3084819
Distributed energy-conserving routing protocols
This paper discusses several distributed power-aware routing protocols in wireless ad-hoc networks (especially sensor networks). We seek to optimize the lifetime of the network. We have developed three distributed power-aware algorithms and analyzed their efficiency in terms of the number of message broadcasts and the overall network lifetime modelled as the time to the first message that can not be sent. These are: (1) a distributed min power algorithm (modelled on a distributed version of Dijkstra's algorithm), (2) a distributed max-min algorithm, and (3) the distributed version of the centralized online max-min zP/sub min/ algorithm presented by Qun Li et al. (2001). The first two algorithms are used to define the third, although they are very interesting and useful on their own for applications where the optimization criterion is the minimum power, respectively the maximum residual power. The distributed max-min zP/sub min/ algorithm optimizes the overall lifetime of the network by avoiding nodes of low power, while not using too much total power.
8816096
Distributed User Access Control in Sensor Networks
User access control in sensor networks defines a process of granting user the access right to the information and resources. It is essential for the future real sensor network deployment in which sensors may provide users with different services in terms of data and resource access. A centralized access control mechanism requires base station to be involved whenever a user requests to get authenticated and access the information stored in the sensor node, which is inefficient, not scalable, and is exposed to many potential attacks along the long communication path. In this paper, we propose a distributed user access control under a realistic adversary model in which sensors can be compromised and user may collude. We split the access control into local authentication conducted by the sensors physically close to the user, and a light remote authentication based on the endorsement of the local sensors. Elliptic Curve Cryptography (ECC), a public key cryptography scheme, is used for local authentication. We implement the access control protocols on a testbed of TelosB motes. Our analysis and experimental results show that our scheme is feasible for real access control requirement.
1284934
Online power-aware routing in wireless Ad-hoc networks
This paper discusses online power-aware routing in large wireless ad-hoc networks for applications where the message sequence is not known. We seek to optimize the lifetime of the network. We show that online power-aware routing does not have a constant competitive ratio to the off-line optimal algorithm. We develop an approximation algorithm called max-min zPmin that has a good empirical competitive ratio. To ensure scalability, we introduce a second online algorithm for power-aware routing. This hierarchical algorithm is called zone-based routing. Our experiments show that its performance is quite good.
1740828
Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks
Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Partial coverage, where a sensing field is partially sensed by active sensors at any time, is an appropriate approach to balancing the two conflicting design requirements of monitoring applications. Under partial coverage, we develop an analytical framework for object detection in sensor networks, and mathematically analyze average-case object detection quality in random and synchronized sensing scheduling protocols. Our analytical framework facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Furthermore, we propose three wave sensing scheduling protocols to achieve bounded worst-case object detection quality. We justify the correctness of our analyses through rigorous proof, and validate the effectiveness of the proposed protocols through extensive simulation experiments
17699208
A study on object tracking quality under probabilistic coverage in sensor networks
Sensor networks are used for a wide range of object tracking applications, such as vehicle tracking in military surveillance and wild animal tracking in habitat monitoring [1]. These applications, by their nature, enforce certain tracking quality and lifetime requirements. These two requirements, however, are two conflicting optimization goals due to the stringent energy constraints of sensor nodes.
10818971
Efficient Implementation of Public Key Cryptosystems on Mote Sensors (Short Paper)
We report our implementation of the RSA and ECC public-key cryptosystem on Berkeley Motes. We detail the implementation of 1024-bit RSA and 160-bit ECC cryptosystems on MICA mote sensors. We have achieved the performance of 0.79s for RSA public key operation and 21.5s for private operation, and 1.3s for ECC signature generation and 2.8s for verification. For comparison, we also show our new ECC implementation on TelosB motes with a signature time 1.60s and a verification time 3.30s. For the detailed description of the implementation, we refer to our technical report [13].
14819927
Gene functional classification by semi-supervised learning from heterogeneous data
Gene function discovery is an important and interesting problem in computational analysis of microarray data. In this paper, we investigate the use of a semi-supervised learning algorithm for inferring gene functional classifications from heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence compassions. The semisupervised learning approach aims at minimizing the disagreement between individual models built from each separate information source by employing a co-updating method and making use of both labeled and unlabeled data. Our results suggest that the semisupervised approach could be used for gene functional classification. The data sets and the program code used for the experiments can be accessed from our webpage.
36836516
Visualization of geostress tensor along oil well trajectory
A simple and efficient method is proposed to visualize a symmetric second order tensor along 3D curves. It is also applied to an oil well model in which, along the well trajectory, the spatial distribution of geostress is visualized to predict the stability of the oil well bore. To give visual representations several forms are used: (1) we visually compare the magnitude of stresses to determine the mud weight; (2) based on a set of ellipse cross-sections along the well trajectory and the ellipticity of the ellipse we predict the shear failure direction and the stability of the oil well bore; (3) from a hinged curved surface, we emphasize the direction of principal stresses; (4) to give useful information we apply other skills such as magnified details, animation and an interactive interface. Our method avoids two defects in the references, that is, the visual cluster caused by discrete point tensor glyphs, and the integration problem of the hyperstreamline.
3013042
Feature extraction via generalized uncorrelated linear discriminant analysis
Feature extraction is important in many applications, such as text and image retrieval, because of high dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature extraction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, we will first propose the ULDA/QR algorithm to simplify the previous implementation of ULDA. Then we propose the ULDA/GSVD algorithm, based on a novel optimization criterion, to address the singularity problem. It is applicable for undersampled problem, where the data dimension is much larger than the data size, such as text and image retrieval. The novel criterion used in ULDA/GSVD is the perturbed version of the one from ULDA/QR, while surprisingly, the solution to ULDA/GSVD is shown to be independent of the amount of perturbation applied. We did extensive experiments on text and face image data to show the effectiveness of ULDA/GSVD and compare with other popular feature extraction algorithms.
663178
Efficient Kernel Discriminant Analysis via QR Decomposition
Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algorithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and space. However, LDA/QR is based on linear projection, which may not be suitable for data with nonlinear structure. This paper first proposes an algorithm called KDA/QR, which extends the LDA/QR algorithm to deal with nonlinear data by using the kernel operator. Then an efficient approximation of KDA/QR called AKDA/QR is proposed. Experiments on face image data show that the classification accuracy of both KDA/QR and AKDA/QR are competitive with Generalized Discriminant Analysis (GDA), a general kernel discriminant analysis algorithm, while AKDA/QR has much lower time and space costs.
17714396
Reliability Analysis of the Retrial Queue with Server Breakdowns and Repairs
Retrial queues have been widely used to model many problems arising in telephone switching systems, telecommunication networks, computer networks and computer systems, etc. It is of basic importance to study reliability of retrial queues with server breakdowns and repairs because of limited ability of repairs and heavy influence of the breakdowns on the performance measure of the system. However, so far the repairable retrial queues are analyzed only by queueing theory. In this paper we give a detailed analysis for reliability of retrial queues. By using the supplementary variables method, we obtain the explicit expressions of some main reliability indexes such as the availability, failure frequency and reliability function of the server. In addition, some special queues, for instance, the repairable M/G/1 queue and repairable retrial queue can be derived from our results. These results may be generalized to the repairable multi-server retrial models.