Physical objects are getting connected to the Internet at an exceptional rate, making the idea of the Internet of Things (IoT) a reality. The IoT ecosystem is evident everywhere in the form of smart homes, health care systems, wearables, connected vehicles, and industries. This has given rise to risks associated with the privacy and security of systems. Security issues and cyber attacks on IoT devices may potentially hinder the growth of IoT products due to deficiencies in the architecture. To counter these issues, we need to implement privacy and security right from the building blocks of IoT. The IoT architecture has evolved over the years, improving the stack of architecture with new solutions such as scalability, management, interoperability, and extensibility. This emphasizes the need to standardize and organize the IoT reference architecture in federation with privacy and security concerns. In this study, we examine and analyze 12 existing IoT reference architectures to identify their shortcomings on the basis of the requirements addressed in the standards. We propose an architecture, the privacy-federated IoT security reference architecture (PF-IoT-SRA), which interprets all the involved privacy metrics and counters major threats and attacks in the IoT communication environment. It is a step toward the standardization of the domain architecture. We effectively validate our proposed reference architecture using the architecture trade-off analysis method (ATAM), an industry-recognized scenario-based approach.

International audience

Average mobile phone price of a region's residents has not been explored as a proxy variable for socio-economic indicators in development literature. Using handset models from mobile data logs of a telecom operator, we calculate average phone prices of subscribers at each cell tower, and then find their relationship with indicators from preexisting census data. Our results show that average phone price correlates well (absolute Pearson's correlation coefficient > 0.35 and p-value < 0.01) with indicators of accessibility to services, infrastructure, hygiene and communication, but does not correlate well with literacy variables.

In this paper we show that typical nonrigid structure can often be approximated well as locally rigid sub-structures in time and space. Specifically, we assume that: 1) the structure can be approximated as rigid in a short local time window and 2) some point- pairs stay relatively rigid in space, maintaining a fixed distance between them during the sequence. First, we use the triangulation constraints in rigid SfM over a sliding time window to get an initial estimate of the nonrigid 3D structure. Then we automatically identify relatively rigid point-pairs in this structure, and use their length-constancy simultaneously with triangulation constraints to refine the structure estimate. Local factorization inherently handles small camera motion, short sequences and significant natural occlusions gracefully, performing better than nonrigid factorization methods. We show more stable and accurate results as compared to the state-of-the art on even short sequences starting from 15 frames only, containing camera rotations as small as 2° and up to 50% contiguous missing data.

This paper proposes Automatic Metric Rectification of projectively distorted 3D structures for man-made scenes using Angle Regularity. Man-made scenes, such as buildings, are characterized by a profusion of mutually orthogonal planes and lines. Assuming the availability of planar segmentation, we search for the rectifying 3D homography which maximizes the number of orthogonal plane-pairs in the structure. We formulate the orthogonality constraints in terms of the Absolute Dual Quadric (ADQ). Using RANSAC, we first estimate the ADQ which maximizes the number of planes meeting at right angles. A rectifying homography recovered from the ADQ is then used as an initial guess for nonlinear refinement. Quantitative experiments show that the method is highly robust to the amount of projective distortion, the number of outliers (i.e. non-orthogonal planes) and noise in structure recovery. Unlike previous literature, this method does not rely on any knowledge of the cameras or images, and no global model, such as Manhattan World, is imposed.

Partial computation elimination techniques are often used for fast template matching. At a particular search location, computations are prematurely terminated as soon as it is found that this location cannot compete with an already known best match location. Due to the nonmonotonic growth pattern of the correlation-based similarity measures, partial computation elimination techniques have been traditionally considered inapplicable to speed up these measures. In this paper, we show that partial elimination techniques may be applied to a correlation coefficient by using a monotonic formulation, and we propose basic-mode and extended-mode partial correlation elimination algorithms for fast template matching. The basic-mode algorithm is more efficient on small template sizes, whereas the extended mode is faster on medium and larger templates. We also propose a strategy to decide which algorithm to use for a given data set. To achieve a high speedup, elimination algorithms require an initial guess of the peak correlation value. We propose two initialization schemes including a coarse-to-fine scheme for larger templates and a two-stage technique for small- and medium-sized templates. Our proposed algorithms are exact, i.e., having exhaustive equivalent accuracy, and are compared with the existing fast techniques using real image data sets on a wide variety of template sizes. While the actual speedups are data dependent, in most cases, our proposed algorithms have been found to be significantly faster than the other algorithms.

A variety of dynamic objects, such as faces, bodies, and cloth, are represented in computer graphics as a collection of moving spatial landmarks. Spatiotemporal data is inherent in a number of graphics applications including animation, simulation, and object and camera tracking. The principal modes of variation in the spatial geometry of objects are typically modeled using dimensionality reduction techniques, while concurrently, trajectory representations like splines and autoregressive models are widely used to exploit the temporal regularity of deformation. In this article, we present the bilinear spatiotemporal basis as a model that simultaneously exploits spatial and temporal regularity while maintaining the ability to generalize well to new sequences. This factorization allows the use of analytical, predefined functions to represent temporal variation (e.g., B-Splines or the Discrete Cosine Transform) resulting in efficient model representation and estimation. The model can be interpreted as representing the data as a linear combination of spatiotemporal sequences consisting of shape modes oscillating over time at key frequencies. We apply the bilinear model to natural spatiotemporal phenomena, including face, body, and cloth motion data, and compare it in terms of compaction, generalization ability, predictive precision, and efficiency to existing models. We demonstrate the application of the model to a number of graphics tasks including labeling, gap-filling, denoising, and motion touch-up.

Most nonrigid objects exhibit temporal regularities in their deformations. Recently it was proposed that these regularities can be parameterized by assuming that the nonrigid structure lies in a small dimensional trajectory space. In this paper, we propose a factorization approach for 3D reconstruction from multiple static cameras under the compact trajectory subspace representation. Proposed factorization is analogous to rank-3 factorization of rigid structure from motion problem, in transformed space. The benefit of our approach is that the 3D trajectory basis can be directly learned from the image observations. This also allows us to impute missing observations and denoise tracking errors without explicit estimation of the 3D structure. In contrast to standard triangulation based methods which require points to be visible in at least two cameras, our approach can reconstruct points, which remain occluded even in all the cameras for quite a long time. This makes our solution especially suitable for occlusion handling in motion capture systems. We demonstrate robustness of our method on challenging real and synthetic scenarios.

Existing approaches to nonrigid structure from motion assume that the instantaneous 3D shape of a deforming object is a linear combination of basis shapes. These bases are object dependent and therefore have to be estimated anew for each video sequence. In contrast, we propose a dual approach to describe the evolving 3D structure in trajectory space by a linear combination of basis trajectories. We describe the dual relationship between the two approaches, showing that they both have equal power for representing 3D structure. We further show that the temporal smoothness in 3D trajectories alone can be used for recovering nonrigid structure from a moving camera. The principal advantage of expressing deforming 3D structure in trajectory space is that we can define an object independent basis. This results in a significant reduction in unknowns and corresponding stability in estimation. We propose the use of the Discrete Cosine Transform (DCT) as the object independent basis and empirically demonstrate that it approaches Principal Component Analysis (PCA) for natural motions. We report the performance of the proposed method, quantitatively using motion capture data, and qualitatively on several video sequences exhibiting nonrigid motions, including piecewise rigid motion, partially nonrigid motion (such as a facial expressions), and highly nonrigid motion (such as a person walking or dancing).

Power control plays significant role in the cognitive network system where secondary users (SUs) co-exist with primary users (PUs), but the interference due to SUs may affect the performance of PUs. The approach of considering the outage probability of PU as quality of service (QoS) constraint, while using the maximization function of secondary user throughput is used in this paper. A generalized regional model is suggested, while using Binary Power Control (BPC) as an example case. The results conclude that under these conditions the cognitive radio relay network (CRRN), where SUs communicate through a cognitive radio base station (CRBS), with three regions (N=3) and three thresholds provides better performance, in terms of more active SUs and outage probability of SUs.

Elimination Algorithms are often used in template matching to provide a significant speed-up by skipping portions of the computation while guaranteeing the same best-match location as exhaustive search. In this work, we develop elimination algorithms for correlation-based match measures by exploiting the transitivity of correlation. We show that transitive bounds can result in a high computational speed-up if strong autocorrelation is present in the dataset. Generally strong intrareference local autocorrelation is found in natural images, strong inter-reference autocorrelation is found if objects are to be tracked across consecutive video frames and strong intertemplate autocorrelation is found if consecutive video frames are to be matched with a reference image. For each of these cases, the transitive bounds can be adapted to result in an efficient elimination algorithm. The proposed elimination algorithms are exact, that is, they guarantee to yield the same peak location as exhaustive search over the entire solution space. While the speed-up obtained is data dependent, we show empirical results of up to an order of magnitude faster computation as compared to the currently used efficient algorithms on a variety of datasets.

Geo-spatial data on village locations, their size, population and other parameters is scarcely available to decision makers in many developing countries. In this paper, we demonstrate an automatic ?crawler? which can segment nucleated villages from satellite imagery freely available in public domain geographic information systems such as Google EarthTM. Our approach is to use frequency and color features to generate a number of weak classifiers, which are then combined through Adaboost to produce the final classifier. We use a total of 69 features in the generation of the weak classifiers, including phase gradients, cornerness measures and color features. Our primary dataset consists of 60 images having more than 345 million pixels and covering more than 100 km2 of area, containing nucleated villages in fifteen countries, spread over four continents and captured by different sensors. Using six manual annotations for ground-truth, we perform five-fold cross validation, using 25% of data for testing. Our results show an Equal Error Rate (EER) of around 3.4%. Using the trained classifier, we detect villages on a 50 km2 image (close to 184 million pixels) from a different site than the images used in training, and demonstrate highly accurate extraction of villages with 2.3% false positives and 0.01% false negatives.

In factorization approaches to nonrigid structure from motion, the 3D shape of a deforming object is usually modeled as a linear combination of a small number of basis shapes. The original approach to simultaneously estimate the shape basis and nonrigid structure exploited ortho-normality constraints for metric rectification. Recently, it has been asserted that structure recovery through ortho-normality constraints alone is inherently ambiguous and cannot result in a unique solution. This assertion has been accepted as conventional wisdom and is the justification of many remedial heuristics in literature. Our key contribution is to prove that ortho-normality constraints are in fact sufficient to recover the 3D structure from image observations alone. We characterize the true nature of the ambiguity in using ortho-normality constraints for the shape basis and show that it has no impact on structure reconstruction. We conclude from our experimentation that the primary challenge in using shape basis for nonrigid structure from motion is the difficulty in the optimization problem rather than the ambiguity in ortho-normality constraints.

In this paper we propose an early termination algorithm for speeding up the detection phase of the Adaboost based detectors. In the basic algorithm, at a specific search location, the AdaBoost ensemble response is computed as monotonic decreasing function of weak learners. As more weak learners are evaluated, the response either decreases or remains the same. As soon as the current response becomes lower than the AdaBoost global threshold, remaining computations may be skipped without any loss of accuracy. We further extend the basic algorithm by integrating it with the Non Maxima Suppression (NMS) process. Any candidate location may be discarded, as soon as its current response becomes lower than another candidate location, within the same non-maxima suppression window. In our experiments, our proposed algorithm has been found to be an order of magnitude faster than the traditionally used AdaBoost detector, for the application of edge-corner detection. Speedup comparisons are also done with other three well known edge corner detectors. The early terminated AdaBoost detector has been found to be significantly faster than all three of these detectors.

In this paper, we propose a spectral efficiency enhancement algorithm through simultaneous accesses to heterogeneous networks, where a mobile station (MS) can simultaneously access different non-interfering networks with different radio access technologies (RATs). This simultaneous access (i.e., RAT diversity) is determined in terms of the distributed optimal solution, which implies that MSs can decide whether they connect with multiple RATs or not. To derive the optimal solution, we first show the advantages of multiaccess (MA) by an example in heterogeneous networks, and analyze the spectral efficiency of MA in multi-RAT system. Through our analysis and simulations, it is shown that the proposed algorithm is able to achieve the increase of spectral efficiency compare with vertical handoff (VHO) method, which selects only one network having the best channel quality. As a result, the proposed MA algorithm can be considered as a viable solution to get more spectral efficiency in heterogeneous environments.

Digital images of natural scenes are usually characterized by strong spatial correlation between adjacent pixels which has been successfully exploited in the coding of still and moving pictures. In this work we show that the strong spatial correlation of natural images can also be used to speedup the video to reference image alignment algorithms. To this end, we divide the search locations in the reference image into groups. The target frames are matched with only one location in each group, while on the remaining locations we evaluate exact theoretic upper bounds on the correlation coefficient. These bounds are used to eliminate majority of the search locations and thus result in significant speedup without effecting the value or location of the global maxima. In our experiments, up to 83.3% search locations are found to be eliminated and the speedup is up to 5.3 times the FFT based implementation and up to 7.9 times the spatial domain techniques.

Existing approaches to nonrigid structure from motion assume that the instantaneous 3D shape of a deforming object is a linear combination of basis shapes, which have to be estimated anew for each video sequence. In contrast, we propose that the evolving 3D structure be described by a linear combination of basis trajectories. The principal advantage of this lateral approach is that we do not need to estimate any basis vectors during computation. Instead, we show that generic bases over trajectories, such as the Discrete Cosine Transform (DCT) bases, can be used to effectively describe most real motions. This results in a significant reduction in unknowns, and corresponding stability, in estimation. We report empirical performance, quantitatively using motion capture data and qualitatively on several video sequences exhibiting nonrigid motions including piece-wise rigid motion, articulated motion, partially nonrigid motion (such as a facial expression), and highly nonrigid motion (such as a person dancing).

Block based motion compensation techniques are commonly used in video encoding to reduce the temporal redundancy of the signal. In these techniques, each block in a video frame is matched with another block in a previous frame. The match criteria normally used is the minimization of the sum of absolute differences (SAD). Traditional encoders take the difference of the current block and its best matching block, and this differential signal is used for further processing. Rather than directly encoding the difference between the two blocks, we propose that the difference between the current block and its first order linear estimate from the best matching block should be used. This choice of using linear compensated differential signal is motivated by observing frequent brightness and contrast changes in real videos. We show two important theoretical results: (1) The variance of the linear compensated differential signal is always less than or equal to the variance of differential signal in traditional encoders. (2) The optimal criteria for finding the best matching block, in our proposed scheme, is the maximization of the magnitude of correlation coefficient. The theoretical results are verified through experimentation on a large dataset taken from several commercial videos. For the same number of bits per pixel, our proposed scheme exhibits an improvement in peak signal to noise ratio (PSNR) of up to 5 dB when compared to the traditional encoding scheme.

Block based motion compensation techniques make frequent use of early termination algorithms (ETA) to reduce the computational cost of block matching process. ETAs have been well studied in the context of sum of absolute differences (SAD) match measure and are effective in eliminating a large percentage of computations. As compared to SAD, the correlation coefficient (rho) is a more robust measure but has high computational cost because no ETAs for rho have been reported in literature. In this paper, we propose two types of ETAs for correlation coefficient: growth based and the bound based. In growth based ETA, rho is computed as a monotonically decreasing measure. At a specific search location, when the partial value of rho falls below the yet known maxima, remaining calculations are discarded. In bound based ETA, a new upper-bound on rho is derived which is tighter than the currently used Cauchy-Schwartz inequality. The search locations where the proposed bound falls shorter than the yet known maxima are eliminated from the search space. Both types of algorithms are implemented in a cascade and tested on a commercial video dataset. In our experiments, up to 88% computations are found to be eliminated. In terms of execution time, our algorithm is up to 13.7 times faster than the FFTW based implementation and up to 4.6 times faster than the current best known spatial domain technique.

Beyond third generation (3 G) mobile communication systems are expected to support various multimedia services. A mobile user, under the constraint of his/her maximum transmission rate and power may launch multiple services simultaneously. In the uplink, the mobile station (MS) will need to allocate its limited resources (processing and transmit power, rate) among the user's different services, based on the user's preferences and the services' quality of service (QoS) requirements. In this paper, we derive an optimum algorithm to allocate uplink available rate and power to multimedia services. The algorithm maximizes user satisfaction under QoS, rate and power constraints.

We address the issue of tracking moving objects in an environment covered by multiple uncalibrated cameras with overlapping fields of view, typical of most surveillance setups. In such a scenario, it is essential to establish correspondence between tracks of the same object, seen in different cameras, to recover complete information about the object. We call this the problem of consistent labeling of objects when seen in multiple cameras. We employ a novel approach of finding the limits of field of view (FOV) of each camera as visible in the other cameras. We show that, if the FOV lines are known, it is possible to disambiguate between multiple possibilities for correspondence. We present a method to automatically recover these lines by observing motion in the environment, Furthermore, once these lines are initialized, the homography between the views can also be recovered. We present results on indoor and outdoor sequences containing persons and vehicles.

Video segmentation is different from segmentation of a single image. While several correct solutions may exist for segmenting a single image, there needs to be a consistency among segmentations of each frame for video segmentation. Previous approaches of video segmentation concentrate on motion, or combine motion and color information in a batch fashion. We propose a maximum a posteriori probability (MAP) framework that uses multiple cues, like spatial location, color and motion, for segmentation. We assign weights to color and motion terms, which are adjusted at every pixel, based on a confidence measure of each feature. We also discuss the appropriate modeling of PDFs of each feature of a region. The correct modeling of the spatial PDF imposes temporal consistency among segments in consecutive frames. This approach unifies the strengths of both color segmentation and motion segmentation in one framework, and shows good results on videos that are not suited for either of these approaches.