Mobile Network Signal Research Articles

Determining the relationship between mobile phone network signal strength and radiofrequency electromagnetic field exposure: protocol and pilot study to derive conversion functions

Mobile phones continuously monitor and evaluate indicators of the received signal strengths from surrounding base stations to optimise wireless services. These signal strength indicators (SSIs) offer the potential for assessing radiofrequency electromagnetic field (RF-EMF) exposure on a population scale, as they can be related to exposure from both base stations and handset devices. Within the ETAIN (Exposure To electromAgnetic fields and plaNetary health) project, an open-access RF-EMF exposure app for smartphones, named "5G Scientist Monitor”, has been developed using citizen science. This paper delineates a measurement protocol for deriving formulas to convert the app SSIs into electric field values to estimate RF-EMF exposure. It presents pilot study results from measurements taken at four locations in Lyon, France (FR), and 14 locations in the Netherlands (NL), using three different phone models and the most common network providers in each country. The measurements were conducted while executing different usage scenarios, such as calls or data transmission. The exposimeter ExpoM-RF4 and on-body electric field probes were used to measure exposure from far-field sources and the handset, respectively. Two-minute aggregates were considered the sample unit for analyses (n=891 in NL and n=395 in FR). Regression analyses showed a positive log-linear relationship between Long Term Evolution (LTE) SSIs and far-field RF-EMF exposure when aggregating data by location (coefficients for the normalised RSSI: 0.91 [95% CI: 0.55 - 1.28] in FR, 1.09 [95% CI: 0.96 - 1.22] in NL). Negative log-linear trends were observed for handset-related RF-EMF exposure at the ear (-0.31 [95% CI: -0.46 - -0.16]) and chest (-0.20 [95% CI: -0.37 - -0.03]) during data transmission scenarios. These results demonstrate that the 5G-Scientist-Monitor app can be implemented for smartphone-based RF-EMF estimation. However, uncertainties in individual measurement points highlight the need for further data collection and analysis to improve the accuracy of exposure estimates.

An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization.

Usage of Unmanned Aerial Vehicles (UAVs) for different tasks is widespread, as UAVs are affordable, easy to manoeuvre and versatile enough to execute missions in a reliable manner. However, there are still fields where UAVs play a minimal role regardless of their possibilities. One of these application domains is mobile network testing and measurement. Currently, the procedures used to measure the main parameters of mobile networks in an area (such as power output or its distribution in a three-dimensional space) rely on a team of specialized people performing measurements with an array of tools. This procedure is significantly expensive, time consuming and the resulting outputs leave a higher degree of precision to be desired. An open-source UAV-based Cyber-Physical System is put forward that, by means of the Galileo satellite network, a Mobile Data Acquisition System and a Graphical User Interface, can quickly retrieve reliable data from mobile network signals in a three-dimensional space with high accuracy for its visualization and analysis. The UAV tested flew at 40.43 latitude and -3.65 longitude degrees as coordinates, with an altitude over sea level of around 600-800 m through more than 40 mobile network cells and signal power displayed between -75 and -113 decibels.

Study of Obstacles Effect on Mobile Network and WLAN Signal Strength

Study of Obstacles Effect on Mobile Network and WLAN Signal Strength

Semi-supervised learning-based coverage hole detection in cellular networks

For any time-critical mobile network-dependent applications and services, coverage is one of the prominent factors for providing the best Quality of Service (QoS) and Quality of Experience (QoE). A simple Coverage Hole (CH) may degrade the performance and the reputation of any operator by reducing the Key Performance Indicators (KPIs). This is one of the important aspects which need to be planned from the phase of network deployment throughout the whole operational stage. Many factors can cause CH such as attenuation, obstacles and improper network planning. Traditionally, a Drive Test (DT) used to be carried out in order to assess the quality of the mobile network signal. With technological advancement, DT has been replaced by the Minimization of Drive Test (MDT) and included as a part of Self-Organizing Networkss (SONs). The MDT process is applicable to networks that operate in 3G, 4G and 5G technologies. With this method, operators are able to measure network performance with the help of end users' devices. Thus, the network can be managed more conveniently, performance is improved, quality is increased, and maintenance costs are reduced for the network. However, the processing of MDT at the operators' side remains time-consuming and complex especially for CH analysis and detection from mobile network data. Therefore, we present a method by utilising Semi-Supervised Learning (SSL) in this paper so that this task becomes uncomplicated with improved accuracy. Our results show that the proposed method achieves better accuracy than the usual classification algorithm.

Development of an Automated Ticketing and Tracking System for the Monitoring of Surface Mine Hauling Operations

Telematics systems that integrate wireless communications and automation for equipment tracking and trip monitoring in mining operations have an advanced impact on the operational efficiency and optimization of the mine's asset inventory. In this study, a system was developed for solution in a mining area with low mobile network signal coverage and no internet service available to monitor and analyze the actual utilization executed by heavy equipment using microcontrollers with communication capabilities (GSM/GPRS, WiFi, and Bluetooth) and sensor modules for fuel monitoring and location tracking. Open-source software was utilized, such as PHP framework and MySQL, to provide a fast and real-time display of daily mine operations. The study provides an online tracking and ticketing system on mine hauling vehicles, introducing a cost-efficient, reliable, interactive platform for web-based monitoring and real-time telematics. The data acquired in real-time provide significant information which will allow innovative equipment optimization.

CSMC

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

Perceptions on using interactive voice response surveys for non-communicable disease risk factors in Uganda: a qualitative exploration.

Decision-makers need up to date information on risk factors for effective prevention and control of non-communicable diseases (NCDs). Currently available surveys are infrequent and costly to implement. The objective of the study was to explore perceptions on using an interactive voice response (IVR) survey for data collection on NCD risk factors. Five focus group discussions (FGDs), including rural and urban, elderly and young adults, male and female groups; and eleven key informant interviews (KIIs) of researchers and NCD policy makers were conducted. Respondents were audio recorded and data were transcribed into text. Data were entered into QDA miner software for analysis. Meaningful units were generated and then merged into codes and categories. Quotes are presented highlighting findings. At the individual level, age, gender, disability, past experience and being technology literate were perceived as key determinants on whether respondents would accept an IVR survey. Receiving the IVR at a time at which people are usually available to take calls increases participation. However, technological accessibility like presence of a mobile network signal and possession of mobile phones were critical for use of IVR. Participants recommended that community sensitization activities be provided, IVR be conducted at appropriate times and frequency, and that incentives may improve survey participation. IVR has the potential to quickly collect data from a wide geographic scope. However, caution needs to be taken to ensure that certain categories of people are not excluded because of their location, ability, age or gender. Sensitization prior to the survey, proper timing and structured incentives could increase participation.

Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems

In many parts of the world, passengers traveling on underground metro systems do not enjoy uninterrupted Internet connectivity. This results in passenger frustration since during such trips the access of online social media services is a highly popular activity. Being the world’s oldest underground metro system, London’s underground is a typical transportation environment, where the Internet connectivity is often not available during journeys which predominantly take place underground along sub-surface and deep-level track lines. To alleviate the absence of continuous connectivity, we designed DeepOpp, a context-aware mobile system that facilitates offline access to online social media content. The DeepOpp operates efficiently due to its opportunistic approach: it executes content prefetching and caching operations when adequate urban 3G or WiFi signal is detected. The functionality of DeepOpp includes the crowdsourcing of measurements of signal characteristics (strength, bandwidth availability, and latency) which are subsequently used in predicting mobile network signal coverage and initiating data prefetching operations. During data prefetching, an optimization scheme selectively specifies the social media content to be cached based on current network conditions and device storage availability. We implemented DeepOpp as an Android application which we trialled during real trips on the London underground. Our evaluations show that the DeepOpp offers significant reduction when compared with existing approaches in terms of power usage and the volume of data downloaded. Even though we only tested DeepOpp in the London underground metro system, its feature set makes it readily applicable in similar underground metro systems (in cities like New York, Paris, and Shanghai) as well as in situations, where mobile device users suffer from significant connectivity interruptions.

Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging.

In order to overcome the limitations of traditional road test methods in 5G mobile communication network signal coverage detection, a signal coverage detection algorithm based on distributed sensor network for 5G mobile communication network is proposed. First, the received signal strength of the communication base station is collected and pre-processed by randomly deploying distributed sensor nodes. Then, the neural network objective function is modified by using the variogram function, and the initial weight coefficient of the neural network is optimized by using the improved particle swarm optimization algorithm. Next, the trained network model is used to interpolate the perceptual blind zone. Finally, the sensor node sampling data and the interpolation estimation result are combined to generate an effective coverage of the 5G mobile communication network signal. Simulation results indicate that the proposed algorithm can detect the real situation of 5G mobile communication network signal coverage better than other algorithms, and has certain feasibility and application prospects.

From Intermittent to Ubiquitous

Accessing online social networks in situations with intermittent Internet connectivity is a challenge. We have designed a context-aware mobile system to enable efficient offline access to online social media by prefetching, caching and disseminating content opportunistically when signal availability is detected. This system can measure, crowdsense and predict network characteristics, and then use these predictions of mobile network signal to schedule cellular access or device-to-device (D2D) communication. We propose several opportunistic optimization schemes to enhance controlled crowdsensing, resource constrained mobile prefetch, and D2D transmissions impacted by individual selfishness. Realistic tests and large-scale trace analysis show our system can achieve a significant improvement over existing approaches in situations where users experience intermittent cellular service or disrupted network connection.

Research on the Technology of the Dual Mode Communication with the Electricity Information Collection System

There are a variety of communications modes are using in the domestic electricity information acquisition system, which has its own characteristics, which can meet the needs of specific applications. However, the application scenarios and requirements of electric information collection system are changeable, and the hierarchical structure of its communication network is very complex, relying on single-mode communication technology is unable to meet the needs of users [1-2]. Therefore, this paper carries out a comprehensive survey and objective evaluation analysis on the communication support network of electricity information collection system, and the objective and detailed test for the current problems. With the using of dual mode communication technology, the problem of the single mobile communication network signal coverage could be solved, and it can improve the reliability of the communication.

Mobility-Enabled Service Selection for Composite Services

Mobile business is becoming a reality due to ubiquitous Internet connectivity, popular mobile devices, and widely available cloud services. However, characteristics of the mobile environment, such as mobility, unpredictability, and variation of mobile network's signal strength, present challenges in selecting optimal services for composition. Traditional QoS-aware methods that select individual services with the best QoS may not always result in the best composite service because constant mobility makes the performance of service invocation unpredictable and location-based. This paper discusses the challenges of this problem and defines it in a formal way. To solve this new research problem, we propose a mobility model, a mobility-aware QoS computation rule, and a mobility-enabled selection algorithm with teaching-learning-based optimization. The experimental simulation results demonstrate that our approach can obtain better solutions than current standard composition methods in mobile environments. The approach can obtain near-optimal solutions and has a nearly linear algorithmic complexity with respect to the problem size.

ECall minimum set of data transmission – results from a field test in Finland

European in-vehicle emergency call system eCall aims to save lives and mitigate consequences of road accidents. eCall minimum set of data (MSD) which contains information on the accident vehicle is transmitted in the beginning of the call in the voice channel opened between in-vehicle system and public safety answering point. The main objectives of the study are to analyse the successfulness of eCall MSD transmission in Finnish fixed-line and GSM networks and to provide information on factors which contribute to the success rate of MSD transmission. The results of the Wilcoxon rank sum test indicated that the signal strength has no major impact on the success or failure of the MSD transmission when the mobile network signal strength measured by IVS is between −80 and −40 dBm. The analysed data included 12 failed MSD transmission attempts and 214 initiated MSDs in total. However, the test results also indicated that the MSD success rate can still be very different in different real-life physical mobile and fixed-line locations.

Bluetooth-Based Room Localization Research Based on NB and SVM Approach

It has been general recognized that the application of localization technology in home environment are beneficial to the development of health monitoring and mobile identification system development. As a kind of highly efficient sensor with obvious advantages such as low cost, the Bluetooth device has been widely used in our daily life. Research is carried out in an integrated environment based on mobile phone network signal measurement and Bluetooth link measurements in developing home localization systems. This paper presented a hybrid classification method, based on the combination of Bayesian network and supported vector machines, to support the development of Bluetooth-based room localization system. The proposed method mainly considers the dependency between features and non-linear overlapping of features between rooms. The results show that the prediction accuracy has been improved greatly in comparison to the traditional Naive Bayes classifier and the hidden Markov model used in previous studies.

Monitoring and Warning System of Highway Environmental Parameters Based on GSM

Mobile network signal is the basic coverage along the highway, and the GSM mobile communication technologies provide a powerful and reliable short message service and data transmission services, a variety of applications based on GSM data transmission platform is also being developed. The paper focuses on highway visibility detection and speed detection, system is constitute of a communication unit that uses the TC35i module and STC89C54 single chip, As well as hardware and software design of the method, GSM precautions in the design process of the highway system are briefly described.

Discovering the Radio Signal Coverage Hole and Weak Coverage Area in Mobile Network by Spatiotemporal Data Mining on Location-Based Services

Locating the radio signal coverage hole (SCH) and signal weak coverage area (SWCA) in the mobile network and taking appropriate actions to improve network quality are the major tasks for mobile network operators (MNO). A novel approach, Signal Coverage Hole and weak coverageArea DIscovering Model (SCHADIM), is proposed based on spatiotemporal data mining on the raw data collecting from location based service (LBS) to achieve this goal. It reuses the communication raw data in LBS-based applications and transforms it into mobile network communication quality monitoring information by integrating the GIS (geographical information system), road network database and mobile network base station database. By this way, the vehicles in the LBS-based applications can be regarded as the mobile network signal probing vehicles, which provides plentiful information for discovering the SCH/SWCA. Comparing to the traditionalmobile network signal probing vehicle method, which is known as radio site verification (RSV) method, the proposed SCHADIM has the spatiotemporal coverage as well as cost advantages. A mobile network monitoring system, cellular network quality safeguard (CQS), has been implemented based on the proposed model which combines with the domain expert heuristics to provide decision support information for optimizing the mobile network.