This document discusses positioning techniques for mobile devices, comparing Android and iPhone approaches. It covers GPS, WiFi, and cellular positioning methods, as well as APIs available on each platform. Location-based services that require continuous tracking can significantly drain batteries; the document discusses various approaches to address this problem and improve energy efficiency of positioning algorithms.
REVIEW OF ANTENNAS FOR USB DONGLE APPLICATIONS IJEEE
In this era of wireless communication there is an integration of more and more radios into a single wireless platform enabling maximum connectivity The multiband antenna approach using PIFA structure enables size reduction, lowers SAR values, augments the bandwidth. These can be obtained by implementing several techniques for the modification of basic structure and use of ground plane. PIFA is also a preferred choice to be incorporated for LTE and WiMAX bands as it inhibits polarization diversity effectively without any decrement in the volume. Single band antenna supports a single frequency of wireless service. And in this era several wireless standards are supported by the equipments. Thus several antennas are employed for each standard leading to a huge space requirement in the handheld devices. Hence the designing of a small PIFA antenna supports multiple bands, small size, improve Gain and good radiation pattern is the prime objective.
RF Planning and Optimization in GSM and UMTS NetworksApurv Agrawal
The report covers various aspects involved in improving the network coverage as well as the parameters used in planning of new network sites for GSM and UMTS networks.
Implementation of Particle Swarm Optimization Technique for Enhanced Outdoor ...Onyebuchi nosiri
This paper describes the development of optimized model for urban outdoor coverage in Long Term Evolution (LTE) network at 2300 MHz frequency band in Port Harcourt urban region, Nigeria. Signal attenuation and fluctuation remain amongst the major channel impairments for mobile radio communication systems. This arises as a result of model incompatibility with terrain and Line of Sight (LOS) obstruction of the channel signals. Some path loss models such as OkumuraHata, COST 231, Ericsson 999, Egli and ECC-33 models were evaluated for suitability and compared with the modified model for the environments. The models were based on data collected from LTE base stations at three geographical locations in Port Harcourt namely- Rumuokoro, Eneka and Ikwerre roads respectively. The simulation was implemented using MATLAB R2014a software. The modified model was further optimized with some selected parameters such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) using Particle Swarm Optimization (PSO) technique. The results obtained gave rise to 3.030dB for RMSE and 0.00162dB for MAE respectively. The results obtained from the PSO optimized model demonstrated a better performance which is suitable for cell coverage planning and smooth handoff processes.
REVIEW OF ANTENNAS FOR USB DONGLE APPLICATIONS IJEEE
In this era of wireless communication there is an integration of more and more radios into a single wireless platform enabling maximum connectivity The multiband antenna approach using PIFA structure enables size reduction, lowers SAR values, augments the bandwidth. These can be obtained by implementing several techniques for the modification of basic structure and use of ground plane. PIFA is also a preferred choice to be incorporated for LTE and WiMAX bands as it inhibits polarization diversity effectively without any decrement in the volume. Single band antenna supports a single frequency of wireless service. And in this era several wireless standards are supported by the equipments. Thus several antennas are employed for each standard leading to a huge space requirement in the handheld devices. Hence the designing of a small PIFA antenna supports multiple bands, small size, improve Gain and good radiation pattern is the prime objective.
RF Planning and Optimization in GSM and UMTS NetworksApurv Agrawal
The report covers various aspects involved in improving the network coverage as well as the parameters used in planning of new network sites for GSM and UMTS networks.
Implementation of Particle Swarm Optimization Technique for Enhanced Outdoor ...Onyebuchi nosiri
This paper describes the development of optimized model for urban outdoor coverage in Long Term Evolution (LTE) network at 2300 MHz frequency band in Port Harcourt urban region, Nigeria. Signal attenuation and fluctuation remain amongst the major channel impairments for mobile radio communication systems. This arises as a result of model incompatibility with terrain and Line of Sight (LOS) obstruction of the channel signals. Some path loss models such as OkumuraHata, COST 231, Ericsson 999, Egli and ECC-33 models were evaluated for suitability and compared with the modified model for the environments. The models were based on data collected from LTE base stations at three geographical locations in Port Harcourt namely- Rumuokoro, Eneka and Ikwerre roads respectively. The simulation was implemented using MATLAB R2014a software. The modified model was further optimized with some selected parameters such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) using Particle Swarm Optimization (PSO) technique. The results obtained gave rise to 3.030dB for RMSE and 0.00162dB for MAE respectively. The results obtained from the PSO optimized model demonstrated a better performance which is suitable for cell coverage planning and smooth handoff processes.
GSM QUALITY OF SERVICE PERFORMANCE IN ABUJA, NIGERIAIJCSEA Journal
There has been tremendous growth in Global System for Mobile (GSM) services in Nigeria in the last two decade. The rapid development of GSM telecommunication industries have given rise to need for how the
GSM services are delivered to customers efficiently. This paper is on GSM quality of service (QoS) performance in, Abuja Nigeria. The study investigates network transmission impairment and offers some useful remedies. The method involved use of Sony Ericson W-995 phone to gather data on physical network impairments in selected densely populated areas of Abuja. The data obtained were from four major GSM
service providers MTN, Etisalat, Glo and Airtel. Assessment in terms of Key Performance Indicator (KPI) using Transmission Environment Monitory System (TEMS) Discovery professional software carried out on the networks under test. The results obtained from the analysis indicated that QoS performance of Airtel is slightly better than the other three GSM services providers tested within Nyanyan, Gwagwalada, and Wuse
areas in Abuja.
Integrated Optical Wireless Network For Next Generation Wireless SystemsCSCJournals
Next generation wireless networks need to support broadband wireless services at significantly reduced cost. The existing wireless systems can hardly provide transmission capacity of the order of few Mbps. However, millimeter waves and optical fiber can provide data capacity of the order of Gbps and Tbps respectively. Hence the requirements of broadband wireless system can be achieved through the integration of optical fiber and millimeter wireless systems. We suggest modified millimeter wireless system, with optical fiber as feeder network. Simulations have been carried out for AWGN and optical fiber channels using MATLAB code, so as to compare their individual performance. When compared it is observed that the performance of multimode optical fiber (MMOF) link even for distance of 80 KM is better than that of the AWGN channel with SNR of 50 dB and above. Hence, an integrated fiber radio network is an excellent cost effective media for higher data rate (>100Mbps).
IOSR Journal of Applied Physics (IOSR-JAP) is an open access international journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The prediction of mobile data traffic based on the ARIMA model and disruptive...TELKOMNIKA JOURNAL
Disruptive technologies, which are caused by the cellular evolution including
the Internet of Things (IoT), have significantly contributed data traffic to the mobile
telecommunication network in the era of Industry 4.0. These technologies cause
erroneous predictions prompting mobile operators to upgrade their network, which
leads to revenue loss. Besides, the inaccuracy of network prediction also creates
a bottleneck problem that affects the performance of the telecommunication network,
especially on the mobile backhaul. We propose a new technique to predict more
accurate data traffic. This research used a univariate Autoregressive Integrated Moving
Average (ARIMA) model combined with a new disruptive formula. Another model,
called a disruptive formula, uses a judgmental approach based on four variables:
Political, Economic, Social, Technological (PEST), cost, time to market, and market
share. The disruptive formula amplifies the ARIMA calculation as a new combination
formula from the judgmental and statistical approach. The results show that
the disruptive formula combined with the ARIMA model has a low error in mobile
data forecasting compared to the conventional ARIMA. The conventional ARIMA
shows the average mobile data traffic to be 49.19 Mb/s and 156.93 Mb/s for the 3G and
4G, respectively; whereas the ARIMA with disruptive formula shows more optimized
traffic, reaching 56.72 Mb/s and 199.73 Mb/s. The higher values in the ARIMA with
disruptive formula are closest to the prediction of the mobile data forecast. This result
suggests that the combination of statistical and computational approach provide more
accurate prediction method for the mobile backhaul networks.
PSO-CCO_MIMO-SA: A particle swarm optimization based channel capacity optimza...IJECEIAES
With the radio channels physical limits, achieving higher data rate in the multi-channel systems is been a biggest concern. Hence, various spatial domain techniques have been introduced by incorporating array of antenna elements (i.e., smart antenna) in recent past for the channel limit expansion in mobile communication antennas. These smart antennas help to yield the improved array gain or bearm forming gain and hence by power efficiency enhanmaent in the channel and antenna range expansion. The use of smart antenna leads to spatial diversity and minimizes the fading effect and improves link reliability. However, in the process of antenna design, the proper channel modelling is is biggest concern which affect the wireless system performance. The recent works of MIMO design systems have discussed the issues in number of antenna selection which suggests that optimization of MIMO channel capacity is required. Hence, a Particle Swarm Optimization based channel capacity optimzation for MIMO system incorporated with smart antenna is introduced in this paper. From the outcomes it is been found that the proposed PSO based MIMO system achieves better convergenece speed which results in better channel capacity.
The relay stations are widely used in major wireless technologies such as WiMAX (Worldwide Interoperability for Microwave Access) and LTE (Long term evolution) which provide cost effective service to the operators and end users. It is quite challenging to provide guaranteed Quality of Service (QoS) in WiMAX networks in cost effective manner.
Impact of Next Generation Cognitive Radio Network on the Wireless Green Eco s...ijeei-iaes
Land mobile communication is burdened with typical propagation constraints due to the channel characteristics in radio systems.Also,the propagation characteristics vary form place to place and also as the mobile unit moves,from time to time.Hence,the tramsmission path between transmitter and receiver varies from simple direct LOS to the one which is severely obstructed by buildings, foliage and terrain. Multipath propagation and shadow fading effects affect the signal strength of an arbitrary Transmitter-Receiver due to the rapid fluctuations in the phase and amplitude of signal which also determines the average power over an area of tens or hundreds of meters. Shadowing introduces additional fluctuations, so the received local mean power varies around the area –mean. The present paper deals with the performance analysis of impact of next generation wireless cognitive radio network on wireless green eco system through signal and interference level based k coverage probability under the shadow fading effects.
TECHNIQUES IN PERFORMANCE IMPROVEMENT OF MOBILE WIRELESS COMMUNICATION SYSTEM...Onyebuchi nosiri
Mobile wireless communication providers are expected by their numerous subscribers to provide network that can allow higher data rates, and good voice quality. However, this may be restricted due to some technical problems such as limited availability of radio frequency spectrum, bandwidth, channel capacity, geographical areas and transmission problems caused by various factors like fading and multipath distortion. All these lead to overall system performance degradation. This has led to various studies on how improvement on the performance of wireless communication can be realized using different techniques. This paper is a review of some scholarly works on this subject. To achieve this some recent scholarly articles were accessed online and their findings were highlighted. It was observed that all the articles reviewed had results drawn only from theoretical analysis. Based on this, one of the recommendations is that theoretical analysis should be supported with data obtained from carrying out RF measurements in the field where possible.
3G Drive Test Analysis for Long Call in University Campusijtsrd
Mobile communication is developing very rapidly with passage of time, new technologies are being introduced to facilitate the mobile user more from the technology. The past technologies are replaced by new ones and needs are growing for the new technologies to be developed. Just within a decade, an evolution of wireless service people used every day can be completely dumb founded from the roots of analog based first generation service 1G to today's truly broadband ready fourth generation networks. Drive Test is conducted for checking coverage criteria of a cell site with RF Drive Test tool. The data collected by Drive Test tool as log files is analyzed to evaluate various RF parameters of the network. And it's very important things for mobile service provider. This paper is to study about the analysis of long call Drive Test for third generation 3G networks to evaluate the performance of QoS Quality of Service using TEMS Investigation 16.3.3 software and TEMS Discovery 10.0.6 software. Aye Myat Myat Myo | Zar Chi Soe "3G Drive Test Analysis for Long Call in University Campus" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26777.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26777/3g-drive-test-analysis-for-long-call-in-university-campus/aye-myat-myat-myo
Intelligent location tracking scheme for handling user’s mobilityeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
GSM QUALITY OF SERVICE PERFORMANCE IN ABUJA, NIGERIAIJCSEA Journal
There has been tremendous growth in Global System for Mobile (GSM) services in Nigeria in the last two decade. The rapid development of GSM telecommunication industries have given rise to need for how the
GSM services are delivered to customers efficiently. This paper is on GSM quality of service (QoS) performance in, Abuja Nigeria. The study investigates network transmission impairment and offers some useful remedies. The method involved use of Sony Ericson W-995 phone to gather data on physical network impairments in selected densely populated areas of Abuja. The data obtained were from four major GSM
service providers MTN, Etisalat, Glo and Airtel. Assessment in terms of Key Performance Indicator (KPI) using Transmission Environment Monitory System (TEMS) Discovery professional software carried out on the networks under test. The results obtained from the analysis indicated that QoS performance of Airtel is slightly better than the other three GSM services providers tested within Nyanyan, Gwagwalada, and Wuse
areas in Abuja.
Integrated Optical Wireless Network For Next Generation Wireless SystemsCSCJournals
Next generation wireless networks need to support broadband wireless services at significantly reduced cost. The existing wireless systems can hardly provide transmission capacity of the order of few Mbps. However, millimeter waves and optical fiber can provide data capacity of the order of Gbps and Tbps respectively. Hence the requirements of broadband wireless system can be achieved through the integration of optical fiber and millimeter wireless systems. We suggest modified millimeter wireless system, with optical fiber as feeder network. Simulations have been carried out for AWGN and optical fiber channels using MATLAB code, so as to compare their individual performance. When compared it is observed that the performance of multimode optical fiber (MMOF) link even for distance of 80 KM is better than that of the AWGN channel with SNR of 50 dB and above. Hence, an integrated fiber radio network is an excellent cost effective media for higher data rate (>100Mbps).
IOSR Journal of Applied Physics (IOSR-JAP) is an open access international journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The prediction of mobile data traffic based on the ARIMA model and disruptive...TELKOMNIKA JOURNAL
Disruptive technologies, which are caused by the cellular evolution including
the Internet of Things (IoT), have significantly contributed data traffic to the mobile
telecommunication network in the era of Industry 4.0. These technologies cause
erroneous predictions prompting mobile operators to upgrade their network, which
leads to revenue loss. Besides, the inaccuracy of network prediction also creates
a bottleneck problem that affects the performance of the telecommunication network,
especially on the mobile backhaul. We propose a new technique to predict more
accurate data traffic. This research used a univariate Autoregressive Integrated Moving
Average (ARIMA) model combined with a new disruptive formula. Another model,
called a disruptive formula, uses a judgmental approach based on four variables:
Political, Economic, Social, Technological (PEST), cost, time to market, and market
share. The disruptive formula amplifies the ARIMA calculation as a new combination
formula from the judgmental and statistical approach. The results show that
the disruptive formula combined with the ARIMA model has a low error in mobile
data forecasting compared to the conventional ARIMA. The conventional ARIMA
shows the average mobile data traffic to be 49.19 Mb/s and 156.93 Mb/s for the 3G and
4G, respectively; whereas the ARIMA with disruptive formula shows more optimized
traffic, reaching 56.72 Mb/s and 199.73 Mb/s. The higher values in the ARIMA with
disruptive formula are closest to the prediction of the mobile data forecast. This result
suggests that the combination of statistical and computational approach provide more
accurate prediction method for the mobile backhaul networks.
PSO-CCO_MIMO-SA: A particle swarm optimization based channel capacity optimza...IJECEIAES
With the radio channels physical limits, achieving higher data rate in the multi-channel systems is been a biggest concern. Hence, various spatial domain techniques have been introduced by incorporating array of antenna elements (i.e., smart antenna) in recent past for the channel limit expansion in mobile communication antennas. These smart antennas help to yield the improved array gain or bearm forming gain and hence by power efficiency enhanmaent in the channel and antenna range expansion. The use of smart antenna leads to spatial diversity and minimizes the fading effect and improves link reliability. However, in the process of antenna design, the proper channel modelling is is biggest concern which affect the wireless system performance. The recent works of MIMO design systems have discussed the issues in number of antenna selection which suggests that optimization of MIMO channel capacity is required. Hence, a Particle Swarm Optimization based channel capacity optimzation for MIMO system incorporated with smart antenna is introduced in this paper. From the outcomes it is been found that the proposed PSO based MIMO system achieves better convergenece speed which results in better channel capacity.
The relay stations are widely used in major wireless technologies such as WiMAX (Worldwide Interoperability for Microwave Access) and LTE (Long term evolution) which provide cost effective service to the operators and end users. It is quite challenging to provide guaranteed Quality of Service (QoS) in WiMAX networks in cost effective manner.
Impact of Next Generation Cognitive Radio Network on the Wireless Green Eco s...ijeei-iaes
Land mobile communication is burdened with typical propagation constraints due to the channel characteristics in radio systems.Also,the propagation characteristics vary form place to place and also as the mobile unit moves,from time to time.Hence,the tramsmission path between transmitter and receiver varies from simple direct LOS to the one which is severely obstructed by buildings, foliage and terrain. Multipath propagation and shadow fading effects affect the signal strength of an arbitrary Transmitter-Receiver due to the rapid fluctuations in the phase and amplitude of signal which also determines the average power over an area of tens or hundreds of meters. Shadowing introduces additional fluctuations, so the received local mean power varies around the area –mean. The present paper deals with the performance analysis of impact of next generation wireless cognitive radio network on wireless green eco system through signal and interference level based k coverage probability under the shadow fading effects.
TECHNIQUES IN PERFORMANCE IMPROVEMENT OF MOBILE WIRELESS COMMUNICATION SYSTEM...Onyebuchi nosiri
Mobile wireless communication providers are expected by their numerous subscribers to provide network that can allow higher data rates, and good voice quality. However, this may be restricted due to some technical problems such as limited availability of radio frequency spectrum, bandwidth, channel capacity, geographical areas and transmission problems caused by various factors like fading and multipath distortion. All these lead to overall system performance degradation. This has led to various studies on how improvement on the performance of wireless communication can be realized using different techniques. This paper is a review of some scholarly works on this subject. To achieve this some recent scholarly articles were accessed online and their findings were highlighted. It was observed that all the articles reviewed had results drawn only from theoretical analysis. Based on this, one of the recommendations is that theoretical analysis should be supported with data obtained from carrying out RF measurements in the field where possible.
3G Drive Test Analysis for Long Call in University Campusijtsrd
Mobile communication is developing very rapidly with passage of time, new technologies are being introduced to facilitate the mobile user more from the technology. The past technologies are replaced by new ones and needs are growing for the new technologies to be developed. Just within a decade, an evolution of wireless service people used every day can be completely dumb founded from the roots of analog based first generation service 1G to today's truly broadband ready fourth generation networks. Drive Test is conducted for checking coverage criteria of a cell site with RF Drive Test tool. The data collected by Drive Test tool as log files is analyzed to evaluate various RF parameters of the network. And it's very important things for mobile service provider. This paper is to study about the analysis of long call Drive Test for third generation 3G networks to evaluate the performance of QoS Quality of Service using TEMS Investigation 16.3.3 software and TEMS Discovery 10.0.6 software. Aye Myat Myat Myo | Zar Chi Soe "3G Drive Test Analysis for Long Call in University Campus" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26777.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26777/3g-drive-test-analysis-for-long-call-in-university-campus/aye-myat-myat-myo
Intelligent location tracking scheme for handling user’s mobilityeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
5G VS WI-FI INDOOR POSITIONING: A COMPARATIVE STUDYIJCSES Journal
Sensing location information in indoor scenes requires a high accuracy and is a challenging task, mainly
because of multipath and NLoS (non-line-of-sight) propagation. GNSS signals cannot penetrate well in
indoor environment. Satellite-based navigation and positioning systems cannot therefore be used for indoor
positioning.. Other technologies have been suggested for indoor usage, among them, Wi-Fi (802.11) and
5G NR (New Radio). The primary aim of this study is to discuss the advantages and drawbacks of 5G and
Wi-Fi positioning techniques for indoor localization
5G Vs Wi-Fi Indoor Positioning: A Comparative StudyIJCSES Journal
Sensing location information in indoor scenes requires a high accuracy and is a challenging task, mainly because of multipath and NLoS (non-line-of-sight) propagation. GNSS signals cannot penetrate well in indoor environment. Satellite-based navigation and positioning systems cannot therefore be used for indoor positioning. Other technologies have been suggested for indoor usage, among them, Wi-Fi (802.11) and 5G NR (New Radio). The primary aim of this study is to discuss the advantages and drawbacks of 5G and Wi-Fi positioning techniques for indoor localization.
Technology which uses the physical location of the end-user to improve the relevance, context, and value of an application is defined as location based technology. Bluetooth, GPS, Wi-Fi and NFC are all location-based technologies. A service or application which utilizes location-based technology is called Location-based service(LBS).
A genetic based indoor positioning algorithm using Wi-Fi received signal stre...IAESIJAI
The recent trend in location-based services has led to a proliferation of studies in indoor positioning technology. Wi-Fi received signal strength indicator (RSSI) Fingerprinting and pedestrian dead reckoning (PDR) are the two best representatives from both approaches. This research proposed a genetic algorithm to combine Wi-Fi Fingerprinting and PDR. By taking advantage of PDR and genetic algorithm, we only need to collect a limited number of points for the fingerprint dataset with known coordinates, then target trajectories' position can be estimated with high accuracy. Results from our experiments and simulations have shown that even in the scenario of noisy inertial measurement unit (IMU) sensors data, using RSSI measurements and the coordinate of 8 points, our proposed method was able to achieve 1.589 meters of average distance error which is 34.4 percent lower than the conventional Fingerprinting method.
Novel Position Estimation using Differential Timing Information for Asynchron...IJCNCJournal
Positioning techniques have been a common objective since the early development of wireless networks. However, current positioning methods in cellular networks, for instance, are still primarily focused on the use of the Global Navigation Satellite System (GNSS), which has several limitations, like high power drainage and failure in indoor scenarios. This study introduces a novel approach employing standard LTE signaling in order to provide high accuracy positioning estimation. The proposed technique is designed in analogy to the human sound localization system, eliminating the need of having information from three spatially diverse Base Stations (BSs). This is inspired by the perfect human 3D sound localization with two ears. A field study is carried out in a dense urban city to verify the accuracy of the proposed technique, with more than 20 thousand measurement samples collected. The achieved positioning accuracy is meeting the latest Federal Communications Commission (FCC) requirements in the planner dimension.
NOVEL POSITION ESTIMATION USING DIFFERENTIAL TIMING INFORMATION FOR ASYNCHRON...IJCNCJournal
Positioning techniques have been a common objective since the early development of wireless networks. However, current positioning methods in cellular networks, for instance, are still primarily focused on the use of the Global Navigation Satellite System (GNSS), which has several limitations, like high power drainage and failure in indoor scenarios. This study introduces a novel approach employing standard LTE signaling in order to provide high accuracy positioning estimation. The proposed technique is designed in analogy to the human sound localization system, eliminating the need of having information from three spatially diverse Base Stations (BSs). This is inspired by the perfect human 3D sound localization with two ears. A field study is carried out in a dense urban city to verify the accuracy of the proposed technique, with more than 20 thousand measurement samples collected. The achieved positioning accuracy is meeting the latest Federal Communications Commission (FCC) requirements in the planner dimension.
ASSESSMENT OF ALTERNATIVE PRECISION POSITIONING SYSTEMSijitcs
The continuous evolution of technology, electronics, and software along with the dramatic decrease in the
cost of electronic devices has led to the spread of sensing, surveillance, and control devices. The Internetof-Things
(IoT) benefits from the spread of devices (things) by processing device feeds using Machine-toMachine
(M2M) technologies. At the heart of the M2M technologies lies the ability of devices (things) to identify their own location on the globe or relative to known landmark. Since location awareness is fundamental to processing sensing and control feeds, it has attracted researchers to identify ways to
identify and improve location accuracy. The article looks at Global Positioning Systems (GPS) along with the enhancements and amendments that apply to satellite based solutions. The article also looks at medium to short-range wireless solutions such as cellular, Wi-Fi, Dedicated Short-Range Communications (5.9 GHz DSRC) and similar solutions.
-> Explanation of various techniques for Localization like Triangulation and Proximity Based Techniques. (OTDOA/ AOA/ GSM Fingerprinting/Hybrid technologies, etc.)
-> Working of GPS and its drawbacks.
-> AGPS- working and advantages.
-> E911 call processing in AGPS.
->Location Based Services in 4G
After the read, you will know:
1. What is spatial perception?
2. A-GNSS system architecture
3. What is UWB?
4. Compared with Wi-Fi and Bluetooth positioning technology, UWB advantages
5. Comparison of UWB and other positioning technologies
6. UWB industry development
7. Conclusion
This article talks about the 7 common positioning technologies comparison, GPS positioning, Beidou positioning, base station positioning, Bluetooth positioning, WI-FI positioning, UWB positioning, and RFID positioning comparison.
Intelligent GIS-Based Road Accident Analysis and Real-Time Monitoring Automat...CSCJournals
It has been a big concern for many people and government to reduce the amount of road accident specially in Malaysia since it could be a big threat to this country. Malaysian government has spent millions of money in order to reduce the number of accident occurrence through several modes of campaign. Unfortunately, from years to years the number keeps increasing. The lack of a comprehensive accident recording and analysis system in Malaysia can be effective in these kinds of problems. By making use of IRAS (Intelligent Road Accident System), the police would be control and manage whole accident events as a real-time monitoring system. This system exploits WiMAX and GPRS communications to connect to the server for transfer the specific data to the data center. This system can be used for a comprehensive intelligent GIS-based solution for accident analysis and management. The system is developed based on object and aspect oriented software design such as .NET technology.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
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Background positioning huber
1. Background Positioning for Mobile Devices Android vs. iPhone
Denis Huber
School of Electrical and
Computer Engineering
Technical University Berlin
Email: hubeealh@mailbox.tu-berlin.de
Abstract—This seminar paper covers the current techniques
available for mobile phone positioning and their application.
GPS, WiFi and cellular based positioning methods are explained
in detail, and current trends regarding the optimization of
existing algorithms are analyzed. The algorithms talked about
include fingerprinting and different time of arrival methods.
Current solutions in the mobile phone market from Apple
and Google which use several positioning technologies in their
products are investigated. Especially the APIs from Apple and
Google who deliver access to these technologies. Location Based
Services (LBS) which need continuous monitoring of the users
position are leading to a high energy demand. This problem is
not solved yet satisfactory, and different approaches for solving
this problem are available.This paper investigates some of the
proposed algorithmic solutions and gives an outlook to the
future.
Location-based services a lot of different techniques are
available today including GSM, WiFi or GPS positioning.
These different methods as well as new approaches of
improving them are covered in section 2. In section 3 a
look under the hood of the positioning capabilities of the
iPhone and Android OS’s will be provided, as well as the
API calls available for developers. In section 4 the problem
of continuous tracking by LBS’s leading to high power
consumption will be discussed. Different approaches to solve
this problem will be presented. The final section gives an
outlook for the future and concludes this paper.
II. OVERVIEW OF P OSITIONING S YSTEMS
I. I NTRODUCTION
Smartphones with high bandwidth internet access and
GPS receivers have become very widespread these days and
have repressed traditional mobile phones. Two of the leading
companies in the mobile phone operating system market
today are Apple with their iPhone iOS and Google with
Android OS. One of the major features of these operating
systems is the ability of running a wide array of third party
applications available for purchase in special App Stores.
A special kind of applications has emerged in the last few
years, the so called Location-based services (LBS) [1]. These
are applications using the ability of smartphones to localize
the geographical position of their users outdoors as well as
indoors. With applications ranging from showing the users
position on the map or routing to new kinds of services
like Location-based gaming, Location-based advertising or
even mobile dating, many companies are developing LBSs
today. But also security applications like for example the
tracking of 911 calls, which is now mandatory by law in the
United states [2], are using the location information provided
by mobile phones these days. While some applications
may need only very rough position fixes others may need
a very accurate location determination. One of the major
challenges in localization today is to deliver this accuracy
without consuming too much of the limited amount of battery
power available on the mobile devices. In order to realize
Modern smartphones today can use up to three different
methods to locate their position. Satellite Positioning,
Wifi Positioning and Cellular Positioning (Figure 1). In
the following subsections all three will be explained in
detail. Generally these positioning methods have different
characteristics which can be measured by the following
criterias like described by Hightower et. al [3].
•
Accuracy. The difference between the true position and
the calculated position.
•
Precision. The closeness of a number of position values
to their mean value.
•
•
•
Power consumption. The amount of electrical power
needed for determining the position measured in Watts
per second.
Latency. The time needed for obtaining the position
fix. Also generally known as TTFF (Time to first fix)
measured in seconds.
Availability. Not all positioning methods are available
in every situation.
2. B. Cellular Positioning
Cellular networks have expanded globally in the last decade
and reached almost worldwide coverage. At the moment
there are mainly two types of cellular networks in operation.
The Global System for Mobile Communication (GSM) and
the Universal Telecommunication System (UMTS) often also
referred as 2G and 3G networks. While the next generation
of networks Long Term Evolution (LTE) is already in the
pipeline, it will take again probably two years since this
standard will be integrated in the network infrastructure and
on the mobile devices. While work is already in progress
in specifying positioning for the next generation of cellular
networks, in this paper only techniques used for 2G and 3G
networks are explained.
Fig. 1.
Overview of Position Systems from [4]
A. Satellite Positioning
Satellite Positioning is a technology originally invented
by the US Military. The most prominent satellite positioning
method today is the Global Positioning System (GPS). It
uses up to 32 satellites to determine the position of a Mobile
Station (MS) [5]. There are two types of signals sent by
the satellites. The Precise Positioning Service (PPS) and the
Standard Positioning Service (SPS). While PPS is reserved
for military use and is encoded, SPS is free for public use.
A mobile device equipped with a GPS Receiver catches up
at least four of these satellite signals which are broadcasting
time and orbital information and calculates the position from
these. GPS is very accurate and precise technology, providing
accuracy up to 20 meters, but it consumes also a lot of power
on the end device. Another downside of GPS is that satellite
signals can not be received indoors, and therefore it is not
suited for indoor positioning. Also the TTFF can be up to 15
minutes long because when the device was off for a longer
period of time the complete Almanac database (location of
all satellites in the orbit) must be reloaded into the device.
Most smartphones today however use Assisted GPS (A-GPS)
like described in [4], a technique where the traditional GPS
Receiver is assisted by cellular positioning methods and
the assisting data is downloaded in the background via IP
connection. This results in better accuracy, faster TTFF times
and less power consumption. However IP connectivity may
not be available in all situations and power consumption is
still higher than in other positioning methods.
As GPS Receivers have become less and less cheap they
are integrated in nearly every smartphone today becoming a
standard. Beside that a lot of improvement in the Hardware
of GPS Receivers has been done lately, and recent studies
have shown that they can be functional in certain indoor
environments which can bee seen in [6].
Cellular networks are basically divided into cells which
ideally have a round shape. Each of this cells is associated
with a cellular tower (base station). As a user moves through
the cells his mobile device will connect to the base station
with the best signal strength available, which should be in
most cases the nearest one according to his position. The
basic form of cellular positioning therefore is to use the
location of this base station, whose position is already known
and stored in a database. This method is referred as cell
identification (cell ID). The accuracy delivered from this
method relies on the cell size which can be up to 35 km
in radius for 2G networks and a little bit less in 3G due to
technical restraints. For this reason a method called timing
advance (figure 2) is used. This technique measures the time
signals take to arrive from the mobile device to the base
station. The mobile station can then be located in a circle
around the base station. This can be improved even further
with the use of multiple antennas which divide the circle
into sections. These method is referred as enhanced cell ID
(E-CID) [7].
When mobile devices are in reach of multiple base stations
more complex algorithms can be used, which utilize the Time
Difference of Arrival (TDOA) or Angle of Arrival (AOA).
See Kuepper [8] for a complete overview of these techniques.
Generally all of these methods need a high density of cell
towers to function properly and TDOA, due the need of
timing measurements reliability, is not well suited for 3G
networks.
The accuracy of cellular positioning lies far behind GPS,
but regarding it’s power consumption, nonavailability indoors
and additional hardware costs, research in cellular positioning
techniques regarding the improvement of their accuracy is
considered well. For example Ibrahim and Youssef propose
a new algorithm based on a hidden Markov model which is
especially suited for low end GSM phones and which delivers
quite good results [9] and Nurmi et. al provide a grid based
solution [10]. Also a probabilistic method based on RSSI
fingerprinting is provided by Ibrahim and Youssef [11]
3. C. WiFi Positioning
In general WiFi positioning takes a similar approach as
the cell ID algorithm in cellular based positioning. Instead
of cell towers the geographical information of WiFi Access
Points (AP) is kept in a database which can be looked up for
positioning. This information is usually gathered through war
driving. This means that people equipped with GPS Receivers
and WiFi clients are driving around collecting the data. As a
key the unique MAC Address of the access points is being
used. As WiFi networks have only an average range of 60
meters a good accuracy is provided naturally. Also the TTFF
times are low, while no connection must be established to the
Wifi Network and the database lookup may only take some
seconds. Traditionally WiFi positioning was regarded as a
indoor only solution, but as wireless Internet access gained
more and more popularity, now there’s also a very good
outside coverage in urban areas. Jones et.al provided a good
research work in analyzing the density of access points in the
United states [12].
WiFi positioning can either be performed passive or
active from the mobile terminal viewpoint. The active
method uses the uplink of a 802.11 WiFi enabled device.
A beacon, which is basically a broadcast signal, is sent by
the terminal.The access points receive this information and
evaluate it in the network.This method is generally referred
as network-based positioning.The other method available
uses the downlink.The terminal passively scans beacons from
the access points, known as Basic Service Set Identifier’s
(BSSI), which include several information including their
signal strength. If no beacon information is available for a
longer period of time, the terminal can send a probe request
and all access points available in range will send their BSSI
back. This is referred as active scanning. Generally WiFi
positioning algorithms which use upstream or downstream
can be divided in three categories as described in [8].
•
•
•
Proximity sensing.The position of the access point with
the best signal quality received is adopted.
Lateration.The distance between the access point and
the terminal is determined by the path loss experienced
by the beacon during transmission.
Fingerprinting.A database with RSS patterns observed
earlier is compared with the actual RSS pattern received
by the access points. The best match is then adopted as
the actual position.
From these methods proximity sensing is the easiest to
implement. Though it suffers from accuracy as the range of
a signal broadcasted by access points can vary from some to
a hundred meters. Accuracy and precision can therefore not
be guaranteed and the algorithm is only suited for granular
positioning. Lateration on the other side is only possible
when the access points are aligned in a certain way. As this
is far from reality in most cases this method is not used in
daily practice. The last method remaining is fingerprinting.
In this method a radio map,in which the signal strength
for a certain position is stored, is generated offline with
the already introduced uplink method. This information is
stored in a database. The database can be either stored on
the device or in the network, the latter being the better
option because of limited processing power and memory
available on the handsets. In the online phase, the terminal
requests it’s position from the network in sending the actual
measured RSSI information. This information is matched
against the existing data from the radio map in the database.
The point with the minimum distance to the received one
from the terminal is then calculated and will be taken. This is
referred as the Nearest Neighbor in Signal Strength method
[13]. Beside this simple deterministic approach also different
probabilistic methods are possible which can deliver better
results but are more complex to implement. Honkarvirta et.
al provide a very good overview of these algorithms [14].
RSSI fingerprinting is very promising in terms of accuracy
when enough access points are used, as already existing
implementations like RADAR [13] show very good results
with an accuracy of up to only a few meters. This can surely
be improved in the future as a lot of researchers are currently
active in this area. A major advantage of WiFi positioning
however is that no additional hardware is required, as most
devices already implement the 802.11 standard and the
access points are already present and don’t need any further
modification.
D. Summary
Fig. 2.
Cell ID with timing advance and sectored antennas
As seen in this section so far, all of the positioning methods
have different characteristics. With GPS in advance in terms
4. of accuracy and position but weak in energy consumption,
and WiFi best suited for indoor application but not available
everywhere. While cellular positioning is almost available
everywhere today, and very energy efficient it still lacks
accuracy and precision. An in depth comparison of all
positioning techniques on the iPhone 3G is provided by
Zandbergen [7].
III. S MARTPHONE S PECIFIC P OSITIONING
In this section an overview of the current smartphone
positioning techniques is provided, by looking at the two
most popular mobile Operating Systems (OS) today. These
are Google’s Android OS and Apple’s iOS running on the
iPhone. Also third party solutions available for different
platforms will be covered.
A. Apple iPhone
Apple recently introduced the fourth version of the iPhone.
Simultaneously they also released their renewed operating
system iOS, currently available in Version 4.2, which added
new features including multitasking. The first generation of
iPhones did not have GPS Receivers. Location determination
was realized with GSM and WiFi positioning using Google’s
location databases. With the second and third models Apple
built in GPS Receivers and used Skyhook as their main
location data service. Skyhook is a company specialized in
providing location data services, and will be discussed in the
Third Party section. On the newest iPhone 4 model however
Apple now use their own database of location information,
and like tests have shown the location determination accuracy
and performance has been improved with it [17].
Developers can access the positioning features of iOS
4.2 via the Core Location API provided by Apple [18].
There are two major options available. The first one is
the standard location service (Figure 3). For the standard
location service a new object of the CLLocationManager
class has to be created.Then a delegate object of type
CLLcationManagerDelegate and a desired accuracy value
must be assigned to it. Also a distanceFilter is set to the
value in meters when the location should be updated. Finally
the startUpdatingLocation method is called, and the device
begins to track it’s position, updating it when the user
moved out of the distance set in the distanceFilter. The
assigned delegate will be notified when location information
becomes available or changes. This method as proposed by
Apple works best for positioning applications running in the
foreground e.g. Turn by Turn Navigation. The higher the
value in the CCLocationManagerAccuracy property is set
the more precise the position data. This also leads to higher
energy consumption as GPS will be used when available.
However the real value of precision relies on the availability
of the methods on the infrastructure side and can change
drastically during runtime. The second method for tracking is
called significant update change. Like in the standard method
a CLLocationManager object must be created and associated
with a delegate. But instead of calling startUpdatingLocation
the method startMonitoringSignificantLocationChanges is
called. With this method the application can be suspended
and is woken up automatically if a significant location
change took place. This is the method proposed by Apple if
location services should run in the background. It consumes
much less energy as it is relying on cellular positioning
only, but is much more inaccurate and therefore not suited
if slight position changes must be tracked [19]. With the
new multitask feature it is also possible to run the standard
location service in the background. But as this consumes
lots of power Apple describes it as the least desirable option
[20]. Another new feature interesting for LBS developers
is the new monitoring region feature introduced with iOS
4. With this new feature it is possible to monitor a shaped
region which can be highlighted on a map. This is realized
via the startMonitoringForRegion method provided by the
API. When the mobile phone reaches the boundary of the
monitored region either the didEnterRegion or didExitRegion
will be called depending if the user wants to be notified
entering or exiting the region. However this method tracks
the user continuously, hence consumes a lot of battery
power. As we can see developers do not have much control
over the positioning hardware. Instead the API manages
and encapsulates the decision which technology is used.
Developers have either the option to use the more precise
standard location service resulting in high energy consumption
or use the significant update feature with less precision. Apple
however provides no data about accuracy and precision of
the positioning features of the iPhone. Results of a field test
done by Zandbergen with the older model iPhone 3 can be
looked up here [7]
Fig. 3.
Starting a standard location service in Core Location [18]
B. Google Android
Google recently unveiled the newest Version of Android
which is version 2.3 now. In contrary to Apple, Google
is focusing their attention on the mobile operating system
5. Android only, and does not produce any hardware. This means
that their operating system is used on a lot different hardware
devices from companies like HTC, Motorola, Samsung and
others and not all positioning methods may be available on
a specific phone e.g. GPS. Google use their own location
databases for localization. Though it is possible for vendors
of Android powered phones to use third-party localization like
Skyhook, Google makes effort in preventing this [21].
Developers can obtain location data using the Android API
[22] in the following way (Figure 4). First a reference to
LocationManager must be acquired from the system. Then
a new LocationListener object is assigned to it, which has to
implement several callback methods. With the call of requestLocationUpdates the device begins to locate it’s position. The
parameters passed to this method are very essential here. The
first parameter expects a LocationProvider. This can be either
Network Provider or GPS Provider. The first option means
that only cell and WiFi based location methods will be used,
while with the second option only GPS will be utilized. It is
also possible activating both with calling requestLocationUpdates twice with both different parameters. With the second
parameter a minimum time interval between location updates
can be set. The third parameter allows to set the minimum
change in distance before getting new location updates. With
the fourth parameter the LocationListener instantiated before
is handed over. As we can see Android developers have a little
more control over the Hardware in comparison to iPhone developers. They can decide which positioning method they want
to use, in choosing the desired LocationProvider. Also the
fact that LocationManager operates systemwide allowing the
developers to run only the LocationListeners in the background
is a good solution. But there are still shortcomings in terms
of controlling the energy consumption and accuracy. Choosing
only between two location providers is still too granular and
in the effort of building energy saving applications which on
the other hand can provide good accuracy. However Google
doesn’t provide any data about the position accuracy on
Android devices.
C. Third Party APIs
There are a couple of third-party solutions for localization
on the market like Skyhook Wireless’s XPS System, SoftGPS
from Devicescape, PlaceEngine, Navizon or Polaris Wireless
[16]. The most prominent and most successful till now being
Skyhook. Skyhook has a large amount of location data from
Wireless LAN access points which they gathered through
wardriving or information by private person’s who can
provide Skyhook their location data in form of MAC-Address
and relating GPS coordinates through their website [24].
According to Skyhook their location data covers up to 70 %
of the US rural areas and many places around the world with
over 250 million access points in their database. Developers
can download their Software Development Kit (SDK) for
a number of platforms including Android, but excluding
the iPhone. For platforms where the SDK is not available,
Skyhook provides a portable Core Engine for developers and
Fig. 4.
Define a Location Listener in Android [23]
manufactures which can be set up on top of the underlying
OS [25]. Once the SDK or the Core Engine is running on the
device, developers are enabled to use a the Skyhook API for
location tracking. Skyhook’s XPS provides hybrid location
determination with GPS, WiFi and cellular techniques and the
company holds several patents most of them regarding WiFi
positioning. They indicate that their hybrid system offers an
accuracy of up to 10 meters with an availability of 99.8 %
and a TTFF time of only 1 second [26] like shown in table
I, though accurate information under what circumstances
this data was collected is not provided. Also no information
about energy consumption is provided. Their website gives
an overview with sample codes how one time location fixes
on certain platforms can be achieved, but no information
regarding constant location tracking and energy saving can be
found there [27]. Skyhook as already mentioned in a section
before lost a big customer with Apple and it’s relationship
with Google is not the best, so their future remains unclear
at the moment.Apple and Google seem to take the location
market seriously and do not want to share it with other
company’s, though they might face some patent trial issues
with Skyhook [28].
TABLE I
S KYHOOK ’ C ORE E NGINE PERFORMANCE ACCORDING TO THEMSELVE
[26]
Core Engine
GPS
A-GPS
Accuracy
10 meters
10 meters
30 meters
Availability
99,8%
80%
95%
TTFF
1 sec
65 sec
30 sec
6. IV. LBS AND E NERGY C ONSUMPTION
As already mentioned in the introduction Location-based
services are gaining more and more popularity these days.
Many of these applications do need a location fix at a time
or need only rough position estimation like for example the
city the user is currently staying in. In this case energy
consumption is not a big problem, while GPS is only turned
on for a short period of time or not needed at all because
the accuracy of other methods is already sufficient. However
another category of applications has evolved in the last years
which are known as proactive Location-based services. The
most prominent examples here are navigation applications.
These applications show the users current position on a map
and therefore need to track him constantly while he is moving
around. As high accuracy is needed in this case GPS is
constantly turned on which leads to a very high energy
consumption and the battery of the mobile phone will be
drained quickly (Figure 5).
fixes energy consumption can be reduced significantly (Figure
6), though accuracy suffers. But in comparison to Enloc the
accuracy achieved is better especially in urban areas with a lot
of turns (Figure 7). Furthermore accuracy can be improved wit
map matching where the position data calculated is mapped
against a street map. This technique is already widely used
in car navigation systems. Youssef et al. recently gained an
technology award from Google for this project. Google also
will implement this system in future versions of Android.
Fig. 6.
Fig. 5.
Energy Consumption with GAC from [31]
Power consumption of typical Location Based Services from [29]
This problem has currently gained a lot of attention in
the LBS research community [29]. One solution provided by
Constandache et al. [30] is to turn on GPS only in certain
time intervals and calculate the track between them with probabilistic methods. This leads to less energy consumption, but
the position errors are quite high in tracks with a lot of turns.
The idea has been improved by Youssef et al. with their GAC
Project [31]. The main idea here is that most of the modern
smartphones are equipped with compasses and accelerometers
which can be used to calculate the track between two GPS
position fixes. With longer time intervals between the GPS
Fig. 7.
Accuracy with GAC from [31]
7. V. C ONCLUSION AND O UTLOOK
Positioning methods have improved a lot in the recent
years, but there are still many challenges to master. GPS
can be improved for indoor positioning and cellular based
algorithms have to become more accurate. Of course also
the infrastructure plays an important role and the density of
WiFi and cellular stations will certainly increase in next years
leading to better accuracy especially with WiFi. Also new
technologies like LTE or the announced european satellite
positioning system Galileo will play an important role in
the future of positioning. On the manufacturer side Apple
and Google should make positioning more transparent for
developers and provide a better energy management. This
could be possible with the solutions presented in this paper.
A good approach would also be to implement a power
simulation already integrated in the SDK, so that developers
can test their applications for energy consumption already in
the early stage of development.
R EFERENCES
[1] C. Post and S. Woodrow, “Location is everything: Balancing innovation,
convenience, and privacy in location-based technologies,” Ethics and
Law on the Electronic Frontier, no. 6.805/STS.487, December 2008.
[2] Y. Zhao, “Standardization of mobile phone positioning for 3g systems,”
Communications Magazine, IEEE, vol. 40, no. 7, pp. 108 –116, July
2002, 13.12.2010.
[3] J. Hightower and G. Borriello, “A survey and taxonomy of location
systems for ubiquitous computing,” University of Washington, Computer
Science and Engineering, Tech. Rep., 2001.
[4] G. Sun, J. Chen, W. Guo, and K. Liu, “Signal processing techniques
in network-aided positioning: a survey of state-of-the-art positioning
designs,” Signal Processing Magazine, IEEE, vol. 22, no. 4, pp. 12 –
23, July 2005.
[5] P. H. Dana. (2010, December) Global positioning system overview.
[Online]. Available: http://www.colorado.edu/geography/gcraft/notes/
gps/gps f.html
[6] M. Kjaergaard, H. Blunck, T. Godsk, T. Toftkjaer, D. Christensen, and
K. Groenbaek, “Indoor positioning using gps revisited,” in Pervasive
Computing, ser. Lecture Notes in Computer Science. Springer Berlin
/ Heidelberg, 2010, vol. 6030, pp. 38-56.
[7] P. A. Zandbergen, “Accuracy of iphone locations: A comparison of
assisted gps, wifi and cellular positioning,” Transactions in GIS, vol. 13,
pp. 5–25, 2009.
[8] A. Kuepper, Location-Based Services: Fundamentals and Operations.
John Wiley & Son, 2005.
[9] M. Ibrahim and M. Youssef, “A hidden markov model for localization
using low-end gsm cell phones,” CoRR, vol. abs/1010.3411, 2010.
[10] P. Nurmi, S. Bhattacharya, and J. Kukkonen, “A grid-based algorithm
for on-device gsm positioning,” in Proceedings of the 12th ACM
international conference on Ubiquitous computing, ser. Ubicomp ’10.
New York, NY, USA: ACM, 2010, pp. 227–236. [Online]. Available:
http://doi.acm.org/10.1145/1864349.1864385
[11] M. Ibrahim and M. Youssef, “Cellsense: A probabilistic rssi-based gsm
positioning system,” Tech. Rep. arXiv:1004.3178, April 2010.
[12] K. Jones and L. Liu, “What where wi: An analysis of millions of wi-fi
access points,” in Portable Information Devices, 2007. PORTABLE07.
IEEE International Conference on, May 2007, pp. 1 –4.
[13] P. Bahl and V. Padmanabhan, “Radar: an in-building rf-based user
location and tracking system,” in INFOCOM 2000. Nineteenth Annual
Joint Conference of the IEEE Computer and Communications Societies.
Proceedings. IEEE, 2000.
[14] V. Honkavirta, T. Perala, S. Ali-Loytty, and R. Piche, “A comparative
survey of wlan location fingerprinting methods,” in Positioning, Navigation and Communication, 2009. WPNC 2009. 6th Workshop on, 2009,
pp. 243 –251.
[15] P. A. Zandbergen, “Accuracy of iphone locations: A comparison of
assisted gps, wifi and cellular positioning,” Transactions in GIS, vol. 13,
2009.
[16] U. Bareth, “Algorithmic optimizations for energy-efficient monitoring
of spatial objects on smartphones,” in Proceedings of the 40th Annual
Conference of the Gesellschaft f¨ r Informatik e.V. (INFORMATIK 2010),
u
vol. P175. Leipzig, Germany: K¨ llen Druck + Verlag GmbH, Sep 2010,
o
pp. 577–582.
[17] F. Schuil. (2010, December) Geopositioning on the iphone
4 is doing just fine without skyhook and google.
[Online].
Available:
http://thenextweb.com/location/2010/08/24/
geo-positioning-on-the-iphone-4-is-doing-just-fine-without-skyhook-and-google
[18] (2010, December) Cllocationmanager class reference. Apple.
[Online].
Available:
http://developer.apple.com/library/ios/
#documentation/CoreLocation/Reference/CLLocationManager Class/
CLLocationManager/CLLocationManager.html
[19] (2010, December) Location awareness programming guide.
Apple. [Online]. Available: http://developer.apple.com/library/ios/
#documentation/UserExperience/Conceptual/LocationAwarenessPG/
Introduction/Introduction.html
[20] (2010, December) Background execution. Apple. [Online]. Available:
http://developer.apple.com/library/ios/#documentation/iPhone/
Conceptual/iPhoneOSProgrammingGuide/BackgroundExecution/
BackgroundExecution.html
[21] (2010,
December)
Skyhook:google
forced
motorola
to
drop
our
location
service.
Engadget.
[Online].
Available:
http://www.engadget.com/2010/09/17/
skyhook-google-forced-motorola-to-drop-our-location-service-de/
[22] (2010,
December)
Android
api
locationmanager.
Google.
[Online]. Available: http://developer.android.com/reference/android/
location/LocationManager.html
[23] (2010, December) Obtaining user location. Google. [Online]. Available: http://developer.android.com/guide/topics/location/
obtaining-user-location.html
[24] (2010, December) Skyhook coverage. Skyhook. [Online]. Available:
http://www.skyhookwireless.com/howitworks/coverage.php
[25] (2010, December) Skyhook device support. Skyhook. [Online].
Available: http://www.skyhookwireless.com/devices/devicesupport.php
[26] (2010, December) Skyhook performance. Skyhook. [Online]. Available:
http://www.skyhookwireless.com/howitworks/performance.php
[27] (2010, December) Skyhook examples. Skyhook. [Online]. Available:
http://www.skyhookwireless.com/developers/example.php
[28] (2010, December) Skyhook will take the location battle to
court. Gigaom. [Online]. Available: http://gigaom.com/2010/08/03/
skyhook-will-take-the-location-battle-to-court/
[29] M. Kjaergaard, “Minimizing the power consumption of location-based
services on mobile phones,” Pervasive Computing, IEEE, 2010.
[30] I. Constandache, S. Gaonkar, M. Sayler, R. R. Choudhury, and L. P. Cox,
“Enloc: Energy-efficient localization for mobile phones,” in INFOCOM,
2009, pp. 2716–2720.
[31] M. Youssef, M. A. Yosef, and M. N. El-Derini, “Gac: Energyefficient hybrid gps-accelerometer-compass gsm localization,” CoRR,
vol. abs/1004.3174, 2010.