A Comparative Analysis of Bloom Filter-based Routing Protocols for Information-Centric Networks

Bloom filter-based routing protocols for Named Data Networking (NDN) aim at facilitating content discovery in NDN. In this paper, we compare the performance of two Bloom filter-based routing protocols, namely BFR and COBRA. BFR is a push-based routing protocol that works based on Bloom filter-based content advertisements, while COBRA is a pull-based routing protocol that operates based on route traces left from previously retrieved content objects, which are stored in Stable Bloom Filters. In this paper, we show that BFR outperforms COBRA in terms of average memory needed for storing routing updates, average round-trip delay, normalized communication overhead, total Interest communication overhead, and mean hit distance

An efficient pending interest table control management in named data network

Named Data Networking (NDN) is an emerging Internet architecture that employs a new network communication model based on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves a significant role of recording Interest packet information which is ready to be sent but in waiting for matching Data packet. In managing PIT, the issue of flow PIT sizing has been very challenging due to massive use of long Interest lifetime particularly when there is no flexible replacement policy, hence affecting PIT performance. The aim of this study is to propose an efficient PIT Control Management (PITCM) approach to be used in handling incoming Interest packets in order to mitigate PIT overflow thus enhancing PIT utilization and performance. PITCM consists of Adaptive Virtual PIT (AVPIT) mechanism, Smart Threshold Interest Lifetime (STIL) mechanism and Highest Lifetime Least Request (HLLR) policy. The AVPIT is responsible for obtaining early PIT overflow prediction and reaction. STIL is meant for adjusting lifetime value for incoming Interest packet while HLLR is utilized for managing PIT entries in efficient manner. A specific research methodology is followed to ensure that the work is rigorous in achieving the aim of the study. The network simulation tool is used to design and evaluate PITCM. The results of study show that PITCM outperforms the performance of standard NDN PIT with 45% higher Interest satisfaction rate, 78% less Interest retransmission rate and 65% less Interest drop rate. In addition, Interest satisfaction delay and PIT length is reduced significantly to 33% and 46%, respectively. The contribution of this study is important for Interest packet management in NDN routing and forwarding systems. The AVPIT and STIL mechanisms as well as the HLLR policy can be used in monitoring, controlling and managing the PIT contents for Internet architecture of the future

Review of name resolution and data routing for information centric networking

Information Centric Networking (ICN) a future Internet, presents a new paradigm by shifting the current network to the modern network protocols. Its goal, to improve the traditional network operations by enabling ICN packet routing and forwarding based on names.This shift will bring advantages, but at the same time, it is leading to a big challenge on routing approaches to implement ICN nodes. Routing approaches must use special techniques to publish messages to all the network nodes.Flooding approach is an easy and stateless, however, results in control overhead, depending on the network size.Moreover, designing, implementing, and evaluating routing approaches with higher capacity is really a key challenge in the overall ICN research area, because the state of ICN brings a significant cost; both in packet processing and router storage.Many approaches were proposed in the literatures over these years for the efficient control of forwarding on the network.This paper provides a classification and review of the routing mechanisms that are proposed on six ICN architectures.A summary in tabular form and a comparative study of these six architectures is also given in the paper as well as few open research challenges are highlighted

A Scalable Name Resolution System for Information Centric Networking

Information Centric Networking (ICN) is a new paradigm, aimed at shifting to the future Internet from host centric to a content centric approach. ICN focuses on retrieval and dissemination of information between pairwise communications of hosts. Information are organized in the form of Information Objects (IO), known as Named Data Objects (NDO). These NDO are location independent. Objects in ICN are stored in the system overlay; popularly known as Name Resolution System (NRS). NDOs are requested by the Subscribers in the network to get the needed information from the Publishers, through NRS. Thus, the NRS is responsible in forwarding the interest packets based on the names of NDOs. This application of ICN depends on the scalability of the NRS. To design NRS, the most significant issue is scalability due to the ever-increasing number of NDOs. This paper aims to present the issues, by proposing balanced binary tree data structure to organize and store the NDOs. The methodology proposed in this work is thus; for every new insertion in the tree, a Balance Factor (BF) is computed to balance the height of left and right sub-tree. According to our investigation, balanced binary tree provides less searching time when compared to the Distributed Hash Table (DHT) approach. Simulation results show that End-to-End delay decreases by increasing the throughput in the network

Private and Mobile inter-network routing for Wireless Sensor Networks and Internet of Things

In the last few years, using the Internet of Things has been expanded in many areas, such as environmental monitoring, industries, and smart home. Since the Internet of Things has a direct relation to human life, its security is of paramount importance. Therefore, the communication between the nodes should be secured and the valuable private information should be kept private so that the attacker cannot detect the network structure. This article provides a protocol that can handle routing privately. To do this, we use the data structure called Spatial Bloom Filter (SBF). In addition, the proposed protocol uses random identifiers instead of IP addresses, so that an attacker cannot collect network structure information and location of nodes from IP addresses. Using a homomorphic encryption scheme, the protocol prevent attackers from retrieving valuable network information, if they can infiltrate to one or more network nodes. Also, since almost all nodes in the internet of things are mobile, the structure of networks and subnets is constantly changing. The proposed protocol has the ability to manage to route in networks with a dynamic structure

A Taxonomy of Information-Centric Networking Architectures based on Data Routing and Name Resolution Approaches

This study presents a vast coverage of current Information-Centric Network (ICN) submission by evaluating eight distinct and popular routing and name resolution approaches. Internet build-up and initial deposition were based on a host-driven approach. With the increasing demands for mediadriven data flooding the cost of the Internet, a new semantic and paradigm shift was envisioned known as ICN. InformationCentrism is an approach that partly dissociates the host dependencies by referring to contents by unique identifiers called name. However, to benefit from the content network, forwarding, naming and routing, among other issues are still in its developmental stages. The taxonomy serves as a basis for research directions, challenges, implementation and future studies for standardizing the ICN routing and naming. Routing and Name Resolution were themed in categories of strategies, contributions, issues and drawbacks. The major findings of this paper are providing a classification and review of the data routing and name resolutions approaches that are proposed on eight ICN architectures; presenting drawback areas in the selected architectures; and finally highlighting some challenges of ICN routing for the ICN research community vending

A novel approach for energy- and memory-efficient data loss prevention to support Internet of Things networks

Internet of Things integrates various technologies, including wireless sensor networks, edge computing, and cloud computing, to support a wide range of applications such as environmental monitoring and disaster surveillance. In these types of applications, IoT devices operate using limited resources in terms of battery, communication bandwidth, processing, and memory capacities. In this context, load balancing, fault tolerance, and energy and memory efficiency are among the most important issues related to data dissemination in IoT networks. In order to successfully cope with the abovementioned issues, two main approaches—data-centric storage and distributed data storage—have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the abovementioned problems, the proposed solutions typically focus on one issue at a time. In this article, we propose a cross-layer optimization approach to increase memory and energy efficiency as well as support load balancing. The optimization problem is a mixed-integer nonlinear programming problem, and we solve it using a genetic algorithm. Moreover, we integrate the data-centric storage features into distributed data storage mechanisms and present a novel heuristic approach, denoted as Collaborative Memory and Energy Management, to solve the underlying optimization problem. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms the existing approaches in various IoT scenarios