Enhancing Data Compression Efficiency in Wireless Sensor Networks: A Study and Modification of Rice Golomb Coding Algorithm | Chapter 5 | Theory and Applications of Engineering Research Vol. 9

 The chapter suggests improvements to the Rice Golomb Coding (RGC) compression for wireless sensor networks (WSN), focusing on nodes with limited resources. To extend the network's life, effective data compression is crucial, especially for nodes periodically sending data to the sink. Five techniques adapting RGC based on adjustable parameters from preprocessed input data is presented in this chapter. The goal is to reduce energy use during communication in WSN nodes. Using various MATLAB datasets, we compare outcomes, where the EMARGC_D method, with auto-differencing, shows better results. Real-time tests with NI WSN nodes in LabVIEW support the practicality of our approach. This aims to deepen understanding of our findings, particularly in aiding resource-constrained nodes sending data regularly to the sink node.

Author(s) Details:

S. Kalaivani,
Department of Electronics and Communication Engineering, B.S.A Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India.

C. Tharini,
Department of Electronics and Communication Engineering, B.S.A Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/TAER-V9/article/view/13828

Optimizing Wireless Sensor Network Communication through Differential Encoding-Enhanced Compressed Sensing Protocol | Chapter 10 | Contemporary Perspective on Science, Technology and Research Vol. 6

Wireless sensor networks (WSNs) face inherent constraints in resources, such as power supply, processing speed, memory requirements, and bandwidth for communication. Given their limited power supply, energy consumption is a critical challenge in the development of protocols and algorithms for WSNs. Various operations in WSNs, including data sensing, computation, node switching, and transmission, necessitate a focus on energy efficiency. Extensive literature reveals that a substantial portion of energy in WSNs is consumed during radio communications. To address this issue, reducing the number of transmitted data bits has been identified as an effective strategy to minimize energy consumption.

Therefore, employing data compression techniques becomes imperative in order to reduce the overall number of bits transmitted. Although researchers have explored numerous energy-efficient lightweight compression algorithms [4] tailored for WSN data, there remains a need for more efficient compression techniques that not only compress data but also minimize the mean square error (MSE) of the received data. In this paper, we propose a novel approach using differential encoding-based compressed sensing (CS) to achieve this goal. Simulation results demonstrate a notable improvement in the compression ratio compared to the standard compressed sensing technique, highlighting the efficacy of the suggested protocol in enhancing the existing WSN system.

Author(s) Details:

Parnasree Chakraborty,
Department of Electronics and Communication Engineering, BSA Crescent Institute of Science & Technology, Chennai, India.

C. Tharini,
Department of Electronics and Communication Engineering, BSA Crescent Institute of Science & Technology, Chennai, India.

Please see the link here: https://stm.bookpi.org/CPSTR-V6/article/view/13567

Exploring the Transition from Classical Approaches to Machine Learning Techniques for Coverage Estimation in Wireless Sensor Networks | Chapter 7 | Recent Developments in Science and Technology for Sustainable Future

In the modern era, wireless sensor networks have become crucial due to their ability to operate within size constraints. Networks can be influenced by various internal and external factors, leading to effective changes. Conventional methods were designed for stable networks, which may not be suitable for dynamic networks. Here, machine learning techniques can be utilized for dynamic networks. In this chapter, we explore machine learning techniques that are well-suited for estimating coverage in wireless sensor networks.

Author(s) Details:

Mini,
Department of Mathematics, S. A. Jain (PG) College, Ambala City, Haryana, India.

Please see the link here: https://stm.bookpi.org/RDSTSF/article/view/13389

Feasibility and Efficacy of Modified Adaptive Rice Golomb Coding for Wireless Sensor Networks | Chapter 2 | Research and Applications Towards Mathematics and Computer Science Vol. 7

Wi-Fi Sensor Networks (WSNs), characterized by energy-forced nodes powered by limited-ability batteries, necessitate energy-effective data compression methods to extend their network lifetime. The ideas module within each sensor node arises as a primary energy services, making data compression a important approach to curtail data broadcast. This paper introduces the Modified Adaptive Edible grain Golomb Coding (MARGC) algorithm as an persuasive compression technique to reinforce the network's lifespan. Simulation results, utilizing various datasets, underscore the feasibility and efficiency of the MARGC algorithm. Furthermore, the invention's real-time exercise on National Instruments Wireless Sensor Network (NI WSN) fittings demonstrates its realistic applicability and performance. To standard our contribution, future work will involve a approximate analysis with existing methods to highlight the superior aspects of the projected MARGC algorithm.

Author(s) Details:

S. Kalaivani,
Department of Electronics and Communication Engineering, B.S.A Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India.

C. Tharini,
Department of Electronics and Communication Engineering, B.S.A Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/RATMCS-V7/article/view/12832

Stochastic-Heap Hybrid Optimizer (SHHO) and Control Strategies for AC to DC Converters in IoT Applications | Chapter 11 | Research and Developments in Engineering Research Vol. 7

The rebellious effects of the Internet of Things (IoT) on tool interaction and communication have happened in significant development in the adoption of IoT-based requests. Effective power source exercise is crucial in IoT uses, especially in settings accompanying constrained strength resources. AC to DC converters plays a crucial part in the present position by converting interspersing current into direct current. This conversion procedure helps to increase strength efficiency and create it easier to include energy from undepletable source sources. The Stochastic-Heap Hybrid Optimizer (SHHO) is a finish that may be used to help the energy efficiency and control plans of AC to DC converters in Internet of Things (IoT) applications. As submitted, the SHHO method combines guessed search methods accompanying a heap-based addition strategy to efficiently investigate the solution room. SHHO successfully overcomes the local optima question by using the capacities of randomization and prioritization, leading to revised convergence and greater acting. This chapter still comprehensively resolves several AC-to-DC converter control algorithms that concede possibility be used in IoT plans. The solutions include containing, procedures like Proportional-Integral-Derivative (PID) control, Maximum Power Point Tracking (MPPT), and Pulse Width Modulation (PWM). These control methods' examination and contrasting shed light on their distinct benefits and restraints in diverse IoT backgrounds. Additionally, this chapter explores the troubles encountered when SHHO (Solar Hybrid Home Optimisation) is linked with IoT-located AC-to-DC converters. Real-time operation, fittings limits, and communication limits are all part of these difficulties. The authors further discuss useful implementation issues and focal point prospective applications place SHHO might considerably increase performance and energy adeptness. To verify the productiveness of the suggested SHHO and control means, extensive simulations and experimental tests are completed activity in typical IoT positions. According to the findings, SHHO performs better than usual optimization systems in terms of influence, dependability, and flexibility. The Stochastic-Heap Hybrid Optimizer and control arrangements for AC to DC converters are thoroughly checked in this book chapter for use in Internet of Things uses. The approaches and conclusions argued in this information improve the development of strength-efficient IoT wholes. This development sets the stage for the next smart and tenable IoT deployments.

Author(s) Details:

I. E. S. Naidu,
Department of EECE, GITAM University, Vishakhapatnam, India.

Praveen Mande,
Department of EECE, GITAM University, Vishakhapatnam, India.

Budidi Udaya Kumar ,
Department of EECE, GITAM University, Vishakhapatnam, India.

S. V. Bharath Kumar Reddy,
Department of EECE, GITAM University, Vishakhapatnam, India.

Sreenivasulu Ummadisetty,
Department of EECE, GITAM University, Vishakhapatnam, India.

Please see the link here: https://stm.bookpi.org/RADER-V7/article/view/11711

Design and Implementation of IoT Based WSN Smart Home Automation Intelligent System using Flask Web Server| Chapter 14 | New Approaches in Engineering Research Vol. 11

 The smart home is changing the way we live today. In this project, we introduce to automate the entire home appliances using a wireless sensor network to control and monitor the connectivity and communication using Raspberry Pi. The Raspberry Pi is used as central coordinator to connect with four NodeMCU wirelessly operating under star network topology to handle the home appliances. The Raspberry Pi and NodeMCU modules are connected to the internet using the hotspot. The coordinator node will monitor and control the process through the HTTP web page protocol. The web page was developed using HTML, CSS, JavaScript, and chart.js, and the hosted website is on the Flask web server. The webpage automation system will control the home appliances through the WiFi module; it makes a complete setup of home automation using the Internet of Things. Finally, the entire core design of hardware and software to manage the home appliances through a Flask web server is done in local storage and can be viewed in a web browser using the client-server model.

Author (S) Details

G. Mallikharjuna Rao
Department of ECE, CBIT, Osmania University, Hyderabad, India. Research and Academic Experience: 15

View Book :- https://stm.bookpi.org/NAER-V11/article/view/3574