Interest Packets Research Articles

A Contextual Multi-Armed Bandit approach for NDN forwarding

Named Data Networking (NDN) is a promising Internet architecture that aims to supersede the current IP-based architecture and shift the host-centric model to a data-centric one. Within NDN, forwarding Interest packets remains a significant challenge and has attracted considerable recent research attention. The momentum behind machine learning techniques, especially reinforcement learning, is steadily growing, offering the potential to deliver intelligent, adaptable, and reliable NDN forwarding algorithms. In this context, this paper proposes efficient NDN forwarding strategies based on Contextual Multi-Armed Bandit (CMAB). Initially, we employ CMAB to address the challenge of forwarding Interest packets and introduce a new CMAB model tailored for NDN, dubbed CMAB4NDN. Subsequently, we construct the CMAB context using information derived from the content name and the network congestion state, which are then fed into the CMAB4NDN learning algorithm to decide on the best forwarding action. Further, we develop three CMAB strategies, namely Lin-ɛ-Greedy, Linear Upper Confidence Bound, and Contextual Thompson Sampling, and deploy them within our proposal. CMAB4NDN was implemented in ndnSIM, thoroughly evaluated, and compared with multiple state-of-the-art NDN forwarding algorithms across various scenarios. The obtained results confirm the relevance and superiority of our approach in terms of delay, throughput, and packet loss.

Evolutionary game based interest forwarding in information centric vehicular networks

In Information Centric Network (ICN) a client generates an interest packet when it is in need of a content. The interest packet carries the content name and propagates through the network till it reaches an appropriate cached content store or the producer of the content. Retrieval of the searched content is faster if the interest packet is forwarded in right direction towards a probable content location. An efficient and optimal interest forwarding technique is the key requirement for the success of any ICN implementation. This paper describes an interest forwarding approach for information centric vehicular networks based on evolutionary game theory. It uses the Public Goods Gaming (PGG) strategy to forward interest packets so that the content can be located at the earliest. The interest packets are modeled as goods and various participating entities are categorized either as cooperator or defector. The purpose of the game is to deliver the goods (the interest packet) to the rightful content store. Cooperator tries to optimally deliver the interest packets and hence they are rewarded with certain incentives. However, defectors are not rewarded as they do not participate in the game. At the beginning, a player selects a strategy and confined to it till the completion of the game. During interest forwarding, a node selects its neighbor(s) having higher credits as next level forwarder. The scheme is analyzed mathematically to establish various claims made in this paper. The mathematically established claims are also validated through simulation in ns-3 based ndnSIM-2.0. The model is compared with four other existing ICN forwarding approaches and simulation results depict that the new algorithm performs better.

NDN Security: Cryptographic Approaches for Safeguarding Content-Centric Networking against Threats

The future of the internet demands innovative approaches to communication paradigms and security mechanisms. Named Data Networking (NDN) introduces a ground breaking data-centric communication paradigm, shifting the focus from host-centric networking to efficient content retrieval. This novel framework relies on the dynamic interplay of Interest and Data packets to enable seamless content dissemination and caching. NDN's security framework, built upon digital keys, certificates, and trust policies, ensures the integrity and confidentiality of data transactions within its ecosystem. Cryptographic keys exchanged in real-time through Interest and Data packets, forming the bedrock of secure interactions within NDN. This dynamic key exchange mechanism bolsters security and facilitates controlled data generation. Certificates play a pivotal role in establishing trust, solidifying the connection between names and public keys, while trust policies align with application-defined guidelines to govern key-data entity associations. This paper delves into the security challenges that NDN effectively addresses. By investigating vulnerabilities and assessing the susceptibility of common attacks, it underscores the resolute defense mechanisms inherent in the NDN framework. The inherently anonymous and non-identifying nature of NDN renders eavesdropping and traffic analysis infeasible, bolstering confidentiality. Digital signatures provide robust data integrity, thwarting any attempts at unauthorized modification. The comprehensive data signing mechanism effectively prevents masquerade attacks, and the integration of unique naming and nonce in Interest packets averts replay attacks. In essence, this research not only sheds light on the essential security components of NDN but also illuminates how this paradigm addresses a multitude of security challenges. NDN's forward-thinking content-centric approach and its robust security mechanisms hold the potential to reshape networking paradigms. As we look to the future of the internet, NDN stands as a promising contender for advancing secure, efficient, and transformative networking models.

Distributed Mobility Management Support for Low-Latency Data Delivery in Named Data Networking for UAVs

Named Data Networking (NDN) has emerged as a promising architecture to overcome the limitations of the conventional Internet Protocol (IP) architecture, particularly in terms of mobility, security, and data availability. However, despite the advantages it offers, producer mobility management remains a significant challenge for NDN, especially for moving vehicles and emerging technologies such as Unmanned Aerial Vehicles (UAVs), known for their high-speed and unpredictable movements, which makes it difficult for NDN to maintain seamless communication. To solve this mobility problem, we propose a Distributed Mobility Management Scheme (DMMS) to support UAV mobility and ensure low-latency content delivery in NDN architecture. DMMS utilizes decentralized Anchors to forward proactively the consumer’s Interest packets toward the producer’s predicted location when handoff occurs. Moreover, it introduces a new forwarding approach that combines the standard and location-based forwarding strategy to improve forwarding efficiency under producer mobility without changing the network structure. Using a realistic scenario, DMMS is evaluated and compared against two well-known solutions, namely MAP-ME and Kite, using the ndnSIM simulations. We demonstrate that DMMS achieves better results compared to Kite and MAP-ME solutions in terms of network cost and consumer quality-of-service metrics.

Reinforcement learning inspired forwarding strategy for information centric networks using Q‐learning algorithm

SummaryContent interest forwarding is a prominent research area in Information Centric Network (ICN). An efficient forwarding strategy can significantly improve data retrieval latency, origin server load, network congestion, and overhead. The state‐of‐the‐art work is either driven by flooding approach trying to minimize the adverse effect of Interest flooding or path‐driven approach trying to minimize the additional cost of maintaining routing information. These approaches are less efficient due to storm issues and excessive overhead. Proposed protocol aims to forward interest to the nearest cache without worrying about FIB construction and with significant improvement in latency and overhead. This paper presents the feasibility of integrating reinforcement learning based Q‐learning strategy for forwarding in ICN. By revising Q‐learning to address the inherent challenges, we introduce Q‐learning based interest packets and data packets forwarding mechanisms, namely, IPQ‐learning and DPQ‐learning. It aims to gain self‐learning through historical events and selects best next node to forward interest. Each node in the network acts as an agent with aim of forwarding packet to best next hop according to the Q value such that content can be fetched within fastest possible route and every action returns to be a learning process, which improves the accuracy of the Q value. The performance investigation of protocol in ndnSIM‐2.0 shows the improvement in a range of 10%–35% for metrics such as data retrieval delay, server hit rate, network overhead, network throughput, and network load. Outcomes are compared by integrating proposed protocol with state‐of‐the‐art caching protocols and also against recent forwarding mechanisms.

MsDD: A novel NDN producer mobility support scheme based on multi-satellite data depot.

The Named Data Networking (NDN) is currently an important future network framework, and the mobility issue of producers within NDN is a primary challenge. However, in environments characterized by frequent producers mobility, traditional producer mobility support schemes still encounter issues such as excessive consumer delays and interest packet loss. With the development of The 6th generation communication technology (6G), integrating ground networks with satellites has emerged as a potential solution to address the aforementioned problems. In this paper, we propose an NDN producer mobility support scheme based on multi-satellite data depot, named MsDD. The proposed scheme proactively caches producer data into a data depot built on a low-earth orbit satellite constellation to minimize the impact of NDN producer mobility on network performance. We design data depot construction strategy, in-network caching strategy, and routing strategy based on forwarding hint to facilitate effective communication in satellite networks. Experimental results using ndnSIM demonstrate that compared with other existing schemes, MsDD can effectively shield the impact of producer mobility on consumer delay, delivery ratio, and signaling overhead, and MsDD has a clear advantage in terms of consumer delay and delivery ratio.

Hop Count Based Interest Selection and Content Forwarding Scheme for Vehicular Named Data Networks

Vehicular Named Data Networks (VNDN) face challenges in efficiently disseminating content due to high mobility and intermittent connectivity. To address these challenges, a Hop Count based Interest Selection and Content Forwarding (HISCF) scheme for VNDNs is proposed. The scheme focuses on mitigating interest flooding, reducing data packet duplication, and alleviating network congestion. HISCF consists of two components: interest selection and content forwarding. The selection process chooses a vehicle based on hop count and Interest Satisfaction Ratio (ISR) to forward the interest packet. Content forwarding is performed considering a hop count limit and pending interests, ensuring efficient content delivery. The HISCF scheme is evaluated using extensive simulations in ns-3 with ndnSIM. Performance metrics such as Data Packet Replication Count (DPRC), total number of interest packets forwarded, Interest Response Time (IRT) and routing overhead are analysed. Results show that HISCF outperforms naïve VNDN, reducing DPRC, minimizing interest packets forwarded, and decreasing average IRT. The findings demonstrate that HISCF effectively mitigates interest broadcast storms, reduces data packet duplication, and improves content delivery efficiency in VNDNs. This study contributes to VNDN research advancement and provides insights for designing effective content forwarding mechanisms in vehicular networks.

ENCache: Improving content delivery with cooperative caching in Named Data Networking

Content centric networks offer better response time and resilience to failures by caching the content in the network routers. Owing to the limited capacity of caches and the quest for maximizing the utilization of available caches has lead to many interesting caching techniques. While on-path caching is a commonly adopted caching technique to reduce content access latency, it does not guarantee better cache hit ratio when considered across multiple receivers in the network. In this paper, we present eNCache which is a cooperative caching method that cooperates among the routers in the neighborhood to improve overall performance. It retains the simplicity of on-path caching and makes caching, routing (queries and replies) decisions by interacting with off-path routers in the neighborhood. We present the working of eNCache with algorithms for request handling and caching decisions at every router. We also formalize the interaction between a router and its neighbors by providing an extension to the Named Data Networking Interest packet structure. We evaluate and compare the performance of eNCache with other popular on-path caching strategies by performing simulations on standard RocketFuel Internet Service Provider (ISP) topologies using a discrete event simulator. We also compare the performance of a variant of enhanced NCache, i.e.,eNCache with hash based off-path caching methods. Our simulation results show that eNCache has reduced latency, better cache hit ratio and diversity.

Dimensioning the pending interest table in content-centric networks

In chunk-based interest-driven content-centric networks, each interest packet forwarded upstream by a node on a face implies the return of at most one data chunk to the node from that face shortly after. As a consequence, the congestion of the downstream transmission buffer in the data path of the node is highly correlated with the occupancy of the pending interest table (PIT). Therefore a systematic study and analysis of the PIT occupancy are of paramount importance to understanding congestion in CCN. In particular, in this paper, we propose an analytical model to estimate the PIT occupancy distribution via a continuous-time Markov chain (CTMC) model that considers the effects of interest blocking, interest timeout and retries. To validate our model and assumptions, we invoke simulation with realistic traffic streams and show how the filtering effects of caching and interest aggregation make the Markov assumption reasonable in the nodes that are the most susceptible to experiencing congestion. To solve the model numerically, we use two alternative approximations, state space truncation and state aggregation, and then give some numerical results to demonstrate the accuracy of our approximations.

AM-IF: Adaptive Multi-Path Interest Forwarding in named data networking

Named data networking (NDN) have been proposed as a new solution to overcome the limitation of TCP/IP architecture. In the proposed designs, researchers have not paid enough attention to the concept of forwarding strategy in NDN, which is a crucial part of this method. An efficient forwarding strategy is essential to maximize the benefits of multiple concurrent connections. To fill this gap, an innovative forwarding strategy called Adaptive Multi-Path Interest Forwarding (AM-IF) strategy is presented in this paper. In the proposed strategy, when a router receives an Interest packet, it checks if there is a sufficient number of paths to the requested content on servers or cache of routers. In case of a lack of enough paths for the Interest packet, AM-IF explores new paths by redirecting the Interest request through all ports of the neighbor routers, and data regarding the detected paths are collected. This data enables AM-IF to identify and select the best paths by computing the degrees of all discovered paths based on the paths’ delay, available bandwidth, and disjointness. After the selecting of the best-detected path, the process of transferring data across multiple paths is started where the quota of each path depends on the efficiency of these paths. In the proposed strategy, less load is allocated to congested paths by monitoring the status of paths. By comparing the proposed strategy with other strategies using the simulation results extracted from ndnSIM, we can conclude that our approach performs better in delivering the content. The proposed strategy improves parameters such as network load balancing, average throughput, average packet drop, average Interest Satisfaction Ratio, and average content retrieval time at a reasonable computational cost.

Lightweight Transmission Behavior Audit Scheme for NDN Industrial Internet Identity Resolution and Transmission Based on Blockchain

The Named Data Network (NDN) enables efficient content dissemination through interest-based retrieval, name-based routing, and content caching. In the industrial Internet architecture based on NDN, device identity distribution, identification, resolution, and routing rely on identification resolution technology. However, this approach presents challenges such as cache poisoning, interest packet flood attacks, and black hole attacks. Existing security schemes primarily focused on routing forwarding and verification fail to address critical concerns, including routing environment credibility and data leakage, while exhibiting poor time and space efficiency. To address these challenges, this paper proposes a lightweight behavior auditing scheme using blockchain technology. The scheme utilizes an improved Bloom filter to compress behavioral information like interest and data packets during the identification transmission process. The compressed data are subsequently uploaded to a blockchain for auditing, achieving efficient space and time utilization while maintaining feasibility.

IBPC: An Approach for Mitigation of Cache Pollution Attack in NDN using Interface-Based Popularity

The performance of named data networking (NDN) depends on the caching efficiency of routers. Cache pollution attack (CPA) refers to colonization of unpopular contents in the content store (CS) of an NDN router, which leads to declined quality of service (QoS) in NDN. CPA has very few solutions proposed for its mitigation. Most of these solutions are based on the statistics of the router itself. However, an attacker can influence these statistics by requesting unpopular contents repeatedly. This article proposes a new parameter for the detection of CPA, which is based on the number of distinct users requesting interest packets for a content over a period of time. The local popularity of the attackers’ content does not affect the proposed approach. The results show that the proposed approach consumes less storage, reduces processing time, and more effectively mitigates the CPA, as compared to the other existing approaches. Compared with the previous approaches, the proposed approach exhibits an improvement of approximately 28.14% to 36.80%.

Efficient congestion control scheme based on caching strategy in NDN

As a pioneering network architecture, Named Data Networking (NDN) leverages the content-centric model and connectionless transmission mode to enhance network capacity. Despite the emergence of numerous congestion control protocols aimed at improving NDN transmission efficiency, most of these approaches implicitly assume that each interest packet elicits one single data packet, which significantly restricts network performance. To address this issue, we introduce C3NDN, a Congestion Control scheme that leverages caching strategy in NDN. C3NDN formulates a One-Interest-Multiple-Data model to improve network efficiency, and employs a probabilistic caching strategy to cache popular content at important nodes and optimize the in-network caching characteristic of NDN. Furthermore, C3NDN incorporates a congestion control algorithm based on the One-Interest-Multiple-Data model, which considers the bandwidth and delay information of the transmission path. Additionally, C3NDN implements a marking method that takes full advantage of node cache and reduces transmission time. Extensive experiments have been conducted to show the performance of C3NDN, and the results demonstrate that C3NDN can reduce the transmission time by up to 41.68% when compared with the other congestion control schemes.

An Efficient Pending Interest Table Content Search In NDN through Stable Bloom Filter

AbstractNamed Data Networking (NDN) has gained importance in today’s era due to a paradigm shift in the Internet usage pattern which revolves around the content rather than the respective host addresses. Three important data structures in NDN are Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB). The search time of PIT is quite high since its size grows with the addition of new content names, and the Interest packets which are not served by CS are searched in millions of existing entries in the PIT. Hence, lookup time can be improved if, instead of checking all the available entries, initial scanning is done to determine whether the required content name exists in the PIT or not. In this paper, we propose a Stable Bloom Filter (SBF) based PIT called S-PIT, to minimize the PIT search time by identifying the existence of query content through SBF. The various experiments performed show that S-PIT outperforms existing data structures in terms of memory consumption, content insertion time, average search time and false positive rate.

Towards Predictive Forwarding Strategy in Vehicular Named Data Networking

Vehicular Named Data Networking (V-NDN) is promising to improve the content delivery efficiency in Vehicular Ad Hoc Networks (VANETs). However, the potential broadcast storm caused by Interest packet flooding and return path failures caused by vehicle mobility can significantly degrade the content delivery performance. Existing forwarding strategies based on outdated position information cannot address these issues well. In this paper, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predictive forwarding strategy</i> (PRFS) for V-NDN. In PRFS, Long Short-Term Memory (LSTM) is employed to amend the NeighBor Table (NBT) for preciser neighboring vehicles' positions. In addition, the next-hop forwarder is selected among the neighbors, taking into account the link reliability and the Distance along the Road (DR) in both directions. Furthermore, a new mechanism is designed to notify the selected next-hop forwarder by embedding the forwarder identity in the Interest packet header, so as to accelerate the forwarding process. Finally, extensive simulations are carried out, and experimental results demonstrate that PRFS can reduce the number of forwarded Interest packets and data packets by 21.29% and 25.75%, respectively, and improve the success ratio of satisfied Interest packets by 35.1% compared to the existing baseline algorithms.

MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking.

The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead.

Homomorphic encryption based privacy-aware intelligent forwarding mechanism for NDN-VANET

Machine learning has been widely used for intelligent forwarding strategy in Vehicular Ad-Hoc Networks (VANET). However, machine learning has serious security and privacy issues. BRFD is a smart Receiver Forwarding Decision solution based on Bayesian theory for Named Data Vehicular Ad-Hoc Networks (NDN-VANET). In BRFD, every vehicle that received an interest packet is required to make a forwarding decision according to the collected network status information. And then decides whether it will forward the received interest packet or not. Therefore, the privacy information of a vehicle can be revealed to other vehicles during information exchange of the network status. In this paper, a Privacy-Aware intelligent forwarding solution PABRFD is proposed by integrating Homomorphic Encryption (HE) into the improved BRFD. In PABRFD, a secure Bayesian classifier is used to resolve the security and privacy issues of information exchanged among vehicle nodes. We informally prove that this new scheme can satisfy security requirements and we implement our solution based on HE standard libraries CKKS and BFV. The experimental results show that PABRFD can satisfy our expected performance requirements.

An efficient producer mobility management technique for real-time communication in NDN-based Remote Health Monitoring systems

The remote Health Monitoring (RHM) system is a remote healthcare platform that provides real-time monitoring services and brings convenience and safety for doctors as well as patients. Named Data Network (NDN) is a future network architecture, which is a kind of Information-Centric Network. NDN’s advantages in data security and performance can make the health system more reliable and efficient. However, in the existing NDN network architecture, there exist some producer mobility issues that can impede the proceeding towards real-time and seamless communication. To solve the problem, a Neighborhood Registration Scheme (NRS) is proposed here. When the mobile producer moves from one AP to another, it has not registered into the new AP and thus cannot receive the Interest Packet (IntP) from the consumer in time, which can cause data losses. Meanwhile, the updating process in the NDN map also takes a considerable time, which can also cause delay, referred to as updating delay. This is due to some consumers having got the new location of the mobile producer while others have not. The scheme proposes in this paper is to register the mobile producer not only into the nearest Access Point (AP) but also multiple neighbor APs. In this way, the delay caused by AP switching and package loss rate can be effectively reduced, which allows mobile producers to move across the cells with low latency and Packet Loss (PL) rate. NRS is a comprehensive routing algorithm that designs the modified packet transmission strategy. Compare to the Anchor-less strategy, the proposed NRS decreases about 80% PL rate and saves 16% delay time, whereas, NRS reduces more than 90% PL rate and saves 40% delay time than Hop-count strategy in high-speed producer mobility scenarios. These improvements are necessary for RHM because low delay and PL are the most important requirements of the RHM that can strengthen the real-time monitoring for patients, information interaction between doctors and patients, and safety assurance. By setting the proper parameters, NRS can promise to have no PL and acceptable network cost.

An Advanced Hierarchical Identity-Based Security Mechanism by Blockchain in Named Data Networking

Named data networking (NDN) has been viewed as a promising future Internet architecture due to its data-centric design. It requires a new security model that is orienting data but not devices. In this paper, an advanced hierarchical identity-based security mechanism by blockchain (AHISM-B) is to be proposed for the NDN networks. On one hand, the hierarchical identity-based cryptology is used to bind the data name to a public key. The valid public parameters would be requested by consumers with the Interest packets so that consumers would compose producers’ public keys to authenticate producers and verify the integrity of the Data packets. On the other hand, a blockchain is employed to manage public parameters to avoid catastrophes due to a single node failure. Both of the security proof result and the formal validation result indicate that the proposed AHISM-B is secure. Moreover, the simulation results show that the performance of our AHISM-B outperforms that of the classic NDN scheme. Especially, the average response delay of the AHISM-B scheme is less by 8% than that of the classic NDN scheme. With the increase of the average arrival rate of Interest packets, the advantage of the AHISM-B could be enhanced further to 11%.

Hybrid 802.11p‐cellular architecture for NDN‐based VANET

SummaryVehicular network communications (VANET) face multiple challenges due to their intermittent connections and the rapid changes in their topologies. In recent years, several research efforts have explored the use of content‐centric approaches to alleviate some of these challenges. One of these promising network designs is Named Data Networking (NDN), which has become a valid solution to support VANET applications. However, in the NDN architecture, the main forwarding mechanism for the interest packets is flooding. This forwarding mechanism will result in excessive collisions, which will lead to the broadcast storm problem. In this paper, we propose VC‐NDN: a hybrid and hierarchical Named Data Networking architecture for VANETs. VC‐NDN improves content retrieval efficiency through an adapted NDN‐based communication model. VC‐NDN includes a new interest forwarding scheme to reduce packet collision in the network and an efficient mechanism to support push‐based traffic. Furthermore, to reduce communication costs, VC‐NDN uses two communication technologies in parallel, namely, IEEE 802.11p and cellular communications, while keeping the usage of the cellular network at a minimum level. Finally, to reduce the impact on vehicle mobility, VC‐NDN follows a hierarchical clustering architecture. Specifically, a density‐based clustering algorithm is designed to create and maintain stable clusters with multihop communication capability. Our performance study shows that VC‐NDN outperforms the basic VNDN solutions in terms of data retrieval delay and packet delivery ratio while minimizing the usage of the cellular network.