Demetrios Zeinalipour-Yazti

A local search mechanism for peer-to-peer networks

One important problem in peer-to-peer (P2P) networks is searching and retrieving the correct information. However, existing searching mechanisms in pure peer-to-peer networks are inefficient due to the decentralized nature of such networks. We propose two mechanisms for information retrieval in pure peer-to-peer networks. The first, the modified Breadth-First Search (BFS) mechanism, is an extension of the current Gnuttela protocol, allows searching with keywords, and is designed to minimize the number of messages that are needed to search the network. The second, the Intelligent Search mechanism, uses the past behavior of the P2P network to further improve the scalability of the search procedure. In this algorithm, each peer autonomously decides which of its peers are most likely to answer a given query. The algorithm is entirely distributed, and therefore scales well with the size of the network. We implemented our mechanisms as middleware platforms. To show the advantages of our mechanisms we present experimental results using the middleware implementation.

Crowdsourcing with Smartphones

Smartphones can reveal crowdsourcings full potential and let users transparently contribute to complex and novel problem solving. This emerging area is illustrated through a taxonomy that classifies the mobile crowdsourcing field and through three new applications that optimize location-based search and similarity services based on crowd-generated data. Such applications can be deployed on SmartLab, a cloud of more than 40 Android devices deployed at the University of Cyprus that provides an open testbed to facilitate research and development of smartphone applications on a massive scale.

A distributed middleware infrastructure for personalized services

In this paper, we present an overview of extensible Retrieval, Annotation and Caching Engine (eRACE), a modular and distributed intermediary infrastructure that collects information from heterogeneous Internet sources according to registered profiles or end-user requests. Collected information is stored for filtering, transformation, aggregation, and subsequent personalized or wide-area dissemination on the wireline or wireless-Internet. We study the architecture and implementation of the main module of eRACE, an HTTP proxy named WebRACE. WebRACE consists of a high-performance, distributed and multithreaded Web crawler, a multithreaded filtering processor and an Object Cache. We discuss the implementation of WebRACE in Java, describe a number of performance optimizations, and present its performance assessment.

A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks

A Wireless Sensor Network (WSN) design often requires the decision of optimal locations (deployment) and transmit power levels (power assignment) of the sensors to be deployed in an area of interest. Few attempts have been made on optimizing both decision variables for maximizing the network coverage and lifetime objectives, even though, most of the latter studies consider the two objectives individually. This paper defines the multiobjective Deployment and Power Assignment Problem (DPAP). Using the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), the DPAP is decomposed into a set of scalar subproblems that are classified based on their objective preference and tackled in parallel by using neighborhood information and problem-specific evolutionary operators, in a single run. The proposed operators adapt to the requirements and objective preferences of each subproblem dynamically during the evolution, resulting in significant improvements on the overall performance of MOEA/D. Simulation results have shown the superiority of the problem-specific MOEA/D against the NSGA-II in several network instances, providing a diverse set of high quality network designs to facilitate the decision makers choice.

The threshold join algorithm for top-k queries in distributed sensor networks

In this paper we present the Threshold Join Algorithm (TJA), which is an efficient TOP-k query processing algorithm for distributed sensor networks. The objective of a top-k query is to find the k highest ranked answers to a user defined similarity function. The evaluation of such a query in a sensor network environment is associated with the transfer of data over an extremely expensive communication medium. TJA uses a non-uniform threshold on the queried attribute in order to minimize the number of tuples that have to be transferred towards the querying node. Additionally, TJA resolves queries in the network rather than in a centralized fashion, which minimizes even more the consumption of bandwidth and delay. Our preliminary experimental results, using our trace driven simulator, show that TJA is both practical and efficient.

Online Social Networks: Status and Trends

The rapid proliferation of Online Social Network (OSN) sites has made a profound impact on the WWW, which tends to reshape its structure, design, and utility. Industry experts believe that OSNs create a potentially transformational change in consumer behavior and will bring a far-reaching impact on traditional industries of content, media, and communications. This chapter starts out by presenting the current status of OSNs through a taxonomy which delineates the spectrum of attributes that relate to these systems. It also presents an overall reference system architecture that aims at capturing the building blocks of prominent OSNs. Additionally, it provides a state-of-the-art survey of popular OSN systems, examining their architectural designs and business models. Finally, the chapter explores the future trends of OSN systems, presents significant research challenges and discusses their societal and business impact.

Information retrieval techniques for peer-to-peer networks

Peer-to-peer (P2P) systems are application-layer networks that let networked hosts share resources in a distributed environment. An important attribute of P2P networks is the ability to efficiently search the contents of other peers. In this article, the authors survey existing search techniques for information retrieval (IR) in P2P networks, including recent techniques that they propose. They also present a realistic experimental evaluation and comparison of these techniques, using a distributed middleware infrastructure they designed and implemented.

Exploiting locality for scalable information retrieval in peer-to-peer networks

An important problem in unstructured peer-to-peer (P2P) networks is the efficient content-based retrieval of documents shared by other peers. However, existing searching mechanisms are not scaling well because they are either based on the idea of flooding the network with queries or because they require some form of global knowledge.We propose the Intelligent Search Mechanism (ISM) which is an efficient, scalable yet simple mechanism for improving the information retrieval problem in P2P systems. Our mechanism is efficient since it is bounded by the number of neighbors and scalable because no global knowledge is required to be maintained.ISM consists of four components: A Profiling Structure which logs queryhit messages coming from neighbors, a Query Similarity function which calculates the similarity queries to a new query, RelevanceRank which is an online neighbor ranking function and a Search Mechanism which forwards queries to selected neighbors.We deploy and compare ISM with a number of other distributed search techniques over static and dynamic environments. Our experiments are performed with real data over Peerware, our middleware simulation infrastructure which is deployed on 75 workstations. Our results indicate that ISM outperforms its competitors and that in some cases it manages to achieve 100% recall rate while using only half of the network resources required by its competitors. Further, its performance is also superior with respect to the total query response time and our algorithm exhibits a learning behavior as nodes acquire more knowledge. Finally ISM works well in dynamic network topologies and in environments with replicated data sources.

Crowdsourced indoor localization for diverse devices through radiomap fusion

Crowdsourcing is an emerging field that allows to tackle difficult problems by soliciting contributions from common people, rather than trained professionals. In the post-pc era, where smartphones dominate the personal computing market offering both constant mobility and large amounts of spatiotemporal sensory data, crowdsourcing is becoming increasingly popular. In this context, crowdsourcing stands as the only viable solution for collecting the large amount of location-related network data required to support location-based services, e.g., the signal strength radiomap of a fingerprinting localization system inside a multi-floor building. However, this benefit does not come for free, because crowdsourcing also poses new challenges in radiomap creation. We focus on the problem of device diversity that occurs frequently as the contributors usually carry heterogeneous mobile devices that report network measurements very differently. We demonstrate with simulations and experimental results that the traditional signal strength values from the surrounding network infrastructure are not suitable for crowdsourcing the radiomap. Moreover, we present an alternative approach, based on signal strength differences, that is far more robust to device variations and maintains the localization accuracy regardless of the number of contributing devices.

The Airplace Indoor Positioning Platform for Android Smartphones

In this demonstration paper, we present an indoor positioning system developed for Android smartphones, coined Airplace. To infer the unknown user location we rely on ubiquitous WLANs and exploit Received Signal Strength (RSS) values from neighboring Access Points (AP) that are constantly monitored by the mobile devices under normal operation. Our system follows a mobile-based network-assisted architecture to eliminate the communication overhead and respect user privacy. In a typical scenario, when a user walks inside a building a smartphone client conducts a single communication with our Distribution Server to receive the RSS radiomap and is then able to position itself independently using the observed RSS values. Moreover, we have implemented an Android application to facilitate the collection of RSS values by users that may contribute their data to our system for constructing and updating the radiomap through crowdsourcing1. We will demonstrate the real-time positioning capabilities of the system during the conference by allowing attendees to carry an Android tablet in order to view their position on a floorplan map, while walking around inside the demo area (interactive scenario). Moreover, we will illustrate how to evaluate the performance of different positioning algorithms using profiled data in a trace-driven scenario. Our objective is to highlight the effectiveness and applicability of our system and at the same time the participants will be able to appreciate the potential of indoor location-oriented services and applications.