An Efficient Probe-Based Routing for Content-Centric Networking

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is non-deterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up to date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(NCS + Nrt + NFIB ∗ NSPT) and O(NFIB) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service (QoS) category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.

An Efficient Content Store-Based Forwarding Scheme for Internet of Things

One of the main advantages of information-centric networking (ICN) is that a requested piece of content can be retrieved from a content store (CS) at any intermediate node, instead of its original content producer. In existing ICN designs, nodes forward Interest packets mainly based on forwarding information base (FIB). FIB is constructed from name prefixes registered by content producers with a list of next hops to the name prefixes. The ICN forwarding engine uses those information to forward Interest packets towards corresponding content producers. CS information of a node is currently used only for checking the availability of cached content objects at the node and is not considered in the data plane of existing ICN forwarding mechanisms. This paper highlights the importance of CS information in an ICN forwarding mechanism and enables neighbor CS information in the data plane to improve the cache hit ratio and forwarding efficiency, especially for resource-constraint Internet of Things (IoT). We propose an efficient CS-based forwarding scheme for IoT. The proposed forwarding scheme exploits CS information of neighbors to find efficient routes to forward Interest packets toward nearby nodes with corresponding cached content. For that, we carefully design an efficient way for CS information sharing using counting bloom filter. We implement the proposed scheme and compare with state-of-the-art ICN forwarding schemes in IoT. Experimental results indicate that the proposed forwarding scheme achieves a significant improvement in terms of cache hit ratio, energy efficiency, content retrieval latency, and response rate.

Towards Pending Interest Table Management Solutions in Named Data Networking

The transition of current Internet from host-centric communication to content-centric communication has been realized due to rapid growth in the number of users and their demands. Named Data Network (NDN) has emerged as Future Internet Architecture to meet the demands related to content oriented communication. Among other data structures such as Forwarding Information Base (FIB) and Content Store (CS), Pending Interest Table (PIT) holds record of the pending interests for which response has not yet received. Despite the benefits of PIT for fast packet forwarding (such as address less communication, loop detection, multi-path routing, packet aggregation, efficient bandwidth utilization), there are challenges faced by PIT. These challenges include name lookup speed, PIT memory consumption and PIT Entry Lifetime (PEL) that may render PIT as a bottleneck and hence will affect the system performance. In this paper, we thoroughly and systematically study the PIT management schemes. Our study covers the solutions for PIT management spanning over the last 7 years. As far as considering our learning, we are the first to study PIT based on both PIT memory size reduction to achieve fast look-up speed and different PEL calculation schemes. The study will help the research community in modeling better and optimized PIT management solutions. The paper also presents the research trends on the basis of different regions, publishing outlets, contributing patterns etc. in the field of PIT management in NDN.

On Sharing an FIB Table in Named Data Networking

As a new networking paradigm, Named Data Networking (NDN) technology focuses on contents, and content names are used as identifiers for forwarding and routing, as opposed to IP addresses in the current Internet. NDN routers forward packets by looking up a Forwarding Information Base (FIB), each entry of which has a name prefix and output faces. An FIB should have the information to forward Interest packets for any contents. Hence, the size of an FIB would be excessively large in NDN routers, and the traffic for building an FIB would be significant. In order to reduce the traffic associated with building an FIB table and memory requirement for storing an FIB table, this paper proposes a new efficient method which combines the routing of network connectivity and the building of a forwarding engine using Bloom filters. We propose to share the summary of an FIB using a Bloom filter rather than to advertise each name prefix. The forwarding engine of the proposed scheme is a combination of Bloom filters, and hence the memory requirement of the forwarding can be much smaller than the regular FIB. Simulation results using ndnSIM under real network topologies show that the proposed method can achieve nearly the same performance as the conventional link state algorithm with 6–8% of the traffic for distributing the connectivity information and 5–9% of the memory consumption.