Energy Efficient IoT Virtualization Framework with Peer to Peer Networking and Processing

TL;DR

This paper proposes an energy-efficient IoT virtualization framework utilizing P2P networking and edge computing, comparing relay, object, and hybrid scenarios to optimize task processing and power consumption.

Contribution

It introduces a novel MILP model and a heuristic for optimizing energy efficiency in IoT P2P networks with virtualized edge computing.

Findings

Hybrid scenario processes up to 77% of tasks but consumes more energy.

Relays only scenario saves 8% power over hybrid.

Objects only scenario saves up to 62% power with 28% task processing.

Abstract

In this paper, an energy efficient IoT virtualization framework with P2P networking and edge computing is proposed. In this network, the IoT task processing requests are served by peers. The peers in our work are represented by IoT objects and relays that host virtual machines (VMs). We have considered three scenarios to investigate the saving in power consumption and the system capabilities in terms of task processing. The first scenario is the relays only scenario, where the task requests are processed using relays only. The second scenario is the objects only scenario, where the task requests are processed using the IoT objects only. The last scenario is a hybrid scenario, where the task requests are processed using both IoT objects and VMs. We have developed a mixed integer linear programming (MILP) model to maximize the number of processing tasks served by the system and minimize the total power consumed by the IoT network. We investigated our framework under the impact of VMs placement constraints, fairness constraints between the objects, tasks number limitations, uplink and downlink limited capacities, and processing capability limitations. Based on the MILP model principles, we developed an energy efficient virtualized IoT P2P networks heuristic (EEVIPN). The heuristic results were comparable to those of the MILP in terms of energy efficiency and tasks processing. Our results show that the hybrid scenario serves up to 77% (57% on average) processing task requests, but with higher energy consumption compared to the other scenarios. The relays only scenario can serve 74% (57% on average) of the processing task requests with 8% saving in power consumption compared to the hybrid scenario. In contrast, 28% (22% on average) of task requests can be successfully handled by applying the objects only scenario with up to 62% power saving compared to the hybrid scenario.