A Survey on Piracy Protection Techniques in Digital Cinema Watermarking Schemes

Watermarking is used in several areas such as CDNs (Content Delivery Networks), as part of the rights management system for counterfeit prevention. Watermarking schemes need some additional features in order to be used in digital cinema. In fact, extra watermarks are added to movies by cinema projectors in projection time, which help identify the cinema hall in which the illegal copy has been recorded. But distortions caused by hand vibrations and the point of view angle make it difficult to recover the watermark. This makes it necessary to be distortion-resistant for the watermarking schemes used in digital cinema. On the other hand, theatre owners would like to locate the camcorder that has recorded the pirate copy. This requires watermarking schemes to be able to estimate the distance and angle using the distributed pirate copy. In this chapter, we present a review on watermarking techniques specifically designed to attack the aforementioned problems

A Holistic Approach to Lowering Latency in Geo-distributed Web Applications

User perceived end-to-end latency of web applications have a huge impact on the revenue for many businesses. The end-to-end latency of web applications is impacted by: (i) User to Application server (front-end) latency which includes downloading and parsing web pages, retrieving further objects requested by javascript executions; and (ii) Application and storage server(back-end) latency which includes retrieving meta-data required for an initial rendering, and subsequent content based on user actions. Improving the user-perceived performance of web applications is challenging, given their complex operating environments involving user-facing web servers, content distribution network (CDN) servers, multi-tiered application servers, and storage servers. Further, the application and storage servers are often deployed on multi-tenant cloud platforms that show high performance variability. While many novel approaches like SPDY and geo-replicated datastores have been developed to improve their performance, many of these solutions are specific to certain layers, and may have different impact on user-perceived performance. The primary goal of this thesis is to address the above challenges in a holistic manner, focusing specifically on improving the end-to-end latency of geo-distributed multi-tiered web applications. This thesis makes the following contributions: (i) First, it reduces user-facing latency by helping CDNs identify and map objects that are more critical for page-load latency to the faster CDN cache layers. Through controlled experiments on real-world web pages, we show the potential of our approach to reduce hundreds of milliseconds in latency without affecting overall CDN miss rates. (ii) Next, it reduces back-end latency by optimally adapting the datastore replication policies (including number and location of replicas) to the heterogeneity in workloads. We show the benefits of our replication models using real-world traces of Twitter, Wikipedia and Gowalla on a 8 datacenter Cassandra cluster deployed on EC2. (iii) Finally, it makes multi-tier applications resilient to the inherent performance variability in the cloud through fine-grained request redirection. We highlight the benefits of our approach by deploying three real-world applications on commercial cloud platforms

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Energy Efficiency of P2P and Distributed Clouds Networks

Since its inception, the Internet witnessed two major approaches to communicate digital content to end users: peer to peer (P2P) and client/server (C/S) networks. Both approaches require high bandwidth and low latency physical underlying networks to meet the users’ escalating demands. Network operators typically have to overprovision their systems to guarantee acceptable quality of service (QoS) and availability while delivering content. However, more physical devices led to more ICT power consumption over the years. An effective approach to confront these challenges is to jointly optimise the energy consumption of content providers and transportation networks. This thesis proposes a number of energy efficient mechanisms to optimise BitTorrent based P2P networks and clouds based C/S content distribution over IP/WDM based core optical networks. For P2P systems, a mixed integer linear programming (MILP) optimisation, two heuristics and an experimental testbed are developed to minimise the power consumption of IP/WDM networks that deliver traffic generated by an overlay layer of homogeneous BitTorrent users. The approach optimises peers’ selection where the goal is to minimise IP/WDM network power consumption while maximising peers download rate. The results are compared to typical C/S systems. We also considered Heterogeneous BitTorrent peers and developed models that optimise P2P systems to compensate for different peers behaviour after finishing downloading. We investigated the impact of core network physical topology on the energy efficiency of BitTorrent systems. We also investigated the power consumption of Video on Demand (VoD) services using CDN, P2P and hybrid CDN-P2P architectures over IP/WDM networks and addressed content providers efforts to balance the load among their data centres. For cloud systems, a MILP and a heuristic were developed to minimise content delivery induced power consumption of both clouds and IP/WDM networks. This was done by optimally determining the number, location and internal capability in terms of servers, LAN and storage of each cloud, subject to daily traffic variation. Different replication schemes were studied revealing that replicating content into multiple clouds based on content popularity is the optimum approach with respect to energy. The model was extended to study Storage as a Service (StaaS). We also studied the problem of virtual machine placement in IP/WDM networks and showed that VM Slicing is the best approach compared to migration and replication schemes to minimise energy. Finally, we have investigated the utilisation of renewable energy sources represented by solar cells and wind farms in BitTorrent networks and content delivery clouds, respectively. Comprehensive modelling and simulation as well as experimental demonstration were developed, leading to key contributions in the field of energy efficient telecommunications

Journal of Telecommunications and Information Technology, 2004, nr 2

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CacheCash: A Cryptocurrency-based Decentralized Content Delivery Network

Online content delivery has witnessed dramatic growth recently with traffic consuming over half of today’s Internet bandwidth. This escalating demand has motivated content publishers to move outside the traditional solutions of infrastructure-based content delivery networks (CDNs). Instead, many are employing peer-to-peer data transfers to reduce the service cost and avoid bandwidth over-provision to handle peak demands. Unfortunately, the open access work model of this paradigm, which allows anyone to join, introduces several design challenges related to security, efficiency, and peer availability. In this dissertation, we introduce CacheCash, a cryptocurrency-based decentralized content distribution network designed to address these challenges. CacheCash bypasses the centralized approach of CDN companies for one in which end users organically set up new caches in exchange for cryptocurrency tokens. Thus, it enables publishers to hire caches on an as-needed basis, without constraining these parties with long-term business commitments. To address the challenges encountered as the system evolved, we propose a number of protocols and techniques that represent basic building blocks of CacheCash’s design. First, motivated by the observation that conventional security assessment tools do not suit cryptocurrency-based systems, we propose ABC, a threat modeling framework capable of identifying attacker collusion and the new threat vectors that cryptocurrencies introduce. Second, we propose CAPnet, a defense mechanism against cache accounting attacks (i.e., a client pretends to be served allowing a colluding cache to collect rewards without doing any work). CAPnet features a bandwidth expenditure puzzle that clients must solve over the content before caches are given credit, which bounds the effectiveness of this collusion case. Third, to make it feasible to reward caches per data chunk served, we introduce MicroCash, a decentralized probabilistic micropayment scheme that reduces the overhead of processing these small payments. MicroCash implements several novel ideas that make micropayments more suitable for delay-sensitive applications, such as online content delivery. CacheCash combines the previous techniques to produce a novel service-payment exchange protocol that secures the content distribution process. This protocol utilizes gradual content disclosure and partial payment collection to encourage the honest collaborative work between participants. We present a detailed game theoretic analysis showing how to exploit rational financial incentives to address several security threats. This is in addition to various performance optimization mechanisms that promote system efficiency and scalability. Lastly, we evaluate system performance and show that modest machines can serve/retrieve content at a high bitrate with minimal overhead

Dynamic Adaptation of Brain Networks from Rest to Task and Application to Stroke Research

Examining how motor task modulates brain activity plays a critical role of understanding how cerebral motor system works and could further be applied in the research of motor disability due to neurological diseases. Recent advances in neuroimaging have resulted in numerous studies focusing on motor-induced modulation of brain activity and most widely used strategy of these studies was identifying activated brain regions during motor task through event-related/block-design experiment paradigms. Despite progress obtained, motor-related activation analysis mainly focused on modulation of brain activities for individual brain regions. However, human brain is known to be an integrated network, and the adaptation of brain in response to motor task could be reflected by the modulation of brain networks. Thus, investigating the spatiotemporal modulation of task-state brain networks during motor task would provide system-level information regarding the underlying adaptation of cerebral motor system in response to the motor task and could be further applied in the research of motor disability due to neurological diseases. Although the task-state functional connectivity (FC) and networks have been examined by previous studies, there are still several aspects needed to be explored: (1) Previous task-state FC studies were mainly based on functional magnetic resonance imaging (fMRI) and potential of applying functional near-infrared spectroscopy (fNIRS), which is a promising complementary modality to fMRI because of its low cost and relatively high temporal resolution (10 Hz in sampling rate compared with less than 1Hz in sampling rate for fMRI), in task-state FC studies should be explored. (2) In the fMRI studies, the task-state brain network was mainly investigated from the perspective of static FC, which focuses on the spatial pattern of the task-state brain networks. However, brain activities and cognitive processes are known to be dynamic and adaptive and the newly emerging dynamic FC analysis could further provide temporal patterns of the task-state brain networks. Thus, the spatiotemporal pattern of task-state brain network during motor task is still needed to be investigated by dynamic FC analysis based on fMRI; (3) Task-state brain network analysis has not been applied in the research of stroke, and the relationship between task-state brain network and stroke recovery has not been investigated; Therefore, in this thesis, we aim to investigate the task-state brain networks during motor task using both static and dynamic FC analysis based on fNIRS and fMRI to reveal the motor task-specific spatiotemporal changes of brain networks compared with resting conditions, and further applied the task-state brain network analysis in the research of stroke recovery In this thesis, fMRI and fNIRS were employed to record the brain hemodynamic signals, and static FC and dynamic FC were used to investigate spatiotemporal pattern of task-state brain network during motor task. In addition, the fMRI data of stroke patients were recorded at four time points post stroke, and the reorganization of task-state brain network as well as its relationship to stroke recovery were examined. Specific results are described as follow: (1) Through static FC analysis of fNIRS during rest and motor preparation, increased FC were identified during motor preparation, especially the FC connecting right dorsolateral prefrontal cortex (DLPFC) with contralateral primary somatosensory cortex (S1) and primary motor cortex (M1) as well as the FC connecting contralateral S1 with ipsilateral S1 and M1. Channels located in sensorimotor networks and right DLPFC were also found activated during motor preparation. Our findings demonstrated that the sensorimotor network was interacting with high-level cognitive brain network to maintain the motor preparation state. (2) Through dynamic FC analysis of fNIRS during rest and motor execution, increased variability of FC connecting contralateral premotor and supplementary motor cortex (PMSMC) and M1 was identified, and the nodal strength variability of these two brain regions were also increased during motor execution. Our findings demonstrated that contralateral M1 and PMSMC were interacting with each other actively and dynamically to facilitate the fist opening and closing. (3) Through dynamic FC analysis on fMRI data, two principal FC states during rest and one principal FC state during motor task were identified. The 1st principal FC state in rest was similar to that in task, which likely represented intrinsic network architecture and validated the broadly similar spatial patterns between rest and task. However, the presence of a 2nd principal FC state with increased FC between default-mode network (DMN) and motor network (MN) in rest with shorter "dwell time" could imply the transient functional relationship between DMN and MN to establish the "default mode" for motor system. In addition, the more frequent shifting between two principal FC states in rest indicated that the brain networks dynamically maintained the "default mode" for the motor system. In contrast, during task, the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity, validating the distinct temporal patterns between rest and task. Our findings suggested that the principal states could show a link between the rest and task states, and verified our hypothesis on overall spatial similarity but distinct temporal patterns of dynamic brain networks between rest and task states. (4) Task-state motor network was applied in the research of motor disability due to stroke and topological reorganization of task-state motor network was identified during sub-acute phase post stroke. In addition, for the first time, our study found the topological configuration of task-state motor network at the early recovery stage were capable of predicting the motor function restoration during sub-acute phase. In general, the findings demonstrated the reorganization and potential prognostic value of task-state brain network after stroke, which provided new insights into understanding the brain reorganization and stroke rehabilitation. In summary, this thesis used two neuroimaging modalities (fMRI and fNIRS) to investigate how brain networks, especially the motor network and high-level cognitive network, would reorganize spatiotemporally from resting-state to motor tasks through both static and dynamic FC analysis, and further applied the task-state brain network analysis in the research of motor disability due to stroke. Our findings revealed the underlying spatiotemporal adaptation of brain networks in response to motor task and demonstrated the potential clinical prognostic value of task-state motor network during stroke recovery. The novelties of this thesis are as follow: (1) dynamic FC was innovatively applied in revealing the motor task-specific spatiotemporal changes of brain networks compared with resting conditions; (2) task-state motor network was applied in the research of stroke recovery; (3) static and dynamic FC analysis were innovatively applied in fNIRS data.Ph.D., Biomedical Engineering -- Drexel University, 201