Wireless Body Area Networks Devices Research Articles

Integrating Neutrosophic Theory for Improved Decision-Making in Wireless Body Area Networks: Enhancing Accuracy and Efficiency in Health Monitoring

Wireless Body Area Networks (WBANs) play a pivotal role in modern healthcare by enabling continuous monitoring of physiological data through sensors placed on or around the human body. Despite their significant benefits, WBANs face challenges such as data uncertainty, complex decision-making processes, and dynamic network conditions. These challenges can lead to inaccuracies and inefficiencies in health monitoring and diagnostics. The paper's main aim is to incorporate neutrosophic theory into Wireless Body Area Networks to provide enhancements in decision-making. In modern healthcare, the use of WBANs for monitoring physiological data by sensors, which are attached to or around the human body, can be continuous. Despite huge advantages, the main challenges that WBANs face are the uncertainties in data, complex decision-making processes, and dynamic network conditions, making health monitoring and diagnostics inaccurate and inefficient. In this paper, authors propose a robust framework to map sensor data into the neutrosophic domain and apply neutrosophic logic for enhanced accuracy and reliability of decision-making. In this paper, a Neutrosophic Decision-Making Algorithm is proposed, and its performance is compared with other decision-making techniques in terms of accuracy, response time, energy efficiency, and reliability. Experimental results show major improvements of around 95.3% in accuracy and a reduction of up to 25% in response time and energy consumption. Results underline the potential of neutrosophic theory for revolutionizing decision-making processes within WBANs to ensure more reliable and efficient health monitoring. This approach enables not only operational life but also improves patient outcome, avoiding a wrong diagnosis, during long-term health monitoring applications using WBAN devices.

Secure Data Aggregation and Transmission System for Wireless Body Area Networks Using Twofish Symmetric Key Generation

Nowadays, Wireless Body Area Networks (WBANs) are mostly used in the healthcare industry. They represent a portable, inexpensive network that exhibition adaptability. The data developed using WBAN devices is vulnerable to transmission-related internal and external attacks; nevertheless, this vulnerability arises due to resource restrictions; by employing data aggregation technologies to conduct statistical analyses of medical data while protecting patient privacy, medical professionals can enhance the precision of diagnoses and assist medical insurance firms in selecting optimal plans for their clients. Maintaining the confidentiality and integrity of sensitive health information becomes more stimulating at the stages of aggregation and transmission due to security issues. This study proposes a novel method, Twofish Symmetric Key Generation (TFSKG), combined into a Secure Data Aggregation (SDA) and transmission system intended for WBANs. The Twofish technique is animatedly employed to make the secure symmetric keys chosen for its robust encryption capabilities. These keys are used to encrypt and decrypt aggregated health data through transmission. The proposed TFSKG-SDA method implements effective algorithms for aggregating data to safeguard end-to-end privacy and preserve data accuracy while reducing bandwidth consumption. Thus, for improved performance, an innovative genetic algorithm for data security is presented in this study. This paper introduces TFSKG-SDA, a system that, by employing rigorous simulation testing, enhances security protocols, resistance against recognized threats, and data transmission efficacy in the context of resource-constrained WBANs. We assess the encryption strength, computational cost, and communication efficiency of the TFSKG- SDA method to prove its significance to real-world healthcare applications.

Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment

Recently, the Wireless Body Area Networks (WBAN) have become a promising and practical option in the tele-care medicine information system that aids for the better clinical monitoring and diagnosis. The trend of using Internet of Things (IoT) has propelled the WBAN technology to new dimension in terms of its network characteristics and efficient data transmission. However, these networks demand the strong authentication protocol to enhance the confidentiality, integrity, recoverability and dependability against the emerging cyber-physical attacks owing to the exposure of the IoT ecosystem and the confidentiality of biometric data. Hence this study proposes the Fog based WBAN infrastructure which incorporates the hybrid symmetric cryptography schemes with the chaotic maps and feed forward networks to achieve the physiological data info security without consuming the characteristics of power hungry WBAN devices. In the proposed model, scroll chaotic maps are iterated to produce the high dynamic keys streams for the real time applications and feed-forward layers are leveraged to align the complex input-output associations of cipher data for subsequent mathematical tasks. The feed forward layers are constructed which relies on the principle of Adaptive Extreme Learning Machines (AELM) thereby increasing randomness in the cipher keys thereby increasing its defensive nature against the different cyber-physical attacks and ensuring the high secured encrypted-decrypted data communication between the users and fog nodes. The real time analysis is conducted during live scenarios. BAN-IoT test beds interfaced with the heterogeneous healthcare sensors and various security metrics are analysed and compared with the various residing cryptographic algorithms. Results demonstrates that the recommended methodology has exhibited the high randomness characteristics and low computational overhead compared with the other traditional BAN oriented cryptography protocol schemes

Advanced Hybrid Mask Convolutional Neural Network with Backpropagation Optimization for Precise Sensor Node Classification in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are crucial in continuous health monitoring, fitness tracking, and other applications where a real-time collection of physiological data is needed by sensors worn on the body. This is important in WBAN to achieve reliable data transmission, energy efficiency, and overall system performance. Still WBANs present several challenges: for example, the data that is being collected is heterogeneous since it originates from diverse IMDs measuring different bio-physiological signals; these can be also quite noisy because of motion artifacts when moving; as well high limitations when it comes to energy, bandwidth or storage push for low-complexity methods rather than standard deep learning techniques. Common Convolutional Neural Networks) CNNs( are successfully utilized for spatial information extraction but they cannot catch temporal dependencies well and also, WBAN sensor data has a noisy and multi-modal structure which acts as an additional challenge for traditional CNNs. These limitations emphasize the need of a flexible, fast and precise classification model based on the specific needs of WBAN applications. To overcome these challenges, this paper presents a novel hybrid neural network architecture consisting of combined 2D and 3D convolutions for spatial-temporal feature extraction along with masked convolution layers to provide an ability to adaptively ignore uninterested parts of the data. The model aims at achieving a high classification performance while also balancing with the system computational efficiency, perfect for its ipsi deployment on resource constrained WBAN devices. Then, we apply further backpropagation optimization measures such as adaptive learning rate scheduling and gradient clipping, to improve the stability of training speed and reduce latency which in return finds its way into supporting real-time processing capabilities of the model. By using each of these components, the model is able to deal with the multi-dimension aspects and high noise level nature of WBANs without excessive computation resources [18]. The Hybrid Masked CNN model is shown to out-perform existing approaches without such masking, yielding substantially higher performance in terms of accuracy, precision, recall and F1-score across all metrics defined for the application as compared to traditional 2DCNNs, 3DCNNs and other hybrid models. Consequently, the latency of the model is significantly decreased as well which confirms its applicability to real-time WBAN applications. The obtained results confirm the efficacy of features from hybrid architectures with masked convolutions along with optimization in training techniques for WBAN sensor node classification. The results of this paper improve WBAN technology by providing a solid and scalable solution which can be implemented when more reliability and flexibility are required from such systems in applications like healthcare, fitness or any other field.

LSNCP: Lightweight and Secure Numeric Comparison Protocol for Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have been deployed in numerous applications, where the most common communication technology is Bluetooth. Bluetooth uses the Numeric Comparison Protocol (NCP) to negotiate session keys based on the Elliptic Curve Cryptography (ECC) and Out-of-Band (OoB) channels. However, the scalar multiplication of ECC is a heavy computing operation for devices in WBANs. To address this issue, we propose the Lightweight and Secure Numeric Comparison Protocol (LSNCP) which requires less scalar multiplication than the NCP in Bluetooth. New logic expressions and rules are proposed to verify the security of LSNCP in GNY logic. The proof shows that LSNCP is secure. We conduct a provable security analysis by integrating the commitment scheme and short hash function. The result shows that LSNCP is secure in the modified Bellare-Rogaway model. Finally, we conduct theoretical analysis and experiments to evaluate the performance of LSNCP. The results confirm that LSNCP has less computation cost than NCP and other benchmark protocols. LSNCP has many potential application scenarios like healthcare, Metaverse, and blockchain.

A CRLH-TL Inspired Dual-Band Antenna With Polarization Diversity for Wrist-Worn Wireless Body Area Network Devices

A wrist-worn dual-band smartwatch antenna with polarization diversity is proposed for smartwatches. The antenna has a cylindrical structure with a diameter of 33.6 mm and a height of 6.8 mm. The radiated structure is inspired by composite right/left handed-transmission lines (CRLH-TLs) to realize a dual-band operation (2.4 GHz and 5.8 GHz WLAN band). The unit cell of the CRLH-TL is analyzed, and its equivalent circuit model and the dispersion curves are studied for mode analyzation. The +1 and -1 modes of CRLH TL are employed for dual-band operating, and two orthogonal-polarized modes are excited with different types of feeding structure to realize the polarization diversity with low coupling and low envelope correlation coefficients (ECC). The wrist model is built in both simulation and experiment to evaluate the performance of the antenna, which shows a good radiation and diversity performances. The measured degree of freedom (DoF) is 1.92 and the capacity is 17.39 bit/s/Hz with 30 dB SNR. The results of specific absorption rate (SAR) are also studied and they are pretty low. These characteristics of the proposed antenna make it a competitive candidate for wrist-worn wireless body area network devices.

BOSS: Bargaining-Based Optimal Slot Sharing in IEEE 802.15.6-Based Wireless Body Area Networks

In this article, we propose a method to solve the problem of optimal distribution of slots among sensor devices associated in wireless body area networks (WBANs). Modern healthcare is witnessing a paradigm shift due to the recent developments in Internet of Things (IoT), and WBAN is a fundamental enabling component of IoT-based healthcare. In a WBAN, sensors and actuators are implanted in patients (both on-body and in-body) connected to a hub to monitor health conditions ubiquitously and in real time. WBANs deal with heterogeneous sensors with diverse resource demands and constraints. Optimal allocation of data transmission slots among these heterogeneous sensors is a real challenge. Thus, in this article, we propose a cooperative game-theoretic approach, based on the Nash bargaining solution (NBS), for allocating slots for sensor devices in WBANs where the sensors communicate with each other following the IEEE 802.15.6 standard. We also compare the performance of our work with this standard and other relevant benchmarks to show the efficacy of the proposed solution. The proposed bargaining-based optimal slot sharing (BOSS) algorithm yields 26.68% better reliability and 38.07% better throughput, on an average, than the traditional IEEE 802.15.6 standard.

Secure and Lightweight Data Aggregation Scheme for Anonymous Multi-Receivers in WBAN

Wireless body area networks (WBANs) provide healthcare providers with early warning of medical emergencies by monitoring vital parameters of a patient's body with wearable devices embedded with biosensors. However, the resource-constrained nature of WBAN devices makes the data collected from them vulnerable to internal and external attacks during transmission. By aggregating medical data, data aggregation technology is used to conduct statistical analysis of the data and achieve confidentiality of patient information, which can help doctors make the correct diagnosis and assist medical insurance companies choose the right insurance for patients. However, the existing data aggregation schemes are inefficient or prone to potential safety risks to patients due to their high computational intensity and known identity of recipients. To address it, we propose a privacy-preserving health data aggregation scheme in the multi-receiver setting, which not only securely collects health data from IoT wearable devices but also supports the anonymity of multiple receivers while avoiding time-consuming pairing operations. Since the patient's health data and the recipient's identities are transmitted in cipher-text, it effectively prevents data leakage and achieves privacy protection of identity information among recipients. Compared with several existing schemes, the proposed scheme not only achieves anonymity of the collected data and the receivers' identities,but also makes it lightweight. Furthermore, experimental results demonstrate that the proposed scheme is cost-efficient in an all-around performance.

FDTD-based SAR calculation of a wearable antenna for wireless body area network devices

AbstractWireless-connected wearable electronics are finding extensive usage for diagnostic and therapeutic purposes after the globally spread pandemic disease of COVID-19. Although they are undoubtedly helpful for keeping physical distance, their health effects are still under investigation from different aspects and are still a concern for the end-users. In this study, a custom M-shaped wearable antenna covering the wireless body area network and wireless local area network frequencies is designed, built, and measured. A beret cap made from a 2 mm thick textile is used as a substrate. The specific absorption rate (SAR) in a realistic human-head model due to electromagnetic energy produced by the antenna is evaluated using the finite-difference time-domain method. The SAR distributions for 1-g and 10-g tissues are calculated at 2.4 and 5.8 GHz. It is shown that the obtained maximum SAR values for 1-g and 10-g tissues at each frequency of interest were less than the limits determined by IEEE RF exposure guidelines and standards.

O³HSC: Outsourced Online/Offline Hybrid Signcryption for Wireless Body Area Networks

Wireless body area networks (WBAN) enable ubiquitous monitoring of patients, which can change the future of healthcare services overwhelmingly. As the collected data of patients usually contain sensitive information, how to collect, transfer, store and share data securely and properly has become a concerning issue. Attribute-based encryption (ABE) can achieve data confidentiality and fine-grained access control simultaneously. Identity-based ring signature (IBRS) allows patients to prove their identity without leaking any extra (private) information. However, the heavy computational burden of ABE and IBRS is intolerable for most power-limited mobile devices, which account for a large proportion of WBAN devices. This paper combines the attribute-based online/offline encryption (ABOOE) and IBRS to achieve an outsourced online/offline hybrid signcryption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}$ </tex-math></inline-formula> HSC) scheme. As far as we know, this scheme is the first signcryption scheme that adopts IBRS and satisfies online/offline signcryption simultaneously. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}$ </tex-math></inline-formula> HSC divides the key generation and signcryption into offline and online phases to increase the throughput of the central authority and save the power resources of mobile devices, respectively. Besides, outsourced decryption and public signature verification are also realized. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}\mathrm {HSC}$ </tex-math></inline-formula> achieves security under CCA and CMIA, and the performance analysis shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}\mathrm {HSC}$ </tex-math></inline-formula> is a lightweight and applicable scheme for WBAN.

Dictionary Learning-Based Multichannel ECG Reconstruction Using Compressive Sensing

Multi-channel electrocardiogram (MECG) compression on lightweight wireless body area network (WBAN) is highly challenging for long-term eHealthcare monitoring. Energy consumption involved in wireless transmission poses an obstacle in the implementation of WBAN. MECG is widely used to diagnose cardiovascular diseases (CVDs), which require a considerable time to extract sufficient clinically relevant data from subjects. Since wireless MECG data transmission dominates energy cost and memory consumption of wireless sensor nodes, it is favorable to reduce the data size without any loss of diagnostic information. Compressed sensing (CS) provides efficient encoding schemes for data reduction and energy consumption in wireless transmission. We propose an adaptive multiple dictionary learning-based joint CS model for MECG compression, which exploits spatial correlation and adaptive features existing in MECG signals. We demonstrate performance of the proposed algorithm with the Physikalisch-Technische Bundesanstalt database (PTB) for MECG compression. Results from the proposed scheme can achieve excellent reconstruction quality with fewer measurements than other existing CS-based approaches. It is the most suitable technique for long-term MECG compression using lightweight WBAN devices.

Designing of Unit EBG Cell Using Conductive Textile for Dual Band Operation

Background/Objectives: Flexible electronics have paved the way for Wireless Body Area Networks (WBAN). The main challenge in accommodating such applications is reducing the impact of radiation on the human body. The presence of body tissues may affect WBAN devices such as wearable sensors and wearable antennas, so reducing back radiations becomes an important task. Methodology: When a microstrip patch antenna is placed on a human body, the artificially formed Electromagnetic Band Gap (EBG) surface mimics the property of a Perfect Magnetic Conductor (PMC) rather than the conventional Perfect Electrical Conductor (PEC). Findings: In this work, the unique property of the EBG surface beneath the patch antenna creates a zero phase shift at the resonance, which improves the antenna’s performance and reduces back radiations as well. The proposed EBG surface structure is the simplest square-shaped structure with no conductor connections Via patch (Via-less). Novelty: The EBG structure designed and presented in this paper has zero reflections for the dual-band of operations. The resonance frequency of 2.45GHz and 5.5 GHz has been designed for the absorption of 80% and 65% respectively. This level of absorption has not yet been reported in the literature. The newly formed EBG cell integrated with the patch antenna and its performance improvements has been shown. The analysis shows that the EBG enhances the return loss by 20.35 % and gain enhances by 16.44 %, on textile materials with the advantage of most simplex EBG cell construction. Keywords: EBG; PMC; PEC; Wearable Microstrip Patch Antenna; WBAN

Retracted] Secure Techniques for Channel Encryption in Wireless Body Area Network without the Certificate

Wireless body area networks (WBANs) have seen an increase in popularity in recent years. Electromagnetic waves created by the body have the capacity to connect nodes all over the epidermis and throughout the body. If the gadget does not cause discomfort or harm, it can be linked to or implanted in the body. This is something that is currently being worked on. Other factors influence an individual’s genuine mobility and the ease with which they can use something. Participating in social networks may enhance the lives of members. WBANs equipped with sensors can monitor a user’s heart rate and communicate that information to the user’s physician. WBAN has been shown to be a dependable electronic health solution. WBAN technology allows you to follow your patient’s data no matter where they are, when they are, or what they are doing. However, because it runs in an open Wi‐Fi environment and can conceal users’ physiological data, it is more vulnerable to assault. To deal with resource‐constrained WBAN sensors and devices, a cryptographic solution that is both very efficient and extremely secure is required. Our primary priority will be the safeguarding of the WBAN network. WBAN contains several significant security weaknesses that must be addressed immediately. WBANs might benefit from certificateless signature encryption that uses a hyperelliptic curve and works over a secure channel. We are outpaced by the opposition by 4.58 milliseconds.

Securing Wireless Body Area Network with Efficient Secure Channel Free and Anonymous Certificateless Signcryption

In the last few years, the wireless body area network (WBAN) has emerged as an appealing and viable option in the e‐health application domain. WBAN technology is primarily used to offer continuous screening of health data to patients, independent of their location, time, or activity. A WBAN, on the other hand, is vulnerable to different cyberattacks due to the openness of the wireless environment and the privacy of people’s physiological data. A highly efficient and secure cryptographic scheme that can fulfill the needs of resource‐constrained WBAN sensors and devices is considered necessary. First, we take a look at the most up‐to‐date security solutions for WBANs. Then, we go through some of the underlying concerns and challenges with WBAN security. We propose a new framework called secure channel free certificateless signcryption scheme for WBANs based on a hyperelliptic curve that can meet security requirements such as confidentiality, anonymity, integrity, resistance against unauthorized users, unforgeability, public verifiability, forward secrecy, and antireplay attack, all of which can be achieved with low computation and communication costs. The computation cost of the proposed scheme is 3.36 ms, which is much better than its counterpart schemes.

Improved unsupervised coloring algorithm for spectrum allocation in multiple wireless body area networks

As electromagnetic waves fade rapidly when passing through the human body, interferences may significantly degrade communication over wireless body area networks (WBANs). However, due to the limited power and resource restrictions in WBAN devices, existing interference mitigation methods may not fully address this issue. In this study, considering the architecture and equipment capacity of WBANs, the clustering mechanism is improved using unsupervised machine learning during the coloring process. Accordingly, a novel unsupervised coloring algorithm (UCA) for spectrum allocation in multiple WBANs is presented in this paper. The nodes in the region are separated by clustering, and the channel group is assigned to the cells by coloring to achieve UCA. We simulated the performance of UCA in different scenarios with different node densities for both indoor and outdoor environments. The experimental results showed that the proposed UCA achieved convergence results with accurate and inaccurate positioning. It outperformed the existing methods by exhibiting less time complexity, lower interference strength, and faster algorithm convergence. Moreover, the proposed UCA was robust against topology changes.

A provably secure and efficient anonymous mutual authentication and key agreement protocol for wearable devices in WBAN

Wireless body area networks (WBAN) is a novel paradigm that is gaining popularity in a scenario of current wireless communication systems. It plays an essential role in healthcare applications like remote monitoring of health data. For instance, the crucial and confidential data about the condition of the patient’s physical health can be gathered and transferred through WBAN. Therefore, authentication and session key-agreements are integral security concerns for wearable sensors in WBAN. Moreover, as the wearable devices are resource-constraints, there is a need to develop a lightweight protocol to ensure authenticity, confidentiality, and integrity of the information. Li et al. presented an anonymous mutual authentication protocol to establish a session-key among wearable sensor nodes and the local hub node. However, after an in-depth analysis, we found that their scheme is susceptible to an intermediate node capture attack, and sensor node/hub node impersonation with intermediate node capture attacks. The scheme also does not provide anonymity with unlinkable sessions. This paper proposes a new anonymous mutual authentication and key agreement protocol in WBAN to overcome the security weaknesses in Li et al.’s protocol. The proposed protocol uses only basic symmetric cryptosystems like simple XOR and cryptographic hash functions; hence, it is efficient and lightweight. The validity and the correctness of the proposed protocol are evaluated using BAN-Logic, Real-Or-Random (ROR) model, and the broadly accepted AVISPA tool. The performance comparison of the proposed protocol with the existing related protocols shows the efficiency regarding communication and computational complexities. Hence, it is suitable to be used in real-life applications.

A low‐profile dual‐band omnidirectional Alford antenna for wearable WBAN applications

AbstractA low‐profile omnidirectional antenna based on folded Alford loop is proposed for wireless body area network (WBAN) applications in industrial, scientific, and medical (ISM, 2.4‐2.4835 GHz and 5.725‐5.875 GHz) bands. By fabricating two complementary symmetrically folded Alford loops on both sides of a round Roger substrate, the proposed antenna has small volume and omnidirectional radiation capability in both bands. Since the omnidirectional radiation plane is parallel to the human body surface, low specific absorption rate (SAR) can be obtained. The proposed antenna has a profile of 0.639 mm and occupies an area of π × 302 mm2, along with a measured peak gain of 6.1 dBi and 14.1% impedance bandwidth (BW) at 2.45 GHz and peak gain of 2.2 dBi and 6.7% impedance BW at 5.8 GHz. Simulated and measured results reveal that the antenna has good omnidirectional radiation in both bands. Moreover, the bending sensitivity study and SAR analysis prove that it is a good candidate for wearable WBAN devices.

IEEE Access Special Section Editorial: Wireless Body Area Networks

Wearable devices and wireless communications, combined with personalized health management, are future trends that healthcare practices and procedures are heading toward. To facilitate this progress, new technologies and methods are required to provide reliable measurements, end-to-end communications, and data analysis mechanisms from the data source to medical health records. The wireless body area network (WBAN) is the major element in this process. This Special Section is not only limited to on-body WBAN devices but also focuses on benefitting technologies which can distribute vital information inside a human body or allow control of implantable gadgets. Dependable wireless communications combined with versatile application areas, such as accurate localization or behavior analysis techniques, remote monitoring, adoption of vital sensors, and actuators, can benefit the increased use of new WBAN technologies in various healthcare-related studies. In the end, this will make healthcare processes more effective and user friendly, and simultaneously increase the safety of (out)patients. This Special Section focuses on various theoretical and experimental views on WBAN applications, technologies, implementations, and utilizations based on invited, extended versions of the best-evaluated articles from BodyNets 2018, held at Oulu, Finland, in October 2018, as well as quality articles submitted from the Open Call. This Special Section includes 16 accepted articles which have undergone a rigorous peer-review process. Three of the accepted articles were invited extensions of the articles presented at the BodyNets 2018 conference. The total number of submissions we received for this Special Section was 45; thus, the acceptance rate was 36%.

A Low-Profile and Wideband Triple-Mode Antenna for Wireless Body Area Network Concurrent On-/Off-Body Communications

In this article, a low-profile and wideband triple-port printed antenna for wireless body area network (WBAN) on-body and off-body communications is presented. Three ports are responsible for exciting three different patterns that are orthogonal to each other, including a vertically polarized omnidirectional pattern and two horizontally polarized broadside patterns with radiated fields polarized along the xand y-directions, respectively. In particular, the vertically polarized pattern is achieved by loading an annular array of metallic vias connected to a pin-fed patch antenna on the bottom, while the horizontally polarized patterns are enabled by a closely spaced modified E-shaped stacked patch on the top with additional slot cuts. The integrated antenna was fabricated and characterized, achieving a measured wide S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> <; -10 dB bandwidth of nearly 14%, a mutual coupling smaller than -15 dB, and an envelope correlation coefficient of less than 0.01. Further numerical and experimental investigations reveal that the proposed antenna has robust electrical properties under both structural deformation and human body loading. The superior performance indicates that the demonstrated antenna is a promising candidate for WBAN devices that can simultaneously support multiple communication modes as well as for other MIMO communications and sensing systems.

Data Storage Mechanism Based on Blockchain with Privacy Protection in Wireless Body Area Network.

Wireless body area networks (WBANs) are expected to play a vital role in the field of patient-health monitoring shortly. They provide a convenient way to collect patient data, but they also bring serious problems which are mainly reflected in the safe storage of the collected data. The privacy and security of data storage in WBAN devices cannot meet the needs of WBAN users. Therefore, this paper adopts blockchain technology to store data, which improves the security of the collected data. Moreover, a storage model based on blockchain in WBAN is proposed in our solution. However, blockchain storage brings new problems, for example, that the storage space of blockchain is small, and the stored content is open to unauthorized attackers. To solve the problems above, this paper proposed a sequential aggregate signature scheme with a designated verifier (DVSSA) to ensure that the user’s data can only be viewed by the designated person and to protect the privacy of the users of WBAN. In addition, the new signature scheme can also compress the size of the blockchain storage space.