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

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

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

Theoretical Models for Video on Demand Services on Peer-to-Peer Networks

Peer-to-peer networks (P2Ps) are becoming more and more popular in video content delivery services, such as TV broadcast and Video on Demand (VoD), thanks to their scalability feature. Such characteristic allows for higher numbers of simultaneous users at a given server load and bandwidth with respect to alternative solutions. However, great efforts are still required to study and design reliable and QoS-guaranteed solutions. In this paper, within the scenario of P2P-based VoD services, we study the phenomenon of peer churns and propose four models of the peer behaviour to evaluate its impact on the system performance, which are based on the Gilbert-Elliot chain, the fluidic representation of the user behavior, and a queuing analysis of the system. The models are compared by computing the resources the system has to add on top of the P2P network to satisfy all the download requests. Simulations show important relationships between playback buffer length, peer request rate, peer average lifetime, and server upload rate

Deployment of Distributed Applications in Wireless Sensor Networks

The increase in computation and sensing capabilities as well as in battery duration of commercially available Wireless Sensors Network (WSN) nodes are making the paradigm of an horizontal ambient intelligence infrastructure feasible. Accordingly, the sensing, computing and communicating infrastructure is set with a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. In this scenario, we face the problem of setting up the desired application in complex scenarios with hundreds of nodes, which consists of identifying which actions should be performed by each of the nodes so as to satisfy the ambient needs while minimizing the application impact on the infrastructure battery lifetime. Accordingly, we approach the problem by considering every possible decomposition of the application’s sensing and computing operations into tasks to be assigned to each infrastructure component. The contribution of energy consumption due to the performance of each task is then considered to compute a cost function, allowing us to evaluate the viability of each deployment solution. Simulation results show that our framework results in considerable energy conservation with respect to sink-oriented or cluster-oriented deployment approaches, particularly for networks with high node densities, non-uniform energy consumption and initial energy, and complex actions