1,305 research outputs found
Security, Privacy, and Access Control in Information-Centric Networking: A Survey
Information-Centric Networking (ICN) is a new networking paradigm, which
replaces the widely used host-centric networking paradigm in communication
networks (e.g., Internet, mobile ad hoc networks) with an information-centric
paradigm, which prioritizes the delivery of named content, oblivious of the
contents origin. Content and client security are more intrinsic in the ICN
paradigm versus the current host centric paradigm where they have been
instrumented as an after thought. By design, the ICN paradigm inherently
supports several security and privacy features, such as provenance and identity
privacy, which are still not effectively available in the host-centric
paradigm. However, given its nascency, the ICN paradigm has several open
security and privacy concerns, some that existed in the old paradigm, and some
new and unique. In this article, we survey the existing literature in security
and privacy research sub-space in ICN. More specifically, we explore three
broad areas: security threats, privacy risks, and access control enforcement
mechanisms.
We present the underlying principle of the existing works, discuss the
drawbacks of the proposed approaches, and explore potential future research
directions. In the broad area of security, we review attack scenarios, such as
denial of service, cache pollution, and content poisoning. In the broad area of
privacy, we discuss user privacy and anonymity, name and signature privacy, and
content privacy. ICN's feature of ubiquitous caching introduces a major
challenge for access control enforcement that requires special attention. In
this broad area, we review existing access control mechanisms including
encryption-based, attribute-based, session-based, and proxy re-encryption-based
access control schemes. We conclude the survey with lessons learned and scope
for future work.Comment: 36 pages, 17 figure
On Content-centric Wireless Delivery Networks
The flux of social media and the convenience of mobile connectivity has
created a mobile data phenomenon that is expected to overwhelm the mobile
cellular networks in the foreseeable future. Despite the advent of 4G/LTE, the
growth rate of wireless data has far exceeded the capacity increase of the
mobile networks. A fundamentally new design paradigm is required to tackle the
ever-growing wireless data challenge.
In this article, we investigate the problem of massive content delivery over
wireless networks and present a systematic view on content-centric network
design and its underlying challenges. Towards this end, we first review some of
the recent advancements in Information Centric Networking (ICN) which provides
the basis on how media contents can be labeled, distributed, and placed across
the networks. We then formulate the content delivery task into a content rate
maximization problem over a share wireless channel, which, contrasting the
conventional wisdom that attempts to increase the bit-rate of a unicast system,
maximizes the content delivery capability with a fixed amount of wireless
resources. This conceptually simple change enables us to exploit the "content
diversity" and the "network diversity" by leveraging the abundant computation
sources (through application-layer encoding, pushing and caching, etc.) within
the existing wireless networks. A network architecture that enables wireless
network crowdsourcing for content delivery is then described, followed by an
exemplary campus wireless network that encompasses the above concepts.Comment: 20 pages, 7 figures,accepted by IEEE Wireless
Communications,Sept.201
Dynamic Video Streaming in Caching-enabled Wireless Mobile Networks
Recent advances in software-defined mobile networks (SDMNs), in-network
caching, and mobile edge computing (MEC) can have great effects on video
services in next generation mobile networks. In this paper, we jointly consider
SDMNs, in-network caching, and MEC to enhance the video service in next
generation mobile networks. With the objective of maximizing the mean
measurement of video quality, an optimization problem is formulated. Due to the
coupling of video data rate, computing resource, and traffic engineering
(bandwidth provisioning and paths selection), the problem becomes intractable
in practice. Thus, we utilize dual-decomposition method to decouple those three
sets of variables. Extensive simulations are conducted with different system
configurations to show the effectiveness of the proposed scheme
Asymptotically-Optimal Incentive-Based En-Route Caching Scheme
Content caching at intermediate nodes is a very effective way to optimize the
operations of Computer networks, so that future requests can be served without
going back to the origin of the content. Several caching techniques have been
proposed since the emergence of the concept, including techniques that require
major changes to the Internet architecture such as Content Centric Networking.
Few of these techniques consider providing caching incentives for the nodes or
quality of service guarantees for content owners. In this work, we present a
low complexity, distributed, and online algorithm for making caching decisions
based on content popularity, while taking into account the aforementioned
issues. Our algorithm performs en-route caching. Therefore, it can be
integrated with the current TCP/IP model. In order to measure the performance
of any online caching algorithm, we define the competitive ratio as the ratio
of the performance of the online algorithm in terms of traffic savings to the
performance of the optimal offline algorithm that has a complete knowledge of
the future. We show that under our settings, no online algorithm can achieve a
better competitive ratio than , where is the number of
nodes in the network. Furthermore, we show that under realistic scenarios, our
algorithm has an asymptotically optimal competitive ratio in terms of the
number of nodes in the network. We also study an extension to the basic
algorithm and show its effectiveness through extensive simulations
PTP: Path-specified Transport Protocol for Concurrent Multipath Transmission in Named Data Networks
Named Data Networking (NDN) is a promising Future Internet architecture to
support content distribution. Its inherent addressless routing paradigm brings
valuable characteristics to improve the transmission robustness and efficiency,
e.g. users are enabled to download content from multiple providers
concurrently. However, multipath transmission NDN is different from that in
Multipath TCP, i.e. the "paths" in NDN are transparent to and uncontrollable by
users. To this end, the user controls the traffic on all transmission paths as
an entirety, which leads to a noticeable problem of low bandwidth utilization.
In particular, the congestion of a certain path will trigger the traffic
reduction on the other transmission paths that are underutilized. Some
solutions have been proposed by letting routers balance the loads of different
paths to avoid congesting a certain path prematurely. However, the complexity
of obtaining an optimal load balancing solution (of solving a Multi-Commodity
Flow problem) becomes higher with the increasing network size, which limits the
universal NDN deployments. This paper introduces a compromising solution -
Path-specified Transport Protocol (PTP). PTP supports both the label switching
and the addressless routing schemes. Specifically, the label switching scheme
facilitates users to precisely control the traffic on each transmission path,
and the addressless routing scheme maintains the valuable feature of retrieving
content from any provider to guarantee robustness. As the traffic on a
transmission path can be explicitly controlled by consumers, load balancing is
no longer needed in routers, which reduce the computational burden of routers
and consequently increase the system scalability. The experimental results show
that PTP significantly increases the users' downloading rates and improved the
network throughput.Comment: journal, 21 page
Cache-enabled Wireless Networks with Opportunistic Interference Alignment
Both caching and interference alignment (IA) are promising techniques for
future wireless networks. Nevertheless, most of existing works on cache-enabled
IA wireless networks assume that the channel is invariant, which is unrealistic
considering the time-varying nature of practical wireless environments. In this
paper, we consider realistic time-varying channels. Specifically, the channel
is formulated as a finite-state Markov channel (FSMC). The complexity of the
system is very high when we consider realistic FSMC models. Therefore, we
propose a novel big data reinforcement learning approach in this paper. Deep
reinforcement learning is an advanced reinforcement learning algorithm that
uses deep network to approximate the value-action function. Deep
reinforcement learning is used in this paper to obtain the optimal IA user
selection policy in cache-enabled opportunistic IA wireless networks.
Simulation results are presented to show the effectiveness of the proposed
scheme
Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks
As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Machine Learning for Multimedia Communications
Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
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