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

Data Analysis and Query Processing in Wireless Sensor Networks

This work minimizes the cost of answering queries in wireless sensor networks. To answer a query, data generated by the sensors needs to be collected and processed. We optimize the cost by constructing sophisticated query trees. Queries are divided into two categories: queries that need data from all the nodes in the network and queries that need data from a subset of nodes only.For the first type of queries we propose a distributed algorithm to construct a near-optimal balanced communication tree with minimum overhead. Such a tree has inherently minimal number of collisions during query execution, and therefore avoids numerous retransmissions. Our algorithm outperforms previous work both in tree construction overhead and in tree balance.For the second type of queries we present methods for constructing query trees to route and perform in-network processing of data. First, we focus on snapshot queries and show that minimizing the problem is NP-hard. We propose a dynamic programming algorithm to compute the optimal solution for small problem instances. We also propose a low complexity, approximate, heuristic algorithm for solving larger problem instances efficiently. Finally, we adapt the Fermat point problem (1-median problem) for a weighted graph, and propose a centralized solution that is used as heuristic in the above algorithms.Dealing with continuous queries of the second category, we present an optimal distributed algorithm to adapt the placement of a single operator. Our parameter-free algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three ideas, proposed here, make this feature possible: 1) identifying the special, and most frequent case, where no flooding is needed, otherwise 2) limitation of the neighborhood to be flooded and 3) variable speed flooding and eves-dropping. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem. In our experiments we show that for the rest of cases our algorithm saves 30\%-80\% of the energy compared to previously proposed techniques

Data Analysis and Query Processing in Wireless Sensor Networks

This work minimizes the cost of answering queries in wireless sensor networks. To answer a query, data generated by the sensors needs to be collected and processed. We optimize the cost by constructing sophisticated query trees. Queries are divided into two categories: queries that need data from all the nodes in the network and queries that need data from a subset of nodes only.For the first type of queries we propose a distributed algorithm to construct a near-optimal balanced communication tree with minimum overhead. Such a tree has inherently minimal number of collisions during query execution, and therefore avoids numerous retransmissions. Our algorithm outperforms previous work both in tree construction overhead and in tree balance.For the second type of queries we present methods for constructing query trees to route and perform in-network processing of data. First, we focus on snapshot queries and show that minimizing the problem is NP-hard. We propose a dynamic programming algorithm to compute the optimal solution for small problem instances. We also propose a low complexity, approximate, heuristic algorithm for solving larger problem instances efficiently. Finally, we adapt the Fermat point problem (1-median problem) for a weighted graph, and propose a centralized solution that is used as heuristic in the above algorithms.Dealing with continuous queries of the second category, we present an optimal distributed algorithm to adapt the placement of a single operator. Our parameter-free algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three ideas, proposed here, make this feature possible: 1) identifying the special, and most frequent case, where no flooding is needed, otherwise 2) limitation of the neighborhood to be flooded and 3) variable speed flooding and eves-dropping. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem. In our experiments we show that for the rest of cases our algorithm saves 30\%-80\% of the energy compared to previously proposed techniques

Minimum-Hot-Spot Query Trees for Wireless Sensor Networks

Weproposeadistributedalgorithmtoconstructabalancedcommunication tree that serves in gathering data from the network nodes toasink. Ouralgorithmconstructsanear-optimallybalancedcommunication tree with minimum overhead. The balancing of the nodedegreesresultsintheminimizationofpacketcollisionsduring query execution, that would otherwise require numerous retransmissions and reduce the lifetime of the network. We compare our simple distributed algorithm against previous work and a centralizedsolutionandshowthatformostnetworklayoutsitoutperforms competition and achieves tree balance very close to the centralized algorithm. Italsohasthesmallestenergyoverheadpossibletoconstructthe tree, increasing the lifetime of the network even more

Operator Placement for Snapshot Multi-Predicate Queries in Wireless Sensor Networks

Abstract—This work aims at minimize the cost of answering snapshot multi-predicate queries in high-communication-cost networks. High-communication-cost (HCC) networks is a family of networks where communicating data is very demanding in resources, for example in wireless sensor networks transmitting data drains the battery life of sensors involved. The important class of multi-predicate queries in horizontally or vertically distributed databases is addressed. We show that minimizing the communication cost for multi-predicate queries is NP-hard and we propose a dynamic programming algorithm to compute the optimal solution for small problem instances. We also propose a low complexity, approximate, heuristic algorithm for solving larger problem instances efficiently and running it on nodes with low computational power (e.g. sensors). Finally, we present a variant of the Fermat point problem where distances between points are minimal paths in a weighted graph, and propose a solution. An extensive experimental evaluation compares the proposedalgorithms tothebest knowntechniqueusedtoevaluate queries in wireless sensor networks and shows improvement of 10 % up to 95%. The low complexity heuristic algorithm is also shown to be scalable and robust to different query characteristics and network size. I