Data Flow Optimization in the Internet of Things

Abstract

The Internet of Things (IoT) is constituted of an important number of constrained nodes limited in terms of power energy, computation capacity, storage capacity. They produce a considerable amount of data, which increases the data flflow in the network. The ineffificient transmission of data via constrained nodes makes the network unstable, the energy consumption increases rapidly, and the data delay increases strictly. To overcome these limitations, we propose a new approach that allows nodes to select the effificient path to transmit data from source nodes to base stations (BSs) to optimize the data flflow in the constrained network. First, we grouped nodes using a density peaks (DP) clustering algorithm based on the coordinate’s location of these nodes. Second, using the group nodes, the assignment of nodes to BSs that are considered as the collectors of data is performed. Third, the nodes make a dynamic and automated path plan to optimize the data flflow in the constrained network. Simulation results on a real network data set demonstrate that our proposal outperforms the state-of-the-art approaches in terms of the number of hops to achieve the cluster head (CH) node, the data delay, the network lifetime, and the number of the alive nodes.

An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs

“…Recently, a technique [61] was proposed based on the DP clustering algorithm that allows nodes to be grouped based on their coordinate locations. It also enables SNs to self-generate decisions depending on the data flow condition in the network and builds an automated path plan that extends the network's lifetime.…”

“…As known in the literature, hierarchical protocols and methods are state-of-the-art and based on clustering algorithms such as LEACH [57], DP clustering [61], and HEEMP [39]. We then compared the previous algorithms with our contribution to demonstrate the proposed technique's performance.…”