Smartphone-based food diagnostic technologies: A review

A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies

Water quality monitoring using wireless sensor network and smartphone-based applications: a review

A wireless sensor network (WSN) has a huge potential in water ecology monitoring applications. The integration of WSN to a portable sensing device offers the feasibility of sensing distribution capability, on-site data measurements, and remote sensing abilities. Due to the advancement of WSN technology, unexpected contamination events in water environments can be observed continuously. Local Area Network (LAN), Wireless Local Area Network (WLAN) and Internet web-based are commonly used as a gateway unit for data communication via local base computer using standard Global System for Mobile Communication (GSM) or General Packet Radio Services (GPRS). However, WSN construction is costly and a growing static infrastructure increases the energy consumptions. Hence, a growing trend of smartphone-based application in the field of water monitoring is a surrogate approach to engage mobile base stations for in-field analysis that are driven by the expanding adaptation of Bluetooth, ZigBee and standard Wi-Fi routers. Owing to the fact that smartphones are portable and accessible, mobile data collection from WSN in remote locations are achievable. This paper comprehensively reviews the detection of water contaminants using smartphone-based applications in accordance with WSN technologies. In this paper, some recommendations and prospective views on the developments of water quality monitoring will be discussed

FacePET: Enhancing Bystanders\u27 Facial Privacy with Smart Wearables/Internet of Things

Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the bystanders’ trust in these systems to protect his/her facial privacy. To address these limitations, we propose FacePET (Facial Privacy Enhancing Technology), a wearable system worn by bystanders and designed to enhance facial privacy. We present the design, implementation, and evaluation of the FacePET and discuss some open research issues

Looking towards the future: the changing nature of intrusive surveillance and technical attacks against high-profile targets

In this thesis a novel Bayesian model is developed that is capable of predicting the probability of a range of eavesdropping techniques deployed, given an attacker's capability, opportunity and intent. Whilst limited attention by academia has focused on the cold war activities of Soviet bloc and Western allies' bugging of embassies, even less attention has been paid to the changing nature of the technology used for these eavesdropping events. This thesis makes four contributions: through the analysis of technical eavesdropping events over the last century, technological innovation is shown to have enriched the eavesdropping opportunities for a range of capabilities. The entry barrier for effective eavesdropping is lowered, while for the well resourced eavesdropper, the requirement for close access has been replaced by remote access opportunities. A new way to consider eavesdropping methods is presented through the expert elicitation of capability and opportunity requirements for a range of present-day eavesdropping techniques. Eavesdropping technology is shown to have life-cycle stages with the technology exploited by different capabilities at different times. Three case studies illustrate that yesterday’s secretive government method becomes today’s commodity. The significance of the egress transmission path is considered too. Finally, by using the expert elicitation information derived for capability, opportunity and life-cycle position, for a range of eavesdropping techniques, it is shown that it is possible to predict the probability of particular eavesdropping techniques being deployed. This novel Bayesian inferencing model enables scenarios with incomplete, uncertain or missing detail to be considered. The model is validated against the previously collated historic eavesdropping events. The development of this concept may be scaled with additional eavesdropping techniques to form the basis of a tool for security professionals or risk managers wishing to define eavesdropping threat advice or create eavesdropping policies based on the rigour of this technological study.Open Acces

Transportation planning via location-based social networking data : exploring many-to-many connections

textToday’s metropolitan areas see changes in populations and land development occurring at faster rates than transportation planning can be updated. This dissertation explores the use of a new dataset from the location-based social networking spectrum to analyze origin-destination travel demand within Austin, TX. A detailed exploration of the proposed data source is conducted to determine its overall capabilities with respect to the Austin area demographics. A new methodology is proposed for the creation of origin-destination matrices using a peer-to-peer modeling structure. This methodology is compared against a previously examined and more traditional approach, the doubly-constrained gravity model, to understand the capabilities of both models with various friction functions. Each method is examined within the constructs of the study area’s existing origin-destination matrix by examining the coincidence ratios, mean errors, mean absolute errors, frequency ratios, swap ratios, trip length distributions, zonal trip generation and attraction heat maps, and zonal origin-destination flow patterns. Through multiple measures, this dissertation provides initial interpretations of the robust Foursquare data collected for the Austin area. Based upon the data analytics performed, the Foursquare data source is shown to be capable of providing immensely detailed spatial-temporal data that can be utilized as a supplementary data source to traditional transportation planning data collection methods or in conjunction with other data sources, such as social networking platforms. The examination of the proposed peer-to-peer methodology presented within this dissertation provides a first look at the potential of many-to-many modeling for transportation planning. The peer-to-peer model was found to be superior to the doubly-constrained gravity model with respect to intrazonal trips. Furthermore, the peer-to-peer model was found to better estimate productions, attractions, and zone to zone movements when a linear function was used for long trips, and was computationally more proficient for all models examined.Civil, Architectural, and Environmental Engineerin

Performance of the Intrac Wireless Activity Tracking System for the Afari Assistive Device

Afari is a mobility device that was designed to be more recreational, aesthetic, and functional outside than the typical mobility devices commonly used today such as walkers, crutches, and rollators. The Afari transfers weight from a user through the arm rests and enforces an upright posture while walking with correct adjustments to the arm rest height. In addition to assisting with walking or running, a sensor system fitted to the Afari device has been designed to analyze different aspects of activity tracking such as the dynamic loading applied to the arm rests, spatial-temporal gait parameters, speed, and distance. This includes various sensors, namely, load cells for each arm rest, an inertial measurement unit, and a speed and distance sensor that wirelessly transmit data via Bluetooth Low Energy (BLE) to either a smartphone or computer. The total distance, pitch angle, right and left loading on each armrest can be viewed in real time by the user. An algorithm was created in MATLAB to process all the raw data and compute cadence, stride length, average toe-off and heel strike angle, swing and stance time, and speed over the duration of active use. An Afari user can monitor these different aspects of their activity and adjust accordingly to potentially improve their balance or gait

Behaviour based anomaly detection system for smartphones using machine learning algorithm

In this research, we propose a novel, platform independent behaviour-based anomaly detection system for smartphones. The fundamental premise of this system is that every smartphone user has unique usage patterns. By modelling these patterns into a profile we can uniquely identify users. To evaluate this hypothesis, we conducted an experiment in which a data collection application was developed to accumulate real-life dataset consisting of application usage statistics, various system metrics and contextual information from smartphones. Descriptive statistical analysis was performed on our dataset to identify patterns of dissimilarity in smartphone usage of the participants of our experiment. Following this analysis, a Machine Learning algorithm was applied on the dataset to create a baseline usage profile for each participant. These profiles were compared to monitor deviations from baseline in a series of tests that we conducted, to determine the profiling accuracy. In the first test, seven day smartphone usage data consisting of eight features and an observation interval of one hour was used and an accuracy range of 73.41% to 100% was achieved. In this test, 8 out 10 user profiles were more than 95% accurate. The second test, utilised the entire dataset and achieved average accuracy of 44.50% to 95.48%. Not only these results are very promising in differentiating participants based on their usage, the implications of this research are far reaching as our system can also be extended to provide transparent, continuous user authentication on smartphones or work as a risk scoring engine for other Intrusion Detection System

A Survey on Security for Mobile Devices

Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has signicantly increased due to the dierent form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research eld is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011. We focus on high-level attacks, such those to user applications, through SMS/MMS, denial-of-service, overcharging and privacy. We group existing approaches aimed at protecting mobile devices against these classes of attacks into dierent categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research