Mobile Computing Technologies Research Articles

Traffic data reconstruction based on compressive sensing with neighbor regularization

AbstractThe production and collection of the mass traffic data in the vehicle network will result in wasted bandwidth and data transmission delay. The mobile edge computing (MEC) technology is able to reduce the transmission cost and provide fast interactive response. However, the amount of data between roadside units (RSUs) and MEC servers has not decreased. Compressive sensing (CS) technology can reduce the sampling frequency and reconstruct the signal with even fewer samples than the sampling theorem requires. Therefore, in this article, we use CS technology to reduce the data in the “last mile” of traffic edge computing network. We propose two traffic data reconstruction models, which use different neighbor regularization methods in the objective function of CS reconstruction model. Different from the previous studies, we leverage the low‐rank nature and the neighbor relationship between detectors to improve the reconstruction accuracy of different missing scenes. To validate the improved reconstruction models, we have carried out extensive experiments based on the actual traffic flow data of the freeway system under different loss rates and loss patterns. The missing data are caused by reduced sampling frequency and accidental transmission loss so that it is closer to the real fact. The experimental results show that even if the traffic data from the RSUs to MEC servers are decreased by 50%, the complete data can be reconstructed accurately.

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

Mobile Health Technology Interventions for Suicide Prevention: Systematic Review.

BackgroundDigital interventions are proposed as one way by which effective treatments for self-harm and suicidal ideation may be improved and their scalability enhanced. Mobile devices offer a potentially powerful medium to deliver evidence-based interventions with greater specificity to the individual when the intervention is needed. The recent proliferation of publicly available mobile apps designed for suicide prevention underlines the need for robust evidence to promote safe practice.ObjectiveThis review aimed to examine the effectiveness of currently available mobile health (mHealth) technology tools in reducing suicide-specific outcomes.MethodsThe following databases were searched: Cochrane Central Register of Controlled Trials (The Cochrane Library), MEDLINE, EMBASE, PsycINFO, and relevant sources of gray literature. All published and unpublished randomized controlled trials (RCTs), pseudo-RCTs, and pre-post observational studies that evaluated the effectiveness of mHealth technology in suicide prevention delivered via mobile computing and communication technology were included. Studies were included if they measured at least one suicide outcome variable (ie, suicidal ideation, suicidal intent, nonsuicidal self-injurious behavior, and suicidal behavior). A total of 2 review authors independently extracted data and assessed study suitability, in accordance with the Cochrane Collaboration Risk of Bias Tool, on July 31, 2018. Owing to the heterogeneity of outcomes found across studies, results were not amenable for pooled synthesis, and a meta-analysis was not performed. A narrative synthesis of the available research is presented here.ResultsA total of 7 studies met criteria for inclusion . Four published articles that reported on the effectiveness of the following mobile phone apps were included: iBobbly, Virtual Hope Box, BlueIce, and Therapeutic Evaluative Conditioning. Results demonstrated some positive impacts for individuals at elevated risk of suicide or self-harm, including reductions in depression, psychological distress, and self-harm and increases in coping self-efficacy. None of the apps evaluated demonstrated the ability to significantly decrease suicidal ideation compared with a control condition. In addition, 3 unpublished and recently completed trials also met criteria for inclusion in the review.ConclusionsFurther research is needed to evaluate the efficacy of stand-alone mHealth technology–based interventions in suicide prevention. The small number of studies reported in this review tentatively indicate that such tools may have a positive impact on suicide-specific outcomes. Future mHealth intervention evaluations would benefit from addressing the following 3 main methodological limitations : (1) heterogeneity of outcomes: a lack of standardized measurement of suicide outcomes across studies; (2) ecological validity: the tendency to exclude potential participants because of the elevated suicide risk may reduce generalizability within clinical settings; and (3) app regulation and definition: the lack of a standardized classification system for mHealth intervention type points to the need for better definition of the scope of such technologies to promote safe practice.Trial RegistrationPROSPERO CRD42017072899; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=72899International Registered Report Identifier (IRRID)RR2-10.2196/resprot.8635

Design and implementation of intelligent creation platform based on artificial intelligence technology

In this article, we have designed a fusion media platform that integrates intelligent creation and editing based on Artificial Intelligence Technology. The system includes a content acquisition and intelligent creation module, a multimedia editing module, and a multi-channel integrated publishing and operation module. The content collection and intelligent creation module utilizes the audio and video capture technology of the mobile computer and natural language processing technology to achieve rich media content acquisition and smart embellishment. The multimedia editing module can realize text matching and modification trace preservation, associated content intelligent extraction, and audio and video processing. The multi-channel integrated publishing module utilizes crawling technology and auth2.0 authentication technology to realize seamless interfacing of new media channels such as WeChat, Weibo, headline, Sohu, TV production system, radio production system, newspaper typesetting system, etc. At the same time, the platform can also capture operational data through crawler technology to meet the needs of intelligent operations. The platform has been piloted in a number of press and publication media organizations. Through the results of the operation and the user feedback, the platform can provide a good service for news media workers from content creation, editorial review to channel release. This research will provide a comprehensive working platform for the news publishing media industry, which is of great value for promoting the comprehensive fusion of the media.

IEEE Access Special Section Editorial: Mobile Edge Computing and Mobile Cloud Computing: Addressing Heterogeneity and Energy Issues of Compute and Network Resources

Mobile applications are advancing toward higher network and compute requirements, which are similar to the requirements of server applications. Users prefer to perform their tasks on the go from mobile devices instead of stationary desktop and server systems. Modern mobile applications limit mobile battery times as charging is lost quickly due to high processing and data demands. Resources other than energy are also limited in mobile devices (memory, CPU millions of instructions per second, etc.).Mobile edge computing (MEC) technologies facilitate resource-scarcemobile devices to enhance their capabilities and execute data/computeintensive applications while collaborating with resource-rich network servers to enable ubiquitous computing. On the other hand, mobile cloud computing provides higher resources for applications that have low response requirements (noninteractive applications). Bothmobile edge computing andmobile cloud computing enable technologies for Internet of Things (IoT), smart grids, and e-health applications.

Two-Stage Computation Offloading Scheduling Algorithm for Energy-Harvesting Mobile Edge Computing

Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario.

Experimental Supplements from Mobile Tools for Cognitive Introspection Towards Cognitive Augmentation

In an increasingly interconnected world with more and more technology surrounding us, more and more people end up with psychological and mental health issues. Mobile Computing technologies is often introduced into society without knowing about impact and consequences. We need new mobile tools for everyday life to better manage our attention and give us introspection into our cognitive self. There are patterns in human physiological signals (facial expressions, heart rate, nasal temperature, eye movements, blinking, etc.) that can reveal information about cognitive functions of individuals and groups.

Application of information technologies for the assessment of occupational stress in athletes

Introduction. To assess occupational stress, it is important to create an effective system of personalized monitoring of the functional state of workers based on information computer technologies.The aim of the study was to carry out personalized physiological and hygienic monitoring using a complex of mobile telemetry systems and computer technologies of laterometry and campimetry in real conditions of professional activity.Materials and methods. 37 athlete rowers aged 15–20 years took part in the research on the basis of voluntary informed consent. In the course of the training, stress responses were recorded according to the results of heart rate telemetry and the stress load index (SLI) was calculated. The blood levels of biochemical markers were detected (creatine kinase — CK-MB, alanine aminotransferase — ALT). Using computer laterometry and campimetry.Results. In persons with identifi ed signs of stress in terms of laterometry and campimetry in the blood recorded increased ALT (18.9±6.9 IU / l against the group “norm” 14.3±3.4 IU/ l, p=0.01), CC-MV (42±17.5 IU / l against 16±3.5 IU/ l, p=0.02). In the course of training, 60% of athletes had increased values of isn (0.30±0.04).Conclusions. The introduction of information technologies for personalized monitoring makes it possible to manage the risk of health disorders of workers with high nervous, emotional and physical stress.

Research on Germplasm Resources Informationization Based on Mobile Information Technology

The traditional collection of germplasm resources adopts the form of paper collection tables. It takes a lot of manpower and material resources to field operations, and then collects, records, and analyzes the collected data. This operation method is not only time-consuming and labor-intensive, but also cannot perform images. And other important multimedia is data collection. The demand for images by germplasm researchers is not limited to its visibility. They urgently need a real-time, mobile, multi-source acquisition method to achieve a higher level of integration, including basic content, geographic location, and location information. And other multimedia information and other image data information. With the further development of information technology, the Internet has begun to be applied to various industrial fields, and has played a huge role. In order to solve the above-mentioned problems, it is necessary to apply information technology to achieve rapid collection of bamboo germplasm resources. The use of mobile computing and other related information technologies to build a data collection system for bamboo germplasm resources based on mobile computing has not only improved the theoretical application level of mobile computing technology, but also promoted the information collection process of germplasm resources. Therefore, in order to achieve the information collection of germplasm resources, it is necessary to closely integrate this work with mobile computing technology.

Decentralized Resource Allocation for Video Transcoding and Delivery in Blockchain-Based System With Mobile Edge Computing

The blockchain-based video systems are designed to build a decentralized and flexible video ecosystem by enabling a direct interaction among users, video providers (VPs), and service providers. In blockchain-based video systems, the heterogeneous qualities and formats of the video streams usually require massive computational resources to transcode them into different versions and formats to meet distinct requirements of users. However, current blockchains cannot handle massive and heterogeneous video streaming due to limited computing capacity and long transaction times. To deal with this issue, in this paper, leveraging mobile edge computing (MEC) technology, we propose a blockchain-based MEC architecture, where small base stations (SBSs) allocate their computation as well as communication resources for providing video streaming in a distributed and secure manner. Moreover, to improve the operation efficiency, we use a series of smart contracts to enable a self-organized video transcoding and delivery service without a centralized controller. Then, users, SBSs, and VP could adjust their strategies according to the transactional information on blockchain. Moreover, we formulate the video transcoding and delivery problem as a three-stage Stackelberg game. We analyze the sub-game equilibrium in each stage and the interplays of the three-stage game. Last, we propose an iterative algorithm to solve the problem. Simulation results show that the proposed approach could obtain the good performance in terms of average time to finality (TTF), average access delay, and network cost.

Vulnerable community identification using hate speech detection on social media

With the rapid development in mobile computing and Web technologies, online hate speech has been increasingly spread in social network platforms since it's easy to post any opinions. Previous studies confirm that exposure to online hate speech has serious offline consequences to historically deprived communities. Thus, research on automated hate speech detection has attracted much attention. However, the role of social networks in identifying hate-related vulnerable community is not well investigated. Hate speech can affect all population groups, but some are more vulnerable to its impact than others. For example, for ethnic groups whose languages have few computational resources, it is a challenge to automatically collect and process online texts, not to mention automatic hate speech detection on social media. In this paper, we propose a hate speech detection approach to identify hatred against vulnerable minority groups on social media. Firstly, in Spark distributed processing framework, posts are automatically collected and pre-processed, and features are extracted using word n-grams and word embedding techniques such as Word2Vec. Secondly, deep learning algorithms for classification such as Gated Recurrent Unit (GRU), a variety of Recurrent Neural Networks (RNNs), are used for hate speech detection. Finally, hate words are clustered with methods such as Word2Vec to predict the potential target ethnic group for hatred. In our experiments, we use Amharic language in Ethiopia as an example. Since there was no publicly available dataset for Amharic texts, we crawled Facebook pages to prepare the corpus. Since data annotation could be biased by culture, we recruit annotators from different cultural backgrounds and achieved better inter-annotator agreement. In our experimental results, feature extraction using word embedding techniques such as Word2Vec performs better in both classical and deep learning-based classification algorithms for hate speech detection, among which GRU achieves the best result. Our proposed approach can successfully identify the Tigre ethnic group as the highly vulnerable community in terms of hatred compared with Amhara and Oromo. As a result, hatred vulnerable group identification is vital to protect them by applying automatic hate speech detection model to remove contents that aggravate psychological harm and physical conflicts. This can also encourage the way towards the development of policies, strategies, and tools to empower and protect vulnerable communities.

Talking Avatar

With the booming of the mobile computing and web technology, virtual and intelligent mobile applications become increasingly popular, e.g. web computing and web-based information retrieval. However, under contemporary network conditions and web application environment, it remains a challenging problem to achieve a trade-off between algorithm complexity and hardware performance. In this article, a Talking Avatar architecture is presented based on third-party cloud services. First, the authors propose a cloud service based multi-level layered software framework, which consists of user interface layer, business logic layer and data layer. Second, human face synthesis, speech conversion and social sharing schemes are introduced to integrate third-party cloud services. Third, experimental results on Android platforms indicate that the proposed Talking Avatar can be served efficiently in terms of memory consumption as well as average response time. In addition, stronger functions are provided compared with existing methods.

Mobile health and implantable cardiac devices: Patients' expectations.

Mobile computing and communication technologies in health services and information (so-called mHealth) have modified the traditional approach in the follow-up of patients with implantable cardiac devices, increased patient engagement and empowerment, reduced healthcare costs and improved patients' outcome. Recent developments in mobile technology, with the introduction of smartphone-compatible devices that can measure various health parameters and transfer automatically generated data, have increased the potential application of remote monitoring and the interest towards mHealth. However, little is known about the patients' interest and expectations of this new technology. The patients' interest in the possibility of receiving data from their implantable cardiac device, clinical and health advice via remote monitoring on their smartphones were investigated. A questionnaire entitled 'Expectations for future possibility of self-management of device data' (Likert scale scored) was submitted to 300 consecutive implantable cardiac device outpatients. The questionnaire was focused on collecting patients' expectations in receiving direct information regarding their implantable cardiac device status (item 1, five questions), their own clinical status (item 2, seven questions) and advice on healthy lifestyle promotion (item 3, nine questions). Patient characteristics associated with greater interest towards mHealth were also investigated. Questionnaires were completed by 268 patients (221 men, aged 69 ± 14 years). The Cronbach test reported an alpha value of 0.98 for item 1, 0.94 for item 2 and 0.97 for item 3. Patients declared to be mainly interested in the device interventions (62%) and in severe arrhythmia occurrence (61%), followed by data on heart failure severity (54%) and their performed physical activity (48%). Patients showed very little interest in ECG tracing (37%), but the lowest interest was expressed towards healthy lifestyle promotion advice (<40%). A higher education degree and the presence of the caregiver positively affected the interest towards remote monitoring information ( P < 0.001). The patients' interests were mainly directed at receiving information related to technical data of the implantable cardiac device and not to the overall management of the disease, underlying the insufficient awareness of patients towards the key role of self-control health status and the promotion of a healthy lifestyle.

Joint Communication and Control for Small Underactuated USV Based on Mobile Computing Technology

To satisfy the requirement of the future ocean communication and maritime transportation, in this paper, an integrated space-air-ground-sea communication control system based on mobile computing technology is proposed to rescue a water container. Specifically, unmanned surface vehicle (USV), as a flexible and lightweight intelligent device deployment integration platform, is the main tool to move towards the water container. However, how to sense the real-time navigation status information of the USV, integrate the information, and eventually distribute it to the ground control center, is still a difficult point for us. To successfully rescue the water container, three aspects of research are needed: information collection of the USV, information fusion, and data distribution to the ground control center. For the information collection part, we mainly use nine-axis, ultrasonic, global positioning system (GPS), and ultra wide band (UWB) sensors to complete the information collection for the USV. For the information fusion part, we set up a computational model to solve it. The USV computational model in previous works is mainly based on large ships, ignoring the modeling of wind, wave, and current for small underactuated USV. We use the classical Nomoto model, combined with the actual any slight wind, wave, and current forces on the USV, and finally combined with the proportion integration differentiation (PID) algorithm to model the USV. For the data distribution to the ground control center part, we have set up a communication protocol for the USV so that it can communicate normally. In addition, we wrote a Browser/Server (B/S) architecture to enable the ground control center to communicate with the host computer. Finally, our proposed control and communication schemes are carried out on actual USV. The real experiments show that the proposed schemes can reduce the ship surge and satisfy the ship navigation.

Design of V-Type Warehouse Layout and Picking Path Model Based on Internet of Things

Mobile computing provides useful, accurate, and timely information to any customer at any time and any place, which greatly changes the traditional way of reading data in the warehouse. The shelf layout of a warehouse center is one of the important factors affecting the efficiency of operation. Modern distribution centers use the mobile computing technology to store warehouse data, and these new layouts can significantly reduce the order turnover time and cost. This paper focuses on order picking at a storage center, a real-time communication system using data read-write devices, removes the limitation of the picking channel being a straight line and performs the curve design of the main channel of V-shaped and fishbone layouts. Using mobile computing technology, we can obtain the order position in real time and store relevant data, gather the order in time, and reduce the operating costs. Afterward, we built a storage area utilization model with the main channel selected as the curve and a stochastic model of return-type picking path with the main channel as the curve and verified the effectiveness of the mathematical model through simulation using the MATLAB software.

An Energy-Efficient and Deadline-Aware Task Offloading Strategy Based on Channel Constraint for Mobile Cloud Workflows

Energy efficiency is a fundamental problem due to the fact that numerous tasks are running on mobile devices with limited resources. Mobile cloud computing (MCC) technology can offload computation-intensive tasks from mobile devices onto powerful cloud servers, which can significantly reduce the energy consumption of mobile devices and thus enhance their capabilities. In MCC, mobile devices transmit data through the wireless channel. However, since the state of the channel is dynamic, offloading at a low transmission rate will result in the serious waste of time and energy, which further degrades the quality of service (QoS). To address this problem, this paper proposes an energy-efficient and deadline-aware task offloading strategy based on the channel constraint, with the goal of minimizing the energy consumption of mobile devices while satisfying the deadlines constraints of mobile cloud workflows. Specifically, we first formulate a task offloading decision model that combines the channel state with task attributes such as the workload and the size of the data transmission to determine whether the task needs to be offloaded or not. Afterward, we apply it to a new adaptive inertia weight-based particle swarm optimization (NAIWPSO) algorithm to create our channel constraint-based strategy (CC-NAIWPSO), which can obtain a near-optimal offloading plan that can consume less energy while meeting the deadlines. The experimental results show that our proposed task offloading strategy can outperform other strategies with respect to the energy consumption of mobile devices, the execution time of mobile cloud workflows, and the running time of algorithms.

Multi-User and Multi-Task Offloading Decision Algorithms Based on Imbalanced Edge Cloud

The mobile edge computing (MEC) technology can provide mobile users (MU) with high reliability and low time-delay computing and communication services. The imbalanced edge cloud deployment can better adapt to the non-uniform spatial-time distribution of tasks and reduce the deployment cost of edge cloud servers. For multi-user and multi-task offloading decision based on the imbalanced edge cloud, a new offloading cost criteria, based on the tradeoff among time delay-energy consumption-cost, is designed to quantify the user experience of task offloading and to be the optimization target of offloading decision. Both the optimization problems of minimizing the sum offloading costs for all MUs (efficiency-based) and minimizing the maximal offloading cost per MU (fairness-based) are discussed. Efficiency-based offloading decision algorithm [centralized greedy algorithm (CGA) and modified greedy algorithm (MGA)] and fairness-based offloading decision algorithm [fairness-based greedy algorithm (FGA)] are proposed, respectively, and the performance bounds of the algorithm are analyzed. The simulation results show that the offloading cost of the MGA is lower than the CGA, the efficiency of resource utilization of the CGA is higher than that of the FGA, and the fairness of the FGA is stronger than that of the CGA.

Combination of RanidSONNI Mobile Laboratory Devices and Rodos DSS computer Technologies to Forecast Consequences of Fires in the ChNPP Exclusion Zone

This study focuses on joint application of the RanidSONNI mobile laboratory hardware and RODOS DSS computer technologies using the example of numerical forecast of radiation consequences for Kyiv residents caused by radioactive cloud under the city resulting from a wildfire in the Chernobyl Exclusion Zone that occurred on 5 June 2018 in Red Forest area.&#x0D; The purpose of the study was, on the one hand, to combine rapid response technologies using mobile environment sampling tools and computer technologies for forecasting the spread of air contamination at long distances based on weather forecast data, and on the other hand, to define true extent of radiation impact of the radiation cloud on Kyiv residents, which according to the meteorological situation had to pass over the city at the moment of wildfire.&#x0D; The interest in the issue raised is preconditioned by difficulties arising when it is necessary to measure short-term air contamination: in many cases it is possible to obtain actual air contamination indices at a long distance from source term location under its short-term impact only in the center of the cloud itself, which is spread from the source term in wind direction. This preconditions a random nature of contamination registering, if registering is done using stationary control posts. As previous studies have shown, the probability that the cloud formed by the source term passes just above the sampling site is low enough.&#x0D; The article presents a general description of implementing the joint operation of computer technologies for numerical forecast for radioactive cloud spread in the environment using RODOS DSS software package and the air monitoring hardware installed on the RanidSONNI mobile laboratory for radiation survey. The article shows specific hardware for sampling and measuring Cs-137 concentrations in the air is shown, presents the results of forecasting radioactive release spread for long distances and the results of assessing exposure doses for Kyiv residents for the whole time of radioactive cloud passage over the city.

A reference framework for social-enhanced Vehicle-to-Everything communications in 5G scenarios

Vehicle-to-Everything (V2X) communications are widely recognized as a key technology enabler of increased road safety, improved traffic efficiency, and more comfortable traveling experience. Notably, vehicles on the road may share common interests and some mobility patterns determined by the road topology and the human habits and they can cooperate by exchanging valuable information. These social aspects of vehicular mobility can be exploited in many ways, for entertainment, public utility and emergency purposes.In this paper we speculate on how the social dimension can be leveraged in future fifth generation (5G) V2X systems. Specifically, we propose a novel social-enhanced 5G-V2X framework which leverages mobile edge computing (MEC) and software-defined networking (SDN) technologies with the aim to exploit social relationships between V2X entities and facilitate data delivery in this context. Early results are provided to showcase the viability of the proposal in a representative scenario, i.e., safety alert dissemination, and demonstrate its benefits compared to a legacy cellular-aided data dissemination approach.

Potential Fit between Geotechnical Tasks and Mobile Computing Technologies

Trend of rapid pace in earthworks has forced geotechnical laboratories to increase their productivity and performance at organizational as well as at individual level. Though geotechnical work involves numerous mobile work processes but the true potential of rapidly growing mobile computing technologies (MCT) has not yet been effectively exploited mainly due to lack of realization of a potential match between features of this specific technological paradigm and major task requirements. To fill this gap, this study investigated and characterized salient task characteristics and features of MCT. Based on the theory of task-technology fit (TTF), an initial conceptual model is proposed which reflects hypothesized relationships for finding a fit between task and technology and consequently assessing its impact on performance of geotechnical field workers. This paper contributes to the knowledge pool of information system through development of an adapted TTF model in geotechnical mobile work context. Lastly, future research plan of using quantitative methodology for finding empirical support of this conceptual model is presented.