14446337
Multitolerant Barrier Synchronization
Abstract We designed a multitolerant program for synchronizing the phases of concurrent processes. The tolerances of the program enable processes to: (i) execute all phases correctly in the presence of faults that corrupt the process state in a detectable manner, and (ii) execute only a minimum possible number of phases incorrectly before resuming correct computation in the presence of faults that corrupt the process state in an undetectable manner.
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.
1711572
Designing masking fault-tolerance via nonmasking fault-tolerance
Masking fault-tolerance guarantees that programs continually satisfy their specification in the presence of faults. By way of contrast, nonmasking fault-tolerance does not guarantee as much: it merely guarantees that when faults stop occurring, program executions converge to states from where programs continually (re)satisfy their specification. In this paper, we show that a practical method to design masking fault-tolerance is to first design nonmasking fault-tolerance and to then transform the nonmasking fault-tolerant program minimally so as to achieve masking fault-tolerance. We demonstrate this method by designing novel fully distributed programs for termination detection, mutual exclusion, and leader election, that are masking tolerant of any finite number of process fail-stops and/or repairs.
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.
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.
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.
1329510
Noise Conditions for Prespecified Convergence Rates of Stochastic Approximation Algorithms
We develop deterministic necessary and sufficient conditions on individual noise sequences of a stochastic approximation algorithm for the error of the iterates to converge at a given rate. Specifically, suppose {/spl rho//sub n/} is a given positive sequence converging monotonically to zero. Consider a stochastic approximation algorithm x/sub n+1/=x/sub n/-a/sub n/(A/sub n/x/sub n/-b/sub n/)+a/sub n/e/sub n/, where {x/sub n/} is the iterate sequence, {a/sub n/} is the step size sequence, {e/sub n/} is the noise sequence, and x* is the desired zero of the function f(x)=Ax-b. Then, under appropriate assumptions, we show that x/sub n/-x*=o(/spl rho//sub n/) if and only if the sequence {e/sub n/} satisfies one of five equivalent conditions. These conditions are based on well-known formulas for noise sequences: Kushner and Clark's (1978) condition, Chen's (see Proc. IFAC World Congr., p.375-80, 1996) condition, Kulkarni and Horn's (see IEEE Trails Automat. Contr., vol.41, p.419-24, 1996) condition, a decomposition condition, and a weighted averaging condition. Our necessary and sufficient condition on {e/sub n/} to achieve a convergence rate of {/spl rho//sub n/} is basically that the sequence {e/sub n///spl rho//sub n/} satisfies any one of the above five well-known conditions. We provide examples to illustrate our result. In particular, we easily recover the familiar result that if a/sub n/=a/n and {e/sub n/} is a martingale difference process with bounded variance, then x/sub n/-x*=o(n/sup -1/2/(log(n))/sup /spl beta//) for any /spl beta/>1/2.
1752947
Learning decision rules for pattern classification under a family of probability measures
In this paper, uniformly consistent estimation (learnability) of decision rules for pattern classification under a family of probability measures is investigated. In particular, it is shown that uniform boundedness of the metric entropy of the class of decision rules is both necessary and sufficient for learnability under each of two conditions: (i) the family of probability measures is totally bounded, with respect to the total variation metric, and (ii) the family of probability measures contains an interior point, when equipped with the same metric. In particular, this shows that insofar as uniform consistency is concerned, when the family of distributions contains a total variation neighborhood, nothing is gained by this knowledge about the distribution. Then two sufficient conditions for learnability are presented. Specifically, it is shown that learnability with respect to each of a finite collection of families of probability measures implies learnability with respect to their union; also, learnability with respect to each of a finite number of measures implies learnability with respect to the convex hull of the corresponding families of uniformly absolutely continuous probability measures.
15475360
Estimating correspondence in digital video
This paper addresses several estimation problems involving correspondence in digital video. We present three cases, in order of increasing complexity: affine transformations, projective transformations, and general correspondence. We review some recent research in the first two cases and present a new framework for discussing the third case. We also discuss how to use the formalism of correspondence graphs to ensure that any estimated correspondence is physically valid.
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.
13800701
A metric entropy bound is not sufficient for learnability
The authors prove by means of a counterexample that it is not sufficient, for probably approximately correct (PAC) learning under a class of distributions, to have a uniform bound on the metric entropy of the class of concepts to be learned. This settles a conjecture of Benedek and Itai (1991). >