Design of personalized location areas for future Pcs networks

In Global Systems for Mobile Communications (GSM), always-update location strategy is used to keep track of mobile terminals within the network. However future Personal Communication Networks (PCS) will require to serve a wide range of services (digital voice, video, data, and email) and also will have to support a large population of users. Under such demands, determining the exact location of a user by traditional strategies would be difficult and would result in increasing the signaling load imposed by location-update and paging procedures. The problem is not only in increasing cost, but also in non-efficient utilization of a precious resource, i.e., radio bandwidth; In this thesis, personalized Location Areas (PLAs) are formed considering the mobility patterns of individual users in the system such that the signaling due to location update and paging is minimized. We prove that the problem in this formulation is of NP complexity. Therefore we study efficient optimization techniques able to avoid combinatorial search. Three known classes of optimization techniques are studied. They are Simulated Annealing, Tabu Search and Genetic Search. Three algorithms are designed for solving the problem. Modeling does not assume any specific cell structure or network topology that makes the proposal widely applicable. The behavior of mobile terminals in the network is modeled as Random Walk with an absorbing state and the Markov chain is used for cost analysis; Numeric simulation carried out for 25 and 100 hexagonal cell networks have shown that Simulated Annealing based algorithm outperforms other two by indicators of the runtime complexity and signaling cost of location management. The ID\u27s of cells populating the calculated area are provided to the mobile terminal and saved in its local memory every time the mobile subscriber moves out its current location area. Otherwise, no location update is performed, but only paging. Thus, at the expense of small local memory, the location management is carried more efficiently

Enhanced distance-based location management of mobile communication systems using a cell coordinates approach

In managing the locations of mobile users in mobile communication systems, the distance-based strategy has been proven to have better performance than other dynamic strategies, but is difficult to implement. In this paper, a simple approach is introduced to implement the distance-based strategy by using the cell coordinates in calculating the physical distance traveled. This approach has the advantages of being independent of the size, shape, and distribution of cells, as well as catering for the direction of movement in addition to the speed of each mobile terminal. An enhanced distance-based location management strategy is proposed to dynamically adjust the size and shape of location area for each individual mobile terminal according to the current speed and direction of movement. It can reduce the location management signaling traffic of the distance-based strategy by half when mobile terminals have predictable directions of movement. Three types of location updating schemes are discussed, namely, Circular Location Area, Optimal Location Area, and Elliptic Location Area. Paging schemes using searching techniques such as expanding distance search based on the last reported location and based on the predicted location, and expanding direction search are also explored to further reduce paging signal traffic by partitioning location areas into paging areas.published_or_final_versio

Learning-based tracking area list management in 4G and 5G networks

© 2020 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 worksMobility management in 5G networks is a very challenging issue. It requires novel ideas and improved management so that signaling is kept minimized and far from congesting the network. Mobile networks have become massive generators of data and in the forthcoming years this data is expected to increase drastically. The use of intelligence and analytics based on big data is a good ally for operators to enhance operational efficiency and provide individualized services. This work proposes to exploit User Equipment (UE) patterns and hidden relationships from geo-spatial time series to minimize signaling due to idle mode mobility. We propose a holistic methodology to generate optimized Tracking Area Lists (TALs) in a per UE manner, considering its learned individual behavior. The k -means algorithm is proposed to find the allocation of cells into tracking areas. This is used as a basis for the TALs optimization itself, which follows a combined multi-objective and single-objective approach depending on the UE behavior. The last stage identifies UE profiles and performs the allocation of the TAL by using a neural network. The goodness of each technique has been evaluated individually and jointly under very realistic conditions and different situations. Results demonstrate important signaling reductions and good sensitivity to changing conditions.This work was supported by the Spanish National Science Council and ERFD funds under projects TEC2014-60258-C2-2-R and RTI2018-099880-B-C32.Peer ReviewedPostprint (author's final draft

EVEREST IST - 2002 - 00185 : D23 : final report

Deliverable públic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version

Security Enhancement of Route Optimization in Mobile IPv6 Networks

Mobile IPv6 is an IP-layer protocol that is designed to provide mobility support.It allows an IPv6 node to arbitrarily change its location in the IPv6 network while maintaining the existing connection by handling the change of addresses at the Internet layer. Route optimization is standard in Mobile IPv6 to eliminate inefficient triangle routing. Several methods were proposed to secure route optimization. Return routability was adopted by Internet Engineering Task Force (IETF) with its security protocol based on RFC 3775. Return routability is an infrastructureless, lightweight procedure that enables a Mobile IPv6 node to request another IPv6 node to check and test the ownership of its permanent address in both home network and current visited network. It authorizes a binding procedure by the use of cryptographically token exchange. However, return routability protocol in route optimization is to protect messages and is not able to detect or prevent an attacker which tampers against data. In this thesis, focus is given on Mobile IPv6 route optimization test-bed with enhanced security in terms of data integrity. The proposed method can be performed on top of the return routability procedure to detect and prevent Man-In-The-Middle attack by using encryption if any attack is detected. This also eliminates the additional delay compared to using encryption from the beginning of a connection. A real-time experimental test-bed has been set up, which is comprised of hardware, software and network analysis tools to monitor the packet flow and content of data packets. The test-bed consists of four computers acting as Mobile Node, Home Agent, Correspondent Node, and Router, respectively. To ensure the accuracy and integrity of the collected data, the Network Time Protocol (NTP) was used between the packet generator (Mobile Node) and packet receiver (Correspondent Node) to synchronize the time. The results show that the proposed method is able to work efficiently, maintaining 99% data security of route optimization in Mobile IPv6 (MIPv6) networks. The overall data integrity (by means of security) is improved 72% compared to existing MIPv6 by at a cost of 0.1 sec added overall delay, which is within the tolerable range by the network

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique

Anticipatory Buffer Control and Quality Selection for Wireless Video Streaming

Video streaming is in high demand by mobile users, as recent studies indicate. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast to that, good channel conditions cause common greedy buffering schemes to pile up very long buffers. Such over-buffering wastes expensive wireless channel capacity. To keep buffering in balance, we employ a novel approach. Assuming that we can predict data rates, we plan the quality and download time of the video segments ahead. This anticipatory scheduling avoids buffer underruns by downloading a large number of segments before a channel outage occurs, without wasting wireless capacity by excessive buffering. We formalize this approach as an optimization problem and derive practical heuristics for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality