Researchers from the Indian Institute of Technology (IIT) Jodhpur, the Indian Institute of Information Technology (IIIT) Guwahati, and the Indian Institute of Technology (IIT) Kharagpur conducted research on the Internet of Things (IoT). The team created architectures and algorithms to improve the efficiency of data gathering and transmission in IoT devices and apps.
“The Internet of Things (IoT) is considered the next Industrial Revolution because it is slowly changing our lives. We have already started connecting everyday objects to the internet via embedded devices; smart homes are already a reality and with advancements in Artificial Intelligence, IoT systems are enabling functional robots, self-driving cars, among others,” Suchetana Chakraborty, assistant professor, Department of Computer Science and Engineering, IIT Jodhpur, remarked.
According to Chakraborty, there is a rising desire among ecosystems to share IoT services. She went on to say that such an architecture begs the question of how many apps may effectively use and govern a single IoT configuration.
“We sought to address the above two problems of resource wastage and data irrelevance through development of novel algorithms,” noted the lead researcher. Context-aware Data Generation (CaDGen) is an extreme edge-based data pre-processing framework created by the team for effective data management and forwarding on shared IoT infrastructure.
CaDGen consists of two modules. The data is filtered by the adaptive sensing module based on the context of the running apps that use the sensing infrastructure. The selective forwarding module determines the data forwarding channels so that different microservices running on edge devices can best utilise the data based on their needs.
The researchers assessed CaDGen’s performance in several configurations and found promising results in terms of network resource use, scalability, energy conservation, and compute allocation for efficient service delivery. The context analysis method might produce a 35% decrease in generated data for a moderately dynamic scenario by filtering out data that was extraneous to the operating programme.
“We believe that such an approach can suit various smart environments in a connected living setup that minimizes the cost of data management while providing an effective service architecture for end-users,” the researchers said in their article explaining their research.