A Cognitive Theory-based Opportunistic Resource-Pooling Scheme for Ad hoc Networks

TL;DR

This paper proposes a cognitive, opportunistic resource pooling scheme for ad hoc networks that enhances resource convergence, reliability, and latency by leveraging behavioral observations and beliefs in a distributed manner.

Contribution

It introduces a novel cognitive computing-based resource pooling scheme that improves response time and reliability in ad hoc networks.

Findings

Faster resource convergence rate achieved

Enhanced reliability in resource pooling

Lower latency in resource management

Abstract

Resource pooling in ad hoc networks deals with accumulating computing and network resources to implement network control schemes such as routing, congestion, traffic management, and so on. Pooling of resources can be accomplished using the distributed and dynamic nature of ad hoc networks to achieve collaboration between the devices. Ad hoc networks need a resource-pooling technique that offers quick response, adaptability, and reliability. In this context, we are proposing an opportunistic resource pooling scheme that uses a cognitive computing model to accumulate the resources with faster resource convergence rate, reliability, and lower latency. The proposed scheme is implemented using the behaviors observations beliefs cognitive model, in which the resource pooling decisions are made based on accumulated knowledge over various behaviors exhibited by nodes in ad hoc networks.