Models and systems for managing sensor and crowd-oriented processes

Business process modeling refers to the design of business process models, using business processes languages, to orchestrate the work executed by employees, their interaction with external entities, and work items that are necessary to achieve a predefined goal. Model-driven development allows people, generally called modelers, to design also sophisticated application logic using high-level abstractions. Process modeling is typically connected with business, hence, existing process languages focus principally on the support and orchestration of activities executed by employees, or by external entities like web services. However, there is a wide range of other application logics that are process-driven and that can benefit from high-level abstractions to model low-level details. Our initial research focuses on distributed UIs, which are a distributed type of actors, and then particularly concentrated on Wireless Sensor Networks (WSNs) and crowdsourcing, which are distributed and also autonomous types of actors (they can execute a part of an application logic in an autonomous and isolated fashion). Developing applications in these areas requires a deep knowledge of the field and a non-trivial programming effort; domain experts have to code an orchestrate the logic executed by these actors. Since these applications are highly process-driven, domain experts could take advantage of high-level, process-oriented modeling conventions to design the internal logic of these kinds of applications. However, the intrinsic complexity of these domains and the current state of the art of modeling paradigms make the design and execution of processes for these new actors challenging. In this dissertation we analyze, design, and present modeling formalism and systems for managing processes in these contexts. We tackle the challenges of the three areas with an approach that analyzes and extends existing process modeling languages, to enable the design of the processes, and with an architecture, similar for the three focuses, to support the development and execution of processes. Starting from our initial work on the orchestration of distributed UIs, for which we present a modeling language with a set of modeling constructs specific for the UIs, we then present our contribution to WSNs and crowdsourcing domains, which are: a modeling convention for the development of WSN applications, with high-level modeling constructs that abstract the low-level details of the networks; and a modeling paradigm to design processes that are partially executed by a crowd of people. These languages are all equipped with prototypes that contain a modeling tool to design processes and a runtime environment to support the execution. The impact of this work is not only to the domains we focused on but also to the business process domain as we demonstrate how a process modeling is a flexible and suitable formalism to design processes with very diverging, domain-specific requirements

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects

Noise-sensing energy-harvesting wireless sensor network nodes

Noise pollution is becoming an increasing concern in many urban regions all over the world. An important step in fighting and mitigating noise pollution is its quantification. Wireless sensor networks (WSNs) can potentially help with these efforts, as they enable the simultaneous and continuous gathering of data over wide geographic regions. The need to replace batteries however makes the maintenance of such physically very large networks impractical. As an alternative to batteries, noise-sensing WSNs could also be powered by energy harvesting. While energy-harvesting WSNs have been demonstrated before, utilizing energy harvesting for powering noise-sensing WSNs still pose a significant challenge because of application’s unique requirements, such as a high power consumption profile for extended periods of time. In this thesis, we address four key areas of research necessary on to make energy-harvesting noise-sensing WSNs possible and, more importantly, practical to use in large-scale settings. The first key area that we address is that of new and emerging energy storage technologies, and how current algorithms and infrastructures must be modified to take advantage of them. The second key area is that of currently-accepted technical requirements, and their assessment on whether they would indeed lead to the attainment of long-term goals. The third key area is that of test methodologies for energy-harvesting designs, and how they should be modified to facilitate validation of results between researchers. The final key area is that of techniques and algorithms for future capabilities that energy-harvesting noise-sending WSNs will or can have, and how we should prepare for them, even though they may not yet exist. We provide research to support all four key areas in this work and provide concrete examples for each

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Instantaneous multi-sensor task allocation in static and dynamic environments

A sensor network often consists of a large number of sensing devices of different types. Upon deployment in the field, these sensing devices form an ad hoc network using wireless links or cables to communicate with each other. Sensor networks are increasingly used to support emergency responders in the field usually requiring many sensing tasks to be supported at the same time. By a sensing task we mean any job that requires some amount of sensing resources to be accomplished such as localizing persons in need of help or detecting an event. Tasks might share the usage of a sensor, but more often compete to exclusively control it because of the limited number of sensors and overlapping needs with other tasks. Sensors are in fact scarce and in high demand. In such cases, it might not be possible to satisfy the requirements of all tasks using available sensors. Therefore, the fundamental question to answer is: “Which sensor should be allocated to which task?", which summarizes the Multi-Sensor Task Allocation (MSTA) problem. We focus on a particular MSTA instance where the environment does not provide enough information to plan for future allocations constraining us to perform instantaneous allocation. We look at this problem in both static setting, where all task requests from emergency responders arrive at once, and dynamic setting, where tasks arrive and depart over time. We provide novel solutions based on centralized and distributed approaches. We evaluate their performance using mainly simulations on randomly generated problem instances; moreover, for the dynamic setting, we consider also feasibility of deploying part of the distributed allocation system on user mobile devices. Our solutions scale well with different number of task requests and manage to improve the utility of the network, prioritizing the most important tasks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo