Healthcare Sensor Networks Research Articles

IoT Sensor Networks- Orchestrating Connectivity, Efficiency, and Intelligence Across Diverse Domains

The advent of the Internet of Things (IoT) has led to the proliferation of sensor networks, enabling a new era of connectivity, data collection, and automation across various domains. IoT-based sensor networks comprise interconnected sensors and actuators that collect, transmit, and process data to provide valuable insights and enable intelligent decision-making. This paper explores the architecture, applications, and challenges of IoT-based sensor networks. The architecture section delves into the components, layers, and communication protocols that constitute these networks, highlighting the roles and interactions of sensors, microcontrollers, gateways, and cloud services. The applications section showcases the diverse use cases of IoT-based sensor networks in smart cities, industrial automation, healthcare, agriculture, and environmental monitoring, illustrating their transformative impact. The challenges section identifies the key issues such as scalability, interoperability, security, reliability, energy efficiency, and data management that need to be addressed to realize the full potential of IoT networks. Finally, the paper discusses future directions, emphasizing the potential of edge computing, 5G, artificial intelligence, and blockchain technology to advance IoT-based sensor networks and unlock new opportunities. Through continued research, innovation, and collaboration, IoT sensor networks are poised to drive significant advancements in technology and society, creating a more connected and intelligent world.

An efficient three-factor authentication protocol for wireless healthcare sensor networks

An efficient three-factor authentication protocol for wireless healthcare sensor networks

Efficient data acquisition with wireless powered sensor networks for smart healthcare in hospitals

Efficient data acquisition with wireless powered sensor networks for smart healthcare in hospitals

Distance Energy-Efficient Soft Computing Model for Data Forwarding in Healthcare Sensor Network

Data forwarding is a crucial process in computer networking and telecommunications. It involves the transmission of data packets from a source to a destination within a network. The forwarding process is fundamental for enabling communication between devices on different segments of a network and plays a vital role in maintaining the overall performance and reliability of the network infrastructure. This paper presents the development and evaluation of the Threshold Congestion Data Forwarding Soft Computing (TCDF-SC) algorithm, designed for healthcare sensor networks. The algorithm combines threshold-based congestion management with soft computing techniques to enhance data forwarding efficiency in dynamic healthcare environments. Through extensive simulations, TCDF-SC demonstrates scalability, achieving high total packets forwarded and maintaining a reliable packet delivery ratio as the network scales. The algorithm minimizes average transmission delay and optimizes energy consumption, ensuring timely and energy-efficient data transmission. Comparative analysis against existing protocols, LEACH and BCP, highlights TCDF-SC’s superior performance across key metrics, positioning it as a promising solution for healthcare sensor networks. This research contributes to the advancement of data transmission optimization in healthcare, addressing critical requirements of reliability, energy efficiency, and adaptability in dynamic healthcare settings. Further validation and real-world experimentation will enhance the algorithm’s applicability in diverse healthcare scenarios, fostering advancements in patient monitoring and healthcare applications.

Wireless Sensor Networks for Digital Agriculture, Environmental Protection, and Healthcare

Wireless Sensor Networks for Digital Agriculture, Environmental Protection, and Healthcare

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare.

Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate.

Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0.

Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model's performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.

Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home

This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user's point of view.

Deep reinforcement learning‐based full‐duplex link scheduling in federated learning‐based computing for IoMT

SummaryThe rapid developments in mini‐hardware manufacturing and wireless network communications have enabled the Internet of Medical Things (IoMT) to provide continuous healthcare services over the Internet. Federated learning (FL) combined with blockchain technology has been a popular way to resolve privacy‐preserving data sharing in IoMT‐based wireless body area networks (WBANs), on the other side, communication payloads become much heavier than traditional healthcare sensor network, because central server should aggregate the model updates and orchestrate the training tasks. The high latency will lead to FL's low system efficiency. However, the existing studies on FL mainly focus on the system design and algorithm optimization, which ignore a critical problem of data transmission in the FL system. To improve the communication performance, we proposed a two‐tier scheduling algorithm in which a full‐duplex (FD) multiple access based scheduling algorithm is employed to improve channel utility and network throughput, and decrease the delay in tier II. A deep reinforcement learning (DRL) framework is used to generate the FD links between hubs and access points (APs) which jointly considers the channel state, fairness, and delay. Therefore, the DRL‐based FD Link Scheduling (R‐FDLS) algorithm is proposed. When the traffic volume is different or in various distribution scenarios, the evaluation results demonstrate that the proposed algorithm significantly improves the network communication quality, as well as has obvious advantages compared to several baselines.

Routing Protocol for Healthcare Applications Data Over the 6LoWPAN-based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have increasingly been adopted as a viable solution in the healthcare industry for many years. One novel application of WSNs in healthcare is real-time health data collection and tele-healthcare service delivery via the Internet. With the increasing number of applications of Wireless Sensor Networks (WSNs) in healthcare has emerged during the past decades, especially during the COVID-19 pandemic which has affected millions of people, particularly the elderly, the advantages and benefits of applying WSNs to monitor patients and improve the quality of healthcare have become clearer. Today's sensing and communications technologies have reached a point where these WSNs can be readily implemented and deployed, albeit with some limitations and security concerns.Collecting patients' health data or vital signs from WSNs deployed in hospitals or elderly nursing homes has been one of the most challenging tasks in the healthcare industry. In this paper, our main focus is on the routing protocol for human psychological data generated by healthcare application over the 6LoWPAN-based Wireless Sensor Networks (WSNs) as well as the development of affordable healthcare applications by leveraging novel WSN technology. Firstly, we present our latest achievements in implementing healthcare systems using the framework proposed in [10]. Additionally, We employ this framework to simulate 6LoWPAN-based Wireless Sensor Networks (WSNs) in two different healthcare settings using the network simulator NS-3. Finally, we provide preliminary results from the application of the developed application in on-site hospitals and nursing homes.

Intelligent Wireless Sensor Networks for Healthcare: Bridging Biomedical Clothing to the IoT Future

The science of achieving a healthy mind, body, and spirit through objectives and activities is known as personal health (PH). We must be aware of our mental, bodily, and social well-being. The term hygiene refers to a wide variety of healthy behaviours. Individuals' healthcare costs and quality of life increased by avoiding or reducing the long-term effects of the disease through knowledge and skills. Biomedical apparel includes sutures, vascular grafts, and biodegradable clothes (BC). Biomedical clothing is anything implanted or incorporated into the human body and used near tissue, blood, or cells. Quick, dependable, and energy-efficient connectivity between wireless sensor networks is necessary (WSNs). Physical layers, media access control, networking layers, and control requirements must be co-designed. For those with lesser means, health insurance will increase in cost. There are difficulties with privacy and cyber security, a higher chance of malpractice claims, and increased time and financial expenditures for doctors and patients. In this study, wireless sensor networks-based personal health biomedical clothing (PH-BC-WSN) was utilized to increase access to high-quality healthcare, increase food production through precision agriculture, and raise the standard of human resources. More effective healthcare and medical asset monitoring systems can be developed thanks to the Internet of Things. Eavesdropping on medical data, modification, fabrication of warnings, denial of service, user tracking and location, physical interference with equipment, and electromagnetic threats were extensively discussed. The article gives several instances of current technology, discusses design challenges including energy efficiency, security, and scalability, provides various demonstrations of current technology, and provides a complete analysis of all the advantages and disadvantages. Despite their many benefits, body sensor networks have several significant obstacles and unresolved research problems, which are described along with some potential answers. As a result, the experimental findings demonstrate that PH-BC-WSN enhances accuracy and reduces response time in inpatient health monitoring.

Time dependency: an efficient biometric-based authentication for secure communication in wireless healthcare sensor networks

Time dependency: an efficient biometric-based authentication for secure communication in wireless healthcare sensor networks

Simple and Robust Log-Likelihood Ratio Calculation of Coded MPSK Signals in Wireless Sensor Networks for Healthcare

The simple and robust log-likelihood ratio (LLR) computation of coded Multiple Phase Shift Keying (MPSK) signals in Wireless Sensor Networks (WSNs) is considered under both phase noncoherent and Rayleigh fading channels for healthcare applications. We first simplify the optimal LLR for phase noncoherent channel, the estimation of the instantaneous channel state information (CSI) for both the fading amplitude and the additive white Gaussian noise (AWGN) is successfully avoided, and the complexity-intensive process for zero-order Bessel function of the first kind is also perfectly eliminated. Furthermore, we also develop the simplified LLR under Rayleigh fading channel. Correspondingly, the variance estimation for both AWGN and the statistical characteristic of the fading amplitude is no longer required, and the complicated process for implementation of the exponential function is also successfully avoided. Compared to the calculation of optimal LLR with full complexity, the proposed method is implementation-friendly, which is practically desired for energy-limited WSNs. The simulations are developed in the context of low-density parity-check (LDPC) codes, and the corresponding results show that the detection performance is extremely close to that of the full-complexity LLR metrics. That is, the performance degradation is efficiently prevented, whereas complexity reduction is also successfully achieved.

The Impact of Healthcare Traffic Over H2H and M2M Networks During the Spread Phase of Pandemic Diseases

Recently, the coronavirus pandemic has caused widespread panic around the world. Modern technologies can be used to monitor and control this highly contagious disease. A plausible solution is to equip each patient who is diagnosed with or suspected of having COVID-19 with sensors that can monitor various healthcare and location parameters and report them to the desired facility to control the spread of the disease. However, the simultaneous communication of numerous sensors installed in the majority of an area’s population results in a huge burden on existing Long-Term Evolution (LTE) networks. The existing network becomes oversaturated because it has to manage two kinds of traffic in addition to normal traffic (text, voice, and video): healthcare traffic generated by a large number of sensors deployed over a huge population, and extra traffic generated by people contacting their family members via video or voice calls. In pandemics, e-healthcare traffic is critical and should not suffer packet loss or latency due to network overload. In this research, we studied the performance of existing networks under various conditions and predicted the severity of network degradation in an emergency. We proposed and evaluated three schemes (doubling bandwidth, combining LTE-A and LTE-M networks, and request queuing) for ensuring quality of service (QoS) of healthcare sensor (HCS) network traffic without perturbation from routine human-to-human or machine-to-machine communications. Finally, we simulated all proposed schemes and compared them with existing network scenarios. The results have showed that when we have doubled the bandwidth the SCR of all traffic was 100% as same as the Queue strategy. However, when we prioritized the HCS traffic the SCR has recorded 100%, while H2H and M2M traffic has recorded 73%. When we used hybrid network LTE-A and LTE-M network, the HCS and H2H traffic has recorded 100% and M2M traffic has recorded 70%. After analyzing the results, we conclude that our proposed queuing schemes performed well in all conditions and provide the best QoS for HCS traffic.

A New Efficient Authentication Protocol for Wireless Healthcare Sensor Networks

Electronic health (E-Health) has become one of the most important research fields over the last decades, which is considered the best solution for many problems in the medical area. E-Health is based on Wireless Healthcare Sensor Network (WHSN). The field of wireless communication has many security challenges. For this prospect, an authentication and session key agreement protocol for WHSN has been proposed in order to secure the transferred information over the wireless channels by WHSN. The proposed protocol has preserved privacy for the shared entities and has withstand the expected different types of electronic attacks (e.g. stolen smart card attack, replay attack, privileged insider attack and denial of service attack, etc.). The formal security analysis using BAN logic has claimed that the protocol is secure. Moreover, the proposed protocol has managed to achieve a noticeable improvement in the computation efficiency compared to its peers.

From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review.

This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.

A Secure and Lightweight Three‐Factor Remote User Authentication Protocol for Future IoT Applications

With the booming integration of IoT technology in our daily life applications such as smart industrial, smart city, smart home, smart grid, and healthcare, it is essential to ensure the security and privacy challenges of these systems. Furthermore, time‐critical IoT applications in healthcare require access from external parties (users) to their real‐time private information via wireless communication devices. Therefore, challenges such as user authentication must be addressed in IoT wireless sensor networks (WSNs). In this paper, we propose a secure and lightweight three‐factor (3FA) user authentication protocol based on feature extraction of user biometrics for future IoT WSN applications. The proposed protocol is based on the hash and XOR operations, including (i) a 3‐factor authentication (i.e., smart device, biometrics, and user password); (ii) shared session key; (iii) mutual authentication; and (iv) key freshness. We demonstrate the proposed protocol’s security using the widely accepted Burrows–Abadi–Needham (BAN) logic, Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool, and the informal security analysis that demonstrates its other features. In addition, our simulations prove that the proposed protocol is superior to the existing related authentication protocols, in terms of security and functionality features, along with communication and computation overheads. Moreover, the proposed protocol can be utilized efficiently in most of IoT’s WSN applications, such as wireless healthcare sensor networks.

A Lightweight Three-Factor Authentication Scheme for WHSN Architecture.

Wireless Healthcare Sensor Network (WHSN) is a benchmarking technology deployed to levitate the quality of lives for the patients and doctors. WHSN systems must fit IEEE 802.15.6 standard for specific application criteria, unlike some standard criteria that are difficult to meet. Therefore, many security models were suggested to enhance the security of the WHSN and promote system performance. Yu and Park proposed a three-factor authentication scheme based on the smart card, biometric, and password, and their scheme can be easily employed in three-tier WHSN architecture. Furthermore, they claimed that their scheme can withstand guessing attack and provide anonymity, although, after cryptanalysis, we found that their scheme lacks both. Accordingly, we suggested a three-factor authentication scheme with better system confusion due to multiplex parametric features, hash function, and higher key size to increase the security and achieve anonymity for the connected nodes. Moreover, the scheme included initialization, authentication, re-authentication, secure node addition, user revocation, and secure data transmission via blockchain technology. The formal analysis of the scheme was conducted by BAN logic (Burrows Abadi Nadeem) and the simulation was carried out by Tamarin prover to validate that the proposed scheme is resistant to replay, session hijacking, and guessing attacks, plus it provides anonymity, perfect forward secrecy, and authentication along with the key agreement.

A robust authentication and access control protocol for securing wireless healthcare sensor networks

Wireless Healthcare Sensor Network (WHSN) has become one of the major research fields over the past decades that play a very prominent role in the medical field. Due to the rapid growth of technology in wireless communication, different security challenges have been raised in WHSN. Authentication protocols are used to secure the information transferred over the public channels by WHSN. For this prospect recently, Liu & Chung proposed an authentication and data transmission mechanism for WHSN. However, Challa et al. identified that Liu-Chung’s scheme is vulnerable to stolen smart-card, offline password guessing, privileged insider, and user impersonation attacks. Challa et al. then proposed an enhanced scheme to overcome beforehand stated flaws. This paper denotes out that in accession to before mentioned attacks, Liu-Chung’s scheme is also prone to users’ private key leakage and user impersonation attacks towards sensors. Moreover, Challa et al.’s scheme suffers from incorrectness, broadcasting problem, lack of authentication between Trusted Authority (TA) and sensor nodes, replay attack, Denial of Service (DoS) attack, forgery attack and delay in communication due to the involvement of the TA. Using the elliptic curve cryptography and bilinear paring, an improved scheme is proposed in this paper, to mitigate the weaknesses of Challah et al. and Liu-Chang schemes. The formal security analysis using simulation tool AVISPA and BAN logic demonstrate that the proposed scheme is secure. The rigorous informal security analysis also attests that our scheme is safe against well-known attacks.

An eHealth System for Monitoring the Relapse of Salmonella Typhi in Outpatients.

Typhoid fever remains a major health problem in developing countries: According to the World Health Organisation, there are 21 Million cases worldwide and 222,000 typhoid-related deaths occurring annually [1]. Mobile monitoring systems have been proposed for observing patient's physiological signals; these hardware and software solutions are broadly categorised as Healthcare Sensor Networks (HSNs). Since no two patients are alike with regards to biometric parameters and access to healthcare professionals can be difficult because of geography or scarcity of resources, monitoring must be observed in situ. Various serological and culture diagnostic tests for enteric fever exist, including the Widal test [44], TUBEX [45], ELISA [46] and the Gold Standard Blood Culture test [32]. The limitations of these existing conventional tests include lack of speed, sensitivity, and specificity, ambiguity in the classification of symptoms, diagnosis by inexperienced healthcare workers and the fact that they are predominantly clinic-based tests. Critical to typhoid care is outpatient support after the completion of courses of antibiotics, and the two to three-week reoccurrence period is crucial in aftercare. Our research is designed for patients during this period. In this paper, we propose a mobile-based monitoring methodology, which uses vital signs monitoring apparatus, which is configured to recognise the characteristics of typhoid recurrence. Key to our design is the implementation of sensors for body temperature monitoring and physiological testing through galvanic skin response (GSR), electrocardiographic testing (ECG) and electromyography (EMG). The paper introduces an invaluable tool for (i) software programmers and algorithm designers working with biometric HSNs and (ii) practitioners working in public health monitoring and disease prevention.