Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522943 | PMC |
| http://dx.doi.org/10.3390/s121114851 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Scalability Analysis of LoRa and Sigfox in Congested Environment and Calculation of Optimum Number of Nodes.
Sensors (Basel)
October 2024
Indian Institute of Information Technology (IIIT), Nagpur 441108, India.
Low-power wide area network (LPWAN) technologies as part of IoT are gaining a lot of attention as they provide affordable communication over large areas. LoRa and Sigfox as part of LPWAN have emerged as highly effective and promising non-3GPP unlicensed band IoT technologies while challenging the supremacy of cellular technologies for machine-to-machine-(M2M)-based use cases. This paper presents the design goals of LoRa and Sigfox while throwing light on their suitability in congested environments.
View Article and Find Full Text PDFSystematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.
Sensors (Basel)
September 2024
Faculty of Engineering, Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0G4, Canada.
The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction.
View Article and Find Full Text PDFA Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.
Sensors (Basel)
September 2024
Department of Radio Communications and Video Technology, Faculty of Telecommunications, Technical University of Sofia, 1000 Sofia, Bulgaria.
One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study.
View Article and Find Full Text PDFElevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing, Apache Spark, and Machine Learning.
Sensors (Basel)
June 2024
Department of Industrial Engineering and Engineering Management, Yuan Ze University, 135, Far-East Rd., Taoyuan 320315, Taiwan.
The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime.
View Article and Find Full Text PDFDevelopment of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies.
Sensors (Basel)
June 2024
Department of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications named Gumarbek Daukeyev, Almaty 050013, Kazakhstan.
This research focuses on developing an artificial vision system for a flexible delta robot manipulator and integrating it with machine-to-machine (M2M) communication to optimize real-time device interaction. This integration aims to increase the speed of the robotic system and improve its overall performance. The proposed combination of an artificial vision system with M2M communication can detect and recognize targets with high accuracy in real time within the limited space considered for positioning, further localization, and carrying out manufacturing processes such as assembly or sorting of parts.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!