Content-Centric Networking Research Articles

Corrections to “A Neighborhood Aware Caching and Interest Dissemination Scheme for Content Centric Networks”

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Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning.

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN.

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

Optimization of content-centric networking based IoT systems using partitioning-heuristics in-network caching

Optimization of content-centric networking based IoT systems using partitioning-heuristics in-network caching

Minimizing delay in content-centric networks using heuristics-based in-network caching

Minimizing delay in content-centric networks using heuristics-based in-network caching

Mitigating Broadcasting Storm Using Multihead Nomination Clustering in Vehicular Content Centric Networks

Vehicles are highly mobile nodes; therefore, they frequently change their topology. To maintain a stable connection with the server in high-speed vehicular networks, the handover process is restarted again to satisfy the content requests. To satisfy the requested content, a vehicular-content-centric network (VCCN) is proposed. The proposed scheme adopts in-network caching instead of destination-based routing to satisfy the requests. In this regard, various routing protocols have been proposed to increase the communication efficiency of VCCN. Despite disruptive communication links due to head vehicle mobility, the vehicles create a broadcasting storm that increases communication delay and packet drop fraction. To address the issues mentioned above in the VCCN, we proposed a multihead nomination clustering scheme. It extends the hello packet header to get the vehicle information from the cluster vehicles. The novel cluster information table (CIT) has been proposed to maintain several nominated head vehicles of a cluster on roadside units (RSUs). In disruptive communication links due to the head vehicle’s mobility, the RSU nominates the new head vehicle using CIT entries, resulting in the elimination of the broadcasting storm effect on disruptive communication links. Finally, the proposed scheme increases the successful communication rate, decreases the communication delay, and ensures a high cache success ratio on an increasing number of vehicles.

Software-Defined Content Dissemination Scheme for Internet of Healthcare Vehicles in COVID-Like Scenarios

The exponential increase in the number of connected devices in the network led to a paradigm shift from the traditional host-centric IP-based network architecture to a content-based paradigm, which eliminates the address-content bindings in traditional IP-based architectures. In content-centric networking (CCN) architecture, contents are accessed by name rather than the physical location where these are stored, which results in more flexible and robust content-based architecture. However, the broadcast nature of devices in CCN causes network congestion and latency. The Internet of Healthcare Vehicles (IoHV) is an evolving paradigm that relies on smart transportation involving ambulances and other healthcare vehicles (for rapid COVID testing) to connect with each other through the Internet, especially in case like COVID 19 testing and contact tracing. However, the applications of IoHV are not similar to other networks and therefore bring new technical challenges due to high mobility and rapid changes of topology. Therefore, to handle these challenges, in this article, we propose a novel scheme that finds an optimal path. Using the optimal path, the interest packet is forwarded among healthcare vehicles in a smart city scenario. The proposed scheme maximizes the probability of finding the requested data while minimizing latency. As the proposed scheme does not involve broadcasting of interest packets, the problem of network congestion in CCN can be solved to a large extent. In order to facilitate the interoperability of heterogeneous healthcare devices in the network, and to improve the routing flexibility, an SDN controller is deployed in the IoHV scenario. It maintains the global information of the network in its flow tables. Moreover, to achieve faster response time and lower latency in the IoHV environment, edge devices are utilized instead of central cloud. Keeping in view the memory constraints of edge devices, deletable Bloom filters are used for storage and caching. Finally, the open issues and challenges related to the proposed solution for the IoHV scenario are also presented.

Content Management Based on Content Popularity Ranking in Information-Centric Networks

Users can access the Internet anywhere they go at any time due to the advancement of communications and networking technologies. The number of users and connected devices are rapidly increasing, and various forms of content are becoming increasingly available on the Internet. Consequently, several research ideas have emerged regarding the storage policy for the enormous amount of content, and procedures to remove existing content due to the lack of storage space have also been discussed. Many of the proposals related to content caching offer to identify the popularity of certain content and hold the popular content in a repository as long as possible. Although the host-based Internet has been serving its users for a long time, managing network resources efficiently during high traffic load is problematic for the host-based Internet because locating the host with their IP address is one of the primary mechanisms behind this architecture. A more strategical networking paradigm to resolve this issue is Content-Centric Networking (CCN), a branch of the networking paradigm Information-Centric Networking (ICN) that is focused on the name of the content, and therefore can deliver the requested content efficiently, securely, and faster. However, this paradigm has relatively simple content caching and content removal mechanisms, as it caches all the relevant content at all the nodes and removes the content based on the access time only when there is a lack of space. In this paper, we propose content popularity ranking (CPR) mechanism, content caching scheme, and content removal scheme. The proposed schemes are compared to existing caching schemes such as Leave Copy Everywhere (LCE) and Leave Copy Down (LCD) in terms of the Average Hop Count, content removal schemes such as Least Recently Used (LRU) and Least Frequently Used (LFU) in terms of the Cache Hit Ratio, and finally, the CCN paradigm incorporating the LCE and the LRU schemes and the host-based Internet architecture in terms of Content Delivery Time. Graphical presentations of performance results utilizing the proposed schemes show that the proposed CPR-based schemes for content caching and content removal provide better performance than the host-based Internet and the original CCN utilizing LCE and LRU schemes.

Intelligent Content-Centric Networking Routing Strategy: A Bacterial Quorum Inspired Pattern

In recent years, the Content-Centric Networking (CCN) has attracted much attention from the global Internet experts; in particular, it has demonstrated the outstanding effect on the application in the field of Internet of Things (IoT). At present, the routing technique of ICN is subjected to the dynamic change of network environment with the development of mobile Internet. Therefore, this paper proposes an Intelligent CCN routing strategy based on Bacterial Quorum pattern (ICBQ). The ICBQ tries to simulate the behaviors of bacteria, including quorum sensing and adaptive chemotaxis. Meanwhile, the quorum sensing can obtain the parameter information on bandwidth, delay, and error rate to facilitate the subsequent forwarding of packets. The adaptive chemotaxis can select the optimal interface to forward the packets through the information measurement. The simulation is driven based on the real Netflix dataset over the GTS network topology, and the experimental results show that the proposed ICBQ has better performance in terms of routing success rate, routing delay, load balance, and energy efficiency.

A Neighborhood Aware Caching and Interest Dissemination Scheme for Content Centric Networks

Content-Centric Networking (CCN) is a promising framework for the next generation Internet architecture that exploits ubiquitous in-network caching to minimize content delivery latency and reduce network traffic. In this paper, we introduce a neighborhood aware mechanism for content caching, named Neighborhood Aware Caching and Interest Dissemination (NACID) that accounts for the popularity of contents and how close the content copies are in the neighborhood. We use a very low-overhead, Bloom Filter based dissemination of caching information in the neighborhood. Given the neighborhood cached contents, the proposed scheme decides when and how to handle the additional caching of content and its eviction. Simulation results show that NACID performs substantially better than the existing CCN caching policies. We also study different heterogeneous cache memory allocation strategies and show that the simpler homogeneous allocation strategies work almost as well.

Designing Cuckoo Based Pending Interest Table for CCN Networks

&lt;p class="JESTECAbstract"&gt;Content Centric Networking (CCN) is a modern architecture that got wide attention in the current researches as a substitutional for the current IP-based architecture. Many studies have been investigated on this novel architecture but only little of them focused on Pending Interest Table (PIT) which is very important component in every CCN router. PIT has fundamental role in packet processing in both upstream process (Interest packets) and downstream process (Data packets). PIT must be fast enough in order to not become an obstruction in the packet processing and also it must be big enough to save a lot of incoming information. In this paper, we suggest a new PIT design and implementation named CF-PIT for CCN router. Our PIT design depends on modifying and utilizing an approximate data structure called Cuckoo filter (CF). Cuckoo filter has ideal characteristics like: high insertion/query/deletion performance, acceptable storage demands and false positive probability which make it with our modification convenient for PIT implementation. The experimental results showed that our CF-PIT design has high performance in different side of views which make it very suitable to be implemented on CCN routers.&lt;/p&gt;

Enabling Content-Centric Device-to-Device Communication in the Millimeter-Wave Band

The growth in wireless traffic and mobility of devices have congested the core network significantly. This bottleneck, along with spectrum scarcity, made the conventional cellular networks insufficient for the dissemination of large contents. The ability of content-centric networking (CCN) and device-to-device (D2D) communication in offloading the network and huge unlicensed spectrum at millimeter-wave (mmWave) band, make the integration of CCN with D2D communication in the mmWave band a viable solution to improve the network's throughput. In this paper, we propose a novel scheme that enables efficient initialization of CCN-based D2D networks in the mmWave band through addressing decentralized D2D peer association and antenna beamwidth selection. The proposed scheme considers mmWave characteristics such as directional communication and blockage susceptibility. We propose a heuristic peer association algorithm to associate D2D users using context information, including link stability time and content availability. We model the beamwidth selection problem as a potential game and propose a synchronous log-linear learning algorithm to obtain the game's optimal Nash equilibrium. The performance of the proposed scheme in terms of data throughput and transmission efficiency is evaluated through extensive simulations. Simulation results show that the proposed scheme improves network performance significantly and outperforms other methods in the literature.

Hierarchical cooperative-peers fog caching strategy to improve the performance of content centric networking

Content Centric Networking (CCN) is a novel paradigm to address challenges faced by traditional TCP/IP architecture. The motivation behind CCN is its inherent feature of caching. Caching at intermediate nodes is essential to reduce delay and to enhance performance of the network. However, determining as to how and where content should be cached to achieve this is the major challenge in CCN architecture. In this paper, we have proposed a Hierarchical Cooperative-peers Architecture (HCPA) and effective caching approaches to reduce the response time and increase the cache hit rate alleviating the server bottleneck and network traffic. The main idea is to utilize the resources of fog peer nodes near the user to deal with high latency and low bandwidth issues of the current communication pattern. This is achieved by pooling the cache space of cooperative fog peers of a region with a Regional demand-based fog Caching (RDFC) approach in two folds. First implementation is with replicated caching (RC) and the second one is using unique caching (UC). Both these approaches are implemented using CcnSim and the performance is evaluated based on cache hit rate, response time and bandwidth usage which has shown the significant improvement in the performance of the network.

Bloom-Quotient Based Name Matching Technique in Content Centric Networks

Content Centric Networking (CCN) is a novel network paradigm in which the communication process focusses on content rather than the host. In the current network architecture, routers forward the incoming packets based on the routing information that already had. Whereas, forwarding decisions in CCN are taken depending on networks situation. The contents in CCN are retrieved depending on their names which are URL like names that are composing of a number of string parts separated by’/’. These names are stored inside a specific data structure, called Forwarding Information Base (FIB), inside CCN routers and they are used to forward any incoming packet. Therefore, the main two challenges that face the design of FIB table are: search speed and storage utilization. Consequently, in this paper, we propose a new name matching technique (named BF-QF FIB) to design and implement a FIB table in CCN routers in order to decrease the storage utilization and to increase the lookup speed. This technique utilizes two kinds of query data structures: Bloom filter (BF) and Quotient filter (QF) as its main data structures. The utilization of these two data structures will ensure low memory usage and high lookup speed. The results of our evaluation show that BF-QF FIB can guarantee high search rate and offer perfect scalability to large FIB tables.

Intelligent Content-Centric Networking Routing Strategy: A Bacterial Quorum Inspired Pattern

In recent years, the Content-Centric Networking (CCN) has attracted much attention from the global Internet experts, especially it has demonstrated the outstanding effect on the application in the field of Internet of Things (IoT). At present, the routing technique of ICN is subjected to the dynamic change of network environment with the development of mobile Internet. Therefore, this paper proposes an Intelligent CCN routing strategy based on Bacterial Quorum pattern (ICBQ). The ICBQ tries to simulate the behaviors of bacteria, including quorum sensing and adaptive chemotaxis. Meanwhile, the quorum sensing can obtain the parameter information on bandwidth, delay and error rate to facilitate the subsequent forwarding of packets; The adaptive chemotaxis can select the optimal interface to forward the packets through the information measurement. The simulation is driven based on the real Netflix dataset over the GTS network topology, and the experimental results show that the proposed ICBQ has better performance in terms of routing success rate, routing delay and load balance.

Comparison of Performance Among Forwarding Strategies in CCN: Disaster Scenarios

Content-Centric Networking (CCN) is a novel modern architecture for the Internet in the future. This architecture concentrates on content retrieval and dissemination solution for communication models. Forwarding strategies are decision-making strategies whose aim is to define the forwarding destination, i.e., where and when request packets will be redirected. They are considered as the most crucial component in a network environment because of their contribution to determining which strategy is suitable to adopt in accordance with the environment and the applications. These strategies have not been initially designed to support complex applications and interactions that require Interest packet send rate, CCN table sizing, refreshing Pending Interest Table (PIT), and Content Store (CS), such as disaster scenarios, social network, and smart applications. The goal of this work is to evaluate the performance of CCN forwarding strategies in disaster scenarios. A network simulator (i.e., ndnSIM) is utilized to measure the performance in many scenarios by modifying Interest packet rate, PIT size, and CS size. Evaluation results achieved after performing the study on the selected strategies are considerably enhanced for the metrics cache hit, PIT size, Interest retransmissions, number of hops, delay, and Interest delivery. The main significance of this work is that it formulates a universal rule as the main function, which can adopt a suitable forwarding strategy accurately in accordance with the PIT and CS conditions and Interest packet rate, thus leading to increased Interest packet satisfaction utilization without increasing Interest packet retransmission and response delay.

Dynamic popularity window and distance-based efficient caching for fast content delivery applications in CCN

Content-Centric Networking (CCN) is being established as an emerging Internet architecture that brings efficient data retrieval technology to access the contents with their names. In CCN, the in-network caching capability plays a critical role in the improvement of network performance by reducing the load of content server and increasing the content availability. Thus, the efficiency of the content caching strategy crucially affects the performance of the entire network. Towards this, a novel content caching scheme has been proposed in this article that efficiently utilizes the available caching capacity and improves Quality-of-Service (QoS) during content delivery. To perform optimized caching operations, the proposed scheme considers a dynamic threshold value and takes caching decisions based on two parameters, first is a novel dynamic size popularity window to determine content popularity, and secondly, the distance navigated by the Content message from the preceding on-path router that cached the content copy. For content caching decisions, the dynamic threshold value decreases with an increase in Content message distance from the preceding on-path router (that cached the content) and the content popularity. Using these heuristics, the scheme places the popular contents near the edges of the network. Through extensive simulations, the QoS delivered by the proposed scheme has been examined for different cache sizes on standard Abilene network topology. The results show that the proposed scheme outperforms the existing caching strategies for various performance parameters such as hit-ratio, average network delay, hop count, and average traffic generated in the network. This makes the proposed solution suitable for futuristic Internet architectures with 5G/6G and Industry 4.0 revolutions and their applications where faster content deliveries are required.

Cooperative Caching in Vehicular Content Centric Network Based on Social Attributes and Mobility

Communications in vehicular ad-hoc network (VANET) are subject to performance degradation as results of channel fading and intermittent network connectivity. The emerging Vehicular Content Centric Network (VCCN) is promising in supporting the needs of contents and alleviating the communication problems in VANET. Specifically, to improve the cache hit ratio and reduce the access delay of content retrieval, it helps to choose the appropriate vehicles to cache the frequently accessed data items. In this paper, we propose a Cooperative Caching scheme based on Social Attributes and Mobility Prediction (CCSAMP) for VCCN. CCSAMP is based on the observation that vehicles move around and are liable to contact each other according to drivers' common interests or social similarities. A caching node sharing more social attributes with the content requester is more likely to be interested in the same contents and distribute the contents to others with similar interests. Furthermore, a caching node that frequently meets other nodes is a better candidate to keep cache copies. To increase the network performance, CCSAMP also exploits the regularity of vehicle moving behaviors to predict the chance for a vehicle to reach hot zones based on Hidden Markov Model (HMM). We evaluate CCSAMP through the ONE simulator to demonstrate its higher cache hit ratio and lower content access delay compared to other state-of-the-art schemes.

Multi-Head Attention Based Popularity Prediction Caching in Social Content-Centric Networking With Mobile Edge Computing

With the rapid growth of social network traffic, the design of an efficient caching strategy is crucial in the social content-centric network (SocialCCN). In order to design a more comprehensive popularity prediction caching strategy, in this letter, we proposed a novel architecture that integrates mobile edge computing (MEC) in SocialCCN (MeSoCCN) and proposed multi-head attention based popularity prediction caching strategy in MeSoCCN. Firstly, we proposed a multi-head attention based popularity prediction model (MAPP) that considers multi-dimensional features including history and future popularity, social relationships, and geographic location to predict content popularity. Then, we design a caching strategy based on the prediction results of MAPP. The simulation results show that the proposed MAPP model achieves lower predictive error and the proposed predictive caching strategy improves cache hit rate and reduces hop redundancy in the network.

Light forwarding based optimal CCN content delivery: a case study in metropolitan area network

Content Centric Networking (CCN) has been introduced in recent years to effectively deliver the named content objects rather than relying on the traditional host-to-host communication model, in which a significant and non-negligible characteristic of CCN is in-network caching. In this paper, we stand on the shoulders of in-network caching to propose the light forwarding based approach in order to optimize CCN content delivery, i.e., the process of data routing irrespective of interest routing. At first, we specially devise an additional packet type which has no data entity at the same time the caching/delivery of data is the granularity of packet. Then, we present a generic content delivery scheme based on packet-level caching and new designed packets; in addition, we make a case study in Metropolitan Area Network (MAN) due to some realistic constraints. Finally, we implement the proposed light forwarding based content delivery scheme and compare it with three existing schemes, and the experimental results reveal that it has more efficient performance optimization, such as network load, delivery delay and energy saving.