Abstract Ransomware threats are growing in frequency and severity, posing significant challenges to cybersecurity defences. Machine learning (ML) has gained attention as a promising tool for detecting ransomware, but the lack of realistic ransomware datasets for training and evaluating ML models has limited progress. This paper introduces RADAR, a comprehensive dataset designed to address this challenge and advance ransomware detection. With over 400,000 system events from seven prominent ransomware families and benign activities, RADAR overcomes the limitations of existing datasets that rely on outdated samples and fail to capture the evolving nature of ransomware. RADAR is structured as a continuous stream of system events and incorporates realistic scenarios, including data drift and class imbalance. The dataset features 48 attributes extracted from Sysmon logs and 19 additional engineered features to improve the analysis of behavioural patterns. By simulating data drift and reflecting the minority-class nature of ransomware, RADAR provides a realistic environment for evaluating ML models in conditions that replicate real-world operations. The utility of RADAR is demonstrated through an experimental framework using an adaptive random forest algorithm in an online incremental learning setting. The results underscore the importance of continually adapting detection methods to effectively address evolving ransomware threats. This research lays a solid foundation for improving ML algorithms and fostering innovative methods for real-time ransomware detection.

The rapid expansion of wireless communication and the Internet of Everything (IoE) has transformed modern technology, necessitating secure and efficient Resource Allocation (RA) to optimize system performance. However, the increasing number of IoE devices introduces security vulnerabilities, particularly from Non-Legitimate Devices (NLDs) that threaten network integrity, data confidentiality, and system availability. This study proposes an RA model based on cyber threat intelligence (CTI) to detect and mitigate malicious devices, integrating a two-state Hidden Markov Model (HMM) for NLD identification and encryption/decryption mechanisms for secure task communication. The model is designed for IoE-Edge fog-based networks, reducing dependency on external cloud servers while leveraging 6G-enabled device clustering for optimized task distribution. A novel CTI-based RA mechanism, namely the Secure-Intelligent Main Task Off-loading Scheduling Algorithm (Sec- i MTOSA), is introduced to enhance intelligent scheduling and secure RA. Experimental results demonstrate that Sec- i MTOSA achieves an average of 93.7% accuracy in detecting NLDs while maintaining a secure RA process with only an average of 7.2% increase in end-to-end delay compared to non-secure traditional methods. These results validate the effectiveness of the model, demonstrating a high accuracy rate in identifying legitimate NLDs while maintaining a low computational overhead suitable for lightweight IoE-Edge environments. Although Sec- i MTOSA introduces minor end-to-end delays due to its embedded security features, it remains efficient for real-time IoE-Edge deployments. These findings establish CTI-driven RA as a scalable and secure approach for next-generation IoE-Edge networks.

The integration of Large Language Models (LLM) with multimodal agentic AI within the Medical Internet of Things (MIoT) ecosystem is redefining modern healthcare intelligence. This convergence enables continuous patient observation, adaptive clinical decision-making, and context-aware interaction between humans and machines across various biomedical data modalities. Healthcare systems generate a wide range of multimodal data, including textual records such as EHRs, prescriptions, and pathology notes; medical imagery such as CT, MRI, fundus, and radiographs; spoken data from consultations and transcriptions; video streams for rehabilitation and physiotherapy monitoring; and sensor readings such as ECG, SpO \({}_{2}\) , and glucose levels. Conventional unimodal algorithms fall short in interpreting this diversity, whereas LLM-augmented agentic frameworks fuse and reason over these heterogeneous sources, grounding their outputs in medical ontologies and coordinating task-specific agents to enhance real-world clinical workflows. This article presents a comprehensive overview of multimodal agentic AI powered by LLM for MIoT-enabled healthcare systems. Introduces a 6D unified taxonomy that covers multimodal input channels, fusion mechanisms, core LLM reasoning capabilities, agentic coordination models, computational deployment layers, and ethical governance frameworks. To contextualize this taxonomy, the discussion includes a Virtual Hospital case study centered on cancer that demonstrates how multimodal signals such as imaging, genomics, patient dialogues, and clinical updates integrate through intelligent agents to enable personalized diagnosis, automated documentation, home rehabilitation, and rapid intervention in emergencies. The survey also consolidates current progress on datasets, benchmarks, and evaluation protocols for AI in multimodal and agentic healthcare. The survey identifies critical research gaps, such as the lack of longitudinal multimodal datasets, standardized evaluation frameworks for multi-agent reasoning, and reliable methods to assess trustworthiness in clinical AI. Furthermore, it examines security and compliance issues such as adversarial manipulation, data leakage, and accountability across distributed agent networks, and it proposes countermeasures through federated data governance, secure MCP-oriented orchestration, and privacy-aware edge deployment strategies. By situating recent advances within the Virtual Hospital paradigm and oncology workflows, this study provides a systematic foundation for developing scalable, secure, and ethically aligned multimodal agentic systems based on LLMs, guiding the next generation of intelligent MIoT-driven healthcare ecosystems.

Sensor devices are prone to errors and sudden node failures, which are difficult to detect in a timely manner when deployed in real-time, hazardous, large-scale harsh environments and in medical emergencies. Therefore, the loss of data can be life-threatening when the sensed phenomenon is not disseminated due to sudden node failure, battery depletion or temporary malfunctioning. We introduce a set of partial differential equations for localizing faults, like Green’s and Maxwell’s equation used in electrostatics and electromagnetism. We introduce a node organization and clustering scheme for self-stabilizing sensor networks. Green’s theorem is applied to regions where the curve is closed and continuously differentiable to ensure network connectivity. Experimental results show that the proposed Green’s Theorem Fault-Detection (GTFD) protocol not only detects faulty nodes but also accurately generates network stability graphs where urgent intervention is required for self-stabilizing the network dynamically.

With the increased usage of Internet of Everything (IoE) capable devices and new communication technologies such as Sixth Generation ( 6G ), more and more services can be made available close to the edge of networks formulating the IoE-based edge networks. In edge networks, several devices communicate with each other for the purpose of sharing information and lending each other various computing resources. This has raised challenges of how efficiently and effectively computing resources can be shared among IoE devices so that users can achieve high quality of service and the network resources are optimally utilised. This paper addresses the issue of computing resource allocation among various devices in such a way that every device can get its task done while lending its unutilised resources to other tasks. We proposed a local scheduler-based architecture where the central scheduler has up-to-date information on the resources available within the network and then allocates them under a certain pre-defined scheduling policy. We introduced the intelligence in the system based on various characteristics of the devices such as each device’s battery level, storage capacity , and computing capability. The proposed algorithm is named the Intelligent Main Task Off-loading Algorithm ( i MTOSA). Novel scheduling schemes using these intelligence-based characteristics for device identification, selection, scheduling, and task management within the IoE cluster at Layer 1 supersedes conventional scheduling policies. To evaluate the performance of the proposed iMTOSA algorithm, we used the Program Evaluation and Review Technique (PERT) and Central Limit Theorem (CLT) to calculate the Z-scores for the successful completion of each task. The proposed algorithm was evaluated through extensive simulations, showing that intelligent scheduling algorithms ( i RR, i SC, i MR, i PF, i PB) achieved task success rates of 89 % to 99.8 % significantly outperforming non-intelligent algorithms, which ranged from 30 % to 40 %. The proposed algorithm enhances the 6G system’s overall performance compared to similar techniques regarding successful task allocation, achieving higher efficiency rates than non-intelligent algorithms. We compared the performance of the proposed algorithm with the existing similar scheme in the literature and it is shown that our proposed algorithm has better performance and stands out when compared under similar network settings. The proposed i MTOSA approach is suitable for IoE-Edge cluster-based industrial environments and business scenarios like 6G to scale its enormous volume of IoE-generated data.

In this paper, we propose a Machine Learning (ML) based approach to address Wireless Channel Estimation (WCE) problem for 5G and beyond wireless networks. Accurate wireless channel estimation plays a crucial role for provision of high quality of service to billions of wireless devices in current and future wireless networks. Our proposed approach uses contemporary ML technique, Federated Learning (FL), in conjunction with the Stochastic Gradient Descent (SGD) algorithm to optimise the WCE problem. Our proposed approach leverages the local user wireless channel information to locally optimise the objective at users and then uses this locally optimised information at the server to optimise the global objective function. The proposed approach is referred to as joint Federated Server Learning and Federated Client Learning (j-FSL-FCL) in the paper. We formulate the WCE problem with a novel loss function to be used for the optimisation problem. To evaluate the performance of our proposed j-FSL-FCL approach (with and without SGD), we consider a Down Link (DL) wireless channel model with Multiple-Input Multiple-Output (MIMO) setting that mimic closely to the wireless channel for 5G and beyond wireless channel models. The performance measuring parameters for j-FSL-FCL are to minimise the difference between actual and estimated wireless channel parameters (channel strength and direction). We compare the results of our proposed approach with the other techniques in the literature based on Least Squares (LS), Linear Regression (LR) and Mean Square Error (MSE). It is shown that the proposed algorithm converges to the optimal solution quickly when used with SGD compared to other existing techniques. It is also shown that the efficiency of the proposed approach for WCE problem is much higher compared to other LS and MSE based techniques. Finally, we present some interesting futuristic applications of FL in the context of 5G and beyond wireless networks.

A vehicular ad-hoc network consists of multiple local networks due to the limited communication range of Onboard Processing Unit utilised by participating vehicles. Therefore, multiple re-transmission attempts by relay vehicles are required to propagate an original information packet. However, these necessary re-transmissions cause Broadcast Storm Problem (BSP) which can in-turn create network congestions. This paper addresses the issues associated with BSP by proposing an Optimised Relay Vehicle Selection (ORVS) mechanism. To devise the strategy for an ORVS mechanism, an Evolutionary Game (EG) strategy is utilised that consists of a novel payoff function. In addition to this, we also propose a (BG) to simulate a VANET environment, which is implemented using Python, for collection of results. The numerical results obtained by the proposed EG are compared with the existing techniques, which show better performance by the ORVS mechanisms produced by the game theoretical approaches.

Smart cities enhance the quality of life for citizens by utilising cutting edge technologies such as 5G and beyond wireless communication. Internet of Everything (IoE) enables a smart city to power and monitor multiple geographically distributed IoE nodes to support a range of applications across various domains such as energy and resource management, intelligent transport systems and E-health to name a few. Due to unprecedented increase in the use of IoE technology and the volume of data it generates, there is need to develop a state-of-the-art architecture to support wide range of applications in order to manage smart city resources in an efficient and intelligent manner. In this work in progress article, we present a conceptual design to establish efficient task off-loading and resource allocation architecture for smart city environment. We first present a novel conceptual design, called conventional model for task off-loading and resource allocation. Secondly, we build upon the conventional model to introduce the intelligence for task off-loading and resource allocation problem. We further develop the specific research questions in order to design and evaluate the performance of various units within the above mentioned models to accommodate the technological advancements such as the use of Artificial Intelligence (AI) in the sixth generation (6G) wireless communication era.

Broadcasting is a part of the communication spectrum supported by VANETs through which information is disseminated to all the vehicles in the network. The dissemination process requires multiple retransmissions to achieve network coverage in a multi-hop environment. However, unsupervised retransmissions cause broadcasting storm, whereas on the other hand network coverage is cannot be achieved without retransmissions. In order to achieve network coverage without creating broadcast storm, the work in [1] proposed the modified Genetic Algorithm based on the analytic fitness function in static highway scenario. In this paper, we use the same approach of the modified GA in a dynamic highway environment. The paper introduces vehicular movement in the model under study by proposing the vehicle speed between 60 and 100 km/h. The result shows minor differences in the number of of retransmissions as well as propagation time for both static and dynamic highway scenario. This is due to the fact that the distance covered by the vehicles movement during the time period required to achieve network coverage is too small.

Microstrip patch antennas are becoming essential component in many emerging medical applications. The increase use of these antennas in such devices is due to a number of attractive properties of these antennas. In this paper, we present a novel design of microstrip patch antenna which can be used in mobile devices for microwave, wireless and eHealth applications. The proposed design of the antenna is based on rectangular structured slots in order to operate at multiple frequency bands. The slots are designed on the rectangular patch and fed by a microstrip feeder line. The combination of the proposed design and quarter wave transformer feeding technique allow the antenna to operate at multiple frequencies in the range of 3 – 12 GHz which is used for most of the wireless applications. It is shown that five different operating frequency bands have VSWR ≤ 2 which is an acceptable range for short to medium range wireless communication. The operating bands of frequency are: 4.7 GHz, 6.7 GHz, 8.9 GHz, 9.8 GHz and 10.9 GHz with VSWR ≤ 2. It is also observed that the gain of proposed design is higher than the conventional patch antenna which makes it more attractive choice for many applications. The proposed design ensures secure and efficient transmission as well as better transmission of input power, i.e. low values of Return Loss.

This paper considers the sum-rate of wireless broadcast systems with multiple antennas at the base station. In a conventional MIMO-BC system with a large number of users, selecting an optimal subset of users to maximizing the overall system capacity is a key design issue. This paper presents a novel approach to investigate the sum-rate using Eigen Value Decomposition (EVD). Particularly, we derive the lower bound on sum-rate of a conventional MIMO-BC using a completely different approach compared to the existing approaches. The paper formulates the rate maximization problem for any number of users and any number of transmitting antennas using EVD approach of the channel matrix. This also shows the impact of channel angle information on the sum-rate of conventional MIMO-BC. Numerical results confirm the benefits of our technique in various MIMO communication scenarios.

Achieving high Quality of Service (QoS) in routing is one of the major issues to be tackled in VANET. This is primarily due to the interference among vehicles and the objects present within the transmission environment. This also causes rapid degradation in transmitted signal and affects the overall system throughput. As a result, the system performance parameters such as delay, packet drop ratio and efficiency are highly effected. This paper proposes a Cross-Layer Decision Based (CLDB) routing protocol that necessitates to choose the best path for routing the packets to meet promised QoS requirements. The protocol considers the parameters from both PHY and MAC layers to optimise the routing objective. Performance of the proposed algorithm is evaluated using extensive computer simulations.

Modeling wireless transmission in stringent networks such as VANETs is a challenging task. This requires mathematically incorporating all the environmental effects present within such a dynamics atmosphere. The key attributes to model the wireless channel are physical constraints inherent to such networks such as lack of permanent infrastructure, limited knowledge in relation to the position of vehicles as well as interference that effects the strength of receive signal at each position of vehicles. The selection of an appropriate transmission model plays a key role in the routing decisions for VANET. This paper investigates such wireless transmission models for vehicular communication. It identifies the situations where a particular model can be beneficial. The paper also provides an insight into the use of practical parameters in theoretical transmission models. An analysis of the proposed transmission model is presented. The performance of different transmission models in terms of receive signal strength (RSS) is also presented. These results help to select a transmission model that suits best to a particular VANET communication scenario.

Routing is an important and critical issue for successful transmission in Vehicular Ad-hoc Networks (VANETs). Most of the traditionally designed routing schemes are based on optimising their parameters individually in the existing VANET architecture. Such approaches may not result in an overall efficient system. Therefore, it is important to consider various parameters from multiple layers such as PHY and MAC, to optimise routing. In this paper while presenting a new cross-layer routing scheme, we subdivide the existing OSI model in three main layers. The routing scheme presented in this paper considers parameters from multiple layers simultaneously to achieve the routing objectives. We argue that the proposed routing scheme results in less packet drops and comparatively smaller delay in packet transmission.