Search Results (162)

Wireless Medical Sensor Networks (WMSNs) collect and transmit patients’ physiological data in real time through various sensors, playing an increasingly important role in intelligent healthcare. Authentication protocols in WMSNs ensure that users can securely access real-time data from sensor nodes. Although many researchers have proposed authentication schemes to resist common attacks, insufficient attention has been paid to insider attacks and ephemeral secret leakage (ESL) attacks. Moreover, existing adversary models still have limitations in accurately characterizing an attacker’s capabilities. To address these issues, this paper extends the traditional adversary model to better reflect practical deployment scenarios, assuming a semi-trusted server and allowing adversaries to obtain users’ temporary secrets. Based on this enhanced model, we design an efficient ECC-based authentication and key agreement protocol that ensures the confidentiality of users’ passwords, biometric data, and long-term private keys during the registration phase, thereby mitigating insider threats. The proposed protocol combines anonymous authentication and elliptic curve cryptography (ECC) key exchange to satisfy security requirements. Performance analysis demonstrates that the proposed protocol achieves lower computational and communication costs compared with existing schemes. Furthermore, the protocol’s security is formally proven under the Random Oracle (ROR) model and verified using the ProVerif tool, confirming its security and reliability. Therefore, the proposed protocol can be effectively applied to secure data transmission and user authentication in wireless medical sensor networks and other IoT environments. Full article

This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) applications. Full article

Novel K band wearable sensors and antennas for Telemonitoring, Telehealth and Telemedicine Systems, Internet of Things (IoT) systems, and communication sensors are discussed in this paper. Only in a limited number of papers are K band sensors presented. One of the major goals in the evaluation of Telehealth and Telemedicine and wireless communication devices is the development of efficient compact low-cost antennas and sensors. The development of wideband efficient antennas is crucial to the evaluation of wideband and multiband efficient Telemonitoring, Telehealth and Telemedicine wearable devices. The advantage of the printed wearable antenna is that the feed and matching network can be etched on the same substrate as the printed radiating antenna. K band slot antennas and arrays are presented in this paper the sensors are compact, lightweight, efficient, and wideband. The antennas’ design parameters, and comparison between computation and measured electrical performance of the antennas, are presented in this paper. Fractal efficient antennas and sensors were evaluated to maximize the electrical characteristics of the communication and medical devices. This paper presents wideband printed antennas in frequencies from 16 GH to 26 GHz for Telemonitoring, Telehealth and Telemedicine Systems. The bandwidth of the K band fractal slot antennas and arrays ranges from 10% to 40%. The electrical characteristics of the new compact antennas in the vicinity of the patient body were measured and simulated by using electromagnetic simulation techniques. The gain of the new K band fractal antennas and slot arrays presented in this paper ranges from 3 dBi to 7.5 dBi with 90% efficiency. Full article

As an emerging information technology, Wireless Body Area Networks (WBANs) provide a lot of convenience for the development of the medical field. A WBAN is composed of many miniature sensor nodes in the form of an ad hoc network, which can realize remote medical monitoring. However, the data transmission between sensor nodes in the WBAN not only consumes the energy of the node but also causes the temperature of the node to rise, thereby causing human tissue damage. Therefore, in response to the energy consumption problem in the Wireless Body Area Network and the hot node problem in the transmission path, this paper proposes a temperature state awareness-based energy-saving routing protocol (TSAER). The protocol senses the temperature state of nodes and then calculates the data receiving probability of nodes in different temperature state intervals. A benefit function based on several parameters such as the residual energy of the node, the distance to sink, and the probability of receiving data was constructed. The neighbor node with the maximum benefit function was selected as the best forwarding node, and the data was forwarded. The simulation results show that compared with the existing M-ATTEPMT and iM-SIMPLE protocols, TSAER effectively prolongs the network lifetime and controls the formation of hot nodes in the network. Full article

Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions. Full article

Time synchronization between geographically separated equipment, such as, for example, that required in sensor networks for radio localization, is often based on global navigation satellite systems (GNSSs). However, in environments that are GNSS-denied due to signal blockage or interference, alternative timing synchronization methods are necessary. In this work, an experimental wireless time synchronization system based on long-range (LoRa) modulation has been developed and tested in the field. LoRa modulation operating in the license-free 2.4 GHz industrial, scientific and medical (ISM) band was chosen due to the potentially large coverage area of several kilometers and the availability of a ranging engine in the SX1280 transceiver by Semtech, which facilitates the implementation of time synchronization. The prototype system was tested over 170 m, where it achieved a time deviation (TDEV) of 30 ps for an average time of 1 s and a maximum TDEV of 3 ns over one day of measurement, improving over existing work on time synchronization with LoRa modulation by around three orders of magnitude. The field tests showed that ns accuracy can be achieved using LoRa modulation, making it suitable for the synchronization of remote sites, for example, for radio localization. Full article

A wireless sensor network (WSN) consists of sensors with wireless transceivers that link autonomously over many hops. It offers various advantages, including less traffic, more stability, extended wireless communication distances, and broader coverage regions at lower cost. Combining emerging Limited Bandwidth Channel Internet of Things (LBC-IoT) technologies with wireless sensor networks offers interesting applications in the defense, medical, smart conveyance, and marketable sectors. This study initially analyzes WSN and LBC-IoT technologies independently before combining them to look into the networking framework of LBC-IoT and WSN, as well as the associated technologies resulting from the fusion. This article describes the typical network node redeployment strategy for wireless sensors, which can lead to poor node connection and inadequate coverage due to a lack of confined subgroup node exploration. The suggested method for localizing WSN nodes, based on the Hop Count Path Vector (HOP-PV) algorithm, enhances the process of calculating the average hop distance and the number of node hops, resulting in the PVHOP-LBCIOT mechanism. Simulation results indicate that the improved PVHOP-LBCIOT algorithm’s three deployment methods (square, central uniform, and cross) outperform the two approaches of HOP-PV (random deployment) and PVHOP-LBCIOT (border uniform deployment) for an equal number of unknown moving anchor positions (11), a disparate number of unspecified nodes (30-13), and a fixed communication radius (6), with a reduced average error rate of 32.79%, from 38%, and improved accuracy for obtaining unknown node locations. The suggested method for localizing WSN nodes using a single node acting as a mobile anchor point, known as the PVHOP-LBCIOT mechanism, enhances and optimizes the process of computing the average hop distance and the number of node hops. A comparison experiment demonstrates that this hopping algorithm has much greater coverage, node power, linkage, and resilience compared to existing methods. Full article

In medical healthcare services, Wireless Body Area Networks (WBANs) are enabler tools for tracking healthcare conditions by monitoring some critical vital signs of the human body. Healthcare providers and consultants use such collected data to assess the status of patients in intensive care units (ICU) at hospitals or elderly care facilities. However, the collected data are subject to anomalies caused by faulty sensor readings, malicious attacks, or severe health degradation situations that healthcare professionals should investigate further. As a result, anomaly detection plays a crucial role in maintaining data quality across various real-world applications, including healthcare, where it is vital for the early detection of abnormal health conditions. Numerous techniques for anomaly detection have been proposed in the literature, employing methods like statistical analysis and machine learning to identify anomalies in WBANs. However, the lack of normal datasets makes training supervised machine learning models difficult, highlighting the need for unsupervised approaches. In this paper, a novel, efficient, and effective unsupervised anomaly detection model for WBANs is developed using the autoencoder convolutional neural network (CNN) technique. Due to their ability to reconstruct data in a completely unsupervised manner using reconstruction error, autoencoders hold great potential. Real-world physiological data from the PhysioNet dataset evaluated the suggested model’s performance. The experimental findings demonstrate the model’s efficacy, which provides high detection accuracy, as reported F1-Score is 0.96 with a batch size of 256 along with a mean squared logarithmic error (MSLE) below 0.002. Compared to existing unsupervised models, the proposed model outperforms them in effectiveness and efficiency. Full article

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies. Full article

Wireless Sensor Networks (WSNs) are rapidly being integrated into various fields, significantly impacting and facilitating many aspects of human life. However, the increasingly prominent security issues associated with WSNs have become a significant challenge. This paper provides an in-depth analysis of the security challenges faced by WSNs in resource constrained and open communication environments. As a key component of the Internet of Things (IoT), a WSN can perceive, collect and transmit physical environmental data in real-time, and is widely used in military, medical, agricultural and other fields. However, the insecurity of communication channels and unauthorized user access pose severe threats to network security and data integrity. To address these challenges, this paper proposes a provably secure two-factor authentication protocol. This protocol utilizes a Chebyshev chaotic map and a two-factor authentication mechanism, which not only enhances security in WSNs but also improves authentication efficiency. The protocol is validated through security proof and performance experiments, demonstrating excellent security, functionality and efficiency. This provides strong support for secure and efficient communication in WSNs across various application scenarios. Full article

Background: Virtual reality (VR), widely used in the medical field, may affect future medical training and treatment. Therefore, this study examined VR’s potential uses and research directions in medicine. Methods: Citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate VR in medicine in articles published between 1 January 2012 and 31 December 2023. These data were analyzed using CiteSpace 6.2. R2 software. Present limitations and future opportunities were summarized based on the data. Results: A total of 2143 related publications from 86 countries and regions were analyzed. The country with the highest number of publications is the USA, with 461 articles. The University of London has the most publications among institutions, with 43 articles. The burst keywords represent the research frontier from 2020 to 2023, such as “task analysis”, “deep learning”, and “machine learning”. Conclusion: The number of publications on VR applications in the medical field has been steadily increasing year by year. The USA is the leading country in this area, while the University of London stands out as the most published, and most influential institution. Currently, there is a strong focus on integrating VR and AI to address complex issues such as medical education and training, rehabilitation, and surgical navigation. Looking ahead, the future trend involves integrating VR, augmented reality (AR), and mixed reality (MR) with the Internet of Things (IoT), wireless sensor networks (WSNs), big data analysis (BDA), and cloud computing (CC) technologies to develop intelligent healthcare systems within hospitals or medical centers. Full article

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability. Full article

Future communication, 5G, medical, and IoT systems need compact, green, efficient wideband sensors, and antennas. Novel linear and dual-polarized antennas for 5G, 6G, medical devices, Internet of Things (IoT) systems, and healthcare monitoring sensors are presented in this paper. One of the major goals in the evaluation of medical, 5G, and smart wireless communication devices is the development of efficient, compact, low-cost antennas and sensors. Moreover, passive and active sensors may be self-powered by connecting an energy-harvesting unit to the antenna to collect electromagnetic radiation and charge the wearable sensor battery. Wearable sensors and antennas can be employed in smart grid applications that provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. A low-cost wearable antenna may be developed by etching the printed feed and matching the network on the same substrate in the printed antenna. Active modules may be placed on the same dielectric board. The antenna design parameters and a comparison between the computation and measured electrical performance of the antennas are presented in this paper. The electrical characteristics of the new compact antennas in the vicinity of the patient’s body were simulated by using electromagnetic simulation techniques. Fractal and metamaterial efficient antennas and sensors were evaluated to maximize the electrical characteristics of smart communication and medical devices. The dual- and circularly polarized antennas developed in this paper are crucial to the evaluation of wideband and multiband compact 5G, 6G, and IoT advanced systems. The new efficient sensors and antennas maximize the system’s dynamic range and electrical characteristics. The new efficient wearable antennas and sensors are compact, wideband, and low-cost. The operating resonant frequency of the metamaterial antennas with circular split-ring resonators (CSRRs) may be 5% to 9% lower than the resonant frequency of the sensor without CSRRs. The directivity and gain of the metamaterial fractal antennas with CSRRs may be up to 3 dB higher than the antennas without CSRRs. The directivity and gain of the metamaterial fractal passive sensors with CSRRs may be up to 8.5 dBi. This study presents new wideband active meta-fractal antennas and sensors. The bandwidth of the new sensors is around 9% to 20%. At 2.83 GHz, the receiving active sensor gain is 13.5 dB and drops to 8 dB at 3.2 GHz. The receiving module noise figure with TAV541 LNA is around 1dB. Full article

In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s vital signs and health parameters. However, the increased connectivity also introduced security challenges, particularly as they related to the presence of attack nodes. This paper proposed a unique solution, an enhanced random forest classifier with a K-means clustering (ERF-KMC) algorithm, in response to these challenges. The proposed ERF-KMC algorithm combined the accuracy of the enhanced random forest classifier for achieving the best execution time (ERF-ABE) with the clustering capabilities of K-means. This model played a dual role. Initially, the security in IoMT networks was enhanced through the detection of attack messages using ERF-ABE, followed by the classification of attack types, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) using K-means. This approach facilitated the precise categorization of attacks, enabling the ERF-KMC algorithm to employ appropriate methods for blocking these attack messages effectively. Subsequently, this approach contributed to the improvement of network performance metrics that significantly deteriorated during the attack, including the packet loss rate (PLR), end-to-end delay (E2ED), and throughput. This was achieved through the detection of attack nodes and the subsequent prevention of their entry into the IoMT networks, thereby mitigating potential disruptions and enhancing the overall network efficiency. This study conducted simulations using the Python programming language to assess the performance of the ERF-KMC algorithm in the realm of IoMT, specifically focusing on network performance metrics. In comparison with other algorithms, the ERF-KMC algorithm demonstrated superior efficacy, showcasing its heightened capability in terms of optimizing IoMT network performance as compared to other common algorithms in network security, such as AdaBoost, CatBoost, and random forest. The importance of the ERF-KMC algorithm lies in its security for IoMT networks, as it provides a high-security approach for identifying and preventing MITM and DDoS attacks. Furthermore, improving the network performance metrics to ensure transmitted medical data are accurate and efficient is vital for real-time patient monitoring. This study takes the next step towards enhancing the reliability and security of IoMT systems and advancing the future of connected healthcare technologies. Full article

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs. Full article