A Failure Detector for Wireless Networks with Unknown Membership

Abstract

International audienceThe distributed computing scenario is rapidly evolving for integrating self-organizing and dynamic wireless networks. Unreliable failure detectors are classical mechanisms which provide information about process failures and can help systems to cope with the high dynamism of these networks. A number of failure detection algorithms has been proposed so far; nonetheless, most of them assume a global knowledge about the membership as well as a fully communication connectivity; additionally, they are timer-based, requiring that eventually some bound on the message transmission will hold. These assumptions are no longer appropriate to the new scenario. This paper presents a new failure detector protocol which implements a new class of detectors, namely ♢SM, which adapts the properties of the ♢S class to a dynamic network with an unknown membership. It has the interesting feature to be time-free, so that it does not rely on timers to detect failures; moreover, it tolerates mobility of nodes and message losses

An Eventual alpha Partition-Participant Detector for MANETs

“…However, the distributed system is not partitionable. In [17], the authors extend the QUERY-RESPONSE communication mechanism of [22] by considering the mobility of nodes, and propose a failure detector ♦S M that eventually detects the set of known and stable processes: a process is known if it has joined the system and has been identified by a stable process; a process is stable if after having entered the system, it never departs. The local value of α of a process p is computed as the value of the neighborhood density of p minus the maximum number of faulty processes in p's neighborhood.…”

“…Distributed models that consider dynamic systems with a stability condition (α processes) can be founded in [22], [17], [19]. In [19], the model involves unreliable failure detectors with α denoting the smallest number of stable processes in the system.…”

“…The local value of α of a process p is computed as the value of the neighborhood density of p minus the maximum number of faulty processes in p's neighborhood. Again, the model in [17] is designed for primary-partition systems. [5] considers sparse MANETs where the node density is relatively low so that disconnections and network partitions are common.…”

Partitionable group membership for Mobile Ad hoc Networks

“…Mais, les processus dans ♢P ART p ne peuvent pas recevoir avec garantie temporelle des messages diffusés par u car il n'existe pas de Dans la section suivante, nous présentons les modèles de systèmes dynamiques avec les condition de stabilité considérés dans les travaux connexes. [Hermant and Le Lann, 2002, Mostefaoui et al, 2005, Greve et al, 2011] présentent des modèles de systèmes dynamiques accompagnés d'une condition de stabilité. Dans [Hermant and Le Lann, 2002], α dénote le nombre minimum de processus qui exécutent l'algorithme et ne sont jamais suspectés d'être défaillants.…”

Reliable Channels for Systems in the Presence of Byzantine Faults

“…Existing schemes that detect or tolerate Byzantine Faults perturbing message content and variables deal with end-to-end communication [6,7,8] but fall short of tolerating/detecting faults in the form of spurious, and replay messages, and perturbation of message parameters during message routing. When message parameters are perturbed and spurious and replay mes sages are sent, additional fau lt types are introduced including routing the message to an unintended destination, mixing up of shares of different messages, replacement messages or false positives, a non-faulty message detected as a faulty message.…”