Implementation of an experimental platform for the social internet of things

The convergence of the Internet of Things (IoT) technologies with the social networking concepts has led to a new paradigm called the Social Internet of Things (SIoT), where the objects mimic the human behavior and create their own relationships based on the rules set by their owner. This is aimed at simplifying the complexity in handling the communications between billions of objects to the benefits of the humans. Whereas several IoT platforms are already available, the SIoT paradigm has represented only a field for pure research and simulations, until now. The aim of this paper is to present our implementation of a SIoT platform. We begin by analyzing the major IoT implementations, pointing out their common characteristics that could be re-used for our goal. We then discuss the major extensions we had to introduce on the existing platforms to introduce the functionalities of the SIoT. We also present the major functionalities of the proposed system: how to register a new social object to the platform, how the system manages the creation of new relationships, and how the devices create groups of members with similar characteristics. We conclude with the description of possible simple application scenarios

A Decentralized Lifetime Maximization Algorithm for Distributed Applications in Wireless Sensor Networks

We consider the scenario of a Wireless Sensor Networks (WSN) where the nodes are equipped with a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. We then address the problem of finding the optimal deployment of the target applications in terms of network lifetime. We approach the problem considering every possible decomposition of an application's sensing and computing operations into tasks to be assigned to each infrastructure component. The contribution of energy consumption due to the energy cost of each task is then considered into local cost functions in each node, allowing us to evaluate the viability of the deployment solution. The proposed algorithm is based on an iterative and asynchronous local optimization of the task allocations between neighboring nodes that increases the network lifetime. Simulation results show that our framework leads to considerable energy saving with respect to both sink-oriented and cluster-oriented deployment approaches, particularly for networks with high node densities and non-uniform energy consumption or initial battery charge

QoE-centric service delivery: A collaborative approach among OTTs and ISPs

The provisioning of the quality to end users is a major objective for the successful deployment of multimedia services over the Internet. It is more and more evident from past research and service deployments that such an objective often requires a collaboration among the different parties that are involved in the delivery of the service. This paper specifically focuses on the cooperation between the Over-The-Top (OTTs) and the Internet Service Providers (ISPs) and proposes a novel service delivery approach that is purely driven by the Quality of Experience (QoE) provided to the final common users. Initially, we identify the need of the collaboration among the OTTs and the ISPs where we not only highlight some of the enterprise level motivations (revenue generation) but also the technical aspects which require collaboration. Later, we provide a reference architecture with the required modules and vertical interfaces for the interaction among the OTTs and the ISPs. Then, we provide a collaboration model where we focus on the modeling of the revenue, whose maximization drives the collaboration. The revenue is considered to be dependent on the user churn, which in turn is affected by the QoE and is modeled using the Sigmoid function. We illustrate simulation results based on our proposed collaboration approach which highlight how the proposed strategy increases the revenue generation and QoE for the OTTs and the ISPs hence providing a ground for ISP to join the loop of revenue generation between OTTs and users

Spectral Classified Vector Quantization (SCVQ) for Multispectral Images

Multi- and hyper-spectral data pose severe problems in terms of storage capacity and transmission bandwidth. Although recommendable, compression techniques require efficient approaches to guarantee an adequate fidelity level. In particular, depending on the final destination of the data, it could be necessary to maximize several parameters, as for instance the visual quality of the rendered data, the correctness of their interpretation, or the performance of their classification. Based on the idea of Spectral Vector Quantization, the approach proposed in this paper aims at combining a compression and a classification methodology into a single scheme, in which visual distortion and classification accuracy can be balanced a- priori according to the requirements of the target application. Experimental results demonstrate that the proposed approach can be employed successfully in a wide range of application domains

Dynamic involvement of real world objects in the IoT: a consensus-based cooperation approach

A significant role in the Internet of Things (IoT) will be taken by mobile and low-cost unstable devices, which autonomously self-organize and introduce highly dynamic and heterogeneous scenarios for the deployment of distributed applications. This entails the devices to cooperate to dynamically find the suitable combination of their involvement so as to improve the system reliability while following the changes in their status. Focusing on the above scenario, we propose a distributed algorithm for resources allocation that is run by devices that can perform the same task required by the applications, allowing for a flexible and dynamic binding of the requested services with the physical IoT devices. It is based on a consensus approach, which maximizes the lifetime of groups of nodes involved and ensures the fulfillment of the requested Quality of Information (QoI) requirements. Experiments have been conducted with real devices, showing an improvement of device lifetime of more than 20%, with respect to a uniform distribution of tasks

A Cross-Layer Survey on Secure and Low-Latency Communications in Next-Generation IoT

The last years have been characterized by strong market exploitation of the Internet of Things (IoT) technologies in different application domains, such as Industry 4.0, smart cities, and eHealth. All the relevant solutions should properly address the security issues to ensure that sensor data and actuators are not under the control of malicious entities. Additionally, many applications should at the same time provide low-latency communications, as in the case for instance of remote control of industrial robots. Low latency and security are two of the most important challenges to be addressed for the successful deployment of IoT applications. These issues have been analyzed by several scientific papers and surveys that appeared in the last decade. However, few of them consider the two challenges jointly. Moreover, the security aspects are primarily investigated only in specific application domains or protocol levels and the latency issues are typically investigated only at low layers (e.g., physical, access). This paper addresses this shortcoming and provides a systematic review of state-of-the-art solutions for providing fast and secure IoT communications. Although the two requirements may appear to be in contrast to each other, we investigate possible integrated solutions that minimize device connection and service provisioning. We follow an approach where the proposals are reviewed by grouping them based on the reference architectural layer, i.e., access, network, and application layers. We also review the works that propose promising solutions that rely on the exploitation of the QUIC protocol at the higher levels of the protocol stack

Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems

Most Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases for an effective training. As these are not easily available, a good solution is to augment the databases with appropriate data augmentation (DA) techniques, which are typically based on either geometric transformation or oversampling augmentations (e.g., generative adversarial networks (GANs)). However, it is not always easy to understand which DA technique may be more convenient for FER systems because most state-of-the-art experiments use different settings which makes the impact of DA techniques not comparable. To advance in this respect, in this paper, we evaluate and compare the impact of using well-established DA techniques on the emotion recognition accuracy of a FER system based on the well-known VGG16 convolutional neural network (CNN). In particular, we consider both geometric transformations and GAN to increase the amount of training images. We performed cross-database evaluations: training with the "augmented" KDEF database and testing with two different databases (CK+ and ExpW). The best results were obtained combining horizontal reflection, translation and GAN, bringing an accuracy increase of approximately 30%. This outperforms alternative approaches, except for the one technique that could however rely on a quite bigger database

Controlling Media Player with Hands: A Transformer Approach and a Quality of Experience Assessment

In this article, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared with two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. We found that (i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; and that (ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high, with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the natural gesture interaction

Overview progetto IKNOS: gestione della qualità attraverso KPI in reti IP di nuova generazione multiservizio, multidominio e multivendor orientata alla standardizzazione

2008-06-27Sardegna Ricerche, Edificio 2, Località Piscinamanna 09010 Pula (CA) - ItaliaNGN'08: Workshop su Next generation networks: architetture e serviz