Georgios Chatzimilioudis

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.

Continuous All k-Nearest-Neighbor Querying in Smartphone Networks

Consider a centralized query operator that identifies to every smartphone user its k geographically nearest neighbors at all times, a query we coin Continuous All k-Nearest Neighbor (CAkNN). Such an operator could be utilized to enhance public emergency services, allowing users to send SOS beacons out to the closest rescuers, allowing gamers and social networking users to establish ad-hoc overlay communication infrastructures, in order to carry out complex interactions. In this paper, we study the problem of efficiently processing a CAkNN query in a cellular or WiFi network, both of which are ubiquitous. We introduce an algorithm, coined Proximity, which answers CAkNN queries in O(n(k + λ)) time, where n denotes the number of users and λ a network-specific parameter (λ <;<; n). Proximity does not require any additional infrastructure or specialized hardware and its efficiency is mainly attributed to a smart search space sharing technique we introduce. Its implementation is based on a novel data structure, coined k+-heap, which achieves constant O(1) look-up time and logarithmic O(log(k*λ)) insertion/update time. Proximity, being parameter-free, performs efficiently in the face of high mobility and skewed distribution of users (e.g., the service works equally well in downtown, suburban, or rural areas). We have evaluated Proximity using mobility traces from two sources and concluded that our approach performs at least one order of magnitude faster than adapted existing work.

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

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 proposed algorithms to the best known technique used to evaluate 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.

Privacy-Preserving Indoor Localization on Smartphones

Indoor Positioning Systems (IPS) have recently received considerable attention, mainly because GPS is unavailable in indoor spaces and consumes considerable energy. On the other hand, predominant Smartphone OS localization subsystems currently rely on server-side localization processes, allowing the service provider to know the location of a user at all times. In this paper, we propose an innovative algorithm for protecting users from location tracking by the localization service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our proposed Temporal Vector Map (TVM) algorithm, allows a user to accurately localize by exploiting a

Minimum-hot-spot query trees for wireless sensor networks

k

A Distributed Technique for Dynamic Operator Placement in Wireless Sensor Networks

-Anonymity Bloom (kAB) filter and a bestNeighbors generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. We have evaluated our framework using a real prototype developed in Android and Hadoop HBase as well as realistic Wi-Fi traces scaling-up to several GBs. Our analytical evaluation and experimental study reveal that TVM is not vulnerable to attacks that traditionally compromise k-anonymity protection and indicate that TVM can offer fine-grained localization in approximately four orders of magnitude less energy and number of messages than competitive approaches.

A novel distributed framework for optimizing query routing trees in wireless sensor networks via optimal operator placement

We propose a distributed algorithm to construct a balanced communication tree that serves in gathering data from the network nodes to a sink. Our algorithm constructs a near-optimally balanced communication tree with minimum overhead. The balancing of the node degrees results in the minimization of packet collisions during 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 centralized solution and show that for most network layouts it outperforms competition and achieves tree balance very close to the centralized algorithm. It also has the smallest energy overhead possible to construct the tree, increasing the lifetime of the network even more.

Distributed In-Memory Processing of All k Nearest Neighbor Queries

We present an optimal distributed algorithm to adapt the placement of a single operator in high communication cost networks, such as a wireless sensor network. Our parameter-free algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three techniques, 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. When no flooding is needed the communication cost overhead for adapting the operator placement is negligible. In addition, our algorithm does not require any extra communication cost while the query is executed. In our experiments we show that for the rest of cases our algorithm saves 30%-85% of the energy compared to previously proposed techniques. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem.

Crowdsourcing for Mobile Data Management

In this paper, we focus the attention on the operator placement problem in Wireless Sensor Networks (WSN). This problem is very relevant for in-network query processing over WSN, where query routing trees are decomposed into three sub-components that must be processed at query time, namely operator tree, operator placement assignment scheme and routing scheme. In particular, the operator placement assignment defines on which node of the network each (query) operator will be hosted and executed. Hence, operator placement plays a key role in the context of query optimization issues in WSN research. In line with this main motivation, in this paper we present an optimal distributed algorithm to adapt the placement of a single operator in high communication cost networks, such as a wireless sensor network. Our parameter-free algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three techniques, 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. When no flooding is needed the communication cost overhead for adapting the operator placement is negligible. In addition, our algorithm does not require any extra communication cost while the query is executed. In our experiments we show that for the rest of cases our algorithm saves 30%-85% of the energy compared to previously proposed techniques. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem. A comprehensive experimental evaluation and the proposal of two solutions that are capable of dealing with adaptive properties of the operator placement problem, which is an innovative perspective of research in this scientific field, represent two further contributions of our research.

Radio Map Prefetching for Indoor Navigation in Intermittently Connected Wi-Fi Networks

A wide spectrum of Internet-scale mobile applications, ranging from social networking, gaming and entertainment to emergency response and crisis management, all require efficient and scalable All k Nearest Neighbor (AkNN) computations over millions of moving objects every few seconds to be operational. Most traditional techniques for computing AkNN queries are centralized, lacking both scalability and efficiency. Only recently, distributed techniques for shared-nothing cloud infrastructures have been proposed to achieve scalability for large datasets. These batch-oriented algorithms are sub-optimal due to inefficient data space partitioning and data replication among processing units. In this paper, we present Spitfire , a distributed algorithm that provides a scalable and high-performance AkNN processing framework. Our proposed algorithm deploys a fast load-balanced partitioning scheme along with an efficient replication-set selection algorithm, to provide fast main-memory computations of the exact AkNN results in a batch-oriented manner. We evaluate, both analytically and experimentally, how the pruning efficiency of the Spitfire algorithm plays a pivotal role in reducing communication and response time up to an order of magnitude, compared to three other state-of-the-art distributed AkNN algorithms executed in distributed main-memory.