LOCATION MANAGEMENT FOR PCS NETWORKS USING USER MOVEMENT PATTERN

ABSTRACT Location management is essential task in current cellular system. Mobility prediction is widely used to assist handoff management, resource reservation and service pre-configuration. Location management methods are to find out mobile unit current location. Location update and paging have to maintain efficiently to minimize location management cost in cellular network. This paper introduce new user movement pattern according to particular time slot based algorithm reducing location management cost. This algorithm is based on user's daily predefined moving geographical activities pattern, according to time. Paging decision for user is based on this predicted location for any instance of time interval. This predicated value again sort by higher probability of user finding in any cell for that time duration. This prediction information is saved by mobile unit in its memory for every fixed time interval. The results confirm the effectiveness of this method compare to existing method for real time in mobile services and proposed method

Location management and fault tolerance issues in mobile networks

Mobile networks has become ubiqutous in todays life. The number of people using this technology is rapidly growing each day. The mobile population is expected to reach the figure of nearly 1 billion in the near future. Several research efforts are being undertaken to address various issues in the design and operation of mobile networks. Some of the active research issues in mobile networking include channel allocation, mobility management, signal processing, fault tolerance, routing protocols and interoperability between different mobile networking protocols. In this dissertation we focus on a couple of these research issues namely mobility management and fault tolerance. Mobility management deals with managing the mobile nodes when they move. The network has to maintain connection to the mobile node inspite of the nodes changing location. We develop fundamental bounds for load balanced location management. We develop novel algorithms for load balanced location management in mobile networks. We later derive fundamental bounds satisfied by any load balanced location management algorithm in the presence of failures in the network. We then present a fast recovery protocol to recover from failures in the network. The salient feature of this protocol being the removal of the use of wireless bandwidth in the recovery process. We conclude the thesis by discussing some of the future issues in the area of mobile networks

Air Force Institute of Technology Research Report 1999

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Journal of Telecommunications and Information Technology, 2001, nr 2

kwartalni

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications