This paper proposes an energy and spectrum efficient IoT network for 5G systems where spectrum is shared with the cellular system for spectrum efficiency and energy harvesting and energy transfer are utilized for energy efficiency. The IoT network, which consists of sensor nodes and a cluster head with a reliable energy source, reuses part of the cellular band whenever the cellular network does not utilize it. The cluster head performs spectrum sensing, random scheduling of the sensor nodes, and schedules some idle time for energy transfer. The sensor nodes harvest RF energy from the cellular traffic and the transferred energy from the cluster head. Provided the sensor nodes have sufficient energy, they transmit collected sensory data when scheduled. The inter-play between the cellular and IoT network introduces trade-offs between the spectrum availability, energy availability, information, and energy transfer. This paper shows that for the same cellular traffic level, as the number of sensor nodes in the network increases, the IoT network utilization increases resulting in a multi-user gain thanks to the broadcast nature of the energy transfer. The results offer insights into different operational regimes and exposes what type of IoT applications may be feasible with such networks.

It is well known that many more than three or four spectral measurements are required for accurate measurement of color. Previous work has shown seven to ten measurements can yield accurate results on average, but with significant numbers of errors above the threshold of obvious visual detection. Furthermore, the filters used for these measurements are very difficult to fabricate. We show that such filters are not needed and, in fact, have much poorer performance, in perceptual quality measured in ΔEab, than simple narrow-band filters. This is especially true in the presence of Poisson noise at a level common in current digital cameras. In realistic Poisson noise, our filter sets of up to 12 filters allow average ΔEab values around 0.5, with maximum errors below 3.

Due to their complementary properties, inertial measurement units (IMU) and cameras are used in ego-motion tracking applications. For this, the relative rotation and displacement between the camera and IMU reference frames has to be known. There are established methods for the accurate estimation of the relative orientation, however, accurate estimation of the displacement is still a challenging problem. When this is not possible, one might resort to the alternative approach of fusing camera and gyroscope data only, as this does not require the displacement information. To be able to asses such alternatives, this paper presents a systematic methodology based on realistic simulations to analyze the effect of the camera - IMU displacement calibration error on tracking performance, and discusses in detailed simulations the dependency of tracker performance metrics on the camera - IMU displacement's magnitude and calibration error.

In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.

An occlusion-aware multiple deformable object tracker for visual surveillance from two cameras is presented. Each object is tracked by a separate particle filter tracker, which is initiated upon detection of a new person and terminated when s/he leaves the scene. Objects are considered as 3D points at their centre of masses as if their mass density is uniform. Point objects and corresponding silhouette centroids in two views together with the epipolar geometry they satisfy resulted in a practical tracking methodology. An occlusion filter is described, that provides the tracker filters conditional occlusion probabilities of the objects, given their estimated positions. Advances over the previous work; in the computation of conditional occlusion probabilities, in incorporation of these probabilities in the particle filter, and in maintaining tracking of separating objects after long periods of moving close-by, are presented on PETS 2006, PETS 2009 and EPFL datasets.

SUMMARY In SOA, composite services depend on a set of partner services to perform their tasks. These partner services may become unavailable because of system and/or network faults, leading to an increased error rate for the composite service. In this paper, we propose an approach to prevent the occurrence of errors that result from the unavailability of partner services. We introduce an external Web service, dubbed Fault Avoidance Service (FAS), to which composite services can register at will. After registration, FAS periodically checks the partner links, detects unavailable partner services, and updates the composite service with available alternatives. Thus, in case of a partner service error, the composite service will have been updated before attempting an ill‐destined request. We provide mathematical analysis regarding the error rate and the false positive rate with respect to the monitoring frequency of FAS for two models. We obtained empirical results by conducting several tests on the Amazon Elastic Compute Cloud to evaluate our mathematical analyses. We also introduce an industrial case study for improving the quality of a service‐oriented system from the broadcasting and content delivery domain. Copyright © 2014 John Wiley & Sons, Ltd.

This article describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, we combine a task-driven approach with camera subset selection. In the task-driven approach, each camera first performs simple local processing to detect the horizontal position of the object in the image. This information is then sent to a cluster head to track the object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is introduced, where occlusions are captured through occlusion indicator functions. An auxiliary particle filter that incorporates the occluder information is used to track the object. The camera subset selection algorithm uses the minimum mean square error of the best linear estimate of the object position as a metric, and tracking is performed using only the selected subset of cameras. Using simulations and preselected subsets of cameras, we investigate (i) the dependency of the tracker performance on the accuracy of the moving occluder priors, (ii) the trade-off between the number of cameras and the occluder prior accuracy required to achieve a prescribed tracker performance, and (iii) the importance of having occluder priors to the tracker performance as the number of occluders increases. We find that computing moving occluder priors may not be worthwhile, unless it can be obtained cheaply and to high accuracy. We also investigate the effect of dynamically selecting the subset of camera nodes used in tracking on the tracking performance. We show through simulations that a greedy selection algorithm performs close to the brute-force method and outperforms other heuristics, and the performance achieved by greedily selecting a small fraction of the cameras is close to that of using all the cameras.

This paper describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, each camera first performs simple local processing to reduce each frame to a scan line. This information is then sent to a cluster head to track a point object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is presented, where occlusions are captured through an occlusion indicator function. An auxiliary particle filter that incorporates the occluder information is used to track the object. Using simulations, we investigate (i) the dependency of the tracker performance on the accuracy of the moving occluder priors, (ii) the tradeoff between the number of cameras and the occluder prior accuracy required to achieve a prescribed tracker performance, and (iii) the importance of having occluder priors to the tracker performance as the number of occluders increases. We generally find that computing moving occluder priors may not be worthwhile, unless it can be obtained cheaply and to a reasonable accuracy. Preliminary experimental results are provided.