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Given the need to reduce water consumption in irrigation to face the challenge of guaranteeing the supply of this resource to the population in the coming decades, this work presents a fuzzy inference system based irrigation control system to determine water irrigation volume for a sweet pepper (capsicum annuum) crop. The system is composed of a Mamdani fuzzy control algorithm which receives data from soil moisture, air humidity and air temperature sensors from the crop field. Also, the rain forecast is gathered from a personal weather station located near to the crop field. Data gathered are used by the control algorithm to ensure that the soil moisture at the root zone is readily available to the plant. After 126 days from seeding, the results indicated that the system was able to reduced irrigation by 18% through controlling soil moisture levels at the plant root zone, saving water content on the irrigation process.

A key point to decrease water usage in agriculture is to employ precision agriculture by adopting technologies such as the Internet of Things (IoT). Many solutions using the IoT rely on the use of soil moisture sensors to gather data, in real-Time, from the plant's root zone. This study presents a linear calibration curve, obtained by the thermogravimetric method, for a capacitive soil moisture sensor for a silty clay soil sampled at the FEI University Center. The curve obtained has an adjusted R2 of 0.86 and a residual standard error of 2.8%. The soil sensor was tested in the field during the end of February revealing that the sensor is capable of indicating changes in soil moisture within the error obtained from the calibration procedure and that it can be used to indicate changes by rain or irrigation procedures.

Layered internet of things (IoT) architectures have been proposed over the last years as they facilitate understanding the roles of different networking, hardware, and software components of smart applications. These are inherently distributed, spanning from devices installed in the field up to a cloud datacenter and further to a user smartphone, passing by intermediary stages at different levels of fog computing infrastructure. However, IoT architectures provide almost no hints on where components should be deployed. IoT Software Platforms derived from the layered architectures are expected to adapt to scenarios with different characteristics, requirements, and constraints from stakeholders and applications. In such a complex environment, a one-size-fits-all approach does not adapt well to varying demands and may hinder the adoption of IoT Smart Applications. In this paper, we propose a 5-layer IoT Architecture and a 5-stage IoT Computing Continuum, as well as provide insights on the mapping of software components of the former into physical locations of the latter. Also, we conduct a performance analysis study with six configurations where components are deployed into different stages. Our results show that different deployment configurations of layered components into staged locations generate bottlenecks that affect system performance and scalability. Based on that, policies for static deployment and dynamic migration of layered components into staged locations can be identified.

As LoRaWAN has been increasingly used in the Internet of Things (IoT) smart agriculture, efficacious deployments of this technology need a clear understanding of its performance and scalability tradeoffs. This paper proposes a two-step methodology to evaluate the performance of LoRaWAN based on simulation for understanding the behavior of the air interface and measurement for understanding the behavior of the IoT Platform. We conducted a performance analysis study in a smart irrigation scenario, varying the distance from sensors to the gateway, the sensor density (number of sensors), and the LoRaWAN spreading factor. Our results show that the LoRa air interface poses the most stringent scalability limits, mainly related to the number of sensors actively transmitting from a farm parcel to the gateway. The IoT Platform adds some delay but does not notably interfere with the overall performance of the solution.

Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations-at-risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.

Most current scientific and industrial efforts in IoT are geared towards building integrated platforms to finally realize its potential in commercial scale applications. The IoT and Big Data contemporary context brings a number of challenges, such as providing quality assurance (defined by availability andveracity) for sensor data. Traditional signal processing approaches are no longer sufficient, requiring combined approaches in both architectural and analytical layers. This paper proposes a discussion on the adequate foundations of a newgeneral approach aimed at increasing robustness and antifragility of IoT-based smart applications. In addition, it shows results of preliminary experiments with real data in the context of precision irrigation using multivariate methods toidentify relevant situations, such as sensor failures and the mismatch of contextual sensor information due to different spatial granularities capture. Our results provide initial indications of the adequacy of the proposed framework.

Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.

The intensive use of technology in precision irrigation for agriculture is getting momentum in order to optimize the use of water, reduce the energy consumption and improve the quality of crops. Internet of Things (IoT) and other technologies are the natural choices for smart water management applications, and the SWAMP project is expected to prove the appropriateness of IoT in real settings with the deployment of on-site pilots. At the same time, the more intense the use of technology is, agriculture turns new security risks, which may affect both crop development and the commodities market. A security breach may irreversibly compromise a crop and data eavesdropping may compromise price and contracts exposing sensitive data such crop quality, development or management. This paper discusses security challenges and technologies for the application of IoT in agriculture and indicates that one of the most relevant challenges to be handled in SWAMP project is dealing with the multitude of behaviors from IoT application and what would be considered as normal and what would be considered as a threat.

This paper presents a roadmap for the transition from current gadget-centric digital services towards a gadget-free services environment called the Naked world. The main idea of the Naked world is that all the services which are currently provided by gadgets will be provided by the infrastructure, thus no gadgets will be needed to use any kind of digital services. When a user in the Naked world intends to use a service, the infrastructure senses the user, the nearby intelligent surrounding launches an interactive user interface, performs identification through biometric identities, provides the service, and then closes the session when the user finishes the job. Therefore, the Naked world comprises highly intelligent and context-aware interactive environments. The vision of the Naked world is an evolution towards a user-friendly and ubiquitously available digital services, which is naturally bounded by the technological advancement. Henceforth, this paper presents the essential technologies and functional requirements along with the current and forthcoming novel technological concepts and challenges for the realization of the Naked world.

In this paper we present fully automatic interpatient electrocardiogram (ECG) signal classification method using deep convolutional neural networks (CNN). ECG is simple and non-invasive way to measure the electric activity of the heart and it provides valuable information about the condition of the heart. It is widely utilized for detecting different abnormalities in heartbeat. Identifying and classification abnormalities is timeconsuming, because it often requires analyzing each heartbeat of the ECG recording. Therefore, automatic classification of the arrhythmias using machine-learning technologies can bring various benefits. In this paper, we are focusing on inter-patient arrhythmia classification, where separate patient data is used in training and test phase. This method is more realistic in clinical environment, where trained model needs to operate with patients, whose ECG data was not available during the training phase. Our proposed method gives 92% sensitivity, 97% positive predictivity and 23% false positive rate for normal heartbeats. For supraventricular ectopic beat, our approach gives 62% sensitivity, 56% positive predictivity and 2% false positive rate. For ventricular ectopic beat, our method gives 89% sensitivity, 51% positive predictivity and 6% false positive rate. These results from our fully automatic feature learning approach are on par with solutions that require manual feature engineering.

Semantics associates meaning with Internet of Things (IoT) data and facilitates the development of intelligent IoT applications and services. However, the big volume of the data generated by IoT devices and resource limitations of these devices have given rise to challenges for applying semantic technologies. In this article, we present Cloud and edge based IoT architectures for semantic reasoning. We report three experiments that demonstrate how edge computing can facilitate IoT systems in terms of data transfer and semantic reasoning. We also analyze how distributing reasoning tasks between the Cloud and edge devices affects system performance.

Future mobile devices will be based on heterogeneous multiprocessing platforms accommodating several stand-alone applications. The network-on-chip communication and device networking combine the design challenges of conventional distributed systems and resource constrained real-time embedded systems. Interoperable design space exploration for both the application and platform development is required. Application designer needs abstract platform models to rapidly check the feasibility of a new feature or application. Platform designer needs abstract application models for defining platform computation and communication capacities. We propose a layered UML application/workload and SystemC platform modelling approach that allow application and platform to be modelled at several levels of abstraction, which enables early performance evaluation of the resulting system. The overall approach has been experimented with a mobile video player case study, while different load extraction methods have been validated by applying them to MPEG-4 encoder, Quake2 3D game, and MP3 decoder case studies previously.

Increasing number of concurrent applications in future mobile devices will be based on parallel heterogeneous multiprocessor system-on-chip platforms using network-on-chip communication to achieve scalability. In this paper we describe a performance modeling and simulation approach to explore efficiently the application-platform solution/design space at system-level. The application behavior is abstracted to workload models that are mapped onto performance models of the execution platform for transaction level simulation. The approach provides separation of application and platform through service-oriented modeling. The experimentation of the approach in virtual network computing and mobile video player case studies is presented.

Integrated Internet of Things (IoT) platforms are needed for realizing IoT potential in commercial-scale applications. The main challenge is to provide solution flexibility to meet custom application needs. We developed an IoT-based platform for smart irrigation, with a flexible architecture to easily connect IoT and machine learning (ML) components to build application solutions. Our architecture enables multiple and customizable analytical approaches to precision irrigation, making room for the improvement of ML approaches. Impacts on different stakeholders can be anticipated, including IoT professionals, by facilitating system deployment, and farmers, by providing cost reduction and safer crop yields. Examples are given based on pilots in Europe and Brazil.

Irrigation for agriculture is the biggest consumer of freshwater in the world, which makes a case for the intensive use of technology to optimize the use of water, reduce the consumption of energy and improve the quality of crops. While the Internet of Things (IoT) and other associated technologies are the natural choice for smart water management applications, their appropriateness is still to be proven in real settings with the deployment of on-site pilots. Also, IoT-based application development platforms should be generic enough to be adapted to different crops, climates, and countries. The SWAMP project develops IoT based methods and approaches for smart water management in precision irrigation domain and pilots them in Italy, Spain, and Brazil. In this paper, we present the SWAMP view, architecture, pilots and the scenario-based development process adopted in the project.