Researchers from the Indian Institute of Technology (IIT) Jodhpur, Indian Institute of Information Technology (IIIT) Guwahati and IIT Kharagpur have performed a research in the area of Internet of Things (IoT). The team has developed architectures and algorithms to enhance the efficiencies of data collection and transmission associated with IoT devices and applications.
“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, said.
According to Chakraborty, there is growing interest to share IoT services among ecosystems. She further added that such an architecture raises a fundamental question – how can multiple applications best utilise and control a single IoT setup?
“We sought to address the above two problems of resource wastage and data irrelevance through development of novel algorithms,” explained the lead researcher. The team has developed an extreme edge-based data pre-processing framework namely Context-aware Data Generation (CaDGen), for efficient data management and forwarding in shared IoT infrastructure.
CaDGen has two modules. The adaptive sensing module filters the data based on the context of the running applications that use the sensing infrastructure. The selective forwarding module decides the forwarding paths for the data so that different microservices running over the edge devices can best utilize the data depending on their requirements.
The team has evaluated the performance of CaDGen under diverse setups, and noted promising results in terms of network resource utilization, scalability, energy conservation, and distribution of computation for optimal service provisioning. By filtering the data irrelevant to the running application, the context analysis method could achieve nearly 35% reduction in the generated data for a moderately dynamic scenario without compromising on the data quality.
“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,” concluded the researchers in their paper describing their research.