Jessica Feng

Model-based calibration for sensor networks

Calibration is the process of mapping raw sensor readings into corrected values by identifying and correcting systematic bias. Calibration is important from both off-line and on-line perspectives. Major objectives of calibration procedure include accuracy, resiliency against random errors, ability to be applied in various scenarios, and to address a variety of error models. In addition, a compact mapping function is attractive in terms of both storage and robustness. We start by introducing the nonparametric statistical approach for conducting off-line calibration. After that, we present the non-parametric statistical percentile method for establishing the confidence interval for a particular mapping function. Furthermore, we propose the first model-based on-line procedure for calibration. The calibration problem is formulated as an instance of nonlinear function minimization and solved using the standard conjugate gradient approach. A number of trade-offs between the effectiveness of calibration and noise level, latency, size of network and the complexity of phenomena are analyzed in a quantitative way. As a demonstration example, we use a system consisting of photovoltaic optical sensors.

System-architectures for sensor networks issues, alternatives, and directions

Our goal is to identify the key architectural and design issues related to Sensor Networks (SNs), evaluate the proposed solutions, and to outline the most challenging research directions. The evaluation has three scopes ndividual components on SN nodes (processor, communication, storage, sensors, actuators, and power supply), node level and networked system level. The special emphasis is placed on architecture and system software, and on new challenges related to the usage of new types of components in networked systems. The evaluation is guided by anticipated technology trends and both current and future applications. The main conclusion of the analysis is that the architectural and synthesis emphasis will be shifted from computation and to some extent communication components to sensors and actuators.

Hiding Data in DNA

Just like disk or RAM, DNA and RNA can store vast amounts of information, and just like data stored in digital media, DNA data can be easily copied or tampered with. However, unlike in the digital realm, there are no techniques for watermarking, annotating, or encrypting information in DNA and RNA. The ability to catalogue genes, place checksums, watermark, and otherwise protect intellectual property in such a medium is of profound importance. This paper proposes the original idea of hiding data in DNA and RNA. Moreover, it defines two new and original techniques for hiding the data. The first is a simple technique that hides data in non-coding DNA such as nontranscribed and non-translated regions as well as non-genetic DNA such as DNA computing solutions. The second technique can be used to place data in active coding segments without changing the resulting amino acid sequence. Combining codon redundancy with arithmetic encoding and public key cryptography, this robust technique can be used simultaneously for encryption and authentication. Protecting genetic discoveries, gene therapy drugs, and a great deal of other intellectual property in medicine, genetics, molecular biology, and even DNA computing, could be made possible by the techniques presented in this paper.

Location Discovery Using Data-Driven Statistical Error Modeling

We have developed statistical error modeling techniques for acoustic signal detection-based ranging measurements in the framework of wireless ad-hoc sensor networks (WASNs). The models are used as the basis for solving the location discovery problem in sensor networks. We first demonstrate that the major difficulty in location discovery is how to treat errors by proving the location discovery in presence of noisy measurements is a NP-complete problem, even in onedimensional space. Consequently, we formulate the location discovery as an instance of nonlinear function minimization that optimizes each of the empirically derived statistical error models. The minimization problem is then solved using a conjugate gradient-based nonlinear function optimization solver. We validate the efficiency of the approach by conducting comprehensive experiments on both deployed and simulated WASNs. The results indicate that the statistical model-based approach significantly improves the location accuracy compared with the approaches using the traditional optimization objectives. In addition, the localized version of our location discovery algorithm is capable of finding competitive solutions using significantly lower communication cost.

Security in sensor networks: watermarking techniques

The actual deployment of the majority of envisioned applications for sensor networks is crucially dependent on resolving associated security, privacy, and digital rights management (DRM) issues. Although cryptography, security, and DRM have been active research topics for the last several decades, wireless sensor networks (WSN) pose a new system of conceptual, technical, and optimization challenges.In this Chapter we survey two areas related to security in WSN. First, we briefly survey techniques for the protection of the routing infrastructure at the network level for mobile multi-hop (ad-hoc) networks. Secondly, we discuss the first-known watermarking technique for authentication of sensor network data and information. We conclude the Chapter by providing a short discussion of future research and development directions in security and privacy in sensor networks.

Sensor calibration using nonparametric statistical characterization of error models

Calibration is the process of identifying and correcting for the systematic bias component of the error in sensor measurements. Traditionally, calibration has usually been conducted by considering a set of measurements in a single time frame and restricted to linear systems with the assumption of equal-quality sensors and single modality. The basis for the new calibration procedure is to construct a statistical error model that captures the characteristics of the measurement errors. Such an error model can be constructed either off-line or on-line. It is derived using the nonparametric kernel density estimation techniques. We propose four alternatives to make the transition from the constructed error model to the calibration model, which is represented by piecewise polynomials. In addition, statistical validation and evaluation methods such as resubstitution, is used in order to establish the interval of confidence for both the error model and the calibration model. Traces of the distance ranging measurements recorded by in-field deployed sensors are used as our demonstrative example.

Consistency-Based on-line localization in sensor networks

We have developed a new on-line error modeling and optimization-based localization approach for sensor networks in the presence of distance measurement noise. The approach is solely based on the concept of consistency, and is developed specifically for the case of on-line localization, which refers to the situation when references are not available a priori. The localization problem is formulated as the task of maximizing the consistency between measurements and calculated distances. In addition, we also present a localized localization algorithm where a specified communication cost or the location accuracy is guaranteed while optimizing the other. We evaluated the approach in (i) both GPS-based and GPS-less scenarios; (ii) 1-D, 2-D and 3-D spaces, on sets of acoustic ranging-based distance measurements recorded by deployed sensor networks. The experimental evaluation indicates that localization of only a few centimeters is consistently achieved when the average and median distance measurement errors are more than a meter, even when the nodes have only a few distance measurements. The relative performance in terms of location accuracy compares favorably with respect to several state-of-the-art localization approaches. Finally, several insightful observations about the required conditions for accurate location discovery are deduced by analyzing the experimental results.

ILP-based engineering change

We have developed a generic integer linear programming(ILP)-based engineering change(EC) methodology. The EC methodology has three components: enabling, fast, and preserving. Enabling EC provides a user with the means to specify the amount of flexibility and how this flexibility should be distributed throughout the solution so that one can guarantee that a specific set of EC demands can be satisfied while preserving the quality of the initially obtained solution. Fast EC conducts changes in a fraction of the time needed to solve the problem while preserving or in some cases improving the quality of the initial solution. Preserving EC maintains either user specified components of the solution or as much as possible of the initial solution while still guaranteeing an optimal solution to the altered problem instance. We applied the generic methodology to Boolean Satisfiability (SAT) problem. The effectiveness of all proposed approaches and algorithms is demonstrated on standard benchmarks.

Actuator-Based Infield Sensor Calibration

In this paper, an online infield nonparametric calibration and error-modeling approach was developed. The approach employs a single source as the external stimulus that creates the differential sensor readings used for calibration. Under very mild assumptions imposed on the calibration functions, error model, and the environment, the technique utilizes the maximal likelihood principle and a nonlinear function minimization solver to derive both the calibration function and the error model of a specified accuracy simultaneously. The approach is intrinsically localized and presents two variants: 1) one where only pairs of neighboring sensors have to communicate in order to conduct the calibration and 2) one where probably a minimum amount of communication is achieved. In addition, the broadcasting tree problem was also formulated as an integer linear programming (ILP) instance; therefore, the broadcasts used in the second variant are optimally resolved. The techniques were evaluated using the traces from the light sensors recorded by the infield deployed sensors, and the statistical evaluations are conducted in order to obtain the interval of confidence to support all the results

Transitive statistical sensor error characterization and calibration

Calibration is the process of identifying and correcting for the systematic bias component of the error in the sensor measurements. On-line and in-field sensor measurement calibration is particularly crucial since manual calibration is expensive and sometimes infeasible. We have developed an on-line and in-field error modeling technique, which is a generalization of the calibration problem, that relies on a small number of inaccurate sensors with known error distributions to develop error models for the deployed in-field sensors. We demonstrate the applicability of our transitive error modeling technique and evaluate its performance in various scenarios by conducting experiments using traces of the light intensity measurements recorded by in-field deployed light sensors. In addition, statistical validation and evaluation methods such as resubstitution are used in order to establish the interval of confidence