Mehmet Keskinoz

Energy Aware Iterative Source Localization for Wireless Sensor Networks

In this paper, the source localization problem in wireless sensor networks is investigated where the location of the source is estimated based on the quantized measurements received from sensors in the field. An energy efficient iterative source localization scheme is proposed where the algorithm begins with a coarse location estimate obtained from measurement data from a set of anchor sensors. Based on the available data at each iteration, the posterior probability density function (pdf) of the source location is approximated using an importance sampling based Monte Carlo method and this information is utilized to activate a number of non-anchor sensors. Two sensor selection metrics namely the mutual information and the posterior Cramér-Rao lower bound (PCRLB) are employed and their performance compared. Further, the approximate posterior pdf of the source location is used to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that with significantly less computation, the PCRLB based iterative sensor selection method achieves similar mean squared error (MSE) performance as compared to the state-of-the-art mutual information based sensor selection method. By selecting only the most informative sensors and compressing their data prior to transmission to the fusion center, the iterative source localization method reduces the communication requirements significantly and thereby results in energy savings.

A Multiobjective Optimization Approach to Obtain Decision Thresholds for Distributed Detection in Wireless Sensor Networks

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

Efficient modeling of volume holographic storage channels (VHSC)

Equalizer design typically requires careful modeling of the underlying storage channel. The aim of this paper is to present an efficient ISI model for volume hololgraphic storage channels (VHSC) in which we consider the frequency plane aperture, spatial light modulator (SLM) and CCD fill factors, and SLM finite contrast ratio as the main sources of ISI.

Two-Dimensional Equalization/Detection for Patterned Media Storage

Although patterned media storage (PMS) is a promising candidate for ultrahigh-capacity magnetic data storage, as the capacity of PMS increases, the bit error rate (BER) performance of the system is degraded by increased two-dimensional intersymbol interference (2D-ISI), which results from intertrack interference (ITI), intersymbol interference (ISI), and noise. To improve the system performance under these adverse effects and to increase the capacity, in this paper, we propose to use and/or devise two-dimensional equalization/detection techniques: iterative decision feedback detection (IDFD) and two-dimensional generalized partial response equalization, which is optimized in minimum mean square error (MMSE), followed by one-dimensional Viterbi algorithm (2D-GPR/1D-VA). We evaluate the performance of the proposed methods by using numerical experiments under different amounts of 2D-ISI and noise. Simulation results suggest that under high storage density, the performance of the IDFD is improved by using more iterations and that under the same computational load, 2D-GPR/1D-VA performs better than IDFD. 2D-GPR/1D-VA, therefore, is a good candidate for ultrahigh-capacity PMS.

Discrete magnitude-squared channel modeling, equalization, and detection for volume holographic storage channels

As storage density increases, the performance of volume holographic storage channels is degraded, because intersymbol interference and noise also increase. Equalization and detection methods must be employed to mitigate the effects of intersignal interference and noise. However, the output detector array in a holographic storage system detects the intensity of the incident lights wave front, leading to loss of sign information. This sign loss precludes the applicability of conventional equalization and detection schemes. We first address channel modeling under quadratic nonlinearity and develop an efficient model named the discrete magnitude-squared channel model. We next introduce an advanced equalization method called the iterative magnitude-squared decision feedback equalization (IMSDFE), which takes the channel nonlinearity into account. The performance of IMSDFE is quantified for optical-noise-dominated channels as well as for electronic-noise-dominated channels. Results indicate that IMSDFE is a good candidate for a high-density, high-intersignal-interference volume holographic storage channel.

Application of linear minimum mean-squared-error equalization for volume holographic data storage

When target densities of volume holographic data storage systems are increased, the systems experience increased interference from adjacent pixels and noise. Here we present a method for designing and applying linear minimum mean-squared-error (LMMSE) equalization to improve the bit error rates (BERs) and hence the storage densities achievable. Numerical results with five defocused data pages indicate that a significant improvement in the BER is possible with LMMSE equalization.

Suppressing data sets to prevent discovery of association rules

Enterprises have been collecting data for many reasons including better customer relationship management, and high-level decision making. Public safety was another motivation for large-scale data collection efforts initiated by government agencies. However, such widespread data collection efforts coupled with powerful data analysis tools raised concerns about privacy. This is due to the fact that collected data may contain confidential information. One method to ensure privacy is to selectively hide confidential information from the data sets to be disclosed. In this paper, we focus on hiding confidential correlations. We introduce a heuristic to reduce the information loss and propose a blocking method that prevents discovery of confidential correlations while preserving the usefulness of the data set.

Evaluation of local decision thresholds for distributed detection in wireless sensor networks using multiobjective optimization

For a distributed detection in a wireless sensor network, sensors arrive at decisions about the event of interest and send their decisions to the central fusion center. The fusion center combines the incoming sensor decisions and reaches a final decision about the absence or presence of the event. For binary sensor decisions, determination of the local sensor decision thresholds is crucial. In this paper, we evaluate the set of local sensor thresholds through multi-objective optimization where the probability of error and the total energy consumption of the network are optimized simultaneously. The optimal threshold sets are generated by using a mathematical programming Normal Boundary Intersection (NBI) method and a multi-objective evolutionary algorithm Non Dominating Sorting Genetic Algorithm (NSGA-II). Simulation results show that both NBI and NSGA-II successfully obtain a set of solutions reflecting the tradeoffs between the objectives.

A Monte Carlo based energy efficient source localization method for wireless sensor networks

In this paper, we study the source localization problem in wireless sensor networks where the location of the source is estimated according to the quantized measurements received from sensors in the field. We propose an energy efficient iterative source localization scheme, where the algorithm begins with a coarse location estimate obtained from a set of anchor sensors. Based on the available data at each iteration, we approximate the posterior probability density function (pdf) of the source location using a Monte Carlo method and we use this information to activate a number of non-anchor sensors that minimize the Conditional Posterior Cramér Rao Lower Bound (C-PCRLB). Then we also use the Monte Carlo approximation of the posterior pdf of the source location to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that the proposed iterative method achieves the mean squared error that gets close to the unconditional Posterior Cramér Rao Lower Bound (PCRLB) for a Bayesian estimate based on quantized data from all the sensors within a few iterations. By selecting only the most informative sensors, the iterative approach also reduces the communication requirements significantly and resulting in energy savings.

Linear minimum mean squared error (LMMSE) equalization for holographic data storage

A holographic data storage system (HDSS), just like other storage systems, suffers from interference between adjacent symbols and from noise. In this paper, we present a method for applying the linear minimum mean squared error (LMMSE) equalization to a HDSS. Numerical results with five defocused pages of data indicate that a significant improvement in the bit error rate (BER) is possible using LMMSE equalization.