Implementation Of Wireless Sensor Network Research Articles

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop.

Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.

Brief Overview on Wireless Sensor Network Technologies in the Internet of Things (IoT)

Wireless systems based on IoT-based has evolved rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has expanded across several different applications in real-time. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. Wireless Sensor Network technologies play a vital role in enabling the IoT. In this analysis, we delve deep into the essential elements, functionalities, and applications of WSN technologies within the context of IoT. We explore the diverse sensor nodes, communication protocols, and network architectures that constitute WSNs, highlighting their crucial function in gathering, transmitting, and processing data from the physical environment. The Internet of Things (IoT), which enables seamless connectivity among a variety of physical objects, has completely changed how we interact with the world around us. Wireless Sensor Network (WSN) technologies, which is the central nervous system of this interconnected ecosystem are a fundamental IoT enabler. This paper overview the essential elements, functionalities, and applications of WSN technologies in context of Internet of Things (IoT). We cover the various sensor nodes, communication protocols, and network architectures that make up WSNs in this thorough analysis, highlighting their functions in gathering, transmitting, and processing data from the physical environment. We explore the particular difficulties. WSN technologies find applications across various domains within the IoT landscape. From smart cities and precision agriculture to healthcare and industrial automation, WSNs play a crucial role in transforming these sectors. We showcase real-world case studies that illustrate the practical implementation and impact of WSNs in these domains, providing concrete examples of their effectiveness. Our goal is to provide you with engaging and content that adds value to your understanding of the Internet of Things and Wireless Sensor Networks.

Oxygen Level System Development in WSN and IoT-Based Factory

The health of workers is essential to factory productivity. The lack of oxygen experienced by factory workers for a prolonged duration can disrupt the brain system. One solution to this problem is to build manufacturing facilities with well-maintained airflow, especially oxygen. The system can flow air from outside the factory into the factory based on the measurement of the oxygen level. In this research, an airflow system using the internet of things (IoT) and wireless sensor network (WSN) technology was developed to ensure no oxygen shortage in the factory space. The system comprises three main parts: an oxygen level sensor, a fan controller circuit, and a cloud-based communication system. The oxygen level sensor can measure the volume of oxygen in the factory room and is also connected to the fan controller to control the airflow to the radio-frequency (RF) communication factory room. Oxygen level monitoring data are also sent to the cloud server so that the condition of the factory space can be monitored remotely using internet computers and mobile devices. Performance tests that have been carried out show that the system can increase the oxygen level by 82% from its pre-installed condition. The system built is easy-to-install, low-power, and reliable, with a data loss value of only 1.67%. WSN implementation at the factory does not require a lot of wiring, thus making the system cheaper.

ML Approach to Improve the Costs and Reliability of a Wireless Sensor Network.

Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental factors, these systems delay decomposition. Wireless sensor networks (WSN) can be used to monitor the temperature levels at different locations within these transportation containers and provide feedback to these systems. However, there are a range of unique challenges in WSN implementations, such as the cost of the hardware, implementation difficulties, and the general ruggedness of the environment. This paper presents the novel results of a real-life application, where a sensor network was implemented to monitor the environmental temperatures at different locations inside commercial temperature-controlled shipping containers. The possibility of predicting one or more locations inside the container in the absence or breakdown of a logger placed in that location is explored using combinatorial input-output settings. A total of 1016 machine learning (ML) models are exhaustively trained, tested, and validated in search of the best model and the best combinations to produce a higher prediction result. The statistical correlations between different loggers and logger combinations are studied to identify a systematic approach to finding the optimal setting and placement of loggers under a cost constraint. Our findings suggest that even under different and incrementally higher cost constraints, one can use empirical approaches such as neural networks to predict temperature variations in a location with an absent or failed logger, within a margin of error comparable to the manufacturer-specified sensor accuracy. In fact, the median test accuracy is 1.02 degrees Fahrenheit when using only a single sensor to predict the remaining locations under the assumptions of critical system failure, and drops to as little as 0.8 and 0.65 degrees Fahrenheit when using one or three more sensors in the prediction algorithm. We also demonstrate that, by using correlation coefficients and time series similarity measurements, one can identify the optimal input-output pairs for the prediction algorithm reliably under most instances. For example, discrete time warping can be used to select the best location to place the sensors with a 92% match between the lowest prediction error and the highest similarity sensor with the rest of the group. The findings of this research can be used for power management in sensor batteries, especially for long transportation routes, by alternating standby modes where the temperature data for the OFF sensors are predicted by the ON sensors.

Multiplayer reputation-based coalition game model (MRCGM) with dominant strategy analysis for detecting malicious attacks in wireless sensor networks

Wireless Sensor Networks (WSNs) are turning into an essential portion of our lives. Devoid of assuring WSNs security (WS), there remain no broad implementations of WSNs. Because of sensor nodes’ constrained capacities concerning computation, communication, and energy, giving protection to WSNs remains competitive. Indeed, the procedure of applying WS remains adaptable and vibrant that develops consistently. Attack-defend’s crux in WS could be conveyed by collaborative schemes of interdependency when Game Theory (GT) could be employed to consider communications amidst schemes of logical decision-makers. Hence, learning WS alongside GT possesses greater logic. This study proffers a Multiplayer Reputation-based Coalition Game Model (MRCGM) for seeking the false data injection amidst the nodes. An attacker attempts to sustain an ideal level of belief by correlating the identifications within the network (NW) to initiate a victorious transfer. Concurrently, the attacker should acquire a few charges for sustaining the trustability of its identifications. This proffered procedure employs this notion and turns the attack expensive by fixing a global threshold for nodes or identifications to be trustable and functional within the NW. Additionally, the beneficial function of the attacker and defender is as well described. The MRCGM has been correlated with 2 advanced methodologies like security-aware routing scheme employing repeated game (SARSRGM) paradigm and a Game-based Fuzzy Q-learning (GBFQL) scheme concerning diverse criteria. Consequently, the proffered MRCGM attains 124 kbps of Packet Drop Rate, 76.14% of energy efficiency, 96.1% of detection accuracy, 25.45% of energy consumption, and 102.45 ms of routing latency.

Secure Wireless Sensor Network using Cryptography for Smart Farming Systems

Internet of Things (IoT) technology has become part of human life. Agriculture in many parts is one of IoT implementation segment including the Cultivation of mold oysters. Internet of Things (IoT) is applied to collect data from combined sensors in the Wireless Sensor Network (WSN) will make it easy for farmers to monitor and control garden they remotely. Regardless of the application system control distance far based WSN that will make it easy for a farmer, the system has gap security, and one of them that is hacking and tapping of communication data The IoT . is really dangerous on effort if condition room mold oyster the known by someone hacker who can just is rival effort. It is a needed something method for secure communication when the data transfer process is over-guaranteed. The method used is application Base64 -based data encryption/decryption on WSN node devices and controlling and monitoring mobile devices. Based on results trials carried out after the implementation of Secure WSN with scenario tapping using the Wireshark tool show no data can be read. Analysis results show that this model could be applied as Secure WSN for Smart Farming System.

Monitoring and Locating Oceanic Sailors Through Wireless Sensor Network

A systematic oceanographic monitoring and management requires an effective marine tracking system. It helps the localization of different marine assets, their border control and in encountering emergency situations. Thus, a reliable marine tracking system is always required to ensure a safe marine environment for shippers, fishers, sailors and other marine assets. Nowadays due to the evolution in wireless sensor network (WSN) technology, their easy and inexpensive deployment can be seen in different fields such as agriculture, medical, industries and IoT. The implementation of WSN in the marine environment comes with various issues due to the complex behavior of the marine environment and thus affects the reliability of the system. In this paper, a WSN-based marine tracking protocol sailing is presented, which identifies the location of marine assets reliably through the genetic firefly (GEFIR) algorithm. The system comprises sensor nodes, sink nodes, server-client terminals and a base station. The protocol uses the application and contact layers to create a group of beacon nodes (boats) based on the geographical location to conserve energy and increase data reliability. GEFIR algorithm is implemented to give a precise and more convenient data to the WSN for the mariners. The results show a better performance for latency calculation through the WSN- and GEFIR-based sailing protocol. The system generates the alert to nodes (sailors) in case they are moving out from a specified region and sends their location for further communication.

Towards People Crowd Detection Using Wireless Sensor Networks

Objective: The objective of this study was to examine and propose the use of wireless sensor networks for people crowd detection in resource constrained environments such as developing economies.
 Methodology: A systematic review was carried out on current technological trends and application of Wireless Sensor Networks (WSNs) in crowd detection. For this study, focus was on WSN implementation in developing economies, where infrastructure is underdeveloped and people crowds are dynamic and spontaneous. Based on a requirement analysis and knowledge of the inherent challenges of WSNs, a WSN implementation for people crowd detection was proposed.
 Findings: Most studies in crowd detection using WSNs, have been in the area of non-people crowds. However issues critical to deployment of WSNs for people crowd detection in developing countries include: the uncontrollable nature of people crowds, under developed physical infrastructure and the inherent challenges of power, computational capacity and broadcast communication characterizing WSNs. Achieving people crowd detection using WSNs therefore, calls for special attention.
 Recommendation: To ensure effective people crowd detection, requires taking into consideration connectivity, scalability, performance, security, accuracy and resource utilization of WSNs.

Particle Filter Based Range Search Approach for Localization of Radioactive Materials

Wireless Sensor Networks (WSNs) have recently become crucial in monitoring operations. The development of a Data Fusion Algorithm for radioactive source localization utilizing WSN based on the Particle Filter (PF) technique is presented. The localization of an unknown-intensity point radioactive source using sensor nodes measured intensities in Count per Minute (CPM) is considered. The surveillance area is covered by several sensors (radiation detectors) n. Instead of using four sensors, as described in previous research, two consecutive sensors are used in sequence (S1, S2), (S2, S3)……, (Sn-1, Sn) till reaching the last sensor available Sn. Apollonius circle calculated range guides a particle filter for estimating the source location using actual measurements. Compared with other approaches such as the Iterative Pruning Clustering algorithm, more accurate estimates in terms of the error between the estimated source position and ground truth are obtained. The comparison is conducted using the same real measurements data. The Particle Filter based algorithm is implemented in a Xilinx FPGA chip. The architecture is a two sequential steps implementation, where particle generation, weight calculation, and normalization are carried out in parallel during the first step, followed by a sequential or parallelized resampling in the second step. This architecture targets a balance between hardware resources and speed of operation. The future work plan includes security-related studies and complete WSN implementation using μC/FPGA devices.

Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review

The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requirements. The most important one is optimizing the battery’s lifetime, which is tightly coupled to the sensor lifetime. End-users usually avoid replacing sensors’ batteries, especially in massive deployment scenarios like smart agriculture and smart buildings. To optimize battery lifetime, wireless sensor designers need to delineate and optimize active components at different levels of the sensor’s layered architecture, mainly, (1) the number of data sets being generated and processed at the application layer, (2) the size and the architecture of the operating systems (OS), (3) the networking layers’ protocols, and (4) the architecture of electronic components and duty cycling techniques. This paper reviews the different relevant technologies and investigates how they optimize energy consumption at each layer of the sensor’s architecture, e.g., hardware, operating system, application, and networking layer. This paper aims to make the researcher aware of the various optimization opportunities when designing WSN nodes. To our knowledge, there is no other work in the literature that reviews energy optimization of WSN in the context of Smart Energy-Efficient Buildings (SEEB) and from the formerly four listed perspectives to help in the design and implementation of optimal WSN for SEEB.

An event-driven multi-agent based distributed optimal control strategy for HVAC systems in IoT-enabled smart buildings

Smart buildings generally adopt a centralized optimal control for heating, ventilation and air-conditioning (HVAC) systems to improve the building system performance. As a crucial technology adopted in smart buildings, Internet of Things (IoT) based wireless sensor networks (WSNs) are promising platforms for implementing novel distributed optimal control to achieve distributed intelligence effectively. Such a future mist computing paradigm can be hindered by the limited energy resource of WSNs. The event-driven optimization method activates the optimization only when events occur. It can save the energy resource of WSNs, and thus pave the way for implementing the distributed optimal control architecture in IoT-based WSNs. This study therefore proposes an event-driven multi-agent based distributed optimal control strategy for HVAC systems for implementation in IoT-based battery-powered WSNs. The strategy consists of two novel schemes. First, an event determination scheme determines the event threshold by comprehensively considering the system performance and the total energy consumption of individual sensors implementing the distributed optimal control architecture. Second, an event-driven distributed optimization scheme solves the optimization problems with distributed optimization algorithms in IoT sensors of limited data processing capacity when an event occurs. Comparison and case studies are conducted to compare different strategies and validate the proposed strategy. Results show that different strategies require very different sensor energy consumption. The proposed strategy can provide satisfactory system performance while reducing the energy consumption of IoT sensors.

Data delivery performance on MongoDB generated by WSN over high data load

Wireless sensor networks (WSN) Environment is a collection of nodes that sensing and sending data to other nodes in a large number of nodes. One of the WSN concepts is continuous or periodic data collection, then the data obtained can be further processed. From this process, it is possible to produce large data, so the process of sending and storing data becomes a high load. Data obtained from WSN nodes vary greatly, which affects on sending and storing of data. Therefore, a study is needed to analyze WSN's ability to send and store large amounts of data. Message query telemetry protocol (MQTT) is used because it could save resources on communication and MongoDB used because it does not have the concept of tables and rows, this is very suitable for variations in data generated by WSN. In this study, it can be concluded that the performance of MongoDB on high data amount is acceptable, so MongoDB is highly recommended for WSN implementation.

Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study.

Owing to automation trends, research on wireless sensor networks (WSNs) has become prevalent. In addition to static sinks, ground and aerial mobile sinks have become popular for data gathering because of the implementation of WSNs in hard-to-reach or infrastructure-less areas. Consequently, several data-gathering mechanisms in WSNs have been investigated, and the sink type plays a major role in energy consumption and other quality of service parameters, such as packet delivery ratio, delay, and throughput. However, the data-gathering schemes based on different sink types in WSNs have not been investigated previously. This paper reviews such data-gathering frameworks based on three different types of sinks (i.e., static, ground mobile, and aerial mobile sinks), analyzing the data-gathering frameworks both qualitatively and quantitatively. First, we examine the frameworks by discussing their working principles, advantages, and limitations, followed by a qualitative comparative study based on their main ideas, optimization criteria, and performance evaluation parameters. Next, we present a simulation-based quantitative comparison of three representative data-gathering schemes, one from each category. Simulation results are shown in terms of energy efficiency, number of dead nodes, number of exchanged control packets, and packet drop ratio. Finally, lessons learned from the investigation and recommendations made are summarized.

Power Grid Surveillance and Control Based on Wireless Sensor Network Technologies: Review and Future Directions

In this paper, the concept of the smart grid and the emerging challenges of wireless communication technology for use on the smart grid (SG)is reviewed. One modern-day technology of great significance is wireless sensor networks (WSNs), which has the potential to ensure the smooth operation of smart grid management and increase its energy efficiency, reliability and cost-effectiveness. The advanced electric power grid infrastructure known as smart grid takes advantage of automated control and cutting-edge communication strategies to achieve greater efficiency, reliability and safety, whilst seamlessly combining renewable and alternative sources of energy. Unlike conventional communication technologies, WSNs are more beneficial to modern electric power systems because they are affordable and operate on a cooperative basis. For this reason, WSNs are considered to have good potential for improving the way electric power is produced, distributed and used. Therefore, they are a key element of the smart grid. Nevertheless, the implementation of WSNs in smart grid settings may be inadequately reliable due to the complexity and unfavourable aspects of electric power system environments. The purpose of the current work is to review WSN uses in electric power systems and the opportunities and difficulties offered by this technology, as well as to delineate possible facets of smart grid applications and wireless technologies that future research could explore. Therefore, this work is significant.

Realization of Autonomous Sensor Networks with AI based Self-reconfiguration and Optimal Data Transmission Algorithms in resource constrained nodes

Wireless sensor networks (WSN) prove to be an enabling technology for Industry 4.0 for their ability to perform in autonomous manner even in regions of extreme conditions. Autonomy brings in independent decision making and exerting controls without manual intervention and frequent maintenance. This paper aims to inculcate intelligence to the WSN exploiting the merits of Artificial Intelligence (AI) algorithms in cheap and most preferred ESP8266 and ESP32 based nodes. Autonomy is brought in by means of optimal data transmission, compressive sensing fault detection and network reconfiguration and energy efficiency. Optimal data transmission is achieved using Q-learning based exploration exploitation algorithm. Compressive sensing performed using Autoencoders ensure reduction in transmission overhead. Fault detection is done using Binary SVM classifier and the net- work re-configures based on physical redundancy. This paper high- lights the implementation of such autonomous WSN in real time along with their performance statistics.

An Input Power-Aware Efficiency Tracking Technique With Discontinuous Charging for Energy Harvesting Applications

This paper proposes a discontinuous charging technique for switched-capacitor converters that improves the power conversion efficiency (PCE) at low power levels and extends the input power harvesting range at which high PCE is achievable. Discontinuous charging delivers current to energy storage only during clock non-overlap time. This enables tuning of the output current to minimize converter losses based on the available input power. Based on this fundamental result, an input power-aware, two-dimensional efficiency tracking technique for Wireless Sensor Network (WSN) nodes is presented. In addition to conventional switching frequency control, clock non-overlap time control is introduced to adaptively optimize PCE according to the sensed ambient power levels. The proposed technique is designed and simulated in 90nm CMOS with post-layout extraction. Under the same input and output conditions, the proposed system maintains at least 45% PCE at 4μW input power, as opposed to a conventional continuous system which requires at least 18.7μW to maintain the same PCE. Therefore, the input power harvesting range is extended by 1.5x. The technique is applied to a wearable WSN implementation utilizing the IEEE 802.15.4-compatible GreenNet communications protocol. This allows the node to meet specifications and achieve energy autonomy when deployed in environments where the input power is 49% lower than what is required for conventional operation.

A Robust Method to Detect Coverage Holes And Network Re-Establishment for NB-IOT Enabled

Sensing coverage with respect to WSN (Wireless Sensor Network) research has gained immense consideration. Implementation of WSNs in the domain of IoT (Internet of Things) ensures consideration of IoT features when thinking of sensing coverage. Both IoT and WSN are with respect to coverage hole metrics that includes network re-establishment, link establishing, throughput etc. Implementing sensor nodes may result in the issue of coverage holes if there is improper deployment of nodes. A coverage hole can exist in any region that is being monitored at any instance due to various purposes. As a result detecting coverage hole stand as a prime concern for gaining absolute coverage. Detecting coverage hole pose a big question for gaining high coverage in the Wireless Sensor Networks. The problem of coverage and connectivity particularly in coverage deployment strategy is analyzed for the weaker nodes and in case network design fails. The proposed algorithm referred to as ‘Store and Forward technique' aids in identifying the coverage holes and network re-establishing in WSNs and IoT. Hence with the help of Store and Forward technique, network re-establishing and network stability can be performed in case of a coverage hole issue. The proposed technique is mostly employed in networks having irregular connectivity, specifically within environments where mobility is high. It’s also beneficial in conditions with long transmission and variable delays, maximum error rates, or cases where there is no direct, end-to-end link. The experiment output represents that the proposed healing method magnifies coverage rate with only a few additional sensors in contrast to the related methods.

Perancangan dan Implementasi WSN (Wireless Sensor Network) Pada Alat Ukur Energi Listrik

WSN (Wireless Sensor Network) is a network consisting of several sensor nodes that form a collaboration in a WSN technology network using sensors. The WSN system can be implemented on an electrical energy meter. Electrical energy measuring instrument (kwh meter) is important for the community because it is related to the amount of the electricity bill. In the use of electrical energy, sometimes people do not know how much energy has been used in a month, so there is often a waste of electricity. The main purpose of this system is in the framework of transparency and provides information on per-kWh load rates in the use of electricity.The method used in making WSN implementation on this electrical energy measuring instrument is analyzing the problem, designing the system, making the system up to system testing. By carrying out these steps, the WSN implementation on this electrical energy measuring instrument can provide monitoring effectiveness and can find out how much per-kWh costs in the use of electrical energy.This system is programmed using the C language on Arduino. The devices used are Arduino Nano as the main controller, nRF24L01, ZMCT103C, LM358, LM2917N, and RTC. From the discussion it can be concluded that this system can help in knowing the cost of electricity usage per-kWh and can monitor the use of electrical energy using a PC (Personal Computer).

Comparison of WSN and IoT approaches for a real-time monitoring system of meal distribution trolleys: A case study

International regulations determine that food in hospitals and elderly homes must be served at given temperature ranges. However, the real-time surveillance of the meal distribution trolleys along all the institutions facilities, guaranteeing conformity to rules from the instant when all the meals are put in the distribution trolley until they are delivered to the patients, is still a challenge.In this paper, we present a comparison of two approaches based on Wireless Sensor Networks (WSN) and Internet of Things (IoT) technologies for implementing a Real-Time Monitoring System of Meal Distribution Trolleys in a hospital. The performance evaluation results show that the IoT implementation yields much lower latencies than the WSN implementation, and the latency does not depend on the location of the trolley or the building size. Also, the IoT approach yields slightly better battery life expectancy than the WSN approach. Thus, the IoT approach seems the best option for real-time surveillance systems installed in meal distribution trolleys.