Industrial IoT (IIoT) deployments demand intelligent transmission schemes that balance monitoring fidelity with limited energy and bandwidth. This paper presents a lightweight adaptive framework that predicts a utility–cost slope via machine learning and refines it through Bayesian fusion with historical priors for anomaly-aware rate adjustment. Signal utility is derived from spectral and statistical features such as entropy and the Hurst exponent, while cost reflects link quality, anomaly likelihood, and SIM data usage. Deployed on a 342-day water-purification testbed, the method achieves up to 60% data reduction with negligible loss in reconstruction accuracy, demonstrating its practicality for resource-constrained IIoT edge devices.

This study presents a novel system that tightly integrates zone-based camera analytics with kiosk interaction logs to provide rich, multidimensional insights into customer behaviour in retail environments, particularly within smart retail settings in smart cities. Our approach enables retail administrators to define arbitrary, polygonal zones on a live floorplan overlay for each camera. In the proposed pipeline, video streams are processed at 30 fps using YOLOv8 for person detection and SORT for tracking, with entry and exit events logged per zone to compute footfall, dwell time, and bounce rates. Zones are classified as kiosk zones or custom zones based on metadata assigned during zone creation in the dashboard, where each polygon is manually linked to a kiosk identifier or defined as a general area of interest. By correlating zone presence with kiosk session data via timestamp overlap, we derive a robust kiosk engagement metric. We deployed the system in a mid-size retail store over four weeks, demonstrating improvements in the granularity and actionability of behavioural analytics and revealing optimal kiosk placements and display configurations in a smart retail context.

High-mobility Internet of Things (IoT) for Vehicle-to-Grid (V2G) Demand Response (DR), including roaming between Charge Point Operators (CPOs), requires privacy-preserving authentication with sub-1 ms responses and cross-domain scalability as devices exceed 200km/h. Mechanisms must run on constrained hardware while remaining compatible with EV-charging message flows such as ISO 15118–20 and OCPP 2.0.1. Many deployed schemes re-authenticate from scratch, which inflates computation and airtime; static credentials also ignore trajectory context and struggle with rapid mobility. We present a Zero-Knowledge Proof-based Authentication Scheme (ZKPAS) for V2G/DR that proves possession without disclosure and replaces heavy handshakes with compact, mobility-aware proofs, targeting latencyLO(n) toO(logn). (iii) Predictive token generation with Long Short-Term Memory (LSTM) models trained on GeoLife and T-Drive pre-computes material, yielding 84.7% token reuse along trajectories. (iv) Cross-domain authentication employs (t,n)-threshold cryptography for Byzantine-tolerant roaming across operators. We prove resistance to impersonation, replay, man-in-the-middle, and trajectory inference; under the Computational Diffie–Hellman Problem (CDHP), the adversary’s success probability satisfies Pr[break] ≤ 2−λ. On real transportation topologies, ZKPAS cuts computation by 71.8%, authentication latency by 93.9%, and energy by 69.5%, while interfacing with V2G/DR control flows. The protocol sustains a 98.5% authentication success rate at 250km/h.

Autonomous vehicles (AVs) are transforming transportation systems, necessitating secure digital infrastructures for reliable operation. Vehicular Digital Twin (VDT) networks address AV limitations by enabling synchronized virtual replicas. However, intra-twin communications over public channels expose systems to severe security threats, including impersonation and data tampering. This paper proposes EDTAP-VDT: an Enhanced Distributed Trust Authentication Protocol for VDT networks, which ensures secure identity verification through a threshold cryptographic model anchored in hardware. The protocol employs a hierarchical edge-fog-cloud architecture to balance authentication loads and leverages Trusted Platform Modules (TPMs) to cryptographically bind credentials. Post-quantum secure primitives and a permissioned blockchain with Byzantine consensus ensure long-term security, pseudonymity, and immutable authentication traceability. Attribute-based access control is integrated into the authentication process for fine-grained data sharing. EDTAP-VDT guarantees confidentiality, forward secrecy, and desynchronization resilience while maintaining decentralized control. The protocol is formally validated using the Random Oracle Model, and additional resistance is demonstrated against active and passive attack vectors. Performance evaluation across realistic vehicular settings shows that EDTAP-VDT achieves up to 24% improvement in computational efficiency and up to 22% reduction in communication overhead compared to state-of-the-art alternatives while fulfilling all standard security attributes. The results establish EDTAP-VDT as a future-ready authentication framework for real-time, secure VDT applications in intelligent transportation environments.

Edge computing improves the Internet of Vehicles (IoV) by offloading heavy computations from in-vehicle devices to high-capacity edge servers, typically roadside units (RSUs), to ensure rapid response times for intensive and latency-sensitive tasks. However, maintaining Quality of Service (QoS) remains challenging in dense urban settings and remote areas with limited infrastructure. To address this, we propose an software-defined networking (SDN)-driven model for uncrewed aerial vehicle (UAV)-assisted vehicular edge computing (VEC), integrating RSUs and UAVs to provide computing services and gather global network data via an SDN controller. UAVs serve as adaptable platforms for mobile-edge computing (MEC), filling gaps left by traditional MEC frameworks in areas with high vehicle density or sparse network resources. An optimal offloading mechanism, designed to minimize the Age of Information (AoI) while balancing energy consumption and rental costs, is implemented through a soft actor-critic (SAC)-based algorithm that jointly optimizes UAV trajectory, user association, and offloading decisions. Experimental results demonstrate the model’s superior performance, achieving up to 87.2% energy savings in energy-limited settings and a 50% reduction in time-sensitive scenarios, consistently outperforming traditional strategies across various task sizes.

Inherent broadcast characteristics can raise privacy risks of wireless networks. The specifics of antenna ports, antenna types, orientation, and beamforming configurations of a transmitter can be susceptible to manipulation by any device within range when the signal is transmitted wirelessly. Personal and location information of users connected to the transmitter can be intercepted and exploited by malicious actors to track user movements and profile behaviors or launch targeted attacks, thus compromising user privacy and security. In this paper, we propose a novel precoding perturbation approach for privacy preservation in wireless communications. Our approach perturbs the precoding matrix of the transmitter using a Riemannian manifold (RM) structure that adaptively adjusts the magnitude and direction of perturbation based on the geometric properties of the manifold. The approach ensures robust privacy protection while minimizing the distortion of the transmitted signals, thus balancing privacy preservation and data utility. Privacy can be preserved without relying on additional cryptographic mechanisms, resulting in the computational and communication overhead reduction. Our approach operates directly on the transmission of signals, making them inherently secure against eavesdropping and interception. Simulation results underscore the superiority of the approach, showing a 17.21% improvement in privacy preservation while effectively maintaining data utility.

Autonomous Vehicles (AVs) represent a transformative advancement in modern transportation systems, offering significant improvements in operational efficiency and user experience. However, their widespread implementation faces critical security challenges, particularly regarding secure remote management during system failures or cyber-attacks. These vulnerabilities potentially compromise system integrity and undermine public confidence in autonomous technologies. We introduce a novel Internet of Autonomous Vehicles (IoAV) architecture integrating an AI-driven intrusion detection system with a Chaotic Map-Based Authenticated Key Agreement protocol to address these security concerns. This integration dynamically mitigates evolving security threats through adaptive system responses. Our framework incorporates Physical Unclonable Function (PUF) technology to generate cryptographically secure private keys, establishing robust communication channels between users, Charging Stations (CS), and AVs coordinated by an Electric Service Provider (ESP). Rigorous evaluation using the Real-or-Random (ROR) model demonstrates the protocol’s resilience against diverse attack vectors, including man-in-the-middle, replay, and adversarial attacks. Experimental validation confirms the framework’s effectiveness (97.8% detection accuracy, AUC-ROC: 0.976), computational efficiency (31.25% reduction in overhead, 4.2ms inference latency), and operational resilience (99.3% authentication integrity under 103requests/second DDoS simulation). The protocol achieves 51.38% reduced communication overhead compared to existing solutions, establishing our framework as demonstrably superior for IoAV security implementation within resource-constrained autonomous transportation infrastructures.

Unmanned aerial vehicles (UAVs) have enabled numerous inventive solutions to multiple problems, considerably facilitating our daily lives; however, UAVs frequently rely on an open wireless channel for communication, making them susceptible to cyber-physical threats. Also, UAVs cannot execute complicated cryptographic algorithms due to their limited onboard computing capabilities. Balancing high-security levels and minimum computation costs is imperative when developing a security solution for UAVs. Consequently, several proxy signature schemes have been proposed in the literature to fulfill these requirements. Nevertheless, many of these solutions face the issue of high computation costs, and some exhibit security vulnerabilities that could not be more feasible options for UAV communication. Considering these constraints in mind, in this article, we introduce an improvised certificate-based proxy signature scheme (ICPS), which leverages the concept of hyperelliptic curve cryptography (HECC) to meet the security and efficiency requirements of UAV networks. The proposed ICPS scheme offers a range of notable features, including its ability to address key escrow and secret key distribution issues. The proposed ICPS scheme’s security hardness has been evaluated using the widely known security tool, the random oracle model (ROM), proving its resilience against known and unknown cybersecurity threats. Finally, this study conducts a performance comparison of the proposed scheme against existing schemes, emphasizing its outstanding cost-efficiency. Notably, the computation cost is measured at 5.3536 ms and the communication cost at 1120 bits, substantially lower than relevant existing schemes.

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, pre-deployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy, but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols.

Monitoring in-bed pose estimation based on the Internet of Medical Things (IoMT) and ambient technology has a significant impact on many applications such as sleep-related disorders including obstructive sleep apnea syndrome, assessment of sleep quality, and health risk of pressure ulcers. In this research, a new multimodal in-bed pose estimation has been proposed using a deep learning framework. The Simultaneously-collected multimodal Lying Pose (SLP) dataset has been used for performance evaluation of the proposed framework where two modalities including long wave infrared (LWIR) and depth images are used to train the proposed model. The main contribution of this research is the feature fusion network and the use of a generative model to generate RGB images having similar poses to other modalities (LWIR/depth). The inclusion of a generative model helps to improve the overall accuracy of the pose estimation algorithm. Moreover, the method can be generalized for situations to recover human pose both in home and hospital settings under various cover thickness levels. The proposed model is compared with other fusion-based models and shows an improved performance of 97.8% at PCKh @0.5. In addition, performance has been evaluated for different cover conditions, and under home and hospital environments which present improvements using our proposed model.

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors.

Due to the extremely important role of data in the industrial Internet of Things (IIoT) network, trust and security of data are among the major concerns. In this article, we develop a cloud-integrated 5G-IIoT network architecture enabled by a three-party authenticated key exchange (AKE) protocol with privacy-preserving to secure data exchanged via wireless communication, cope with unauthorized entities, and ensure data integrity. Moreover, we develop a trust model based on the Dempster–Shafer theory to check the trustworthiness of data collected by smart devices/sensor nodes. Security analysis performed on our scheme demonstrates that it can withstand different well known attacks in the IIoT environment. We also analyzed the validity of our scheme by using the automated validation of internet security protocols and applications tool. Additionally, the performance evaluation and experimental results prove the effectiveness of the proposed scheme compared to the existing works in terms of accuracy, delay, trust, and throughput.

In real-time medical monitoring systems, given the significance of medical data and disease symptoms, a secure and always-on connection with the medical centre over the public channels is essential. To this end, an edge-enabled Internet of Medical Things (IoMT) scheme is designed to improve flexibility and scalability of the network and provide seamless connectivity with minimum latency. The entities involved in such network are vulnerable to various attacks and can potentially be compromised. To address this issue, an authentication scheme comprised of digital signature and Authenticated Key Exchange (AKE) protocol is proposed which guarantees only authorized entities get access to the services available in the medical system. Moreover, to fulfill the privacy-preserving, each entity is mapped to a different pseudo-identity. The non-mathematical and performance analysis show that the proposed scheme is robust against various attacks such as impersonation and replay attacks.

Security and privacy of data-in-transit are critical issues in Industry 4.0, which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this article, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. In addition, we conduct a security analysis that proves that the scheme resists several attacks in the Industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

In wireless communication, the performance of the network highly relies on the accuracy of channel state information (CSI). On the other hand, the channel statistics are usually unknown, and the measurement information is lost due to the fading phenomenon. Therefore, we propose a channel estimation approach for downlink communication under channel uncertainty. We apply the Tobit Kalman filter (TKF) method to estimate the hidden state vectors of wireless channels. To minimize the maximum estimation error, a robust minimax minimum estimation error (MSE) estimation approach is developed while the QoS requirements of wireless users is taken into account. We then formulate the minimax problem as a non-cooperative game to find an optimal filter and adjust the best behavior for the worst-case channel uncertainty. We also investigate a scenario in which the actual operating point is not exactly known under model uncertainty. Finally, we investigate the existence and characterization of a saddle point as the solution of the game. Theoretical analysis verifies that our work is robust against the uncertainty of the channel statistics and able to track the true values of the channel states. Additionally, simulation results demonstrate the superiority of the model in terms of MSE value over related techniques.

The role of maintenance in modern manufacturing systems is becoming a more significant contributor to organizational benefit. World-class enterprises are pushing forward with “predict-and prevent” maintenance instead of embracing the drawbacks of reactive maintenance (or a “fail-and fix” approach). The advancement towards Artificial Intelligence (AI), Internet of Things (IoT) and cloud computing has led to a shift in maintenance paradigms with the rising interest in Machine Learning (ML) and in particular deep learning. However, opaque box AI models are complex and difficult to understand and explain to the lay user. This limits the use of these models in predictive maintenance where it is crucial to understand and analyze the model before deployment and it is imperative to understand the logic behind any given decision. This paper introduces a Type-2 Fuzzy Logic System (FLS) optimized by the Big-Bang Big-Crunch algorithm that allows maximizing the interpretability of a model as well as its prediction accuracy for the faults which may occur in future. We tested the proposed type-2 FLS model on water pumps where data was collected in real-time by our proprietary hardware deployed at Aquatronic Group Management Plc. The observations indicate that the proposed system provides a highly interpretable and accurate model for predicting the faults in equipment for building services, process and water industries. The system predictions are used to understand why a particular fault may occur, leading to improved and better-informed service visits for the customers thus reducing the disruptions faced due to equipment failures.

In vehicular ad hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, big storage and high computation platform are needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of the resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with the resource allocation problem assisted by cloud computation. By integration of EC and software-defined networking (SDN), this study provides a new SDN edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multiagent RL-based approach using experience reply. The proposed algorithm stores the users' communication information and the network tracks' state in real time. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case study.

This article proposes a novel asset operation detection (AOD) solution by applying the fuzzy logic reasoning concept to the phase portraits (PPs) of time series data. Around the benefits of business insight and climate impact, we firstly provide relevant context to highlight the importance of asset operation features and necessity for efficient operation detection algorithms in the facility management industry. Then we will review several existing approaches for detecting asset operations and discuss their advantages and disadvantages. With these concerns in mind, we come to the operation detection solution proposed in this research, explaining the technical idea and mentioning two approaches regarding the algorithm inputs: physical phases and derivative phases. All the proposed analysis will be based on a real-case industrial dishwasher. Finally, we will come back to the benefits in terms of business insight and climate impact to showcase the application of detected operational features.

Recent advancements in technology have led to a great interest in the use of Unmanned Aerial Vehicles (UAVs) for a vast array of applications such as real time site monitoring, target search and destroy and UAVs being used as mobile sinks to collect data from Internet of Things (IoT) devices. This is mainly due to their autonomy, high mobility, ease of deployment and affordable nature. A group of UAVs can be used collectively to bring a coordinated effort in task execution, allowing more tasks to be completed in a wider area and in the shortest possible time. However, using multiple UAVs presents some challenges for efficient cooperation. UAVs are resource constrained due to being battery powered and this limits the permissible flight time. Therefore, it is necessary to intelligently manage their operation given the limited resources and other constraints associated with the mission. In this paper, we propose a multi-objective UAV task assignment model to support spatio-temporally distributed events raised by static IoT devices, using a discrete Non- Dominated Sorting Genetic Algorithm II (NSGA-II). This model assigns the most suitable UAV(s) to serve at the different event locations ensuring that none of the constraints are violated. The performance of the algorithm was evaluated through numerical simulations and compared to a similar implementation using Mixed Integer Linear Programming (MILP). Results show an improvement of 7.9% in the total energy consumption for all UAVs while ensuring that all the temporal constraints are not violated.

The technological advancements in the field of internet of things (IoT) have paved way for the evolution of internet of vehicles (IoV), where the things are replaced with vehicles through enabling technologies such as vehicular adhoc networks (VANETs) and intelligent transportation systems (ITS).The technology of simultaneous wireless information and power transfer (SWIPT) provides potential opportunity for increasing the lifetime of the energy-autonomous nodes in vehicular networks. Moreover, the integration of SWIPT with cognitive radio sensor networks (CRSN) has been proved as a vital technique for increasing throughput and effective utilization of the spectrum.In this paper, we consider a cooperative CRSN with near field SWIPT technique for autonomous vehicles (AUV) to monitor the air quality, where the secondary user (SU) forwards and sends the information to the destination through the energy harvested from the radio frequency (RF) signal, thereby focusing towards achieving maximum throughput and also address the trade-off between performance and energy consumption. The problem is formulated using Energy Harvesting and Information Transfer(EHIT) technique and the proof-of-concept is presented based upon a small-scale hardware model to test the performance of the sensors. The results indicate that the proposed SWIPT method for energy autonomous sensors provides optimal solution and has higher performance in comparison to the state-of-art solutions.

Ubiquitous computing is foreseen to play an important role for data production and network connectivity in the coming decades. The Internet of Things (IoT) research which has the capability to encapsulate identification potential and sensing capabilities, strives toward the objective of developing seamless, interoperable, and securely integrated systems which can be achieved by connecting the Internet with computing devices. This gives way for the evolution of wireless energy harvesting (EH) and power transmission using computing devices. Radio frequency (RF) based energy management (EM) has become the backbone for providing energy to wireless integrated systems. The two main techniques for EM in RF identification sensor networks (RSN) are EH and energy transfer (ET). These techniques enable the dynamic energy level maintenance and optimization as well as ensuring reliable communication which adheres to the goal of increased network performance and lifetime. In this paper, we present an overview of RSN, its types of integration and relative applications. We then provide the state-of-the-art EM techniques and strategies for RSN from August 2009 till date, thereby reviewing the existing EH and ET mechanisms designed for RSN. The taxonomy on various challenges for EM in RSN has also been articulated for open research directives.

Image size reduction for energy-efficient transmission without losing quality is critical in Visual Sensor Networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime.