Wireless Medical Sensor Research Articles

Multi-OLT PON-based open access network for multi-service performance tuning via the Event-Horizon algorithm

A multi-optical line terminal (multi-OLT) passive optical network (PON) is gaining attention for its multi-access, wide coverage, and integration capabilities. It outperforms conventional single OLT PON, reducing operational complexities, packet delay, and loss. A novel multi-OLT PON-based open-access network (OAN) architecture is proposed that integrates various service providers (SPs) as well as different access networks. Optical network unit structure is also designed to support differential access. The challenging issue here is to incorporate heterogeneous SPs and provoke downstream service with their application diversity. This paper presents what we believe is a novel algorithm named Event-Horizon (EH) to afford heterogeneous downstream services from different SPs for IEEE PONs [Ethernet PON (EPON) and 10 gigabit EPON (10G EPON)]. It incites SP competitiveness and ensures a user experience of seamless migration from service to service. Downstream resource allocation is an atypical issue in the PON area. There are reported numerous online schemes with prediction for the upstream side. Since network data is highly bursty and unpredictable, the EH algorithm uses the machine-learning-based bootstrapping method as its prediction tool. The algorithm improves downstream performance by four distinctive features. First, bandwidth is guaranteed among shared SPs according to service level agreements. Second, bandwidth is allocated for reported data; then excess bandwidth is reallocated as predicted (by bootstrapping) differed data. Third, a donor SP preserves margin before excess bandwidth release, which is a nonlinear process featuring network dynamics. Fourth, the margin satisfaction factor is formulated for tuning multi-service user satisfaction according to quality of service. The simulation is performed by a model OAN showing an emergency medical wireless sensor network (WSN) as the master shareholder OLT and a fiber to the home (FTTH) as a subsidiary one. Performance tuning on the margin reservation of 1% for the WSN case study shows 99.4% network efficiency with maximum jitter of 50 µs up to the full load owing 97.26% throughput. Network delay justifies CISCO and ITU-T requirements for real-time IP traffic. FTTH experiences the packet loss of 0.68% using the EH algorithm at the full load, maintaining WSN emergency service packet loss<0.003%.

A Secure and Anonymous Authentication Protocol Based on Three-Factor Wireless Medical Sensor Networks

Wireless medical sensor networks (WMSNs), a type of wireless sensor network (WSN), have enabled medical professionals to identify patients’ health information in real time to identify and diagnose their conditions. However, since wireless communication is performed through an open channel, an attacker can steal or manipulate the transmitted and received information. Because these attacks are directly related to the patients’ lives, it is necessary to prevent these attacks upfront by providing the security of WMSN communication. Although authentication protocols are continuously developed to establish the security of WMSN communication, they are still vulnerable to attacks. Recently, Yuanbing et al. proposed a secure authentication scheme for WMSN. They emphasized that their protocol is able to resist various attacks and can ensure mutual authentication. Unfortunately, this paper demonstrates that Yuanbing et al.’s protocol is vulnerable to smart card stolen attacks, ID/password guessing attacks, and sensor node capture attacks. In order to overcome the weaknesses and effectiveness of existing studies and to ensure secure communication and user anonymity of WMSN, we propose a secure and anonymous authentication protocol. The proposed protocol can prevent sensor capture, guessing, and man-in-the-middle attacks. To demonstrate the security of the proposed protocol, we perform various formal and informal analyses using AVISPA tools, ROR models, and BAN logic. Additionally, we compare the security aspects with related protocols to prove that the proposed protocol has excellent security. We also prove the effectiveness of our proposed protocol compared with related protocols in computation and communication costs. Our protocol has low or comparable computation and communication costs compared to related protocols. Thus, our protocol can provide services in the WMSN environment.

PCAS: Cryptanalysis and improvement of pairing-free certificateless aggregate signature scheme with conditional privacy-preserving for VANETs

Certificateless aggregate signature (CLAS) is an effective approach for the multiple authentications in multi-users and multi-information environments like the vehicular ad hoc networks (VANETs), which can address the certificate management issue in the traditional public key cryptography (PKC) and solve the key escrow problem in identity-based cryptography (IBC). In recent years, a new CLAS mechanism (LICLAS) is presented for the authentication of sensors in healthcare wireless medical sensor networks (HWMSNs). In this paper, we make an analysis and prove that LICLAS is not able to against forgery attacks. Thus, we present an improved CLAS scheme (PCAS) for vehicle authentication in VANETs, which satisfies the privacy and security requirements. PCAS constructs the signature algorithm based on Elliptic Curve Cryptography (ECC) to avoid bilinear pairing and Map-to-Hash function operations. In order to further improve security and efficiency of PCAS, the vehicle key generation and signature algorithm is revised. The pseudonym mechanism is also adopted to achieve conditional privacy preservation. Besides, PCAS allows to execute a batch verification of messages, where RSUs may verify the aggregated signatures from vehicles to reduce time overhead. Under the assumption that the elliptic curve discrete logarithm problem (ECDLP) is hard, PCAS is proved to be existentially unforgeable against adaptive chosen message attacks in the random oracle model. Through the performance analysis, under the same time complexity, PCAS is shown to more effective. Compared with LICLAS, for a single message and 2000 messages, the transmission overhead of PCAS is reduced by 25% and 25% respectively, and the computation overhead is reduced by 16.56% and 25.34% respectively.

Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning.

Medical cyber-physical systems (MCPS) represent a platform through which patient health data are acquired by emergent Internet of Things (IoT) sensors, preprocessed locally, and managed through improved machine intelligence algorithms. Wireless medical cyber-physical systems are extensively adopted in the daily practices of medicine, where vast amounts of data are sampled using wireless medical devices and sensors and passed to decision support systems (DSSs). With the development of physical systems incorporating cyber frameworks, cyber threats have far more acute effects, as they are reproduced in the physical environment. Patients' personal information must be shielded against intrusions to preserve their privacy and confidentiality. Therefore, every bit of information stored in the database needs to be kept safe from intrusion attempts. The IWMCPS proposed in this work takes into account all relevant security concerns. This paper summarizes three years of fieldwork by presenting an IWMCPS framework consisting of several components and subsystems. The IWMCPS architecture is developed, as evidenced by a scenario including applications in the medical sector. Cyber-physical systems are essential to the healthcare sector, and life-critical and context-aware health data are vulnerable to information theft and cyber-okayattacks. Reliability, confidence, security, and transparency are some of the issues that must be addressed in the growing field of MCPS research. To overcome the abovementioned problems, we present an improved wireless medical cyber-physical system (IWMCPS) based on machine learning techniques. The heterogeneity of devices included in these systems (such as mobile devices and body sensor nodes) makes them prone to many attacks. This necessitates effective security solutions for these environments based on deep neural networks for attack detection and classification. The three core elements in the proposed IWMCPS are the communication and monitoring core, the computational and safety core, and the real-time planning and administration of resources. In this study, we evaluated our design with actual patient data against various security attacks, including data modification, denial of service (DoS), and data injection. The IWMCPS method is based on a patient-centric architecture that preserves the end-user's smartphone device to control data exchange accessibility. The patient health data used in WMCPSs must be well protected and secure in order to overcome cyber-physical threats. Our experimental findings showed that our model attained a high detection accuracy of 92% and a lower computational time of 13 sec with fewer error analyses.

ECCbAS: An ECC based authentication scheme for healthcare IoT systems

Smart technology is a concept for efficiently managing smart things such as vehicles, buildings, home appliances, healthcare systems and others, through the use of networks and the Internet. Smart architecture makes use of technologies such as the Internet of Things (IoT), fog computing, and cloud computing. The Smart Medical System (SMS), which is focused on communication networking and sensor devices, is one of the applications used in this architecture. In a smart medical system, a doctor uses cloud-based applications such as mobile devices, wireless body area networks, and other cloud-based apps to provide online therapy to patients. Consequently, with the advancement and growth of IoT and 6G wireless technology, privacy and security have emerged as two of the world’s most important issues. Recently, Sureshkumar et al. proposed an authentication scheme for medical wireless sensor networks (MWSN) by using an Elliptic Curve Cryptography (ECC) based lightweight authentication protocol and claimed that it provides better security for smart healthcare systems. This paper will demonstrate that this protocol is susceptible to attacks such as traceability, integrity contradiction, and de-synchronization with the complexity of one run of the protocol and a success probability of one. Furthermore, we also propose an ECC based authentication scheme called ECCbAS to address the Sureshkumar et al. protocol’s vulnerabilities and demonstrate its security using a variety of non-formal and formal methods.

An Efficient Lightweight Provably Secure Authentication Protocol for Patient Monitoring Using Wireless Medical Sensor Networks

An Efficient Lightweight Provably Secure Authentication Protocol for Patient Monitoring Using Wireless Medical Sensor Networks

A Robust Authentication Protocol for Wireless Medical Sensor Networks Using Blockchain and Physically Unclonable Functions

Wireless medical sensor networks (WMSNs)-based medical systems are an emerging paradigm of the Internet of Medical Things (IoMT) in which the patients and doctors can access various healthcare services via wireless communication technology without visiting the hospital in person. However, an adversary attempts a variety of security attacks because the sensitive information in various fields is exchanged via an insecure channel. Thus, robust and lightweight authentication protocols are essential for providing dependable healthcare services in WMSN-based medical systems. Recently, Wang et al. (IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3117762) proposed blockchain and physically unclonable functions (PUFs)-based lightweight authentication protocol for WMSN. They claimed that their protocol is resistant to cyber and physical security threats and also does provide necessary security requirements. However, we prove that their protocol is vulnerable to various security attacks, such as man-in-the-middle and session key disclosure attacks and also lacks mutual authentication. As a result, we propose a robust authentication protocol for WMSN using blockchain and PUF to address the security problems raised by Wang et al.’s scheme. we assess the security of the proposed scheme by using informal and formal security analyses, such as AVISPA simulation and the ROR oracle model. Furthermore, we present the testbed experiments using Raspberry PI 4 based on MIRACL Crypto SDK. Then, we show the performance of the enhanced scheme compared with related schemes based on testbed experiments. Consequently, our scheme is better suited for practical WMSN-based medical systems because it provides greater security and efficiency than competing schemes.

A Novel Construction Of Certificateless Aggregate Signature Scheme For Healthcare Wireless Medical Sensor Networks

Abstract To ensure privacy and security of healthcare wireless medical sensor networks (HWMSNs), several concrete constructions of efficient certificateless aggregate signature (CLAS) scheme without bilinear pairing were proposed in the last few years. However, many previous constructions of CLAS scheme were found to be impractical, which either fail to meet the claimed security or contain design flaws. For example, in some of the previous proposals, any adversary can forge a valid signature on any new message. In this paper, we first demonstrate some security issues and design flaws in the previous proposals of CLAS scheme. As follows, to further address the above deficiencies, a new construction of CLAS scheme with improved security is presented, and the formal security proof is given using Forking Lemma in the random oracle model, assuming that the discrete logarithm problem is hard. Compared with the previous CLAS schemes, our construction has similar computational costs, and it provides better security guarantees. Therefore, compared with the existing solutions, our proposal with strong security and high computational efficiency is more suitable for use in HWMSNs.

A Quantum Secure and Noninteractive Identity-Based Aggregate Signature Protocol From Lattices

The aggregate signature is a special signature mode widely used in the wireless medical sensor network. The aggregate signature can reduce bandwidth and storage space in this sensor network by aggregating multiple signatures from different signers into a short signature. Traditional aggregate signature protocols based on the discrete logarithm problem and the great integer factorization problem cannot resist quantum attacks. In this article, we propose a new noninteractive identity-based aggregate signature protocol from lattices. We prove that the proposed protocol is secure in the quantum random oracle model. Additionally, the performance of the proposed protocol is much better than most previous protocols according to our experiment. Meanwhile, the size of the aggregate signature in our protocol is a logarithmic function of the number of signatures being aggregated.

A PUF-based anonymous authentication protocol for wireless medical sensor networks

A PUF-based anonymous authentication protocol for wireless medical sensor networks

Secured Health Monitoring System Using AES

Wireless medical sensor network is used in healthcare applications that have the collections of biosensors connected to a human body or emergency care unit to monitor the patient's physiological vital status. The real-time medical data collected using wearable medical sensors are transmitted to a diagnostic centre. The data generated from the sensors are aggregated at this centre and transmitted further to the doctor's personal digital assistant for diagnosis. The unauthorised access of one's health data may lead to misuse and legal complications while unreliable data transmission or storage may lead to life threatening risk to patients. So, this system uses Advanced Encryption Standard (AES) algorithm to encrypt the data to make it secured transmission and access control system for medical sensor network. Further the data is sent to a centralised server through a wireless network. In this case this server can be a Personal Computer (PC) connected to the same network, for this transmission User Datagram Protocol (UDP) can be used. This data can be accessed at the receiver side only.

Authentication Schemes for Healthcare Applications Using Wireless Medical Sensor Networks: A Survey.

Many applications are developed with the quick emergence of the Internet of things (IoT) and wireless sensor networks (WSNs) in the health sector. Healthcare applications that use wireless medical sensor networks (WMSNs) provide competent communication solutions for enhancing people life. WMSNs rely on highly sensitive and resource-constrained devices, so-called sensors, that sense patients’ vital signs then send them through open channels via gateways to specialists. However, these transmitted data from WMSNs can be manipulated by adversaries without data security, resulting in crucial consequences. In light of this, efficient security solutions and authentication schemes are needed. Lately, researchers have focussed highly on authentication for WMSNs, and many schemes have been proposed to preserve privacy and security requirements. These schemes face a lot of security and performance issues due to the constrained devices used. This paper presents a new classification of authentication schemes in WMSNs based on its architecture; as far as we know, it is the first of its kind. It also provides a comprehensive study of the existing authentication schemes in terms of security and performance. The performance evaluation is based on experimental results. Moreover, it identifies some future research directions and recommendations for designing authentication schemes in WMSNs.

Certificate-Based Anonymous Authentication With Efficient Aggregation for Wireless Medical Sensor Networks

Wireless medical sensor networks (WMSNs) have aroused widespread attention in recent years with the development of Internet of Things (IoT) technology. WMSNs offer many new opportunities for healthcare professionals to monitor patients and patient self-monitoring. To overcome the resource (such as memory and power) limitations of sensors and attain data security of patients’ private medical information, researchers have designed plenty of work for securing WMSNs. For years, certificate-based aggregate signature (CBAS) schemes have been put forward for WMSNs to prevent patients’ sensitive medical data from being tampered with and damaged. In this work, we analyze the security flaws of a very recent CBAS scheme proposed by Verma <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2021) by presenting two types of security attacks. We later propose a CBAS scheme with user anonymity protection for WMSNs and prove its security based on the standard cryptographic assumption. The performance comparison results from theory and experiment illustrate the practicality of our design.

An improved pairing-free certificateless aggregate signature scheme for healthcare wireless medical sensor networks.

In healthcare wireless medical sensor networks (HWMSNs), the medical sensor nodes are employed to collect medical data which is transmitted to doctors for diagnosis and treatment. In HWMSNs, medical data is vulnerable to various attacks through public channels. In addition, leakage of patients’ information happens frequently. Hence, secure communication and privacy preservation are major concerns in HWMSNs. To solve the above issues, Zhan et al. put forward a pairing-free certificateless aggregate signature (PF-CLAS) scheme. However, according to our cryptanalysis, the malicious medical sensor node (MSNi) can generate the forged signature by replacing the public key in the PF-CLAS scheme. Hence, to address this security flaw, we design the improved PF-CLAS scheme that can achieve unforgeability, anonymity, and traceability. Since we have changed the construction of the partial private key, the improved PF-CLAS scheme can resist Type I and Type II attacks under the Elliptic Curve Discrete Logarithm assumption. In terms of the performance evaluation, the proposed scheme outperforms related CLAS schemes, which is more suitable for HWMSNs environments.

Protocol Wireless Medical Sensor Networks in IoT for the Efficiency of Healthcare

In this article, a new medical communication scheme, protocol wireless medical sensor networks for the efficiency of healthcare (PWMSN4EoCH), shorten by (PEH), which uses hasty strategy and random network coding (RNC), is proposed. The new concept improves the performance of the healthcare network. It quickly analyzes the medical network description, focusing on some basic parameters for narrowband Internet of Things (NB-IoT) systems in wireless mesh networks (WMNs). This PEH effectively meets the requirements prescribed for wireless telemedicine applications in which medical sensors (MSs) share the downlink and uplink resources to its neighborhood, including wireless health hubs (WHHs) and wireless base stations (WBSs) for controlling the health of the human body. The PEH scheme substantially accelerates the implementation devices of telemedicine for patient satisfaction. In contrast, the state-of-the-art technique (SoAT) scheme, which is currently used, misses the entirety of the proposed principle. The proposed scheme is compared with the SoAT in terms of message size (bytes), round-trip time (RTT) (ms), overall network capacity (ONC) (bytes/s, and delivery delay (DD) in ms. Our investigation has proved that the RTT, ONC, and DD of the proposed PEH are much better than the SoAT schemes, achieving 64%, 66%, and 71%, respectively. The simulation studies clearly indicate that the PEH introduces more than 64% performance enhancement over the SoAT scheme.

Public auditing for real-time medical sensor data in cloud-assisted HealthIIoT system

With the advancement of industrial internet of things (IIoT), wireless medical sensor networks (WMSNs) have been widely introduced in modern healthcare systems to collect real-time medical data from patients, which is known as HealthIIoT. Considering the limited computing and storage capabilities of lightweight HealthIIoT devices, it is necessary to upload these data to remote cloud servers for storage and maintenance. However, there are still some serious security issues within outsourcing medical sensor data to the cloud. One of the most significant challenges is how to ensure the integrity of these data, which is a prerequisite for providing precise medical diagnosis and treatment. To meet this challenge, we propose a novel and efficient public auditing scheme, which is suitable for cloud-assisted HealthIIoT system. Specifically, to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices, a new online/offline tag generation algorithm is designed to improve preprocessing efficiency; to protect medical data privacy, a secure hash function is employed to blind the data proof. We formally prove the security of the presented scheme, and evaluate the performance through detailed experimental comparisons with the state-of-the-art ones. The results show that the presented scheme can greatly improve the efficiency of tag generation, while achieving better auditing performance than previous schemes.Graphical abstract

Blockchain and PUF-Based Lightweight Authentication Protocol for Wireless Medical Sensor Networks

Due to the emergence of heterogeneous Internet of Medical Things (IoMT) (e.g., wearable health devices, smartwatch monitoring, and automated insulin delivery systems), large volumes of patient data are dispatched to central cloud servers for disease analysis and diagnosis. Although this direct mode brings a lot of convenience for both patients and medical professionals (MPs), the open communication channel between them also incurs several security and privacy issues, such as man-in-the-middle attacks, eavesdropping attacks, and tracking attacks. Based on the unsolved challenges in wireless medical sensor networks (WMSNs), several researchers have proposed various authentication and key agreement (AKA) protocols for this type of healthcare system recently. However, most of these protocols do not perceive physical-layer security and over-centralized server problem in WMSN. In this article, to address these two open problems, we propose a lightweight and reliable authentication protocol for WMSN, which is composed of cutting-edge blockchain technology and physically unclonable functions (PUFs). In addition, a fuzzy extractor scheme is introduced to deal with biometric information. Subsequently, two security evaluation methods are used to prove the high reliability of our proposed scheme. Finally, performance evaluation experiments illustrate that the proposed mutual authentication protocol requires the least computation and communication cost among the compared schemes.

A smart secure healthcare monitoring system with Internet of Medical Things

The new era of information technologies made smart healthcare by including edge, Internet of Things (loT), Internet of Medical Things (IoMT), cloud computing etc. This adaptation has partially replaced the old medical system, making healthcare more efficient, easy, and customized. Wireless medical sensors are used in the smart healthcare sector to capture the patient’s health data and transmit it to the remote server for treatment or diagnostic purposes. Every time a cloud-based solution is adopted to collect and preserve the sensitive and personal health information. This collected patient data is shared between the different stakeholders across the network. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. Secure data access is a big challenge in such systems. In this paper, a lightweight authentication technique with the privacy-preserving schema using fully homomorphic encryption is proposed. This approach can be used for a secure access of patient data through online mode, and also the data can be shared in an encrypted format for desired purposes of stakeholders. The proposed authentication scheme is for Internet of Medical Things that can resist all network attacks and is easy to implement. The performance analysis of the proposed work shows that overall execution time (80.03%), communication cost (15.62%) and energy consumption (79.19%) is reduced in comparison with existing work.

A Review on WMSN (Wireless Medical Sensor Networks) for Health Monitoring Systems

Wireless Medical Sensor (WMSN) networks have become a great technology to allow for the best kind of applications. Recent developments at WMSN have helped to create a comprehensive health monitoring concept in both home and hospital care settings. Current technological advances in sensors, integrated dynamic circuits, and wireless connectivity have allowed the event of smaller, heavier, less expensive, and clever body sensors. These modules are able to hear, process, and communicate with one or more important signals. In addition, they will be used on local wireless networks (WPANs) or wireless nerve networks (WBSNs) to monitor health. Many studies are being conducted and/or ongoing to develop flexible, reliable, secure, realistic, and effective WBSNs for health applications. The importance of wireless nerve networks in health surveillance is constantly evolving, due to the growing need for safety and security in cities. The rapid development of wireless technology has significantly aided the advancement of monitoring systems that have been developed through the combination of wireless sensors. The wireless health monitoring introduces emerging innovations with significant advantages when compare to conventional communication networks, which has the advantages of lowering the cost of installation and maintenance while increasing the effectiveness of the health monitoring system in the field of medicine.

Efficient data aggregation technique for medical wireless body sensor networks

AbstractA central issue in Wireless Body Sensor Networks (WBSNs) is the large amount of measurement data for monitoring vital parameters, which need to be continuously measured, immediately processed and timely transmitted. This requires a big storage space and computing effort leading to a high-power consumption. Reducing the amount of transmitted data contributes significantly to an extension of the sensor operation time. In this contribution, we focus exactly at this aspect. We propose a data aggregation method based on Artificial Neural Networks (ANN) combining multiple physiological signals, which are the ElectroCardioGram (ECG), ElectroMyoGram (EMG) and Blood Pressure (BP), in one signal before transmission. The simulation and implementation results reveal a reduction of energy consumption to 87.32 %, ensuring a high accuracy level (80.53 %) and a relatively execution time (48.47 ms).