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.

Local Differential Privacy (LDP) has emerged as a popular mechanism for crowd-sourced data collection, but enforcing a uniform level of perturbation may hinder the participation of individuals with higher privacy needs, while high privacy levels that satisfy more users can reduce utility. To address this, we propose a cohort-based mechanism that allows participants to choose the privacy level from a predefined set. We investigate optimal cohort configurations and uncover insights about utility convexity, enabling the identification of privacy-utility balanced settings. Our proposed mechanism, called UD-LDP, empowers users, promotes transparency, and facilitates suitable privacy budget selection. We demonstrate the effectiveness of cohortisation through experiments on synthetic and real-world datasets.

Purpose The current advancements in technologies and the internet industry provide users with many innovative digital devices for entertainment, communication and trade. However, simultaneous development and the rising sophistication of cybercrimes bring new challenges. Micro businesses use technology like how people use it at home, but face higher cyber risks during riskier transactions, with human error playing a significant role. Moreover, information security researchers have often studied individuals’ adherence to compliance behaviour in response to cyber threats. The study aims to examine the protection motivation theory (PMT)-based model to understand individuals’ tendency to adopt secure behaviours. Design/methodology/approach The study focuses on Australian micro businesses since they are more susceptible to cyberattacks due to the least security measures in place. Out of 877 questionnaires distributed online to Australian micro business owners through survey panel provider “Dynata,” 502 (N = 502) complete responses were included. Structural equational modelling was used to analyse the relationships among the variables. Findings The results indicate that all constructs of the protection motivation, except threat susceptibility, successfully predict the user protective behaviours. Also, increased cybersecurity costs negatively impact users’ safe cyber practices. Originality/value The study has critical implications for understanding micro business owners’ cyber security behaviours. The study contributes to the current knowledge of cyber security in micro businesses through the lens of PMT.

Advanced and autonomous defence systems are needed to detect and mitigate new cyber attacks that target emerging technologies. This research defines cybersecurity preliminaries and stimulates questions to expose the complexities of automation and autonomous cybersecurity. A concept of autonomous anomaly management is introduced, where anomalies are defined not just as anomalous human readable strings, but as “unknown changes” and the need to better manage changes that can not be easily explained. Cybernetic principles are explored, and experiments are undertaken for quantitative analysis. Biodiversity Indices that compare relationships between species and communities in ecology are applied to cybernetic environments, to compare dynamic relationships between business applications. The proposed Cyber Diversity Index applies to all software-driven systems and environments. Changes of application's system calls are measured in real-time and monitored for unknown diversity changes, or “the anomaly,” when the system is placed under a zero-day attack. Experimental data is collected from continuous sampling of applications' activity states, which contributes to both the behavioral change profiles and the dynamic normal baselines for the autonomous anomaly management. The findings in this research show that it is possible to define unique system entropic behaviors from applications' activity that is triggered from internal or external stimulus.

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.

Due to the increased proliferation of WiFi in public and private spaces, there is interest in exploiting WiFi for spatial monitoring. In this paper, we aim to characterize movement or objects in a channel using Channel State Information (CSI). Channel state information represents the degree to which a wireless signal has been attenuated and delayed, and hence we hope to characterize different objects and multipath channel characteristics from CSI. We place different static objects and moving humans in a channel and inspect the CSI for each channel condition. From the variations in CSI Amplitude we can accurately distinguish between a person walking, squatting, or standing still in the channel. To identify static objects, we present a novel approach by inspecting the CSI of different Orthogonal Frequency Division Multiplexing (OFDM) subcarriers. This paper makes a novel contribution, by observing frequency selective behavior of CSI for different channel stimuli. This can be used to improve channel detection accuracy.

Affective computing is an emerging multidisciplinary research field that is increasingly drawing the attention of researchers and practitioners in various fields, including artificial intelligence, natural language processing, cognitive and social sciences. Research in affective computing includes areas such as sentiment, emotion, and opinion modelling. The internet is an excellent source of data required for sentiment analysis, such as customer reviews of products, social media, forums, blogs, etc. Most of these data, called big data, are unstructured and unorganized. Hence there is a strong demand for developing suitable data processing techniques to process these rich and valuable data to produce useful information. Early surveys on sentiment and emotion recognition in the literature have been limited to discussions using text, audio, and visual modalities. So far, to the author's knowledge, a comprehensive survey combining physiological modalities with these other modalities for affective computing has yet to be reported. The objective of this paper is to fill the gap in this surveyed area. The usage of physiological modalities for affective computing brings several benefits in that the signals can be used in different environmental conditions, more robust systems can be constructed in combination with other modalities, and it has increased anti-spoofing characteristics. The paper includes extensive reviews on different frameworks and categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications, trends and future directions to serve as guidelines for readers towards this emerging research area.

Besides its enormous benefits to the industry and community the Internet of Things (IoT) has introduced unique security challenges to its enablers and adopters. As the trend in cybersecurity threats continue to grow, it is likely to influence IoT deployments. Therefore it is eminent that besides strengthening the security of IoT systems we develop effective digital forensics techniques that when breaches occur we can track the sources of attacks and bring perpetrators to the due process with reliable digital evidence. The biggest challenge in this regard is the heterogeneous nature of devices in IoT systems and lack of unified standards. In this paper we investigate digital forensics from IoT perspectives. We argue that besides traditional digital forensics practices it is important to have application-specific forensics in place to ensure collection of evidence in context of specific IoT applications. We consider top three IoT applications and introduce a model which deals with not just traditional forensics but is applicable in digital as well as application-specific forensics process. We believe that the proposed model will enable collection, examination, analysis and reporting of forensically sound evidence in an IoT application-specific digital forensics investigation.

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.

This paper identifies the role of electronic Health (eHealth) in supporting healthy ageing (HA) and explores the factors which impacts on technology acceptance. An exploratory study was conducted to capture experiences of fifteen participants. The results show that seniors require awareness about the benefits of eHealth technologies and how the advances in ehealth technologies can assist benefiting seniors. The outcomes of this study can play a vital role for policy makers and relevant health bodies in determining the adoptability of eHealth technologies to cost effectively manage needs of increasing aged population in many developed countries.

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.

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.

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.

Wireless Body Area Networks (WBAN) have emerged as an extension to conventional wireless sensor networks in recent years to comply with the needs in providing timely and effective response in healthcare as one of the many target applications such networks have. The traffic of a WBAN is diverse due to different monitoring tasks carried on by sensor nodes. It brings difficulty in how to efficiently organize the access to the medium for the dynamic and various generated traffic. This paper analyses the traffic diversity problem in WBAN for healthcare applications and proposes a dynamic delayed Medium Access Control (MAC) algorithm. A fuzzy logic system is used to incorporate both application and protocol related parameters of the real time traffic to make the backoff time produced in IEEE 802.15.4 MAC protocol traffic adaptive. The simulation results demonstrate a significant reliability in packet transmissions and decrease in the latency with no change in energy consumption level.

In-house healthcare monitoring applications are continuous time-critical applications often built upon Body Area Wireless Sensor Networks (BAWSNs). Our Assistive Care Loop Framework (ACLF) is an in-house healthcare application capable of monitoring the health conditions of aged/patients over a dedicated period of time by deploying the BAWSN as the monitoring component. However, the wireless medium used in the BAWSN for communications is prone to vulnerabilities that could open a door to attackers tampering with or compromising the user's data privacy. Hence, it is imperative to maintain the privacy and integrity of the data to gain the confidence and hence, the acceptance of the users of the healthcare applications. Furthermore, in time-critical applications, the vital health conditions must be monitored at regular intervals within their specified critical time. Therefore, the security model proposed for the BAWSN must not incur undue overheads when meeting the critical time requirements of the application. In this paper, we propose and implement a secure adaptive triple-key scheme (aTKS) for the BAWSN to achieve the privacy and integrity of the monitored data with minimal overheads. We then present the performance results of our scheme for the BAWSN, using real-time test-bed implementations and simulations.

Electromagnetic fields naturally and invisibly exist around us. We have been gradually engulfed by them due to our dependency on technology. Even with concerns that continuous exposure to power and communication fields has devastating effects on our health, we are increasing our demands for it every year. This increase does not seem to end or slow down. With our desire for new technology and with the increasing population, we are potentially placing ourselves in more and more danger. This paper focuses on an interesting and novel approach in detecting Extremely Low Frequency (ELF) magnetic power fields using a Wireless Sensor Network (WSN). This approach is introduced by the work in progress towards a sensor that will perform data collection on ELF magnetic fields for the forthcoming research.

Deployment of wireless sensor networks in sensitive applications such as healthcare, defence, habitat monitoring and early bushfire detection requires a careful consideration. These networks are prone to security attacks due to their wireless and deployment nature. It is very likely that after deployment of the network, sensor nodes are left unattended which causes serious security concerns. Insecure wireless communication aggravates the inherent vulnerabilities of wireless sensor networks. Several countermeasures have been proposed in literature to counter the threats posed by attacks in sensor networks; however, security does not come for free. Especially for the resource limited nodes it is very costly to deploy computationally extensive security solutions. This paper studies the notion of trust in wireless sensor networks and proposes a solution based on communal reputation and individual trust (CRIT) in sensor nodes. A very important aspect which determines the viability of this study is the simulation results and performance analysis.

Abstract Quality and security are two intertwined concepts where quality seeks perfection while security countermeasures the elements which weakens the quality. Wireless sensor networks have emerged as a promising future of many sensitive applications. Due to the wireless media and the deployment nature, sensor networks are more prone to security attacks. Several security solutions have been proposed, mostly adding overheads to already resource limited sensor networks. Delivering security and quality simultaneously is challenging because security mechanism severely affects quality mechanisms in terms of performance, data confidentiality, availability, integrity, and authentication. This paper presents a quality of security (QoSec) concept through a triple key scheme (TKS) which has comparatively low security overheads hence providing a quality security solution against the known attacks in wireless sensor networks. Three metrics; time to transmit, latency overheads, and energy overheads are used to do comparative analysis with other existing solutions. Experimental results show that TKS has low overheads hence delivering a quality security at low computational cost. Copyright © 2009 John Wiley & Sons, Ltd.

One of the most arising and essential issues in wireless sensor networks is running concurrent applications on a single network. This would not only reduce the deployment and administrative cost, but also serve the usability and efficiency of the network. In this paper we propose a middleware layer model where multiple applications can run on a single network on a one at a time based sequence in a predefined order. In the proposed model concurrency of applications in a sequenced order is achieved which results in prolonging the lifetime of sensor nodes and better network usability and efficiency. This is mainly achieved by having a switching architecture of network sub-sets between sleep and active modes based on which application is currently running. The proposed model takes into consideration a dense wireless sensor networks where multiple applications are running on different 'sets' of sensor nodes alternatively executing the applications and saving the computational and memory resources.

Key management is critical to meet the security goals to prevent the sensor networks being compromised by an adversary. Due to ad-hoc nature and resource limitations of sensor networks, providing a right key management is challenging. Traditional key management schemes based on trusted third parties like a certification authority are impractical due to unknown topology prior to deployment. In this paper we present a secure triple-key management scheme to provide resilience security against attacks in sensor networks.