Search Results (27)

Healthcare has been fundamentally changed by the expansion of IoT, which enables advanced diagnostics and continuous monitoring of patients outside clinical settings. Frequently interconnected medical devices often encounter resource limitations and lack comprehensive security safeguards. Therefore, such devices are prone to intrusions, with DDoS attacks in particular threatening the integrity of vital infrastructure. To safe guard sensitive patient information and ensure the integrity and confidentiality of medical devices, this article explores the critical importance of robust security measures in healthcare IoT systems. In order to detect DDoS attacks in healthcare networks supported by WBSN-enabled IoT devices, we propose a hybrid detection model. The model utilizes the advantages of Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in network traffic and Convolutional Neural Networks (CNNs) for extracting spatial features. The effectiveness of the model is demonstrated by simulation results on the CICDDoS2019 datasets, which indicate a detection accuracy of 99% and a loss of 0.05%, respectively. The evaluation results highlight the capability of the hybrid model to reliably detect potential anomalies, showing superior performance over leading contemporary methods in healthcare 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

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

In medical environments, a wireless body sensor network (WBSN) is used to operate remotely, and sensor nodes are employed. It consists of sensor nodes installed on a human body to monitor a patient’s condition, such as heartbeat, temperature, and blood sugar level, and are functionalized and controlled by remote devices. A WBSN consists of nodes that are actually sensors in nature and are operated with a short range of communication. These sensor nodes are fixed with limited computation power and the main concern is energy consumption and path loss. In this paper, we propose a new protocol named energy-efficient distance- and link-aware body area (EEDLABA) with a clustering mechanism and compare it with the current link-aware and energy-efficient body area (LAEEBA) and distance-aware relaying energy-efficient (DARE) routing protocols in a WBSN. The proposed protocol is an extended type of LAEEBA and DARE in which the positive features have been deployed. The clustering mechanism has been presented and deployed in EEDLABA for better performance. To solve these issues in LAEEBA and DARE, the EEDLABA protocol has been proposed to overcome these. Path loss and energy consumption are the major concerns in this network. For that purpose, the path loss and distance models are proposed in which the cluster head (CH) node, coordinator (C) node, and other nodes, for a total of nine nodes, are deployed on a human body. The results have been derived from MATLAB simulations in which the performance of the suggested EEDLABA has been observed in assessment with the LAEEBA and DARE. From the results, it has been concluded that the proposed protocol can perform well in the considered situations for WBSNs. Full article

Due to the rapid development of information and emerging communication technologies, developing and implementing solutions in the Internet of Medical Things (IoMTs) field have become relevant. This work developed a novel data security algorithm for deployment in emerging wireless biomedical sensor network (WBSN) and IoMTs applications while exchanging electronic patient folders (EPFs) over unsecured communication channels. These EPF data are collected using wireless biomedical sensors implemented in WBSN and IoMTs applications. Our algorithm is designed to ensure a high level of security for confidential patient information and verify the copyrights of bio-signal records included in the EPFs. The proposed scheme involves the use of Hahn’s discrete orthogonal moments for bio-signal feature vector extraction. Next, confidential patient information with the extracted feature vectors is converted into a QR code. The latter is then encrypted based on a proposed two-dimensional version of the modified chaotic logistic map. To demonstrate the feasibility of our scheme in IoMTs, it was implemented on a low-cost hardware board, namely Raspberry Pi, where the quad-core processors of this board are exploited using parallel computing. The conducted numerical experiments showed, on the one hand, that our scheme is highly secure and provides excellent robustness against common signal-processing attacks (noise, filtering, geometric transformations, compression, etc.). On the other hand, the obtained results demonstrated the fast running of our scheme when it is implemented on the Raspberry Pi board based on parallel computing. Furthermore, the results of the conducted comparisons reflect the superiority of our algorithm in terms of robustness when compared to recent bio-signal copyright protection schemes. Full article

Wireless body sensor networks (WBSNs) monitor the changes within the human body by having continuous interactions within the nodes in the body network. Critical issues with these continuous interactions include the limited energy within the node and the nodes becoming isolated from the network easily when it fails. Moreover, when the node’s burden increases because of the failure of other nodes, the energy utilization as well as the heat dissipated increases much more, causing damage to the network as well as human body. In this paper, we propose a threshold-based fail proof lifetime enhancement algorithm which schedules the nodes in an optimal way depending upon the available energy level. The proposed algorithm is experimented with a real time system setup and the proposed algorithm is compared with different routing mechanisms in terms of various network parameters. It is inferred that the proposed algorithm outperforms the existing routing mechanisms. Full article

The wireless body sensor network (WBSN) is a wireless communication that might enable 24/7 patient monitoring and health findings through the online platform. Although BSN design is becoming simpler, building a secure BSN seems to be more challenging than designing conventional solutions, and the recent study provides little guidance to designers and developers. The proposed study summarizes the multivocal literature study of security mechanisms for BSN. The investigation found 10,871 academic publications and 697 grey content; duplicates were removed, and selection criteria were employed, resulting in 73 academic papers and 30 grey publications. Various conventional security techniques, scope, and security contexts were used to classify the stated security solutions within each publication. It was crucial to inquire about the frequency of publications, research methods, security mechanisms, and contexts to answer the proposed questions. Our survey concludes that security methods and assessments are categorized into 15 categories, with the most frequently referenced being authentication and authorization; the majority of strategies concentrate on preventing and mitigating security breaches, with a limited number of works focusing on detection and recovery; and the techniques used to conduct the survey vary between the two types of publications. This evaluation might be the first step toward making the BSN platform more consistent by giving professionals and researchers a complete set of security strategies and methods. Experts will apply these solutions to fix security issues while establishing a trustworthy BSN after they have been identified through the process of discovering the most commonly utilized security solutions. Full article

This article presents the design of a low-cost Wireless Body Sensor Network (WBSN) for monitoring vital signs including a low-cost smart wristwatch that contains an ESP-32 microcontroller and three sensors: heart rate (HR), blood pressure (BP) and body temperature (BT), and an Internet of Things (IoT) platform. The vital signs data are processed and displayed on an OLED screen of the patient’s wristwatch and sent the data over a wireless connection (Wi-Fi) and a Cloud Thing Board system, to store and manage the data in a data center. The data can be analyzed and notified to medical staff when abnormal signals are received from the sensors based on a set parameters from specialists. The proposed low-cost system can be used in a wide range of applications including field hospitals for asymptotic or mild-condition COVID-19 patients as the system can be used to screen those patients out of symptomatic patients who require more costly facilities in a hospital with considerably low expense and installation time, also suitable for bedridden patients, palliative care patients, etc. Testing experiments of a 60-person sample size showed an acceptable accuracy level compared with standard devices when testing with 60 patient-samples with the mean errors heart rate of 1.22%, systolic blood pressure of 1.39%, diastolic blood pressure of 1.01%, and body temperature of 0.13%. According to testing results with 10 smart devices connected with the platform, the time delay caused by the distance between smart devices and the router is 10 s each round with the longest outdoor distance of 200 m. As there is a short-time delay, it does not affect the working ability of the smart system. It is still making the proposed system be able to show patient’s status and function in emergency cases. Full article

The growing aging of the world population is leading to an aggravation of diseases, which affect the autonomy of the elderly. Wireless body sensor networks (WBSN) are part of the solutions studied for several years to monitor and prevent loss of autonomy. The use of optical wireless communications (OWC) is seen as an alternative to radio frequencies, relevant when electromagnetic interference and data security considerations are important. One of the main challenges in this context is optical channel modeling for efficiently designing high-reliability systems. We propose here a suitable optical WBSN channel model for tracking the elderly during a walk. We discuss the specificities related to the model of the body, to movements, and to the walking speed by comparing elderly and young models, taking into account the walk temporal evolution using the sliding windowing technique. We point out that, when considering a young body model, performance is either overestimated or underestimated, depending on which windowing parameter is fixed. It is, therefore, important to consider the body model of the elderly in the design of the system. To illustrate this result, we then evaluate the minimal power according to the maximal bandwidth for a given quality of service. Full article

An accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon using a wireless body sensor network consisting of sEMG sensors in association with a wearable device that integrates accelerometer, gyroscope, and magnetometer. The raw data collected from the sensors during the flexion relaxation test are processed by an algorithm able to identify the phases of which the test is composed, provide an evaluation of the myoelectric activity and automatically detect the phenomenon presence/absence. The developed algorithm was used to process the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on back muscles of subjects with and without Low Back Pain. The results have shown that the proposed method is significant for myoelectric silence detection and for clinical assessment of electromyography activity patterns. Full article

Internet of Medical Things (IoMT) technologies provide suitability among physicians and patients because they are useful in numerous medical fields. Wireless body sensor networks (WBSNs) are one of the most crucial technologies from within the IoMT evolution of the healthcare system, whereby each patient is monitored by low-powered and lightweight sensors. When the WBSNs are integrated into IoMT networks, they are quite likely to overlap each other; thus, cooperation between WBSN sensors is possible. In this paper, we consider communication between WBSNs and beyond their communication range. Therefore, we propose inter-WBAN cooperation for the IoMT system, which is also known as inter-WBAN cooperation in an IoMT environment (IWC-IoMT). In this paper, first, a proposed architecture for the IoT health-based system is investigated. Then, a mathematical model of the outage probability for the IWC-IoMT is derived. Finally, the energy efficiency of the IWC-IoT is analysed and inspected. The simulation and numerical results show that the IWC-IoMT (cooperative IoMT) system provides superior performance compared to the non-cooperative system. Full article

Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies. Full article

Current medical facilities usually lead to a very high cost especially for developing countries, rural areas and mass casualty incidents. Therefore, advanced electronic health systems are gaining momentum. In this paper, we first compared our novel off the shelf experimental wired Body Sensor Networks (BSN), that is, Digital First Aid (DigiAID) with the existing commercial product called as Hexoskin. We showed the viability of DigiAID through extensive real measurements during daily activities by both male and females. It was found that the major hurdle was wires to be worn by the subjects. Accordingly, we proposed and characterized the wireless DigiAID platform for wireless BSN (WBSN). Understanding the effect of body movements on wireless data transmission in WBSN is also of major importance. Therefore, this paper comprehensively evaluates and analyzes the impact of body movements, (a) to ensure transmission of data at different radio power levels and (b) its impact on the topology of the WBSN. Based on this we have proposed a dynamic power control algorithm that adapts the transmitting power according to the packet reception in an energy efficient manner. The results show that we have achieved substantial power savings at various nodes attached to the human body. Full article

For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10⁻⁵° and 2.01 × 10⁻⁵° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient’s locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time. Full article

Wireless Body Sensor Networks (WBSNs) are gaining importance in the era of the Internet of Things (IoT). The modern medical system is a particular area where the WBSN techniques are being increasingly adopted for various fundamental operations. Despite such increasing deployments of WBSNs, issues such as the infancy in the size, capabilities and limited data processing capacities of the sensor devices restrain their adoption in resource-demanding applications. Though providing computing and storage supplements from cloud servers can potentially enrich the capabilities of the WBSNs devices, data security is one of the prevailing issues that affects the reliability of cloud-assisted services. Sensitive applications such as modern medical systems demand assurance of the privacy of the users’ medical records stored in distant cloud servers. Since it is economically impossible to set up private cloud servers for every client, auditing data security managed in the remote servers has necessarily become an integral requirement of WBSNs’ applications relying on public cloud servers. To this end, this paper proposes a novel certificateless public auditing scheme with integrated privacy protection. The multi-user model in our scheme supports groups of users to store and share data, thus exhibiting the potential for WBSNs’ deployments within community environments. Furthermore, our scheme enriches user experiences by offering public verifiability, forward security mechanisms and revocation of illegal group members. Experimental evaluations demonstrate the security effectiveness of our proposed scheme under the Random Oracle Model (ROM) by outperforming existing cloud-assisted WBSN models. Full article