10924262
Automated synthesis of multitolerance
We concentrate on automated synthesis of multitolerant programs, i.e., programs that tolerate multiple classes of faults and provide a (possibly) different level of fault-tolerance to each class. We consider three levels of fault-tolerance: (1) failsafe, where in the presence of faults, the synthesized program guarantees safety, (2) nonmasking, where in the presence of faults, the synthesized program recovers to states from where its safety and liveness are satisfied, and (3) masking where in the presence of faults the synthesized program satisfies safety and recovers to states from where its safety and liveness are satisfied. We focus on the automated synthesis of finite-state multitolerant programs in high atomicity model where the program can read and write all its variables in an atomic step. We show that if one needs to add failsafe (respectively, nonmasking) fault-tolerance to one class of faults and masking fault-tolerance to another class of faults then such addition can be done in polynomial time in the state space of the fault-intolerant program. However, if one needs to add failsafe fault-tolerance to one class of faults and nonmasking fault-tolerance to another class of faults then the resulting problem is NP-complete. We find this result to be counterintuitive since adding failsafe and nonmasking fault-tolerance to the same class of faults (which is equivalent to adding masking fault-tolerance to that class of faults) can be done in polynomial time, whereas adding failsafe fault-tolerance to one class of faults and nonmasking fault-tolerance to a different class of faults is NP-complete.
12287966
Complexity issues in automated synthesis of failsafe fault-tolerance
We focus on the problem of synthesizing failsafe fault-tolerance where fault-tolerance is added to an existing (fault-intolerant) program. A failsafe fault-tolerant program satisfies its specification (including safety and liveness) in the absence of faults. However, in the presence of faults, it satisfies its safety specification. We present a somewhat unexpected result that, in general, the problem of synthesizing failsafe fault-tolerant distributed programs from their fault-intolerant version is NP-complete in the state space of the program. We also identify a class of specifications, monotonic specifications, and a class of programs, monotonic programs, for which the synthesis of failsafe fault-tolerance can be done in polynomial time (in program state space). As an illustration, we show that the monotonicity restrictions are met for commonly encountered problems, such as Byzantine agreement, distributed consensus, and atomic commitment. Furthermore, we evaluate the role of these restrictions in the complexity of synthesizing failsafe fault-tolerance. Specifically, we prove that if only one of these conditions is satisfied, the synthesis of failsafe fault-tolerance is still NP-complete. Finally, we demonstrate the application of monotonicity property in enhancing the fault-tolerance of (distributed) nonmasking fault-tolerant programs to masking.
37856387
Collision-Free Communication in Sensor Networks
In this paper, we provide a stabilizing solution for collision-free diffusion in sensor networks. Such diffusions are often necessary in sensor networks when information from one sensor needs to be communicated to other sensors that satisfy certain geographic properties. Our solution deals with several difficulties, e.g., unidirectional links, unreliable links, long links, failed sensors, and sensors that are sleeping in order to save energy, that occur in sensor networks. It also ensures that there are no collisions during the diffusion and that the time required for the diffusion is O(D) where D is the diameter of the network. Moreover, while the solution can be applied to an arbitrary topology, it is more suitable for a commonly occurring topology, a two-dimensional grid. 
 
We show how our solution for collision-free diffusion can be used for time-division multiplexing (TDM) in sensor networks. TDM ensures that the message communication (other than the messages sent by diffusion) among sensors is also collision-free. While collision-free diffusion and time-division multiplexing are interdependent, we show how both these properties can be achieved simultaneously. Our algorithms are stabilizing fault-tolerant, i.e., collision-free diffusion and time-division multiplexing are restored even if the system reaches an arbitrary state where the sensors are corrupted or improperly initialized.
11497337
Secret instantiation in ad-hoc networks
Abstract In this paper, we focus our attention on the problem of assigning initial secrets to users in ad-hoc network (respectively, sensors in a sensor network) so that they can use those secrets to ensure authentication and privacy during their communication. The goal of this assignment is to ensure that any two users can communicate securely with each other even though each user maintains only a small number of secrets. With this motivation, we present a protocol that maintains O ( n ) secrets per user where n is the number of users in the system. We show that our secret distribution protocol suffices for privacy and authentication as well as secure multihop communication between two users. Furthermore, we show that the number of secrets maintained in this protocol is within a constant factor of the optimal. For the case where user capability prevents them from maintaining the necessary secrets, we propose two probabilistic protocols that maintain O(log n) secrets and where the probability of security compromise between two users is inversely proportional to the number of secrets they maintain. Thus, our protocols provide a continuum where the level of privacy and authentication depends upon user requirements and capabilities.
6279302
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensor nodes at MacDill Air Force Base in Tampa, FL, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.
52887342
Low-cost fault-tolerance in barrier synchronizations
We show how fault-tolerance can be effectively added to several types of faults in program computations that use barrier synchronization. We divide the faults that occur in practice into two classes, detectable and undetectable, and design a fully distributed program that tolerates the faults in both classes. Our program guarantees that every barrier is executed correctly even if detectable faults occur, and that eventually every barrier is executed correctly even if undetectable faults occur. Via analytical as well as simulation results we show that the cost of adding fault-tolerance is low, in part by comparing the times required by our program with that required by the corresponding fault-intolerant counterpart.
9782780
A paradigm for class identification problems
The following problem arises in many applications involving classification, identification, and inference. There is a set of objects X, and a particular x /spl isin/ X is chosen (unknown to us). Based on information obtained about x in a sequential manner, one wishes to decide whether x belongs to one class of objects A/sub 0/ or a different class of objects A/sub 1/. The authors study a general paradigm applicable to a broad range of problems of this type, which they refer to as problems of class identification or discernibility. They consider various types of information sequences, and various success criteria including discernibility in the limit, discernibility with a stopping criterion, uniform discernibility, and discernibility in the Cesaro sense. They consider decision rules both with and without memory. Necessary and sufficient conditions for discernibility are provided for each case in terms of separability conditions on the sets A/sub 0/ and A/sub 1/. They then show that for any sets A/sub 0/ and A/sub 1/, various types of separability can be achieved by allowing failure on appropriate sets of small measure. Applications to problems in language identification, system identification, and discrete geometry are discussed. >
325979
Recursive propagation of correspondences with applications to the creation of virtual video
This paper is concerned with the efficient temporal propagation of correspondences between frames of two video sequences, an integral component of many video processing tasks. The main contribution is a framework for the recursive propagation of these correspondences. The propagation consists of a time update step and a measurement update step. The time update depends only on the dynamics of the rotating source cameras, while the measurement update can be tailored to any member of a general class of image correspondence algorithms. Using these results, the correspondence between points of each frame pair can be propagated and updated in a fraction of the time required to estimate correspondences anew at every frame. We discuss an application of the recursive correspondence propagation framework to the creation of virtual video. Previous virtual view algorithms have been used to generate synthetic video of a static scene, in which objects seem frozen in time. In contrast, the algorithms described here allow the creation of "true" virtual video, in the sense that the synthetic video evolves dynamically along with the scene. While virtual video is our motivating application, the recursive correspondence propagation framework applies to any two-camera video application in which correspondence is difficult and prohibitively time-consuming to estimate by processing frame pairs independently.
14885154
On-line learning of functions of bounded variation under various sampling schemes
We consider the problem of learning of an arbitrary function selected from the non-smooth class of functions that are of bounded variation. Bounds on the prediction errors resulting from sequential type algorithms are achieved for various scenarios. It is shown that for any algorithm there exists a sequence of samples and a function that results in a unit error at each step. If the samples are i.i.d. uniform then there exists an algorithm such that for any function of bounded variation the expected error is less than O(log n/n). Furthermore, for any algorithm there exists a function such that the expected error is greater than 0(1/n). We then introduce a mixed sampling setting in which an adversary can choose points but the actual samples are drawn uniformly from a 6 neighborhood of his selection. We show that there exists an algorithm such that for any function of bounded variation and for any sequence of adversarychosen points the expected cumulative error is less than 0(+ log n). Finally, results are derived for the uniform sampling case with noisy observations. We show that the order of growth of the expected error with noise sequences in Z1 remains unaltered. However, for i.i.d. zero mean and finite variance noise there exists an algorithm with expected error less than 0(1/n*), and for i.i.d. Gaussian noise any algorithm has expected error of at least o(l/@). Permission to copy without fee all or part of this material is granted provided that the copies ars not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying IS by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. ACM COLT ’93 171931CA, USA @ 1993 Acfvf 0.89791-61 ~-5/93 /o(307/0439... $1 .50
15603808
Active Learning Using Arbitrary Binary Valued Queries
The original and most widely studied PAC model for learning assumes a passive learner in the sense that the learner plays no role in obtaining information about the unknown concept. That is, the samples are simply drawn independently from some probability distribution. Some work has been done on studying more powerful oracles and how they affect learnability. To find bounds on the improvement in sample complexity that can be expected from using oracles, we consider active learning in the sense that the learner has complete control over the information received. Specifically, we allow the learner to ask arbitrary yes/no questions. We consider both active learning under a fixed distribution and distribution-free active learning. In the case of active learning, the underlying probability distribution is used only to measure distance between concepts. For learnability with respect to a fixed distribution, active learning does not enlarge the set of learnable concept classes, but can improve the sample complexity. For distribution-free learning, it is shown that a concept class is actively learnable iff it is finite, so that active learning is in fact less powerful than the usual passive learning model. We also consider a form of distribution-free learning in which the learner knows the distribution being used, so that “distribution-free” refers only to the requirement that a bound on the number of queries can be obtained uniformly over all distributions. Even with the side information of the distribution being used, a concept class is actively learnable iff it has finite VC dimension, so that active learning with the side information still does not enlarge the set of learnable concept classes.
11400186
On probably correct classification of concepts
We consider the problem of classifying an unknown concept into one of two subclasses of concepts. Specifically, if C is a concept class and Co and Cl are two disjoint subsets of C, given an unknown c E Co U Cl we wish to decide whether c c Co or c E Cl based on a set of random examples. We consider both uniform and non-uniform probably correct classification for which the number of samples is or is not required to be independent of c, respectively. For both cases, we obtain necessary and sufficient conditions on Co and Cl that allow probably correct classification. The conditions obtained are in terms of separability and/or coverability conditions on the classes Co and Cl. Furthermore, in the non-uniform case we show that this is equivalent to classification in the limit. Several examples of the applicability of our results are also provided.