We study coverage algorithms for deployment of WiFO - a hybrid femtocell architecture based on WiFi and Free Space Optical (FSO) technologies to significantly increase wireless throughput of existing WiFi networks. In contrast with existing WiFi networks, WiFO incorporates small cell size, line of sight transmissions, and the Gaussian attenuation model of light intensity into the design of the coverage algorithms. Specifically, two types of coverage scenarios - the non- overlapped and the overlapped coverage are presented. For the non-overlapped coverage, we show the bounds of coverage efficiency for a finite rectangle area based on a simple model. For the overlapped coverage, user's SNR profiles are assumed to be given. Using these, three algorithms for determining the optimized deployment based on user's SNR profiles and Gaussian light attenuation models are proposed and evaluated. Simulations results are provided to verify the proposed algorithms.

Recent years have witnessed an explosive growth in the number of wireless devices. This development has fueled much research in wireless access technologies to efficiently use radio frequency spectrum. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. In this paper, we describe WiFO, a hybrid WiFi and FSO high-speed wireless local area network of femtocells that can provide high bit rates while maintaining seamless mobility. Importantly, we introduce a novel location-assisted coding (LAC) technique, based on which, the number of novel rate allocation algorithms is proposed to increase throughput and reduce interference for multiple users in a dense array of overlapped femtocells. Both theoretical analysis and numerical results show orders of magnitude increase in throughput using LAC over existing schemes for various random topologies.

We describe WiFO, a novel communication system that integrates free space optical communication and WiFi technologies to provide an order of magnitude improvement in wireless capacity. To provide high bit rates and large coverage, WiFO employs an array of LED-based transmitters. Each modulates a beam of invisible light using a Pulse Amplitude Modulation (PAM) scheme. A WiFO receiver is capable of receiving signals from multiple transmitters simultaneously. We describe a PAM decoding scheme that estimates the channel parameters, then use them to determine the decoding thresholds for minimizing the bit error rate. Furthermore, we characterize the conditions on channel parameters for perfect signal recovery. Simulations and theoretical analyses are provided to validate the proposed scheme.

While existing virtual machine technologies provide easy-to-use platforms for distributed computing applications, many are far from efficient and not designed to accommodate diverse objectives, which dramatically penalizes their performance. These shortcomings arise from 1) not having a formal optimization framework that readily leads to algorithmic solutions for diverse objectives; 2) not incorporating the knowledge of the underlying network topologies and the communication/interaction patterns among the virtual machines/services, and 3) not considering the time-varying aspects of real-world environments. This paper formalizes an optimization framework and develops corresponding algorithmic solutions using Markov Decision Process and Q-Learning for virtual machine/service placement and migration for distributed computing in time-varying environments. Importantly, the knowledge of the underlying topologies of the computing infrastructure, the interaction patterns between the virtual machines, and the dynamics of the supported applications will be formally characterized and incorporated into the proposed algorithms in order to improve performance results for small-scale and large-scale networks are provided to verify our solution approach.

We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from site to site to collect data from the sensors periodically. There are different costs associated with the mobile element moving from one site to another, and different rewards for obtaining data at different sensors. Furthermore, the costs and the rewards are assumed to change abruptly. The goal is to find a “fast” optimal movement pattern/policy of the mobile element that optimizes for the costs and rewards in non-stationary environments. We formulate and solve this problem using a novel optimization framework called fast Markov decision process (FMDP). The proposed FMDP framework extends the classical Markov decision process theory by incorporating the notion of mixing time that allows for the trade-off between the optimality and the convergence rate to the optimality of a policy. Theoretical and simulation results are provided to verify the proposed approach.

Efficiently managing virtual machines (VMs) plays a key role in improving the resource usage and power consumption of datacenters. However, most of the existing techniques have been optimized for each system criterion separately (e.g., either platform layer or virtualization layer). Such approaches are simple to implement, but they usually result in poor system performance or under-utilization of the resources. In this paper, we investigate the problem of optimal VM management (i.e., placement and migration) in datacenters via a multi-objective function. We show that optimizing the proposed multi-objective function reduces not only the energy consumption, but also the cross network traffic among platforms. Due to the high complexity of the combinatorial optimization problem, we propose an efficient heuristic algorithm to find a near optimal solution based on the relaxed convex optimization framework. We provide both theoretical analysis and simulations to show the effectiveness of the proposed approach over the existing approaches.

We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the mobile element moving from one site to another, and different rewards for obtaining data at different sensors. Furthermore, the costs and the rewards are assumed to change abruptly. The goal is to find a “fast” optimal movement pattern/policy of the mobile element that optimizes for the costs and rewards in non-stationary environments. We propose a novel optimization framework called Fast Markov Decision Process (FMDP) to solve this problem. The proposed FMDP framework extends the classical Markov Decision Process theory by incorporating the notion of mixing time that allows for the tradeoff between the optimality and the convergence rate to the optimality of a policy. Theoretical and simulation results are provided to verify the proposed approach.

With the recent rise of mobile and multimedia applications, other considerations such as power consumption and/or Quality of Service (QoS) are becoming increasingly important factors in designing network protocols. As such, we present a new framework for designing robust network protocols under varying network conditions that attempts to integrate various given objectives while satisfying some pre-specified levels of Quality of Service. The proposed framework abstracts a network protocol as a queuing policy, and relies on convex relaxation methods and the theory of mixing time for finding the fast queuing policies that drive the distribution of packets in a queue to a given target stationary distribution. In addition, we show how to augment the basic proposed framework to obtain a queuing policy that produces ε-approximation to the target distribution with faster convergence time which is useful in fast-changing network conditions. Both theoretical and simulation results are presented to verify the effectiveness of the proposed framework.

We present an analytical framework for providing Quality of Service (QoS) using queuing policies that achieves a given target distribution of packets in a network queue. To a large extent, the stationary distribution of packets in the queue resulted from employing a certain queuing policy directly controls the typical QoS metrics for multimedia applications. Therefore, using the packet distribution in the queue as the metric, the proposed framework allows for a more general and precise control of QoS beyond the standard metrics such as bandwidth, jitter, loss, and delay. Moreover, the proposed framework aims to find a fast queuing policy that achieves a given target stationary distribution. This fast adaptation is especially useful for multimedia applications in fast-changing network conditions. As an example, we present a general procedure for obtaining a queuing policy that optimizes for a given arbitrary objective along with the standard QoS requirements. Both theory and simulation results are presented to verify our framework.

We propose a distributed data replenishment mechanism for some distributed peer-to-peer-based storage systems that automates the process of maintaining a sufficient level of data redundancy to ensure the availability of data in presence of peer departures and failures. The dynamics of peers entering and leaving the network are modeled as a stochastic process. A novel analytical time-backward technique is proposed to bound the expected time for a piece of data to remain in P2P systems. Both theoretical and simulation results are in agreement, indicating that the data replenishment via random linear network coding (RLNC) outperforms other popular strategies. Specifically, we show that the expected time for a piece of data to remain in a P2P system, the longer the better, is exponential in the number of peers used to store the data for the RLNC-based strategy, while they are quadratic for other strategies.

We investigate an underlying mathematical model and algorithms for optimizing the performance of a class of distributed systems over the Internet. Such a system consists of a large number of clients who communicate with each other indirectly via a number of intermediate servers. Optimizing the overall performance of such a system then can be formulated as a client-server assignment problem whose aim is to assign the clients to the servers in such a way to satisfy some prespecified requirements on the communication cost and load balancing. We show that 1) the total communication load and load balancing are two opposing metrics, and consequently, their tradeoff is inherent in this class of distributed systems; 2) in general, finding the optimal client-server assignment for some prespecified requirements on the total load and load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex optimization for finding the approximate solution. Our simulation results indicate that the proposed algorithm produces superior performance than other heuristics, including the popular Normalized Cuts algorithm.

Conventional Quality of Service (QoS) for multimedia networking applications are typically specified by a certain set of requirements on latency, jitter, bandwidth, and packet loss rate. In this paper, we introduce a novel approach to QoS via the notion of distribution shaping in which a pre-specified distribution of packets in a queue is achieved via queuing policies. In a way, the distribution-based QoS is more general since the distribution of packets in the queue captures all the statistical information regarding the throughput, latency, delay jitter, and packet loss rate. We present a convex optimization framework for obtaining the optimal queueing policy that drives any initial distribution of packets in the queue to the desired distribution in the fastest time. We then augment the proposed framework to obtain a queueing policy that produces ∈-approximation to the target distribution with even faster convergence time. The augmented framework is useful in dynamic settings where traffic statistics change frequently, and thus fast adaptation is preferable. Both simulation and theoretical results are provided to verify our approach.

Multiple measurement vector (MMV) problem addresses the recovery of a set of sparse vectors that have common sparsity pattern. In this paper, we consider a variant of the MMV problem where the common sparsity pattern is obfuscated by an additive noise. Specifically, we study the conditions for perfect reconstruction of the original sparsity pattern. Based on these, we develop a tuning-free algorithm for recovering jointly sparse solutions via the transfer optimization approach. We provide a preliminary numerical evaluation to illustrate our approach.

For many Internet systems, clients (users) interact with each other indirectly via intermediate servers. In such systems, it is crucial to assign users to servers based on the interaction patterns among the users, in such a way to reduce communication costs and to balance loads among the servers. Finding the optimal client-server assignment has been previously shown to be NP-complete, and approximate centralized solutions have been proposed. In this paper, we present a distributed scheme for finding an approximately optimal client-server assignment in which the servers exchange and update information about their loads iteratively among themselves. Based on these, they make decisions to reassign their users to others iteratively so that final client-server assignment is approximately optimal. Theoretical results on the convergence rate and complexity of the proposed algorithm are presented. Simulations results show the effectiveness of the proposed algorithm for different patterns of user interactions.

We investigate a class of randomized peer-to-peer (P2P) approach to Internet-wide distributed data storage systems that promises to reduce the coordination complexity and increases performance scalability. The core of these randomized P2P data storage systems is the data replenishment mechanism. The data replenishment automates the process of maintaining a sufficient level of data redundancy to ensure the availability of data in presence of peer departures and failures. The dynamics of peers entering and leaving the network is modeled as a stochastic process. A novel analytical time-backward technique is proposed to bound the expected time for a piece of data to remain in P2P systems. Both theoretical and simulation results are in agreement, indicating that a proposed data replenishment via random linear network coding (RLNC) outperforms other strategies that employ popular repetition and channel coding techniques. Specifically, we show that the expected time for a piece of data to remain in a P2P system, the longer the better, is exponential in the redundancy amount for the RLNC-based strategy, while they are quadratic for other strategies.

We investigate an underlying mathematical model and algorithm for optimizing the performance of a class of distributed systems over the Internet. Such a system consists of a large number of clients who communicate with each other indirectly via a number of intermediate servers. Optimizing the overall performance of such a system then can be formulated as a client server assignment problem whose aim is to assign the clients to the servers in such a way to satisfy some prespecified requirements on the communication cost and load balancing. We show that 1) the total communication load and load balancing are two opposing metrics, and consequently, their trade-off is inherent to this class of distributed systems; 2) in general, finding the optimal client-server assignment for some pre-specified requirements on the total load and load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex optimization for finding the approximate solution to the client-server assignment problem. Our simulation results indicate that the proposed algorithm produces superior performance than other heuristics, including the popular Normalized Cuts algorithm.

In this paper, we present our initial results of improving communication performance in chip multicore processors by applying Network Coding (NC) at intermediate routers. Routers with NC capability enable two flit flows on the same communication links at the same time. Consequently, simulation results show that throughput and delay are improved with the proposed NC architecture. For saturated uni-cast communications in a 6-core network-on-a-chip, throughput can be improved up to 2.3 times with a negligible penalty of average latency at about 2.5 cycles. On the other hand, for saturated multi-cast communications in a 9-core network-on-a-chip, average latency can be reduced from over 1000 cycles to just about 37 cycles and throughput can be increased by 1.4 times.

The design of network protocols is a complicated and tedious endeavor. For instance, designing a MAC layer protocol for the 802.11 standard typically involves a number of high-level decisions (e.g., conditions for backoff steps) followed by an individual tuning of numeric parameters (e.g., backoff factors), for a variety of network conditions. A different way to view this design process is that of a designer being forced to fully specify a solution to a complex problem. At the other extreme of the programming spectrum lie reinforcement learning techniques which only require a minimal problem specification from the programmer. Adaptation-Based Programming (ABP) is a novel programming paradigm which bridges the gap between the above extremes. ABP allows a programmer to specify as much of the solution they want and have the learning system optimize the rest.

We propose a framework for optimizing the quality of service of multiple simultaneous flows in wireless access networks via network coding. Specifically, we consider the typical scenario in which multiple flows originate from multiple sources in the Internet and terminate at multiple users in a wireless network. In the current infrastructure, the wireless base station is responsible for relaying the packets from the Internet to the wireless users without any modification to the packet content. On the other hand, in the proposed approach, the wireless base station is allowed to perform network coding by appropriate linear mixing and channel coding of packets from different incoming flows before broadcasting a single flow of mixed or coded packets to all wireless users. Each user then uses an appropriate decoding method to recover its own packets from the set of coded packets that it receives. We show that in principle, for the given channel conditions and QoS requirements, appropriate mixing and channel coding of packets across different flows can lead to substantial quality improvement for both real-time and non-real time flows. On the other hand, blind mixing can be detrimental. We formulate this mixing problem as a combinatorial optimization problem, and propose a heuristic algorithm based on simulated-annealing method to approximate the optimal solution. Simulation results verify the performance improvement resulting from the proposed approach over the non-network coding and the state-of-the-art network coding approaches.

We propose a joint optimization of Network Coding and MIMO techniques to improve the downlink channel throughput of a wireless base station. Specifically, we consider a MIMO base station with multiple transmit antennas that serves multiple users simultaneously by generating multiple signal beams with well-defined beamforming weight vectors where each beam intends for a particular user. Given a large number of users and a small number of transmit antennas, a base station must decide, at any transmission opportunity, which group of users it should transmit packets to, in order to maximize the overall throughput. To that end, we propose a method for grouping users that takes advantages of NC technique and the orthogonality of user channels to improve the overall throughput on both unicast and broadcast transmissions. Our simulation results indicate that the proposed method can efficiently increase the throughput over existing techniques, especially in highly lossy environments.

This work addresses the uneven energy consumption problem in data gathering sensor networks where the nodes closer to the sink tend to consume more energy than those of the farther nodes. This energy unfairness can significantly shorten the life time of a sensor network. We propose a novel cross-sensor coding technique using On-Off keying which exploits (a) the trade-off between delay and bandwidth and (b) the network topology in order to reduce the overall energy consumption of the network, and to alleviate the problem of unequal energy consumption. We formulate our coding problem as an integer programming problem, and construct a number of codes based on different criteria. We show that using our proposed technique under certain assumptions, the energy consumption can be reduced significantly and the unequal energy consumption problem can be practically eliminated.

For many P2P systems, implementing right incentives and policies to promote efficient and fair resource sharing is the key to improve the overall system performance. In this paper, we propose a points-based incentive mechanism named Global Contribution (GC) approach that efficiently and naturally maintains fairness in a P2P network. In this approach, a proposed GC algorithm first calculates a global score for each peer that accurately reflects its bandwidth contribution to the entire network. Then, these scores are used in a proposed data transfer policy to determine whether one peer can download data from other peers. Thus, the GC approach achieves: 1) efficiently preventing free-riding, 2) naturally balancing the upload and download amounts in each peer, and 3) reducing rejections in transactions between cooperative peers. Moreover, the GC algorithm requires only private transaction history as an input and can be fully decentralized. Also, its time complexities are approximately O(N^2) in a centralized system and O(N) per peer in a decentralized system.

Current video streaming and storage systems such as YouTube, are based on the client-server model, thus do not scale well in terms of bandwidth and computation. This paper describes a peer-to-peer video delivery network (P2P-VDN) that provides both performance improvement and scalability based on three architectural elements. First, the proposed P2P-VDN employs a random network coding (RNC) scheme that breaks a video stream into multiple smaller pieces, codes, and disperses them throughout peers in the network, in such a way to maximize the probability of recovering the original video under peer departures and failures. Second, the proposed P2P-VDN employs a scalable mechanism for automating the data replenishment process using RNC that is necessary to maintain a sufficient level of redundancy for video stored in the network. Third, the proposed P2P-VDN employs a path-diversity protocol for a client to simultaneously stream a video from multiple peers in the P2P-VDN. Simulations demonstrate that under certain scenarios, our proposed P2P-VDN can result in bandwidth saving up to 60% over the traditional architecture.

Target tracking techniques usually assume that the target is within the detection range of one or more sensor nodes at known locations, so that the target's location can be determined based on these locations. Instead, we are interested in networks where most sensor nodes do not know their location; hence, it is most likely that the target is many hops away from the closest location-known sensors. For such networks, we propose a target localization system that can estimate the target location based on hop-count information only. We evaluate the performance of this system via simulation.

We investigate the achievable throughput regions for scenarios involving prioritized transmissions. Prioritized transmissions are useful in many multimedia networking applications where the transmitted data have an inherent hierarchy such that a piece of data at one level is only useful if all the pieces of data at all the lower levels are present. Formally, the prioritized transmission refers to the notion that, given M prioritized packets in the decreasing order of importance, a1, a2, ..., amto be delivered to a receiver, then the packet aiis useful to a receiver only if it has received all packets ajwith j < i successfully. Thus, the prioritized throughput can be abstractly represented j - 1 where j is the position of the first lost packet. In this paper we investigate the achievable throughput regions of prioritized transmissions from a source to multiple receivers via a shared and lossy channel. We assume that the source is an oracle such that it knows precisely whether a packet is lost or received at any receiver in any future time slot, thus it can schedule the packet transmission in such a way to maximize the receiver throughputs. We show that using network coding technique, the achievable throughput region for the broadcast scenarios can be substantially enlarged. Furthermore, for some erasure patterns, the achievable throughput region using network coding technique is optimal in the sense that no scheme can do better.

In this paper, we investigate a hybrid network coding technique to be used at a wireless base station (BS) or access point (AP) to increase the throughput efficiency of single-hop wireless networks. Traditionally, to provide reliability, lost packets from different flows (applications) are retransmitted separately, leading to inefficient use of wireless bandwidth. Using the proposed hybrid network coding approach, the BS encodes these lost packets, possibly from different flows together before broadcasting them to all wireless users. In this way, multiple wireless receivers can recover their lost packets simultaneously with a single transmission from the BS. Furthermore, simulations and theoretical analysis showed that when used in conjunction with an appropriate channel coding technique under typical channel conditions, this approach can increase the throughput efficiency up to 3.5 times over the automatic repeat request (ARQ), and up to 1.5 times over the HARQ techniques.

CAN is a well-known DHT technique for content- based P2P networks, where each node is assigned a zone in a virtual coordinate space to store the index of the data hashed into this zone. The dimension of this space is usually lower than the data dimension, thus we have the problem of dimension mismatch. This problem is widely addressed in the context of data retrieval that follows the traditional request/response model. However, little has been done for the publish/subscribe model, which is the focus of our paper. We show that dimension mismatch in CAN-based publish/subscribe applications poses new challenges. We furthermore investigate how a random projection approach can help reduce the negative effects of dimension mismatch. Our theoretical findings are complemented by a simulation-based evaluation.

Privacy protection of visual information is increasingly important as pervasive camera networks becomes more prevalent. The proposed scheme addresses the problem of preserving and controlling of the privacy visual data through two innovations. First, unlike the existing centralized control of privacy data, the proposed system allows individual users to make the final decision on every access to their privacy data. As such, it offers a much stronger form of privacy protection as the user no longer needs to trust, adhere or register his/her privacy preferences with a server. The second innovation is the development of a secure reversible data hiding scheme for embedding all the ownership information and privacy data into the obfuscated video bitstream. Not only has it resulted in an efficient design of protocols, the reversible data hiding allows perfect reconstruction of original data and supports arbitrary types of video obfuscation techniques. Impact of data hiding on bitrate and distortion is minimized through a rate-distortion optimization procedure and experimental results are provided to demonstrate its efficiency.

In recent years, the number of Peer-to-Peer (P2P) applications has increased significantly. One important problem in many P2P applications is how to efficiently disseminate data from a single source to multiple receivers on the Internet. A successful model used for analyzing this problem is a graph consisting of nodes and edges, with a capacity assigned to each edge. In some situations however, it is inconvenient to use this model. To that end, we propose to study the problem of efficient data dissemination in asource constraintnetwork. A source constraint network is modeled as a graph in which, the capacity is associated with a node, rather than an edge. The contributions of this paper include (a) a quantitative data dissemination in any source constraint network, (b) a set of topologies suitable for data dissemination in P2P networks, and (c) an architecture and implementation of a P2P system based on the proposed optimal topologies. We will present the experimental results of our P2P system deployed on PlanetLab nodes demonstrating that our approach achieves near optimal throughput while providing scalability, low delay and bandwidth fairness among peers.

In recent years, multimedia applications over the Internet become increasingly popular. However, packet loss, delay, and time-varying bandwidth of the Internet have remained the major problems for multimedia streaming applications. As such, a number of approaches, including network infrastructure and protocol, source and channel coding, have been proposed to either overcome or alleviate these drawbacks of the Internet. In this article, we propose the MultiTCP system, a receiver-driven, TCP-based system for multimedia streaming over the Internet. Our proposed algorithm aims at providing resilience against short term insufficient bandwidth by using multiple TCP connections for the same application. Our proposed system enables the application to achieve and control the desired sending rate during congested periods, which cannot be achieved using traditional TCP. Finally, our proposed system is implemented at the application layer, and hence, no kernel modification to TCP is necessary. We analyze the proposed system, and present simulation and experimental results to demonstrate its advantages over the traditional single-TCP-based approach.

We consider the problem of estimating the geographic locations of nodes in a wireless sensor network where most sensors are without an effective self-positioning functionality. We propose LSVM-a novel solution with the following merits. First, LSVM localizes the network based on mere connectivity information (that is, hop counts only) and therefore is simple and does not require specialized ranging hardware or assisting mobile devices as in most existing techniques. Second, LSVM is based on Support Vector Machine (SVM) learning. Although SVM is a classification method, we show its applicability to the localization problem and prove that the localization error can be upper bounded by any small threshold given an appropriate training data size. Third, LSVM addresses the border and coverage-hole problems effectively. Last but not least, LSVM offers fast localization in a distributed manner with efficient use of processing and communication resources. We also propose a modified version of mass-spring optimization to further improve the location estimation in LSVM. The promising performance of LSVM is exhibited by our simulation study.

Subscription covering detection is useful to improving the performance of any publish/subscribe system. However, an exact solution to querying coverings among a large set of subscriptions in high dimension is computationally too expensive to be practicable. Therefore, we are interested in an approximate approach. We focus on spherical subscriptions and propose a solution based on random projections. Our complexities are substantially better than that of the exact approach. The proposed solution can potentially find exact coverings with a success probability 100% asymptotically approachable.

Limited bandwidth and high packet loss pose a serious challenge for video streaming over wireless networks. Even when packet loss in the medium is not present, the fluctuating available bandwidth due to varying number of active flows in a network causes problem for video streaming applications. In this paper, we propose to employ a novel admission control together with a rate-distortion optimized framework to maintain reasonable qualities for multiple concurrent video streams. In particular, we formulate an optimization problem to allocate the optimal transmission rates for each layered video streams jointly with the MAC protocol of a slightly modified 802.11x network. We show the hardness results of the optimization problem under various conditions. Furthermore, we show that a simple greedy layer-allocation algorithm is typically not optimal, although it can approximate the solution reasonably well under certain assumptions.

In recent years, Content Delivery Networks (CDN) and Peer-to-Peer (P2P) networks have emerged as two effective paradigms for delivering multimedia contents over the Internet. An important feature in CDN and P2P networks is the data redundancy across multiple servers/peers which enables efficient media delivery. In this paper, we propose a network coding framework for efficient media streaming in either content delivery networks or P2P networks in which, multiple servers/peers are employed to simultaneously stream a video to a single receiver. Unlike previous multi-sender schemes, we show that network coding technique can (a) reduce the redundancy storage, (b) eliminate the need for tight synchronization between the senders, and (c) be integrated easily with TCP. Furthermore, we propose the Hierarchical Network Coding (HNC) technique to be used with scalable video bit stream to combat bandwidth fluctuation on the Internet. Simulation results demonstrate that our proposed scheme can result in bandwidth saving up to 40% for many cases over the traditional schemes.

One of the most important problems facing any distributed application over a heteroge-neous network is the protection of private sensitive information in local terminals. A subfield of cryptography called Secure Multiparty Computation (SMC) is the study of such distributed computation protocols that allow distrusted parties to perform joint computation without dis-closing private data. SMC is increasingly used in diverse fields from data mining to computer vision. This paper provides a tutorial on SMC for non-experts in cryptography and surveys some of the latest advances in this exciting area including various schemes for reducing commu-nication and computation complexity of SMC protocols, doubly homomorphic encryption and private information retrieval. The proliferation of capturing and storage devices as well as the ubiquitous presence of com-puter networks make sharing of data easier than ever. Such pervasive exchange of data, however, has increasingly raised questions on how sensitive and private information can be protected. For example, it is now commonplace to send private photographs or videos to the hundreds of online photo processing stores for storage, development and enhancement like sharpening and red-eye removal. Few companies provide any protection of the personal pictures they receive. Hackers or

In today's heterogeneous network environment, there is a growing demand for distrusted parties to jointly execute distributed algorithms on private data whose secrecy needed to be safeguarded. Protocols that support such kind of joint computation without complete sharing of information are called secure multiparty computation (SMC) protocols. Applying SMC protocols in image processing is a challenging problem. Most of the existing SMC protocols are implemented based on cryptographic primitives like oblivious transfer that are too computational intensive for pixel-based operations. In this paper, we develop two efficient SMC protocols for distributed linear image filtering between two parties, one party with the original image and the other with the image filter. The first protocol is based on a combination of rank reduction and random permutation. The second one uses random perturbation with the help of a non-colluding third party. Experimental results show that both of them execute significantly faster than oblivious-transfer based techniques.

In this paper, we propose a structured hybrid P2P mesh for optimal video dissemination from a single source node to multiple receivers in a bandwidth-asymmetric network such as digital subscriber line (DSL) access network. Our hybrid P2P structured mesh consists of one or more supernodes responsible for node and mesh management and a large number of streaming nodes, Peers. The peers are interconnected in a special manner designed for streaming and real-time video dissemination and are responsible for the actual data delivery. Our proposed hybrid P2P structured mesh is designed to achieve scalability, low delay and high throughput. Our experimental Internet-wide system consisting of PlanetLab nodes demonstrates the aforementioned qualities

Mining repeated patterns in television broadcast is important to advertisers in tracking a large number of television commercials. It can also benefit long-term archival of television because historically significant events are usually marked by repeated airing of the same video clips or sound-bytes. In this paper, we describe a system that can efficiently mine repeated patterns of arbitrary lengths from television broadcast. Compared with existing work, our system has two main innovations: first, our system is robust against minor temporal variations among repeated patterns. This is important as broadcasters often perform temporal editing on commercials so as to fit them into different time slots. Second, our system does not rely on any temporal segmentation algorithm, which may lead to over- or under-segmentation of important patterns. Instead, our system scans the television broadcast with a fixed-size sliding window, summarizes each window into a hash value, and maintains a running frequency count and a reference time-stamp on each hash value. The boundaries of a repeated pattern are identified by the changes in frequency counts and reference time-stamps. Initial experiments show that our system is very efficient in identifying all the repeated commercials from 12 hours of television broadcast.

As broadband Internet becomes widely available, multimedia applications over the Internet become increasingly popular. However, packet loss, delay, and time-varying bandwidth of the Internet have remained the major problems for multimedia streaming applications. As such, a number of approaches, including network infrastructure and protocol, source and channel coding have been proposed to either overcome or alleviate these drawbacks of the Internet. In this paper, we propose the MultiTCP system, a receiver-driven, TCP-based system for multimedia streaming over the Internet. Our proposed algorithm aims at providing resilience against SHORT TERM insufficient bandwidth by using MULTIPLE TCP connections for the same application. Furthermore, our proposed system enables the application to achieve and control the desired sending rate during congested periods, which cannot be achieved using traditional TCP. Finally, our proposed system is implemented at the application layer, and hence, no kernel modification to TCP is necessary. We analyze the proposed system, and present simulation results to demonstrate its advantages over the traditional single TCP based approach.