62417564
Global Clock Synchronization in Sensor Networks
Global synchronization is important for many sensor network applications that require precise mapping of collected sensor data with the time of the events, for example, in tracking and surveillance. It also plays an important role in energy conservation in MAC layer protocols. This paper describes four methods to achieve global synchronization in a sensor network: a node-based approach, a hierarchical cluster-based method, a diffusion-based method, and a fault-tolerant diffusion-based method. The diffusion-based protocol is fully localized. We present two implementations of the diffusion-based protocol for synchronous and asynchronous systems and prove its convergence. Finally, we show that, by imposing some constraints on the sensor network, global clock synchronization can be achieved in the presence of malicious nodes that exhibit Byzantine failures.
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].
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.
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.
8351252
Using object deputy model to prepare data for data warehousing
Providing integrated access to multiple, distributed, heterogeneous databases and other information sources has become one of the leading issues in database research and the industry. One of the most effective approaches is to extract and integrate information of interest from each source in advance and store them in a centralized repository (known as a data warehouse). When a query is posed, it is evaluated directly at the warehouse without accessing the original information sources. One of the techniques that this approach uses to improve the efficiency of query processing is materialized view(s). Essentially, materialized views are used for data warehouses, and various methods for relational databases have been developed. In this paper, we first discuss an object deputy approach to realize materialized object views for data warehouses which can also incorporate object-oriented databases. A framework has been developed using Smalltalk to prepare data for data warehousing, in which an object deputy model and database connecting tools have been implemented. The object deputy model can provide an easy-to-use way to resolve inconsistency and conflicts while preparing data for data warehousing, as evidenced by our empirical study.
8409782
Super edge connectivity properties of connected edge symmetric graphs
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.
12908496
Spatial interest pixels (SIPs): useful low-level features of visual media data
Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dimensionality degrades not only the effectiveness but also the efficiency of visual recognition algorithms. We present a comparative study on spatial interest pixels (SIPs), including eight-way (a novel SIP detector), Harris, and Lucas‐Kanade, whose extraction is considered as an important step in reducing the dimensionality of visual media data. With extensive case studies, we have shown the usefulness of SIPs as low-level features of visual media data. A class-preserving dimension reduction algorithm (using GSVD) is applied to further reduce the dimension of feature vectors based on SIPs. The experiments showed its superiority over PCA.