The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

Introducing a new Workflow for Pig Posture Classification based on a combination of YOLO and EfficientNet

This paper introduces a pipeline for image-based pig posture classification by applying YOLOv5 for pig detection and EfficientNet for subsequent pig posture classification into 'lying' and 'notLying'. A high-quality dataset consisting of 5311 heterogeneous images from different sources with 78215 bounding box annotations was created. The bounding box annotations were then used to create a separate dataset for image classification, consisting of 9209 and 7855 images for each 'lying' and 'notLying'. The YOLOv5 model achieves an AP of 0.994 for pig detection, while EfficientNet achieves a precision of 0.93 for pig posture classification. Comparing the results of the proposed method with other approaches found in literature, it shows that significant improvements in terms of accuracy can be achieved by splitting the classification of pig posture into separate models. This research provides a foundation for the continued development of real-time monitoring and assistance systems in pig Precision Livestock Farming

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

PALANTIR: Zero-trust architecture for Managed Security Service Provider

The H2020 PALANTIR project aims at delivering a Security-as-a-Service solution to SMEs and microenterprises via the exploitation of containerised Network Functions. However, these functions are conceived by third-party developers and can also be deployed in untrustworthy virtualisation layers, depending on the subscribed delivery model. Therefore, they cannot be trusted and require a stringent monitoring to ensure their harmlessness, as well as adequate measures to remediate any nefarious activities. This paper justifies, details and evaluates a Zero-Trust architecture supporting PALANTIR’s solution. Specifically, PALANTIR periodically attests the service and infrastructure’s components for signs of compromise by implementing the Trusted Computing paradigm. Verification addresses the firmware, OS and software using UEFI measured boot and Linux Integrity Measurement Architecture, extended to support containerised application attestation. Mitigation actions are supervised by the Recovery Service and the Security Orchestrator based on OSM to, respectively, determine the adequate remediation actions from a recovery policy and enforce them down to the lower layers of the infrastructure through local authenticated enablers. We detail an implementation prototype serving a baseline for quantitative evaluation of our work

The Role of Web Services at Home

The increase in computational power and the networking abilities of home appliances are revolutionizing the way we interact with our homes. This trend is growing stronger and opening a number of technological challenges. From the point of view of distributed systems, there is a need to design architectures for enhancing the comfort and safety of the home, which deal with issues of heterogeneity, scalability and openness. By considering the evolution of domotic research and projects, we advocate a role for web services in the domestic network, and propose an infrastructure based on web services. As a case study, we present an implementation for monitoring the health of an elder adult using multiple sensors and clients

Securing Real-Time Internet-of-Things

Modern embedded and cyber-physical systems are ubiquitous. A large number of critical cyber-physical systems have real-time requirements (e.g., avionics, automobiles, power grids, manufacturing systems, industrial control systems, etc.). Recent developments and new functionality requires real-time embedded devices to be connected to the Internet. This gives rise to the real-time Internet-of-things (RT-IoT) that promises a better user experience through stronger connectivity and efficient use of next-generation embedded devices. However RT- IoT are also increasingly becoming targets for cyber-attacks which is exacerbated by this increased connectivity. This paper gives an introduction to RT-IoT systems, an outlook of current approaches and possible research challenges towards secure RT- IoT frameworks