Michal Gorawski

Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks.

The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.

Review of Mobility Models for Performance Evaluation of Wireless Networks

Performance evaluation of protocols and mechanisms in wireless networks require a good representation of client mobility. The number of mobility models has been developed, to emulate the changes of location in time of mobile wireless devices in communication networks, such as e.g. mobile phones, tablets, netbooks, palmtops.Mobility models are used to verify the protocols and algorithms developed for wireless networks in simulation and using analytical tools. The mobility patterns of such devices converges with human movement patterns, as the mobile devices bearers. Among many propositions of human mobility modeling in the literature this paper presents and reviews techniques which are most commonly used or that give very good estimation of actual mobile device bearer behavior. The models are divided into 3 groups: random, social and hybrid.

Collecting data streams from a distributed radio-based measurement system

Nowadays it becomes more and more popular to process rapid data streams representing real-time events, such as large scale financial transfers, road or network traffic, sensor data. Analysis of data streams enables new capabilities. It is possible to perform intrusion detection while it is happening, it is possible to predict road traffic basing on the analysis of the past and current vehicle flow. We addressed the problem of real-time analysis of the stream data from a radio-based measurement system. The system consists of large number of water, gas and electricity meters. Our work is focused on data delivery from meters to the stream data warehouse as quick as possible even if transmission failures occur. The system we designed is intended to increase significantly system reliability and availability. During this demonstration we want to present an example of the system capabilities.

Modified R-MVB tree and BTV algorithm used in a distributed spatio-temporal data warehouse

Structural and software modifications of MVB-tree (reverse pointers, aggregations) in exchange for higher space consumption enable answering the timestamp and time aggregated queries in a fast and easy way. Software extensions are new algorithms that accelerate query processing. This paper contains a brief description of temporal data and ways of handling them in modified R-MVB tree and presents distributed system in which above-mentioned index was tested along with a load balancing algorithm used in this solution.

Exploration of Continuous Sequential Patterns Using the CPGrowth Algorithm

In the following paper we present the UCP-Tree and a new algorithm called CPGrowth for continuous pattern mining. The UCP-Tree is an aggregation tree that stores common subsequences of input sequences in the same nodes. The characteristic feature of the CPGrowth algorithm is that it does not require transitional trees at the next recursion levels. Moreover, new sequences can be inserted into the UPC-Tree without rebuilding, which is a considerable advantage considering that Trajectory Data Warehouses store massive amounts of data. In this paper we compared the efficiency of the proposed index with one of the fastest continuous pattern mining algorithms.

Selection of structures with grid optimization, in multiagent data warehouse

The query optimization problem in data base and data warehouse management systems is quite similar. Changes to Joins sequences, projections and selections, usage of indexes, and aggregations are all decided during the analysis of an execution schedule. The main goal of these changes is to decrease the query response time. The optimization operation is often dedicated to a single node. This paper proposes optimization to grid or cluster data warehouses / databases. Tests were conducted in a multi-agent environment, and the optimization focused not only on a single node but on the whole system as well. A new idea is proposed here with multi-criteria optimization that is based on user-given parameters. Depending on query time, result admissible errors, and the level of system usage, task results were obtained along with grid optimization.

The real-life mobility model: RLMM

The performance evaluation of mobile wireless networks requires a representation of user mobility. Mobility models are the essential part of such evaluation because they assure examples of mobile nodes movement for typical scenarios. Mobility models provide synthetic traces adjusted to users needs. We propose a novel RLMM model that produces synthetic traces of a working human and includes clear division on working days and weekend activities. We describe the model and evaluate it using simulation. The model provides better representation of changes in human behavior on different days than the models available in the literature.

The Use of a Cloud Computing and the CUDA Architecture in Zero-Latency Data Warehouses

The growing importance of data warehousing [1-3] and the need to provide up-to-date information, changed procedures of data processing [4-8]. Classic data warehouses which are based on a traditional ETL process, proved to be ineffective and limited further development, due to the need of time-sharing of an access time between updates and analysis [9,10]. Introduction of the zero-latency data warehouse, solved the problem of data mining time limit, however it enforces the need to use larger computing power for processing updates and queries in the ETL process. The article presents two ETL systems for zero-latency data warehouses which implement the WINE-HYBRIS algorithm. The first ETL system processes tasks in CUDA and CPU architectures, while the second uses Cloud Computing. The purpose of the article is to describe advantages and disadvantages of each solution.

Encrypted adaptive storage model : analysis and performance tests /

The presented paper describes an efficient method of storing cryptograms in a relational database management system. We focus solely on the encryption of tables with columns defined using fixed size data types. Next, we propose a new storage model called EASM that improves page utilization and minimizes encryption related padding overhead and reduces encryption computational overhead. This model is compared to PPC since they both originate from NSM and are designed to protect data confidentiality. Both of these models utilize symmetric key encryption. Finally we present and analyze the results of the conducted tests.

Neural Networks in Petrol Station Objects Calibration

The fuel tank autocalibration problem is an important issue in managing the amount of fuel stored in the tank. Current values are calculated basing on fuel sold going out through nozzles - dispensing and fuel pumped into the tank by a tanker delivered. The difference in these values may point to different reasons - leakage, theft, or other errors. To pinpoint the cause it is important to rule out the case of wrong tank calibration, hence the tank autocalibration method is required. In this paper we present autocalibration method based on a neural networks algorithm, along with methods drawbacks and an alternative calibration method proposition.