Neural-Network-Assisted Packet Accelerators for Internet of Things Network Systems

Abstract

Major device nodes within the internet of things (IoT) system collects and store information in bit forms of 0’s and 1’s regardless of its repetition. The nodes do not possess the capability of processing redundant data information except for outright rejection/replacement of packets of similar sizes. This becomes a research problem since high volumes of packet redundancy are prevalent owing to repetitive information. Many optimal solutions have been provided to reprocess redundant packets and to store them in edge server for accessibility by other connected IoT systems and networks servers. To do so, major IoT platforms implements tier-based network layers which primarily aid seamless communication among nodes. These network layers perform near-similar tasks of guaranteeing packet sensing and exchange although at often-higher energy requirement. To mitigate the energy concerns, packets are clustered and compressed, allowing exchange of essential information only. But the approach continues to present heavy packet-losses and/or redundancies. In this paper, two-tier layered network | where packet exchange is conducted at the top layer in order to lower energy consumption and promote system-reliability is investigated. All packets are first segregated into multiple clusters using the Voronoi cell-based correlation cluster formation (VC3F) technique. Cluster heads (CHs) are identified by their multi-path (M-Score) value, thus, assuming sole responsibility of redundant packet removal within each cluster. The redundant packets are then moved to the edge-tier layer using optimized multi-objective flower pollination (MO-FPO) routing, and finally processed using hybrid models of novel fast-fully connected neural network (F2CNN) accelerator and Lempel-Ziv-Welch (LZW) data compression. The F2CNN and LZW models are harmonized to further collectively explore potential benefits of both models. These benefits include the neural capability to work on the sensitivity level of the packets determined by the packet classification and validation approaches. The system is evaluated with detailed experimental investigations where higher system throughput, packet delivery ratio (PDR), end-to-end delay and system reliability are corroborated.