AFT: Adaptive Fibonacci-based Tuning Protocol for Service and Resource discovery in the Internet of Things

Abstract

This paper explores Adaptive Fibonacci-based Tuning Protocol for Service and Resource discovery in the Internet of Things (AFT). Through using constrained application protocol (CoAP), Internet of Things (IoT) can support Machines to Machines (M2M) communications. CoAP has centralized and distributed operation modes to discover resources. In the centralized mode, resource directory (RD) is used to maintain and host updated services description of each resource in the network. RD requires periodical updates by other nodes. However, regularly updates resulted in additional signaling overhead, drain the node's battery and reduce the overall network lifetime. Hence, the proposed AFT intelligently varies and adjusts the update frequency of the services using Fibonacci sequence. We evaluated the performance using an inclusive experiments' number performed by employing emulated Tmote Sky motes in the COOJA environment. The results prove that the proposed AFT protocol consistently accomplished the lowest control overhead that ultimately increased the energy saving of the resources. They also confirm that this AFT protocol outperforms its traditional counterpart by 75% regarding the overall network lifetime.