Orthogonal multiple access assigns disjoint time-frequency resources and limits reuse in dense 6G settings. Non orthogonal multiple access mitigates this by superposing users and separating streams with successive interference cancellation, but performance is sensitive to SIC errors and channel uncertainty. Many RIS-NOMA studies assume perfect channels and ideal SIC, ignore feedback delay and noise, and restrict RIS to diagonal control, while the wideband joint design of base-station precoding, RIS configuration, and inter/intra-cluster power is nonconvex. In this research we address these gaps with a wideband OFDM downlink that employs beyond diagonal RIS with two-bit eigen-phase quantization, imperfect feedback (delay, noise, random phase flips), MMSE precoding from estimated cluster representatives, and an explicit per-subcarrier SIC feasibility rule. We develop OFE-REDQ, a reinforcement-learning agent that combines an optimized feature encoder with a randomized ensemble of critics and a deterministic actor to jointly control RIS settings, inter-cluster power shares, and intra-cluster splits. Stability is ensured by Polyak target updates, subset-minimum bootstrapping, and target-value clipping. In 3GPP TR 38.901 CDL-D UMi simulations, OFE-REDQ achieves higher sum rate, faster and more stable convergence, and higher SIC success than DDPG and vanilla REDQ, indicating suitability for practical RIS-aided NOMA under realistic hardware and feedback constraints.

LoRaWAN has become a leading Low Power Wide Area Network (LPWAN) technology for Industrial Internet of Things (IIoT) applications, offering long range communication with low energy consumption. A fundamental challenge lies in selecting the appropriate Spreading Factor (SF) for each device, since this directly influences coverage, packet success ratio (PSR), and airtime. The default Adaptive Data Rate (ADR) mechanism is static and fails to adapt under dynamic network conditions such as mobility. This paper proposes a lightweight linear upper confidence bandit (LinUCB–SF) based reinforcement learning approach for adaptive SF selection. Each end device uses locally observable features to autonomously select its SF, balancing exploration and exploitation. The method is implemented and validated in ns-3 simulations across a range of deployment densities. Results show that our proposed LinUCB-SF algorithm reduces energy consumption by 19.3% in mobile scenarios and 37.8% in static scenarios, while improving PSR by 9.1% and 9.0%, respectively, compared to the EXP3 baseline.

Task failures in decentralized Internet of Things (IoT)-edge computing environments not only lead to inefficiencies, increased latency, and resource wastage but can also introduce system instability and cause application malfunctions. These failures may arise due to network disruptions, resource constraints, or inefficient task scheduling, ultimately affecting the overall reliability and performance of IoT-edge systems. This study presents a novel Long Short-Term Memory (LSTM)-based Federated Learning (FL) framework for proactive task failure prediction, ensuring adaptive scheduling and efficient resource utilization. Unlike existing conventional methods, our approach personalizes failure prediction per device, addressing heterogeneous execution characteristics while preserving data privacy. By integrating LSTM with FL, we improve the failure detection accuracy and reduce unnecessary task executions. We first trained all models using Federated Learning (FL) and then conducted a comparative analysis of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and LSTM. Our findings show that LSTM achieves the highest accuracy and F1 score, while CNN excels in recall and energy efficiency. These insights validate the effectiveness of our FL-based failure prediction framework and highlight the advantages of model personalization for dynamic decentralized IoT-edge environments.

The exponential growth of encrypted network traffic in IoT ecosystems has created a critical challenge: maintaining privacy while enabling effective malware detection. This paper presents a machine learning (ML) and deep learning (DL) framework for detecting sophisticated malware (e.g., ransomware, trojans, and spyware) in encrypted Internet of Things (IoT) traffic, combining feature engineering with model fusion techniques. We evaluate Random Forest, LSTM, and RNN models on the CIC MalMem 2022 dataset, achieving an 87.1% accuracy with Random Forest - significantly outperforming sequential models (74.6%). Our proposed methodology includes: (1) a novel feature selection pipeline using mutual information for encrypted IoT traffic analysis, and (2) comprehensive benchmarking of traditional ML versus DL approaches. The results demonstrate this framework can potentially be deployed in smart cities and healthcare IoT systems where encrypted traffic analysis must balance detection accuracy with computational efficiency.

This paper presents a detailed exploration of the convergence between blockchain technology and the Internet of Things (IoT) in healthcare, termed Blockchain-enabled IoT (BCIoT). Through a systematic bibliometric analysis, the study investigates the evolution of blockchain and its transformative potential in the healthcare domain. It emphasizes the integration of blockchain and IoT as a cutting-edge approach to safeguarding medical data storage and exchange. The research highlights the increasing significance of blockchain-IoT studies and identifies leading journals, authors, and institutions driving innovation in this field. Using network analysis, the study visualizes keyword co-occurrence, revealing key research clusters such as artificial intelligence, cybersecurity, data protection, and healthcare advancements enabled by blockchain-IoT synergy. Emerging trends indicate a growing emphasis on security enhancements, the integration of cloud and edge computing for healthcare delivery, and the application of machine learning in diagnostics and predictive analytics. The findings suggest continued growth in blockchain-IoT research, paving the way for transformative advancements in global healthcare technologies.

Vehicular Edge Computing (VEC) is a key enabler of real-time intelligence in next-generation transportation systems. However, conventional Federated Learning (FL) in VEC typically depends on static edge-server aggregation, resulting in high communication overhead, increased latency, and poor responsiveness under dynamic mobility. To overcome these challenges, we propose Proximity-Aware Federated Learning (PA-FL), a decentralized framework that integrates vehicle-to-vehicle (V2V) collaboration and edge-assisted synchronization to enhance learning efficiency, scalability, and robustness. PA-FL introduces three core innovations: (i) Collaborative Local Aggregation, where vehicles perform proximity-based model fusion before forwarding updates to the edge, reducing uplink traffic and accelerating convergence; (ii) Adaptive Neighbor Selection, which dynamically filters peers based on spatiotemporal proximity and link stability to ensure context-relevant learning; and (iii) Context-Aware Synchronization, which adjusts aggregation frequency based on vehicular density and mobility to improve energy efficiency and learning consistency. Extensive experiments demonstrate that PA-FL achieves an average accuracy of 87.08% ± 0.49, surpassing state-of-the-art FL baselines by over 13% in accuracy and 11% in F1 score. It reduces task failure rates across all proximity ranges and lowers per-round energy consumption to 0.038 J, achieving a 6× improvement in communication efficiency. Delay per communication round is also reduced to 0.85 seconds, supporting real-time responsiveness. These results validate PA-FL as a resilient and scalable framework for symbiotic FL where vehicles collaboratively learn from local context while contributing to global intelligence in AI-integrated, 6G-enabled vehicular edge environments.

Intelligent resource allocation maintains a better quality of service among devices in next-generation heterogeneous network infrastructures (NG-HetNetIs). NG-HetNetIs include industry 5.0 enabled infrastructures like Internet of Things (IoT), cognitive radio (CR) enabled B5G and 6G networks, unmanned aerial vehicles (UAVs), wireless sensor networks (WSNs) and autonomous vehicles (AVs). Digital twin (DT) joins hand with cognitive radio and resource aggregation technologies to provide the integrated framework for intelligent resource allocation in NG-HetNetIs. In NG-HetNetIs, the obtained statistics of measured radio activity as prior information play an instrumental role in enabling optimized resource allocation using context awareness. Unfortunately, the already available static approaches are inefficient to replicate (DT) the radio activity in a heterogeneous radio environment. To address the issue, static implementation framework is extended as dynamic radio activity characterization framework (DRAC) to have context awareness in NG-HetNetIs. The proposed DRAC replicates (DT) the wide sense stationarity of time and carrier aggregated radio activity due to its exploitation of more localized temporal and spectral information in NG-HetNets. The obtained localized statistics using DRAC can be exploited as appropriate prior knowledge and test statistics during the spectrum sensing phase of NG-HetNetIs for intelligent resource allocation instead of a single statistic obtained by the static approach.

Smart grid is a two-way communication technology power system that sends information between the control server and consumer. It consists of different IoTs connected to a smart meter, creating a network known as the HAN home area network, and collections of these smart meters form a NAN neighbor area network. This data has been transferred to a WAN-wide area network, where the control server will share and analyze the information. The information shared in all layers has been secured in order to maintain this infrastructure. Traditional systems like firewalls’ general cryptographic techniques can detect anomalies for known attacks, but they sometimes fail to provide efficient security for unknown or real-time attacks. There should be a complete framework to detect real-time intruders and attacks. Here, NIDS using machine learning approach has been discussed in this survey report. Most ML techniques are able to detect real-time attacks with less time overhead and higher accuracy. On the basis of accuracy, detection rate, and F1 score, ten different types of datasets were evaluated and analyzed.

Wireless technology and sensing devices are playing an important role in healthcare, known as Body Sensor Networks (BSNs). Existing wearable technologies process vast amounts of data with critical quality of service (QoS) requirements in terms of delay, reliability, and throughput. This study provides a traffic prioritizing strategy that ensures synchronization, optimal traffic control, and resource optimization. It includes a method for reducing delay and enhancing throughput, and the energy efficiency of BSNs. In addition, we investigated that implementation of access periods improves the channel accessing strategy for high priority nodes with increased starvation for high data rates in low priority nodes. M/G/1/K queue with finite buffer is implemented to overcome poor resource utilization. Simulation results showed that implementing a finite buffer had enhanced resource utilization in terms of higher throughput and bandwidth efficiency.

The growing world population is facing challenges such as increased chronic diseases and medical expenses. Integrate the latest modern technology into healthcare system can diminish these issues. Internet of medical things (IoMT) is the vision to provide the better healthcare system. The IoMT comprises of different sensor nodes connected together. The IoMT system incorporated with medical devices (sensors) for given the healthcare facilities to the patient and physician can have capability to monitor the patients very efficiently. The main challenge for IoMT is the energy consumption, battery charge consumption and limited battery lifetime in sensor based medical devices. During charging the charges that are stored in battery and these charges are not fully utilized due to non-linearity of discharging process. The short time period needed to restore these unused charges is referred as recovery effect. An algorithm exploiting recovery effect to extend the battery lifetime that leads to low consumption of energy. This paper provides the proposed adaptive Energy efficient (EEA) algorithm that adopts this effect for enhancing energy efficiency, battery lifetime and throughput. The results have been simulated on MATLAB by considering the Li-ion battery. The proposed adaptive Energy efficient (EEA) algorithm is also compared with other state of the art existing method named, BRLE. The Proposed algorithm increased the lifetime of battery, energy consumption and provides the improved performance as compared to BRLE algorithm. It consumes low energy and supports continuous connectivity of devices without any loss/ interruptions.

Body Sensor Networks (BSNs) is the emerging driver to revolutionize the entire landscape of the medical field. However, sensor-based handheld devices suffer from high power drain and limited battery life, due to their resource-constrained nature. Hybridization of different energy control methods and protocol layers is a best approach to enhance the performance perimeters for smart and connected healthcare. Thus, this paper mainly contributes in two ways. First, adaptive duty-cycle optimization algorithm (ADO), is proposed which optimizes the active time by considering the specific power level which leads to more energy saving instead of increasing the sleep period unlike the traditional methods. Second, joint Green and sustainable healthcare framework is proposed. Extensive theoretical and experimental analysis is performed by adopting real-time data sets with Monte Carlo simulation in MATLAB, and it is revealed that proposed algorithm enhance reliability and energy saving by 24.43%, 36.54%, respectively. Thus it can be said that proposed algorithm have more potential for energy constrained sensor devices in smart and connected healthcare platform.

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. Quality-of-Service (QoS) optimization during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes is the main purpose of this research. This article contributes in four distinct ways. First, to propose a novel lazy video transmission algorithm (LVTA). Second, a novel video transmission rate control algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators, i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission), and standard deviation, is investigated while comparing LVTA, VTRCA, and baseline approaches. The experimental results demonstrate that the reduction in encoding (32% and 35.4%) and transmission (37% and 39%) energy drains, PMR (5 and 4), and standard deviation (3 and 4 dB) for VTRCA and LVTA, respectively, is greater than that obtained by baseline during video streaming through WMMD.

Energy-efficient communication has become the center of attention from various interdisciplinary fields, such as industrial automation, healthcare, and transportation, among others. Besides, proliferation in the artificial intelligence (AI)-based sixth-generation (6G) technology for achieving the smart automation system has caught the attention of both academia and industry. Most of the intelligent automation systems are formed by IoT-based user terminal (UT) devices for multimedia (i.e., video, audio, image, and text) content delivery with high clarity and efficiency. Customer satisfaction/perception, i.e., Quality of Experience (QoE), is an essential factor to be analyzed because the Quality of Service (QoS) is not a suitable candidate to portray the feelings and expectations of users during multimedia transmission. Therefore, the energy-efficient, entropy-aware communication, and QoE analysis through IoT devices are the dire need. This article focuses on how the energy-efficient communication and user's QoE level can be captured through the UT device during multimedia transmission. Thus first, QoS-based joint energy and entropy optimization (QJEEO) algorithm is proposed. Second, the 6G-driven multimedia data structure model and framework are developed for modeling and evaluation of QoE with acquisition time. Third, the relationship between subjective test score (i.e., surveyed data) and objective performance metrics with mobility/speed of IoT-based devices for multimedia service is established. Fourth, the correlation model is proposed for integrating QoS parameters with estimated QoE perceptions. The experimental results indicate that QoE is modeled and evaluated with acquisition time and correlated with QoS parameter, i.e., packet loss ratio (PLR), and average transfer delay during energy-efficient multimedia transmission in 6G-based networks to improve the satisfaction level of customers.

Machine learning (ML) techniques in association to emerging sixth generation (6G) technologies, i.e., massive Internet of Things (IoT), big data analytics have caught too much attention from academia to the business world since last few years due to their high and fast computing capabilities. The role of ML-based 6G techniques is to reshape the imaginary idea into physical world for resolving the challenging issues of energy, quality of service (QoS), and quality of experience (QoE). Besides, ML techniques with better association to 6G reshapes the industrial network in box (NIB) platform. In the mean-time rapidly increasing market of the IoT devices to deliver multimedia content has caught the attention of various fields such as, industrial, and healthcare. The challenging issue that end-users are facing is the unsatisfactory and annoyed performance of portable devices while surfing the video, and image to/from desired entity, i.e., low QoE. To resolve these issues this research first, proposes a novel ML-driven mobility management method for the efficient communication in industrial NIB applications. Second, a novel architecture of 6G-based intelligent QoE and QoS optimization in industrial NIB is proposed. Third, a 6G-based NIB framework is proposed in association to the long-term evolution. Forth, use-case for 6G-empowered industrial NIB is recommended for an energy efficient communication. Experimental results are extracted with high energy efficiency, better QoE, and QoS in 6G-based industrial NIB.

Fifth generation (5G) technologies have become the center of attention in managing and monitoring high-speed transportation system effectively with the intelligent and self-adaptive sensing capabilities. Besides, the boom in portable devices has witnessed a huge breakthrough in the data driven vehicular platform. However, sensor-based Internet of Things (IoT) devices are playing the major role as edge nodes in the intelligent transportation system (ITS). Thus, due to high mobility/speed of vehicles and resource-constrained nature of edge nodes more data packets will be lost with high power drain and shorter battery life. Thus, this research significantly contributes in three ways. First, 5G-based self-adaptive green (i.e., energy efficient) algorithm is proposed. Second, a novel 5G-driven reliable algorithm is proposed. Proposed joint energy efficient and reliable approach contains four layers, i.e., application, physical, networks, and medium access control. Third, a novel joint energy efficient and reliable framework is proposed for ITS. Moreover, the energy and reliability in terms of received signal strength (RSSI) and hence packet loss ratio (PLR) optimization is performed under the constraint that all transmitted packets must utilize minimum transmission power with high reliability under particular active time slot. Experimental results reveal that the proposed approach (with Cross Layer) significantly obtains the green (55%) and reliable (41%) ITS platform unlike the Baseline (without Cross Layer) for aging society.

Due to the high mobility, dynamic nature, and legacy vehicular networks, the seamless connectivity and reliability become a new challenge in software-defined internet of vehicles based intelligent transportation systems (ITS). Thus, effieicnt optimization of the link with proper monitoring of the high speed of vehicles in ITS is very vital to promote the error-free and trustable platform. Key issues related to reliability, connectivity and stability optimization for vehicular networks are addressed. Thus, this study proposes a novel reliable connectivity framework by developing a stable, and scalable link optimization (SSLO) algorithm, state-of-the-art system model. In addition, a Use-case of smart city with stable and reliable connectivity is proposed by examining the importance of vehicular networks. The numerical experimental results are extracted from software defined-Internet of Vehicle (SD-IoV) platform which shows high stability and reliability of the proposed SSLO under different test scenarios, such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to anything (V2X). The proposed SSLO and Baseline algorithms are compared in terms of performance metrics e.g. packet loss ratio, transmission power (i.e., stability), average throughput, and average delay transfer. Finally, the validated results reveal that SSLO algorithm optimizes connectivity (95%), energy efficiency (67%), throughput (4Kbps) and delay (3 sec).

The pervasive and smart healthcare is important for elderly patients which has revolutionized the medical world and caught the attention from industry and academia with the help of portable sensor-enabled devices. Tiny size and resource-constrained nature restricts them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the key challenges to be tackled carefully for ubiquitous healthcare. Energy efficiency, reliability and longer battery cycle are the vital ingredients for wearable devices to empower cost-effective and pervasive medical environment. Thus, this research work has three key contributions. First, a novel transmission power control driven energy efficient algorithm (EEA) is proposed to enhance energy, battery lifetime and reliability while monitoring the health status of elderly patients. Proposed EEA and conventional constant transmission power control (TPC) are evaluated by adopting real-time datasets of static (i.e., wheelchair sitting) and dynamic (i.e., wheelchair moving) body postures of elderly patients. Second, smart healthcare framework is proposed. Third, performance metrics such as, energy drain, battery lifetime and reliability are introduced and calculated by considering average and threshold RSSI and TPC values. Finally, it is observed through experimental analysis that the proposed EEA enhances energy efficiency with acceptable PLR than the constant TPC during data transmission.

Fourth industrial revolution ally for elderly patients. As QoS is not the appropriate entity to express the feelings and expectations of the end-users i.e., elderly patients. Therefore, the quality of experience in medical media (QoEMM) services is quite important. This research optimizes the medical media service such as, electrocardiogram (ECG) for the elderly patients by adopting wearable devices with large screen. Due to small size, and resource-constrained nature of those handheld devices it is hard to satisfy the end user's perception while monitoring the elderly emergency patients. Besides, how the elderly patient's QoE during medical media ECG data transmission can be captured, for this purpose first, framework of QoEMM is developed by adopting acquisition time. Second, the relationship between subjective test score (i.e. surveyed data) and objective performance metrics (i.e., energy consumption and entropy) with acquisition time and actual time of ECG service is established. It is revealed through extensive real-time subjective data sets in experimental setup that QoEMM is optimized through portable devices, and correlated with QoS parameters during medical media ECG service to improve the satisfaction level of end-users.

Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (~0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.

An important constraint in wireless body area network (WBAN) is to maximise the energy‐efficiency of wearable devices due to their limited size and light weight. Two experimental scenarios; ‘right wrist to right hip’ and ‘chest to right hip’ with body posture of walking are considered. It is analyzed through extensive real‐time data sets that due to large temporal variations in the wireless channel, a constant transmission power and a typical conventional transmission power control (TPC) methods are not suitable choices for WBAN. To overcome these problems a novel energy‐efficient adaptive power control (APC) algorithm is proposed that adaptively adjusts transmission power (TP) level based on the feedback from base station. The main advantages of the proposed algorithm are saving more energy with acceptable packet loss ratio (PLR) and lower complexity in implementation of desired tradeoff between energy savings and link reliability. We adapt, optimise and theoretically analyse the required parameters to enhance the system performance. The proposed algorithm sequentially achieves significant higher energy savings of 40.9%, which is demonstrated by Monte Carlo simulations in MATLAB. However, the only limitation of proposed algorithm is a slightly higher PLR in comparison to conventional TPC such as Gao's and Xiao's methods.