Data query is one of the most important issues in wireless ad hoc networks, since the ultimate goal of these networks is to support efficient data sharing among wireless nodes. In this paper, we study the issue of data query for delay-sensitive applications in dense wireless ad hoc networks. We focus our attention on step-by-step expanding ring-based data query, which provides an upper bound on query delay to any expanding ring based query strategies. Two replication strategies including Index Replication (IR) and Data Replication (DR) are considered, to improve the delay performance of data query. We analyze the probabilistic behavior of query delay for both DR and IR by theoretical methods, and develop analytical models to approximate the minimum number of replicas required for both query strategies if an application-specified delay bound is imposed. Our work is validated through extensive simulations.

Intelligent traffic monitoring provides information support for autonomous driving, which is widely used in intelligent transportation systems (ITSs). A method for estimating vehicle moving target parameters based on millimeter-wave radars is proposed to solve the problem of low detection accuracy due to velocity ambiguity and Doppler-angle coupling in the process of traffic monitoring. First of all, a MIMO antenna array with overlapping elements is constructed by introducing them into the typical design of MIMO radar array antennas. The motion-induced phase errors are eliminated by the phase difference among the overlapping elements. Then, the position errors among them are corrected through an iterative method, and the angle of multiple targets is estimated. Finally, velocity disambiguation is performed by adopting the error-corrected phase difference among the overlapping elements. An accurate estimation of vehicle moving target angle and velocity is achieved. Through Monte Carlo simulation experiments, the angle error is 0.1° and the velocity error is 0.1m/s. The simulation results show that the method can be used to effectively solve the problems related to velocity ambiguity and Doppler-angle coupling, meanwhile the accuracy of velocity and angle estimation can be improved. An improved algorithm is tested on the vehicle datasets that are gathered in the forward direction of ordinary public scenes of a city. The experimental results further verify the feasibility of the method, which meets the real-time and accuracy requirements of ITSs on vehicle information monitoring.

Multicast routing with Quality-of-Service (QoS) guarantees is the key to efficient content distribution and sharing. Developing QoS-aware multicast routing algorithm is an important open topic. This paper investigates QoS-aware multicast routing problem with K constraints where K > 2. The contributions made in this paper include a heuristic that employs the concept of nonlinear combination to extend the existing well-known algorithm for fast computation of a QoS multicast tree, and a Fully Polynomial Time Approximation Scheme (FPTAS) to approximate a multicast routing tree with QoS guarantees. The theoretical analyses and simulations conducted on both algorithms show that the algorithms developed in this paper are general and flexible, thus are applicable to the various networking systems.

Cloud computing is an emerging computing paradigm that may have a significant impact on various aspects of the development of information infrastructure. In a Cloud environment, different types of network resources need to be virtualized as a series of service components by network virtualization, and these service components should be further composed into Cloud services provided to end users. Therefore Quality of Service (QoS) aware service composition plays a crucial role in Cloud service provisioning. This paper addresses the problem on how to compose a sequence of service components for QoS guaranteed service provisioning in a virtualization-based Cloud computing environment. The contributions of this paper include a system model for Cloud service provisioning and two approximation algorithms for QoS-aware service composition. Specifically, a system model is first developed to characterize service provisioning behavior in virtualization-based Cloud computing, then a novel approximation algorithm and a variant of a well-known QoS routing procedure are presented to resolve QoS-aware service composition. Theoretical analysis shows that these two algorithms have the same level of time complexity. Comparison study conducted based on simulation experiments indicates that the proposed novel algorithm achieves better performance in time efficiency and scalability without compromising quality of solution. The modeling technique and algorithms developed in this paper are general and effective; thus are applicable to practical Cloud computing systems.

The quality of edge detection is related to detection angle, scale, and threshold. There have been many algorithms to promote edge detection quality by some rules about detection angles. However these algorithm did not form rules to detect edges at an arbitrary angle, therefore they just used different number of angles and did not indicate optimized number of angles. In this paper, a novel edge detection algorithm is proposed to detect edges at arbitrary angles and optimized number of angles in the algorithm is introduced. The algorithm combines singularity detection with Gaussian wavelet transform and edge detection at arbitrary directions and contain five steps: 1) An image is divided into some pixel lines at certain angle in the range from 45° to 90° according to decomposition rules of this paper. 2) Singularities of pixel lines are detected and form an edge image at the certain angle. 3) Many edge images at different angles form a final edge images. 4) Detection angles in the range from 45° to 90° are extended to range from 0° to 360°. 5) Optimized number of angles for the algorithm is proposed. Then the algorithm with optimized number of angles shows better performances.

The propagation characteristic of 670 nm laser light on the array of 10 µm diameter polystyrene micro-sphere was studied. For the linearly arranged array of micro-spheres from one to 12, the propagated light intensity was decreased from 700 mV to 45 mV. However, the propagated light intensity in the air was significantly decreased and it became 2 mV at 60 µm from the optical fiber light source. For the micro-sphere array on the curvilinear line, the light intensity at 12th micro-sphere became 35 mV. This fact means the light was propagated almost same as that on the linear line. Whereas it is expected that three dimensionally crossing optical wave-guide is possible to be fabricated by arranging the micro-spheres.

The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability Pc. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.

The cognitive radio technique promises to manage and allocate the scarce radio spectrum in the highly varying and disparate modern environments. This paper considers a cognitive radio scenario composed of two queues for the primary (licensed) users and cognitive (unlicensed) users. According to the Markov process, the system state equations are derived and an optimization model for the system is proposed. Next, the system performance is evaluated by calculations which show the rationality of our system model. Furthermore, discussions among different parameters for the system are presented based on the experimental results.

Mobile Ad Hoc Networks (MANETs) have inherently dynamic topologies. Due to the distributed, multi-hop nature of these networks, random mobility of nodes not only affects the availability of radio links between particular node pairs, but also threatens the reliability of communication paths, service discovery, even quality of service of MANETs. In this paper, a novel Markov chain model is presented to predict link availability for MANETs. Based on a rough estimation of the initial distance between two nodes, the proposed approach is able to accurately estimate link availability in a random mobility environment. Furthermore, the proposed link availability estimation approach is integrated into Max-Min d-clustering heuristic. The enhanced clustering heuristic, called M4C, takes node mobility into account when it groups mobile nodes into clusters. Simulation results are given to verify the approach and the performance improvement of clustering algorithm. It also demonstrates the adaptability of M4C, and shows that M4C is able to achieve a tradeoff between the effectiveness of topology aggregation and cluster stabilities. The proposed algorithm can also be used to improve the availability and quality of services for MANETs.