Luís M. B. Lopes

Distribution and Mobility with Lexical Scoping in Process Calculi

Abstract We propose a simple model of distribution for mobile processes, independent of the underlying calculus. Conventional processes compute within sites; inter-site computation is achieved by message sending and object migration, both obeying a lexical scope. We focus on the semantics of networks, on programming practice, and on physical realization with current technology.

Clustering Distributed Sensor Data Streams

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, presenting both empirical and theoretical evidence of its advantages.

Programming Wireless Sensor Networks

Sensor networks can be viewed as a collection of tiny, low-cost devices programmed to sense the physical world and that communicate over radio links [12]. The devices are commonly called motes or smart dust [676], in allusion to their computational and sensing capabilities, as well as their increasingly small size.

Parallel discovery of network motifs

Many natural structures can be naturally represented by complex networks. Discovering network motifs, which are overrepresented patterns of inter-connections, is a computationally hard task related to graph isomorphism. Sequential methods are hindered by an exponential execution time growth when we increase the size of motifs and networks. In this article we study the opportunities for parallelism in existing methods and propose new parallel strategies that adapt and extend one of the most efficient serial methods known from the Fanmod tool. We propose both a master-worker strategy and one with distributed control, in which we employ a randomized receiver initiated methodology capable of providing dynamic load balancing during the whole computation process. Our strategies are capable of dealing both with exact and approximate network motif discovery. We implement and apply our algorithms to a set of representative networks and examine their scalability up to 128 processing cores. We obtain almost linear speedups, showcasing the efficiency of our proposed approach and are able to reach motif sizes that were not previously achievable using conventional serial algorithms.

Clustering distributed sensor data streams using local processing and reduced communication

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.

P3: Parallel Peer to Peer An Internet Parallel Programming Environment

P3 is a next-generation Internet computing platform, building upon other experiments and implementing new ideas for high-performance parallel computing in the Internet environment. This paper describes its run-time system, programming model and how it compares to current state-of-the-art systems.P3 is a next-generation Internet computing platform, building upon other experiments and implementing new ideas for high-performance parallel computing in the Internet environment. This paper describes its run-time system, programming model and how it compares to current state-of-the-art systems.

Efficient Parallel Subgraph Counting Using G-Tries

Finding and counting the occurrences of a collection of subgraphs within another larger network is a computationally hard problem, closely related to graph isomorphism. The subgraph count is by itself a very powerful characterization of a network and it is crucial for other important network measurements. G-tries are a specialized data-structure designed to store and search for subgraphs. By taking advantage of subgraph common substructure, g-tries can provide considerable speedups over previously used methods. In this paper we present a parallel algorithm based precisely on g-tries that is able to efficiently find and count subgraphs. The algorithm relies on randomized receiver-initiated dynamic load balancing and is able to stop its computation at any given time, efficiently store its search position, divide what is left to compute in two halfs, and resume from where it left. We apply our algorithm to several representative real complex networks from various domains and examine its scalability. We obtain an almost linear speedup up to 128 processors, thus allowing us to reach previously unfeasible limits. We showcase the multidisciplinary potential of the algorithm by also applying it to network motif discovery.

Towards the Safe Programming of Wireless Sensor Networks

Sensor networks are rather challenging to deploy, program, and debug. Current programming languages for these platforms suffer from a significant semanti c gap between their specifications and underlying implementations. This fact precludes the development of (type-)safe applications, which would potentially simplify the task of programming and debugging deployed networks. In this paper we define a core calculus for programming sensor networks and propose to use it as an assembly language for developing type-safe, high-level programming languages.

Muscular fitness and metabolic and inflammatory biomarkers in adolescents: Results from LabMed Physical Activity Study

This study aimed to evaluate the associations between muscular fitness and inflammatory biomarkers and to investigate the relationship between muscular fitness and selected clustered inflammatory biomarkers in adolescents. This is a cross‐sectional analysis with 529 adolescents (267 girls) aged 12‐18 years. Handgrip strength and standing long jump tests assessed MF. Continuous scores of clustered inflammatory biomarkers (sum of Z‐scores of C‐reactive protein [CRP], C3, C4, fibrinogen, and leptin); metabolic risk factor (MRF) score (sum of Z‐scores of SBP, triglycerides, ratio total cholesterol [TC]/HDL, HOMA‐IR, and waist circumference [WC]) were computed. Regression analyses showed an inverse association between muscular fitness score (β=−.204; P<.021) and clustered score of inflammatory biomarkers, adjusted for age, sex, pubertal stage, socioeconomic status, adherence to the Mediterranean diet, cardiorespiratory fitness (CRF), MRF score, and body fat. Analysis of covariance showed that adolescents with an adverse inflammatory profile with low levels of muscular fitness exhibit the poorest MRF score (F3,525=6.461; P<.001), adjusted for age, sex, pubertal stage, socioeconomic status, adherence to the Mediterranean diet, CRF, and body fatness. The inflammatory state seems to explain a significant part of the highest MRF score and in adolescents with high inflammatory status and low muscular strength.

A Multi-threaded Asynchronous Language

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.