DNSR: Domain Name Suffix-based Routing in Overlay Networks

Abstract. Overlay Peer-to-Peer (P2P) networks are application layer networks which facilitate users in performing distributed functions such as keyword searches over the data of other users. An important problem in such networks is that the connection among peers are arbitrary, leading in that way to a topology structure which doesn't match the underlying physical topology. This phenomenon leads to excessive network resource consumption in Wide Area Networks as well as degraded user experience because of the incurred network delays. Most state-of-the-art research concentrates on structuring overlay networks in a way that query messages can reach the appropriate nodes within some hop-count boundaries. These approaches are not taking into account the underlying network topology mismatch making it therefore inappropriate for wide area routing. In this work we propose and evaluate DNSR (Domain Name Suffix-based Routing), which is a novel technique to route query messages in Overlay Networks, based on the "domain closeness" of the node sending the message. We describe DNSR and show simulation experiments which are performed over PeerWare, our distributed infrastructure which runs on a network of 50 workstations. Our simulations are based on real data gathered from one of the largest open P2P networks, namely Gnutella

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

FireWatch: G.i.S. -assisted wireless sensor networks for forest fires

Traditional satellite and camera-based systems, are currently the predominant methods for detecting forest fires. Our study has identified that these systems lack immediacy as detected fires must gain some momentum before they are detected. In addition, they suffer from decreased accuracy especially during the night, where visibility is diminished. In this paper, we present FireWatch, a system that aims to overcome the aforementioned limitations by combining a number of technologies including Wireless Sensor Networks, Computer-supported Cooperative Work and Geographic Information Systems in a transparent manner. Compared to satellite and camera-based approaches, FireWatch is able to detect forest fires more accurately and forecast the forest fire danger more promptly. FireWatch is currently scheduled to be deployed at the Cypriot Department of Forests

Power efficiency through tuple ranking in wireless sensor network monitoring

In this paper, we present an innovative framework for efficiently monitoring Wireless Sensor Networks (WSNs). Our framework, coined KSpot, utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. For ease of exposition, consider a set of sensors acquiring data from their environment at a given time instance. The generated information can conceptually be thought as a horizontally fragmented base relation R. Furthermore, the results to a user-defined query Q, registered at some sink point, can conceptually be thought as a view V . Maintaining consistency between V and R is very expensive in terms of communication and energy. Thus, KSpot focuses on a subset V′ (⊆ V ) that unveils only the k highest-ranked answers at the sink, for some user defined parameter k. To illustrate the efficiency of our framework, we have implemented a real system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from University of California-Berkeley, the University of Washington and Intel Research Berkeley, show that KSpot provides an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted over the network (up to 77%), and prolongs the longevity of a WSN deployment to new scales

Towards Robust Methods for Indoor Localization using Interval Data

International audienceIndoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then, we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates than state-of-the-art methods

In-network data acquisition and replication in mobile sensor networks

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of values from the network in cases of failures. We also extend DRA with a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles to form the Hierarchical-DRA (HDRA) algorithm, which enables the approximate retrieval of events from the network. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures across all nodes are less than 60%, our framework can recover over 80% of detected values exactly

Mint views: Materialized in-network top-k views in sensor networks

In this paper we introduce MINT (materialized in-network top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V(sube. V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively- defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models

ACCES:Offline Accuracy Estimation for Fingerprint-Based Localization

In this demonstration we present ACCES, a novel framework that enables quality assessment of arbitrary fingerprint maps and offline accuracy estimation for the task of fingerprint-based indoor localization. Our framework considers collected fingerprints disregarding the physical origin of the data. First, it applies a widely used statistical instrument, namely Gaussian Process Regression (GPR), for interpolation of the fingerprints. Then, to estimate the best possibly achievable localization accuracy at any location, it utilizes the Cramer-Rao Lower Bound (CRLB) with interpolated data as an input. Our demonstration entails a standalone version of the popular and open-source Anyplace Internet-based indoor navigation service in which the software modules of ACCES are integrated. At the conference, we will present the utility of our method in two modes: (i) Collection Mode, where attendees will be able to use our service directly to collect signal measurements over the venue using an Android smartphone, and (ii) Reflection Mode, where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap.© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. Nikitin, C. Laoudias, G. Chatzimilioudis, P. Karras and D. Zeinalipour-Yazti, "ACCES: Offline Accuracy Estimation for Fingerprint-Based Localization," 2017 18th IEEE International Conference on Mobile Data Management (MDM), Daejeon, 2017, pp. 358-359. doi: 10.1109/MDM.2017.6

Indoor Localization Accuracy Estimation from Fingerprint Data

The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data origin and at any location of interest, so as to provide deployment staff with the information on the quality of localization. Even though FM-based localization algorithms usually provide accuracy estimates during system operation (e.g., visualized as uncertainty circle or ellipse around the user location), they do not provide any information about the expected accuracy before the actual deployment of the localization service. In this paper, we develop a novel frame-work for quality assessment on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our extensive experimental study with magnetic FMs, comparing empirical localization accuracy against derived bounds, demonstrates that the navigability score closely matches the accuracy variations users experience.© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. Nikitin, C. Laoudias, G. Chatzimilioudis, P. Karras and D. Zeinalipour-Yazti, "Indoor Localization Accuracy Estimation from Fingerprint Data," 2017 18th IEEE International Conference on Mobile Data Management (MDM), Daejeon, 2017, pp. 196-205. doi: 10.1109/MDM.2017.3

ETC: energy-driven tree construction in wireless sensor networks

Continuous queries in Wireless Sensor Networks (WSNs) are founded on the premise of Query Routing Tree structures (denoted as T), which provide sensors with a path to the querying node. Predominant data acquisition systems for WSNs construct such structures in an ad-hoc manner and therefore there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. In this paper we present the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions, thus reducing energy consumption, during data acquisition in WSNs. We show through real micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UCBerkeley that ETC can provide significant energy reductions under a variety of conditions prolonging the longevity of a wireless sensor network