Direct Data Transmission Research Articles

Relay Assisted Device-to-Device Communication for Future Generation Wireless Networks: A Review from Architectural and Challenges Perspective

The rapid evolution of wireless communication technologies, along with the increasing demand for efficient and reliable data transfer, has driven the rethinking of traditional cellular network architectures in the context of next-generation networks. In conventional cellular systems, communication between users typically occurs through base stations. However, to improve efficiency, scalability, and spectral utilization, device-to-device (D2D) communication has been introduced, enabling direct data transmission between users without base station involvement. In this paper, we present a comprehensive review of D2D communication from architectural and challenges perspective for future generation networks. Additionally, the study focuses on relay-assisted D2D (RAD2D) communication, examining the role of machine learning (ML) and artificial intelligence (AI) in optimizing relay selection processes. In light of the existing literature, challenges for implementing RAD2D are discussed from different perspectives such as relay selection, energy efficiency, secure communication, resource allocation, and the management of dynamic network conditions

Edge-assisted U-Shaped Split Federated Learning with privacy-preserving for Internet of Things

In the realm of the Internet of Things (IoT), deploying deep learning models to process data generated or collected by IoT devices is a critical challenge. However, direct data transmission can cause network congestion and inefficient execution, given that IoT devices typically lack computation and communication capabilities. Centralized data processing in data centers is also no longer feasible due to concerns over data privacy and security. To address these challenges, we present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers to assist IoT devices in model training and optimization process. In this framework, we leverage Federated Learning (FL) to enable data holders to collaboratively train models without sharing their data, thereby enhancing data privacy protection by transmitting only model parameters. Additionally, inspired by Split Learning (SL), we split the neural network into three parts using U-shaped splitting for local training on IoT devices. By exploiting the greater computation capability of edge servers, our framework effectively reduces overall training time and allows IoT devices with varying capabilities to perform training tasks efficiently. Furthermore, we proposed a novel noise mechanism called LabelDP to ensure that data features and labels can securely resist reconstruction attacks, eliminating the risk of privacy leakage. Our theoretical analysis and experimental results demonstrate that EUSFL can be integrated with various aggregation algorithms, maintaining good performance across different computing capabilities of IoT devices, and significantly reducing training time and local computation overhead.

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems. This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits. A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention. This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

An adaptive MLP-based joint optimization of resource allocation and relay selection in device-to-device communication using hybrid meta-heuristic algorithm

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

Integrating Laboratory Testing Results at Point-of-Care in Hospital@Home Care Settings: A FHIR-Based Approach.

Using the HL7 FHIR standard and cloud infrastructure, we developed a concept for direct transmission of laboratory data collected at POC. Requirements were derived from literature and discussions with a POC testing device producer. An architecture for data exchange was developed based on these requirements. Our concept enables access to laboratory data collected at POC, facilitating efficient data transfer and enhancing interoperability. A hypothetical scenario demonstrates the concept's feasibility and benefits, showcasing improved patient care and streamlined processes in Hospital@Home settings. Integration of POC data into Hospital@Home models using the HL7 FHIR standard and cloud infrastructure offers potential to enhance patient care and streamline processes. Addressing challenges such as data security and privacy is crucial for its successful implementation into practice.

Continuous heart monitoring in patients with pulmonary hypertension smartwatches and direct transmission to their electronic health records: A trial design.

BackgroundPulmonary hypertension is a progressive disease for which early treatment interventions are essential. Traditionally, patients undergo periodic clinical assessments. However, recent advances in wearable technology could improve the quality and efficiency of follow-up monitoring in patients with pulmonary hypertension. Trial designTo our knowledge, this is the first study describing direct data transmission from a smartwatch to patients' electronic health records. It implements a novel update and customised program to continuously and automatically transmit data from a smartwatch to the patient's electronic healthcare records. It will evaluate continuous monitoring in patients with pulmonary hypertension and monitor their physical activity time, heart rate variability, and heart rate at rest and during physical activity via a smartwatch. It will also evaluate the data transmission method, and its data will be assessed by the treating physicians supplemental to clinical practice.Smartwatch integration promises numerous advantages: comprehensive cardiovascular monitoring and improved patient experience. Our continuous smartwatch monitoring approach offers a solution for earlier detection of clinical worsening and could be included as a combined endpoint in future clinical trials. It could improve patient empowerment, enhance precision medicine, and reduce hospitalisations. The user-friendly smartwatch is designed to minimise disruption in daily life. ConclusionThe ability to transfer real-time data from wearable devices to electronic health records could help to transform the treatment of patients with pulmonary hypertension and their follow-up monitoring outside a clinical setting, enhancing the efficiency of healthcare delivery.

Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers.

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m-1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle's position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.

Linear Potentiometer Sensor-Based of Athlete Flexibility Measurement Tool

This study aims to develop a novel "sit and reach test" flexometer device utilizing a linear potentiometer sensor to quantitatively evaluate an individual's body flexibility. The innovation in this research lies in the utilization of electronic means to measure flexibility, replacing conventional manual methods. The device employs a linear potentiometer sensor that translates analog voltage into a digital value (ADC) and subsequently converts the digital data into distance measurements in centimeters (cm). The processed data, representing the distance measurements, is wirelessly transmitted to a personal computer (PC) for automatic capture and analysis. The design considerations prioritize modernity, practicality, effectiveness, and efficiency. The experimental outcomes demonstrate the efficacy of the developed tool. The readings from the linear potentiometer sensor exhibit a high linearity, indicated by an R2 value of 0.9999. The average error percentage is minimal, measuring at 0.17%. Moreover, the device allows for direct wireless transmission of data to a PC immediately after assessing an athlete's flexibility. This study not only introduces a novel electronic approach for assessing body flexibility but also validates its accuracy and efficiency through comprehensive testing and analysis.

Rancang Bangun Particle Counter untuk Monitoring Konsentrasi PM1, PM2.5 dan PM10 di Udara Berbasis IoT

Poor air quality is a serious health and environmental issue. Microscopic particles such as PM1, PM2.5, and PM10 cause respiratory disorders and other health problems. Therefore, accurate and continuous air quality monitoring is crucial to mitigate the impacts of air pollution. This research aims to design an Internet of Things (IoT)--based particle counter capable of real-time air quality monitoring and reporting via an online platform. The system utilizes a PMS5003 sensor to measure PM1, PM2.5, and PM10 concentrations precisely. Data from the sensor is processed by an ESP8266 microcontroller connected to the internet, enabling direct data transmission to an online platform for further analysis and visualization. Testing is done by creating a 1x1x1 meter testing chamber to simulate various environmental conditions and validate the device's performance. Results show that the particle counter provides accurate data, with an error rate of less than 10% compared to standard devices. The device demonstrates reliable operation across different environmental conditions, showcasing its robustness in practical applications. This IoT-based particle counter offers an innovative solution for effective and efficient air quality monitoring. It is expected to significantly contribute to human health protection efforts and minimize the adverse environmental impacts of air pollution.

A Study on the Operational Mechanism and Practical Experience of Digital Agricultural Industrial Chain Finance: A Case Study of Leading Enterprise Dominant Model

To alleviate the credit constraint problem of farmers, a financial model of the agricultural industry chain has emerged in China, and has undergone digital transformation with the development of digital technology. In order to explore the operation mechanism of the digital agricultural industry chain finance, this study, based on the review of its typical models, takes the leading enterprise dominant model as an example. Combining the case of chili agricultural industry chain finance in Xiangcheng County, Henan Province, this paper starts from the problems existing in the local traditional industry chain finance, and analyzes the specific process and operation mechanism of the leading enterprise dominant digital agricultural industry chain finance model. The study finds that the local digital agricultural industry chain finance has realized direct data transmission between farmers and financial institutions by building its own e-commerce platform within the leading enterprises. This helps to improve the efficiency of farmer loans, reduce the cost of risk identification for financial institutions, optimize the efficiency of capital turnover, and thus break through the drawbacks of traditional industry chain financing methods, easing the credit constraints faced by farmers. The possible contribution of this paper is that by analyzing the breakthroughs achieved in the digital transformation process of the chili industry chain finance in Xiangcheng County, it provides empirical references for the development of digital agricultural industry chain finance in China.

Toward Smart and Efficient Service Systems: Computational Layered Federated Learning Framework

As increasing concerns have arisen on privacy leakage in data-driven smart services, federated learning (FL) has been introduced to collaboratively learn an efficient model across multiple participants without sharing raw data. However, existing FL schemes hinge on participating nodes to perform intensive ondevice training and network communication, which is a significant burden for energy-constrained mobile devices. In this work, we present the computational layered FL (CLFL) framework to enable resource-constrained devices to perform computationefficient on-device training and lightweight message transmitting. We first introduce the network structure and key aspects of the entities in the framework. Then, we give the implementation principles of CLFL, and present two instance schemes that allow devices to participate in joint training without the need for direct gradient computation or continuous data transmission. In order to more intuitively reflect the performance and efficiency of the proposed methods, we carry out a preliminary implementation and give the comparison with traditional FL. Finally, for the future exploration, we present four related research challenges of CLFL and offer possible solutions.

Exclusive-Region-Map-Based Medium Access Control in Mobile Networks With Directional Antennas Through Deep Interference Learning

The medium access control (MAC) design in mobile networks with directional antennas is challenging due to the difficulty of defining the exact RF interference range between two neighboring directional links and the frequent changes of interference range due to node mobility. This research targets directional data reception (Rx) and transmission (Tx) coordination issues based on the computation of directional interference ranges from nearby directional links. An innovative MAC mechanism is designed with three features: (1) ER-map, i.e., the spatial expression of exclusive region (ER) model in the format of a heatmap. The ER-map helps to determine the directional interference range in typical communication scenarios. Different ER-map cases are analyzed based on the spatial layout differences for two nearby directional links. (2) Spatio-temporal ER-map evolution prediction: a Spatio-Temporal Residual Network (ST-ResNet+) model is used to describe the spatial correlations (for the ERs among neighboring links) and temporal correlations (for the ERs across different time instants) as well as the ER map evolution patterns. Such a Deep ST-ResNet+ model is used to predict the next-time ER map’s snapshot. (3) Optimized directional MAC protocol based on ER map predictions: The ST-ResNet+ prediction results are used to determine the MAC operations, such as Tx/Rx schedule arrangement in the one-hop area, sending rate adjustments, etc. Comprehensive simulations are conducted to validate the throughput efficiency for the proposed directional MAC scheme.

Machine Learning Supported Nano-Router Localization in WNSNs

Sensing data from the environment is a basic process for the nano-sensors on the network. This sensitive data need to be transmitted to the base station for data processing. In Wireless Nano-Sensor Networks (WNSNs), nano-routers undertake the task of gathering data from the nano-sensors and transmitting it to the nano-gateways. When the number of nano-routers is not enough on the network, the data need to be transmitted by multi-hop routing. Therefore, there should be more nano-routers placed on the network for efficient direct data transmission to avoid multi-hop routing problems such as high energy consumption and network traffic. In this paper, a machine learning-supported nano-router localization algorithm for WNSNs is proposed. The algorithm aims to predict the number of required nano-routers depending on the network size for the maximum node coverage in order to ensure direct data transmission by estimating the best virtual coordinates of these nano-routers. According to the results, the proposed algorithm successfully places required nano-routers to the best virtual coordinates on the network which increases the node coverage by up to 98.03% on average and provides high accuracy for efficient direct data transmission.

Power‐Free Contact Lens for Glucose Sensing

AbstractRecent advances in wearable devices have enabled noninvasive monitoring for healthcare applications. Smart contact lenses have gained substantial attention for medical diagnosis through the analysis of vital signs in tear fluids. However, previous studies have mostly focused on designs embedded with electronic devices or antennas for wireless transmission, which are power‐intensive and require external receivers around the ocular system. Here, the study reports a power‐free smart contact lens for noninvasive glucose sensing according to the color changes of multiple electrochromic electrodes to achieve direct data transmission without the external wireless system. The device detects various glucose concentrations, from the ordinary range (0.16–0.5 mm) to abnormally high concentrations (0.9 mm). The multi‐electrode design exhibits acceptable accuracy, with a correlation coefficient r = 0.99543 to the controlled sample and allowed low‐glucose detections with concentrations down to 0.05 mm. The device shows good reproducibility, with standard deviations of determined glucose levels of 0.0462 and 0.025 for four continuous cycles and for an interval of several days, respectively. It is believed that the reported smart contact lens has the potential for daily health monitoring by ordinary users without a power supply and external devices. Its simple electronics‐free structure will allow for immediate application to the market with cost‐effective manufacturing.

A novel approach for FPGA-to-server data transmission over an Ethernet-based network using the eXpress Data Path technology

In the context of the upgrade of the Large Hadron Collider at CERN for high-luminosity operation, the particle detectors have to cope with much higher data rates and therefore need to upgrade their data acquisition systems. This upgrade is taken as an opportunity to exchange the currently used highly customized hardware by commercial solutions. Nevertheless, some part of the data processing still needs to be done within Field Programmable Gate Arrays (FPGA), requiring the transfer of data between the FPGAs and the commercial servers. This paper reports on a study of direct data transmission from FPGAs to servers via a commercial network. Large data buffers as required for reliable data-transmission protocols are avoided by using an emerging technique named eXpress Data Path (XDP). Based on XDP, the transmission of 5.2 PB (i.e. 2.92 × 1012 packets) was achieved within 168 h without a single missing packet.

TOWARDS THE DEVELOPMENT OF A PATIENT MONITORING SYSTEM: REVIEW OF AVAILABLE SOLUTIONS AND ASSUMPTIONS FOR BUILDING A FUNCTIONALLY OPTIMAL SYSTEM

Abstract: The article reviews the technical solutions of devices that enable the patient to monitor selected medical parameters in time. The authors referred to solutions that record data in internal memory as well as solutions enabling direct transmission of medical data, including information on the current location of the patient in the open space and inside buildings. The latest technical solutions of subassemblies allowing us to design a functionally optimised patient monitoring system in the field of sensor and telecommunications technology were also reviewed. Finally, the authors proposed preliminary assumptions for a functionally optimised patient monitoring system.

One Bit Aggregation for Federated Edge Learning With Reconfigurable Intelligent Surface: Analysis and Optimization

As one of the most popular and attractive frameworks for model training, federated edge learning (FEEL) presents a new paradigm, which avoids direct data transmission by collaboratively training a global learning model across multiple distributed edge devices, thus overcoming the disadvantage of centralized machine learning in resource limitations, delay constraints, and privacy issues. However, due to the heavy cost of communicating gradient among edge devices, sharing the parameters of a large-scale neural network can still be time-intensive. To alleviate this bottleneck, an efficient scheme, called SignSGD has been recently proposed, where the one-bit gradient quantization with majority vote is featured at edge devices. Nevertheless, the performance of one-bit aggregation will inevitably deteriorate due to the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a novel reconfigurable intelligent surface (RIS)-aided one-bit communication optimization scheme under orthogonal frequency division multiple access (OFDMA) to relieve the negative influence of communication error on the SignSGD-based FEEL. Specifically, a learning convergence analysis is firstly presented to quantitatively characterize the impact of wireless communication error measured by the union bound on pairwise bit error rate (BER) on the performance of SignSGD-based FEEL. Immediately, a unified communication-learning optimization problem is further formulated to jointly optimize the sub-band assignment strategy, the power allocation vector, and the RIS configuration matrix. Numerical experiments show that the proposed design achieves substantial performance improvement compared with the state-of-the-art approaches.

A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm

INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem. OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime. METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins. RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network. CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.

Lightweight Symmetric Encryption and Attribute Based Encryption Method to Increase Information Safety in Wireless Sensor Network

Direct data transmission in a wireless sensor network raises the data transfer cost. In addition, the lifetime of sensor networks is shortened because of the rise in energy required for data exchange. As a result, data aggregation is utilized in WSN to lessen the burden of transmission costs and lengthen the useful life of the sensor networks. The sensor nodes and their collected data are vulnerable to destruction because they are broadcasting in a hostile environment. Therefore, data security is a major topic of study for WSN. Due to the limited resources of the sensor network, conventional wireless network security measures are ineffective. With Speck encryption and CP-ABE, the proposed Lightweight Secured remote Health monitoring System (LSHS) can protect health data and restrict who can access it while using less power. Lightweight block ciphers are optimal for protecting medical records, according to the research. Using the LSHS, we evaluate how well-known lightweight block ciphers like AES, Simon, and Speck perform. Both encrypting and decrypting with the Speck technique require less processing time. Therefore, medical records are encrypted using the Speck algorithm.

An Upgraded Data Security Based on Homomorphic Encryption and Aggregate Signature Method in Wireless Sensor Network

Wireless sensor networks (WSN) have been implemented in nearly every field of use because they offer a solution to practical problems that can also be affordably implemented. The sensor nodes have limited computing resources, weak batteries, and limited storage space. The environmental or physical data collected by these nodes is transmitted straight to the BS. The data transfer cost is raised due to the direct data transmission. In addition, the lifetime of sensor networks is shortened because of the rise in energy required for data exchange. As a result, data aggregation is utilized in WSN to lessen the burden of transmission costs and lengthen the useful life of the sensor networks. Each sensor node's transmission is encrypted with cipher text generated by the Paillier homomorphic cryptosystem. In addition, the Bilinear aggregate signature method is used to create a digital signature at each sensor node. The cluster head BS is where the aggregation takes place once the cipher text and signature have been combined. Before deciding whether to accept or reject the message, the BS checks the aggregate signature. The homomorphic cryptosystem saves power because it does not perform intermediate-level or cluster-head decryption. Data integrity, authenticity, and confidentiality are all maintained while using less power with this technology. The Intel laboratory dataset is used in the implementation. When compared to current systems, the proposed SDA method requires less time and energy to calculate.