2109911
Serialized optimal relay schedules in two-tiered wireless sensor networks
In two-tiered wireless sensor networks (WSNs), sensor nodes (SNs) are scattered in clusters, and are responsible for collecting relevant information from designated areas and transmitting to an application node (AN) in the cluster. The AN then constructs a local-view for the cluster by exploring correlations among information received from nearby SNs, and sends the local-view toward a base-station that creates a global-view for the entire WSN. ANs can also relay local-views for other ANs, if the resultant network lifetime is longer. In this paper, we want to arrange inter-AN relaying optimally, which is an important process in topology control for maximizing the topological lifetime of a WSN with regard to a certain amount of initial energy provisioning. We first propose some criteria on relay candidates preselection, which can considerably reduce the overhead of obtaining an optimal relaying. We then design an algorithm to serialize the parallel relay allocation, so that each AN only needs to have one relaying AN at any time. Finally, we demonstrate the equivalency in network lifetime of the serialized inter-AN relay schedules.
6313972
Analysis and evaluation of expiration-based hierarchical caching systems
This paper investigates some fundamental properties and performance issues of the expiration-based caching systems. We focus on the hierarchical caching systems based on the time-to-live (TTL) expiration mechanism, and present a basic model for such systems. By analyzing the intrinsic timing behavior in this model, we derive some important performance metrics from the perspectives of caching systems and end users, respectively. We use network simulation results to further substantiate the efficacy of our analysis. Our results show some basic properties and trade-offs for a hierarchical caching system based on the weak consistency mechanism.
12032696
TCP Performance and Behaviors with Local Retransmissions
TCP has been the dominant transport protocol over the global Internet, and its performance over a hybrid wireless/wireline network has attracted much attention in recent years. This paper investigates the end-to-end TCP performance, in terms of normalized throughput, effective goodput, and packet delay, over wireless lossy links with local retransmissions. The results reveal that local retransmissions can increase the normalized TCP throughput in different wireless bandwidth, delay, and error settings, at the cost of a decrease in effective goodput and an increased packet delay. The performance observation is explained by the explored TCP endpoint behaviors, including the spurious timeout and duplicated acknowledgment. Analysis shows that spurious timeouts with local retransmissions are rare due to the conservative TCP timeout algorithm. However, spurious duplicated acknowledgments have negative impact and a further improvement with the D-SACK proposal is evaluated.
2042793
Peer Collaboration in Wireless Ad Hoc Networks
Voluntary peer collaboration is often assumed in media access, route discovery, packet forwarding, and upper-layer protocols for wireless ad hoc networks. This assumption is seriously challenged when some peers are autonomous, selfish, or malicious in large-scale, heterogeneous networks. In this paper, based on the latest advances in identity-based cryptography, we design a lightweight and cheat-resistant micropayment scheme to stimulate and compensate collaborative peers that sacrifice their resources to relay packets for other peers. We also demonstrate that when security and collaboration measures are properly enforced, profitable collaboration is a preferable strategy for all peers.
18117251
Multimedia annotations on the semantic Web
Multimedia in all forms (images, video, graphics, music, speech) is exploding on the Web. The content needs to be annotated and indexed to enable effective search and retrieval. However, recent standards and best practices for multimedia metadata don't provide semantically rich descriptions of multimedia content. On the other hand, the World Wide Web Consortium's (W3Cs) semantic Web effort has been making great progress in advancing techniques for annotating semantics of Web resources. To bridge this gap, a new W3C task force has been created to investigate multimedia annotations on the semantic Web. This article examines the problems of semantically annotating multimedia and describes the integration of multimedia metadata with the semantic Web.
40837902
VQ image coding using sub-vector techniques
A fast codeword search algorithm for vector quantization is presented. Three inequalities based on the characteristics of sums and variances of a vector and its two sub-vectors are introduced to reject a larger number of codewords. Simulation results demonstrate the effectiveness of the proposed algorithm.
18784921
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing
In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts kernel canonical correlation analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matern kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods
7197213
A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking
The ability to accurately detect the location of a mobile node in a sensor network is important for many artificial intelligence (AI) tasks that range from robotics to context-aware computing. Many previous approaches to the location-estimation problem assume the availability of calibrated data. However, to obtain such data requires great effort. In this paper, we present a manifold regularization approach known as LeMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. We compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use Crossbow MICA2 to setup the network and USB camera array to obtain the ground truth. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared to several previous systems.
1155416
Co-Localization from Labeled and Unlabeled Data Using Graph Laplacian
This paper addresses the problem of recovering the locations of both mobile devices and access points from radio signals, a problem which we call colocalization, by exploiting both labeled and unlabeled data from mobile devices and access points. We first propose a solution using Latent Semantic Indexing to construct the relative locations of the mobile devices and access points when their absolute locations are unknown. We then propose a semi-supervised learning algorithm based on manifold to obtain the absolute locations of the devices. Both solutions are finally combined together in terms of graph Laplacian. Extensive experiments are conducted in wireless local-area networks, wireless sensor networks and radio frequency identification networks. The experimental results showthat we can achieve high accuracy with much less calibration effort as compared to several previous systems.