Goal-driven context-sensitive production processes : a case study using BPMN

The Fourth Industrial Revolution, also known as Industry 4.0 or Industrial Internet, predicts that Smart Factories driven by Internet of Things (IoT) and Cyber-Physical Systems, will reinvent the traditional manufacturing industry into a digitalized, a context-aware, and an automated manufacturing that will flourish with contemporary Information and Communication Technology (ICT). As the IoT are being deployed across production cites of the manufacturing companies, the need of decision making inside a business process based upon the received contextual data such as employee availability, machine status, etc. from the execution environment has transpired. Production processes need to be updated and optimized frequently to stay competitive in the market. Context-sensitive Adaptive Production Processes is an adept concept that illustrates how a business process can be context-sensitive keeping itself aligned with the abstract organizational goals. The notion of Context-sensitive Adaptive Production Processes leads us to Context-sensitive Execution Step (CES), a logical construct, that encompasses multiple alternative processes, albeit the best-fitting alternative can only be selected, optimized, and executed in runtime. Realization of the context-sensitive business processes requires a model-driven approach. Being Business Process Model and Notation (BPMN) the de-facto standard for business processes modeling, business experts of manufacturing companies can use custom CES construct of BPMN to model and execute context-sensitive business processes in a model-driven approach. This case study is based upon a scenario where there exists multiple alternatives to achieve the same goal in production, nevertheless all the alternatives are not suitable at a certain point of time as changes in business objectives and execution environment makes adaption tougher. Properties of intelligent production processes are different from traditional processes. Such properties along with the scrutinized properties of standard BPMN facilitates modeling CES integrated processes in BPMN. From the requirements inferred from these properties, standard BPMN is extended with extensions such that context-sensitive business processes can be modeled and executed seamlessly. Developed extensions include a new type of process construct and a new type of process definition that are technology agnostic. Thus, CES approach provides a comprehensive solution that makes production processes contextsensitive as well as goal-driven in unison

Eficiência energética em redes de sensores sem fios: medição adaptativa num sistema de rega inteligente usando o CupCarbon

Dissertação de mestrado integrado em Informatics EngineeringToday, there are many cities that offer to citizens smart solutions to make their daily lives easier so that the available resources can be better managed and the global quality of life improved. These solutions generally rely on a variety of Wireless Sensor Networks (WSN), which are applied in a wide range of scenarios. Most of these solutions work without human intervention, therefore, there has been a lot of interest in increasing the longevity of these sensor networks. In this context, the main purpose of this work is to study and optimize an adaptive, energy-aware sensing algorithm for WSNs, e-LiteSense [11], wich is an algorithm capable of auto-regulate how data is sensed, adjusting it to each applicational scenario. This work, resorts to a simulation scenario representing a case in real life, namely, an Intelligent Irrigation system. In this study, CupCarbon is used as a simulation tool to implement WSN-based system and the e-LiteSense algorithm. The aim is to adapt the number of measurement events of environmental parameters so that the energy consumption of the different nodes of the network can be reduced while maintaining the correct evaluation of the measurement data and increasing the lifetime of the sensor network. The versatility of the algorithm in relation to its effectiveness and ability to self-configure in different types of sensing scenarios is also evaluated.Atualmente, são várias as cidades que disponibilizam aos seus cidadãos soluções inteligentes para facilitar o seu dia a dia, de maneira a haver uma melhor gestão dos recursos existentes e uma melhoria da qualidade de vida proporcionada aos seus habitantes. Muitas destas soluções recorrem geralmente a uma série de redes de sensores sem fios (RSSF), sendo que estas são aplicadas a uma grande variedade de cenários. A maioria destes soluções funcionam sem intervenção humana, havendo assim cada vez mais interesse em aumentar a longevidade destas redes de sensores. Neste contexto, o principal objetivo deste trabalho é estudar e otimizar uma solução de sensing adaptativo e de eficiência energética para RSSF's, o e-LiteSense [11]. Este algoritmo é capaz de regular automaticamente a forma como os dados são deteta-dos, adaptando-se a cada cenário aplicacional. Este trabalho recorre a um cenário de simulação que representa um caso da vida real, nomeadamente, um sistema de Rega Inteligente. Neste estudo, a ferramenta de simulação CupCarbon é usada para implementar esse sistema, baseado em RSSF, e o algoritmo e-LiteSense. O objetivo é adaptar o número de eventos de medição de parâmetros ambientais para que o consumo energético dos diferentes nós da rede possa ser reduzido, mantendo a avaliação correta dos dados de medição e aumentando a vida útil da rede de sensores. A versatilidade do algoritmo relativamente à sua eficácia e capacidade de auto-configuração em diferentes tipos de sensing cenários também será avaliada

Stochastic Optimization and Machine Learning Modeling for Wireless Networking

In the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, including multiple input multiple output architectures or software defined networking: as an example, the next generation of mobile communication, known as 5G, is expected to provide data rates of tens of megabits per second for tens of thousands of users and only 1 ms latency. In order to achieve such demanding performance, these systems need to effectively model the considerable level of uncertainty related to fading transmission channels, interference, or the presence of noise in the data. In this thesis, we will present how different approaches can be adopted to model these kinds of scenarios, focusing on wireless networking applications. In particular, the first part of this work will show how stochastic optimization models can be exploited to design energy management policies for wireless sensor networks. Traditionally, transmission policies are designed to reduce the total amount of energy drawn from the batteries of the devices; here, we consider energy harvesting wireless sensor networks, in which each device is able to scavenge energy from the environment and charge its battery with it. In this case, the goal of the optimal transmission policies is to efficiently manage the energy harvested from the environment, avoiding both energy outage (i.e., no residual energy in a battery) and energy overflow (i.e., the impossibility to store scavenged energy when the battery is already full). In the second part of this work, we will explore the adoption of machine learning techniques to tackle a number of common wireless networking problems. These algorithms are able to learn from and make predictions on data, avoiding the need to follow limited static program instructions: models are built from sample inputs, thus allowing for data-driven predictions and decisions. In particular, we will first design an on-the-fly prediction algorithm for the expected time of arrival related to WiFi transmissions. This predictor only exploits those network parameters available at each receiving node and does not require additional knowledge from the transmitter, hence it can be deployed without modifying existing standard transmission protocols. Secondly, we will investigate the usage of particular neural network instances known as autoencoders for the compression of biosignals, such as electrocardiography and photo plethysmographic sequences. A lightweight lossy compressor will be designed, able to be deployed in wearable battery-equipped devices with limited computational power. Thirdly, we will propose a predictor for the long-term channel gain in a wireless network. Differently from other works in the literature, such predictor will only exploit past channel samples, without resorting to additional information such as GPS data. An accurate estimation of this gain would enable to, e.g., efficiently allocate resources and foretell future handover procedures. Finally, although not strictly related to wireless networking scenarios, we will show how deep learning techniques can be applied to the field of autonomous driving. This final section will deal with state-of-the-art machine learning solutions, proving how these techniques are able to considerably overcome the performance given by traditional approaches

Enhancement of precise underwater object localization

Underwater communication applications extensively use localization services for object identification. Because of their significant impact on ocean exploration and monitoring, underwater wireless sensor networks (UWSN) are becoming increasingly popular, and acoustic communications have largely overtaken radio frequency (RF) broadcasts as the dominant means of communication. The two localization methods that are most frequently employed are those that estimate the angle of arrival (AOA) and the time difference of arrival (TDoA). The military and civilian sectors rely heavily on UWSN for object identification in the underwater environment. As a result, there is a need in UWSN for an accurate localization technique that accounts for dynamic nature of the underwater environment. Time and position data are the two key parameters to accurately define the position of an object. Moreover, due to climate change there is now a need to constrain energy consumption by UWSN to limit carbon emission to meet net-zero target by 2050. To meet these challenges, we have developed an efficient localization algorithm for determining an object position based on the angle and distance of arrival of beacon signals. We have considered the factors like sensor nodes not being in time sync with each other and the fact that the speed of sound varies in water. Our simulation results show that the proposed approach can achieve great localization accuracy while accounting for temporal synchronization inaccuracies. When compared to existing localization approaches, the mean estimation error (MEE) and energy consumption figures, the proposed approach outperforms them. The MEEs is shown to vary between 84.2154m and 93.8275m for four trials, 61.2256m and 92.7956m for eight trials, and 42.6584m and 119.5228m for twelve trials. Comparatively, the distance-based measurements show higher accuracy than the angle-based measurements