406 research outputs found
Named data networking for efficient IoT-based disaster management in a smart campus
Disasters are uncertain occasions that can impose a drastic impact on human life and building infrastructures. Information and Communication Technology (ICT) plays a vital role in coping with such situations by enabling and integrating multiple technological resources to develop Disaster Management Systems (DMSs). In this context, a majority of the existing DMSs use networking architectures based upon the Internet Protocol (IP) focusing on location-dependent communications. However, IP-based communications face the limitations of inefficient bandwidth utilization, high processing, data security, and excessive memory intake. To address these issues, Named Data Networking (NDN) has emerged as a promising communication paradigm, which is based on the Information-Centric Networking (ICN) architecture. An NDN is among the self-organizing communication networks that reduces the complexity of networking systems in addition to provide content security. Given this, many NDN-based DMSs have been proposed. The problem with the existing NDN-based DMS is that they use a PULL-based mechanism that ultimately results in higher delay and more energy consumption. In order to cater for time-critical scenarios, emergence-driven network engineering communication and computation models are required. In this paper, a novel DMS is proposed, i.e., Named Data Networking Disaster Management (NDN-DM), where a producer forwards a fire alert message to neighbouring consumers. This makes the nodes converge according to the disaster situation in a more efficient and secure way. Furthermore, we consider a fire scenario in a university campus and mobile nodes in the campus collaborate with each other to manage the fire situation. The proposed framework has been mathematically modeled and formally proved using timed automata-based transition systems and a real-time model checker, respectively. Additionally, the evaluation of the proposed NDM-DM has been performed using NS2. The results prove that the proposed scheme has reduced the end-to-end delay up from 2% to 10% and minimized up to 20% energy consumption, as energy improved from 3% to 20% compared with a state-of-the-art NDN-based DMS
Access Control Mechanisms in Named Data Networks:A Comprehensive Survey
Information-Centric Networking (ICN) has recently emerged as a prominent
candidate for the Future Internet Architecture (FIA) that addresses existing
issues with the host-centric communication model of the current TCP/IP-based
Internet. Named Data Networking (NDN) is one of the most recent and active ICN
architectures that provides a clean slate approach for Internet communication.
NDN provides intrinsic content security where security is directly provided to
the content instead of communication channel. Among other security aspects,
Access Control (AC) rules specify the privileges for the entities that can
access the content. In TCP/IP-based AC systems, due to the client-server
communication model, the servers control which client can access a particular
content. In contrast, ICN-based networks use content names to drive
communication and decouple the content from its original location. This
phenomenon leads to the loss of control over the content causing different
challenges for the realization of efficient AC mechanisms. To date,
considerable efforts have been made to develop various AC mechanisms in NDN. In
this paper, we provide a detailed and comprehensive survey of the AC mechanisms
in NDN. We follow a holistic approach towards AC in NDN where we first
summarize the ICN paradigm, describe the changes from channel-based security to
content-based security and highlight different cryptographic algorithms and
security protocols in NDN. We then classify the existing AC mechanisms into two
main categories: Encryption-based AC and Encryption-independent AC. Each
category has different classes based on the working principle of AC (e.g.,
Attribute-based AC, Name-based AC, Identity-based AC, etc). Finally, we present
the lessons learned from the existing AC mechanisms and identify the challenges
of NDN-based AC at large, highlighting future research directions for the
community.Comment: This paper has been accepted for publication by the ACM Computing
Surveys. The final version will be published by the AC
Caching Video-on-Demand in Metro and Access Fog Data Centres
This paper examines the utilization of metro fog data centres and access fog datacentres with integrated solar cells and Energy Storage Devices (ESDs) to assist cloud data centres in caching Video-on-Demand content and hence, reduce the networking power consumption. A Mixed Integer Linear Programming (MILP) model is used to optimize the delivery of the content from cloud, metro fog, or access fog datacentres. The results for a range of data centre parameters show that savings by up to 38% in the transport network power consumption can be achieved when VoD is optimally served from fully renewable-powered cloud or metro fog data centres or from access fog data centres with 250 m2 solar cells. Additional 8% savings can be achieved when using ESDs of 100 kWh capacity in the access fog data centres
Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks
Trust can be defined as a measure to determine which source of information is
reliable and with whom we should share or from whom we should accept
information. There are several applications for trust in Online Social Networks
(OSNs), including social spammer detection, fake news detection, retweet
behaviour detection and recommender systems. Trust prediction is the process of
predicting a new trust relation between two users who are not currently
connected. In applications of trust, trust relations among users need to be
predicted. This process faces many challenges, such as the sparsity of
user-specified trust relations, the context-awareness of trust and changes in
trust values over time. In this dissertation, we analyse the state-of-the-art
in pair-wise trust prediction models in OSNs. We discuss three main challenges
in this domain and present novel trust prediction approaches to address them.
We first focus on proposing a low-rank representation of users that
incorporates users' personality traits as additional information. Then, we
propose a set of context-aware trust prediction models. Finally, by considering
the time-dependency of trust relations, we propose a dynamic deep trust
prediction approach. We design and implement five pair-wise trust prediction
approaches and evaluate them with real-world datasets collected from OSNs. The
experimental results demonstrate the effectiveness of our approaches compared
to other state-of-the-art pair-wise trust prediction models.Comment: 158 pages, 20 figures, and 19 tables. This is my PhD thesis in
Macquarie University, Sydney, Australi
Reservoir SMILES: Towards SensoriMotor Interaction of Language and Embodiment of Symbols with Reservoir Architectures
Language involves several hierarchical levels of abstraction. Most models focus on a particular level of abstraction making them unable to model bottom-up and top-down processes. Moreover, we do not know how the brain grounds symbols to perceptions and how these symbols emerge throughout development. Experimental evidence suggests that perception and action shape one-another (e.g. motor areas activated during speech perception) but the precise mechanisms involved in this action-perception shaping at various levels of abstraction are still largely unknown. My previous and current work include the modelling of language comprehension, language acquisition with a robotic perspective, sensorimotor models and extended models of Reservoir Computing to model working memory and hierarchical processing. I propose to create a new generation of neural-based computational models of language processing and production; to use biologically plausible learning mechanisms relying on recurrent neural networks; create novel sensorimotor mechanisms to account for action-perception shaping; build hierarchical models from sensorimotor to sentence level; embody such models in robots
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