Abstract

Information Centric Networks (ICNs) have been considered one of the most promising candidates to overcome the disadvantages of host-centric architectures when applied to IoT networks, having the potential to address the challenges of a smart city. One of the foundations of a smart city is its sensory capacity, which is obtained through devices associated with the IoT concept. The more sensors spread out, the greater the ability to sense the city. However, such a scale demands high energy requirements and an effective improvement in the energy management is unavoidable. To improve the energy management, we are proposing an efficient forwarding scheme in energy-constrained wireless ICNs. To achieve this goal, we consider the type of devices, their internal energy and the network context, among other parameters. The proposed forwarding strategy extends and adapts concepts of ICNs, by means of packet domain analysis, neighbourhood evaluation and node sleeping and waking strategies. The proposed solution takes advantage of the neighbourhood to be aware of the moments to listen and forward packets in order to consistently address mobility, improving the quality of content delivery. The evaluation is performed by simulation with real datasets of urban mobility, one from the lagoon of “Ria de Aveiro” and the other from a vehicular network in the city of Porto. The results show that the proposed forwarding scheme resulted in significant improvements in network content availability, in the overall energy saving and, consequently, in the network lifetime.

Highlights

  • Received: 28 January 2022In recent years we have seen a radical change in the way devices connect to the Internet.The best example is the Internet of Things (IoT), a recent communication paradigm in which objects of everyday life will be able to, among others, communicate with one another, becoming an integral part of the Internet

  • These features increase the challenge in building efficient energy management solutions for Information Centric Networks (ICNs), as traditional solutions for Internet Protocol (IP) networks are of little value in ICN-based environments

  • The remainder of this paper is organised as follows: Section 2 discusses the related work; Section 3 shows the basic characteristics of the ICN paradigm; Section 4 details the proposed efficient forwarding in relation with energy management; Section 5 describes the scenarios and metrics and discusses the results obtained by simulation using real mobility traces; and, Section 6 enumerates the conclusions and points out the future work

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Summary

Introduction

ICNs enable the deployment of in-network caching and content replication, facilitating the efficient and timely delivery of information These features increase the challenge in building efficient energy management solutions for ICNs, as traditional solutions for IP networks are of little value in ICN-based environments. The proposed forwarding strategy extends and adapts concepts of ICNs, by means of packet domain analysis, neighbourhood evaluation and node sleeping and waking strategies to increase the energy saving and reduce the use of resources in unnecessary situations. This strategy is important in this scenario because IoT nodes may have small and limited batteries. The remainder of this paper is organised as follows: Section 2 discusses the related work; Section 3 shows the basic characteristics of the ICN paradigm; Section 4 details the proposed efficient forwarding in relation with energy management; Section 5 describes the scenarios and metrics and discusses the results obtained by simulation using real mobility traces; and, Section 6 enumerates the conclusions and points out the future work

Related Work
ICN Basics
Energy-Efficient Wireless NDN Architecture
Interfaces and Mobility Module
Neighbourhood Status
Node Heading and Direction
Energy Consumption Module
Forwarding and Content Discovery Module
Content Priority
Energy-Efficient Forwarding
Performance Evaluation
Satisfied Interests Rate
Transmission Delay
Network Overhead
Energy Consumption
Findings
Conclusions
Full Text
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