Forwarding in Energy-Constrained Wireless Information Centric Networks

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.

Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments

“…Each PIT entry contains the name of the Interest packet and the respective interfaces through which it arrived at the node. On the other hand, the CS stores the data packets and works as a buffer managed by caching policies [3,23]. In short, in Named Data Networks the contents are independent of their physical location, such as IP addresses, being accessible from anywhere on the network.…”

“…Our NDN solution was specially designed for IoT infrastructures taking advantage of mobility prediction information. In our project, we developed improvements regarding the Forwarding [3], Caching and Energy-efficiency [23], as well as exploring mobilityaware prediction capabilities [4]. For all these developments, we have used the NDN implementation available in the ndnSIM network simulator, an open source NS-3-based NDN simulator [25].…”