Online fraud detection poses increasing challenges as malicious actors continuously adapt their tactics to evade recognition. Conventional detection systems, whether rule-based or machine learning–driven, process transactions in isolation and fail to capture the interdependent, dynamic, and adversarial nature of fraud. Graph Neural Networks (GNNs) have shown promise in modeling such relational data, yet most existing models assume homophily, neglect temporal evolution, and rely heavily on supervision or graph restructuring, therefore limiting their robustness in real-world fraud scenarios characterized by heterophily, class imbalance, and camouflage. To address these issues, we propose HOT-GNN, a Heterophily Outlier Temporal-aware Graph Neural Network designed to detect complex and camouflaged fraud patterns in heterogeneous, evolving graphs. HOT-GNN introduces a Hybrid Outlier-aware Similarity (HOS) mechanism that quantifies structural, semantic, and anomaly alignment to distinguish genuine from deceptive relations. It further employs a decoupled multi-view message-passing framework to separately aggregate information from homophilic and heterophilic neighbors, preventing noise propagation and preserving discriminative heterophilic signals. Temporal positional encoding and relation-specific modules enable HOT-GNN to capture behavioral dynamics without requiring explicit dynamic graph snapshots. Extensive experiments on four benchmark datasets demonstrate that HOT-GNN achieves consistent improvements over state-of-the-art GNN-based detectors.

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.

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.

E-health systems have revolutionized healthcare by enabling efficient data sharing and management. However, they face significant security and privacy challenges, including unauthorized access, data breaches, identity fraud, and insurance fraud. Existing solutions attempt to address these issues but suffer from single points of failure, lack of patient-defined access control, and inadequate privacy-preserving mechanisms. This paper proposes a dual-blockchain architecture integrated with Self-Sovereign Identity and Zero-Knowledge Proofs to enhance security, privacy, and fraud resilience. The framework employs Decentralized Identifiers and Verifiable Credentials for secure authentication while leveraging the InterPlanetary File System for decentralized Electronic Health Records storage. By addressing the limitations of current systems, the proposed solution ensures a more secure, scalable, and privacy-preserving e-health environment.

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.

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.

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 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.

Due to the recorded success by Internet of Things (IoT) technology, more and more domains use it as a communications and exchange network such as e- health, smart cities, vehicles, etc. IoT do not stop integrating an important number of components and objects that are characterized by their complexity and heterogeneity. Such constraints make the existing routings protocols unsuitable for IoT communications. To accomplish all the expected tasks and satisfy the user services, it is important to guarantee a quality of communication that answers to the requirements of the various applications in terms of data and processing (availability, integrity, efficiency, etc.). The objective of our work in this paper is to propose a smart and efficient routing algorithm to improve the IoT communications performance. The proposed method is called SERA (Smart and Efficient Routing Algorithm) which is based on self-organization of the communications between devices according to their new parameters. The evaluation of SERA has shown that SERA improves considerably the IoT communications performance in terms of some QoS and energy efficiency parameters.

The objective of the work carried out in this paper is to propose a novel load balancing algorithm that adapts the functioning of transport layer to the characteristics of the IoT (Internet of Things) communications when applied to e-health applications. The proposed algorithm is called LBA-Ie (Load Balancing Algorithm for IoT communications within e-health environment) and it is based on the integration of IoT communication parameters in the flow control process supported by TCP (Transmission Control Protocol). LBA-Ie is self-organized and adaptive algorithm by taking into account the changes occurred within the network, the links situations and the objects parameters. LBA-Ie is evaluated in terms of QoS (Quality of Service) and energy efficiency. The simulation results are compared to those obtained by three other solutions. LBA-Ie improves the QoS of IoT communications by increasing the data reliability which improves then the e-health applications. LBA-Ie allows also economizing the consumed energy by the objects and increase then their average lifetime.

The wireless mobile networks (WMNs) are nowadays faced to several challenges such as the guarantee of quality of service (QoS) in the presence of multiple constraints specific to the wireless communication environments. The limited energy of the different nodes is one of these constraints which constitute the main source of the connectivity breaks within the network. Such breaks when repeated may lead to the appearance of uncovered areas which degrade the performance of some applications for which network coverage is very important such as e-health. In order to overcome to this problem, we present in this paper a new solution called MCoS (Maximization of the network Coverage by Self-organization) for better performance communications in WMNs when applied to e-health. MCoS aims to allow an intelligent control of the network topology and a self-organization between nodes in order to cover as possible the operating area of the e-health applications (such as home or hospital). MCoS maximizes the network coverage and then improves, in addition to the coverage quality, important QoS parameters such as the end to end delay, the energy consumption and the nodes lifetime.

Computer security is something that brings to mind the greatest developers and companies who wish to protect their data. Major steps forward are being taken via advances made in the security of technology. The main purpose of this paper is to provide a view of different mechanisms and algorithms used to ensure big data security and to theoretically put forward an improvement in the health-based environment using a proposed model as reference. A search was conducted for information from scientific databases as Google Scholar, IEEE Xplore, Science Direct, Web of Science, and Scopus to find information related to security in big data. The search criteria used were “big data”, “health”, “cloud”, and “security”, with dates being confined to the period from 2008 to the present time. After analyzing the different solutions, two security alternatives are proposed combining different techniques analyzed in the state of the art, with a view to providing existing information on the big data over cloud with maximum security in different hospitals located in the province of Valladolid, Spain. New mechanisms and algorithms help to create a more secure environment, although it is necessary to continue developing new and better ones to make things increasingly difficult for cybercriminals.

Our contribution in this paper is to propose a new solution called EQ-AODV (Energy and QoS supported AODV) for better performance in WMSNs (Wireless Multimedia Sensor Networks). EQ-AODV aims to improve AODV protocol and make it effective for multimedia data in WMSNs. This improvement is based on the adaptation of the routing process of AODV according to two parameters. The first one is the energy of sensors belonging to the routing roads and the second parameter is the nature of the packets received by these sensors. The considered data are text data, audio and video streaming data. After the evaluation of the performance and based on the obtained results, EQ-AODV showed a better performance compared to AODV. EQ-AODV improves important QoS parameters namely the network load and the end to end delay. Important improvement in terms of the life time of sensors and the consumed energy is recorded too.

The objective of our work is to propose a new approach called ES-WSN (Energy Efficiency by Switching between roles in WSNs) for the efficient use of energy in wireless sensor networks (WSN). Our solution ES-WSN extends the lifetime of the nodes in the network and guarantees a fair evolution of their level of energy. This is based on the exchanging of roles between relays and sensors nodes based on their positioning and deployment in the WSN. This process is provided according to two parameters from the communication environment. The first one is the distance between the sensors and relays nodes. The second one is the remaining energy of nodes in the network. After evaluation, ES-WSN has achieved better performance compared to two other solutions proposed in the literature.

The energy efficient in medical ad hoc sensors network (MASN) is one of the most important areas of researches to ensure better services for applications using such an environment. The challenge is how to use of energy of the sensors nodes in a fair way to avoid a break of connectivity in the network as long as possible. Our work focus on how to optimize the energy consumption and to increase the performance of the user applications in medical context. Our proposed approach is called M-EE (Medical Energy Efficient) which is based on the routing protocols with adding a new algorithm for energy fairness. M-EE takes into account the medical communication environment to manage efficiency the energy of the sensor nodes. With this mechanism, sensor nodes which are using image and video medical data are allowed more energy than other sensors nodes. The simulation results showed that our proposed M-EE approach allow significant energy consumption of the network, a reduction of the data loss and an increase in average working time of the sensors.