Wireless Body Area Network Nodes 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 .

WHOOPH: whale optimization-based optimal placement of hub node within a WBAN

Biosensor nodes of a wireless body area network (WBAN) transmit physiological parameter data to a central hub node, spending a substantial portion of their energy. Therefore, it is crucial to determine an optimal location for hub placement to minimize node energy consumption in data transmission. Existing methods determine the optimal hub location by sequentially placing the hub at multiple random locations within the WBAN. Performance measures like link reliability or overall node energy consumption in data transmission are estimated for each hub location. The best-performing location is finally selected for hub placement. Such methods are time-consuming. Moreover, the involvement of other nodes in the process of hub placement results in an undesirable loss of network energy. This paper shows the whale optimization algorithm (WOA)-based hub placement scheme. This scheme directly gives the best location for the hub in the least amount of time and with the least amount of help from other nodes. The presented scheme incorporates a population of candidate solutions called "whale search agents". These agents carry out the iterative steps of encircling the prey (identifying the best candidate solution), bubble-net feeding (exploitation phase), and random prey search (exploration phase). The WOA-based model eventually converges into an optimized solution that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The results show that the proposed WOA-based hub placement scheme outperforms various state-of-the-art related WBAN protocols by achieving a network lifetime of 8937 data transmission rounds with 93.8% network throughput and 9.74 ms network latency.

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.

Securing IoT-Based Healthcare Systems Against Malicious and Benign Congestion

The Internet of Things (IoT) has made it possible to gather patient data through a network of sensors, referred to as the wireless body area network (WBAN). However, the variability of the wireless channels can pose a challenge to the real-time functionality of medical IoT systems. These delays can occur due to either natural or malicious congestion in the wireless channels. To address this issue, we present an efficient algorithm that partitions the WBAN nodes within a medical IoT system to balance the traffic load across the entire system. In our model, each network partition includes an access point (AP) responsible for managing all the WBANs within its coverage range. Our proposed algorithm enables the APs to dynamically readjust their coverage range based on the overall traffic load, thereby evenly distributing the load among APs to alleviate congested areas. Moreover, the APs continuously monitor the traffic to detect and mitigate congested areas, regardless of whether the congestion is due to a natural load or a malicious traffic injection attack. Based on the simulations done using NS2, the proposed algorithm i) can resolve congestion of both cases very efficiently, ii) improves the network delay variation by at least 40%, and iii) improves the network energy consumption and network delay by at least 30% and 44% respectively.

An Integrated Grasshopper Optimization Algorithm with Artificial Neural Network for Trusted Nodes Classification Problem

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classification approach, hence strengthening the safety of such networks. Feature extraction process is done by using Linear Regression-Based Principal Component Analysis (LR-PCA). The test results demonstrated that the proposed IGO-ANN method attains the greatest performance in terms of accuracy, end to end delay and packet delivery ratio regarding trusted WBAN nodes classification than certain existing methods.

Reinforcement learning based effective communication strategies for energy harvested WBAN

This paper proposes effective communication strategies for Wireless Body Area Networks (WBANs) that consist of wearable or implantable sensor nodes placed in, on/around the human body to send body vitals to a sink. The main research challenges for communication strategy formulation include limited energy resources and varying link conditions. Though energy harvested sensor nodes partially address the problem of energy efficiency, finding an optimal balance between the energy constraint of the nodes and communication reliability is still challenging. Since data loss in such networks may prove to be fatal, it is important to investigate the problem prior to deployment and come up with effective communication strategies for initiating post-deployment operations. Hence, in this paper, the nodes are stochastically modeled as a Markov Decision Process. There is a need to adapt to the changing ambient conditions through exploration and exploitation. So, a modified Q-learning technique is proposed for post-deployment decision-making by the WBAN nodes subject to the dynamic ambient conditions. The effectiveness of the proposed strategy is validated through extensive simulation and compared with state-of-the-art works. The performance of the proposed approach is also verified with a real-life dataset. The results demonstrate that around 90% successful data delivery to sink could be made with the proposed scheme in the real-life scenario.

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.

An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks

Wireless Body Area Networks (WBANs) comprise a network of sensors subcutaneously implanted or placed near the body surface and facilitate continuous monitoring of health parameters of a patient. Research endeavours involving WBAN are directed towards effective transmission of detected parameters to a Local Processing Unit (LPU, usually a mobile device) and analysis of the parameters at the LPU or a back-end cloud. An important concern in WBAN is the lightweight nature of WBAN nodes and the need to conserve their energy. This is especially true for subcutaneously implanted nodes that cannot be recharged or regularly replaced. Work in energy conservation is mostly aimed at optimising the routing of signals to minimise energy expended. In this article, a simple yet innovative approach to energy conservation and detection of alarming health status is proposed. Energy conservation is ensured through a two-tier approach wherein the first tier eliminates “uninteresting” health parameter readings at the site of a sensing node and prevents these from being transmitted across the WBAN to the LPU. The second tier of assessment includes a proposed anomaly detection model at the LPU that is capable of identifying anomalies from streaming health parameter readings and indicates an adverse medical condition. In addition to being able to handle streaming data, the model works within the resource-constrained environments of an LPU and eliminates the need of transmitting the data to a back-end cloud, ensuring further energy savings. The anomaly detection capability of the model is validated using data available from the critical care units of hospitals and is shown to be superior to other anomaly detection techniques.

An Energy Efficient and Reliable Routing Scheme to enhance the stability period in Wireless Body Area Networks

Wireless Body Area Network (WBAN) is a wireless network of wearable sensing and computing devices connected through a wireless communication channel, thereby offering a plethora of enticing applications in the area of remote health monitoring, sports, and entertainment. However, WBANs nodes are highly resource-constrained; therefore, energy-efficient and reliable data transmission is very vital in the design and implementation of most of its applications. Furthermore, on time and accurate data delivery with minimum delay is also highly required. Over time, numerous energy-efficient routing solutions have been proposed for WBANs; however, the significant feature of reliability in these solutions has not been adequately addressed. Therefore, in this paper, we propose a new Energy-Efficient and Reliable Routing Scheme (ERRS) to enhance the stability period and reliability for resource-constrained WBAN. ERRS comprises two novel solutions, namely, the Forwarder Node Selection and Forwarder Node Rotation techniques. The proposed ERRS takes advantage of the adaptive static clustering routing technique and achieves enhanced stability period and longer network lifetime, ultimately maximizing reliability. Through extensive simulation-based evaluation using MATLAB, ERRS showed an improvement of 26% over the benchmark protocol in terms of network stability and throughput. Whereas the end-to-end delay of the proposed ERRS is improved by 17% and 40% than by SIMPLE and M-ATTEMPT protocols, respectively, which proves ERRS to be an efficient and reliable routing solution for WBANs.

Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN.

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

Investigation and Modeling of Multi-Node Body Channel Wireless Power Transfer.

Insufficient power supply is a huge challenge for wireless body area network (WBAN). Body channel wireless power transfer (BC-WPT) is promising to realize multi-node high-efficiency power transmission for miniaturized WBAN nodes. However, the behavior of BC-WPT, especially in the multi-node scenario, is still lacking in research. In this paper, the inter-degeneration mechanism of a multi-node BC-WPT is investigated based on the intuitive analysis of the existing circuit model. Co-simulation in the Computer Simulation Technology (CST) and Cadence platform and experiments in a general indoor environment verify this mechanism. Three key factors, including the distance between the source and the harvester, frequency of the source, and area of the ground electrodes, are taken into consideration, resulting in 15 representative cases for simulation and experiments studies. Based on the simulation parameters, an empirical circuit model to accurately predict the received power of multiple harvesters is established, which fits well with the measurement results, and can further provide guidelines for designs and research on multi-node BC-WPT systems.

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.

A Hybrid Fuzzy-Genetic Algorithm for Performance Optimization of Cyber Physical Wireless Body Area Networks

The use of fuzzy decision-making in datapath selection extends the sensor network lifetime with a uniform distribution of routing load among network nodes. Fuzzy-logic based routing protocols are mostly designed for general wireless sensor networks (WSN). However, such protocols are not compatible with a Wireless Body Area Network (WBAN) comprised of biosensor nodes. WBAN nodes carry inferior computational, communication and energy resources as compared to general WSN nodes. A WBAN routing protocol needs to be designed as per IEEE 802.15.6 WBAN standards to meet high-end QoS requirements of medical applications. This paper presents a fuzzy-logic-based clustering protocol for data routing in WBANs. Nodes are grouped into clusters and cluster head nodes are selected through a Fuzzy-Genetic Algorithm termed as EB-fg-MADM. EB-fg-MADM makes an assessment of dual attributes of each cluster node in terms of node residual energy and CH selection cost. CH selection cost of a node is the forecasted value of network energy consumption if the node acts as a cluster head. EB-fg-MADM utilizes a fuzzy-TOPSIS function which makes a quantitative comparison of cluster nodes and selects the cluster head node possessing the aforementioned attributes closest to their ideally desired values. A Genetic Algorithm-based optimization process adapts the attribute weights for cluster head selection. EB-fg-MADM provides enhanced network lifetime with a uniform distribution of routing load. Protocol performance is obtained in terms of network lifetime, throughput and latency. Results are compared with existing WBAN routing protocols and are found to be better.

A High-Throughput Subspace Pursuit Processor for ECG Recovery in Compressed Sensing Using Square-Root-Free MGS QR Decomposition

Sensor nodes in wireless body-area networks are expected to use compressed sensing (CS) for ultralow-power consumption. For signal recovery in CS systems, compared with the software implementation, the hardware processor has superiority on power, real-time ability, and so on. Although orthogonal matching pursuit (OMP) is conventionally selected in hardware implementations, its low recovery performance is still a problem. We propose to use the subspace pursuit (SP) algorithm that can achieve higher recovery performance with less iteration to implement the hardware processor. First, a new SP by a square-root-free method and a computation reuse scheme are proposed to reduce the SP’s complexity. Then, the fully paralleled architectures and elaborate pipelines are proposed to improve the processing throughput. Finally, we give an FPGA implementation prototype. Simulations compared with OMP show that the traditional SP can improve more than 8 dB on recovery performance or it can reduce more than 31% sampling paths in sensor nodes. These benefits are kept by our proposed SP because our improvement does not modify the algorithmic nature. Nevertheless, the significant complexity reduction is achieved by our proposal. The proposed SP processor can recover ECG signals with 28.2 dB and achieve the processing throughput of 44.4 K vectors/s.

A Critical Review on Recent Research on Reliable Communication for Wireless Body Area Network

: Recently the Wireless Body Area Network (WBAN) technology has appeared as a subcategory of wireless sensor network (WSN) to improve the quality of life. For such medical system, the improving of reliability becomes very critical research issue because the patient requires continuous monitoring via network. On the other hand, a special attention must be given to the energy consumption because the WBAN nodes must be able to operate for long duration without being recharged or replaced. In this Paper, we have carried out survey of various existing mechanisms, recent techniques and protocols used for reliable communication and energy efficiency under low delay constraints in remote patient monitoring system. This survey depicts that the existing solutions can be further modified to devise more reliable solutions.

Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network—A Survey

This paper offers a review of different types of Error Correction Scheme (ECS) used in communication systems in general, which is followed by a summary of the IEEE standard for Wireless Body Area Network (WBAN). The possible types of channels and network models for WBAN are presented that are crucial to the design and implementation of ECS. Following that, a literature review on the proposed ECSs for WBAN is conducted based on different aspects. One aspect of the review is to examine what type of parameters are considered during the research work. The second aspect of the review is to analyse how the reliability is measured and whether the research works consider the different types of reliability and delay requirement for different data types or not. The review indicates that the current literatures do not utilize the constraints that are faced by WBAN nodes during ECS design. Subsequently, we put forward future research challenges and opportunities on ECS design and the implementation for WBAN when considering computational complexity and the energy-constrained nature of nodes.

Joint Transmission Power Control and Relay Cooperation for WBAN Systems.

Improving transmission reliability is a crucial challenge for Wireless Body Area Networks (WBANs) because of the instability of channel conditions and the stringent Packet Loss Ratio (PLR) requirement for many WBANs applications. On the other hand, limited by the size of WBAN nodes, the energy consumption of WBAN nodes should be minimized. In this paper, we jointly consider transmission power control, dynamic slot scheduling and two-hop cooperative mechanism and propose an Autocorrelation-based Adaptive Transmission (AAT) scheme that achieves a better trade-off between transmission reliability and energy consumption for WBAN systems. The new scheme is designed to be compatible with IEEE 802.15.6. We evaluated the performance of the newly proposed scheme by importing the real channel datasets into our simulation model. Simulation results demonstrate that the AAT method can effectively improve the transmission reliability while reducing the energy consumption. We also provide the performance evaluation from three perspectives, namely packet error ratio, energy consumption and energy efficiency, and provide recommendations on the application of the two-hop cooperative mechanism associated with the proposed AAT in the contexts of WBANs.

An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach

Integrating wireless body area networks (WBANs) with cloudlet introduces an edge-of-things computing environment for pervasive applications. The variation in the number of active WBANs nodes and its data transmission rate requires optimal computing resources to avoid performance degradation and data loss. We argue the research gap in terms of optimal resource provisioning that predicts and automatically adjusts the computing resources on the basis of sensory data volume and application’s type. In this paper, we propose a hybrid autonomic resource provisioning framework, which is the combination of autonomic computing, fuzzy logic control and linear regression model. The proposed framework is built over CloudSim toolkit with autonomic resource provisioning framework inspired by the cloud layer model. The effectiveness of the proposed approach is evaluated under a real workload trace. The experimental results show that the proposed approach minimizes the cost by at least 27% and SLA violations by at least 78% as compared to other approaches.