On latency in the internet of things

In this thesis, we propose the use of IoT-Fog-Cloud (IFC) and Restricted Access Window (RAW) mechanism of IEEE 802.11ah standard to reduce latency in large-scale deployment of IoT devices having limited storage, computation and transmission capabilities. To achieve this end, we use a service discovery protocol to assess the capabilities of the devices and propose a strategy to offload tasks from one layer to the other in IFC paradigm using a queuing-theoretic framework which considers the storage, computation and transmission capabilities of the devices in each layer before actually offloading tasks to it. Moreover, we develop an efficient selection strategy in this thesis for the acquisition of nodes in the fog layer when many candidates are offering their services in the vicinity. The results show that our proposed strategy enhances the overall capability of fog layer by inclusion of efficient fog nodes in the network and thus reduces the latency in processing of tasks being offloaded to the fog layer. Finally, we develop a comprehensive framework based on Discrete Time Markov Chain (DTMC) to characterize RAW which allows nodes to be divided into groups, and permits only one group to access the shared medium within a certain duration of time (RAW slot). By the use of the proposed framework, we determine the size of group of nodes for each RAW slot by quantifying the duration of the RAW slot required to transmit a given number of data packets when nodes in the group have only one, or more (finite or infinite) number of data packets. In this way, the number of collisions due to contention among large number of nodes in IoT is reduced significantly which results in reduction of latency of data packets in the network.