ABSTRACT
Wireless body area networks (WBANs) are helpful for remote health monitoring, especially during the COVID-19 pandemic. Due to the limited batteries of bio-sensors, energy-efficient routing is vital to achieve load-balancing and prolong the network's lifetime. Although many routing techniques have been presented for WBANs, they were designed for an application, and their performance may be degraded in other applications. In this paper, an ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP) is introduced as an adaptive real-time remote health monitoring in WBANs. The motivation behind this technique is to utilize the superior route optimization solutions offered by metaheuristics and to integrate them with the real-time routing capability of machine learning. The proposed method involves two phases: offline model tuning and online routing. During the offline pre-processing step, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is used to optimize routes across various WBAN configurations. By applying WOA for multiple WBANs, a comprehensive dataset is generated. This dataset is then used to train and test a machine learning regressor that is based on support vector regression (SVR). Next, the optimized MDML-RP model is applied as an adaptive real-time protocol, which can efficiently respond to just-in-time requests in new, previously unseen WBANs. Simulation results in various WBANs demonstrate the superiority of the MDML-RP model in terms of application-specific performance measures when compared with the existing heuristic, metaheuristic, and machine learning protocols. The findings indicate that the proposed MDML-RP model achieves noteworthy improvement rates across various performance metrics when compared to the existing techniques, with an average improvement of 42.3% for the network lifetime, 15.4% for reliability, 31.3% for path loss, and 31.7% for hot-spot temperature.
ABSTRACT
Growth in technology has witnessed the comfort of an individual in domestic and professional life. Although, such existence was not able to meet the medical emergencies during the pandemic COVID-19 and during other health monitoring scenarios. This demand is due to the untouched Quality of Service network parameters like throughput, reliability, security etc. Hence, remote health monitoring systems for the patients who have undergone a medical surgery, bed ridden patients, autism affected subjects etc is in need that considers postural change and then forward to the caretaker in hospitals through wireless body area networks (WBAN). Security in these data are very important as it deals with the life of a subject. In this work, a Hierarchical Energy Efficient Secure Routing protocol (HEESR) is proposed that categorizes the deployed body nodes in to direct node and relay node based on the threshold vale. Unlike other conventional protocols the cluster head selection is based on the energy levels and the traffic priority data like critical and non-critical data, followed by an optimal route to forward the acquired data is identified and the data is compressed using Huffman encoding technique and encrypted using asymmetric cryptographic algorithm for secure data transmission. This protocol mainly appends security and routing efficiency in a hierarchical pattern through data prioritization and out performs the other conventional routing protocols by yielding a better energy consumption of 6%, throughput 92% and security of 93%, which has balanced the packet drop rate considerably and deliver the data within the stipulated time period. © 2022 The Authors
ABSTRACT
Smart health is a relatively new paradigm where information and communication technology is utilized to improve health care and medical services. In this article, we provide a literature-based overview of smart health systems, their components, architecture, technologies, benefits, applications, challenges, and opportunities. In addition, we discuss the potential benefits of big data, data analytics, artificial intelligence (AI), and machine learning (ML) in smart health systems. Moreover, we discuss the challenges as well as the open research issues that need further investigation to facilitate the implementation of smart health systems.
ABSTRACT
Growth in technology has witnessed the comfort of an individual in domestic and professional life. Although, such existence was not able to meet the medical emergencies during the pandemic COVID-19 and during other health monitoring scenarios. This demand is due to the untouched Quality of Service network parameters like throughput, reliability, security etc. Hence, remote health monitoring systems for the patients who have undergone a medical surgery, bed ridden patients, autism affected subjects etc is in need that considers postural change and then forward to the caretaker in hospitals through wireless body area networks (WBAN).Security in these data are very important as it deals with the life of a subject. In this work, a Hierarchical Energy Efficient Secure Routing protocol (HEESR) is proposed that categorizes the deployed body nodes in to direct node and relay node based on the threshold vale. Unlike other conventional protocols the cluster head selection is based on the energy levels and the traffic priority data like critical and non-critical data, followed by an optimal route to forward the acquired data is identified and the data is compressed using Huffman encoding technique and encrypted using asymmetric cryptographic aalgorithm for secure data transmission. This protocol mainly appends security and routing efficiency in a hierarchical pattern through data prioritization and out performs the other conventional routing protocols by yielding a better energy consumption of 6%, throughput 92% and security of 93%, which has balanced the packet drop rate considerably and deliver the data within the stipulated time period.
ABSTRACT
At present, COVID-19 is still spreading and affecting millions of people worldwide. Minimizing the need for travel can significantly reduce the probability of infection and improve patients’quality of life. The wireless body area network (WBAN) transmits the patients’physiological data to the doctor remotely through the sensors in a way that minimizes physical contact with others. However, existing WBAN security authentication schemes have core limitation that includes weak authentication performance and over-consumption of resources that precludes their widespread adoption in practical applications. Therefore, in this paper, an enhanced dual-factor authentication system that address the mentioned drawbacks is proposed for securing WBAN resources. By combining iris and electrocardiogram (ECG) features, users would be required to pass the first-level iris authentication before performing the second-level ECG authentication, thus enhancing the overall security scheme of a WBAN system. Furthermore, we examined the existing Inter-Pulse-Intervals (IPI) encoding methods and propose a more efficient ECG IPI encoding algorithm which can effectively shorten the encoding time without affecting the overall encoding performance. Finally, extensive experiments were performed to verify the performance of the proposed dual-factor iris and ECG based WBAN authentication system using public iris and ECG databases. The experimental results show that the false acceptance rate (FAR) is close to 0.0% and the false rejection rate (FRR) is close to 3.2%. Findings from this study suggest that the proposed dual-factor authentication scheme could aid adequate deployment of security schemes to protect WBAN resources in practical applications. IEEE
ABSTRACT
The detection of traces of patients with novel coronavirus pneumonia (COVID-19) is a prerequisite for avoiding the rapid spread of the virus. However, too much patient privacy data uploaded to the cloud centre will overwhelm the network and cause user information security to not be guaranteed. In this paper, we propose a personal prediction method for COVID-19 infections by perceiving the information of worn biosensors and monitoring equipment in a body area network (BAN). Edge computing and blockchain technology are introduced to solve the problems of user privacy protection and perceptual data transmission and storage. We first construct an edge body area network (EBAN) and characterize the maximization function of the edge blockchain cost by considering the constraints on the bandwidth, storage space, and energy consumption. Then we build a blockchain without redundant perception information and select effective transmission paths by using the edge blockchain construction efficiency maximization (EBCEM) algorithm. Finally, we use the network simulator (NS-2) to simulate the performance of the EBCEM algorithm and compare it with the excellent assignment game algorithm (AGA) in terms of the effective requester ratio (ERR), effective provider ratio (EPR), edge blockchain construction success ratio (EBCSR), and average storage usage ratio (ASUR) in the EBAN. Author
ABSTRACT
COVID-19 was first reported in China Wuhan and rapidly grown up to more than 58 countries based on the World Health Organization (WHO). Well ahead of any health emergency, the health care server has the ability to access these data via authorization and then s/he performs necessary actions. In order to protect medical data from malicious activities, authentication is the starting point for this. Authentication systems represent a network support factor to reduce ineffective users and radically eliminate phishing because authentication determines the identity of the real user. Many schemes and technologies have been suggested for authentication in wireless body area networks (WBANs). In this paper, we suggest a strong dynamic password authentication system for WBANs. We adopt a (different/new) way to calculate a password and make it coherent and dynamic for each login session. Our work also provides additional security properties to get rid of hub node impersonation attacks and resolve key escrow issues. Our scheme resist fishing attach which keep patient from any illegal change of drugs. By comparison, the proposed scheme is considered active and has strong security based on formal security analysis tools such as AVISPA. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.
ABSTRACT
Si dettaglia l'evoluzione del prototipo IDEAs (Intelligent Domestic Ecosystem for an Ageing Society) quale modello abitativo integrato da un sistema finalizzato al monitoraggio e supporto delle attivita di vita quotidiane dell'anziano fragile. Dal punto di vista trans-scalare, appare utile ridefinire la smart city in chiave cittadino-centrica: «una citta si puo definire smart quando gli investimenti nel capitale umano e sociale, nelle infrastrutture di collegamento e informatiche alimentano uno sviluppo economico sostenibile che preveda un'alta quality della vita e una gestione oculata delle risorse, all'interno di processi decisionali partecipativi» (Magaro et al., 2020). Le smart homes implicano YInternet of Things (IoT), espressione attribuita a Kevin Ashton del Massachusetts Institute of Technology: alla fine degli anni Novanta, egli sosteneva che una nuova generazione di computer avrebbe utilizzato le reti per lo scambio di informazioni tra le cose (Ashton, 2009). La smart home si caratterizza per la presenza di reti multilivello (Fig. 2) (Marcelino et al., 2018): - BAN, (Body Area Network), attraverso la quale una serie di sensori scambiano dati;- HAN, (Home Area Network) che supporta l'IoT;- WAN, (Wide Area Network) e la rete di prossimita tra l'abitazione, il vicinato e sistema sanitario nazionale. Il prototipo prendera il nome di IDEAs (Intelligent Domestic Ecosystem for an Ageing Society).
ABSTRACT
The rise of antenna technology, smartphones, and the Internet-of-things (IoT) has enabled wearable antennas for wireless communication between implantable devices such as pacemakers, infusion pumps, etc., and external devices for health monitoring. This work describes the key challenges that need to be addressed for such wireless body area network (WBAN) technologies to be integrated into large-scale health monitoring programs. These include the miniaturization of antennas, fabrication techniques to enable mass production, and methods to protect patients from data infringement and hackers. Furthermore, the role of wearable and implantable antennas is pivotal to realize devices for continuous healthcare monitoring especially during Pandemic situations such as Coronavirus Disease-2019 (COVID-19). © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic has had a huge worldwide effect on everyone. A major issue that it has brought to light is the requirement for social distancing resulting in working from home with the ability to utilize video conferencing for meetings. This has become an essential part of everyday life, hence the requirement for reliable computer networks. Coping with social distancing in a medical environment is far more challenging however the use of Wireless Body Area Network (WBAN) and the ability to share the data has become enormous aid. A great deal of development in body sensors has taken place and these devices are becoming commercially available. WBAN devices can be used to measure many human conditions e.g. heart rate, temperature, sleep-patterns etc. to help with diagnosis. Most papers on WBAN concentrate on sensors but this paper investigates the suitability of the technology and the support network required for use in a medical environment. An integrated system using AWS IoT services to support WBAN devices in different environments is covered. The implementation of a smart hub for healthcare monitoring is proposed. The concept of an Intelligent Bed is introduced and a solution is provided consisting of a smart hub attached to a bed. Parameters associated with the wireless characteristics are calculated and use of the reliability of the AWS IoT services for use as a healthcare monitoring system is investigated. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Wireless body area networks (WBANs) play a paramount role in modern health-care systems and bring numerous benefits. Modern WBANs provide a promising service especially for elderly people suffering from heart disease, Alzheimer, etc., which enable them to live safely and independently. Medical institutions tend to use WBANs to provide real-time monitoring of remote patients outside hospitals, which helps saving their lives by means of instant and proper responses at emergency situations. However, in 2020, the world was affected by COVID-19 pandemic and thousands of new cases are discovered daily all over the world. The pandemic provoked a serious lack in professional staff and in many countries there were not enough beds in hospitals for patients with COVID-19 symptoms. This in turn increases the demand for WBANs, which can help medical institutions to withstand harmful consequences of the pandemic and to ensure regular patients monitoring. In this context, very recently, Fotouhi et al. proposed a new lightweight authentication scheme to secure patient's sensitive data in WBANs. The authors claimed that their scheme is secure against various known attacks and is efficient to be applied in practice. However, we analyze Fotouhi et al.'s scheme and find out that their scheme is prone to several attacks. In this paper, we point out the weaknesses associated with their proposed lightweight authentication scheme. © 2021 IEEE
ABSTRACT
Contemporary healthcare systems contain diverse computing devices that construct very complex systems to manage patients‘ data more efficiently. Connected computing devices, such as the Internet of Things (IoT) that may have limited processing powers, have contributed more than ever with the advent of wearable body area networks (WBAN). These devices are connected to other medical devices to share sensitive health data with corresponding entities like hospitals, research institutions, and insurance companies. Since health data are very sensitive, they should be always available to authorized entities and unavailable to other entities. Moreover, COVID-19 pandemic has added additional value to health data which case increases cyber-attacks on (Electronic health) E-health systems with different tools dramatically. In this paper, several cyber-attacks on E-health systems are explored. Particularly, we have focused on attacks to IoT based wearable health devices for body area networks. The paper contains the architecture of wearable health devices to show the potential attack surface. One of the main contributions of the paper is to present cyber-attacks on wearable e-health devices with ground robots. A tactical ground robot is portable devices that may be used to carry out several cyber-attacks on E-health systems. Moreover, the paper contains analyses of the attacks with ground robots. © 2021, TUBITAK. All rights reserved.
ABSTRACT
The rise in life expectancy of humans, COVID-19 pandemic and growing cost of medical services has brought up huge challenges for the government and healthcare industry. Due to unhealthy lifestyle, there is an increased need for continual health monitoring and diagnosis of diseases. Wireless Body Area Network (WBAN) is attracted attention of researchers as various biosensors can be embedded in or worn on the body of human beings for the measurement of health parameters. The patient's health data is then sent wirelessly to the physician for health analysis. The biosensors used to measure physiological parameters have limited power due to its small size and hence smaller form factor. For the longevity of the network, it is imperative to transmit the data in an energy-efficient manner. Moreover, the health information of the patient is stringently private. Hence, the privacy and security of transmitted information needs to be ensured. It necessitates the development of effective, lightweight and secure routing protocols that provides security with minimal use of resources. This paper has identified the numerous security requirements in WBANs and has provided the extensive review on existing secure routing protocols reported in the literature. A comparative analysis of the various existing state-of-the art secure routing protocols and critical analysis based on security techniques along with different performance parameters has been presented.