Source localization and tracking in a dispersive medium using wireless sensor network

It is well known that an arbitrary graphical model of statistical inference defined on a tree,i.e. on a graph without loops, is solved exactly and efficiently by an iterative beliefpropagation (BP) algorithm convergent to the unique minimum of the so-called Bethe freeenergy functional. For a general graphical model on a loopy graph, the functional may showmultiple minima, the iterative BP algorithm may converge to one of the minimaor may not converge at all, and the global minimum of the Bethe free energyfunctional is not guaranteed to correspond to the optimal maximum likelihood (ML)solution in the zero-temperature limit. However, there are exceptions to this generalrule, discussed by Kolmogorov and Wainwright (2005) and by Bayati et al (2006,2008) in two different contexts, where the zero-temperature version of the BPalgorithm finds the ML solution for special models on graphs with loops. Thesetwo models share a key feature: their ML solutions can be found by an efficientlinear programming (LP) algorithm with a totally uni-modular (TUM) matrix ofconstraints. Generalizing the two models, we consider a class of graphical modelsreducible in the zero-temperature limit to LP with TUM constraints. Assuming thata gedanken algorithm, g-BP, for finding the global minimum of the Bethe freeenergy is available, we show that in the limit of zero temperature, g-BP outputsthe ML solution. Our consideration is based on equivalence established betweengapless linear programming (LP) relaxation of the graphical model in the T → 0 limit and the respective LP version of the Bethe free energy minimization.

In this paper, we consider the connected coverage problem and aim to construct a minimal connected cover set that is sufficient for a given query in wireless sensor networks. We propose a centralized, Voronoi tessellation (CVT) based algorithm to select the minimum number of active sensor nodes needed to cover the target region completely. The constructed sensor set proves to be connected when sensor node’s communication range is at least twice of its sensing range. For other situations where the CVT algorithm alone cannot guarantee the network connectivity, we design a Steiner minimum tree (SMT) based algorithm to ensure the network connectivity. Theoretical analysis and simulation results show that our algorithm outperforms the greedy algorithm in terms of both the time complexity and the needed number of sensor nodes that must be kept active to respond to a given query.

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

We investigate the localization of multiple signal sources based on sensors performing time-of-arrival (TOA) measurement in wireless sensor networks. Moving beyond the widely studied single source localization problem, concurrently active multiple sources substantially complicate the problem since anchored sensor nodes are unaware of associations between measured signals and source nodes. At the same time, as the total number of possible source-measurement associations grows exponentially with the number of sensor nodes, it is inefficient to attempt conventional single-source localization algorithm for each possible association in a brute-force manner. In this work, we address this difficult problem from a joint optimization perspective. Specifically, we consider simultaneous estimation of source-measurement associations and the source locations, in addition to finding the initial signal transmission time. This joint optimization problem includes both discrete and continuous variables. We propose an efficient three-step algorithm that progressively simplifies the original problem through convex relaxation and sensible approximations. Our proposed algorithm demonstrates results comparable to a genie-aided method that utilizes known source-measurement associations.

Wireless sensor networks are a collection of specially designed nodes to provide support to sensor information propagation. Sensor nodes are basically designed to sense data as sensor and distribute to servers for further analysis. Based on the specific requirements, the numbers of sensor nodes can range from hundreds to thousands. All the communication will take place through predefined network characteristics. WSNs generally face energy issues. LEACH is designed for efficient cluster formation. It provides an efficient energy solution for data transmission from node to base station. Various enhanced version of LEACH has been proposed by Assuming LEACH as base protocol. The energy constraints are a critical aspect in developing the routing protocols for WSNs. Designers and researchers have contributed their effort through enhancement in energy saving procedure into WSNs. In this work, the LEACH based protocols and their performance in WSN has been analysed. This research paper evaluates recent advancement in LEACH based approaches for Wireless Wide Area Sensor Network with comparison table.

In this paper a context based super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator identifies local gradients and textures for selecting the optimal SR method for the region of interest. Texture segmentation and gradient map estimation are done prior to the reconstruction stage. Gradient direction is used for optimal noise reduction along the edges for non-textured regions. On the other hand, regularization term is cancelled for textured regions so that the resultant method reduces to maximum likelihood (ML) solution. It is demonstrated on Brodatz Texture Database that ML solution gives the best PSNR values on textures compared to the regularized SR methods in the literature. Experimental results show that the proposed hybrid method has superior performance in terms of Peak Signal-to-Noise-Ratio (PSNR), Structural Similarity Index Measure (SSIM) compared the SR methods in the literature.

Wireless sensor network (WSN) is composed of a large number of sensor nodes, it can real-time monitor and collect various information of monitored objects and passed to the user for analysis and use. This paper studies and designs a hierarchical WSN network management architecture and communication model, focus on the wireless sensor network topology control mechanism and based on the analysis of the existing hierarchical topology control algorithms. On the introduction of node load, the judgment method and the backup cluster head, design a load balancing topology control algorithm, the cluster management WSN, effectively balance the node energy consumption, reducing the number of network clusters, and the cluster head failure. Design of WSN network management protocol has a simple data type, operation type and the format of the message level HWSNMP, the improved MER protocol code to PDU code, the coded data length ratio after using the BER code length is reduced by 10%. Based on the hierarchical structure as the research object and energy-saving topology, a comprehensive analysis of the LEACH algorithm in layered structures (physical model, algorithm), and compares the advantages and disadvantages and the existing typical algorithms based on LEACH, the cluster algorithm based on node energy balance, balance the node energy consumption to meet the clusters in the theory history

We consider the source and network localization problems, seeking to strengthen the relationship between the Weighted-Least-Square (WLS) and the Maximum-Likelihood (ML) solutions of these problems. To this end, we design an optimization algorithm for source and network localization under the principle that: a) the WLS and the ML objectives should be the same; and b) the solution of the ML-WLS objective does not depend on any information besides the set of given distance measurements (observations). The proposed Range-Global Distance Continuation (R-GDC) algorithm solves the localization problems via iterative minimizations of smoothed variations of the WLS objective, each obtained by convolution with a Gaussian kernel of progressively smaller variances. Since the last (not smoothed) WLS objective derives directly from the ML formulation of the localization problem, and the R-GDC requires no initial estimate to minimize it, final result is maximum-likelihood approach to source and network localization problems. The performance of the R-GDC method is compared to that of state-of-the-art techniques such as semidefinite programming (SDP), nonlinear Newton least squares (NLS), and the Stress-of-a-MAjorizing-Complex-Objective-Function (SMACOF) algorithms, as well as to the Cramér-Rao Lower Bound (CRLB). The comparison reveals that the solutions obtained with the R-GDC algorithm is insensitive to initial estimates and provides a localization error that closely approaches that of the corresponding fundamental bounds. The R-GDC is also found to achieve a complexity order comparable to that of the SMACOF, which is known for its efficiency.

With great development of wireless sensor networks (WSNs) technology, one of its important applications, source localization, has attracted numerous interests of recent scientific researches. Among different kinds of methods, techniques based on time difference of arrival (TDOA) and time of arrival (TOA) are practical and of high accuracy. In consideration of TDOA-based methods' inherent noise correlation and loss in signal-to-noise ratio (SNR), this paper focuses on TOA-based source localization method in WSNs. We formulate the localization problem as a maximum-likelihood (ML) problem, and exploit the relationship between the emitting time constant of signal and the coordinate of source to remove the time constant in the ML function. Then the conjugate gradient method is utilized to obtain the final solution. Simulation results validate the convergence of the proposed algorithm and demonstrate its better performance than some existing TDOA based techniques.

This work investigates three penalized-likelihood expectation maximization (EM) algorithms for image reconstruction with Poisson data where the images are known ap riorito be sparse in the space domain. The penalty functions considered are the � 1 norm, the � 0 “norm,” and a penalty function based on the sum of logarithms of pixel values, R(x )= � n p j=1 log � xj δ +1 � . Our results show that the � 1 penalized algorithm reconstructs scaled versions of the maximum-likelihood (ML) solution, which does not improve the sparsity over the traditional ML estimate. Due to the singularity of the Poisson log-likelihood at zero, the � 0 penalized EM algorithm is equivalent to the maximum-likelihood EM algorithm. We demonstrate that the penalty based on the sum of logarithms produces sparser images than the ML solution. We evaluated these algorithms using experimental data from a position-sensitive Compton-imaging detector, where the spatial distribution of photon-emitters is known to be sparse.

Wireless sensor networks (WSNs) include small sensor nodes with battery and processing power and limited memory units then improving power consumption is a major design challenge for any sensor network. In this paper, a new algorithm for routing in the wireless sensor network is proposed using the ultra-innovative algorithm for bacterial Foraging and mobile sink, which leads to energy efficiency. In the proposed method, the number of sensor nodes is determined according to two criteria: the amount of energy on the battery surface and the distance to the sink ahead, which leads to the formation of regular clusters in the network. Nodes adopt a multi-step routing scheme within the network to communicate with the sink. also, the mobile sink is used to balance the load and help consume uniform energy throughout the network. The simulation results show better performance of the proposed method in terms of energy consumption by 17.99%, throughput rate by 30.04%, end-to-end delay by 46.04%, signal-to-noise ratio by 32.81%, delivery rate successfully Data to the sink is 0.80 times higher than the AFSRP (Artificial Fish Swarm Routing Protocol).

Minimum-Latency Aggregation Scheduling (MLAS) is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for global-scale information to compute the cumulative interference at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log3n) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the non-linear power assignment strategy in [9]. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results.

This paper is a theoretical analysis of the maximum likelihood (ML) channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in the presence of unknown interference. The following theoretical results are presented. Firstly, the uniqueness of the ML solution for practical applications, i.e., when thermal noise is present, is analytically demonstrated when the number of transmitted OFDM symbols is strictly greater than one. The ML solution is then derived from the iterative conditional ML (CML) algorithm. Secondly, it is shown that the channel estimate can be described as an algebraic function whose inputs are the initial value and the means and variances of the received samples. Thirdly, it is theoretically demonstrated that the channel estimator is not biased. The second and the third results are obtained by employing oblique projection theory. Furthermore, these results are confirmed by numerical results.

Quantification of local variations in the optical properties of tumor tissue introduced by the presence of gold-silica nanoparticles (NP) presents significant opportunities in monitoring and control of NP-mediated laser induced thermal therapy (LITT) procedures. Finite element methods of inverse parameter recovery constrained by a Pennes bioheat transfer model were applied to estimate the optical parameters. Magnetic resonance temperature imaging (MRTI) acquired during a NP-mediated LITT of a canine transmissible venereal tumor in brain was used in the presented statistical inverse problem formulation. The maximum likelihood (ML) value of the optical parameters illustrated a marked change in the periphery of the tumor corresponding with the expected location of NP and area of selective heating observed on MRTI. Parameter recovery information became increasingly difficult to infer in distal regions of tissue where photon fluence had been significantly attenuated. Finite element temperature predictions using the ML parameter values obtained from the solution of the inverse problem are able to reproduce the NP selective heating within 5°C of measured MRTI estimations along selected temperature profiles. Results indicate the ML solution found is able to sufficiently reproduce the selectivity of the NP mediated laser induced heating and therefore the ML solution is likely to return useful optical parameters within the region of significant laser fluence.

Intrusion detection in wireless sensor networks is crucial for ensuring network security. This study focuses on the problem of estimating the number of barriers necessary for effective intrusion detection in WSNs. The aim is to make accurate predictions to improve security optimization in WSNs. To this end, various regression models (Linear Regression, Ridge and Lasso Regression, Random Forest, Support Vector and Gradient Boosting) were applied on a dataset including parameters such as field size, sensing range, transmission range, and the number of sensor nodes. The performance of the models was evaluated with metrics such as R2, RMSE, MAE, and MSE, and validated with 5-fold cross-validation. The results show that the Linear Regression model achieved the best performance with the lowest error values (RMSE 0.0181, MAE 0.0136, and MSE 0.0003), followed closely by Ridge Regression. These findings highlight the effectiveness of simple linear models in accurately predicting barrier requirements, supporting the optimization of WSN security systems