Architecture framework of IoT-based food and farm systems: A multiple case study

The Internet of Things (IoT) is expected to be a real game changer in food and farming. However, an important challenge for large-scale uptake of IoT is to deal with the huge heterogeneity of this domain. This paper develops and applies an architecture framework for modelling IoT-based systems in the agriculture and food domain. The framework comprises a coherent set of architectural viewpoints and a guideline to use these viewpoints to model architectures of individual IoT-based systems. The framework is validated in a multiple case study of the European IoF2020 project, including different agricultural sub sectors, conventional and organic farming, early adopters and early majority farmers, and different supply chain roles. The framework provides a valuable help to model, in a timely, punctual and coherent way, the architecture of IoT-based systems of this diverse set of use cases. Moreover, it serves as a common language for aligning system architectures and enabling reuse of architectural knowledge among multiple autonomous IoT-based systems in agriculture and food

Business Process Model for IOT Based Systems Operations

The internet of things (IoT) is an innovative and advanced high-level IT development that provides the connection between a large network of devices equipped with numerous computing capabilities, actuation, and sensing with the help of internet connection, consequently providing multifarious novel services regarding smart systems. All around the globe the attractive big data analytics and IoT services are allowing initiatives regarding smart systems. Business processes are commonly executed inside the application systems where computers, objects of IoT as well as humans participate. However, for the system-supported processes, the use of IoT technology is still facing the problem of the absence of a standard system architecture that is essential to manage the coordination in a smart IoT environment. Business process management (BPM) is regarded as a substantial technique for designing, controlling, and improving the processes of a system. This article introduces a BPM modeling approach for IoT-based systems operation exploits IoT using BPM by adopting an IoT framework architecture and considering IoT data for interaction in a defined process model. The methodology has been carried out on top of current BPM modeling notions and system techniques for formal representations of the system and also to get through the challenges of collaboration and connection

IoT-based systems for soil nutrients assessment in horticulture

Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.info:eu-repo/semantics/publishedVersio

Smart, Autonomous and Reliable Internet of Things

AbstractThe dynamic rapidly changing and technology-rich digital environment enables the provision of added-value applications that exploit a multitude of devices contributing services and information. As the Internet of Things (IoT) techniques mature and become ubiquitous, emphasis is put upon approaches that allow things to become smarter, more reliable and more autonomous. In this paper we present challenges and enablers as technologies that will allow things to evolve and act in a more autonomous way, becoming more reliable and smarter. We describe decentralized management mechanisms targeting IoT-based systems in order to enable the exploitation of millions of devices, while we also present an architecture that allows things to learn based on others experiences. The proposed architectural approach also introduces situational knowledge acquisition and analysis techniques in order to make things aware of conditions and events affecting IoT-based systems behavior

Enabling Design of Middleware for Massive Scale IOT-based Systems

Recently, the Internet of Things (IoT) technology has rapidly advanced to the stage where it is feasible to discover, locate and identify various smart sensors and devices based on the context, situation, characteristics, and relevancy to query for their data or control actions. Taking things a step further when developing Large Scale Applications requires that two serious issues be overcome. The first issue is to find a solution for data sensing and collection from a massive number of various ubiquitous devices when converging these into the next generation networks. The second important issue is to deal with the “Big Data” that arrive from a very large number of sources. This research emphasizes the need for finding a solution for a large scale data aggregation and delivery. The paper introduces biomimetic design methods for data aggregation in the context of large scale IoT-based systems

Validation of a development methodology and tool for IoT-based systems through a case study for visually impaired people

In this article, we validate the Test-Driven Development Methodology for Internet of Things (IoT)-based Systems (TDDM4IoTS) and its companion tool, called Test-Driven Development Tool for IoT-based Systems (TDDT4IoTS). TDDM4IoTS consists of 11 stages, including activities ranging from system requirements gathering to system maintenance. To evaluate the effectiveness of TDDM4IoTS and TDDT4IoTS, in the last four academic years from 2019, System Engineering students have developed several IoT-based systems as part of their training, from the sixth semester (third academic year). Ñawi (phonetically, Gnawi), which is the case study presented herein, is one of them, and intends to assist visually impaired people to move through open environments. Ñawi consists of a device, a mobile application and a web application. The device interacts with the environment and issues alerts to the user whenever it recognizes obstacles in their path. The mobile application targets two user roles: assisted person and caregiver. Assisted people can use the device and log in into a server when they leave home, so that the mobile application identifies and notifies obstacles in their path. All the collected data is gathered into the server, so that caregivers receive notifications and can monitor the location of their assisted people at any place and time. The web application allows caregivers to query and view more extensive information (details of events, trajectories, etc.). TDDM4IoTS has been evaluated regarding both the roles of the project members and the development cycle stages. A survey was used to evaluate the methodology. Out of a total of 47 respondents, 30 had used TDDM4IoTS and 96.66% of them were very satisfied or satisfied, with nobody unsatisfied

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas