Real-time Mobile Data Research Articles

Optimizing Wireless Networks Performance Through Adaptive Machine Learning Strategies

This research aims to enhance wireless network performance through the development and implementation of a novel machine learning-based algorithm. The methodology involves comprehensive data collection in the Uttarakhand region, specifically in Dehradun, Haridwar, and Rishikesh, through LTE network drive tests. The collected data, over six months, serves as the foundation for the proposed algorithm. The research design focuses on parameterizing essential factors of path loss to optimize coverage. Various drive test tools, including NEMO, Agilent, TEMS, and XCAL-Mobile, are employed to measure and analyze field Received Signal Strength Indicators (RSSI) data. Statistical tools are utilized to collect network parameters, assess coverage, and analyze real-time mobile network data. The research objectives include the development and evaluation of the machine learning algorithm, comparing its performance across diverse network scenarios, and benchmarking against existing state- of-the-art algorithms. The proposed methodology provides a structured and relevant framework to achieve these objectives, fostering advancements in wireless network optimization through innovative machine-learning approaches.

Impact of the 2022 national formula shortage on clinical decision-making of healthcare providers in switching amino acid formulas for infants with cow's milk protein allergy: a survey-based study.

In 2022, the United States experienced a national shortage of infant formula due to a global supply chain crisis and a large-scale domestic formula recall. The existing literature on healthcare providers' (HCPs) clinical decision-making during formula shortages is limited. This study aims to analyze the factors influencing pediatric HCP clinical decision-making when switching between amino acid formulas (AAF) for managing cow's milk protein allergy (CMPA) in infants under 24 months of age during an unprecedented national formula shortage. The study included pediatric HCPs with experience managing CMPA in infants and toddlers under 24 months during the formula shortage from January 2022 to November 2022. A de-identified survey comprising 26 questions examining driving factors used in clinical decision-making was administered to pediatric HCPs using a real-time mobile data collection tool. Among the surveyed pediatric HCPs (n = 75), the factors most frequently considered as "extremely important" when switching to another AAF included safety (85%), tolerability (73%), and efficacy (83%). No statistically significant differences were found in HCP ratings among the listed examined factors of the four AAFs. The availability of specific formulas was the only factor that exhibited a statistically significant difference in perceived performance among pediatric HCPs when comparing the four AAFs (p < 0.05). This study elucidates the crucial aspects that influenced pediatric HCPs' selection of AAFs for CMPA management during the 2022 formula shortage. The findings highlight the significance of safety, tolerability, efficacy, and availability in the pediatric HCP decision-making processes.

Stochastic Digital-Twin Service Demand With Edge Response: An Incentive-Based Congestion Control Approach

The emergence of Digital Twin Edge Networks (DTENs) achieves the mapping of real physical entities to digital models of cyberspace. By offloading real-time mobile data to Mobile Edge Computing (MEC) servers for processing and modeling, communication-efficient Digital Twin (DT) services could be achieved. However, the spatio-temporal dynamic DT service demand stochastically generated by mobile users easily causes service congestion, which challenges the long-term DT service stability. Meanwhile, current DT services still lack long-term effective incentive designs for participants. To solve these issues, we design an incentive-based congestion control scheme for stochastic demand response in DTENs. First, we adopt the Lyapunov optimization theory to decompose the long-term congestion control decision into a sequence of online edge association decisions, with no need for future system information. We then present a contract-based incentive design to optimize the long-term profit of the DT service provider, comprehensively considering the delay sensitivity, incentive compatibility, and individual rationality. Finally, experimental simulations are carried out to verify the superiority of the proposed scheme with the base station dataset of Shanghai Telecom. Theoretical and simulation analysis demonstrates that compared with benchmarks, our scheme could effectively avoid long-term service congestion with an arbitrarily near-optimal profit.

Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning

The need to solve public transport planning challenges using 5G is demanding. In 2019, the world started using 5G technology. Unfortunately, many countries have no equipment that is compatible with 5G infrastructures. There are two main deployment options for countries willing to accept 5G. They can directly venture to install relatively expensive infrastructure, called 5G SA (standalone access). However, more countries use the 5G NSA (non-standalone access) alternative, a 5G network supported by existing 4G infrastructure. One of the considerations for choosing NSA 5G is that it still performs 4G equalisation in its area. The data throughput is faster but still uses the leading 4G network. Interestingly, there are three types of 5G: low-band (sub-6), middle-band (sub-6), and high-band (millimetre-wave (mmWave)). The problem is determining the kind of 5G needed for public transport planning. Meanwhile, mobile network big data (MNBD) requires robust and stable internet access, with broad coverage in real time. MNBD movement includes the movement of people and vehicles, as well as logistics. GPS and internet connections track the activity of private vehicles and public transportation. The difference between mmWave and sub-6 5G can complement transportation planning needs. The density and height of buildings in urban areas and the affordability of the range of the connections determine 5G. This study examines the literature on 5G and then, using the bibliographic method, matches the network coverage obtained in Indonesia using nPerf data services. According to the data, urban areas are becoming more densely populated. Thus, this could show the differences in the data quality outside of metropolitan areas. This study also discusses the current conditions in terms of market potential and the development of smart cities and provides an overview of how real-time mobile data can support public transport planning. This article provides beneficial insight into the stability and adjustment of 5G, where the connectivity can be adequately maintained so that the MNBD can deliver representative data for analysis.

A measure to estimate the risk of imported COVID-19 cases and its application for evaluating travel-related control measures

High connectivity between nations facilitates the spread of infectious diseases. We introduce an improved measure to estimate the risk of COVID-19 importation. The measure was applied to identify the effectiveness of travel-related control measures. We estimated the risk of importation, using the product of air-travel volume and COVID-19 prevalence in the area-of-origin. Travel volumes were acquired through real-time mobile data, and prevalence was calculated considering the time-varying strength of the COVID-19 testing policy. With the measure, the number of expected-imported cases was calculated, and compared with the reported-imported COVID-19 cases before and after post-entry quarantine for all entrants. The expected and reported-imported cases were well fitted (R2 = 0.8). A maximum of 35 undetected-imported cases was estimated to have entered Seoul, before the first imported COVID-19 case was confirmed. With the travel-related control measures, at most, 48 (73%) imported cases could be isolated from the local community. Our measure predicted trends in imported COVID-19 cases well. The method used to develop the measure can be applied to future emerging infectious diseases. Our results provide a ‘real-world’ evidence that travel-related control measures are effective at curbing further COVID-19 transmission.

Population Exposure Changes to One Heat Wave and the Influencing Factors Using Mobile Phone Data—A Case Study of Zhuhai City, China

The frequent occurrence of extreme high temperature weather and heat waves has greatly affected human life. This paper analyzes population exposure and its influencing factors during a heat wave incident in Zhuhai from 6 to 12 September 2021 based on real-time mobile phone data and meteorological data. The results show that the most areas of Zhuhai are affected by high temperature during this heat wave incident. The hourly population exposure is directly proportional to hourly heat wave coverage. In terms of time dimension, the overall population exposure shows a trend of decreasing and then increasing. In terms of spatial dimensions, high population exposure is concentrated in areas such as primary and secondary schools, colleges and universities, office buildings, and residential areas. Low exposure is distributed in most of the mountainous areas along the southern coast. In addition, the leading factors that cause changes in population exposure in different periods of the heat wave cycle are different, which rely more on either climatic factors or population factors.

Using real-time mobile phone data to characterize the relationships between small-scale farmers’ planting dates and socio-environmental factors

Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision. We investigate whether antecedent agro-meteorological variables and household-level attributes can be used to predict planting dates of small-scale maize producers in central Kenya. Using random forest models, we compare remote estimates of SOS with field-level survey data of actual planting dates. We compare three years of planting dates (2016–2018) for two rainy seasons (the October-to-December short rains, and the March-to-May long rains) gathered from weekly Short Message Service (SMS) mobile phone surveys. In situ data are compared to SOS from the Water Requirement Satisfaction Index (SOSWRSI) and other agro-meteorological variables from Earth observation (EO) datasets (rainfall, NDVI, and evaporative demand). The majority of farmers planted within 20 days of the SOSWRSI from 2016 to 2018. In the 2016 long rains season, many farmers reported planting late, which corresponds to drought conditions. We find that models relying solely on EO variables perform as well as models using both socio-economic and EO variables. The predictive accuracy of EO variables appears to be insensitive to differences in reference periods that were tested for deriving EO anomalies (1, 3, 5, or 10 years). As such, it would appear that farmers are either responding to short-term weather conditions (e.g., intra-seasonal variability), or longer trends than were included in this study (e.g., 25–30 years), when planting. The methodologies used in this study, weekly SMS surveys, provide an operational means for estimating farmer behaviors–information which is traditionally difficult and costly to collect.

Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks—Part I

Analyzing massive amounts of data using complex machine learning models requires significant computational resources. The conventional approach to such problems involves centralizing training data and inference processes in the cloud, i.e., in data centers. However, with the proliferation of mobile devices and increasing application of the Internet-of-Things (IoT) paradigm, very large amounts of data are collected at the edges of wireless networks, and due to privacy constraints and limited communication resources, it is undesirable or impractical to upload this data from mobile devices to the cloud for centralized learning. This problem can be solved by distributed learning at the network edge, by which edge devices collaboratively train a shared learning model using real-time mobile data. The avoidance of raw-data uploading not only helps to preserve privacy but may also alleviate network-traffic congestion and minimize latency. With that said, distributed training still requires a substantial amount of information exchange between devices and edge servers over wireless links. In the process, wireless impairments such as noise, interference, and imperfect knowledge of channel states can significantly slow down distributed learning (e.g., convergence speed) and degrades its performance (e.g., learning accuracy). This makes it crucial to optimize wireless network performance so as to support the efficient deployment of distributed learning algorithms. On the other hand, distributed learning algorithms provide a powerful tool-set for solving complex problems in wireless communication and networking. One important framework, called federated learning (FL), enables users to collaboratively learn a shared model while helping to preserve local data privacy. The application of FL can endow edge devices with capabilities of user behavior prediction, user identification, and wireless environment analysis. As another example, distributed reinforcement learning is capable of leveraging distributed computation power and data to solve complex optimization and control problems that arise in various use cases, such as network control, user clustering, resource management, and interference alignment. To cover this paradigm of distributed learning over wireless networks, this two-part Special Issue features papers dealing with two main research challenges: a) optimization of wireless network performance for efficient implementation of distributed learning in wireless networks, and b) distributed learning for solving communication problems and optimizing network performance.

Smart Futures Based Resource Trading and Coalition Formation for Real-Time Mobile Data Processing

Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.

Research on IRP of Perishable Products Based on Mobile Data Sharing Environment

Inventory routing problem (IRP) has always been a hot issue. Due to its particularity, perishable products have high requirements for inventory and transportation. In order to reduce the losses of perishable goods and improve the storage efficiency of perishable goods, based on the general inventory path problem, this paper further has studied the IRP of perishable goods. In addition, in the process of product distribution and transportation, there are a lot of real-time product information generated dynamically. These real-time mobile data must be shared by the whole distribution network, which will also dynamically affect the efficiency of IRP research. On the basis of some assumptions, the mathematical model has been established with inventory and vehicle as constraints and the total cost of the system as the objective. In view of the particularity of perishable inventory path problem, this paper proposed an improved differential evolution algorithm (IDE) to improve the differential evolution algorithm from two aspects. Firstly, the population has been initialized by gridding and the greedy local optimization algorithm has been used to assist the differential evolution algorithm, with these measures to improve the convergence speed of the algorithm. Then, the accuracy of the algorithm is improved by the adaptive scaling factor, two evolution modes and changing the constraints of the problem. Then the improved algorithm has been used to solve the inventory path problem. The results of numerical experiments show that the algorithm is effective and feasible and can improve the accuracy and speed up the convergence of the algorithm.

An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning

The 5G network connecting billions of Internet of things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization architecture will enable cloud computing at the (network) edge. The availability of both rich data and computation power at the edge has motivated Internet companies to deploy artificial intelligence (AI) there, creating the hot area of edge-AI. Edge learning, the theme of this project, concerns training edge-AI models, which endow on IoT devices intelligence for responding to real-time events. However, the transmission of high-dimensional data from many edge devices to servers can result in excessive communication latency, creating a bottleneck for edge learning. Traditional wireless techniques deigned for only radio access are ineffective in tackling the challenge. Attempts to overcome the communication bottleneck has led to the development of a new class of techniques for intelligent radio resource management (RRM), called data-importance aware RRM. Their designs feature the interplay of active machine learning and wireless communication. Specifically, the metrics that measure data importance in active learning (e.g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers. This article aims at providing an introduction to the emerging area of importance-aware RRM. To this end, we will introduce the design principles, survey recent advancements in the area, discuss some design examples, and suggest some promising research opportunities.

Fast Fading Mobile Channel Modeling for Wireless Communication

In highly populated urban canyons, the mobile communication signal propagate from the base station and arrives at the mobile station(or mobile phone) as a multitude of partial waves from different directions. This is known as multipath propagation. This effect gives rise to multipath fading. Due to this, received signal strength decreases and sometimes unable to recognise. So characterisation and modelling of wireless channel is important. The received signal strength in terms of power is measured using RF recorder for analysis at the mobile station at certain time intervals and the signal (in dBm) assumed to be received in multipath environment and is composed of fast fading caused by local multipath propagation and slow fading due to shadowing. In this paper, the real time mobile data is analyzed by separating fast fading components using moving average filter and then approximation of its and probability distribution functions (PDF) and cummulative distributions(CDF) are analysed.

Scalable Energy-Efficient Distributed Data Analytics for Crowdsensing Applications in Mobile Environments

We are witnessing a new revolution in computing and communication involving symbiotic networks of people (social networks), intelligent devices, smart mobile computing, and communication devices that will form cyber-physical social systems. The emergence of intelligent devices with monitoring, sensing, and actuation capabilities referred to as Internet of Things and social networks have increased the popularity of novel social applications such as crowdsourcing and crowdsensing. The upsurge of such applications has fostered the need for scalable cost-efficient platforms that can enable distributed data analytics. In this paper, we propose CARDAP, a scalable, energy-efficient, generic and extensible component-based distributed data analytics platform for mobile crowdsensing (MCS) applications. CARDAP incorporates on-the-move activity recognition and a number of energy efficient data delivery strategies using real-time mobile data stream mining. We propose and develop theoretical cost models for typical crowdsensing application scenarios. Experimental evaluations of CARDAP using a proof-of-concept MCS scenario validate the theoretical cost model estimates and demonstrate the platform's ability to deliver significant benefits in energy, resource, and query processing efficiency.

Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data

With the rapid development of the society, the transportation system has become more complicated and vulnerable. For simulating the real-time traffic condition of the whole city, a wide range of OD matrix data are needed which are hard to collect in whole based on the present conventional methods. The paper raises a feasible design of the traffic simulation platform based on the real-time mobile phone data. The popularity and development of mobile phones make the vast amounts of real-time traffic data can be collected and usable. With the help of the GIS module, dynamic OD traffic generation module and other related modules, the real-time mobile phone data will be converted to the valuable traffic data and applied to the traffic simulation platform.

Mobile Geographic Information Handling Technologies to Support Disaster Management

Geographic information, easily accessible in real time and capable of being shared amongst users through different emerging technologies (particularly mobile technologies), is essential for a wide range of public and privately-owned agencies responsible for disaster management tasks. Such tasks cannot be successfully undertaken without the real-time ability to visualise map locations, determine the scale of emergency, identify and evacuate at-risk populations, and expedite and direct rescue efforts. Considerable progress has been made in developing geographic information systems (GIS) over the past 30 years, such that the current state of geographic information technology can provide decision makers with the information they need to confront a wide variety of operations. However, little of this effort has gone into researching the unique requirements of field (mobile) geographic information managements, or into developing relevant technologies that support integration and interoperability among databases and GIS to enable real-time mobile data processing. This article investigates the potential of mobile geographic information handling for disaster management, particularly oil spill emergency response. It considers measures to improve geographic information handling and to support rescue efforts during emergency response.