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.

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.

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.