Sylvie Delaët

Transient fault detectors

We present fault detectors for transient faults, (i.e., corruptions of the memory of the processors, but not of the code of the processors). We distinguish fault detectors for tasks (i.e., the problem to be solved) from failure detectors for implementations (i.e., the algorithm that solves the problem). The aim of our fault detectors is to detect a memory corruption as soon as possible. We study the amount of memory needed by the fault detectors for some specific tasks, and give bounds for each task. The amount of memory is related to the size and the number of views that a processor has to maintain to ensure a quick detection. This work may give the implementation designer hints concerning the techniques and resources that are required for implementing a task.

Self-Stabilization with r-Operators revisited

We present a generic distributed algorithm for solving silents tasks such as shortest path calculus, depth-first-search tree construction, best reliable transmitters, in directed networks where communication may be only unidirectional. Our solution is written for the asynchronous message passing communication model, and tolerates multiple kinds of failures (transient and intermittent). First, our algorithm is self-stabilizing, so that it recovers correct behavior after finite time starting from an arbitrary global state caused by a transient fault. Second, it tolerates fair message loss, finite message duplication, and arbitrary message reordering, during both the stabilizing phase and the stabilized phase. This second property is most interesting since, in the context of unidirectional networks, there exist no self-stabilizing reliable data-link protocol. The correctness proof subsumes previous proofs for solutions in the simpler reliable shared memory communication model.

Tolerating Transient and Intermittent Failures

Fault tolerance is a crucial property for recent distributed systems. We propose an algorithm that solves the census problem (list all processor identifiers and their relative distance) on an arbitrary strongly connected network.This algorithm tolerates transient faults that corrupt the processors and communication links memory (it is self-stabilizing) as well as intermittent faults (fair loss, reorder, finite duplication of messages) on communication media. A formal proof establishes its correctness for the considered problem. Our algorithm leads to the construction of algorithms for any silent problems that are self-stabilizing while supporting the same communication hazards.

Deterministic Secure Positioning in Wireless Sensor Networks

Position verification problem is an important building block for a large subset of wireless sensor networks (WSN) applications. As a result, the performance of the WSN degrades significantly when misbehaving nodes report false location information in order to fake their actual position. In this paper we propose the first deterministic distributed protocol for accurate identification of faking sensors in a WSN. Our scheme does notrely on a subset of trustednodes that cooperate and are not allowed to misbehave. Thus, any subset of nodes is allowed to try faking its position. As in previous approaches, our protocol is based on distance evaluation techniques developed for WSN. On the positive side, we show that when the received signal strength (RSS) technique is used, our protocol handles at most

Probabilistic self-stabilizing mutual exclusion in uniform rings

\lfloor \frac{n}{2} \rfloor-2

Transient Fault Detectors

faking sensors. When the time of flight (ToF) technique is used, our protocol manages at most

Practically Self-stabilizing Paxos Replicated State-Machine

\lfloor \frac{n}{2} \rfloor - 3

A 1-strong self-stabilizing transformer

misbehaving sensors. On the negative side, we prove that no deterministic protocol can identify faking sensors if their number is

Self-stabilization with r-operators revisited

\lceil \frac{n}{2}\rceil -1

Universe Detectors for Sybil Defense in Ad Hoc Wireless Networks

. Thus, our scheme is almost optimal with respect to the number of faking sensors. We discuss application of our technique in the trusted sensor model. More specifically, our results can be used to minimize the number of trusted sensors that are needed to defeat faking ones.