Wireless Body Area Networks (WBANs) deal with variety of healthcare services with diverse Quality of Service (QoS) requirements. However, QoS handling is a challenging problem in such networks. In general, QoS related problems can be addressed from different layers in the networking protocol suite. Design of an efficient QoS aware Medium Access Control (MAC) protocol can address this problem in MAC layer. This paper analyzes the QoS requirements of WBAN, identifies the requisites of QoS handling system, and outlines the trends that are being followed for its advancement with focus on QoS issues at MAC layer. We review some prior works, compare them, and analyze the current research concerned with problem of providing QoS in WBAN. We also explore some open issues and discuss them.

In this paper, the resource allocation problem with proportional fairness rate in cognitive OFDM-based wireless network is studied. It aims to maximize the total system throughput subject to constraints that include total transmit power for secondary users, maximum tolerable interferences of primary users, bit error rate, and proportional fairness rate among secondary users. It is a nonlinear optimization problem, for which obtaining the optimal solution is known to be NP-hard. An efficient bio-inspired suboptimal algorithm called immune clonal optimization is proposed to solve the resource allocation problem in two steps. That is, subcarriers are firstly allocated to secondary users assuming equal power assignment and then the power allocation is performed with an improved immune clonal algorithm. Suitable immune operators such as matrix encoding and adaptive mutation are designed for resource allocation problem. Simulation results show that the proposed algorithm achieves near-optimal throughput and more satisfying proportional fairness rate among secondary users with lower computational complexity.

In the draft of the IEEE 802.11s standard, a tree topology is established by the proactive tree-building mode of the Hybrid Wireless Mesh Protocol (HWMP). It is used for cases in which the root station (e.g., gateway) is an end point of the majority of the data connections. In the tree topology, the root or central stations (e.g., parent stations) are connected to the other stations (e.g., leaves) that are one level lower than the central station. Such mesh stations are likely to suffer heavily from contention in bottleneck links when the network has a high traffic load. Moreover, the dependence of the network on such stations is a point of vulnerability. A failure of the central station (e.g., a crash or simply going into sleep mode to save energy) can cripple the whole network in the tree topology. This causes performance degradation for end-to-end transmissions. In a connected mesh topology where the stations having two or more radio links between them are connected in such a way that if a failure subsists in any of the links, the other link could provide the redundancy to the network. We propose a scheme to utilize this characteristic by organizing the network into concentric tiers around the root mesh station. The tier structure facilitates path recovery and congestion control. The resulting mode is referred to as Tier-based Proactive Path Selection Mode (TPPSM). The performance of TPPSM is compared with the proactive tree mode of HWMP. Simulation results show that TPPSM has better performance.

Formation flying is an important technology that enables high cost-effective organization of outer space aircrafts. The ad-hoc wireless network based on direct-sequence ultra-wideband (DS-UWB) techniques is seen as an effective means of establishing wireless communication links between aircrafts. In this paper, based on the theory of matched filter and error bits correction, a hybrid detection algorithm is proposed for realizing multiuser detection (MUD) when the DS-UWB technique is used in the ad-hoc wireless network. The matched filter is used to generate a candidate code set which may contain several error bits. The error bits are then recognized and corrected by an novel error-bit corrector, which consists of two steps: code mapping and clustering. In the former step, based on the modified optimum MUD decision function, a novel mapping function is presented that maps the output candidate codes into a feature space for differentiating the right and wrong codes. In the latter step, the codes are clustered into the right and wrong sets by using the K-means clustering approach. Additionally, in order to prevent some right codes being wrongly classified, a sign judgment method is proposed that reduces the bit error rate (BER) of the system. Compared with the traditional detection approaches, e.g., matched filter, minimum mean square error (MMSE) and decorrelation receiver (DEC), the proposed algorithm can considerably improve the BER performance of the system because of its high probability of recognizing wrong codes. Simulation results show that the proposed algorithm can almost achieve the BER performance of the optimum MUD (OMD). Furthermore, compared with OMD, the proposed algorithm has lower computational complexity, and its BER performance is less sensitive to the number of users.

The robustness of a network is usually measured by error tolerance and attack vulnerability. Significant research effort has been devoted to determining the network design with optimal robustness. However, little attention has been paid to the problem of how to improve the robustness of existing networks. In this paper, we investigate how to optimize attack tolerance and communication efficiency of an existing network under the limited link addition. A survival fitness metric is defined to measure both the attack tolerance and the communication efficiency of the network. We show that network topology reconfiguration optimization with limited link addition (NTRLA) problem is NP-hard. Two approximate solution methods are developed. First, we present a degree-fitness parameter to guide degree-based link addition method. Second, a preferential configuration node-protecting cycle (PCNC) method is developed to do trade-off between network robustness and efficiency. The performance of PCNC method is demonstrated by numerical experiments.

Route establishment in Mobile Ad Hoc Network (MANET) is the key mechanism to a successful connection between a pair of source and destination nodes. An efficient routing protocol constructs routing path with minimal time, less routing overhead and capable of utilizing all possible link connectivity. In general, most on-demand MANET routing protocols operates over symmetrical and bidirectional routing path, which is infeasible due to the inherent heterogeneous properties of wireless devices. Simulation results show that the presence of unidirectional links on a network severely affect the performance of a routing protocol. In this paper, a robust protocol independent scheme is proposed, which enable immediate rediscovery of alternative route for a path blocked by a unidirectional link. The proposed scheme is efficient; route rediscovery is locally computed, which results in significant minimization of multiple route packets flooding. Nodes may exploit route information of immediate neighbors using the local reply broadcast technique, which then redirect the control packets around the unidirectional links, therefore maintaining the end-to-end bidirectional connection. The proposed scheme along with Ad Hoc On-demand Distance Vector (AODV) and AODV-Blacklist routing protocol is investigated over three types of mobility models. Simulation results show that the proposed scheme is extremely reliable under poor network conditions and the route connectivity can be improved by as much as 75%.

Contextual advertising is an important revenue source for major service providers on the Web. Ads classification is one of main tasks in contextual advertising, and it is used to retrieve semantically relevant ads with respect to the content of web pages. However, it is difficult for traditional text classification methods to achieve satisfactory performance in ads classification due to scarce term features in ads. In this paper, we propose a novel ads classification method that handles the lack of term features for classifying ads with short text. The proposed method utilizes a vocabulary expansion technique using semantic associations among terms learned from large-scale search query logs. The evaluation results show that our methodology achieves 4.0% ~ 9.7% improvements in terms of the hierarchical f-measure over the baseline classifiers without vocabulary expansion.

This paper proposes a quality metric based on a No-Reference Bitstream (NR-B) having least computational complexity for the assessment of the human-perceptual quality of H.264 encoded video. The proposed NR-B method performs a modeling of encoding distortion with three bit-stream information (i.e. frame-rate, motion-vector, and quantization-parameter) that can be directly extractable from the encoded bitstream and does not require additional complex processing of final pictures. From performance evaluation using 165 compressed video sequences, the experiment results show that the proposed metric has a higher correlation with subjective quality than is achieved with other comparable methods.

Object detection and tracking using visual sensors is a critical component of surveillance systems, which presents many challenges. This paper addresses the enhancement of object detection and tracking via the combination of multiple visual sensors. The enhancement method we introduce compensates for missed object detection based on the partial detection of objects by multiple visual sensors. When one detects an object or more visual sensors, the detected object's local positions transformed into a global object position. Local and global information exchange allows a missed local object's position to recover. However, the exchange of the information may degrade the detection and tracking performance by incorrectly recovering the local object position, which propagated by false object detection. Furthermore, local object positions corresponding to an identical object can transformed into nonequivalent global object positions because of detection uncertainty such as shadows or other artifacts. We improved the performance by preventing the propagation of false object detection. In addition, we present an evaluation method for the final global object position. The proposed method analyzed and evaluated using case studies.

Mobile devices such as Android devices are emerging as a convenient client computing device with mobility and context-sensing capability. However, the computing power and hardware resource of the devices are limited due to their small form-factor. Consequently, large-scaled applications could not be deployed on these devices. Nonetheless, if the large-scaled applications are deployed and executed on the devices, high performance of the applications cannot be guaranteed. To remedy the limitation in terms of performance, it is inevitable to let some heavy-weight functionality executed on the server side and let a client application invoke the functionality in the server. To realize this kind of mobile applications, we adopt well-defined architecture design principles; being thin-client, being layered with Model-View-Controller (MVC), and being balanced between client side and server side. By adopting the principles, we propose a unique, ideal and practical architecture for mobile applications, called balanced MVC architecture. By considering the principles, key design considerations of realizing balanced MVC architecture lie in functionality partitioning. Hence, we define key criteria of determining the degree of performance. And, we define a method to design a balanced MVC architecture which embodies functionality partitioning for high performance, and a simulation-based evaluation method of balanced MVC architectures.

While botnets are used for various malicious activities, it is well known that they are widely used for email spam. Though the spam filtering systems currently in use block IPs that send email spam, simply blocking the IPs of zombie PCs participating in a botnet is not enough to prevent the spamming activities of the botnet because these IPs can easily be changed or manipulated. This IP blocking is also insufficient to prevent crimes other than spamming, as the botnet can be simultaneously used for multiple purposes. For this reason, we propose a system that detects botnets and zombie PCs based on email spam analysis. This study introduces the concept of "group pollution level" - the degree to which a certain spam group is suspected of being a botnet - and "IP pollution level" - the degree to which a certain IP in the spam group is suspected of being a zombie PC. Such concepts are applied in our system that detects botnets and zombie PCs by grouping spam mails based on the URL links or attachments contained, and by assessing the pollution level of each group and each IP address. For empirical testing, we used email spam data collected in an "email spam trap system" - Korea's national spam collection system. Our proposed system detected 203 botnets and 18,283 zombie PCs in a day and these zombie PCs sent about 70% of all the spam messages in our analysis. This shows the effectiveness of detecting zombie PCs by email spam analysis, and the possibility of a dramatic reduction in email spam by taking countermeasure against these botnets and zombie PCs.

Based on the theory of local-world network, the composition self-similarity (CSS) of network traffic is presented for the first time in this paper for the study of DoS detection. We propose the concept of composition distribution graph and design the relative operations. The $(R/S)^d$ algorithm is designed for calculating the Hurst parameter. Based on composition distribution graph and Kullback Leibler (KL) divergence, we propose the composition self-similarity anomaly detection (CSSD) method for the detection of DoS attacks. We evaluate the effectiveness of the proposed method. Compared to other entropy based anomaly detection methods, our method is more accurate and with higher sensitivity in the detection of DoS attacks.