The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data. First, the traceability and tamper-proof of blockchain are used to realize the secure transaction of IoMT data, transforming the secure transaction of IoMT data into a two-stage Stackelberg game. The dual-chain architecture is used to ensure the security and privacy protection of the transaction. The main-chain manages regular IoMT data transactions, while the side-chain deals with data trading activities aimed at resale. Simultaneously, the perceptual hash technology is used to realize data rights confirmation, which maximally protects the rights and interests of each participant in the transaction. Subsequently, medical time-series data is modeled using bidirectional simple recurrent units to detect anomalies and cyberthreats accurately while overcoming vanishing gradients. Lastly, an adversarial sample generation method based on local interpretable model-agnostic explanations is provided to evaluate, secure, and improve the anomaly detection model, as well as to make it more explainable and resilient to possible adversarial attacks. Simulation results are provided to illustrate the high performance of the integrated secure data management framework leveraging blockchain and XAI, compared with the benchmarks.

Cancer patients are known to have a higher likelihood of developing Cardiovascular Disease (CVD) compared to non-cancer individuals. Although various types of cancer can contribute to the onset of CVD, lung cancer is inherently linked with increased susceptibility. To bridge this hypothesis, we propose a Lung cancer detection and Cardiovascular Disease Prediction (LCDP) system through lung Computed Tomography (CT) scan images. The lung cancer detection module of the LCDP system utilizes Transfer Learning (TL) with AdaDenseNet for classification. It employs the improvised Proximity-based Synthetic Minority Over-sampling Technique (Prox-SMOTE), improving accuracy. In the CVD prediction module, the feature extraction was performed using the VGG-16 model, followed by classification using a Support Vector Machine (SVM) classifier. The impact and interdependence of lung cancer on CVD were evident in our evaluation, with high accuracies of 98.28% for lung cancer detection and 91.62% for CVD prediction.

An efficient traffic light control system (TLCS) is an integral part of monitoring the flow of autonomous vehicles (AVs) through road junctions. Existing TLCS systems have limitations, focusing on specific scenarios like emergency vehicles or considering only the queue length at intersections. Furthermore, the absence of privacy protection in these systems exposes vehicles to potential tracking risks. We propose an intuitive and privacy-preserving TLCS (IPTLCS) to improve the performance of the TLCS for multiple traffic scenarios and prevent tracking of vehicles at traffic signals. The proposed IPTLCS uses a deep Q-learning (DQN) algorithm based on the quantity versus priority concept, adaptive to all scenarios, including pedestrians and emergency vehicles, making the framework applicable to real-time situations. Further, we propose a novel anonymity preserving protocol (APP) to protect the privacy of vehicles using two-party computation (2PC) that can prevent the tracking of AVs in our environment. Extensive experimental studies of the IPTLCS reveal that the model can achieve a reduced waiting time of 3.45 s/vehicle, a queue length of 3.32 vehicles/lane, a run time of 0.002 s, and a communication overhead of 3 KB. The increase in the efficiency of the proposed IPTLCS model in comparison with existing models in terms of waiting time, queue length, run time, and communication overhead is 11.08%–43.25%, 2.35%–24.23%, 33.3%–65.5%, and 38.77%–54.38%, respectively. Impact Statement The infusion of deep learning (DL) methodologies into traffic light systems signifies a substantial leap in advancing the management of AVs on roadways. Confronting the limitations of current traffic light systems, especially in adapting to diverse scenarios with varying quantities and priorities of vehicles, this study introduces a framework for efficient AV navigation that minimizes traffic collisions with reduced delays. Incorporating the proposed privacy measures ensures vehicular data protection, enhancing the overall system’s safety. Results demonstrate that the proposed method addresses the secure management of vehicular data and achieves reduced processing times, paving the way for progressing more intelligent and secure urban mobility solutions and fostering a seamless coexistence between AVs and pedestrians.

Post COVID-19 and recent advancement in terms of language models, researchers have shown a lot of interest in analyzing social media posts for analyzing mental state of the users. Social media platforms are the epitome of sharing individual thoughts and feelings through textual posts and linguistic cues. Therefore, the textual modality from social media posts can be leveraged for detecting early signs of stress, depression or other mental health conditions, accordingly. Existing methods mainly focus on the feature engineering, shallow learning, and employing of deep learning architectures to improve the mental state recognition performance. Seldom the study uses an established knowledge-base that is available to model mentalization and emotional aspect to improving the depression and stress recognition. In this regard, we propose emotion aware contrastive networks (EAC-net) that leverages the existing knowledge-base and propose some new ones to model the emotional and mentalization aspect in order to improve the recognition of stress and depression state from textual posts. Furthermore, we propose a feature-level fusion and weighting mechanism using gated recurrent units (GRUs) and self-attention layers to weight and select the important features. Last, the EAC-Net uses a supervised contrastive learning strategy to train the network. The proposed method is evaluated on four publicly available datasets. Experimental results reveal that the EAC-Net achieves state-of-the-art results by outperforming baselines and existing methods by atleast 1.86%, 0.72%, 3.43%, and 3.64% on four publicly available datasets using F1-measure as the evaluation metric.

In past, due to data and model security concerns, modern communication systems mainly focus on the use of edge computing devices for enabling immersive applications and services. Federated learning is one of the preferred solutions but it stresses the computation capability of the edge devices for immersive applications. Much research is now focusing on split learning as an alternative due to its ability of performing joint training with limited computing resources. However, split learning is also vulnerable to data reconstruction, feature space hijacking, and model inversion attacks, which are quite common concerning immersive applications such as Metaverse. In this regard, we propose a ZEro-Trust Attack (ZETA) framework for data reconstruction and model inversion attacks for autonomous vehicles opting for split learning strategies. We propose the joint training of client, server, and shadow models for both the reconstruction and main task to fool existing methods. Our experimental results demonstrate that the proposed method is capable of reconstructing client's data with an error of 0.0032. This study is proposed as a basis to design more sophisticated defense mechanisms for autonomous vehicles to protect user services in 5G/6G networks.

Artificial Intelligence of Medical Things (AIoMT) requires storing, preprocessing, monitoring, and analytics of large-scale sensor data fusion in the cloud. However, migrating to the cloud possesses intrinsic issues of cost, performance constraints, and sustainable computing. This research explores the potential of AIoMT in crafting intelligent models for daily activity patterns and predicting unusual occurrences. It delves into power-efficient and sustainable computing tailored for the IoT sensors, methods, and systems geared toward crafting digitally enhanced smart homes for the elderly. Fusion data is collected from heterogenous sensors to track daily patterns and processed for anomaly detection and alert generation. The AIoMT model has employed the time and energy minimization scheduler (TEMS) algorithm, which considers energy consumption, processing duration, data transmission expenses, and standby device power consumption. This enables local computing in the IoMT systems, mobile edge servers, and cloud controllers, promoting sustainability in healthcare. To optimize execution time and cost-effectiveness, task scheduling options include local Internet of Things devices, cloud infrastructure, and multiaccess edge computing (MEC). This approach could benefit digitally enhanced communities significantly, promoting low-carbon, power-efficient, sustainable computing (LCPESC). The LCPESC AIoMT approach demonstrates precision close to a 95% confidence level. Further, the proposed model is extended beyond individual households to encompass digitally augmented communities.

This study aims to present a comparative analysis of existing waveforms for integrated sensing and communication (ISAC) in vehicular environments. A novel multicarrier framework called generalized adaptive spreading modulation (GASM) is proposed for ISAC-enabled vehicular environments. The GASM waveform offers symbol spreading in both time and frequency domains with tunable spreading parameters, which allows the proposed GASM-based waveform to adapt according to the rapid time-frequency variations of the fading channel. This helps to combat the most common system impairments, such as carrier frequency offset (CFO) and symbol timing offset (STO). The GASM scheme is the generalization of various existing waveforms, such as orthogonal frequency-division multiplexing (OFDM), fractional Fourier transform-based OFDM (FrFT-based OFDM), and orthogonal chirp division multiplexing (OCDM). The proposed GASM-based ISAC system is evaluated in terms of average bit error rate (ABER) for the communication and ambiguity function (AF) for sensing capabilities. The performance of the GASM-based ISAC system is found superior as compared to the existing waveforms, i.e., OFDM, FrFT-based OFDM, OCDM, generalized frequency division multiplexing (GFDM), and orthogonal time frequency space (OTFS) modulation.

Internet-connected robotic systems today predominantly rely on isolated sensing, computing, and communication modules, limiting cross-layer optimizations. However, emerging applications like industrial automation and augmented reality necessitate tight coupling between complementary capabilities for versatility, precision, and autonomy improvements. To this end, this paper proposes an adaptive sensing algorithm for Internet of Robotic Things (IoT) platforms with integrated sensing, computing, and communication (as-ISCC-IoRT) capabilities. The framework leverages a model-driven methodology to dynamically harness the benefits of complementary techniques for improving localization accuracy and operational efficiency. First, three classical sensing algorithms are introduced to realize multi-target ranging and speed measurement, and the algorithms are analyzed in terms of sensing accuracy, communication performance, and computational complexity, which shows that any one of the algorithms alone cannot achieve the optimization of sensing accuracy, sensing capacity, and communication rate simultaneously. Then, combining the characteristics of different sensing algorithms, an adaptive sensing algorithm is proposed, and the receiver selects the appropriate sensing algorithm based on the ratio of the measured received signal to the interference plus noise. Extensive simulations under varying signal-to-interference-plus-noise ratio levels, number of sensors, and quality of service constraints validate the effectiveness of as-ISCC-IoRT -consistently showing the fastest convergence, lowest weighted MSE, highest communications rate gain, and minimum transmit power by adaptively switching between component algorithms. The consistent performance gains of the proposed as-ISCC-IoRT scheme across key metrics like accuracy, latency, and efficiency validate the benefits of integrating sensing, computing, and communication capabilities in Internet-connected robotic systems.

The future communication technologies like 6G are capable to provide higher mobility and better quality-of-service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large-scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next-generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next-generation networks. In this article, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next-generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of the PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost and load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next-generation large-scale IoT networks.

There has been an asymmetric shift toward harnessing cloud-based technologies as the world focuses on shifting operations remotely. Data security for remote operations is crucial for the protection and preservation of critical infrastructure. Furthermore, there has been an emerging trend to integrate IoT-based devices with the expanding cloud infrastructure. In this work, mobile cloud-based infrastructure is considered where contact opportunities are developed in an opportunistic manner so as to facilitate efficient data forwarding and secure process handling. A probabilistic framework is proposed that facilitates data routing between the nodes and local cloud in an IoT network coupled with a multitier trust and encryption scheme for secure data delivery in the cloud-based IoT network. The proposed scheme is evaluated with simulations over two data sets against attack resilience and routing efficiency-based performance metrics, comparing to standard protocols, such as PRoPHET, MaxProp, ProWait, and TCAFE, which displays the enhancement in operations of Sec-CIoT.

Internet of Things (IoT) comprises of a huge number of connected devices that can communicate within the same network and across the networks. IoT-enabled wireless sensor networks (WSNs) are getting growing interest due to its wide applicability in healthcare, patient monitoring, transportation, and surveillance. The main issue is that the network is mostly deployed in hostile environments where an attacker may physically destroy the CHs or other technical fault may occur. It isolates the cluster and causes loss of sensitive data from that region. This article presents a critical data reclamation (CDR) protocol that provides secure data transmission for isolated clusters. We present the data transfer and data aggregation algorithms for sensing nodes and data receiving and extraction at CH and sink. We performed extensive simulations using NS-2.35. The results prove the dominance of CDR in contrast to counterparts in terms of communication cost, energy consumption, and resilience.