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Demographic information is usually treated as private data ( e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. Then, by considering the category-unbalance problem, we make use of the correlation between users’ demographic information and their requested Internet resources to make the prediction, and propose an optimal method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21 percent of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance ( e.g. , 73.84 percent of Accuracy in the prediction of age group and 66.42 percent of Accuracy in the prediction of phone level).

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization (HPSO) algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

This work investigates a robust energy-efficient solution for multiple-input–multiple-output (MIMO) transmissions in cognitive vehicular networks. Our goal is to design an optimal MIMO beamforming for secondary users (SUs), considering imperfect interference channel-state information (CSI). Specifically, we optimize the energy efficiency (EE) of SUs, given that the transmission power constraint, the robust interference power constraint, and the minimum transmission rate are satisfied. To solve the optimization problem, we first characterize the uncertainty of CSI by bounding it in a Frobenius-norm-based region and then equivalently convert the robust interference constraint to a linear matrix inequality (LMI). Furthermore, a feasible ascent direction approach is proposed to reduce the optimization problem into a sequential linearly constrained semidefinite program, which leads to a distributed iterative optimization algorithm for deriving the robust and optimal beamforming. The feasibility and convergence of the proposed algorithm is theoretically validated, and the final experimental results are also supplemented to show the strength of the proposed algorithm over some conventional schemes in terms of the achieved EE performance and robustness.

The tag collision problem is considered as one of the critical issues in RFID system. To further improve the identification efficiency of an UHF RFID system, a frame breaking policy is proposed with dynamic framed slotted aloha algorithm. Specifically, the reader makes effective use of idle, successful, and collision statistics during the early observation phase to recursively determine the optimal frame size. Then the collided tags in each slot will be resolved by individual frames. Simulation results show that the proposed algorithm achieves a better identification performance compared with the existing Aloha-based algorithms.

To solve the problems of occlusion and fast motion of small targets in unmanned aerial vehicle target tracking, an adaptive algorithm that fuses the improved color histogram tracking response and the correlation filter tracking response based on multichannel histogram of oriented gradient features is proposed to realize small target tracking with high accuracy. The state judgment index is used to determine whether the target is in a fast motion or an occlusion state. In the fast motion state, the search area is enlarged, and the color optimal model that suppresses the suspected area is used for rough detection. Then, redetection in the location of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. In an occlusion state, the model stops updating, the search area is expanded, and the current color model is used for rough detection. Then, redetection in the place of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. Experimental results show that the proposed method can track small targets accurately. The frame rate of the proposed method is 40.23 frames/s, indicating usable real-time performance.

Internet-of-Things (IoT) is a new paradigm of wireless technology, where smart sensors and machines communicate through amalgamating multivaried protocols and devices such as Zigbee, Bluetooth, Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN). IoT systems, requires sensing, gathering, storing, processing and transmitting of data from real time sensors as well as virtual online sensors. Thus, energy efficiency and robust data delivery to users are the key requirements. In this paper, we introduce active RFID tags based cluster head (CH) selection, data-awareness and energy harvesting in the IoT systems. The results show that the IoT based WSN heterogeneous systems are better equipped to deal with energy efficiency and data delivery problems. Simulation results support our proposed method and show significant improvement over state-of-the art techniques.

Within recent years, the service demand for rich multimedia over mobile networks has kept being soaring at a tremendous pace. To solve the critical problem of mobile traffic explosion, substantial efforts have been made from researchers to try to offload the mobile traffic from infrastructured cellular links to direct short-range communications locally among nearby users. In this article, we discuss the potential of combining users’ online and offline social impacts to exploit the device-to-device (D2D) opportunistic sharing for offloading the mobile traffic. We propose Tag-Assisted Social-Aware D2D sharing framework, TASA, with corresponding optimization models, architecture design, and communication protocols. Through extensive simulations based on real data traces, we demonstrate that TASA can offload up to 78.9% of the mobile traffic effectively.

In smart cities, cellular network plays a crucial role to support wireless access for numerous devices anywhere and anytime. The future 5G network aims to build the infrastructure from mobile internet to connected world. Small Cell is one of the most promising technologies of 5G to provide more connections and high data rate. In order to make the best use of small cell technology, smart cell planning should be implemented to guarantee connectivity and performance for all end nodes. It is particularly a challenging task to deploy dense small cells in the presence of dynamic traffic demands and severe co-channel interference. In this paper, we model various traffic patterns using stochastic geometry approach and propose an energy-efficient scheme to deploy and plan small cells according to the prevailing traffic pattern. The simulation results indicate that our scheme can meet dynamic traffic demands with optimized deployment of small cells and enhance the energy efficiency of the system without compromising on quality-of-service (QoS) requirements. In addition, our scheme can achieve very close performance compared with the leading optimization solver CPLEX and find solutions in much less computational times than CPLEX.

In this paper, we propose an efficient identification algorithm for RFID systems based on EPC C1 Gen2 RFID standard1. Specifically, the proposed anti-collision algorithm is based on the observation of sub-frame during an identification process, and makes effective use of idle and collision statistics to accurately estimate the tag backlog and determine the proper frame size for the next inventory round. Simulation results are supplemented to demonstrate the advantages of the proposed algorithm in achieving time and computation efficiency.

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

Turbulence grids are widely used in wind tunnels to produce representative turbulence levels when testing aerodynamic phenomena around models. Although the purpose of the grid is to introduce a desired turbulence level in the freestream flow, the wall boundary layers of the tunnel are subjected to modification due to the presence of such grids. This could have major implications to the flow around the models to be tested and hence there is a need to further understand this interaction. The study described in this paper examines wind tunnel wall boundary layer modification by turbulence grids of different mesh sizes and porosities to understand the effect of these parameters on such interaction. Experimental results are presented in the form of pressure loss coefficients, boundary layer velocity profiles and the statistics of turbulence modification.