Iterative Merging Algorithm for Cooperative Data Exchange

We consider the problem of finding the minimum sum-rate strategy in cooperative data exchange systems that do not allow packet-splitting (NPS-CDE). In an NPS-CDE system, there are a number of geographically close cooperative clients who send packets to help the others recover a packet set. A minimum sum-rate strategy is the strategy that achieves universal recovery (the situation when all the clients recover the whole packet set) with the the minimal sum-rate (the total number of transmissions). We propose an iterative merging (IM) algorithm that recursively merges client sets based on a lower estimate of the minimum sum-rate and updates to the value of the minimum sum-rate. We also show that a minimum sum-rate strategy can be learned by allocating rates for the local recovery in each merged client set in the IM algorithm. We run an experiment to show that the complexity of the IM algorithm is lower than that of the existing deterministic algorithm when the number of clients is lower than $94$.Comment: 9 pages, 3 figure

Coded Cooperative Data Exchange for a Secret Key

We consider a coded cooperative data exchange problem with the goal of generating a secret key. Specifically, we investigate the number of public transmissions required for a set of clients to agree on a secret key with probability one, subject to the constraint that it remains private from an eavesdropper. Although the problems are closely related, we prove that secret key generation with fewest number of linear transmissions is NP-hard, while it is known that the analogous problem in traditional cooperative data exchange can be solved in polynomial time. In doing this, we completely characterize the best possible performance of linear coding schemes, and also prove that linear codes can be strictly suboptimal. Finally, we extend the single-key results to characterize the minimum number of public transmissions required to generate a desired integer number of statistically independent secret keys.Comment: Full version of a paper that appeared at ISIT 2014. 19 pages, 2 figure

Network Codes for Real-Time Applications

We consider the scenario of broadcasting for real-time applications and loss recovery via instantly decodable network coding. Past work focused on minimizing the completion delay, which is not the right objective for real-time applications that have strict deadlines. In this work, we are interested in finding a code that is instantly decodable by the maximum number of users. First, we prove that this problem is NP-Hard in the general case. Then we consider the practical probabilistic scenario, where users have i.i.d. loss probability and the number of packets is linear or polynomial in the number of users. In this scenario, we provide a polynomial-time (in the number of users) algorithm that finds the optimal coded packet. The proposed algorithm is evaluated using both simulation and real network traces of a real-time Android application. Both results show that the proposed coding scheme significantly outperforms the state-of-the-art baselines: an optimal repetition code and a COPE-like greedy scheme.Comment: ToN 2013 Submission Versio

Optimal Deterministic Polynomial-Time Data Exchange for Omniscience

We study the problem of constructing a deterministic polynomial time algorithm that achieves omniscience, in a rate-optimal manner, among a set of users that are interested in a common file but each has only partial knowledge about it as side-information. Assuming that the collective information among all the users is sufficient to allow the reconstruction of the entire file, the goal is to minimize the (possibly weighted) amount of bits that these users need to exchange over a noiseless public channel in order for all of them to learn the entire file. Using established connections to the multi-terminal secrecy problem, our algorithm also implies a polynomial-time method for constructing a maximum size secret shared key in the presence of an eavesdropper. We consider the following types of side-information settings: (i) side information in the form of uncoded fragments/packets of the file, where the users' side-information consists of subsets of the file; (ii) side information in the form of linearly correlated packets, where the users have access to linear combinations of the file packets; and (iii) the general setting where the the users' side-information has an arbitrary (i.i.d.) correlation structure. Building on results from combinatorial optimization, we provide a polynomial-time algorithm (in the number of users) that, first finds the optimal rate allocations among these users, then determines an explicit transmission scheme (i.e., a description of which user should transmit what information) for cases (i) and (ii)

Cooperative Data Exchange based on MDS Codes

The cooperative data exchange problem is studied for the fully connected network. In this problem, each node initially only possesses a subset of the $K$ packets making up the file. Nodes make broadcast transmissions that are received by all other nodes. The goal is for each node to recover the full file. In this paper, we present a polynomial-time deterministic algorithm to compute the optimal (i.e., minimal) number of required broadcast transmissions and to determine the precise transmissions to be made by the nodes. A particular feature of our approach is that {\it each} of the $K-d$ transmissions is a linear combination of {\it exactly} $d+1$ packets, and we show how to optimally choose the value of $d.$ We also show how the coefficients of these linear combinations can be chosen by leveraging a connection to Maximum Distance Separable (MDS) codes. Moreover, we show that our method can be used to solve cooperative data exchange problems with weighted cost as well as the so-called successive local omniscience problem.Comment: 21 pages, 1 figur

Improving Network Reliability: Analysis, Methodology, and Algorithms

The reliability of networking and communication systems is vital for the nation's economy and security. Optical and cellular networks have become a critical infrastructure and are indispensable in emergency situations. This dissertation outlines methods for analyzing such infrastructures in the presence of catastrophic failures, such as a hurricane, as well as accidental failures of one or more components. Additionally, it presents a method for protecting against the loss of a single link in a multicast network along with a technique that enables wireless clients to efficiently recover lost data sent by their source through collaborative information exchange. Analysis of a network's reliability during a natural disaster can be assessed by simulating the conditions in which it is expected to perform. This dissertation conducts the analysis of a cellular infrastructure in the aftermath of a hurricane through Monte-Carlo sampling and presents alternative topologies which reduce resulting loss of calls. While previous research on restoration mechanisms for large-scale networks has mostly focused on handling the failures of single network elements, this dissertation examines the sampling methods used for simulating multiple failures. We present a quick method of nding a lower bound on a network's data loss through enumeration of possible cuts as well as an efficient method of nding a tighter lower bound through genetic algorithms leveraging the niching technique. Mitigation of data losses in a multicast network can be achieved by adding redundancy and employing advanced coding techniques. By using Maximum Rank Distance (MRD) codes at the source, a provider can create a parity packet which is e ectively linearly independent from the source packets such that all packets may be transmitted through the network using the network coding technique. This allows all sinks to recover all of the original data even with the failure of an edge within the network. Furthermore, this dissertation presents a method that allows a group of wireless clients to cooperatively recover from erasures (e.g., due to failures) by using the index coding techniques

Realtime Streaming with Guaranteed QOS over Wireless D2D Networks

The increase in the processing power of mobile devices has led to an explosion of available services and applications. However, the cost of mobile data is a hindrance to the adoption of data intensive applications. We consider a group of co-located wireless peer devices that desire to synchronously receive a live content stream. Devices desire to minimize the usage of their B2D interfaces (3G/4G) to reduce cost, while maintaining synchronous reception and playout of content. While it might be possible for a cellular base station to broadcast or multicast live events to multiple handsets, such content would be restricted to a few selected channels, and only available to subscribers of a single provider. Utilizing both B2D and D2D (WiFi) interfaces enables users to pick any event of interest, and "stitch together" their B2D capacities regardless of provider support. Our objective is to enable users to listen or watch real time streams while incurring only a fraction of the original costs. Our system setup is as follows. The real-time stream is divided into blocks, which must be played out soon after their initial creation. If a block is not received within a specific time after its creation, it is rendered useless and dropped. The blocks in turn are divided into random linear coded chunks to facilitate sharing across the devices. We transform the problem into the two questions of (i) deciding which peer should broadcast a chunk on the D2D channel at each time, and (ii) how long B2D transmissions should take place for each block. The thesis studies the performance of a provably-minimum-cost algorithm that can ensure that QoS targets can be met for each device. We use a Lyapunov stability argument to show that a stable delivery ratio can be achieved using our mechanism. We show that the optimal D2D scheduling algorithm has a simple and intuitive form under reliable broadcast, which allows for easy implementation and development of good heuristics. We study this via simulations, and present an overview of the implementation on Android phones using the algorithm as a basis. Additionally, we design an incentive framework that promotes cooperation among devices. We show that under this incentive framework, each device benefits by truthfully reporting the number of chunks that it received via B2D and its deficit in each frame, so that a system-wide optimal allocation policy can be employed. The incentive framework developed is lightweight and compatible with minimal amounts of history retention. The Android testbed used in the experiments consisted of multiple Google Nexus 4 phones. A modified version of Android Jelly Bean (v 4.3) was built in order to conduct the experiments which removes the limitation wherein the phone switches off its 3G data connection (B2D) whenever a known WiFi network (D2D) becomes available. Since the Nexus 4 devices are incapable of operating in ad-hoc mode, we used a WiFi network (without Internet connectivity) to emulate the D2D part. Hence, devices must use their 3G interfaces to receive chunks for the server (via the Internet). We present experimental results, and show that it would be possible to follow popular streams on hand held devices incurring only a fraction of the costs while achieving a high QoS