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Information-Centric Networking (ICN) is promising system architecture for distributing popular information across the network. Its important feature is the node cache. ICN with caching is a very promising featured network structural design. There are numerous cache nodes distributed in the ICN. Excessive consumption of cache information on a tremendous number of nodes dissipates a large storage space and causes data redundancy, which reduces the cache hit rate. Therefore, an efficient caching deployment approach is required to improve the cache hit rate. A novel RAndom probit regressive Bucklin DEcision Forest classifier (RADEF) technique is introduced in Information-Centric Networking (ICN) for minimal network latency and higher cache hit rate. In RADEF Technique, probit regression and random forest classification processes are carried out for ICN with healthcare patient data. First, the patient information is collected from different IoT devices and registered. Every request to the router node is analyzed in the content storage (CS) by using a random probit regressive Bucklin decision forest classifier. The Bucklin decision forest classifier is an ensemble technique that includes a set of weak learners as decision trees (i.e., probit regression tree) and IoHT data are selected randomly for each decision tree. The Probit regression tree is constructed to analyze the patient healthcare data request search in the content storage (CS) of the router node in the ICN network by using a simple matching coefficient. If the copy is present in the content storage, the particular router nodes are chosen and deliver the content. If the copy is absent, the patient information is stored in the cache. Then, the weak learner's results are combined to make a strong output. Then the votes are generated for each decision tree. The votes of all decision trees are combined to identify the majority votes of data for classification by minimizing the error using the Bucklin voting method. In this way, content catching is effectively performed in ICN with minimum latency and a higher cache hit ratio. Experimental evaluation is carried out on factors such as cache hit rate, network latency, average request length, average response time, server traffic ratio, and hop reduction ratio regarding respect to the number of patient healthcare data. The analyzed results demonstrate the superior performance of our proposed RADEF technique when compared with existing methods. [ABSTRACT FROM AUTHOR]

Information-Centric Networking (ICN) is a new paradigm of network architecture that focuses on content rather than hosts as first-class citizens of the network. As part of these architectures, in-network storage devices are essential to provide end users with close copies of popular content, to reduce latency and improve the overall experience for the user but also to reduce network congestion and load on the content producers. To be effective, in-network storage devices, such as content storage routers, should maintain copies of the most popular content objects. Adversaries that wish to reduce this effectiveness can launch cache pollution attacks to eliminate the benefit of the in-network storage device caches. Therefore, it is crucial to protect these devices and ensure the highest hit rate possible. This paper demonstrates Per-Face Popularity approaches to reducing the effects of cache pollution and improving hit rates by normalizing assessed popularity across all faces of content storage routers. The mechanisms that were developed prevent consumers, whether legitimate or malicious, on any single face or small number of faces from overwhelmingly influencing the content objects that remain in the cache. The results demonstrate that per-face approaches generally have much better hit rates than currently used cache replacement techniques.

Information-Centric Networking (ICN) is a new paradigm of network architecture that focuses on content rather than hosts as first-class citizens of the network. As part of these architectures, in-network storage devices are essential to provide end users with close copies of popular content, to reduce latency and improve the overall experience for the user but also to reduce network congestion and load on the content producers. To be effective, in-network storage devices, such as content storage routers, should maintain copies of the most popular content objects. Adversaries that wish to reduce this effectiveness can launch cache pollution attacks to eliminate the benefit of the in-network storage device caches. Therefore, it is crucial to protect these devices and ensure the highest hit rate possible. This paper demonstrates Per-Face Popularity approaches to reducing the effects of cache pollution and improving hit rates by normalizing assessed popularity across all faces of content storage routers. The mechanisms that were developed prevent consumers, whether legitimate or malicious, on any single face or small number of faces from overwhelmingly influencing the content objects that remain in the cache. The results demonstrate that per-face approaches generally have much better hit rates than currently used cache replacement techniques. [ABSTRACT FROM AUTHOR]

Information-Centric Networking (ICN) and Software-Defined Networking (SDN) are both new evolving network architectures that are receiving a lot of attention from researchers. ICN is a Future Internet architecture which tries to transform the current Internet architecture from location- and host-centric to content-centric, where obtaining requested data is achieved by the contents' names regardless of the location of the data. From another angle, SDN is considered a new Internet architecture that moves the control plane management from network devices to a centralized controller. The SDN controller enhances network robustness and improves its scalability, reliability, and flexibility. The integration of ICN and SDN results in massive benefits, where SDN enhances ICN networks' manageability, controllability, and functionality, and ICN reshapes the SDN design to make it compatible with ICN features and to enhance ICN in terms of network caching, routing, mobility, and security.. In this review paper, a comprehensive survey of the issues and challenges of integrating ICN and SDN is presented. Firstly, ICN's main characteristics are summarized, and a short comparison between different ICN architectures is completed. Then, the key details of SDN are highlighted. Moreover, the motivation and benefits of merging ICN with SDN are summarized and the state-of-the-art work on merging ICN and SDN is reviewed and classified from several aspects. Finally, several open research issues are highlighted. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC) is a novel edge computing paradigm that moves cloudbased processing and storage capabilities closer to mobile users by implementing server resources in the access nodes. MEC helps fulfill the stringent requirements of 5G and beyond networks to offer anytimeanywhere connectivity for many devices with ultra-low delay and huge bandwidths. Information-Centric Networking (ICN) is another prominent network technology that builds on a content-centric network architecture to overcome host-centric routing/operation shortcomings and to realize efficient pervasive and ubiquitous networking. It is envisaged to be employed in Future Internet including Beyond 5G (B5G) networks. The consolidation of ICN with MEC technology offers new opportunities to realize that vision and serve advanced use cases. However, various integration challenges are yet to be addressed to enable the wide-scale co-deployment of ICN with MEC in future networks. In this paper, we discuss and elaborate on ICN MEC integration to provide a comprehensive survey with a forward-looking perspective for B5G networks. In that regard, we deduce lessons learned from related works (for both 5G and B5G networks). We present ongoing standardization activities to highlight practical implications of such efforts. Moreover, we render key B5G use cases and highlight the role for ICN MEC integration for addressing their requirements. Finally, we layout research challenges and identify potential research directions. For this last contribution, we also provide a mapping of the latter to ICN integration challenges and use cases.

Summary: Named data networking (NDN) is gaining momentum as a future Internet architecture. NDN is a type of information‐centric networking (ICN) that attempts to change the current Internet architecture from host/location‐centric to content‐centric, where data retrieving is done by the names of the contents irrespective of the location of the data. A mechanism to advertise the name‐based prefixes between different domains is necessary to accelerate the NDN deployment. In IP‐based routing, border gateway protocol (BGP) is the de facto inter‐domain routing protocol that plays a vital role in Internet communication by enabling different Internet domains to exchange routing information. In its current form, BGP can advertise and process IP‐based prefixes, but it cannot advertise or process NDN name‐based prefixes. Accordingly, BGP needs to be extended to support NDN technology. This paper proposes an NDN extension for BGP, referred to as N‐BGP, that offers a solution to exchange name‐based routes in the current BGP networks. This proposed extension modifies the traditional BGP speaker into a hybrid one. This hybrid speaker is qualified to understand, advertise, receive, process, and store both IP‐based and name‐based routes simultaneously and efficiently, without disturbing or breaking the current Internet operation; that is, it can coexist along with traditional speakers. We also validate and evaluate our proposed solution in a hybrid environment, and the results show that N‐BGP has the capability to exchange and process both Name and IP‐based routes efficiently. [ABSTRACT FROM AUTHOR]

The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission. [ABSTRACT FROM AUTHOR]

The domain of information-centric networking (ICN) is expanding as more devices are becoming a part of connected technologies. New methods for serving content from a producer to a consumer are being explored, and Named Data Networking (NDN) is one of them. The NDN protocol routes the content from a producer to a consumer in a network using content names, instead of IP addresses. This facility, combined with content caching, efficiently serves content for very large networks consisting of a hybrid and ad hoc topology with both wired and wireless media. This paper addresses the issue of the quality-of-service (QoS) dimension for content delivery in NDN-based networks. The Internet Engineering Task Force (IETF) classifies QoS traffic as (prompt, reliable), prompt, reliable, and regular, and assigns corresponding priorities for managing the content. QoS-linked privileged content caching (QLPCC) proposes strategies for Pending Interest Table (PIT) and content store (CS) management in dedicated QoS nodes for handling priority content. QoS nodes are intermediately resourceful NDN nodes between content producers and consumers which specifically manage QoS traffic. The results of this study are compared with EQPR, PRR probability cache, and Least Frequently Used (LFU) and Least Fresh First (LFF) schemes, and QLPCC outperformed the latter-mentioned schemes in terms of QoS-node CS size vs. hit rate (6% to 47%), response time vs, QoS-node CS size (65% to 90%), and hop count vs. QoS-node CS size (60% to 84%) from the perspectives of priority traffic and overall traffic. QLPCC performed predictably when the NDN node count was increased from 500 to 1000, showing that the strategy is scalable. [ABSTRACT FROM AUTHOR]

Information-centric networking (ICN) introduces in-network caching mechanism to enable routers to have content caching function, changing the network from IP addressing to content name addressing, aiming to better provide services for content distribution applications. However, interest flooding attack (IFA) will lead to the exhaustion of router resources and make it discard a large number of legitimate interest packets, thus becoming the “bane” of ICN security. Combined with human immune defense theory, a two-stage ICN secure routing mechanism is proposed to resist interest flooding attacks. During the immunization time, non-specific immunization is completed through immune feedback and isolation strategy to prevent the pending interest table (PIT) of the router from being maliciously occupied. However, non-specific immunity cannot alleviate persistent IFA attacks, so further specific immunity is completed through backtracking strategy to form immune memory and completely block interest flooding attacks. Experimental results show that the proposed routing mechanism can effectively resist interest flooding attacks, reduce resource depletion and invalid computation caused by attacks, and ensure network performance.

Named data networking (NDN) aims to change the traditional content delivery method and caching by router nodes caching and participating in forwarding. NDN-caching can reduce the expected flood of global data traffic by providing cache storage at intermediate nodes for transmitted content objects, making data broadcasting in an efficient way. In this paper, a novel caching strategy based on entropy weighting method and TOPSIS is proposed for efficient content dissemination to improve NDN’s in-network caching performance. Firstly, the consumer’s request process is modeled by the entropy weighting method and TOPSIS to obtain the best cache node for the cache object according to the real-time status of the node. Secondly, two cache replacement algorithms (composed of an active cache replacement algorithm and a passive cache replacement algorithm) are proposed to reduce the cache redundancy on the delivery path and improve the utilization of data packets in the nodes. Finally, an effective cache mechanism and data packet migration scheme are proposed to further improve the cache performance according to the different types of cache nodes. The performance evaluation shows that the proposed scheme performs better in terms of cache hit rate, latency, and link load compared with some existing strategies.

One unique architectural benefit of Named Data Networking (NDN) is adaptive forwarding, i.e., the forwarding plane is able to observe past data retrieval performance and use it to adjust forwarding decisions for future Interests. To be effective, adaptive forwarding assumes that Interest Routing Locality is related to Interests’ common name prefix, meaning that Interests sharing the same prefix are likely to follow a similar forwarding path within a short period of time. Since Interests can have multiple common prefixes with different lengths, the real challenge is determining which prefix length should be used in adaptive forwarding to record path performance measurements - we refer to this as the Prefix Granularity Problem. The longer the common prefix is, the better the Interest Routing Locality, and the larger the forwarding table. Given the limited FIB size, route names are designed to be considerably shorter than Interest names. Existing adaptive forwarding designs use route names to record path performance measurements, which looses forwarding adaptability as it promises in the event of partial network failures. In this work, we propose to dynamically aggregate and de-aggregate name prefixes in the forwarding table in order to use the prefixes that are the most appropriate given current network situation. In addition, to reduce the overhead of adaptive forwarding, we propose mechanisms to minimize the use of the longest prefix matching in Data packet processing. Simulations demonstrate that the proposed techniques can result in better forwarding decisions in the event of partial network failures with significantly reduced overhead. [ABSTRACT FROM AUTHOR]

Information-Centric Networking (ICN) emerges as a promising architecture for the future Internet to keep up with the tremendous growth of Internet users and the explosive increase of mobile data traffic. ICN can be coupled with Disruption Tolerant Networking (DTN) to overcome the limitations of the current Internet architecture in some challenging environments where we cannot establish a network connection, and also lighten the load on the Internet backhaul. Unfortunately, resources constraints, such as energy and buffer space, are among the most serious issues in Mobile Information-Centric Disruption Tolerant Networking (MICDTN). In this paper, we introduce an opportunistic DTN-based forwarding mechanism of Interest and Data for ICN, named Fast-MICDTN. The propagation of the content have been modeled using Markov-Decision Process (MDP). Hence, we characterize the living period in transit of the content to reach the maximum possible number of mobiles. Through extensive simulations based on synthetic mobility model and real-world traces, we prove the efficiency of the new approach in sharing content within a large number of users under a low cost. [ABSTRACT FROM AUTHOR]

As an innovative networking paradigm in the Internet of Things, information-centric networking (ICN) has improved security than the traditional IP networks. However, it still faces a large number of security threats, especially those threats from internal attacks. The trust management technology is an effective approach for defending against internal attacks. In this paper, our contributions lie in our investigating the requirements of cybersecurity in ICN and analyzing the typical attack behaviors and defense schemes. Then, we propose a fast and efficient trust management scheme (FETMS) for defending against the on-off attack, which is an intelligent internal attack. The simulation results show that FETMS can efficiently detect and remove the malicious node in a short period of time; meanwhile, it can achieve lower latency and add its mobility.

The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission.

The domain of information-centric networking (ICN) is expanding as more devices are becoming a part of connected technologies. New methods for serving content from a producer to a consumer are being explored, and Named Data Networking (NDN) is one of them. The NDN protocol routes the content from a producer to a consumer in a network using content names, instead of IP addresses. This facility, combined with content caching, efficiently serves content for very large networks consisting of a hybrid and ad hoc topology with both wired and wireless media. This paper addresses the issue of the quality-of-service (QoS) dimension for content delivery in NDN-based networks. The Internet Engineering Task Force (IETF) classifies QoS traffic as (prompt, reliable), prompt, reliable, and regular, and assigns corresponding priorities for managing the content. QoS-linked privileged content caching (QLPCC) proposes strategies for Pending Interest Table (PIT) and content store (CS) management in dedicated QoS nodes for handling priority content. QoS nodes are intermediately resourceful NDN nodes between content producers and consumers which specifically manage QoS traffic. The results of this study are compared with EQPR, PRR probability cache, and Least Frequently Used (LFU) and Least Fresh First (LFF) schemes, and QLPCC outperformed the latter-mentioned schemes in terms of QoS-node CS size vs. hit rate (6% to 47%), response time vs, QoS-node CS size (65% to 90%), and hop count vs. QoS-node CS size (60% to 84%) from the perspectives of priority traffic and overall traffic. QLPCC performed predictably when the NDN node count was increased from 500 to 1000, showing that the strategy is scalable.

Information-Centric Networking (ICN) empowered by information-centric paradigm takes popular paradigm place of host-centric networking of communication networks, which in turn helps prioritizing the labeled content delivery, with no information on the origin of the contents. Security of client and content, originating place, and identity privacy are inherent in ICN paradigm design in contrast to present host centric concept where they are introduced as a second-thought. But, with its genesis, the ICN paradigm exhibits different unresolved challenges in privacy and security. In this work, current literature in ICN privacy and security are explored and open challenges are presented. Especially, three extensive subjects: security threats, risks involved with privacy, access control management techniques are explored. Primary objective of ICN is to modify the present location-based IP network architecture to location-free and content-oriented network framework. ICN can satisfy the demands for caching to the neighbouring edge devices with no more storage deployed. In this work, an several architecture for effective caching at the edge devices for data-centric IoT applications and a rapid content access that depends on novel deep learning techniques and caching processes in ICN. The novel learning-oriented effective caching technique yields the solution to the problem involving the available hash and on-path caching techniques, and the newly introduced content popularity scheme improves the availability content at the devices in the vicinity for minimizing the content transfer time and packet loss ratio.

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Information-Centric Networking (ICN) empowered by information-centric paradigm takes popular paradigm place of hostcentric networking of communication networks, which in turn helps prioritizing the labeled content delivery, with no information on the origin of the contents. Security of client and content, originating place, and identity privacy are inherent in ICN paradigm design in contrast to present host centric concept where they are introduced as a second-thought. But, with its genesis, the ICN paradigm exhibits different unresolved challenges in privacy and security. In this work, current literature in ICN privacy and security are explored and open challenges are presented. Especially, three extensive subjects: security threats, risks involved with privacy, access control management techniques are explored. Primary objective of ICN is to modify the present location-based IP network architecture to location-free and content-oriented network framework. ICN can satisfy the demands for caching to the neighbouring edge devices with no more storage deployed. In this work, an several architecture for effective caching at the edge devices for data-centric IoT applications and a rapid content access that depends on novel deep learning techniques and caching processes in ICN. The novel learning-oriented effective caching technique yields the solution to the problem involving the available hash and on-path caching techniques, and the newly introduced content popularity scheme improves the availability content at the devices in the vicinity for minimizing the content transfer time and packet loss ratio. [ABSTRACT FROM AUTHOR]

This research proposes a novel wireless information-centric networking (ICN) architecture, namely, Context-Aware Green ICN Model (CAGIM), which can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The power adaption in ICN nodes is conducted through dynamically adjusting the link-rate corresponding to content popularity and traffic load to reduce wasteful energy consumption. Moreover, we propose a smart popularity-based caching strategy, called distinguished caching scheme (DCS), with the introduction of hot and cold-caching partitions of ICN node's cache storage for popular and non-popular content objects, respectively. DCS improves the content diversity of the cache storage by adjusting, for each content, the number of chunks to be cached at ICN nodes based on its type and popularity level. DCS thus can further decrease the network system power consumption, thanks to its improved cache hit that reduces network traffic load. Toward the goal of realizing a context-aware green wireless network system with efficient content delivery, we also design a WiFi Direct based scheme as an alternative approach to minimize power consumption and latency by sharing essential/important content objects via direct communications with power-saving mechanisms in the case that wireless local area network connections are not available. The evaluation results show that CAGIM can improve network efficiency by reducing both hop-count and power consumption considerably compared with existing wireless network systems with different well-known caching schemes. This proposal enables a flexible and efficient content delivery mechanism for future networks with various real-life scenarios, like Green building, Green company, and Green campus content accesses.

The recent advances in vehicular communications and networking are bringing self-driving vehicles closer to reality. Once full automation (i.e., levels 4 and 5 as defined in the five levels of autonomous driving) becomes a reality, entertainment services for both drivers and passengers will shift from listening to radios (or music) to watching videos. Hence, how to improve the quality of experience (QoE) for autonomous vehicle (AV) users, and to reduce the network load will become a crucial problem. Information-centric networking (ICN) is seen as one of the potential paradigms for next-generation networks, and could potentially be used for content distribution in vehicular networks. Caching, an important feature in ICN-based networks, is an efficient way to reduce network load, and to improve QoE for users. However, traditional reactive caching approaches are inefficient for AV users due to the high delay caused by their high speed. In this paper, we propose a novel hierarchical proactive caching approach that considers both users’ future demands and AV user mobility. The proposed approach uses the non-negative matrix factorization (NMF) technique to predict user's preferences which are then used to predict users’ future demands by considering the historical popularity of videos. A user mobility prediction model is used to predict the AV users’ next location based on the current location and the planned route information which can be retrieved from the self-driving system. Based on the predicted users’ future demands and locations of AVs, the proposed caching approach can proactively cache videos that are likely to get requested at the next base station (BS) or roadside unit (RSU) that the users are moving to. The proposed approach has been evaluated under two scenarios: a highway scenario and a grid street scenario. Results show that the proposed approach can significantly improve the efficiency of caching in terms of hit ratio and the average number of hops. [ABSTRACT FROM AUTHOR]

As an instantiation of information-centric networking, named data networking was proposed. Since the Interests are recorded in the Pending Interest Table (PIT) of the intermediate routers until they receive corresponding Data packets or exceed the expiring time, the attackers can send an excessive number of spoofed Interests to occupy the PITs, which is called Interest Flooding Attack (IFA), to degrade the network performance. Traditional IFA detection methods are simply based on the PIT expiration rate or the Interest satisfaction rate, which cannot distinguish the IFA from other attack approaches. Besides, the traditional threshold-based methods are easily affected by the value of the thresholds. In this article, we propose an IFA detection mechanism based on the support vector machine (SVM), which can detect the IFA timely and accurately. In order to improve the accuracy of the attack detection, we present to use the entropy of the Interest names, the satisfaction rate of the Interests, and the usage of the PIT as the extracted features in the SVM classifier. In addition, we put forward a Jensen–Shannon-divergence-based malicious Interest prefix recognition mechanism. The simulation results validate that the mechanism can help the network to resist the IFA accurately and effectively. [ABSTRACT FROM AUTHOR]

Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased. [ABSTRACT FROM AUTHOR]

With the rapid growth of mass content retrieval on the Internet, Information-Centric Network (ICN) has become one of the hotspots in the field of future network architectures. The in-network cache is an important feature of ICN. For better network performance in ICN, the cache size on each node should be allocated in proportion to its importance. However, in some current studies, the importance of cache nodes is usually determined by their location in the network topology, ignoring their roles in the actual content transmission process. In this paper, we focus on the allocation of cache size for each node within a given total cache space budget. We explore the impact of heterogeneous cache allocation on content dissemination under the same ICN infrastructure and we quantify the importance of nodes from content dissemination and network topology. To this purpose, we implement a hierarchy partitioning method based on content dissemination, then we formulate a set of weight calculation methods for these hierarchies and to provide a per-node cache space allocation to allocate the total cache space budget to each node in the network. The performance of the scheme is evaluated on the Garr topology, and the average hit ratio, latency, and load are compared to show that the proposed scheme has better performance in these aspects than other schemes.

The current internet architecture is inefficient in fulfilling the demands of newly emerging internet applications. To address this issue, several over-the-top application-level solutions have been employed, making the overall architecture very complex. Information-centric-networking (ICN) architecture has emerged as a promising alternative solution. The ICN architecture decouples the content from the host at the network level and supports the temporary storage of content in an in-network cache. Fundamentally, the ICN can be considered a multisource, multicast content-delivery solution. Because of the benefits of network coding in multicasting scenarios and proven benefits in distributed storage networks, the network coding is apt for the ICN architecture. In this study, we propose a solvable linear network-coding scheme for the ICN architecture. We also propose a practical implementation of the network-coding scheme for the ICN, particularly for the content-centric network (CCN) architecture, which is termed the coded CCN. The performance results show that the network-coding scheme improves the performance of the CCN and significantly reduces the network traffic and average download delay. [ABSTRACT FROM AUTHOR]

The Internet, the de facto platform for large-scale content distribution, suffers from two issues that limit its manageability, efficiency, and evolution. First, the IP-based Internet is host-centric and agnostic to the content being delivered and, second, the tight coupling of the control and data planes restrict its manageability, and subsequently the possibility to create dynamic alternative paths for efficient content delivery. Here, we present the CURLING system that leverages the emerging Information-Centric Networking paradigm for enabling cost-efficient Internet-scale content delivery by exploiting multicasting and in-network caching. Following the software-defined networking concept that decouples the control and data planes, CURLING adopts an interdomain hop-by-hop content resolution mechanism that allows network operators to dynamically enforce/change their network policies in locating content sources and optimizing content delivery paths. Content publishers and consumers may also control content access according to their preferences. Based on both analytical modeling and simulations using real domain-level Internet subtopologies, we demonstrate how CURLING supports efficient Internet-scale content delivery without the necessity for radical changes to the current Internet. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has enabled unprecedented interactions with our physical world, with the aim to deliver a wide range of customizable services in many domains. With recent advancements in IoT technology, users are increasingly expecting these services to be intelligent and context aware. Nevertheless, there is still no framework capable of delivering personalized IoT services on a large scale. For such a framework to be conceived, it is likely that technologies from many domains have to be utilized. This paper examines the readiness of the leading state-of-the-art technologies in several key fields for realizing the goal of a truly scalable and personalized IoT experience. We discuss the important requirements and challenges for realizing this goal. Then, we identify the major approaches that can contribute to this goal and categorize them into: technologies for adaptive personalized sensing, scalable solutions for user-centric networking, and intelligence techniques that leverage context awareness and adaptability at the application and system levels. In the first category, our discussion centers around virtualization and reprogrammability at the sensing layer. In the second category, we investigate the readiness of Fog computing and information-centric networking to develop scalable personalized IoT infrastructures. These approaches were chosen for their combined ability to match dynamic user requirements with available system resources, while guaranteeing overall efficient utilization. Finally, in the third category, we examine context awareness, reasoning, and machine learning techniques as well as semantic technologies for realizing proactive and adaptive intelligent IoT systems and applications. This paper offers a focused discussion of the key topics that drive the research in the important and timely topic of scalable and personalized IoT networks. [ABSTRACT FROM AUTHOR]

Various Internet solutions take their power processing and analysis from cloud computing services. Internet of Things (IoT) applications started discovering the benefits of computing, processing, and analysis on the device itself aiming to reduce latency for time-critical applications. However, on-device processing is not suitable for resource-constraints IoT devices. Edge computing (EC) came as an alternative solution that tends to move services and computation more closer to consumers, at the edge. In this letter, we study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network (RNN), and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept. Therefore, a CNN model can be used in the IoT area to exploit reliably data from a complex environment. Moreover, RL and RNN have been recently integrated into IoT, which can be used to take the multi-modality of data in real-time applications into account. [ABSTRACT FROM AUTHOR]

The dynamic adaptive streaming technique flexibly adapts the video bit-rate to link fluctuations, which can improve the quality of experience (QoE). In this paper, we present a systematic framework of video streaming in the context of information-centric networking, in order to facilitate the large-scale deployment of the dynamic adaptive streaming technique. Specifically, we design the network as a two-layer coordinating structure, namely, the control layer and the transmission layer. The control layer employs the statistical data recorders to record the variations of the video popularity, link states, and user demands. On the other side, the network forwards user requests and caches data packets in the transmission layer, based on the statistical data which is obtained in the control layer. In addition, the network executes the real-time monitoring of link conditions in the transmission layer, and adjusts the video bit-rate accordingly. Under the above feedback circumstance, we first develop a distributed algorithm of joint dynamic forwarding and caching to theoretically maximize the total user demand rates within the network stability region. Then, we modify the distributed algorithm with a practical caching strategy to make the system applicable to real scenarios. Simulation results show the superior performance of the modified distributed algorithm in terms of low user delay and high QoE performance.

With the exponential growth of Cyber-Physical Systems (CPSs) technologies, the Internet of Things (IoT) infrastructure has evolved from built-in static infrastructure to a flexible structure applicable to various mobile environments. In this Internet of Mobile Things (IoMT) environment, each IoT device could operate simultaneously as a provider and consumer of information, and could provide new services through the exchange of such information. Named Data Networking (NDN), which could request data by content name rather than location (IP address), is suitable for such mobile IoT environments. However, in the current Named Data Networking (NDN) specification, producer mobility is one of the major problems in need of remedy. Previously proposed schemes for producer mobility use an anchor to hide the producer’s movement from consumers. As a result, they require a special anchor node and a signaling procedure to track the current locations of contents. A few anchorless schemes have also been proposed, but they still require mobility signaling and all NDN routers on the signaling path must understand the meaning of the signaling. We therefore propose an anchorless producer mobility scheme for the NDN. This scheme uses a dual-connectivity strategy that can be expressed as a soft handover. Whenever a producer changes its NDN Access Router (NAR), the new mobility link service located on the mobile producer’s old NDN face repairs the old link so that the connectivity with the pNAR can be maintained for a while. The old NDN face is removed after the new location information on the contents of the producer is disseminated over the NDN network by the Named-data Link State Routing Protocol (NLSR) routing protocol at the nNAR. The new mobility link service decouples connection and transaction to hide the collapse of the link. Therefore, the NDN’s mobility procedure could be simplified as the handover is defined as transaction completion as opposed to a breakdown of links. The proposed scheme prevents the routing information from being abruptly outdated due to producer mobility. Our simulation results show seamless handover when the producer changes its default access router.

Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased.

Mobility has become a basic premise of network communications, thereby requiring a native integration into 5G networks. Despite numerous efforts to propose and standardize effective mobility-management models for IP, the result is a complex, poorly flexible set of mechanisms. The natural support for mobility offered by information centric networking (ICN) makes it a good candidate to define a radically new solution relieving limitations of the traditional approaches. If consumer mobility is supported in ICN by design, in virtue of its connectionless pull-based communication model, producer mobility is still an open challenge. In this paper, we look at two prominent ICN architectures, content centric networking (CCN) and named data networking (NDN) and we propose MAP-Me, an anchor-less solution to manage micro-mobility of content producers via a name-based CCN/NDN data plane, with support for latency-sensitive streaming applications. We analyze MAP-Me performance and provide guarantees of correctness, stability, and bounded stretch, which we verify on real ISP topologies. Finally, we set up a comprehensive simulation environment in NDNSim 2.1 for MAP-Me evaluation and comparison against the existing classes of solutions, including a realistic trace-driven car-mobility pattern under a 802.11n radio access. The results are encouraging and highlight the superiority of MAP-Me in terms of user performance and network cost metrics. All the code is available as open-source. [ABSTRACT FROM AUTHOR]

Information-centric networking (ICN), as a novel network paradigm, is expected to natively support mobility, multicast, and multihoming in vehicular ad hoc networks (VANETs). In this paper, the adoption of ICN principles for multimedia streaming in multihomed VANETs is investigated, with a major emphasis on the tradeoff between the quality of experience and energy efficiency (EnE). To formalize this problem, a cost optimization model is first proposed, based on queueing theory arguments. Then, a novel green information-centric multimedia streaming (GrIMS) framework is designed to drive the system toward optimal working points in practical settings. GrIMS consists of three enhanced mechanisms for on-demand cloud-based processing, adaptive multipath transmission, and cooperative in-network caching. Finally, a massive simulation campaign has been carried out, demonstrating that, thanks to its core components, the GrIMS enables flexible multimedia service provisioning and achieves an improved performance in terms of start-up delay, playbacks continuity, and EnE with respect to state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

In-network caching is one of the core issues in information-centric networking (ICN) which will directly restrict the data dissemination performance of the network. Considering the cache cost contains not only the path cost raised by accessing a cache node on the path but also the replacement cost of old information， a path and node cost based cooperative caching strategy (PNC3S) is proposed. The PNC3S considers the path cost and replacement cost comprehensively， and whether information matches a node or not depends on the total cost. Then the optimization algorithm is introduced into the proposed strategy to make caching decision. The simulation experiments demonstrate that the proposed PNC3S， compared with CEE (cache everything everywhere) and LCD (leave copy down)， improves such performance as cached information ratio， server load ratio， average link utilization ratio and hop reduction ratio. [ABSTRACT FROM AUTHOR]

Information dissemination in current Vehicular Sensor Networks (VSN) depends on the physical location in which similar data is transmitted multiple times across the network. This data replication has led to several problems, among which resource consumption (memory), stretch, and communication latency due to the lake of data availability are the most crucial. Information-Centric Networking (ICN) provides an enhanced version of the internet that is capable of resolving such issues efficiently. ICN is the new internet paradigm that supports innovative communication systems with location-independent data dissemination. The emergence of ICN with VSNs can handle the massive amount of data generated from heterogeneous mobile sensors in surrounding smart environments. The ICN paradigm offers an in-network cache, which is the most effective means to reduce the number of complications of the receiver-driven content retrieval process. However, due to the non-linearity of the Quality-of-Experience (QoE) in VSN systems, efficient content management within the context of ICN is needed. For this purpose, this paper implements a new distributed caching strategy (DCS) at the edge of the network in VSN environments to reduce the number of overall data dissemination problems. The proposed DCS mechanism is studied comparatively against existing caching strategies to check its performance in terms of memory consumption, path stretch ratio, cache hit ratio, and content eviction ratio. Extensive simulation results have shown that the proposed strategy outperforms these benchmark caching strategies.

With the rapid development of cloud computing, big data, and Internet of Things, Information-Centric Networking (ICN) has become a novel hotspot in the field of future Internet architecture, and new problems have appeared. In particular, more researchers consider information naming, delivery, mobility, and security in ICN. In this paper, we mainly focus on the cache placement strategy and network performance of ICN, and propose a cache placement strategy with energy consumption optimization. In order to optimize the energy consumption of the ICN, the best cache placement node is selected from the view of users. First of all, the distance sequence of different nodes arriving at each user is obtained in terms of detection results of network distribution channels, and the corresponding energy consumption of information distribution is obtained from the distance sequence. Secondly, the reward function of the cache node is derived using two factors of energy consumption, which includes the additional energy consumed by the change of the cache node and the energy consumption of the content distribution. Finally, we construct the optimal stopping theory problem to solve the maximum expected energy saving. In simulations, we give the comparison results of energy savings, caching benefit, and delivery success rate. The results show that the strategy proposed by this paper has higher delivery success rate and lower energy consumption than other strategies.

This article proposes a novel chunk-based caching scheme known as the Progressive Popularity-Aware Caching Scheme (PPCS) to improve content availability and eliminate the cache redundancy issue of Information-Centric Networking (ICN). Particularly, the proposal considers both entire-object caching and partial-progressive caching for popular and non-popular content objects, respectively. In the case that the content is not popular enough, PPCS first caches initial chunks of the content at the edge node and then progressively continues caching subsequent chunks at upstream Content Nodes (CNs) along the delivery path over time, according to the content popularity and each CN position. Therefore, PPCS efficiently avoids wasting cache space for storing on-path content duplicates and improves cache diversity by allowing no more than one replica of a specified content to be cached. To enable a complete ICN caching solution for communication networks, we also propose an autonomous replacement policy to optimize the cache utilization by maximizing the utility of each CN from caching content items. By simulation, we show that PPCS, utilizing edge-computing for the joint optimization of caching decision and replacement policies, considerably outperforms relevant existing ICN caching strategies in terms of latency (number of hops), cache redundancy, and content availability (hit rate), especially when the CN’s cache size is small.