Device-Free Localization for Human Activity Monitoring

Over the past few decades, human activity monitoring has grabbed considerable research attentions due to greater demand for human-centric applications in healthcare and assisted living. For instance, human activity monitoring can be adopted in smart building system to improve the building management as well as the quality of life, especially for the elderly people who are facing health deterioration due to aging factor, without neglecting the important aspects such as safety and energy consumption. The existing human monitoring technology requires additional sensors, such as GPS, PIR sensors, video camera, etc., which incur cost and have several drawbacks. There exist various solutions of using other technologies for human activity monitoring in a smartly controlled environment, either device-assisted or device-free. A radio frequency (RF)-based device-free indoor localization, known as device-free localization (DFL), has attracted a lot of research effort in recent years due its simplicity, low cost, and compatibility with the existing hardware equipped with RF interface. This chapter introduces the potential of RF signals, commonly adopted for wireless communications, as sensing tools for DFL system in human activity monitoring. DFL is based on the concept of radio irregularity where human existence in wireless communication field may interfere and change the wireless characteristics

Runtime reconfiguration of physical and virtual pervasive systems

Today, almost everyone comes in contact with smart environments during their everyday’s life. Environments such as smart homes, smart offices, or pervasive classrooms contain a plethora of heterogeneous connected devices and provide diverse services to users. The main goal of such smart environments is to support users during their daily chores and simplify the interaction with the technology. Pervasive Middlewares can be used for a seamless communication between all available devices and by integrating them directly into the environment. Only a few years ago, a user entering a meeting room had to set up, for example, the projector and connect a computer manually or teachers had to distribute files via mail. With the rise of smart environments these tasks can be automated by the system, e.g., upon entering a room, the smartphone automatically connects to a display and the presentation starts. Besides all the advantages of smart environments, they also bring up two major problems. First, while the built-in automatic adaptation of many smart environments is often able to adjust the system in a helpful way, there are situations where the user has something different in mind. In such cases, it can be challenging for unexperienced users to configure the system to their needs. Second, while users are getting increasingly mobile, they still want to use the systems they are accustomed to. As an example, an employee on a business trip wants to join a meeting taking place in a smart meeting room. Thus, smart environments need to be accessible remotely and should provide all users with the same functionalities and user experience. For these reasons, this thesis presents the PerFlow system consisting of three parts. First, the PerFlow Middleware which allows the reconfiguration of a pervasive system during runtime. Second, with the PerFlow Tool unexperi- enced end users are able to create new configurations without having previous knowledge in programming distributed systems. Therefore, a specialized visual scripting language is designed, which allows the creation of rules for the commu- nication between different devices. Third, to offer remote participants the same user experience, the PerFlow Virtual Extension allows the implementation of pervasive applications for virtual environments. After introducing the design for the PerFlow system, the implementation details and an evaluation of the developed prototype is outlined. The evaluation discusses the usability of the system in a real world scenario and the performance implications of the middle- ware evaluated in our own pervasive learning environment, the PerLE testbed. Further, a two stage user study is introduced to analyze the ease of use and the usefulness of the visual scripting tool

Energy-efficient Continuous Context Sensing on Mobile Phones

With the ever increasing adoption of smartphones worldwide, researchers have found the perfect sensor platform to perform context-based research and to prepare for context-based services to be also deployed for the end-users. However, continuous context sensing imposes a considerable challenge in balancing the energy consumption of the sensors, the accuracy of the recognized context and its latency. After outlining the common characteristics of continuous sensing systems, we present a detailed overview of the state of the art, from sensors sub-systems to context inference algorithms. Then, we present the three main contribution of this thesis. The first approach we present is based on the use of local communications to exchange sensing information with neighboring devices. As proximity, location and environmental information can be obtained from nearby smartphones, we design a protocol for synchronizing the exchanges and fairly distribute the sensing tasks. We show both theoretically and experimentally the reduction in energy needed when the devices can collaborate. The second approach focuses on the way to schedule mobile sensors, optimizing for both the accuracy and energy needs. We formulate the optimal sensing problem as a decision problem and propose a two-tier framework for approximating its solution. The first tier is responsible for segmenting the sensor measurement time series, by fitting various models. The second tier takes care of estimating the optimal sampling, selecting the measurements that contributes the most to the model accuracy. We provide near-optimal heuristics for both tiers and evaluate their performances using environmental sensor data. In the third approach we propose an online algorithm that identifies repeated patterns in time series and produces a compressed symbolic stream. The first symbolic transformation is based on clustering with the raw sensor data. Whereas the next iterations encode repetitive sequences of symbols into new symbols. We define also a metric to evaluate the symbolization methods with regard to their capacity at preserving the systems' states. We also show that the output of symbols can be used directly for various data mining tasks, such as classification or forecasting, without impacting much the accuracy, but greatly reducing the complexity and running time. In addition, we also present an example of application, assessing the user's exposure to air pollutants, which demonstrates the many opportunities to enhance contextual information when fusing sensor data from different sources. On one side we gather fine grained air quality information from mobile sensor deployments and aggregate them with an interpolation model. And, on the other side, we continuously capture the user's context, including location, activity and surrounding air quality. We also present the various models used for fusing all these information in order to produce the exposure estimation

Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy

Urban mobility is known to have a relevant impact on work related car accidents especially during commuting. It is characterized by highly dynamic spatial–temporal variability. There are open questions about the size of this phenomenon; its spatial, temporal, and demographic characteristics; and driving mechanisms. A case study is here presented for the city of Rome, Italy. High-resolution population presence and demographic data, derived from mobile phone traffic, were used. Hourly profiles of a defined mobility factor (NPM) were calculated for a gridded domain during working days and cluster analyzed to obtain mean diurnal NPM mobility patterns. Age distributions of the population were calculated from demographic data to get insight in the type of population involved in mobility, and spatially linked with the mobility patterns. Census data about production units and their employees were related with the classified NPM mobility patterns. Seven different NPM mobility patterns were identified and mapped over the study area. The mobility slightly deviates from the census-based demography (0.15 on average, in a range of 0 to 1). The number of employees per 100 inhabitants was found to be the main driving mechanism of mobility. Finally, contributions of people employed in different economic macrocategories were assigned to each mobility time-pattern. Results provide a deeper knowledge of urban dynamics and their driving mechanisms in Rome

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledg

Ubiquitous Computing

The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

Combining Multiple Granularity Variability in a Software Product Line Approach for Web Engineering

[Abstract] Context: Web engineering involves managing a high diversity of artifacts implemented in different languages and with different levels of granularity. Technological companies usually implement variable artifacts of Software Product Lines (SPLs) using annotations, being reluctant to adopt hybrid, often complex, approaches combining composition and annotations despite their benefits. Objective: This paper proposes a combined approach to support fine and coarse-grained variability for web artifacts. The proposal allows web developers to continue using annotations to handle fine-grained variability for those artifacts whose variability is very difficult to implement with a composition-based approach, but obtaining the advantages of the composition-based approach for the coarse-grained variable artifacts. Methods: A combined approach based on feature modeling that integrates annotations into a generic composition-based approach. We propose the definition of compositional and annotative variation points with custom-defined semantics, which is resolved by a scaffolding-based derivation engine. The approach is evaluated on a real-world web-based SPL by applying a set of variability metrics, as well as discussing its quality criteria in comparison with annotations, compositional, and combined existing approaches. Results: Our approach effectively handles both fine and coarse-grained variability. The mapping between the feature model and the web artifacts promotes the traceability of the features and the uniformity of the variation points regardless of the granularity of the web artifacts. Conclusions: Using well-known techniques of SPLs from an architectural point of view, such as feature modeling, can improve the design and maintenance of variable web artifacts without the need of introducing complex approaches for implementing the underlying variability.The work of the authors from the Universidad de Málaga is supported by the projects Magic P12-TIC1814 (post-doctoral research grant), MEDEA RTI2018-099213-B-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/FEDER, UE), LEIA UMA18-FEDERIA-157, TASOVA MCIU-AEI TIN2017-90644-REDT and, European Union’s H2020 research and innovation program under grant agreement DAEMON 101017109. The work of the authors from the Universidade da Coruña has been funded by MCIN/AEI/10.13039/501100011033, NextGenerationEU/PRTR, FLATCITY-POC: PDC2021-121239-C31 ; MCIN/AEI/10.13039/501100011033 EXTRACompact: PID2020-114635RB-I00 ; GAIN/Xunta de Galicia/ERDF CEDCOVID: COV20/00604 ; Xunta de Galicia/FEDER-UE GRC: ED431C 2021/53 ; MICIU/FEDER-UE BIZDEVOPSGLOBAL: RTI-2018-098309-B-C32 ; MCIN/AEI/10.13039/501100011033 MAGIST: PID2019-105221RB-C41Junta de Andalucía; P12-TIC-1814Universidad de Málaga; UMA18-FEDERIA-157Xunta de Galicia; COV20/00604Xunta de Galicia; ED431C 2021/53Junta de Andalucía; P18-FR-108

Using Hidden Markov Models to Segment and Classify Wrist Motions Related to Eating Activities

Advances in body sensing and mobile health technology have created new opportunities for empowering people to take a more active role in managing their health. Measurements of dietary intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require considerable manual effort, leading to underreporting of consumption, non-compliance, and discontinued use over the long term. We are investigating the use of wrist-worn accelerometers and gyroscopes to automatically recognize eating gestures. In order to improve recognition accuracy, we studied the sequential ependency of actions during eating. In chapter 2 we first undertook the task of finding a set of wrist motion gestures which were small and descriptive enough to model the actions performed by an eater during consumption of a meal. We found a set of four actions: rest, utensiling, bite, and drink; any alternative gestures is referred as the other gesture. The stability of the definitions for gestures was evaluated using an inter-rater reliability test. Later, in chapter 3, 25 meals were hand labeled and used to study the existence of sequential dependence of the gestures. To study this, three types of classifiers were built: 1) a K-nearest neighbor classifier which uses no sequential context, 2) a hidden Markov model (HMM) which captures the sequential context of sub-gesture motions, and 3) HMMs that model inter-gesture sequential dependencies. We built first-order to sixth-order HMMs to evaluate the usefulness of increasing amounts of sequential dependence to aid recognition. The first two were our baseline algorithms. We found that the adding knowledge of the sequential dependence of gestures achieved an accuracy of 96.5%, which is an improvement of 20.7% and 12.2% over the KNN and sub-gesture HMM. Lastly, in chapter 4, we automatically segmented a continuous wrist motion signal and assessed its classification performance for each of the three classifiers. Again, the knowledge of sequential dependence enhances the recognition of gestures in unsegmented data, achieving 90% accuracy and improving 30.1% and 18.9% over the KNN and the sub-gesture HMM

Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey

Source anonymity in wireless sensor networks (WSNs) becomes a real concern in several applications such as tracking and monitoring. A global adversary that has sophisticated resources, high computation and full view of the network is an obvious threat to such applications. The network and applications need to be protected and secured to provide the expected outcome. Source anonymity is one of the fundamental WSNs security issues. It is all about preventing the adversary from reaching the origin by analyzing the traffic of the network. There are many methods to provide source anonymity, which is also known as Source Location Privacy (SLP). One of these methods is based on dummy packets. The basic notion is to inject the network with dummy packets to confuse the adversary about the location of the transmitting source node. This paper provides a survey of protocols for anonymity that use dummy packet injections. We discuss each technique from the point of their advantages and disadvantages. Further, We provide a comparison for the most promising techniques provided in the literature which use dummy packet injections. A comparison for the adversary assumptions and capabilities will be provided as well.http://dx.doi.org/10.3390/electronics710025