Healthcare Wireless Sensor Networks Research Articles

Enhancing Data Privacy in Wireless Sensor Networks: Investigating Techniques and Protocols to Protect Privacy of Data Transmitted Over Wireless Sensor Networks in Critical Applications of Healthcare and National Security

The article discusses the emergence of Wireless Sensor Networks (WSNs) as a groundbreaking technology in data processing and communication. It outlines how WSNs, composed of dispersed autonomous sensors, are utilized to monitor physical and environmental factors, transmitting data wirelessly for analysis. The article explores various applications of WSNs in healthcare, national security, emergency response, and infrastructure monitoring, highlighting their roles in enhancing patient care, public health surveillance, border security, disaster management, and military operations. Additionally, it examines the foundational concepts of data privacy in WSNs, focusing on encryption techniques, authentication mechanisms, anonymization techniques, and access control mechanisms. The article also addresses vulnerabilities, threats, and challenges related to data privacy in healthcare and national security contexts, emphasizing regulatory compliance, ethical considerations, and socio-economic factors. Furthermore, it introduces the Diffusion of Innovation Theory as a framework for understanding the adoption of privacy-enhancing technologies in WSNs. Finally, the article reviews empirical studies demonstrating the efficacy of security solutions in preserving data privacy in WSNs, offering insights into advancements in safeguarding sensitive information.

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks.

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol's methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415-443, representing 85-90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0-3.6 joules, representing 88-95% energy efficiency of the network; effected network range of 2.98, representing 72-89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72-89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.

Secure and scalable data aggregation techniques for healthcare monitoring in WSN

Wireless Sensor Networks (WSN) are crucial in the healthcare field as they allow for the immediate monitoring and collection of data from medical sensors. The growing incorporation of WSN in healthcare requires the creation of authentication protocols that are both strong and energy-efficient. This study presents Lightweight Authentication Protocol for Cloud-based Health-care Systems (LAPCHS) using BLAKE2 designed specifically for WSN in medical settings for secure and scalable data aggregation. The proposed method tackles the difficulties presented by limited resources in sensor nodes, with the goal of achieving a careful equilibrium between strong security and effective energy usage. LAPCHS utilizes innovative techniques to improve the energy efficiency in WSN. The system employs the BLAKE2 cryptographic hash algorithm to establish robust mutual authentication and key agreement between the tag and the cloud server. The protocol is characterized by its low weight and high efficiency, rendering it appropriate for deployment in medical sensor networks with limited resources, which minimizes the need for global key distribution and decreases communication overhead. The proposed method is useful to meet the distinct needs of medical sensor networks, where energy preservation is of utmost importance to ensure extended device scalability. The findings demonstrate that LAPCHS surpasses current approaches in terms of energy efficiency and latency, while simultaneously upholding a robust level of security. The protocol’s novel strategy for managing encryption keys and selecting cryptographic algorithms makes it a promising solution for securing medical sensor networks while maintaining energy sustainability. This study makes a valuable contribution to the developing field of WSN security by proposing a practical solution for ensuring strong and energy-efficient authentication in healthcare applications.

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN).

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network's real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

An efficient intrusion detection framework for mitigating blackhole and sinkhole attacks in healthcare wireless sensor networks

A Wireless Sensor Network (WSN) is made up of physically dispersed autonomous sensors which monitor the network and gather data about its surroundings. A sensor effectively captures information securely with the goal to detect potentially dangerous user conduct in the network. However, identifying an attack from ordinary nodes of sensors remains a difficult task. As a result, the suggested efforts concentrate on building effective attack discovery methods for secured data packet broadcasting from source to destination in the WSN. Proportional Overlapping Score-Based Minkowski K-Means Clustering (POS-MKC) is proposed to improve attack detection accuracy with lesser computational complexity in WSN healthcare applications. The simulation results illustrate that the proposed MK-Means framework widely generates an optimized performance with the reduction of delay, computational complexity and improvements in packet delivery ratio with high attack detection accuracy as compared to the state-of-the-art works.

Certificateless Anonymous Signcryption Scheme With Provable Security in the Standard Model Suitable for Healthcare Wireless Sensor Networks

In healthcare wireless sensor networks(HWSNs), wireless sensors are placed on the patient, which collect the patient’s vital signs parameters and various environmental information, and send these data to the doctor, so that the doctor is able to perform real-time remote monitoring for the patient. The patient’s vital signs parameters contain a lot of private information. It is very likely to cause the leakage of patient’s private information if these parameters are transmitted through the public channel. In addition, many patients don’t want doctors to know their true identity. We designed a certificateless anonymous signcryption (CL-ASC) scheme for HWSNs in this paper. It encrypts the patient’s vital signs parameters, authenticates the legitimacy of the sensor, and realizes the anonymity of the sensor. We then showed the security proofs of new scheme in the standard model. Finally, we compared the performance of the six schemes. Since only three pairing operations are used, the new scheme enjoys higher computation efficiency and is suitable for HWSNs.

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.

A priority‐based congestion avoidance scheme for healthcare wireless sensor networks

One of the most important challenges of wireless sensor networks is controlling network congestion and transmitting data in a way that improves the quality of service (QoS) parameters. Thus, it increases network performance and reduces energy consumption. Energy consumption increases due to various reasons, such as unsuccessful delivery of packets to the receiver, congestion in the network, retransmission of packets, delay in delivering packets to the base station, and so on. Given the importance of some data in the field of health, congestion should be avoided and secure data transmission should be ensured. This study divides the collected data into two groups based on their intrinsic characteristics by presenting a congestion management protocol: (1) critical data and (2) non-critical data. The proposed protocol provides a dynamic routing algorithm based on the TOPSIS model for non-critical data transmission. In addition, an algorithm for transmitting critical data through the shortest possible path is also provided based on support vector machines (SVMs). This improves the network performance through using multi-classification that is obtained from SVMs. The simulation results indicate that the proposed method works better than other methods and leads to better performance in delay, network performance, and power consumption.

A Survey on MAC-Based Physical Layer Security over Wireless Sensor Network

Physical layer security for wireless sensor networks (WSNs) is a laborious and highly critical issue in the world. Wireless sensor networks have great importance in civil and military fields or applications. Security of data/information through wireless medium remains a challenge. The data that we transmit wirelessly has increased the speed of transmission rate. In physical layer security, the data transfer between source and destination is not confidential, and thus the user has privacy issues, which is why improving the security of wireless sensor networks is a prime concern. The loss of physical security causes a great threat to a network. We have various techniques to resolve these issues, such as interference, noise, fading in the communications, etc. In this paper we have surveyed the different parameters of a security design model to highlight the vulnerabilities. Further we have discussed the various attacks on different layers of the TCP/IP model along with their mitigation techniques. We also elaborated on the applications of WSNs in healthcare, military information integration, oil and gas. Finally, we have proposed a solution to enhance the security of WSNs by adopting the alpha method and handshake mechanism with encryption and decryption.

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.

A Priority Based Congestion Avoidance Scheme for Healthcare Wireless Sensor Networks

A Priority Based Congestion Avoidance Scheme for Healthcare Wireless Sensor Networks

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.

Current Progress on Passion Efficient Cluster Based Routing Protocols of Wireless Sensor Networks for Medicine Systems

Detected modified works by getting to cellar organizes as clear in A measure of doctor's facilities and therapeutic focuses are investigating utilizations of remote sensor course of action (WSN) innovation to a propelled ambit of restorative applications, including pre-healing center, and in-clinic crisis mind, misfortune reaction, and accomplishment obliging recovery. Current Healthcare Wireless Sensor Networks (HCWSN) examination patterns concentrate on patients, dependable correspondence, obliging versatility, and vitality proficient steering, a some portion of others. The reviews concentrate on modified bunch based procurement conventions which are accustomed in gradual addition movement capacity of WSN for medicinal services apparatus and to call attention to imperative concerns in group established obtaining (CBR) understanding that counselor to propel them in acclimation to develop their machine go. Today, Wireless Sensor Networks are adequate acknowledged and flourishing securing conventions acknowledge been proposed in theory with an attention on the various leveled directing. This cardboard overviews the WSNs vitality productive CBR strategies that are accustomed for Healthcare Advice framework. Late progress and restrictions of forerunner studies were highlighted. The procurement conventions are classified by comparing action proficiency. We mean for counselors to bound investigate territories that hunger for added retention and to illustrate an atypical arrangement for healing the capacity of outright conventions.

Development of a remote health care wireless sensor network based on wireless spread spectrum communication networks

When medical technology, communication, and computers were developed and put into practice in the treatment and follow-up of remote health care patients, the difficulty of communicating with the medical team or patients was due to bandwidth limitations. Sending a significant volume of data between medical personnel and patients, as well as power usage, is not permitted. Wireless sensors use a direct spread spectrum based on CDMA, especially mobile wireless third generation with a wide bandwidth that can send large data such as multimedia between patients and doctors at anytime and anywhere. The wireless sensor network decreases the power consumption of the transmitter sensor and the receiver sensor by using binary transmission and multipath. The accuracy of the measurement depends on the bandwidth of the unit to calculate the time of arrival (TOA). The TOA forecasts come and go together to pass them to the receiver because of the delay of the MPC and the reduction of replicas of the initial signal. The diffusion time caused by the moving signal through barriers, adding a positive bias to the TOA.

Certificateless Designated Verifier Anonymous Aggregate Signature Scheme for Healthcare Wireless Sensor Networks

The contradiction between the growing demand for healthcare and the lack of professional medical staff urgently needs to be resolved. Healthcare wireless sensor technology is an important option to resolve this contradiction. Patients use wearable devices to collect various health indicators and send them to doctors for diagnosis in a timely manner. This breaks the time and space constraints in traditional medical methods. Patient's data is transmitted over a public network. Therefore, protecting data security and patient privacy has become an urgent issue for healthcare wireless sensor networks (HWSNs). In this article, we first presented a system model for certificateless designated verifier anonymous aggregate signature (CL-DVAAS) scheme for HWSNs, and investigated the security attributes. We then gave a concrete construction and showed the security proofs. We finally compared the new scheme with several other schemes, and the results show that the new scheme is more computationally efficient.

A provably secure lightweight certificateless aggregate signature scheme for healthcare wireless sensor network

In healthcare wireless sensor networks (HWSNs), a patient's body usually contains several wearable or implantable wireless sensors which generate and transmit physiological data to a medical server (MS) where authorised medical professionals can access relevant medical data for efficient patient's diagnosis and treatments. To achieve data integrity and authenticity, aggregate signature is generally used. Several certificateless aggregate signature (CL-AS) schemes have been proposed to address the problems in the traditional public key cryptography. However, the performances of the existing CL-AS schemes are sometimes unsatisfactory. In this work, we propose a novel pairing-free certificateless aggregate signature scheme with strong anonymity for HWSN. We show that the scheme is provably secure against Type I and Type II adversaries in the random oracle model based on the elliptic curve discrete logarithm problem (ECDLP) assumption. The performance analysis and comparison show that the scheme is very efficient in terms of computation and communication overhead.

A provably secure lightweight certificateless aggregate signature scheme for healthcare wireless sensor network

In healthcare wireless sensor networks (HWSNs), a patient's body usually contains several wearable or implantable wireless sensors which generate and transmit physiological data to a medical server (MS) where authorised medical professionals can access relevant medical data for efficient patient's diagnosis and treatments. To achieve data integrity and authenticity, aggregate signature is generally used. Several certificateless aggregate signature (CL-AS) schemes have been proposed to address the problems in the traditional public key cryptography. However, the performances of the existing CL-AS schemes are sometimes unsatisfactory. In this work, we propose a novel pairing-free certificateless aggregate signature scheme with strong anonymity for HWSN. We show that the scheme is provably secure against Type I and Type II adversaries in the random oracle model based on the elliptic curve discrete logarithm problem (ECDLP) assumption. The performance analysis and comparison show that the scheme is very efficient in terms of computation and communication overhead.

A Lightweight Data Aggregation Protocol With Privacy-Preserving for Healthcare Wireless Sensor Networks

In recent years, healthcare wireless sensor networks (HWSNs) have received extensive attention. Due to the direct participation of patients' personal health data, privacy issues, and security are very sensitive in HWSNs. How to aggregate the sensitive patient information securely and efficiently is still a challenging task to the HWSNs communication scenario. In this article, a lightweight data aggregation protocol with privacy-preserving is proposed to achieve enhancing security in HWSNs. With the proposed protocol, patients' personal health data collected by the data collect client can be aggregated by data aggregator and efficiently obtained by the medical service provider in a privacy-preserving manner. In particular, we first propose an efficient certificateless aggregate signcryption scheme, and then, design a data aggregation protocol for patient condition monitoring with information confidentiality, integrity, mutual authenticity, and public verifiability. Performance comparisons show that our newly proposed protocol significantly reduces computing overhead compared to the latest protocols in the field.

Performance of RPL in Healthcare Wireless Sensor Network

The new advances of the Internet of Things (IoT) technology can be utilized to promote service delivery in several real-life applications such as healthcare systems. The Routing Protocol for Low Power and Loss Network (RPL) is a routing protocol designed to serve as a proper routing protocol for packets in Wireless Sensor Networks (WSN). Among the most prominent issues exist in the RPL protocol are packet loss within the WSN and sensors power consumption especially in healthcare WSNs. Multiple Objective Functions (OF) in RPL intended to find the routes from source nodes to a destination node. This paper presents an evaluation to discover which OF is more efficient for a WSN in a healthcare scenario where the Packet Delivery Ratio (PDR) of WSN and the sensors' power consumption are prominent concerns. Expected transmission Count (ETX) and Objective Function Zero (OF0) of RPL were examined in various network densities and network topologies such as the grid and random topology. The simulation outcomes revealed that the OF0 is more efficient regarding the PDR and power consumption compared to the ETX in random