Synthetic Data Generation for the Internet of Things

The concept of Internet of Things (IoT) is rapidly moving from a vision to being pervasive in our everyday lives. This can be observed in the integration of connected sensors from a multitude of devices such as mobile phones, healthcare equipment, and vehicles. There is a need for the development of infrastructure support and analytical tools to handle IoT data, which are naturally big and complex. But, research on IoT data can be constrained by concerns about the release of privately owned data. In this paper, we present the design and implementation results of a synthetic IoT data generation framework. The framework enables research on synthetic data that exhibit the complex characteristics of original data without compromising proprietary information and personal privacy

Privacy, Risk, Anonymization and Data Sharing in the Internet of Health Things

This paper explores a specific risk-mitigation strategy to reduce privacy concerns in the Internet of Health Things (IoHT): data anonymization. It contributes to the current academic debate surrounding the role of anonymization in the IoHT by evaluating how data controllers can balance privacy risks against the quality of output data and select the appropriate privacy model that achieves the aims underlying the concept of Privacy by Design. It sets forth several approaches for identifying the risk of re-identification in the IoHT as well as explores the potential for synthetic data generation to be used as an alternative method to anonymization for data sharing

STEM materials: a new frontier for an intelligent sustainable world

Materials are addressed as possible enablers for solutions to many global societal challenges. A foresight exercise has been conducted to identify research paths to design, with a new approach, a generation of materials which can provide multi-functionalities. These material systems have been named ???stem???, in analogy to living cells where a base of primitive units can be designed and assembled for self-reacting to external inputs. These materials will embed a concept of ???internet in things???, where their processing capacity will enable the systems to interact with the environment and express diverse functionalities. Stem materials do not exist yet, but many clues from diferent theoretical and experimental results suggest they can be developed, and because living organisms exist. This article aims at launching this new approach and promoting the structuring of a multi-disciplinary community to fll the research gaps

VIoLET: A Large-scale Virtual Environment for Internet of Things

IoT deployments have been growing manifold, encompassing sensors, networks, edge, fog and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose VIoLET, a virtual environment for defining and launching large-scale IoT deployments within cloud VMs. It offers a declarative model to specify container-based compute resources that match the performance of the native edge, fog and cloud devices using Docker. These can be inter-connected by complex topologies on which private/public networks, and bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation on these devices as well. We validate VIoLET for deployments with > 400 devices and > 1500 device-cores, and show that the virtual IoT environment closely matches the expected compute and network performance at modest costs. This fills an important gap between IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31, 2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for presentation at the Plenary Session of the conferenc

Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable