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18
Differentiated Data Persistence with Priority Random Linear Codes
, 2006
"... Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challeng ..."
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Cited by 18 (2 self)
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Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in such systems, without the use of centralized servers. To better cope with node dynamics and failures, we propose priority random linear codes, as well as their affiliated predistribution protocols, to maintain measurement data in different priorities, such that critical data have a higher opportunity to survive node failures than data of less importance. A salient feature of priority random linear codes is the ability to partially recover more important subsets of the original data with higher priorities, when it is not feasible to recover all of them due to node dynamics. We present extensive analytical and experimental results to show the effectiveness of priority random linear codes. 1
Fountain codes based distributed storage algorithms
, 2007
"... We consider largescale networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because of, for example, limited energy or a hostile environment, it is desirable to disse ..."
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Cited by 12 (2 self)
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We consider largescale networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because of, for example, limited energy or a hostile environment, it is desirable to disseminate the acquired information throughout the network so that each of the n nodes stores one (possibly coded) packet and the original k source packets can be recovered later in a computationally simple way from any (1 + ǫ)k nodes for some small ǫ> 0. We developed two distributed algorithms for solving this problem based on simple random walks and Fountain codes. Unlike all previously developed schemes, our solution is truly distributed, that is, nodes do not know n, k or connectivity in the network, except in their own neighborhoods, and they do not maintain any routing tables. In the first algorithm, all the sensors have the knowledge of n and k. In the second algorithm, each sensor estimates these parameters through the random walk dissemination. We present analysis of the communication/transmission and encoding/decoding complexity of these two algorithms, and provide extensive simulation results as well 1. 1
Geometric Random Linear Codes in Sensor Networks
"... Wireless sensor networks consist of unreliable and energyconstrained sensors connecting to each other wirelessly. As measured data may be lost due to sensor failures, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in sensor networks, ..."
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Cited by 2 (0 self)
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Wireless sensor networks consist of unreliable and energyconstrained sensors connecting to each other wirelessly. As measured data may be lost due to sensor failures, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in sensor networks, without the use of centralized servers. To cope with node failures, while providing convenient access to measured data, we propose geometric random linear codes, to encode data in a hierarchical fashion in geographic regions with different sizes, such that data are easy to access, if the original sensors producing the data are alive. Otherwise, data are persistently available elsewhere in the network. Although our coding scheme is simple, we have shown that it enjoys the same low encoding cost as sparse random linear codes, while dramatically decreasing the decoding cost. We present extensive analytical and experimental results to show the effectiveness of geometric random linear codes.
Raptor Codes Based Distributed Storage Algorithms for Wireless Sensor Networks
, 903
"... Abstract—We consider a distributed storage problem in a largescale wireless sensor network with n nodes among which k acquire (sense) independent data. The goal is to disseminate the acquired information throughout the network so that each of the n sensors stores one possibly coded packet and the o ..."
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Cited by 2 (1 self)
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Abstract—We consider a distributed storage problem in a largescale wireless sensor network with n nodes among which k acquire (sense) independent data. The goal is to disseminate the acquired information throughout the network so that each of the n sensors stores one possibly coded packet and the original k data packets can be recovered later in a computationally simple way from any (1 + ǫ)k of nodes for some small ǫ> 0. We propose two Raptor codes based distributed storage algorithms for solving this problem. In the first algorithm, all the sensors have the knowledge of n and k. In the second one, we assume that no sensor has such global information. I.
InNetwork Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection
 In Proceedings of 6th International Workshop on Algorithms for Sensor Systems, Wireless Ad Hoc Networks, and Autonomous Mobile Entities (ALGOSENSORS 2010
, 2010
"... Abstract. In a sensor network of n nodes in which k of them have sensed interesting data, we perform innetwork erasure coding such that each node stores a linear combination of all the network data with random coefficients. This scheme greatly improves data resilience to node failures: as long as t ..."
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Cited by 2 (1 self)
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Abstract. In a sensor network of n nodes in which k of them have sensed interesting data, we perform innetwork erasure coding such that each node stores a linear combination of all the network data with random coefficients. This scheme greatly improves data resilience to node failures: as long as there are k nodes that survive an attack, all the data produced in the sensor network can be recovered with high probability. The innetwork coding storage scheme also improves data collection rate by mobile mules and allows for easy scheduling of data mules. We show that using spatial gossip we can compute the erasure codes for the entire network with a total of near linear message transmissions, thus improving substantially the communication cost in previous scheme [5]. We also extend the scheme to allow for online data reconstruction, by interleaving spatial gossip steps with mule collection. We present simulation results to demonstrate the performance improvement using erasure codes.
Distributed Floodingbased Storage Algorithms for Largescale Sensor Networks
, 908
"... Abstract—In this paper we propose distributed storage algorithms for largescale wireless sensor networks. Assume a wireless sensor network with n nodes that have limited power, memory, and bandwidth. Each node is capable of both sensing and storing data. Such sensor nodes might disappear from the n ..."
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Cited by 1 (0 self)
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Abstract—In this paper we propose distributed storage algorithms for largescale wireless sensor networks. Assume a wireless sensor network with n nodes that have limited power, memory, and bandwidth. Each node is capable of both sensing and storing data. Such sensor nodes might disappear from the network due to failures or battery depletion. Hence it is desired to design efficient schemes to collect data from these n nodes. We propose two distributed storage algorithms (DSA’s) that utilize network flooding to solve this problem. In the first algorithm, DSAI, we assume that every node utilizes network flooding to disseminate its data throughout the network using a mixing time of approximately O(n). We show that this algorithm is efficient in terms of the encoding and decoding operations. In the second algorithm, DSAII, we assume that the total number of nodes is not known to every sensor; hence dissemination of the data does not depend on n. The encoding operations in this case take O(Cµ 2), where µ is the mean degree of the network graph and C is a system parameter. We evaluate the performance of the proposed algorithms through analysis and simulation, and show that their performance matches the derived theoretical results. I.
Distributed Storage Allocations
, 2010
"... We examine the problem of allocating a given total storage budget in a distributed storage system for maximum reliability. A source has a single data object that is to be coded and stored over a set of storage nodes; it is allowed to store any amount of coded data in each node, as long as the total ..."
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Cited by 1 (0 self)
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We examine the problem of allocating a given total storage budget in a distributed storage system for maximum reliability. A source has a single data object that is to be coded and stored over a set of storage nodes; it is allowed to store any amount of coded data in each node, as long as the total amount of storage used does not exceed the given budget. A data collector subsequently attempts to recover the original data object by accessing only the data stored in a random subset of the nodes. By using an appropriate code, successful recovery can be achieved whenever the total amount of data accessed is at least the size of the original data object. The goal is to find an optimal storage allocation that maximizes the probability of successful recovery. This optimization problem is challenging in general because of its combinatorial nature, despite its simple formulation. We study several variations of the problem, assuming different allocation models and access models. The optimal allocation and the optimal symmetric allocation (in which all nonempty nodes store the same amount of data) are determined for a variety of cases. Our results indicate that the optimal allocations often have nonintuitive structure and are difficult to specify. We also show that depending on the circumstances, coding may or may not be beneficial for reliable storage.
On the Reliability of LargeScale Distributed Systems A Topological View 1
"... In largescale, selforganized and distributed systems, such as peertopeer (P2P) overlays and wireless sensor networks (WSN), a small proportion of nodes are likely to be more critical to the system's reliability than the others. This paper focuses on detecting cut vertices so that we can either n ..."
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In largescale, selforganized and distributed systems, such as peertopeer (P2P) overlays and wireless sensor networks (WSN), a small proportion of nodes are likely to be more critical to the system's reliability than the others. This paper focuses on detecting cut vertices so that we can either neutralize or protect these critical nodes. Detection of cut vertices is trivial if the global knowledge of the whole system is known but it is very challenging when the global knowledge is missing. In this paper, we propose a completely distributed scheme where every single node can determine whether it is a cut vertex or not. In addition, our design can also confine the detection overhead to a constant instead of being proportional to the size of a network. The correctness of this algorithm is theoretically proved and a number of performance measures are verified through trace driven simulations. 1.
unknown title
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: