We propose an energy-efficient semantic filtering framework for smart grid forecasting in edge computing environments. A Variational Autoencoder (VAE) estimates the semantic relevance of input windows via reconstruction error, and a quantile rule removes low-information windows from the training set. A lightweight Gradient Boosting Regressor (GBR) is then trained on the retained data for next-step load forecasting. On the UCI Individual Household Electric Power Consumption dataset, up to 80% of training windows can be discarded with negligible loss in accuracy, yielding up to 90% lower CO2 emissions and a 5 & times; reduction in inference time, as measured with the CodeCarbon toolkit. These results indicate that semantic filtering is a promising path toward sustainable, low-latency AI for edge-enabled smart grid systems.

The rapid proliferation of the Internet of Things (IoT) has heightened exposure to diverse security threats, particularly Distributed Denial of Service (DDoS) attacks, which underscore the limitations of traditional security mechanisms in protecting resource-constrained devices. This study introduces a novel threat detection framework that exploits transport layer congestion control features to detect security breaches in real time by monitoring key indicators such as packet loss, round-trip time (RTT), and retransmission rates. The proposed lightweight solution integrates machine learning techniques to effectively distinguish between genuine network congestion and congestion induced by malicious activity. Through extensive IoT simulations, the framework demonstrated a Detection Rate (DR) of 96% with a minimal False Positive Rate (FPR) of 1.2%. It further achieved a Packet Delivery Ratio (PDR) of 95%, an average latency of 150 ms, a throughput of 5000 bytes/s, and a Response Time (RT) of 120 ms. These results confirm the framework’s ability to provide reliable, real-time IoT security while preserving high network performance.

The rapid growth of IoT in healthcare demands reliable, secure, and energy-efficient communication solutions. We propose CLEHTO, a novel cross-layer optimization framework that dynamically adapts to network conditions by integrating real-time energy monitoring, joint mobility-security assessment, and adaptive congestion control. Unlike conventional approaches, CLEHTO introduces a unified reliability scoring system that simultaneously evaluates physical channel quality, link reliability, node mobility, and transport-layer congestion. Experimental results demonstrate CLEHTO's exceptional performance: a 92.8% Packet Delivery Ratio under 20% link failure while maintaining 4.5 Mbps throughput, a 98.6% authentication success rate using SSL/TLS (outperforming IPSec's 98.3%), and optimal energy consumption of 0.55 mAh for battery-powered devices. CLEHTO maintains 105 ms latency (vs. IPSec's 95 ms) for secure medical data flows, showing significant improvements over single-layer approaches. These results establish CLEHTO as a robust and efficient solution for IoT-based healthcare systems.

Ensuring reliable communication and energy efficiency remains challenging in Internet of Everything (IoE) environments, where devices are heterogeneous, mobile, and energy-limited. This paper presents a cross-layer optimization framework that leverages the physical, data link, network, and transport layers to enable adaptive route selection, balanced traffic management, and energy-aware data transmission. Coordinating these layers improves data delivery, reduces latency, and extends device operational lifetime. Experimental results show a 24% increase in delivery rate, 0.85 ns reduction in latency, 37% longer device activity, and up to 35% energy savings. These findings demonstrate the framework’s effectiveness for robust, high-performance, and energy-efficient communication in heterogeneous IoE systems. Overall, this work provides a holistic cross-layer design integrating routing, traffic adaptation, and energy management for reliable and sustainable IoE networking.

Over recent years, research has continued on the subject of Ambient Backscatter Communication (AmBC), owing to the low-power, passive nature of this technology. It shows great promise for the growing networks of the Internet of Things (IoT), which now include billions of devices. Despite the potential, AmBC system applications are still limited by low data rates and short range. To help overcome these challenges, Intelligent Reflective Surfaces (IRS) have been suggested as a means to improve the quality of the backscattered signal. This paper investigates the effect of integrating IRS with an AmBC system to enhance the quality of the backscattered signal. The simulation results show that the implementation of IRS significantly improves the bit error rate (BER), as well as the received signal power. These findings suggest that IRS shows promise for scalable and robust AmBC networks.

The Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT), have transformed sectors such as home automation, healthcare, and manufacturing by enhancing data management through advanced networking. However, the rapid growth of IIoT has introduced significant cybersecurity challenges, necessitating a comprehensive approach to securing data across the TCP/IP model. This paper presents a novel cybersecurity investment strategy formulated as a bi-objective optimization problem, validated through genetic and iterative algorithms. The strategy effectively balances security and cost, achieving nearly 50% efficiency in solution effectiveness. By utilizing these optimization techniques, the approach provides a practical and cost-effective solution to improve IIoT security within budget constraints, offering valuable insights for cybersecurity professionals seeking robust and economically viable solutions.

The rapid proliferation of connected vehicles has transformed modern transportation, while introducing critical security and performance challenges in dynamic vehicular networks. To address sophisticated threats such as DDoS, Sybil, and routing manipulation attacks-without compromising operational efficiency-we propose ROADS-VN, a novel Routing Optimization and Adaptive Defense System for Vehicular Networks. ROADS-VN integrates machine learning-based anomaly detection, mobility-aware route adaptation, and historical threat intelligence into a modular, context-aware mechanism enabling real-time, adaptive decision-making under dynamic vehicular conditions. The architecture of ROADS-VN is designed to be compatible with federated learning, but federated learning is not implemented in the current experiments, to avoid any misinterpretation regarding deployment. Extensive simulations demonstrate that ROADS-VN achieves a Packet Delivery Ratio (PDR) of 98.5% in low-mobility, low-traffic scenarios and 94.0% PDR under high-mobility, high-traffic conditions, while maintaining average communication latency as low as 45 ms and detection accuracy up to 96.0%. The protocol exhibits strong scalability, energy efficiency, and resilience against evolving cyber threats. By seamlessly combining adaptive routing with proactive security mechanisms, ROADS-VN provides a robust foundation for secure, reliable, and intelligent vehicular communications in next-generation transportation ecosystems.

The rapid expansion of connected devices has ushered in the Internet of Everything (IoE), enabling seamless integration among machines, people, and systems across diverse applications. However, the IoE faces significant challenges in ensuring efficient, reliable, and energy-conscious data transmission at scale. To address these issues, we present CLIC-IoE (Cross-Layer Solutions to Improve Communications under IoE), an innovative cross-layer framework designed to significantly enhance communication performance within IoE environments. By intelligently coordinating multiple communication layers, CLIC-IoE achieves remarkable results: a 39.47% reduction in data errors, a 38.33% increase in delivery rates, and a decrease of 0.8 nanoseconds in end-to-end delays. Additionally, it optimizes energy consumption, demonstrating a 51.67% improvement in energy efficiency (CEA) and a 20% boost in Active Things Rate (ATR). These advancements position CLIC-IoE as a transformative solution that enhances the scalability and reliability of IoE systems while promoting sustainable energy use. This manuscript provides a comprehensive exploration of the CLIC-IoE architecture, algorithms, and performance evaluation, emphasizing its potential impact on future IoE deployments. By addressing the critical challenges faced in IoE environments, CLIC-IoE not only enhances communication performance but also paves the way for more sustainable and efficient IoT systems.

The proliferation of industrial IoT and cyber-physical systems demands Medium Access Control (MAC) protocols that simultaneously address security threats and operational efficiency in resource-constrained environments. Existing solutions frequently fail to provide adequate protection against real-time threats like jamming and denial-of-service (DoS) attacks while maintaining performance. We present the Adaptive MAC-layer Backoff Algorithm (AMBA), a novel protocol that enhances security, efficiency, and resilience through dynamic backoff adaptation based on real-time traffic analysis and physical-layer feedback. AMBA achieves: (1) 20 Mbps peak throughput (15.5 Mbps under jamming; 17 Mbps under DoS), (2) 50% lower latency than JR-MAC, (3) 75% improvement in packet loss resilience, and (4) 20%-30% higher Security Threat Resilience Metric (STRM) scores against diverse attacks. Evaluations demonstrate AMBA's superiority over existing protocols while meeting the stringent reliability requirements of industrial IoT and vehicular networks. The solution's lightweight design and scalability make it particularly suitable for next-generation cyber-physical systems where security and performance must coexist.

The rapid expansion of connected devices has ushered in the Internet of Everything (IoE), enabling seamless integration among machines, people, and systems across diverse applications. However, the IoE faces significant challenges in ensuring efficient, reliable, and energy-conscious data transmission at scale. To address these issues, we present CLIC-IoE (Cross-Layer Solutions to Improve Communications under IoE), an innovative cross-layer framework designed to significantly enhance communication performance within IoE environments. By intelligently coordinating multiple communication layers, CLIC-IoE achieves remarkable results: a 39.47% reduction in data errors, a 38.33% increase in delivery rates, and a decrease of 0.8 nanoseconds in end-to- end delays. Additionally, it optimizes energy consumption, demonstrating a 51.67% improvement in energy efficiency (CEA) and a 20% boost in Active Things Rate (ATR). These advancements position CLIC-IoE as a transformative solution that enhances the scalability and reliability of IoE systems while promoting sustainable energy use. This manuscript provides a comprehensive exploration of the CLIC-IoE architecture, algorithms, and performance evaluation, emphasizing its potential impact on future IoE deployments. By addressing the critical challenges faced in IoE environments, CLIC-IoE not only enhances communication performance but also paves the way for more sustainable and efficient IoT systems.

The Internet of Things (IoT) continues to grow at remarkable speed, connecting different devices and facilitating communication and data exchange. Ambient Backscatter Communication (AmBC) has emerged as a low-power, low-cost alternative suitable for the connectivity of IoT. However, this technology still requires development to be adopted on a large scale. In this paper, we present a brief overview of AmBC, and highlight how it is appealing for IoT. We then go over the major challenges that AmBC faces with a concise explanation for each, as well as outline the directions future research should take to enhance the performance AmBC and push its integration with IoT forward. We also include a survey of some contributions in this domain.

The rapid expansion of the Internet of Things (IoT) has exacerbated security and management challenges, particularly in routing protocols. Existing solutions often prioritize performance but overlook dynamic security needs specific to IoT environments. To address this gap, we propose the Secure Adaptive Routing Protocol (SARP), leveraging data aggregation and machine learning-based anomaly detection. SARP dynamically assesses key network metrics—including node reliability, mobility patterns, historical attack data, and information change rates—to generate a holistic view of the network and detect attacks such as blackhole, sinkhole, and APTs. The protocol operates in two phases: a training phase to establish baseline behavior, and a real-time monitoring phase for anomaly detection. Experimental results under low-mobility, low-traffic and high-mobility, high-traffic scenarios demonstrate SARP’s effectiveness: it achieves up to 98.5

This paper introduces AMBA, an adaptive backoff algorithm that strengthens security at the Medium Access Control (MAC) layer for IoT and vehicular networks. AMBA effectively counters jamming and denial-of-service (DoS) attacks, delivering impressive results: 15 Mbps throughput, a 60% reduction in latency compared to JRMP, and a 67% improvement in packet loss resilience. The Security Threat Resilience Metric (STRM) shows a 20% boost in resilience in hostile environments. Leveraging real-time traffic analysis and physical layer feedback, AMBA detects and mitigates malicious activity while maintaining optimal network performance. Its lightweight design is perfect for resource-limited environments, offering a scalable, efficient solution for securing next-generation wireless networks.

The rapid expansion of the Internet of Things(IoT) has underscored the critical need for efficient and autonomous communication systems to sustain the massive, interconnected network of smart devices. Central to this challenge is optimizing communication protocols to ensure energy efficiency, reliability, and self-configurability across diverse IoT applications. This paper reviews the essence of leveraging artificial intelligence, specifically deep reinforcement learning, distributed AI services, swarm intelligence, metaheuristic optimization, and cross-layer approaches, for autonomous optimization and configuration in IoT and IoE (Internet of Everything) environments within the context of emerging 6 G technologies. It also implies a focus on comparing various methodologies and approaches to achieve efficient communication systems for IoT. The key criteria used in this comparison study are energy efficiency, transmission power, protocols, scalability, and Quality of service (QoS). By synthesizing these findings, our study highlights the strengths, limitations, and potential synergies between different approaches, offering insights into the future direction of IoT communication optimization.

We propose a novel approach to energy-efficient radio frequency (RF) communication based on uncertainty-aware semantic filtering and multi-task learning. The framework utilizes a Bayesian Neural Network (BNN) with Monte Carlo Dropout to estimate predictive uncertainty and filter semantically redundant RF frames. This adaptive mechanism enables efficient data reduction based on confidence levels, optimizing bandwidth without sacrificing performance. The retained high-confidence frames are used for multi-class modulation classification and signal-to-noise ratio (SNR) prediction, supporting intelligent transmission under varying channel conditions while minimizing unnecessary processing. Experimental results show that entropy-based filtering achieves bandwidth savings of up to 40%, maintaining 90.80% classification accuracy, and reducing energy consumption by 10% compared to the baseline. Uniform Manifold Approximation and Projection (UMAP) visualizations confirm improved latent space separability after filtering. Energy consumption is tracked using CodeCarbon, showing a significant reduction in energy per transmitted frame. The proposed framework is lightweight, ideal for real-time inference in resource-constrained environments, and offers applications in edge artificial intelligence (AI), low-power Internet of Things (IoT), and vehicular networks (V2X), paving the way for energy-efficient communication in future 6G systems.

In the era of smart cities, efficient resource allocation is critical for seamless task execution and minimizing latency. This study introduces a hybrid computing framework integrated with SUMO (Simulation of Urban Mobility) to address real-time allocation challenges across distributed and centralized edge servers. The framework ensures reliable connectivity between IoT devices and edge servers in urban environments. In the simulation, self-organizing IoT devices generate computational tasks, transmitted to self-adaptive edge servers strategically deployed in high-traffic areas, while the main server manages system-wide load balancing. The framework integrates two algorithms: RAIDER (Resource Allocation and Integration for Distributed Edge Routing), which optimizes task distribution among edge servers, and TRAX (Task Resource Allocation eXecutor), which clusters tasks and balances load at the main server. Results show that RAIDER reduces bandwidth by 13%, power consumption by 11.4%, and latency by 25% compared to greedy approaches. TRAX achieves 62.3% lower delay and 89% reduced variability under heavy workloads, ensuring stable performance at scale. These results validate the framework’s ability to optimize resource use across the computing infrastructure, providing a strong foundation for managing future urban demands.

In recent years, the rise in cyber threats targeting online platforms has led to significant advancements in cyber-security research. This paper offers a comprehensive overview of current trends and persistent challenges in the cybersecurity landscape. The study evaluates the effectiveness of various cyber-security measures, including attack detection and simulation, incident response, and blockchain-based security frameworks. Despite progress, many cybersecurity solutions are still limited by factors such as insufficient datasets, computational inefficiency, and susceptibility to adversarial attacks. By analyzing recent literature and highlighting these ongoing gaps, this paper aims to direct future research toward developing more robust and adaptive cybersecurity solutions, enhancing protection for online platforms and IT systems against evolving cyber threats.

Nowadays, almost all applications use Internet of Things (IoT) to modernize their process of data communication. The area of smart cities is considered as an industry whose the exploitation of IoT is in constant increasing. Due to the specific requirements of the smart cities' applications, especially in terms of QoS (Quality of Services), IoT communications face multiple constraints. The limited resources, whether for devices or for communication links, is considered one important constraint. The frequent changes in the states and situations of devices and communication links make this constraint more complex. Considering this obstacle when designing communication algorithms for the IoT is an active research axis that is conducted in the IoT context. Given the unpredictable and uncertain nature of the situations that can be occurred in the network, it is important that these algorithms be adaptive and intelligent. The objective of the presented work in this paper is the proposition of a new communication solution, named IAAC-IoT (Intelligent and Adaptive Algorithms for IoT Communications), gathering adaptive and intelligent algorithms to improve the QoS and energy consumption within IoT. During the performance evaluation of the IAAC-IoT, we obtained satisfactory performance results in terms of QoS and energy efficiency.

Internet of Everything (IoE) technology is increasingly being used by companies to modernize their activities. The very specific characteristics of such environments, in particular, their vulnerable exchanged data and the weak nature of the connected things, expose these companies to risks and security breaches. The principal objective of our work is to provide a cybersecurity strategy capable to consider all types of attacks that can affect an IoE environment while respecting the specified budget. For this purpose, a financial approach based on portfolio management is exploited by allowing to select a portfolio of security controls that minimizes the direct costs and maximizes the security level control. To be solved, the considered problem is assimilated to a combinatorial optimization technique and more precisely to that of the knapsack. We start by modeling the cybersecurity problem under cardinality and budget constraints that should be respected. To tackle the uncertainty of the problem, a robust optimization is used by considering the min-max criterion al-lowing to consider all the possible threats that can be generated by an attacker over the IoE environment. To solve the considered problem, we use a new iterative method under constraints and we compare it to the Non-dominated Sorting Genetic Algorithm (NSGA-II) meta-heuristic to evaluate its performances. The obtained numeric results when evaluating the performances of the proposed strategy have shown its efficiency by finding efficient Pareto fronts for the two considered objective functions. Based on the iterative method, our strategy greatly outperforms the genetic algorithm by allowing good results for different problem sizes and respecting cardinality constraints in a reasonable time.

Internet of Things (IoT) is considered nowadays as the most important and indispensable support to ensure all types of communication, over almost all sectors of activities. The specificity of each area, as well as its own requirements in terms of Quality of Service (QoS), make this communication difficult to ensure and thus, face multiple challenges. One of these challenges is related to the needed autonomy for IoT, not only in terms of available resources (energy and bandwidth for example) but also in terms of self-configuring and self-organizing within the network. It is in this context that we propose a new cross layers approach for better self-configuring and self-organizing of devices and communications within IoT environments. The proposed approach is named 2SAEC-IoT (self-organizing and self-configuring algorithms for efficient communications within IoT) that leads to guarantee an efficient data communication for IoT applications. 2SAEC-IoT is a cross layers solution since it considers important communication parameters related to three levels which are MAC, network, and transport. The proposed approach allows the continuity of services for IoT applications, especially for those with very sensitive data (e-health for example), by tolerating possible communications failures or devices breakdown. The evaluation of the proposed approach shows a clear improvement in terms of QoS, and energy efficiency compared to those obtained by three other IoT networks using different communication algorithms.