Wireless Nodes Research Articles

Performing Magnetic Boundary Modulation to Broaden the Operational Wind Speed Range of a Piezoelectric Cantilever-Type Wind Energy Harvester

Small piezoelectric wind-induced vibration energy harvesting systems have been widely studied to provide long-term sustainable green energy for a large number of wireless sensor network nodes. Piezoelectric materials are commonly utilized as transducers because of their ability to produce high output power density and their simple structure, but they are prone to material fracture under large deformation conditions. This paper proposes a magnetic boundary modulated stepped beam wind energy harvesting system. On the one hand, the design incorporates a composite stepped beam with both high- and low-stiffness components, allowing for efficient vibration and electrical energy output at low wind speeds. On the other hand, a magnetic boundary constraint mechanism is constructed to prevent the piezoelectric sheet from breaking due to excessive deformation. Experiments have confirmed that the effective operational wind speed range of the harvester with magnetic boundary constraints is doubled compared to that of the harvester without magnetic boundary constraints. Furthermore, by adjusting the magnetic pole spacing of the boundary, the harvesting system can generate sufficiently high output power under high-wind-speed conditions without damaging the piezoelectric sheet.

Self-Supplying Photovoltaic-Hygroelectric Coupling System Powering Internet of Things Sensors.

The rapid advancement in the Internet of Things (IoT) has prompted a proliferation of sensor applications with increasing focus on harnessing environmental energy sources for powering these sensors. However, owing to the variability inherent in climatic and geographic conditions, a singular approach to environmental energy harvesting often fails to deliver sustainable and reliable power. Hygroelectric technology, leveraging the electrical coupling between nanostructured materials and water to convert moisture-derived energy into electricity, complements the advantages of light energy collection technology. In this study, an ion diode moisture-based power generation array was fabricated to yield an open-circuit voltage of 8 V and a short-circuit current of 3 mA under an 86.9% relative humidity (RH). Subsequently, integration of a photovoltaic module with the hygroelectric generator assembly via an energy management circuit augmented the system's overall power generation, manifesting a remarkable 1150% increase over the original moisture-based power generation configuration. Furthermore, the incorporation of such a hybridized system bolstered the operational efficiency, extending the duration of continuous power supply cycles by up to 78.2% in comparison with the control group with no moisture-derived energy input. This pioneering endeavor unveils a novel light-moisture coupling energy-harvesting paradigm tailored for sustaining uninterrupted power provision in wireless sensor network nodes, and the harvesting of one energy source is not affected by the other energy source at all. With its inherent adaptability, this approach holds promise for deployment in outdoor environments characterized by low illumination levels and high humidity, presenting a versatile solution to address sensor powering challenges.

Securing the Internet of Things with Ascon-Sign

With a Cryptographically-Relevant Quantum Computer (CRQC) estimated to be viable within the next 15 years, the development of post-quantum security is imperative. Previously secure networks may soon fall victim to these CRQCs as they will likely attack the weakest link in a network. In modern networks, these weak-links are often present in the form of Internet of Things (IoT) devices, as the resource constrains imposed by these wireless nodes leads to lowered security. We offer the first Ascon-Sign implementation for resource constrained FPGAs, which allows a wireless sensor network to verify nodes. Our design runs twice as fast as similarly-area constrained devices, and shows a 33% reduction in power per operation. We demonstrate the capability of our design by integrating it with a wireless sensor network for weather detecting. We also propose an amendment to the Ascon-Sign specification that allows for shortened processing time and lower memory requirements.

Bio-inspired wireless sensor networks - a protocol for an enhanced hybrid energy optimization routing

Recently, there has been a focus on the significance of swarm intelligence-inspired routing algorithms for achieving optimum solutions in biologically inspired wireless sensor networks (WSNs). These protocols depict a network of wireless mobile nodes forming an infrastructure that is agile, dynamic, and independent of a central administrative facility. Among the challenges faced by bio-inspired WSNs, mobility awareness and excessive energy consumption (EC) stand out as significant hurdles, particularly in dynamic models with intermittent connections. This project seeks to tackle these obstacles by deploying the hybrid energy efficiency (HEED) approach to distributed clustering for network system cluster formation, along with fusion routing protocol of particle swarm optimization (PSO) and PIO to select cluster-heads and optimize solutions in bio-inspired WSNs. The success of the suggested approach is assessed using a variety of criteria, such as energy usage, rate of packet delivery, EC, and routing overhead and network lifetime. The methods like ad hoc on-demand distance vector's (AODV) and ant colony optimization (ACO) methods are employed in the testing and validation. In comparison to the reactive AODV routing protocol and ACO, the suggested routing protocol (HPSOPIO) reduces energy usage and increases network lifespan.

A deep experimental analysis of energy‐proficient firewall policies and security practices for resource limited wireless networks

AbstractThe role of firewalls and security principles in resource‐limited wireless networks and Wireless Sensor Networks (WSN) is more expected than in any dedicated network environment. The advanced wireless technologies and emerging fashion of autonomous wireless network nodes are mostly expecting resilient firewall services with multi‐level security policies. Wireless networks are classified under sensor networks, Internet of Things (IoT) mobile networks, and so forth. According to the expectations, security frameworks are gradually invented around the communication platform using various ideologies. The novel ideology and respective implementation effort open better solutions against wireless network attacks. Anyhow, the minimal production of time complexity, energy complexity, and computation overhead from any novel security approach is always considered under the best practices. The complexity levels directly affect the wireless node's energy consumption ability and operational spans severely. The energy optimization techniques and load‐balancing techniques integrated into multi‐class firewall rules are extremely useful solutions for resource‐limited wireless networks. This article has been motivated to analyze the recent firewall techniques and secure data communication mechanisms used for securing wireless networks. Consequently, the practice of comparative literature analysis helps to improve the current limitations identified under the classified categories of security mechanisms such as energy‐optimized security principles and load‐balanced security principles. The establishment of secure and energy‐optimized multi‐class firewall rules in each wireless node assures a healthy focus on next‐generation networks. The experiment section shows the benefits of energy‐optimized secure network communication in terms of better accuracy and the benefits of load‐balanced secure network communication through minimal overhead in computing platforms.

Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement

This study introduces WirelessGridBoost, an innovative framework designed to revolutionize real-time fault detection in wireless electrical grids by harnessing the power of the LightGBM machine learning algorithm. Traditional fault detection systems in electrical grids often face challenges such as latency and scalability due to the intricate nature of grid operations and limitations in communication infrastructure. To overcome these challenges, WirelessGridBoost integrates LightGBM, a highly efficient gradient boosting decision tree algorithm, with wireless technology to facilitate advanced fault detection capabilities. Trained on historical sensor data, the LightGBM model demonstrates exceptional proficiency in discerning complex fault patterns inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, WirelessGridBoost enables prompt identification of anomalies in real-time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of WirelessGridBoost, achieving a fault detection accuracy of 96.80% and reducing latency by 38% compared to conventional methods. This research presents a promising avenue for enhancing fault detection efficiency in wireless electrical grids through the innovative WirelessGridBoost framework.

IoT-based trusted wireless communication framework by machine learning approach

The traditional Radio-Frequency Systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system.

Predictive-Cognitive Maintenance for Advanced Integrated railway Management

Railway systems play a vital role in modern transportation and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management for ensuring the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail track vibrations. The node contains a microcontroller that runs embedded machine-learning models to preprocess the vibration data after train crossing. Abnormal vibrations, indicative of defects, were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for system-level assessment of the RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas the cloud digital twin enables system-level prognosis insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrate that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this work proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.

EVALUATING ROUTING ALGORITHMS ACROSS DIFFERENT WIRELESS MESH NETWORK TOPOLOGIES USING NS-3 SIMULATOR

Wireless Mesh Networks are gaining traction as a solution for delivering reliable connectivity without centralized infrastructure. They operate through wireless node interconnections, forming self-configuring networks ideal for scenarios where wired networks are impractical. Routing is crucial in Wireless Mesh Networks to ensure efficient communication among nodes. However, the suitability of routing algorithms for Wireless Mesh Network’s topology requires further investigation. This paper proposes an investigation into the effectiveness of routing algorithms like AODV, DSDV, and OLSR across various Wireless Mesh Networks topologies using NS-3 simulation. It also aims to determine the optimal number of nodes and protocols to maximize throughput and minimize packet loss within a limited area. Through rigorous NS-3 simulations, the study demonstrates that AODV, DSDV, and OLSR exhibit differing effectiveness across random, mesh grid, and Fruchterman-Reingold topologies. These results emphasize the importance of considering topology-specific factors when selecting and optimizing routing protocols for Wireless Mesh Networks. In summary, Wireless Mesh Networks offer decentralized connectivity, but the effectiveness of routing algorithms in different topologies remains understudied. This investigation addresses this gap by evaluating routing algorithms across various topologies, shedding light on their suitability and performance in Wireless Mesh Networks.

Impact of Propagation Environment on the Performance of Direction Oriented Forwarding through Minimum Number of Edge Nodes (DOF-MEN) Routing Protocol In Ad Hoc Networks

One sort of wireless ad-hoc network is called MANET (Mobile Ad-hoc Network), which is an autonomous network made up of wireless nodes and routers connected by wireless connections. Transmission of data in effective way is important. The DOF-MEN (Direction Oriented Forwarding through Minimum Number of Edge Nodes) protocol is the protocol which lessens the amount of messages for route discovery. It attempts to choose just single node as the next following hop. The node's address is added by the sender. Therefore, only the chosen node will receive and subsequently transmit the data. This protocol increases the throughput, Packet Delivery Ratio, and cuts down on the Routing Overhead. Several aspects affect the routing protocol's execution accuracy in mobile ad hoc networks (MANET). The propagation model is a crucial thing to be considered among many. MANET experiences an enormous loss in performance due to blockage between transmission devices and fluctuation in the receiver's signal intensity. Therefore, it's crucial to determine how Radio Propagation Model and study of performance of routing protocol in MANET. Here we do simulation and analysis using NS2 (Network Simulator 2). The outcome demonstrates that the MANET routing protocol's performance is more influenced by the propagation model.

A Blockchain-Enabled Trust Management Framework for Energy-Efficient and Secure Routing in Mobile Ad-Hoc Networks

This study principally addresses the challenges associated with streaming videos in wireless Mobile ad hoc networks (MANETs), primarily constrained by wireless channels and node mobility. This study proposes a novel routing protocol incorporating a feedback mechanism and cross-layer architecture, supported by three key enhancements. First, it incorporates a route recovery strategy for detecting link failures and re-establishing connections based on predefined Quality of Service (QoS) metrics. Second, a novel algorithm estimates the available bandwidth, enabling dynamic adaptation of bitrate at the application layer concerning the source video. This novel rate-adaptive approach utilizes a scalable layered structure of the video coding, thereby removing layers that the network cannot efficiently support due to bandwidth limitations. Third, a gateway discovery procedure enhances the connectivity among MANETs and other infrastructure networks. This method employs available bandwidth as a parameter while selecting appropriate gateways and dynamically adjusts operational parameters like proactive area size and advertisement message frequency. The performances of the proposed Enhanced OLSR (E-OLSR) protocol are compared with the state-of-the-art routing procedures such as Adhoc on-demand distance vector (AODV), destination sequenced distance vector (DSDV), and optimized link state routing (OLSR), in terms of metrics like packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, as well as energy consumption. The simulation results demonstrated notable improvements, including reduced packet delay, fewer dropped packets, and decreased link failures, indicating effective utilization of available bandwidth. The overall performance of the proposed E-OLSR protocol exceeds other traditional protocols and its security is enhanced through the integration of Blockchain Technology.

LPHD: A low power and high diffusion lightweight block cipher

AbstractSmart door locks pose a large number of threats such as network attacks. Its storage area and power of cipher are severely limited because the wireless nodes of smart door locks are mostly battery‐powered. Therefore, effective security solutions are urgently needed. In this paper, a new lightweight block cipher with low power named LPHD is proposed to ensure the security of the master control chip of the smart door lock terminal. We design a scheme of low power S‐box and construct the two‐stage permutation layer (TP structure) suitable for LPHD by filtering the sets of 8‐bit permutations. LPHD proposes a variant of the 8‐branch generalized Feistel structure (GFS) to realize that the bits of all branches are affected in one encryption round. The problem of slow diffusion in the standard Feistel structure is solved. The key schedule adopts the nonlinear design and reuses the encryption process of LPHD. It improves the security of the cipher and reduces hardware overhead. Moreover, we evaluate the hardware implementation and security of LPHD. The results show that LPHD for the unified encryption and decryption circuits requires only 1276 Gate Equivalents (GEs) and 1.914 W on UMC 0.18 m, which is better than other lightweight block ciphers such as SKINNY, PRESENT, and IVLBC. In summary, LPHD provides sufficient security for the master control chip of the smart door lock terminal.

Wireless Underground Sensor Communication Using Acoustic Technology.

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin-Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs.

Enhanced radioactive waste drum monitoring: A sensorized LoRa-based network for identification and integrity assessment

A reliable monitoring and oversight system is essential to guarantee the safe operation and eventual closure of radioactive waste disposal facilities. With recent technological advancements, there is now the capability to deploy distributed wireless sensor networks that are cost-effective and low in power consumption. Such networks are versatile, finding applications in several civil, environmental, and industrial sectors. This work presents a novel system and method for identifying and continuously monitoring the integrity of stored radioactive waste packages. The proposed approach, developed by the University of Pisa in the context of the H2020 European Project PREDIS, employs a network of wireless node devices, provided with solid-state micro-power radiation detectors tasked with the measurement of gamma-rays and thermal neutrons coming from within cemented waste drums. Collected data are automatically transmitted across the storage site via Long Range radio technology and forwarded to cloud-based or offline platforms, allowing for further visualization, data processing, and safe storage. The system highlights the feasibility of autonomously and passively gathering data from radioactive waste drums over extended distances (more than 250 m in non-line-of-sight conditions), decreasing the reliance on portable or automated scanning mechanisms. In line with its capability for autonomous, long-range data collection, the system satisfies the requirement for structural integrity, containment, and confinement of packaging throughout both interim and long-term disposal of radioactive wastes, thereby playing a key role in reinforcing the safe management of radioactive waste.

Landslide monitoring and prediction system using geosensors and wireless sensor network

Landslides in hilly regions are a frequently occurring natural phenomenon which takes a heavy toll of human lives and causes damage to different properties. Hence, prediction of landslide is essential for averting its deleterious effects by providing early warning to neighboring residents about the impending hazard of landslide. A landslide monitoring system has been designed using geosensors and high-range wireless network for on-line monitoring and prediction of landslides with the application of multivariate statistical analysis of prevailing site parameters. The system consists of various geosensors, wireless sensor network, server, and landslide monitoring and forecasting software. Crackmeter, in-place inclinometer, raingauge, tiltmeter, piezometer and other sensors are set up in the selected hill slope prone to landslide for continuous monitoring of influencing parameters. The system measures real-time landslide parameters using the said geosensors connected with wireless nodes and establishing a dynamic wireless network to overcome redundancy issue using wireless nodes of around 1200 m communication range using a high performance low power microcontroller, integrated solar panel and additional external omni-directional antenna for monitoring landslide in a large and hazardous hilly region from a distant safer location. The application software consists of different modules, namely data monitoring, analysis, storing, viewing, prediction, and generation of audio-visual, SMS and email alerts for 3 levels of landslides situations. The paper enumerates the system architecture and the application software details.Graphical

Temperature sensing and energy harvesting with A MEMS parametric coupling device under low frequency vibrations

The frequency-based self-powered sensor has an important place in the era of the Internet of Things. In existing reports, the resonator is used either as an energy harvester or a sensor, yet it cannot undertake both demands simultaneously in a single structure. A parametric coupling device (abbreviated as PCD), capable of both temperature sensing and energy harvesting, is proposed with a common wings-inspired structure. Under one low frequency vibration excitation, the vibration energy can be potentially converted to electric energy, while the output frequency is observed to have a linear relationship with the temperature. The wings-inspired structure is comprised of three parts bonded in series, two side beams for collecting vibration energy one of them synchronously for temperature sensing, and a middle flexible beam for achieving parametric coupling. Once the heat is applied to the sensing one, the internal thermal stress is generated in the beam, while the reaction force acts on the anchor end of the beam to create compressive stress, for which the stiffness is decreased to cause frequency shifts and turn the working frequency domain. The parametric coupling is designed to improve the PCD performance, which is supported by theoretical analysis and experimental verification. With a manufactured MEMS PCD prototype, under a harmonic vibration excitation, when the temperature varies from 35.85 °C to 48.35 °C, both frequency shifts for sensing and working frequency domain for harvesting are 13.7 Hz which is 3.5 times larger than those of a non-parametric coupling device (abbreviated as NPCD). The sensing sensitivity of PCD is measured as 2.91 times higher than that of NPCD under a harmonic vibration excitation, while that of PCD is analyzed as 1.69 times higher than that of NPCD under a random one. Theoretical analysis shows the maximum temperature detection range is from 33.32 °C to 84.07 °C and can be further enlarged from 33.32 °C to 108.54 °C by adjusting the length of the sensing beam. The proposed PCD has the potential to be applied to temperature compensation systems or to maintain the operating environments of sensing nodes in self-powered wireless sensing networks (WSN).

Performance Analysis of Non-Realistic Routing Protocol using Random Waypoint Model in MANET

A mobile ad hoc network (MANET) is a collection of wireless nodes connected via wireless networks and has no set structure. MANETs feature a self-organized routing topology in which mobile nodes are free to move, making it difficult and crucial to construct a stable and reliable network. Thus, failure of the route is also regarded as a prime factor affecting the efficiency of any MANET routing protocol. The breaking of the connection between two routes or more nodes will cause the failure of the route specifically in the non-realistic routing protocol. In a network of mobile nodes, the link break is mainly based on the mobility of individual nodes. Therefore, the objective of this research is to investigate the performance of proactive DSDV and reactive AODV routing protocol using the Random Waypoint(RWP) mobility model in MANET. NS-2 network simulator is used to simulate the MANET environment and BonnMotion is to create a movement of mobile nodes that integrate with the routing protocol. The network performance metrics used are throughput, packet delivery ratio, and average end-to-end delay. In addition, three simulation scenarios have been conducted to compare AODV and DSDV routing protocols with varying numbers of nodes, a comparison of AODV and DSDV routing protocols with varying pause time, and a comparison of AODV and DSDV routing protocols with varying mobility speed. The result from the three scenarios analysed and concluded that the RWP mobility model with AODV gives a better performance of throughput with 869.69 kbps and Packet Delivery Ratio (PDR) with 83.00% meanwhile, RWP with DSDV is better for the average end-to-end delay(EED) with 212.970 bps.

Gibbs Haversine Reinforcement Learning Based Handover For 5g Enabled Seamless Mobility in Wireless Network

Seamless Mobility (SM) is crucial for bringing about better Quality of Service (QoS) like minimum handover latency with maximum throughput in 5G networks. In this work a method called, Jenkin Impulse Response Filtering and Reinforcement Learning-based Gibbs Haversine Distribution (JIRF-RLGHD) for optimal selection of target cells for the handover process to ensure seamless mobility is designed. The JIRF-RLGHD method is split into two sections. They are predicting the signal quality of both serving and adjacent wireless nodes using the Box Jenkin Impulse Response Filtering model. The second task remains in applying Reinforcement Learning-based Gibbs Haversine Distribution for optimal selection of target cells for handover to ensure seamless mobility in a wireless network. The overall proposed method was simulated on a Python programming interface language. The simulation results reveal that the JIRF-RLGHD method offers a greater delivery rate, and handover success with lesser handover latency at minimal packet loss rate. Numerical results show that the JIRF-RLGHD method performs better in terms of data delivery rate by 18%, and handover latency by 33% compared to existing methods.

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes.

Privacy-Preserving Wireless Sensor Networks for E-Healthcare Applications

Wireless sensor networks (WSNs) are a powerful support system for the fundamental infrastructure that is required to monitor physiological and activity parameters (WSN). Wearable devices, which are also referred to as wireless nodes in the scientific world, are what are used in order to measure one or more of the user’s vital signs. Each and every wireless node is a teeny-tiny device that is meant to be supplied with enough amounts of storage space, power, and transmission capability. The loss of data packets may occur during the transmission of data via a wireless medium for a number of reasons. These reasons include interferences, improper deployment circumstances, distance, and inadequate signal strength. The monitoring of a user’s physiological information and postural activity information in various applications, such as home care and hospital care, is the primary emphasis of this study. In this work, the WSN was shown thanks to the introduction of wireless sensor nodes that were created locally. These wireless sensor nodes are used in the process of analyzing many aspects of a network, such as the received signal strength, transmission offset, packet delivery ratio (PDR), and signal-to-noise interference. The work significantly improves the capabilities of conventional WSN by implementing a variety of alternative communication approaches, such as network-coded cooperative communication (NC-CC) and cooperative communication (CC). The system that is being shown makes it feasible to localize the user’s approximate position inside an indoor setting without making use of any camera network connections. This is made possible by the system’s ability to determine the user’s location via triangulation. This is one of the benefits that the system provides. A hospital sensor network, an example of which is being shown here, is capable of doing real-time monitoring of a patient’s postural activity as well as their general health. The method is being promoted in order to ensure that the patient will get assistance in a timely manner that is adequate to his/her needs. Involving NC-CC enables the effective sharing of real-time data among the group of privileged duty nurses while simultaneously minimizing the amount of network traffic, latency, and throughput. This is possible because of NC-CC protocol. The findings of the experiments showed that the proposed method of communication, which is known as dynamic retransmit/rebroadcast decision control, is a significant advancement in the network coding approach that is presently being utilized. This was demonstrated by the fact that the method was shown to be significantly more effective.