Resilient Service Embedding In IoT Networks

TL;DR

This paper introduces a new resilience framework for IoT networks that improves data recovery, reduces power consumption, and enhances network performance through a service-oriented architecture and optimized routing strategies.

Contribution

The paper presents a novel resilience scheme for IoT networks using a service-oriented architecture, optimized with mixed integer linear programming and heuristics.

Findings

Reduced power consumption compared to existing schemes

Lower data delivery time with the proposed splitting technique

Enhanced network performance through energy-efficient routing

Abstract

The Internet of Things (IoT) has been applied to a large number of heterogeneous devices and is used in the deployment of a variety of applications on the basis of its distributed open architecture. The majority of these IoT devices are battery-powered and are interconnected via a wireless network. IoT devices may be used to carry out critical tasks. Thus, the IoT network requires a resilient architecture that supports semantic search, failure discovery, data recovery, and dynamic and autonomous network maintenance. In this paper, we present a new resilience scheme for IoT networks. We evaluate the proposed scheme in terms of its power consumption and data delivery time, and then compare the results with those of recent resilience schemes such as schemes based on redundancy and replication. The proposed framework was optimized using mixed integer linear programming and real-time heuristics were developed, thus embedding a virtual layer into a physical layer based on a service-oriented architecture (SOA). The proposed framework offers different combinations of packet resilience in terms of recovering the lost data by using end- to-end mechanisms. We further analyzed these schemes by investigating the power consumption, data delivery time, and network overhead of these techniques. The results showed that the proposed splitting technique enhanced the network performance by reducing the power consumption and the data delivery time of service embedding by selecting energy-efficient nodes and routes in IoT networks.