Sensor Nodes In Wireless Body Area Networks Research Articles

HT-WSO: A hybrid meta-heuristic approach-aided multi-objective constraints for energy efficient routing in WBANs

Generally, Wireless Body Area Networks (WBANs) are regarded as the collection of small sensor devices that are effectively implanted or embedded into the human body. Moreover, the nodes included in the WBAN have large resource constraints. Hence, reliable and energy-efficient data transmission plays a significant role in the implementation and in constructing of most of the merging applications. Regarded to complicated channel environment, limited power supply, as well as varying link connectivity has made the construction of WBANs routing protocol become difficult. In order to provide the routing protocol in a high energy-efficient manner, a new approach is suggested using hybrid meta-heuristic development. Initially, all the sensor nodes in WBAN are considered for experimentation. In general, the WBAN is comprised of mobile nodes as well as fixed sensor nodes. Since the existing models are ineffective to achieve high energy efficiency, the new routing protocol is developed by proposing the Hybrid Tunicate-Whale Swarm Optimization (HT-WSO) algorithm. Subsequently, the proposed work considers the multiple constraints for deriving the objective function. The network efficiency is analyzed using the objective function that is formulated by distance, hop count, energy, path loss, and load and packet loss ratio. To attain the optimum value, the HT-WSO derived from Tunicate Swarm Algorithm (TSA) and Whale Optimization Algorithm (WOA) is employed. In the end, the ability of the working model is estimated by diverse parameters and compared with existing traditional approaches. The simulation outcome of the designed method achieves 13.3%, 23.5%, 25.7%, and 27.7% improved performance than DHOA, Jaya, TSA, and WOA. Thus, the results illustrate that the recommended protocol attains better energy efficiency over WBANs.

HCEL: Hybrid Clustering Approach for Extending WBAN Lifetime

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.

Identification of Faulty Sensor Nodes in WBAN Using Genetically Linked Artificial Neural Network

Wireless Body Area Networks (WBANs) have risen as a promising innovation for checking humanphysiological parameters in real time. Be that as it may, the unwavering quality and precision of WBANs dependon the right working of sensor hubs. The distinguishing proof of defective sensor hubs is vital for guaranteeing thequality of information collected by WBANs. In this paper, we propose a novel approach to recognizing faultysensor hubs in WBAN employing a hereditarily linked artificial neural organize (GLANN). The GLANN isprepared to employ a crossbreed fuzzy-genetic calculation to optimize its execution in distinguishing defectivesensor hubs. The proposed approach is assessed employing a dataset collected from a real-world WBAN. Thecomes about appears that the GLANN-based approach beats existing strategies in terms of exactness andproficiency. The proposed approach has potential applications within the field of healthcare, where exact and solidobserving of human physiological parameters is basic for conclusion and treatment. By and large, this ponderpresents a promising approach to progressing the unwavering quality and exactness of WBANs by identifyingflawed sensor hubs utilizing GLANN .

Energy-efficient classification strategy for detecting interference and malicious sensor nodes in wireless body area Networks

Wireless Body Area Networks (WBANs) play a vital role in healthcare monitoring, using wireless sensors to track physiological parameters and predict illness onset. This study proposes a novel approach for detecting interference and malicious sensor nodes in WBANs, crucial for maintaining system integrity and performance. The method combines feature-based techniques with classification strategies to accurately identify anomalies. Features are taken from WBAN nodes and used to train Support Vector Machine (SVM) classifiers, which makes interference detection work well. A neurofuzzy inference system (ANFIS) classifier is also used to train the system on trusted and untrusted nodes at the start, which makes classification easier in real-world WBAN situations. Link failures due to rogue sensor nodes can severely impact WBAN performance, emphasizing the need for efficient detection and correction mechanisms. The proposed strategy introduces a weight metric to identify broken links, enhancing system reliability. Evaluation metrics, including LFD latency and packet delivery ratio, are analyzed to assess the efficacy of the approach. By improving interference detection and addressing link failures, this study contributes to enhancing the efficiency and reliability of WBAN networks, critical for advancing healthcare monitoring technologies.

Healthcare: A priority-based energy harvesting scheme for managing sensor nodes in WBANs

The eHealth service has been considered a potential resource issue for industry and academia and is remarkably similar to a promising technology for continuous monitoring of biomedical signals in the human body. Indeed, it deployed modern digital technologies to maintain patient health data in digital environments such as the Internet of Things (IoT). In this vein, Wireless Body Area Networks (WBANs) are essential components of eHealth systems for early detection and successful treatment. Because sensor batteries in WBANs are usually operated and inconvenient to recharge, an energy-efficient resource allocation scheme is critical to extending the length of networks while still meeting the stringent quality of service requirements inherent in WBANs. As a result, this paper investigates resource allocation issues for WBAN. Our objective is to maximize energy efficiency by considering the effect of data transmission, relay selection, power consumption, and each sensor's energy constraints. Due to the current problems' sophistication, we present a Q-learning Agent (QLA) system to obtain the optimal allocation approach. A Q-Sensor Network Management Unit (Q-SNMU) is implemented and designed to synchronize all body sensors appropriately. The results show that the proposed scheme works well and that the proposed Q-SNMU approach is very efficient at running.

Adaptive Body Area Networks Using Kinematics and Biosignals

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.

Link quality and energy utilization based preferable next hop selection routing for wireless body area networks

The rising population and cost of medical services triggered the new technologies including Wireless Body Area Networks (WBANs) based on smart and intelligent biosensors nodes for sensing and monitoring the patient vital signs. The biosensor nodes have implanted inside or outside the human body to send the medical information to medical centers. For data dissemination in these services, different types of solutions and model have been designed to address the interference, body movement, disconnection quality of services issues in the network. This paper presents an Energy Aware Routing (EAR) protocol to minimize energy utilization and select preferable next hop by evaluating the link quality of sensor nodes. The proposed protocol evaluates the energy level, link quality, and remaining energy level to balance the load, minimize the energy utilization, and enhance the data transmission. Various simulations have conducted to evaluate the proposed protocols performance in terms of energy consumption, data delivery, delay, and data throughput. Experimental results indicated that the proposed protocol has a better mechanism for date routing and better solution to minimize the energy of sensor nodes in WBANs.

Reinforcement Learning Based Clock Synchronization in WBAN

Wireless Body Area Network (WBAN) is an application of wireless sensor network (WSN). WBAN therefore forms a comprehensive collection of devices that are not only capable of providing continuous information about the health status of a person but also offers helpful details about the activities and environment of the person. In this paper, we have evaluated TDMA based MAC protocol performance through several metrics and TDMA approach is used to avoid packet collision which leads to higher packet loss rate. Reinforcement Based Clock synchronization is the solution of problem like packet collision. After clocks of WBAN sensor nodes are synchronized, data can be transferred between sensor nodes and sink efficiently and rapidly. Reinforcement learning iteratively optimizes the clock synchronization technique. Experimental results indicate that the proposed algorithm is more efficient than existing techniques.

Anonymous hybrid mutual authentication and key agreement scheme for wireless body area network

Wireless body area networks (WBANs) play a significant role in remote health monitoring as a key application of the Internet of Things (IoT). Mutual authentication and key agreement are vital for the security and privacy of health information involved in the WBAN. Li et al. proposed a lightweight authentication and key agreement scheme for the sensor nodes in WBAN. Their authentication and key agreement scheme protects against various existing attacks. But on detailed analysis, we could find that their scheme is prone to sensor node impersonation attack. Also, security of their scheme relies on the assumption that the hub node is trustworthy, which is practically infeasible. Hence, we propose a hybrid anonymous authentication and key agreement scheme using the physiological signal to overcome the shortcomings in Li et al.’s scheme. The proposed scheme also provides additional security features to resist hub node impersonation attack and key escrow problem. Burrows-Abadi-Needham (BAN) logic is used to prove the correctness of the proposed scheme and the Automated Validation of Internet Security Protocols and Applications (AVISPA) is used to evaluate the security of the proposed scheme.

Wireless Body Area Networks MAC Protocol For Energy Efficiency and Extending Lifetime

Wireless body area networks (WBANs), as an important part of the Internet of things and sensor networks, are becoming more important in health monitoring and entertainment services. The sensor nodes in these applications need to transmit information data to personal station and receive control information from personal station to adjust working modes. However, unlike an easily rechargeable personal station, body sensor nodes suffer from energy shortages, which affect the nodes lifetimes. To solve this problem, we design a novel medium access control (MAC) layer protocol to enhance the energy efficiency and extend the lifetime of body sensor nodes in WBANs. The proposed protocol utilizes a hybrid scheme by taking advantage of carrier-sense multiple accesses with the collision avoidance and time division multiple access schemes. In addition, we allocate the main transmission overhead at the personal station side and design a novel awaiting order state for sensor nodes to improve energy efficiency. Also, the analysis and simulation results demonstrate that the proposed protocol can provide a 6 to 15 decrease in energy consumption from other protocols.

A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks.

Network Lifetime is one of the most important metrics in Wireless Body Area Networks (WBANs). In this paper, a relay selection scheme is proposed under the topology constrains specified in the IEEE 802.15.6 standard to maximize the lifetime of WBANs through formulating and solving an optimization problem where relay selection of each node acts as optimization variable. Considering the diversity of the sensor nodes in WBANs, the optimization problem takes not only energy consumption rate but also energy difference among sensor nodes into account to improve the network lifetime performance. Since it is Non-deterministic Polynomial-hard (NP-hard) and intractable, a heuristic solution is then designed to rapidly address the optimization. The simulation results indicate that the proposed relay selection scheme has better performance in network lifetime compared with existing algorithms and that the heuristic solution has low time complexity with only a negligible performance degradation gap from optimal value. Furthermore, we also conduct simulations based on a general WBAN model to comprehensively illustrate the advantages of the proposed algorithm. At the end of the evaluation, we validate the feasibility of our proposed scheme via an implementation discussion.

Efficient Anonymous Authenticated Key Agreement Scheme for Wireless Body Area Networks

Wireless body area networks (WBANs) are widely used in telemedicine, which can be utilized for real-time patients monitoring and home health-care. The sensor nodes in WBANs collect the client’s physiological data and transmit it to the medical center. However, the clients’ personal information is sensitive and there are many security threats in the extra-body communication. Therefore, the security and privacy of client’s physiological data need to be ensured. Many authentication protocols for WBANs have been proposed in recent years. However, the existing protocols fail to consider the key update phase. In this paper, we propose an efficient authenticated key agreement scheme for WBANs and add the key update phase to enhance the security of the proposed scheme. In addition, session keys are generated during the registration phase and kept secretly, thus reducing computation cost in the authentication phase. The performance analysis demonstrates that our scheme is more efficient than the currently popular related schemes.

Performance Analysis of Traffic Sensitive Wireless Body Area Networks

The cooperation of micro-electronics, wireless communications, integrated circuits and sensor networks have led to rise of Wireless Body Area Networks (WBANs). The aim of WBANs is to keep a human body under control by means of a set of small-size, lightweight, and low-power sensor nodes placed in, on, or around the human body. The WBANs are also used in a number of new applications namely health monitoring, entertainment, sports, and military applications. The sensor nodes in WBANs have limited energy resources as a result of communicating with each other wirelessly. Therefore, the medium access control protocols for WBANs must be energy efficient protocols. Also, WBAN’s importance of having the different data traffics has increased determination of operation sequence at the coordinator nodes. In this study, data traffic sensitive WBAN is proposed. End to end delay and throughput results are examined for performance evaluation.