Real-Time Data Fusion Approaches for Sensor-Integrated IoT Applications

The Internet of Things (IoT) facilitates the seamless interconnection of various items and devices, enabling them to communicate and collaborate towards common objectives. The Internet of Things (IoT) facilitates the seamless interconnection of various items and devices, enabling them to communicate, share data, and perform tasks autonomously, leading to enhanced efficiency and convenience in both personal and industrial settings. Addressing challenges in efficiency, practicality, and navigation tool limitations can be achieved through the implementation of sensor-integrated navigation systems.

A fundamental component of these integrated positioning systems is data fusion technology. Addressing challenges in efficiency, practicality, and navigation tool limitations can be achieved through innovative technologies, streamlined processes, and user-centric design. IoT devices have the capability to gather context-specific data, which can then be merged using data fusion techniques to create new, sophisticated information. However, these techniques must work with dispersed nodes, decentralized interaction and enable scalability and nodal dynamism, among other limitations, in IoT scenarios. In many cases, identifying faults promptly using conventional maintenance approaches can be challenging for property managers or inspectors, potentially resulting in system failure. In many cases, identifying faults promptly using conventional maintenance approaches can be challenging due to limited visibility into system conditions, especially in complex or interconnected systems.

 Real-time data fusion for sensor-integrated IoT applications faces several challenges. One prominent issue is data heterogeneity, where diverse sensors generate data in different formats and rates, complicating integration and analysis. Scalability is also a significant challenge, especially as IoT deployments grow in size and complexity, necessitating efficient algorithms and architectures for handling large volumes of data in real-time. Lastly, interoperability remains an ongoing issue, requiring standardized protocols and frameworks to facilitate seamless communication and integration across diverse IoT devices and platforms.

The integrated mapping system, with extra sensors for relative steering, has received increased attention as efforts to enhance accuracy and performance. Decentralized filtering emerges as a method used in multi-sensor data combination systems. The issue of installation deterioration often arises due to aging materials, oxidation, and blockages. To accomplish the goal of handling facilities, IoT application seeks to provide a more intuitive representation of building and sewage infrastructure, visualizing them on a digital model with precise geometric detail. This collaboration uses low-cost, efficient sensors to provide real-time input and surveillance. Therefore, programs can be significantly enhanced through fundamental supplementation, allowing modules reliant on each other's information to function independently. The multi-sensor apparatus positioning system integrates directions and localization, incorporating computer, sensory network and location communication technologies. Furthermore, it uses a distributed data fusion technique for multi-sensor integrated routing systems and an algorithm aimed at enhancing the precision and reliability of the positioning and guidance system.

In recent years, the Internet of Things (IoT) has emerged as a transformative technology, enabling seamless connectivity and data exchange between physical devices. One of the critical aspects of the IoT is the integration of sensors that collect vast amounts of data from the environment. However, the real value of IoT lies in extracting actionable insights from this data in real time. Data fusion techniques play a crucial role in achieving this goal by combining information from multiple sources to provide a comprehensive view of the system.

This special issue presents a unique approach to handling data fusion and acquisition based on a decentralized system structure model, enhancing data handling in the ubiquitous IoT framework.

Papers addressing the following questions and themes are encouraged:

• Data fusion for multi-sensor integrated systems: moving from analytics-to learning-based methods
• IoT circumstances: a global service-based strategy for sensor data fusion
• Technique for communication based on integration of multi-sensor information
• Utilizingdata fusion in an integrated multisensor positioning scheme
• Benefits of combined sensor and data in an integrated guidance system
• Localizingrailways using several sensors in an automatic train control system
• Data fusion technology-based integrated system design for the transportation 
• Sensor and data fusion-based pedestrian geolocation technology
• Internal sensor network multi-sensor fusion in a medical interaction setting
• Developing integrated guiding systems based on multisensor data fusion
• A productive sensor-integrated technique for deploying apps for real-time data monitorin

Deadline:

• Submission Deadline: 31 December 2024

Submission Instructions:

• Please read the Guide for Authorsbefore submitting. All articles should be submitted online; please select “Sensor-Integrated IoT” on submission.

Guest Editors: