Wireless Body Area Networks Data Research Articles

Quantum spider prioritized routing and Cramer Shoup cryptosecured data transmission in wireless body area network

The wireless body area network (WBAN) offers healthcare applications for remote patient monitoring. In healthcare, prioritizing patient data has a high impact on the energy usage, delays, and congestion involved in routing. Energy usage, battery life, and absorption rates are current research obstacles in WBAN. The path loss ratio and residual energy were generally the focus of attention during data transfer. Despite this, compromised transaction security caused serious problems with the delivery of vital health data. The Quantum Spider Cramer Shoup Public Key Cryptosystem (QS-CSPKC), which is proposed in this study, is an optimized priority-aware (for many patients) and transaction security technique for vital sensed data in WBAN, selects the optimum energy efficient and reliable path to assure secure data transfer. The proposed QS-CSPKC method employs Dual Contention Access Periods (DCAPs) for managing regular data (i.e., normal sensed or low priority data) and life-threatening data (i.e., abnormal sensed data or high priority data). After prioritizing, the Quantum Spider Monkey Optimized Routing algorithm is used to allocate an optimal shortest route for normal low priority data (i.e., with 0 as the quantum value) and an energy-efficient emergency route for critical high priority data (i.e., with 1 as the quantum value). In addition, to prevent illegal access during transmission, security keys are generated using the Cramer Shoup public key cryptosystem. By using this model, only legitimate users have access to information for which they are authorized. The evaluation results were validated through simulations, showing that the proposed QS-CSPKC method performs efficient routing and secure data transmission better than conventional techniques in terms of throughput, packet loss, average power consumption, and energy use.

Current security and privacy posture in wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for remote health monitoring and healthcare applications. However, ensuring the security and privacy of sensitive health data in WBANs is crucial to foster user trust and prevent unauthorized access or data breaches. This paper provides an overview of the key challenges, techniques, and research gaps in WBAN security and privacy. The findings indicate that the challenges in WBAN security and privacy include resource constraints, compatibility issues, privacy concerns, dynamic network environments, security and usability trade-offs, emerging threat landscape, and user awareness and education. To address these challenges, various security techniques have been developed, such as authentication and authorization mechanisms, encryption, access control, secure communication protocols, intrusion detection systems, and privacy-preserving data handling techniques. Despite the progress made, there are research gaps that require further investigation. These research gaps include the development of secure and lightweight authentication mechanisms, privacy-preserving data analysis techniques, trust and security management frameworks, resilience to insider threats, security of data aggregation and fusion, user-centric security designs, and addressing legal and ethical considerations. Addressing these research gaps and challenges requires collaboration between researchers, device manufacturers, policymakers, and end-users. Ongoing research and innovation are necessary to develop robust security techniques, privacy-enhancing technologies, and user-friendly solutions tailored for WBANs. Additionally, compliance with privacy regulations, user education, and awareness are critical for responsible and ethical use of WBANs.

Implementation and experimental evaluation of dynamic capabilities in wireless body area networks: different setting parameters and environments

It is well known that a wireless body area network (WBAN) is a special proposed wireless sensor network (WSN) that can assist in monitoring physiological signals for the evaluation and planning of patient treatment. One of the most challenging issues for WBANs is communication reliability, with acceptable communication efficiency and packet loss. To obtain such network characteristics, collision-free data transmission in networks of wireless sensor nodes is an interesting research problem. In this paper, the experiments of dynamic capabilities in several WBAN scenarios are focused, where the novelty and major contribution of our tests is that the effects of packet inter-arrival times, packet sizes, and the number of nodes deployed in the network, including human movements, indoor and outdoor environments, and transmitter and receiver positions, are all taken into consideration and evaluated. This is achieved by implementing the WBAN using IEEE 802.15.4 low-power sensor nodes. Experimental results illustrate the significant factors that impact the communication reliability of WBANs as measured by the packet delivery ratio (PDR). The experimental results show that the diverse environment testbed can affect network performance for WBAN data transmission. Our findings also show that the best network reliability needs to be set at more than 15 ms in packet interval time to achieve over 90% PDR for every test scenario. More details of the experimental results related to WBAN reliability obtained from all test cases are also discussed and summarized in the paper. To the best of our knowledge, our findings can be useful for users and researchers to consider the optimal point for WBAN setting and configuration to achieve the communication reliability requirements and also to deploy and develop a more reliable WBAN system.

Traffic priority based delay aware scheduling for co-located WBANs

Wireless Body Area Network (WBAN) is a promising solution that has gained significant attraction in the human health monitoring system. Body sensors measure the physiological parameters of the human body and send the data to the access point via coordinator or local processing unit (e.g., smartphones, PDA) for further analysis. Data transmission from the local processing unit to the access point, known as inter-WBAN transmission, is challenging since many problems such as interference, poor link quality, and scarcity of resources can arise. As a result, data may be lost, or transmission may be delayed, which turn can endanger the user’s health. The situation could be worse when the user suffers from critical diseases and has to coordinate with other WBANs co-located in the same vicinity. This paper focuses on dynamic slot scheduling and topology handling of WBAN, whose neighborhood is constantly changing due to its mobility. To guarantee the performance of different WBAN’s criticality; we propose Critically Aware Threshold Sensitive (CATS) scheduling algorithm for coexisting WBANs data transmission. CATS treats all the nodes in the same WBAN as a whole and allocates the same slot to nodes of the WBAN so that the data transmission delay of a critical WBAN can be minimized. The proposed method categories and schedules the WBANs based on the sensor data priority and WBAN’s criticality index. Furthermore, we have considered for categorizing the WBANs into various groups. In this work, the network performance is evaluated using these threshold values. Simulations are conducted to evaluate the performance of the proposed CATS against the existing scheduling algorithms with respect to various performance metrics. Simulation results show that the proposed method has lower transmission delay and higher spatial reuse factor.

A Q-Learning Approach for Real-Time NOMA Scheduling of Medical Data in UAV-Aided WBANs

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible and cost-effective solution for remote monitoring of the vital signs of patients in large-scale Internet of Medical Things (IoMT) Wireless Body Area Networks (WBANs). This paper deals with the problem of using UAVs for real-time scheduling of the transmission of vital signs in delay-sensitive IoMT WBANs. The main challenge for such a network is to timely and reliably transmit the vital signs of patients to the remote monitoring center without interrupting their daily lifestyles. To achieve this goal, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning-based algorithm to optimize the trajectory of each UAV, as the mobile Base Station (BS), to harvest vital signs of patients in outdoor applications, especially in unreachable areas. In this algorithm, UAVs learn to reach the best 3D position by discovering the network environment step-by-step. It stands for the position in which the covered patients by each UAV have the highest transmission rate, the least delay and energy consumption. Moreover, we employ the Non-Orthogonal Multiple Access (NOMA) technique to simultaneously schedule multiple transmissions by accepting a degree of interference between them in order to enhance the spectrum efficiency of the network. Eventually, the performance of our proposed scheme is evaluated via extensive simulations in terms of throughput, energy consumption, and delay. The simulation results show that our proposed scheme iteratively converges to the benchmark value of the mentioned factors by increasing the information of cluster environment through episodes.

A survey on energy efficiency intrabody communication techniques for wearable devices

Currently, wireless body area networks (WBANs) are developing rapidly. Miniature wearable devices which are light weight and low power consumption can be developed using modern technology. However, the WBANs research challenge is power consumption. This paper provides the two intrabody communication (IBC) methods review. The equivalent body channel models and modeling methods are analyzed. The conventional IBC WBANs architecture is reviewed and novel energy-efficient architecture and an IBC WBANs data transmission technique are proposed. The experiment was conducted in order to measure the BodyCom mobile module output power. The comparative analysis showed that the power consumption of the IBC proposed technique is 7 times lower than Bluetooth LE, and 14 times lower than ZigBee. The proposed technique can be applied in areas such as home and industrial automation, medical appliances, wearable devices, security, and internet of things.

Detection of epileptic seizures from compressively sensed EEG signals for wireless body area networks

Wireless body area networks (WBANs) are gaining popularity for tele-monitoring of biomedical signals such as the electroencephalogram (EEG), with diagnosing and monitoring of epileptic seizures being one of the most important applications. Most seizure-detection algorithms cannot be applied directly to the compressed data in WBANs as they require full reconstruction of original EEG signals. In this study, we propose a novel feature for real-time automatic single-channel seizure detection which does not require complete reconstruction of original EEGs. The feature is based on iteratively applying the orthogonal matching pursuit (OMP) algorithm on the compressed EEG data and computing the rate by which the energies of partially reconstructed signals are increased. The feature, i.e. partial energy difference (PED), is then used for classifying seizure and non-seizure states. We also extend this method to the case for multichannel EEG. In multichannel case, the simultaneous OMP (SOMP) with a low number of iterations (1 and 15 iteration) is applied to the compressed data and the difference between the Frobenius norms of partially reconstructed signals is used as a multivariate feature for the classification of seizure and non-seizure states. The proposed features are used to detect seizure intervals in the EEG database provided by the University of Bonn and the CHB-MIT database. The results show that the proposed features can classify seizure epochs from non-seizures even for compression ratios as small as 0.05. The results also show that the proposed single-channel method achieves improvement of up to 4% for the area under the curve (AUC) with significantly less execution time compared to the benchmark matrix determinant (MD) classification approach. The results of applying the proposed multivariate feature to seizure and non-seizure segments of length 5 s from the CHB-MIT database show that it achieves mean AUC values of 0.941, 0.941, and 0.939 with mean execution times of 9.5, 10.7, and 13.1 s for compression ratios of 0.05, 0.1, and 0.2, respectively. Applying a threshold to the PED in a leave-one-out cross-validation (LOO-CV) scenario generates a sensitivity of 0.873,specificity of 0.710, and accuracy of 0.791 at the compression ratio of 0.05. The proposed single- and multichannel features have the potential for deployment in WBANs for the real-time tele-monitoring of epilepsy patients in healthcare applications.

Efficient Anonymous Authentication Scheme in Body Area Networks Via Signal Propagation Characterization

Owing to its capability to measure the sensitive biological data of patients through embedded sensors and transmit them via open wireless channels to remote medical experts, wireless body area network (WBAN) has been playing an important role in pervasive healthcare systems. However, the open nature of the wireless channels renders the data susceptible to being eavesdropped by an adversary and linked to the identities of the transmitting devices, which can enable the adversary to gain sensitive information and launch targeted physical attacks. Therefore, anonymous authentication and confidentiality of the data in WBAN are vital. In the last few years, numerous anonymous authentication schemes based on cryptographic primitives and physiological features were designed to enhance security in WBAN. However, most of the existing schemes are not computationally efficient or require additional sensing hardware. To address these limitations, we propose an efficient anonymous authentication scheme for WBAN based on signal propagation characteristics. The key idea in the proposed scheme is to utilize the distinct received signal strength (RSS) variation profiles between on-body and off-body communication channels to conceal the identities of communicating devices, thereby ensuring their anonymity during authentication. We perform security and performance analyses of the proposed approach to prove its security strength and computational efficiency, respectively. Moreover, extensive experiments are conducted on human volunteers in indoor and outdoor environments to show the robustness of our approach. The results of the analyses and the experiments show that our scheme can successfully mitigate 88.8% of active attack attempts with less computation overhead.

A logistic mapping-based encryption scheme for Wireless Body Area Networks

In recent years, data security becomes a critical issue restricting the wider acceptance of Internet of Things (IoT) devices and Cyber-physical systems since they have limited hardware resources and power supply while high data security protection requires relatively large hardware resources and power supply utilization. This contradiction is particularly prominent in the field of Wireless Body Area Networks (WBANs) which is a segment of the IoT field. WBANs are dedicated to transmit and process biomedical data collected from human beings, any kind of tampering or hacking may cause severe consequences to users. However, the limited computing ability and battery supply of biomedical sensors attached or implanted in the users restrict the security protection strength of the data in WBANs. In this paper, a quantized Logistic mapping-based stream encryption scheme for WBANs is proposed. Meanwhile, Power spectral entropy (PSD) and Peak-to-average Power Ratio (PAPR) analysis of the quantized chaotic sequences have been performed to evaluate the chaotic characteristic among different quantization precision to resolve the ineffectiveness of Lyapunov factor in quantized systems. This encryption scheme utilizes chaotic systems with different quantization precision based on the security requirement of every individual communication, which leads to higher hardware and power efficiency. Finally, the proposed encryption scheme is implemented with VHDL and synthesized using SMIC 60 CMOS technology. The evaluation results illustrate that the proposed encryption scheme has the advantages of high-security performance and high-efficiency hardware resources utilization.

Efficient and privacy-preserving authentication scheme for wireless body area networks

As a significant part of electronic health, wireless body area networks (WBANs) have greatly improved the quality and efficiency of healthcare. With the help of WBANs, comprehensive and uninterrupted health monitoring becomes possible, and the user can enjoy the medical services provided by application providers with no limitations on space and time. Since the physiological data of user are transmitted on the public channels, it is essential to protect user’s privacy and ensure secure communications in WBANs. In the past few years, several authentication schemes were proposed to solve the security issues of transferring data in WBANs. However, most of these schemes are not efficient and secure enough. As a step towards this direction, in this paper, we propose an efficient and privacy-preserving authentication scheme for WBANs using elliptic curve cryptography (ECC). In the proposed scheme, the method of certificateless authentication based on identity-based cryptography is adopted, which makes it suitable for multi-server architecture without online third party participation. Security analysis and comparisons with five related authentication schemes demonstrate that the proposed privacy-preserving authentication scheme can not only provide the desired security features, but also reduce the computation cost effectively.

Research on Cloud Storage Integrity of Wireless Body Area Network Data Based on Blockchain Technology

Research on Cloud Storage Integrity of Wireless Body Area Network Data Based on Blockchain Technology

Self-heat controlling energy efficient OPOT routing protocol for WBAN

Recent technological advancements in miniaturization of sensor and wireless communication lead to development of Wireless Body Area Networks (WBAN). It is similar to Wireless Sensor Networks but mainly focuses on health care applications to monitor the health condition of patient continuously. It consists of implantable and wearable computing devices placed inside or outside the surface of human body for sensing and data communication. Due to continuous operation, the sensor node introduces electromagnetic radiations which causes damage to delicate human tissues as well as degrades the performance of the network. In addition, WBAN data are sensitive to delay and it is necessary to send critical patient data to the remote server in a timely manner to protect patient’s life. However, alternating the routing path of critical data leads to collision, packet loss, high power consumption, maximum delay and reduced network life time. In this paper, Optimum Path Optimum Temperature Routing Protocol is introduced to address the above issues. The proposed protocol chooses optimum routing path by determining the temperature of sensor nodes and by defining two threshold limits (minimum and maximum). It also considers the critical data signals to be sent when the temperature of node exceeds the admissible threshold limit. The obtained simulation results are compared with conventional routing protocols and was analyzed that the proposed protocol has decreased delay, minimum energy, reduced power, uniform temperature distribution and maximum lifetime of sensor node.

Efficient light-weight private auditing scheme for cloud-based wireless body area networks

With the rapid development of cloud computing, cloud-based wireless body area networks (WBANs) provide powerful storage services, and process massive medical data efficiently. As the medical data are exploited to perform the clinical diagnosis and other special medical treatments, the integrity of the medical data stored in the cloud server is increasingly important. In this paper, based on the elliptic curve digital signature algorithm (ECDSA), we propose an efficient light-weight cloud storage private auditing scheme for medical data in WBANs. Our scheme enables a data owner to check the medical data integrity effectively personally, and does not need to retrieve the entire medical dataset, thereby dramatically reducing the communication overhead. Moreover, we further extend our private auditing scheme to private batch auditing. Thus, the data owner can perform auditing task for multiple different medical data files simultaneously. The performance comparison demonstrates that our scheme is much more light-weight, and more practical in cloud-based wireless body area networks.

Efficient light-weight private auditing scheme for cloud-based wireless body area networks

With the rapid development of cloud computing, cloud-based wireless body area networks (WBANs) provide powerful storage services, and process massive medical data efficiently. As the medical data are exploited to perform the clinical diagnosis and other special medical treatments, the integrity of the medical data stored in the cloud server is increasingly important. In this paper, based on the elliptic curve digital signature algorithm (ECDSA), we propose an efficient light-weight cloud storage private auditing scheme for medical data in WBANs. Our scheme enables a data owner to check the medical data integrity effectively personally, and does not need to retrieve the entire medical dataset, thereby dramatically reducing the communication overhead. Moreover, we further extend our private auditing scheme to private batch auditing. Thus, the data owner can perform auditing task for multiple different medical data files simultaneously. The performance comparison demonstrates that our scheme is much more light-weight, and more practical in cloud-based wireless body area networks.

LDVAS: Lattice-Based Designated Verifier Auditing Scheme for Electronic Medical Data in Cloud-Assisted WBANs

Data integrity has become an extremely important security issue in cloud-assisted wireless body area networks (WBANs). As accurate medical diagnostic analysis is heavily based on outsourced electronic medical data in cloud storage, any corrupted data may lead to fateful consequences. Data auditing contributes to medical data integrity verification, but most of existing data auditing schemes are vulnerable to attackers equipped with practical quantum-computing devices, which are very likely to be invented in the near future. Besides, in many scenarios, a patient has no capability to execute medical data integrity verification personally, he/she has to specify a verifier to complete data auditing. To this end, we present an efficient lattice-based designated verifier auditing scheme (LDVAS), which could be well deployed in cloud-assisted WBANs. In particular, LDVAS enables a patient to designate a unique verifier to execute data auditing in post-quantum secure settings, and any others cannot fulfil such task without the approval of the patient. We formally prove the security of LDVAS based on the hardness assumptions of lattice-based problems. The performance evaluation demonstrates the high efficiency and feasibility of LDVAS on the side of the designated verifier.

Using Bloom Filter to Generate a Physiological Signal-Based Key for Wireless Body Area Networks

Wireless body area networks (WBANs) are often used to provide communication services for the data from the body. Since the data in WBANs are always closely related to individuals, they need to be kept secret with integrity. Key management is critical to data security. The resource-constraint biosensors make it difficult for traditional key management mechanisms to work well in WBAN. Using physiological signals to realize key agreement has the advantages of low overhead, timely key updating, and no key material predeployment and key store requirements, etc. The existing physiological signal-based key agreement schemes are always unable to balance the overhead and security well. To overcome these problems, we make two efforts. One is that we try to enhance the randomness of the interpulse-interval (IPI) from electrocardiograms in the process of digitizing physiological signals. And the other is that we attempt to use the Bloom filter rather than lots of chaff points to conceal the features exchanged for key agreement. The comparative analysis and experiments indicate that the proposed scheme can simultaneously achieve high security strength and low overhead.

Research on Security for Wireless Body Area Network Data in Cloud Storage Based on Blockchain

Cloud computing enables users with limited resources to accomplish complex computation. Meanwhile, the massive human body sensing data generated by Wireless Body Area Network (WBAN) can be stored in cloud servers. However, cloud storage of human body sensing data is challenged by some issues. Users need to ensure that cloud servers can return the results and that they are correct. To address this problem, based on the argument system of commitment scheme, this paper designs a DMP verification algorithm. By compiling the hash of the header node into a constraint set and verifying the accuracy of the data of the intermediate node through a hash list, the algorithm reduces the compiling time of the intermediate node and improves the verification efficiency.

Distributed privacy protection strategy for MEC enhanced wireless body area networks

With the rapid development and widespread application of Wireless Body Area Networks (WBANs), the traditional centralized system architecture cannot handle the massive data generated by the edge devices. Meanwhile, in order to ensure the security of physiological privacy data and the identity privacy of patients, this paper presents a privacy protection strategy for Mobile Edge Computing(MEC) enhanced WBANs, which leverages the blockchain-based decentralized MEC paradigm to support efficient transmission of privacy information with low latency, high reliability within a high-demand data security scenario. On this basis, the Merkle tree optimization model is designed to authenticate nodes and to verify the source of physiological data. Furthermore, a hybrid signature algorithm is devised to guarantee the node anonymity with unforgeability, data integrity and reduced delay. The security performance analysis and simulation results show that our proposed strategy not only reduces the delay, but also secures the privacy and transmission of sensitive WBANs data.

Design, implementation and analysis of cognitive radio enabled intelligent WBAN gateway for cost-efficient remote health monitoring

In this paper, we design and implement an intelligent gateway for Wireless Body Area Network (WBAN) that seeks opportunistic access of licensed Primary User (PU) channels using Cognitive Radio (CR) to facilitate spectral-efficient, cost-efficient and reliable Non-Real Time (NRT) backhaul transmission of WBAN data for ubiquitous healthcare monitoring. The controller is termed herein as BodyCog-BNC. We develop and implement a cross-layer based modified protocol stack at the BodyCog-BNC, comprising of BodyCog-BNC Management Entity (BME) and BodyCog-BNC CR Medium Access Control (BCR-MAC) units, to exploit cross layer message passing to support the novel idea of spectrum agility. A spectral agile BodyCog-BNC is capable of intelligently switching to conventional licensed Wide-Area Access Network (WAN) technology in case of lack of proper PU channels or occurrence of successive handoffs in multiple PU channels. Furthermore, the protocol stack facilitates session management and energy-efficient backhaul CR transmission over a PU channel employing a proposed Inter-Sensing Time Optimization (ISTO) algorithm. ISTO leverages on convex optimization to select the optimum inter-sensing time for a PU channel to maximize the energy-efficiency within a PU interference constraint under sensing uncertainties. Closed form analytical expressions are also derived to estimate the average consumed energy, switching probability, switching time and the cost-efficiency of BodyCog-BNC. Exhaustive performance analysis shows that the BodyCog-BNC (WBAN gateway), provides a cost-efficient solution for different healthcare applications under varied operating conditions and CR cost regimes. Finally, through a prototype implementation and proof of concept we validate the ability of BodyCog-BNC in enabling NRT remote healthcare monitoring.

Cloud-Based Wireless Body Area Networks: Managing Data for Better Health Care

Patients with chronic diseases and the elderly will benefit greatly from a close-monitoring healthcare system. A wireless body area network (WBAN) has been widely deployed for this purpose. WBAN sensors collect vast amounts of biosignal data from a patient for long periods of time. These biosignal data need to be efficiently managed and stored for further processing and future reference. This article reviews and compares the frameworks and also evaluates the various methods used to manage WBAN data. We aim to identify the best method of managing WBAN data and present an enhanced architectural platform. It is intended to help researchers and practitioners to further study, modify, and develop quality-of-service (QoS)-based WBAN health-care systems.