Abstract
Mobile agents are the latest software technology to program flexible and efficient distributed applications. Most current systems implement semantics that are hard if not impossible to prove correct. In this paper we present MOB, a scripting language for Internet agents encoded on top of a process calculus and with provably sound semantics.

Abstract
We describe a reference implementation of a multi-threaded run-time system for a core programming language based on a process calculus. The core language features processes running in parallel and communicating through asynchronous messages as the fundamental abstractions. The programming style is fully declarative, focusing on the interaction patterns between processes. The parallelism, implicit in the syntax of the programs, is effectively extracted by the language compiler and explored by the run-time system.

Abstract
We present an architecture that allows the seamless configuration of computer labs to work as dedicated computing clusters during periods of user inactivity. The operation of the cluster is fully automated by making use of differentiated network booting and a job management system. We have prepared it to be plugged to a larger computational grid. We provide some preliminary performance results obtained.

Abstract
Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, focusing on three outcomes: loss to real centroids, communication prevention, and processing reduction. The experimental work on synthetic data supports our proposal, presenting robustness to a high number of sensors, and the application to real data from physiological sensors exposes the aforementioned advantages of the system.

Abstract
In ubiquitous streaming data sources, such as sensor networks, clustering nodes by the data they produce is an important problem that gives insights on the phenomenon being monitored by such networks. However, if these techniques require data to be gathered centrally, communication and storage requirements are often unbounded. The goal of this paper is to assess the feasibility of computing local clustering at each node, using only neighbors' centroids, as an approximation of the global clustering computed by a centralized process. A local algorithm is proposed to perform clustering of sensors based on the moving average of each node's data over time: the moving average of each node is approximated using memory-less fading average; clustering is based on the furthest point algorithm applied to the centroids computed by the node's direct neighbors. The algorithm was evaluated on a state-of-the-art sensor network simulator, measuring the agreement between local and global clustering. Experimental work on synthetic data with spherical Gaussian clusters is consistently analyzed for different network size, number of clusters and cluster overlapping. Results show a high level of agreement between each node's clustering definitions and the global clustering definition, with special emphasis on separability agreement. Overall, local approaches are able to keep a good approximation of the global clustering, improving privacy among nodes, and decreasing communication and computation load in the network. Hence, the basic requirements for distributed clustering of streaming data sensors recommend that clustering on these settings should be performed locally. © 2011 ACM.

Abstract