Wireless Medical Sensor Networks Research Articles

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

Authentication ensures the privacy of patients by enabling access control within wireless medical sensor networks. However, many schemes do not consider the resource-constrained environments, making those protocols unusable. Meanwhile, sensitive patient information continues to be stored and accessed in a central location, increasing the risk of “single points of failure.” To solve this problem, the researchers designed a lightweight anonymous authentication protocol based on blockchain technology and fuzzy extraction. First, they created a multiround session key negotiation mechanism. Then, they utilized the fuzzy extraction function to extract and recover multimodal biometric features. Decentralization was achieved through blockchain and smart contracts. Simultaneously, the researchers also provided a formal security proof by Burrows-Abadi-Needham logic. Finally, experiments using the Java Pairing-Based Cryptography Library show that this scheme outperforms the comparison schemes in terms of computational overhead and communication overhead.

An improved three factor authentication protocol for wireless body area networks

Wireless Body Area Networks (WBAN) is a concrete solution for the healthcare system, and it is paramount to ensure the sensed data by the WBAN sensors is secure against vulnerable threats and inaccessible by unauthorized entities. In light of this, highly robust authentication schemes and solutions are essential for the successful adoption of WBANs. To this end, a plethora of secure authentication solutions have been recommended by investigators over the past few decades. However, the absence of cohesive research work in view of secure mutual authentication schemes does not light on the extensive purpose of offering a bird’s-eye solution to the domain. To address the security issues in the previous schemes, we approach them in the following manner: We reviewed the Ali et al. scheme, a recently proposed authentication scheme for wireless medical sensor networks for healthcare monitoring, asserting that their scheme can withstand various attacks. Despite this, we proved that their scheme still has significant security weaknesses and is vulnerable to various attacks through rigorous cryptanalysis. Afterward, we proposed our three-factor authentication scheme to overcome the vulnerable attacks found in the Ali et al. scheme. Thereafter, we performed an extensive informal and formal analysis using Burrows-Abadi-Needham (BAN) logic to verify the robustness of the proposed scheme. From the analysis, we proved that the proposed authentication scheme is able to withstand various attacks on WBAN. Finally, a critical performance evaluation is done on our scheme. From the results, we identified that our scheme is a robust and identical solution for feature-rich WBAN health-care applications.

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

Abstract Recently, Qiao et al. proposed a novel construction of certificateless aggregate signature (CLAS) scheme to ensure the integrity and authenticity of medical data in healthcare wireless medical sensor networks (HWMSNs). They first created an underlying certificateless signature (CLS) scheme, and then proposed a CLAS scheme from the underlying CLS scheme by adding an aggregation algorithm and a verification algorithm. In this paper, we point out that their CLS scheme is insecure because the Type I adversary can forge valid signatures. That is, the unforgeability is not actually captured by their CLS scheme. Finally, we map our cryptanalysis to the practical application. That is, in the practical application of HWMSNs, the attacker can launch real attack to their CLS scheme using our cryptanalysis to forge signatures. Therefore, Qiao et al.’s CLS scheme can be totally broken.

On the Security of a Blockchain and PUF-Based Lightweight Authentication Protocol for Wireless Medical Sensor Networks

On the Security of a Blockchain and PUF-Based Lightweight Authentication Protocol for Wireless Medical Sensor Networks

FATE: flexible attribute-based traceable encrypted data sharing scheme using smart contracts in wireless medical sensor networks

FATE: flexible attribute-based traceable encrypted data sharing scheme using smart contracts in wireless medical sensor networks

A smart emergency response system based on deep learning and Kalman filter: the case of COVID-19

During an epidemic, the transportation of patients to emergency departments and the monitoring of their physiological parameters pose significant challenges in this critical scenario. Swift and efficient diagnosis has the potential to rescue the lives of these patients. The objective is accomplished through the utilization of deep learning to categorize information into emergencies, prioritizing its dispatch. In this article, we present a sophisticated emergency system that employs deep learning to swiftly transmit vital information from emergency patients to the hospital that can provide the highest quality healthcare for these individuals. The fusion method integrates data obtained and refined from patients' electronic medical records with data acquired by the wireless medical sensor network during the transportation phase. Subsequently, the process of choosing the parameters is employed as inputs to the learning model. The data gathered and educational outcomes, such as emergency notifications, are transmitted through Wi-Fi and 5G devices in our sophisticated system. The proposed contribution achieves a 98% accuracy with a runtime of 1.53 seconds. This discovery demonstrates the efficacy of our system, particularly in the context of epidemic situations such as COVID-19.

A pairing-free certificate-based key-insulated aggregate signature scheme for wireless medical sensor networks

A pairing-free certificate-based key-insulated aggregate signature scheme for wireless medical sensor networks

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Improved Reinforcement Learning for Reliable Routing in Medical Wireless Sensor Networks

Improved Reinforcement Learning for Reliable Routing in Medical Wireless Sensor Networks

A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

AbstractThe dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority.

Enhanced Authentication Scheme for Mobility Model in Medical Wireless Sensor Networks

The advent of wireless sensor networks has brought significant advancements in healthcare, enabling remote interactions between medical professionals and patients. However, ensuring the security of communication in Medical Wireless Sensor Networks (WSNs) poses different challenges. To address this, this paper introduces a novel authentication framework designed for doctors and patients. The proposed mechanism incorporates essential features such as mutual authentication, anonymity, and data integrity, safeguarding the Medical Wireless Sensor Networks (MWSN). Symmetric encryption techniques are employed to maintain the overall security of the system.

3R: A reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead, and energy efficiency. This paper proposes 3R, a reliable multi agent reinforcement learning routing protocol for WMSN. 3R uses a novel resource-conservative Reinforcement Learning (RL) model to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, an energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model only uses local information to avoid the resource burdens and the security concerns of exchanging energy information. Experimental results prove the lightweightness, attacks resiliency and energy efficiency of 3R, making it a potential routing candidate for WMSN.

Data Security in Healthcare Industrial Internet of Things With Blockchain

The Industrial Internet of Things has gained more attention because of the self-governing nature of system configuration with interoperable application connectivity. In the E-Healthcare environment, it cooperates with medical sensors to capture, examine, analyze, preserve, and document day-to-day transactions of patients in real-time. The integration of E-healthcare and IIoT provides an analytical platform to handle a large amount of data with a low cost of cloud-enabling scalable storage. Throughout the transformation, patients’ personal information is at risk while exchanging records over centralized server-based systems. Thus, the rate of node connectivity, failure of parallel data sharing, and deliverance-related issues increase. In this paper, we present solutions in three folds. First, the paper proposes a novel and secure architecture for E-healthcare data security using a blockchain-distributed ledger technology named BHIIoT. Second, the transformation in the lifecycle of medical wireless sensor networks for data management and optimization with a distributed layered hierarchy is developed, which enhances the network resources and increases trust in the blockchain-enabled peer-to-peer environment. Third, the proposed BHIIoT uses the NuCypher threshold re-encryption mechanism for data encryption and protects shared resources in the form of blocks preserved in a blockchain immutable storage. For instance, chain codes are deployed to automate authentication, logging, index information deliverance, and trace transactions to resist illegal activities in the E-Healthcare distributed-application. The customized lightweight blockchain multi-proof-of-work and multi-proof-of-stake are designed with a digital signature for improving the consumption of resources and reducing the load of storage while the transaction process of E-healthcare IIoT is scheduled.

Track and field training information acquisition and feedback of the based wireless medical sensor network

AbstractThe real‐time embedded system is a computer‐based special‐purpose intelligent computer system that meets the strict requirements of the application system for power consumption. With the development of the Internet era, people are increasingly using the Internet to obtain relevant information, while traditional data collection mainly relies on manual operations. Therefore, the collection, storage and transmission of track and field training information based on existing equipment and databases has become a research hotspot. Based on wireless medical sensor network, this paper proposes an efficient track and field training information collection and feedback system communication protocol for real‐time transmission of various data of track and field athletes, and a real‐time embedded system for data collection and storage. Through research and analysis, it is found that the collection and feedback of track and field training information based on wireless medical sensor network can improve the accuracy of collection by 8.625. This shows that the collection and feedback of sports training information based on wireless medical sensor network is feasible.

Improved Security of a Pairing-Free Certificateless Aggregate Signature in Healthcare Wireless Medical Sensor Networks

Recently, Zhan et al. presented a pairing-free certificateless aggregation signature scheme (ZH-CLAS) to address security issues in healthcare wireless medical sensor networks (HWMSNs), and proved that their scheme is secure against adaptive chosen message attacks. In this article, we analyze the security of the ZH-CLAS scheme by utilizing two types of concrete attacks. Unfortunately, we demonstrate that their scheme cannot withstand public key replacement attacks and is not secure against coalition attacks from malicious sensor nodes. To solve these security challenges, we further improve the security of the ZH-CLAS scheme. Our enhanced scheme has a fixed-length aggregate signature, which efficiently minimizes the transmission bandwidth. Additionally, our scheme outperforms related pairing-free CLAS schemes in terms of security and communication performance.

Bandwidth and power efficient lightweight authentication scheme for healthcare system☆☆☆☆☆☆

With the emergence of the Internet of Things (IoT) and the rapid advancement in sensor technology, wireless medical sensor networks (WMSNs) have become increasingly important in improving health services. WMSNs are deployed for remote monitoring and collecting physiological data of patients for diagnosis and treatment. With the assistance of wearable and mobile devices, IoT-based innovative healthcare solution is extensively adopted to provide better, more precise, and quicker diagnoses. However, the WMSN of healthcare is bandwidth limited and has low latency, while the resource-limited sensors and tremendous IoT output cannot run on practiced security protocols that involve complex encryption/decryption operations. A security system that requires minimal computation and communication time to reduce delay and save the devices’ power source is much needed. Therefore, this article presents a lightweight and robust authentication protocol for the healthcare system that can be used with power and bandwidth-limited devices. Furthermore, this scheme uses random numbers instead of complex encryption/decryption to reduce computation time and energy consumption. The security and performance analysis results show that the proposed method performs better than its competitors regarding security, communication, and computation costs and prolongs the network lifetime.

A Security-Enhanced and Privacy-Preserving Certificateless Aggregate Signcryption Scheme-Based Artificial Neural Network in Wireless Medical Sensor Network

With the recent advances in mobile sensing and beyond-5G technologies, wireless medical sensor networks (WMSNs) have become an emerging hot topic in smart healthcare. Under the condition of limited resources of medical sensor nodes (MSNs), ensuring the accuracy and integrity of a large number of patients’ medical data is a crux for security and data privacy issues. In order to overcome the limitations of medical sensors in communication and computing capabilities, and to achieve privacy in WMSN, we propose a low complexity certificateless aggregate signcryption (CL-ASC) scheme-based artificial neural network (NN) for safely achieving authentication and unforgeability of medical nodes. We use an adaptive sigma filter for denoising, adopt Levenshtein entropy for encoding, and design cryptographic protocol for signcrypting in the hidden layer of deep feedforward artificial NN (DFANN). The CL-ASC can authenticate all sensing bioinformation at once in a privacy-preserving way. Given the challenge of applying a discrete logarithm and computational Diffie-Hellman problems based on an elliptic curve, the proposed scheme satisfies the indistinguishability against chosen-ciphertext attacks (IND-CCA) and the adaptive chosen message attack (UF-CMA) security properties under the random oracle model (ROM). Finally, the benchmark evaluation shows that ours is ahead of similar state-of-the-art schemes, due to its low complexity and higher computational efficiency. Researchers interested in the provable security protocol design and framework of NNs can gain useful insights from this research and identify future cross domain of cybersecurity and NNs research directions based on the proposed scheme herein.

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.