Adaptive neighborhood selection in peer-to-peer networks based on content similarity and reputation

To address the two most critical issues in P2P file-sharing systems: efficient information discovery and authentic data acquisition, we propose a Gnutella-like file-sharing protocol termed Adaptive Gnutella Protocol (AGP) that not only improves the querying efficiency in a P2P network but also enhances the quality of search results at the same time. The reputation scheme in the proposed AGP evaluates the credibility of peers based on their contributions to P2P services and subsequently clusters nodes together according to their reputation and shared content, essentially transforming the P2P overlay network into a topology with collaborative and reputed nodes as its core. By detecting malicious peers as well as free-riders and eventually pushing them to the edge of the overlay network, our AGP propagates search queries mainly within the core of the topology, accelerating the information discovery process. Furthermore, the clustering of nodes based on authentic and similar content in our AGP also improves the quality of search results. We have implemented the AGP with the PeerSim simulation engine and conducted thorough experiments on diverse network topologies and various mixtures of honest/dishonest nodes to demonstrate improvements in topology transformation, query efficiency, and search quality by our AGP.

Managing cohort movement of mobile sensors via GPS-free and compass-free node localization

A critical problem in mobile ad hoc wireless sensor networks is each node’s awareness of its position relative to the network. This problem is known as localization. In this paper, we introduce a variant of this problem, directional localization, where each node must be aware of both its position and orientation relative to its neighbors. Directional localization is relevant for applications that require uniform area coverage and coherent movement. Using global positioning systems for localization in large scale sensor networks may be impractical in enclosed spaces, and might not be cost effective. In addition, a set of pre-existing anchors with globally known positions may not always be available. In this context, we propose two distributed algorithms based on directional localization that facilitate the collaborative movement of nodes in a sensor network without the need for global positioning systems, seed nodes or a pre-existing infrastructure such as anchors with known positions. Our first algorithm, GPS-free Directed Localization (GDL) assumes the availability of a simple digital compass on each sensor node. We relax this requirement in our second algorithm termed GPS- and Compass-free Directed Localization (GCDL). Through experimentation, we demonstrate that our algorithms scale well for large numbers of nodes and provide convergent localization over time, despite errors introduced by motion actuators and distance measurements. In addition, we introduce mechanisms to preserve swarm formation during directed sensor network mobility. Our simulations confirm that, in a number of realistic scenarios, our algorithms provide for a mobile sensor network that preserves its formation over time, irrespective of speed and distance traveled. We also present our method to organize the sensor nodes in a polygonal geometric shape of our choice even in noisy environments, and investigate the possible uses of this approach in search-and-rescue type of missions.     

DOLAR: virtualizing heterogeneous information spaces to support their expansion

Users expect applications to successfully cope with the expansion of information as necessitated by the continuous inclusion of novel types of content. Given that such content may originate from ‘not‐seen thus far’ data collections and/or data sources, the challenging issue is to achieve the return of investment on existing services, adapting to new information without changing existing business‐logic implementation. To address this need, we introduce DOLAR (Data Object Language And Runtime), a service‐neutral framework which virtualizes the information space to avoid invasive, time‐consuming, and expensive source‐code extensions that frequently break applications. Specifically, DOLAR automates the introduction of new business‐logic objects in terms of the proposed virtual ‘content objects’. Such user‐specified virtual objects align to storage artifacts and help realize uniform ‘store‐to‐user’ data flows atop heterogeneous sources, while offering the reverse ‘user‐to‐store’ flows with identical effectiveness and ease of use. In addition, the suggested virtual object composition schemes help decouple business logic from any content origin, storage and/or structural details, allowing applications to support novel types of items without modifying their service provisions. We expect that content‐rich applications will benefit from our approach and demonstrate how DOLAR has assisted in the cost‐effective development and gradual expansion of a production‐quality digital library. Experimentation shows that our approach imposes minimal overheads and DOLAR‐based applications scale as well as any underlying datastore(s). Copyright © 2011 John Wiley & Sons, Ltd.     

Decision-Support System for the Rehabilitation of Deteriorating Sewers

This paper describes an automated and integrated detection, structural assessment, and rehabilitation method selection system for sewers based on the processing of video footage obtained by closed circuit television surveys. The system is based on a neural network classifier (NNC) trained to identify longitudinal cracks in sewers. Results obtained from experimentation with the NNC indicate that crack detection based on single-frame processing is not sufficient, and frame sequence processing substantially improves crack recognition rates. Based on the location of the cracks, local and global structural damage is assessed and a rehabilitation method is selected. Based on the significance of damaged sewers, the rehabilitation projects are being prioritized. An expert system coordinates the various modules in the system and connects them to a geographic information system.     

Regional cerebral volume loss associated with verbal learning and memory in dementia of the Alzheimer type.

Twenty-seven research participants with dementia of the Alzheimer type were studied with the California Verbal Learning Test (D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 1987) and standardized volume measures of the mesial temporal cortical gray matter, neocortical gray matter, thalamus, and caudate nuclei, from magnetic resonance imaging. A pattern of atrophic brain changes in the mesial temporal lobes (MTL) and the thalamus, with relatively less severe atrophy in the neocortical gray matter, was associated with poorer learning of the word list. Similar patterns of brain atrophy were observed for measures of delayed recall and recognition hits. However, for delayed recall, neither contribution was statistically significant, and for recognition hits, MTL was only at the trend level for significance. These results provide evidence that the verbal memory deficit of Alzheimer's disease (AD) is associated not only with the mesial temporal limbic cortex, thought to be the site of earliest and most severe pathology in AD, but also with damage in the thalamus. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Performance comparison of three modern DBMS architectures

The introduction of powerful workstations connected through local area networks (LANs) inspired new database management system (DBMS) architectures that offer high performance characteristics. The authors examine three such software architecture configurations: client-server (CS), the RAD-UNIFY type of DBMS (RU), and enhanced client-server (ECS). Their specific functional components and design rationales are discussed. Three simulation models are used to provide a performance comparison under different job workloads. Simulation results show that the RU almost always performs slightly better than the CS, especially under light workloads, and that ECS offers significant performance improvement over both CS and RU. Under reasonable update rates, the ECS over CS (or RU) performance ratio is almost proportional to the number of participating clients (for less than 32 clients). The authors also examine the impact of certain key parameters on the performance of the three architectures and show that ECS is more scalable that the other two