Redemptive Resource Allocation Scheme for IoT-Assisted Smart Healthcare Systems

Jiechao Gao, University of Virginia
Tu Ngoc Nguyen, Kennesaw State University
Gunasekaran Manogaran
Ankit Chaudhary


Internet of Things-assisted healthcare services grants reliable clinical diagnosis and analysis by exploiting heterogeneous communication and infrastructure elements. The communication is enabled through point-to-point or cluster-to-point between the users and the diagnosis center. In this process, the complication is the resource sharing and diagnosis swiftness in validating multiple resources. The open and ubiquitous nature of IoT results in proactive resource sharing, resulting in delayed transmissions. For addressing this issue, this manuscript introduces the Redemptive Resource Sharing and Allocation (R2SA) scheme. The available health data is accumulated based on a first-come-first-serve basis, and the transmitting infrastructure is selected. In this process, the data-to-capacity of the available infrastructure is identified for non-redemptive resource allocation. The extremity of the capacity and unavailability of the resource is then analyzed for parallel processing and allocation. Therefore, the data accumulation and exchange rely on concurrent sharing and resource allocation processes, deferring a better accumulation ratio. The concurrent redemptive selection and sharing reduces transmission delay, improves the resource allocation rate, and reduces transmission complexity. The entire process is managed for the data-to-capacity validation and concurrent recommendation using transfer learning. The first validation knowledge base remains the same/ shared for different data accumulation and sharing intervals.