Abstract
Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.

Abstract
Due to its pervasiveness and communication capabilities, Bluetooth can be used as an infrastructure for several situated interaction and massive sensing scenarios. This paper shows how Bluetooth scanning can be used in gate counting scenarios, where the main goal is to provide an accurate count for the number of unique devices sighted. To this end, we present an analysis of several stochastic counting techniques that not only provide an accurate count for the number of unique devices, but offer privacy guarantees as well.

Abstract
Report on the poster exhibition that took place at ECOOP'99.

Abstract
Version vectors play a central role in update tracking under optimistic distributed systems, allowing the detection of obsolete or inconsistent versions of replicated data. Version vectors do not have a bounded representation; they are based on integer counters that grow indefinitely as updates occur. Existing approaches to this problem are scarce; the mechanisms proposed are either unbounded or operate only under specific settings. This paper examines version vectors as a mechanism for data causality tracking and clarifies their role with respect to vector clocks. Then, it introduces bounded stamps and proves them to be a correct alternative to integer counters in version vectors. The resulting mechanism, bounded version vectors, represents the first bounded solution to data causality tracking between replicas subject to local updates and pairwise symmetrical synchronization.

Abstract

Abstract