Advances on networked ehealth information access and sharing: Status, challenges and prospects

With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.

The upcoming paradigm in Internet of Things (IoT) based applications is to afford effective interactional communication strategies between the devices in the smart home system. With the rapid growth of IoT services, the incorporation of security measures becomes a vital concern. The general issue faced in the security of the intercommunication between the devices and the users is improper authentication between them. Also, the access control of devices must be ensured with reliable features for establishing secure communication between the users and devices. Hence, we propose a protocol called Modified Honey Encryption using Inverse Sampling-Conditional Probability Model Transform (MHE-IS-CPMT) with Elliptic Curve Cryptography (ECC) to authenticate and perform the key agreement. Here, we employ the following steps: (1) Initialization, (2) Registration, (3) Login and data access Request, (4) Authentication and Session key agreement, and (5) Key update. At the commencement of the session, the users (u), Mobile Users (MU), and the other devices participating in the smart home system are initialized to the Home network head (H). Then, for the registration process, the user and the devices register them into H via the smart gateway (SG) by providing their own identities. The user details and the data about the devices are secured using the MHE-IS-CPMT with the ECC method. Next, during the login process, the registered users connect to the smart home system and send a request to SG to gain access to the devices. After verification, the user is authenticated and the system enables them to acquire the device access control by providing them with the private key of the device. In addition, the proposed system facilitates the secure key change procedure for the legitimate user to update their key whenever required. Hence, the performance of the model is secured against different types of attacks and also obtains more security features than existing methods.

Wireless body area networks (WBANs) are expected to play an important role in monitoring the health information and creating a smart, reliable, and ubiquitous healthcare system. Only authorized users can access the network since the collected data by the WBANs are used to diagnosed and treated. However, it is still a challenging task to design a cost-effective and secure access control scheme because of inherent characteristics of the WBANs, such as open medium channel, limited resources of sensor nodes, and the absence of fixed infrastructure. In this paper, we first propose a novel certificateless signcryption scheme, and then, design a cost-effective and anonymous access control scheme for the WBANs using the novel signcryption. The proposed access control scheme achieves anonymity, confidentiality, authentication, integrity, and nonrepudiation. Compared with existing three access control schemes for the WBANs, our proposed scheme has the least computational cost and total energy consumption for the controller.

An adaptive access control scheme based on trust degrees for edge computing

Chapter 4 - The role of Internet of Things for adaptive traffic prioritization in wireless body area networks

Whenever thought about mass deaths of people, it was either from a flood, drought, or an explosion caused by a missile or bomb, but these days the significant risk to the human lives from the disease named “Cancer”. Cancer is largest cause of death in the world . To prevent the Cancer Disease from disseminate, regular identifying and monitoring of infected patients is needed. In this regard, Wireless Body Area Network (WBAN) can be used in conjunction with the Internet of Things (IoT) and Multi-Criteria Decision- Making Method (MCDM) for identifying and monitoring the human body for health-related information, which in turn can aid in early diagnosis of diseases. WBAN makes use of the biosensors in, on or around the body, to ensure the measurement of the biological specifications of the human body, which are designed for the healthcare application. This paper proposes a novel WBAN framework for detection of an early stage of Cancer which based on IoT to receive the biosensor signals from the patient and evaluate symptoms of disease and help healthcare professionals to improve and enhance cancer treatment . Due to resemblance of Cancer symptoms with several other diseases, this paper also presents the Promethee MCDM to help medical professionals identify the disease that the patient might be suffering from or suffer without Cancer. This methodology relies on collected information from the proposed WBAN framework, and it can deal with the complexity of imprecision and confusion arising from the relative priority scales of various disease symptoms. Keywords: Cancer; Wireless Body Area Network; Internet of Things; Multi-Criteria Decision-Making; Preference ranking organization method for Enrichment Evaluation

The proliferation of Internet of Things (IoT) devices in smart environments has created unprecedented challenges in identity management and security. Traditional centralized identity management systems face scalability, privacy, and single-point-of-failure issues when applied to IoT ecosystems. This paper presents a novel blockchain-based framework for decentralized identity management in smart IoT environments. Our proposed framework leverages blockchain technology's immutable ledger, smart contracts, and cryptographic mechanisms to provide secure, scalable, and privacy-preserving identity management for IoT devices. The framework incorporates a multi-layered security architecture that includes device authentication, access control, and identity verification mechanisms. Experimental results demonstrate that our approach achieves 99.7% authentication accuracy with reduced latency compared to traditional centralized systems. The framework also provides enhanced privacy protection through zero-knowledge proofs and selective disclosure mechanisms. This research contributes to the advancement of secure IoT identity management and provides a foundation for future developments in decentralized IoT security

The current access control of IoT devices adopts the traditional method of centralized control, which leads to the shortcomings of single point of failure and low efficiency of the entire system. Due to the increasing amount of data generated by the Internet of Things network and the more complex data structure, the traditional access control system has exposed the problem of insufficient security when communicating. Based on the characteristics of blockchain decentralization, non-tampering, and programmable Hyperledger, an attribute-based access control mechanism combining blockchain with the Internet of Things is proposed to realize dynamic changes to the access control strategy and ensure the access control of the Internet of Things Safety and reliability.

The Internet of Things (IoT) is an essential component of the digital age, particularly in ensuring reliable and efficient operations as well as the timely and precise recognition of anomalies within IoT systems. However, anomaly detection in time-series data—especially data collected by edge devices—poses several challenges, including concerns over data privacy and communication overhead. To address these issues, this research proposes a novel deep learning (DL)-based anomaly detection model capable of being trained in real-time on edge devices within an IoT environment. The framework ensures user privacy by enabling distributed edge devices to cooperatively train an anomaly recognition system. This study introduces a new, Intelligent Shark Smell Tuned Deep Isolation Forest (ISS-DIF), which effectively detects anomalies and identifies outliers in industrial IoT sensor data with high accuracy. The hybrid model isolates anomalies rather than profiling normal data, making it particularly suitable for identifying rare anomalies within large datasets. Industrial data are collected from real-world manufacturing environments using IoT edge devices. Following data collection, median filtering is applied to reduce noise, and min-max scaling is employed for data normalization. The ISS component allows for fine-tuning the hyperparameters of the Deep Isolation Forest (DIF) to optimize detection performance. The DIF model is tailored to enhance anomaly detection capabilities in sensor data from industrial IoT applications. For validation, 80% of the dataset was used for training, while the remaining 20% served as the test set. The results demonstrate that the proposed ISS-DIF framework achieved superior training accuracy of 99.30%, with precision of 97.89%, recall of 98.76%, and an F1-score of 97.21%, outperforming the testing metrics. This approach integrates real-time anomaly detection with the processing capabilities of edge devices, thereby improving IoT data analytics and providing a scalable, efficient solution that preserves privacy in irregularity recognition within IoT environments.

With rapid advancements in the technology, almost all the devices around are becoming smart and contribute to the Internet of Things (IoT) network. When a new IoT device is added to the network, it is important to verify the authenticity of the device before allowing it to communicate with the network. Hence, access control is a crucial security mechanism that allows only the authenticated node to become the part of the network. An access control mechanism also supports confidentiality, by establishing a session key that accomplishes secure communications in open public channels. Recently, blockchain has been implemented in access control protocols to provide a better security mechanism. The foundation of this survey article is laid on IoT, where a detailed description on IoT, its architecture and applications is provided. Further, various security challenges and issues, security attacks possible in IoT and their countermeasures are also provided. We emphasize on the blockchain technology and its evolution in IoT. A detailed description on existing consensus mechanisms and how blockchain can be used to overpower IoT vulnerabilities is highlighted. Moreover, we provide a comprehensive description on access control protocols. The protocols are classified into certificate-based, certificate-less and blockchain-based access control mechanisms for better understanding. We then elaborate on each use case like smart home, smart grid, health care and smart agriculture while describing access control mechanisms. The detailed description not only explains the implementation of the access mechanism, but also gives a wider vision on IoT applications. Next, a rigorous comparative analysis is performed to showcase the efficiency of all protocols in terms of computation and communication costs. Finally, we discuss open research issues and challenges in a blockchain-envisioned IoT network.

The Internet of things (IoT) is an active, real-world area in need of more investigation. One of the top weaknesses in security challenges that IoTs face, the centralized access control server, which can be a single point of failure. In this paper, Dynamic-IoTrust, a decentralized access control smart contract based aims to overcome distrusted, dynamic, trust and authentication issues for access control in IoT. It also integrates dynamic trust value to evaluate users based on behavior. In particular, the Dynamic-IoTrust contains multiple Main Smart Contract, one Register Contract, and one Judging Contract to achieve efficient distributed access control management. Dynamic-IoTrust provides both static access rights by allowing predefined access control policies and also provides dynamic access rights by checking the trust value and the behavior of the user. The system also provides to detected user misbehavior and make a decision for user trust value and penalty. There are several levels of trusted users to access the IoTs device. Finally, the case study demonstrates the feasibility of the Dynamic-IoTrust model to offer a dynamic decentralized access control system with trust value attribute to evaluate the internal user used IoTs devices.

Internet of Medical Things (IoMT) has revolutionized the modern healthcare by integrating medical devices and real-time patient monitoring into it. However, the limited resource environment in IoMT devices makes the deployment of strong security mechanisms a great challenge. In this paper, lightweight cryptography-based security mechanisms particularly for node authentication and access control in IoMT systems are analyzed. The particular security needs of IoMT systems are considered, and reviewed the new developments in lightweight cryptography for defending against specific cyber-attacks including impersonation, replay and unauthorized access. A comparison of current authentication protocols and access control technologies which evaluates computational cost, scalability, and attack tolerance is presented. The recent trends and future research directions on improving the resilience of IoMT networks with minimal overhead is also discussed. With the proliferation of IoMT usage, it is more important than ever for patient data to remain confidential and for the medical systems to function correctly. This paper focuses on the security versus device trade-offs, and presents cryptography tools that achieve a good trade-off between the strength obtained and the power, memory, and processing limitations. By examining the present situation and open challenges, this research work is an attempt to pave the way for designing next-generation lightweight cryptography for secure and efficient IoMT systems. This research is helpful for numerous scholars and medical experts to analyse the advantages and limitations of traditional authentication and access control models in IoMT and to design innovative solutions.

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

The application of tiny body sensors to collect, process, store, analyze, and retrieve medical information from a human body is a part of the Internet of Medical Things (IoMT). IoMT helps to monitor and track human vital health parameters, predict disease, notify the patients and the health care professionals with relevant data for analyzing the problems before they become severe and for earlier invention. By 2022, more than 60 % of IoT applications will be health-related. The convergence of biomedical sensors, wireless body area networks (WBAN), Information technology, and bioinformatics will help improve the efficiency of saving human lives. In a WBAN, network longevity is challenging because of the limited supply of low power battery energy in tiny body sensor nodes. Here, we proposed an energy-efficient transmission power control (TPC) algorithmto extend the network lifetime in IoMT networks for healthcare applicationsby eliminating the transceiver overhearing problem. In TPC, human tissue resistivity properties are considered to adjust the transmission power, which reduces the communication power and extends the network lifetime. The simulation results show that network power consumption is reduced by 35%.

In this article, the Internet of Medical Things (IoMT) framework based on Apache Spark big data processing technology is proposed for real‐time analysis of health data obtained from wireless body area networks (WBANs), which is one of the most important components of IoMT. The proposed framework consists of four layers: data source, data collection, data analytics and visualization. In addition, the proposed IoMT framework is presented with two different disease prediction scenarios, diabetes and heart disease. Diabetes and heart disease prediction processes are carried out using the random forest (RF), logistic regression (LR) and support vector machine (SVM) algorithms belonging to the Apache Spark machine learning library (MLlib). The analysis of health data generated in WBANs takes place in real‐time in the Apache Spark‐based data analytics layer. In this study, the performances of MLlib algorithms in the real‐time model developed for heart and diabetes disease are examined. The SVM algorithm with an accuracy rate of 93.33% for heart disease and the LR algorithm with an accuracy rate of 78.89% for diabetes are found to provide the best performances.