The Internet of Things (IoT) is a global network of intelligent interconnected objects, enabling remote recognition, detection, and control. Through virtualization, these devices exchange knowledge in the Cloud, bridging physical and virtual worlds. However, the rapid growth in connected objects and data production necessitates innovative system designs. While virtualization offers scalability and flexibility, it proves insufficient alone. Recent studies recommend integrating social network concepts into IoT, forming the Social Internet of Things (SIoT). SIoT, mirroring human social networks, enhances smart devices with relational capabilities, addressing challenges in managing the surge in connected devices. This paper introduces the “Virtual User” concept-an autonomous entity streamlining user-device interactions in the digital realm. The research proposes, elucidates, and evaluates a Cloud infrastructure integrating the Virtual User into the SIoT paradigm. This novel approach offers a modular, extendable, and secure communication platform, marking the first Cloud solution tailored for the Virtual User concept, leveraging SIoT virtualization through containerization and automated service setup processes.

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface. This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

In the last years, the smart city (SC) paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new SC based on the Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real-time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city.

This article analyzes how streams of people move around a city. Wi-Fi sniffing techniques and camera systems are used for classifying vehicles, people, bicycles and scooters. The system is able to detect the presence of people by sniffing the mac address of the smartphone’s Wi-Fi radio interface and with the use of cameras to monitor the presence of people, vehicles, and other means of transport, classifying them through the contribution of neural networks. The proposed solution has a high level of reliability overcoming the limits of a single technological system that offers imprecise and inaccurate monitoring.

The idea of equipping smart devices with the capacity to form social connections with their peers is gaining popularity. Indeed, it is a feature with a lot of promise for fostering device collaboration, speeding up and improving service discovery, and evaluating the reliability of devices by utilizing appropriate features based on peer evaluations in social networks. While numerous architectural solutions have been offered along with various concepts for creating the devices’ social network, the question of how to build and sustain the devices’ social connections has been neglected. This paper proposes a possible implementation of a Social Virtual Object as a constituent entity of the Social Internet of Things (SIoT) based on the concept of virtualization. In the experimental evaluation, the performances in the management of the relationships that originate from the proximity among objects and that represent the greatest difficulties of realization are analyzed. The field trials show the effectiveness of the proposed system in creating relationships and highlight some problems that should be addressed in the formalization of the social relationship in the SIoT.

The climatic conditions of places with a high tourist vocation greatly affect the flow of visitors. In the coastal context, measurements of environmental values such as sea water temperature, wave period and height or direction of currents, are carried out using buoys of considerable size and at a great distance from the coast. This results in a lack of information to satisfy the preferences of tourists and guarantee adequate safety in certain sea conditions. In this paper, we show the implementation of a small buoy that can be positioned near the beaches and that, through a Social Internet of Things platform, can share information with tourists. The first results of a real installation are also shown and what challenges need to be addressed for an efficient use of such a system.

Coast erosion is a process that degrades a coastal profile and is mainly due to natural factors (e.g., related to climate change) and overcrowding (e.g., urbanization and massive tourism). While the first cause can be considered as a slow process, the growing presence of humans is leading to rapid aging of coasts. The Mediterranean Sea authorities are focusing on the necessity for a systematic and comprehensive approach to the management of littoral areas. In this context, Italy is searching for a promising solution to safeguard coasts but, at the same time, to manage in an intelligent and “green” way the big amount of tourists. Research communities all over the world indicated the Internet of Things (IoT) as a valid technology to develop solutions in order to try solving or mitigating the coastal erosion problem. IoT-based techniques allow to manage heterogeneous and massive data for real-time monitoring and decision making and can be used for coastal environment and crowd level monitoring. This article presents a monitoring system based on the Social IoT (SIoT), a new paradigm that defines a network where every node is an object capable of establishing the social relationships with other things in an autonomous way according to specific rules. Thanks to social relationships, all involved devices in the monitoring system (i.e., sensors, cameras, and smartphones) are able to collect and exchange information. The proposed system, developed and installed in Cagliari (Italy), is able to evaluate the occupational state of a beach considering environmental and crowding data collected by devices and feedback sent by users.

Large industrial environments are inherently complex systems to be monitored and controlled, given the presence of vehicles, people and industrial plants. In a context dominated by strict regulations for workplace safety, systems for monitoring and preventing workplace risks can have a particular importance. This paper describes the work in progress on designing and implementing an RFID-based sensor network for workplace safety management. The proposed system is based on Body Area Networks and passive RFID tags and sensors. The goal of the proposed system is to offer a detailed, constantly updated overview of an industrial environment in terms of workplace safety.

Large industrial environments and specifically cargo terminals are inherently very complex systems to be monitored and controlled, given the presence of vessels, vehicles, people and industrial or commercial plants. In this context, dominated by strict regulations for workplace safety, a centralized control system for monitoring, reporting and preventing workplace risks can have a particular importance. This work describes the concepts of a real-time control system for acquiring and processing information for the continuous monitoring of workplace safety conditions. In the proposed system, we make use of specialized wearable sensor networks and specific aggregation methods, CCTV cameras combined with pattern recognition algorithms and data networks. The goal of the control system is to offer a detailed, constantly updated overview of the port's situation in terms of workplace safety.

We propose a source-rate control scheme for streaming video over a wireless channel. The scheme is designed to maximize the quality of the decoded video as perceived at the user-side by resorting on a reduced-reference video-quality estimation approach. The advantage is that the measured video quality, which drives the rate-control algorithm, is obtained after channel errors and error concealment. The rate control algorithm works adjusting the rate on a per-window basis to compensate low-throughput periods with high-throughput periods so as to avoid the “saw” effect that is typically observed in frame-based rate control.

Image data compressed with JPEG XR when transmitted over error-prone channels is severely distorted. In fact, due to the adaptive coding strategies used by the codec, even a single bit error causes a mismatch in the alignment of the reading position from the bitstream, leading to completely different images at the decoder side. We propose an extension to the JPEG XR algorithm, consisting in an error recovery process enabling the decoder to realign itself to the right bitstream position and to correctly decode the most part of the image. We have performed several experiments using different encoder parameter and different error probabilities while image distortion is measured by PSNR objective metric. The simplicity of the proposed algorithm adds very little computational overhead and seems very promising as confirmed by objective image quality results in experimental tests.

This paper illustrates the Digital Mobile Radio (DMR) technology for mission critical applications, and evaluates its performance against other professional mobile radio (PMR) standards, with a special focus on TETRA. We also describe the design of a practical DMR network, i.e. the radio system for the health emergency service in Sardinia (Italy), and we illustrate the advantages of migrating from analog to digital technology. Finally, we discuss the capabilities of DMR technology to provide the best solution for a wide regional emergency network with cost saving constraints.

In this paper we deal with multifractal theory and its application to image analysis. Based on multifractal theory fundamentals, we illustrated an estimation algorithm for singularity spectrum that fits the case of digital images, that is a discrete set of points resulting from sampling a continuous function. The singularity spectrum is defined, and its interesting properties are presented and discussed, together with a novel estimation technique. Such a method is based on the concept of box-dimension and box-counting, but innovations have been introduced. Finally, some experimental results are presented and discussed.

A simple image scrambling method for image-based authentication (IBA) is proposed and evaluated. The devised scheme may be embedded into any wavelet-based IBA framework to provide mutual authentication with challenge-response architecture. The severe constraints on security, data transmission capability and user friendliness are addressed by the complete system, especially in the wireless environment. Data and application scalability is provided through the JPEG2000 standard and JPIP framework

The paper aims on the study of a system for user authentication based on the use of JPEG2000 images as passwords. The proposed method is thought to solve the traditional problems related to the authentication process in the Internet environment by exploiting the human brain's remarkable ability in image recognition. The architecture proposed is user-friendly, secure and cost effective and it can be used both in heterogeneous network and with different devices. The whole system is described in M.T. Carta et al., (2006), this paper compares the security level of this image based authentication system with the traditional authentication methods. The image password demonstrates to be more secure than the common alphanumeric password and at the same time more easy-to-use.

This paper presents an innovative framework for user authentication based on images. Common user authentication based on passwords has the main drawback of the human difficulty in recalling them. Images are instead easier to remember than passwords. Moreover, modern compression and transmission techniques make image exchange between different devices (e.g. mobile phones, personal digital assistants, laptops, and workstations) in heterogeneous networks practically feasible. In the proposed approach, images are coded using the emerging JPEG2000 standard and taking advantage of many of its features (e.g. image scalability, embedded bitstream, image tiling, and interactivity protocol). The described image based authentication is more secure than the common approach based on passwords.

This paper presents a novel no-reference image blockiness index which exploits the properties of the Sobel operator. The blockiness index is a combination of two measures. The first quantifies the luminance variation of block boundaries pixels. The second quantifies the luminance variation of remaining pixels. Experimental results illustrating the performance of the proposed method show improvements over similar recently published methods.

In line with the recent call for technology on Image Based Authentication (IBA) in JPEG committee [1], we present a novel graphical password design in this paper. It rests on the human cognitive ability of association-based memorization to make the authentication more user-friendly, comparing with traditional textual password. Based on the principle of zero-knowledge proof protocol, we further improve our primary design to overcome the shoulder-surfing attack issue without adding any extra complexity into the authentication procedure. System performance analysis and comparisons are presented to support our proposals.

A novel framework for image-based authentication (IBA) is proposed and evaluated. The proposed system relies on data and application scalability in order to optimize its performance in several environments, from wired applications to mobile authentication. Data scalability is exploited through the JPEG2000 standard and JPIP framework. The wireless environment, in particular, imposes severe constraints on data transmission capability and user friendliness, which are satisfied by the devised system

A new coding scheme for lossy to lossless compression of volumetric data using a three-dimensional packet wavelet transform is presented. The need of a unitary transform via the integer lifting scheme and the performance comparison of different integer filter kernels is discussed. A state-of-the-art coder, set partitioning in hierarchical trees (SPIHT), is considered and modified in order to adapt its tree-structure to the 3D packet wavelet structure defined. The algorithm is extensively tested and shows very good performance, both for lossy and lossless coding.

In this paper, we address the problem of concealing high-frequencies errors at the lower decomposition levels in JPEG2000, by proposing a new approach based on the theory of projections onto convex sets. The error effects are masked by iteratively applying two important procedures: low-pass (LP) filtering in the spatial domain and restoration of the uncorrupted wavelet coefficients in the transform domain. It has been observed that a uniform LP filtering brought to some undesired side-effects that negatively compensated the advantages. This problem has been overcome by applying an adaptive solution exploiting an edge map to choose the optimal filter mask dimension. Simulation results demonstrated the efficiency of the proposed approach.

In this paper, the problem of bit-errors recovery in JPEG2000 images transmission is addressed, with particular attention to errors in the high-frequency components. In the HL and LH sub-bands, differently from the LL band, interpolation based recovering results tend to be often ineffective when more than a few adjacent wavelet coefficients are missing. The solution proposed is then to apply a wavelet patch repetition procedure by predicting the similitude between the contour structure in the damaged area and its surroundings. To this aim, the correlation in the spatial structure, that is the contour information, existing between different sub-bands has been analyzed and exploited. Accordingly, the patch used to conceal the corrupted region is that obtained by minimizing a correlation measure with the spatial structure extracted from an adjacent not corrupted subband. Objective and subjective improvements have been obtained.

This paper presents a new technique for video segmentation and tracking. As most of segmentation techniques it consists on an initial model generation process with a subsequent object tracking phase. The model generation is accomplished by means of a combination of temporal and spatial transitions detection approaches. The novelty of the method is that these approaches are performed block-by-block. This has the advantages to reduce problems relevant to the object connectivity and to drastically decrease the algorithm computational complexity respect to a pixel-by-pixel processing procedure. According to the proposed strategy, edged blocks are firstly extracted in an active region selected by the user. From these, the subset of blocks that represents the object contour is selected by minimizing a cost function that exploits a multiple edged block feature vector: motion, smoothness, continuity, strongness and position. The tracking task is then performed by estimating the model blocks motion. Experiments are presented that show comparable results accuracy respect to existing segmentation techniques while requiring a reduced computational complexity.

The paper presents a comparison of the principal lossy compression algorithms, vector quantization (VQ), JPEG and wavelets (WV) posterior KLT applied to multispectral remotely sensed images and evaluated by the classification algorithm K-NN (K-nearest neighbor). The main goal of the compression of remotely sensed images is a reduction of the huge requirements for downlink and storage. The Karhunen Loeve transform first removes the interband correlation to produce the principal components of the image which are then compressed by the principal algorithms. The quality evaluation was done by a supervised classification with the well known algorithm K-NN for remote sensing applications and the MSE for visual aspects. The obtained results of these accurate and particular analysis of the current compression techniques are quite surprisingly compared to other previous works.

This paper presents a new strategy that exploits artificial neural networks (ANNs) for a direct selection of edge points from an image. First, a spatial filtering for edge enhancement (the Canny filter) is used to obtain a set of candidate edge points which turn out to be the local maxima of the filtered image (MPS). A preliminary coarse selection of these points that exploits neighborhood information is performed to produce an extended pseudo-edges set (PES). Then, a features vector is extracted from the PES and is used by a neural classifier to decide whether or not a point belongs to the target edge set (TES).

This paper presents a novel predictive coding scheme for image-data compression by vector quantization (VQ). On the basis of a prediction, further compression is achieved by using a dynamic codebook-reordering strategy that allows a more efficient Huffman encoding of vector addresses. The proposed method is lossless, for it increases the compression performances of a baseline vector quantization scheme, without causing any further image degradation. Results are presented and a comparison with Cache-VQ is made.

The paper addresses the analysis of singular distributions defined on a fractal support, called fractal measures. In general, a fractal measure has an infinite number of singularities of infinitely many types. The term multifractals expresses the fact that points, corresponding to a given type of singularity, typically form a fractal subset whose dimensions depend on the type of singularity. The theory of the q-th order generalized fractal dimensions supplies a tool for the characterization of such multifractal measures. This theory results from an extension of the fractal dimension to different-order statistics. The paper exploits such concepts in order to face the problem of texture recognition. In particular the fractal measure taken into account is the 2D distribution of the optical mass of an image; some theoretical aspects related to this problem are addressed. Results on real images are presented and discussed.

This paper presents a novel multiresolution wavelet-based algorithm for filtering SAR images in order to remove speckle noise. The basic idea is to apply to the wavelet coefficients a size-decreasing half-interpolated median filter. The size of the median filter is adapted to the noise energy reduction between the image pyramid levels and different filter shapes are used in each wavelet subbands according to the dominant frequencies. Experimental results showed, that the proposed algorithm results in significant noise removal while the edges are preserved in the images.

The paper focuses on a new method for block based disparity estimation (BBDE) that is specially suited for real time coding of stereo sequences. The estimation is performed by exploiting a preliminary disparity field, obtained from the coarsest level of a multiresolution pyramid, and its successive refinements in the finer ones. A strong correction strategy for wrong estimates has been implemented by using an appropriate propagation of disparity vectors from one level to the next one and an adaptive search range. Finally, a variable-resolution disparity field is achieved stopping the propagation at an intermediate level of resolution, if a vector allows a good reconstruction quality of its block.

The paper presents an adaptive scheme for image-data compression. It is a region-based approach that suitably integrates two different approaches to image coding, vector quantization (VQ) and polynomial approximation (PA). The scheme is adaptive from the point of view of the human observer: the perceptually most significant areas are those near edges or details. In smoothed areas, PA can be used with notable results, but there VQ must be employed to ensure high fidelity. The two techniques exhibit a complementarity in both advantages and drawbacks. PA is not efficient in compressing high-frequency areas, but yields the best results when applied to highly correlated data. VQ is unable to reach high-compression ratios because of its low adaptability, but is quite suitable for compressing uncorrelated data. The means to achieve the integration of the two techniques is a control image containing information about edge and texture locations. In the paper, edge encoding and restoration are also addressed, which are closely related to the proposed hybrid scheme; block prediction is also utilized to further reduce the residual redundancy between VQ blocks. The exploitation of the best features of both approaches results in high compression factors, and in perceivable good quality. In particular, bit rates range from 0.15 to 0.07 bpp. Main applications of this compression scheme are in the areas of very-low bit rate image transmission and image archiving.>

A novel predictive coding scheme for VQ is presented, called dynamic codebook reordering VQ (DCRVQ). Residual correlations between neighboring codevectors are exploited by a nonlinear prediction, that is a neural one. As a matter of fact, on the basis of the previously decoded codevectors, a multilayer neural network makes a prediction, and this result is used to reorganize the codebook in a dynamic way. This allows for efficient Huffman compression of codevector addresses after reordering.>

The paper deals with the theory of qth-order fractal dimensions and its application to texture analysis. In particular, a novel algorithm for estimating such dimensions is proposed and its use in digital-image processing is described. Results on classical Brodatz textures are reported.>

A novel approach to video coding at very low bit rates is presented, which differs significantly from most of previous approaches, as it uses a spline-like interpolation scheme in a spatiotemporal domain. This operator is applied to a non-uniform 3D grid (built on sets of consecutive frames) so as to allocate the information adaptively. The proposed method allows a full exploitation of intra/inter-frame correlations and a good objective and visual quality of the reconstructed sequences.>

A new strategy for video coding at very low bit rates is proposed in the paper, where an image sequence is encoded in a two-source way. The main difference from state-of-the-art approaches lies in the use of a 3D spatio-temporal interpolation, based on a non-uniform grid (frames are processed in overlapped blocks), in order to allocate the information in an adaptive way. Experimental results show a good fidelity of the decoded sequences, without blocking effect.>

A data compression technique based on neural networks is presented. The schema consists of multiple multilayer perceptron networks, which produce a transformation of the original image with a reduced redundancy. A perceptron with a hidden layer is used; the input and output layers have the same number of nodes, while in the middle the number is reduced, thus producing a data compression of the original information. The transformation is carried out by the neural networks in an adaptive way. A split segmentation, based on spatial activities of regions, is applied to the original image in order to locate uniform blocks. A higher ratio between the input and the hidden nodes is used with large blocks and a lower one with smaller blocks; details are then retained in a good way. Major advantages of the proposed approach lie in its good performance, even with images outside the training set.>

A system for the processing and interpretation of underwater images acquired with an acoustic sensor is presented. As a first objective, the system aims to recognize simple objects with known characteristics located at different positions so as to create a scenario. The proposed recognition system exhibits the features of a knowledge-based system, as it is based on ideal models (provided by an expert) of the possible objects present in the scene, and the matching of such models with the real objects detected by processing data coming from the acquisition system. The numerical data provided by different types of sensors are first processed by some low-level modules. The high-level modules operate a matching of the objects detected with the models, according to criteria of maximum fuzzy reliability. Results are presented and discussed.>

An approach to natural surface analysis and characterization that is based on multifractals is presented. Such an approach presents many interesting peculiarities in the extraction of textural features from an image. A new method to compute such features is suggested, together with their use in the image segmentation process. Comparative results on SAR (synthetic aperture radar) images, by applying a data-fusion segmentation module, are presented and discussed.>

The authors discuss with the development of a coding system able to reach high compressions of picture data, without notable distortion, by integrating vector quantization and polynomial approximation. The whole process is driven by the latter, while the former is used in textured areas only. The polynomial approximation module is described in depth, together with its integration with the other module. Results obtained on standard images are presented and discussed.>

An advanced vector quantizer for coding true-color images is presented. The starting point is the classical algorithm proposed by Y. Linde, A. Buzo, and A.M. Gray (LBG, 1980). On the basis of the LBG algorithm, a neural net able to furnish a starting codebook quite close to the optimal one is developed. As several vectors are infrequently used, a merge-and-split process has been performed to merge such vectors into adjacent ones, thus allowing free space to be reutilized to redistribute the most numerous partitions. Another aspect to be exploited is the existence of statistical redundancies between neighboring blocks. To sharply reduce the bit rate, an RGB predictor based on some transition matrices is developed. The basic idea of the development of an RGB predictor is provided by the address vector quantization (AVQ) technique. Results obtained by processing standard images are discussed.>

A knowledge-based vision system called DOORS (Distributed Object-Oriented Recognition System), for obstacle detection and tracking, is presented. It integrates multisensory information sources by adaptively selecting appropriate fusion strategies at the abstraction levels of both physical and virtual sensors. The basic methodology adopted for data representation and processing control has several points in common with object-oriented programming techniques and with blackboard approaches. Some preliminary results are presented for the case of an obstacle moving on a country-road scene.>