

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user's preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme.

Cloud Computing (CC) has gained popularity in industry and academia. CC is implemented by the industries on a large scale. Still, a lot of efforts in research for cloud computing are required. Improving security helps in abolishing the major problems of a domain. Therefore, research and development in security techniques are required. It is important to state the existing status of research. As publications are practically multiplying since 2008, a bibliometric analysis is a need of the hour. This study provides a comprehensive view of cloud security for a relevant time frame. Total 15,591 publications related to security of CC were investigated based on the Scopus database. This study analyses research publications on various parameters such as (i) publishing patterns (e.g. contributing authors, affiliations), (ii) analysis of common key terms, (iii) key term bunching to identify domain of interest, (iv) citation patterns, (v) publications medium, and (vi) researchers who aid in exploring research productivity in this specific domain. It analyses the literature based on the quantitative features and characteristics of cloud security based on meta‐perspectives. The proposed analytical study will serve as an important tool for significant debate on future research schemas.

Chikungunya is a mosquito instinctive disease that spreads hurriedly in various parts of the country. For the awareness and prevention measure of this disease a new paradigm in Smart Health (S-Health) required to be devised. The auspicious prospective of evolving Internet of Things (IoT) technologies for interconnected heterogeneous devices and objects has played a vital role in the next generation health care systems for eminent patient care to protect the citizens from these types of diseases. Still there is a need for real time health monitoring to analyze the patients for early preventive measures and precautions for healthy life. S-Health care IoT has substantial impending for the cognizance of analogues monitoring. It includes the interconnected apps, objects (devices and people), communication technologies, tracking system, and patients' knowledge base. This paper presents an IoT-enabled model where data collected from the sensors, objects, and people will be gathered at the cloud to take the preventive actions by healthcare professionals. Precautionary measures will be taken by collecting the information about causes of growth of mosquitoes. The suitability of the approach is validated at the base layer of the IoT and data is transmitted to the cloud with the help of edge nodes. From simulations, it is endorsed that the proposed approach is better over ME-CBCCP protocol.

The futuristic trend is toward the merging of cyber world with physical world leading to the development of Internet of Things (IoT) framework. Current research is focused on the scalar data-based IoT applications thus leaving the gap between services and benefits of IoT objects and multimedia objects. Multimedia IoT (IoMT) applications require new protocols to be developed to cope up with heterogeneity among the various communicating objects. In this paper, we have presented a cross-layer protocol for IoMT. In proposed methodology, we have considered the cross communication of physical, data link, and routing layers for multimedia applications. Response time should be less, and communication among the devices must be energy efficient in multimedia applications. IoMT has considered both the issues and the comparative simulations in MATLAB have shown that it outperforms over the traditional protocols and presents the optimized solution for IoMT.

Recently, incredible growth in communication technology has given rise to the hot topic, big data. Distributed wireless sensor networks (WSNs) are the key provider of big data and can generate a significant amount of data. Various technical challenges exist in gathering the real-time data. Energy-efficient routing algorithms can overcome these challenges. The signal transmission features have been obtained by analyzing the experiments. According to these experiments, an energy-efficient big data algorithm (big data efficient gathering, BDEG) for a WSN is proposed for real-time data collection. Clustering communication is established on the basis of a received signal strength indicator and residual energy of sensor nodes. Experimental simulations show that BDEG is stable in terms of the network lifetime and the data transmission time because of the load-balancing scheme. The effectiveness of the proposed scheme is verified through numerical results obtained in MATLAB.