scholarly journals Data Reduction with Quantization Constraints for Decentralized Estimation in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yang Weng
Keyword(s):  
Closed Form Solution ◽  
Optimal Solution ◽  
Form Solution ◽  
Estimation Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Decentralized Estimation ◽  
Final Estimate ◽  
Unknown Vector

The unknown vector estimation problem with bandwidth constrained wireless sensor network is considered. In such networks, sensor nodes make distributed observations on the unknown vector and collaborate with a fusion center to generate a final estimate. Due to power and communication bandwidth limitations, each sensor node must compress its data and transmit to the fusion center. In this paper, both centralized and decentralized estimation frameworks are developed. The closed-form solution for the centralized estimation framework is proposed. The computational complexity of decentralized estimation problem is proven to be NP-hard and a Gauss-Seidel algorithm to search for an optimal solution is also proposed. Simulation results show the good performance of the proposed algorithms.

Capacity Analysis for Correlated Multi-Hop MIMO Channels under Colored Noise

2015 ◽  
Vol 1 ◽  
pp. 41
Author(s):  
Nguyen N. Tran ◽  
Ha X. Nguyen
Keyword(s):  
Computational Complexity ◽  
Mutual Information ◽  
Closed Form Solution ◽  
Optimal Solution ◽  
Form Solution ◽  
Maximization Problem ◽  
Capacity Analysis ◽  
Mimo Channels ◽  
Average Mutual Information ◽  
Channel Output

A capacity analysis for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels is presented in this paper. The channel at each hop is spatially correlated, the source symbols are mutually correlated, and the additive Gaussian noises are colored. First, by invoking Karush-Kuhn-Tucker condition for the optimality of convex programming, we derive the optimal source symbol covariance for the maximum mutual information between the channel input and the channel output when having the full knowledge of channel at the transmitter. Secondly, we formulate the average mutual information maximization problem when having only the channel statistics at the transmitter. Since this problem is almost impossible to be solved analytically, the numerical interior-point-method is employed to obtain the optimal solution. Furthermore, to reduce the computational complexity, an asymptotic closed-form solution is derived by maximizing an upper bound of the objective function. Simulation results show that the average mutual information obtained by the asymptotic design is very closed to that obtained by the optimal design, while saving a huge computational complexity.

A Model for Integrated Inventory and Assortment Planning

2021 ◽  
Author(s):  
Victor Martínez-de-Albéniz ◽  
Sumit Kunnumkal
Keyword(s):  
Closed Form Solution ◽  
Optimal Solution ◽  
Integer Program ◽  
Form Solution ◽  
Assortment Planning ◽  
Dimensional Parameter ◽  
Planning Decisions ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Random Instances

Integrating inventory and assortment planning decisions is a challenging task that requires comparing the value of demand expansion through broader choice for consumers with the value of higher in-stock availability. We develop a stockout-based substitution model for trading off these values in a setting with inventory replenishment, a feature missing in the literature. Using the closed form solution for the single-product case, we develop an accurate approximation for the multiproduct case. This approximated formulation allows us to optimize inventory decisions by solving a fractional integer program with a fixed point equation constraint. When products have equal margins, we solve the integer program exactly by bisection over a one-dimensional parameter. In contrast, when products have different margins, we propose a fractional relaxation that we can also solve by bisection and that results in near-optimal solutions. Overall, our approach provides solutions within 0.1% of the optimal policy and finds the optimal solution in 80% of the random instances we generate. This paper was accepted by David Simchi-Levi, optimization.

scholarly journals Separable MSE-Based Design of Two-Way Multiple-Relay Cooperative MIMO 5G Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6284
Author(s):  
Donatella Darsena ◽  
Giacinto Gelli ◽  
Ivan Iudice ◽  
Francesco Verde
Keyword(s):  
Multiple Input Multiple Output ◽  
Closed Form Solution ◽  
Iterative Algorithms ◽  
Optimal Solution ◽  
Form Solution ◽  
5G Networks ◽  
Amplify And Forward ◽  
Input Multiple Output ◽  
Local Optimal Solution ◽  
Expected Benefits

While the combination of multi-antenna and relaying techniques has been extensively studied for Long Term Evolution Advanced (LTE-A) and Internet of Things (IoT) applications, it is expected to still play an important role in 5th Generation (5G) networks. However, the expected benefits of these technologies cannot be achieved without a proper system design. In this paper, we consider the problem of jointly optimizing terminal precoders/decoders and relay forwarding matrices on the basis of the sum mean square error (MSE) criterion in multiple-input multiple-output (MIMO) two-way relay systems, where two multi-antenna nodes mutually exchange information via multi-antenna amplify-and-forward relays. This problem is nonconvex and a local optimal solution is typically found by using iterative algorithms based on alternating optimization. We show how the constrained minimization of the sum-MSE can be relaxed to obtain two separated subproblems which, under mild conditions, admit a closed-form solution. Compared to iterative approaches, the proposed design is more suited to be integrated in 5G networks, since it is computationally more convenient and its performance exhibits a better scaling in the number of relays.

scholarly journals Reduce Effect of Dependent Malicious Sensor Nodes in WSNs using Pairs Counting and Fake Packets

2020 ◽  
Vol 15 (5) ◽  
Author(s):  
Ashraf Ahmad ◽  
Mohammed Hababeh ◽  
Alaa Abu-Hantash ◽  
Yousef AbuHour ◽  
Husam Musleh
Keyword(s):  
Detection Method ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Simulation Experiments ◽  
Intelligent Decision ◽  
Local Decision ◽  
Prevention Method ◽  
A New Technique ◽  
Prevention Methods

In this paper, we propose a new technique for the enhancement of target detection in Wireless Sensor Networks (WSNs) in which sensor nodes are responsible for taking binary decisions about the presence or absence of a given target and reporting the output to the fusion center. We introduce the algorithm; Fail Silent Pair (FSP) to calculate global decision in the fusion center. The FSP algorithm randomly distributes all sensor nodes into pairs then considers pairs of the same local decision. Also, we present new detection and prevention methods to reduce the effect of dependent malicious sensor nodes. The detection method is based on the deception of suspicious sensor nodes with fake packets to detect a subset of the malicious sensor nodes, as these nodes eavesdrop on other sensor nodes packets to use their local decisions as a reference to build an intelligent decision. While the prevention method allows the fusion center to correct local decisions of some malicious sensor nodes with identified strategies, assisting in the increase of the accuracy of global decisions. We introduce a mathematical analysis to verify our methods, in addition to simulation experiments to validate our technique.

scholarly journals Typical Set Cognitive Sensing

2011 ◽  
Vol 2011 ◽  
pp. 1-10
Author(s):  
Oussama Souihli ◽  
Tomoaki Ohtsuki
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Fusion Rule ◽  
Decision Fusion ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Cooperative Sensing ◽  
Wireless Sensor Nodes ◽  
Detection Probabilities

In cognitive radio (CR) cooperative sensing schemes, wireless sensor nodes deployed in the network sense the licensed spectrum and send their local sensing decisions to a fusion center (FC) that makes a global decision on whether to allow the unlicensed user transmit on the licensed spectrum, based on a decision (fusion) rule. k-out-of-N is widely used in the literature owing to its practical simplicity. Regrettably, it exhibits a tradeoff between the achievable probabilities of false alarm and miss detection, which could have consequent effects on the performance of CR. In this paper, based on the notion of typical sequences, we propose a novel fusion rule in which the false alarm and miss detection probabilities can be simultaneously made as small as desired (asymptotically zero as the number of sensors goes to infinity).

scholarly journals AN-Aided Secure Beamforming in Power-Splitting-Enabled SWIPT MIMO Heterogeneous Wireless Sensor Networks

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 459 ◽  
Author(s):  
Weili Ge ◽  
Zhengyu Zhu ◽  
Wanming Hao ◽  
Yi Wang ◽  
Zhongyong Wang ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Taylor Series Expansion ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Artificial Noise ◽  
Power Splitting ◽  
Green Communications ◽  
Convex Approximation ◽  
Heterogeneous Wireless Sensor Networks ◽  
Heterogeneous Wireless Sensor Network

In this paper, we investigate the physical layer security in a two-tier heterogeneous wireless sensor network (HWSN) depending on simultaneous wireless information and power transfer (SWIPT) approach for multiuser multiple-input multiple-output wiretap channels with artificial noise (AN) transmission, where a more general system framework of HWSN only includes a macrocell and a femtocell. For the sake of implementing security enhancement and green communications, the joint optimization problem of the secure beamforming vector at the macrocell and femtocell, the AN vector, and the power splitting ratio is modeled to maximize the minimal secrecy capacity of the wiretapped macrocell sensor nodes (M-SNs) while considering the fairness among multiple M-SNs. To reduce the performance loss of the rank relaxation from the SDR technique while solving the non-convex max–min program, we apply successive convex approximation (SCA) technique, first-order Taylor series expansion and sequential parametric convex approximation (SPCA) approach to transform the max–min program to a second order cone programming (SOCP) problem to iterate to a near-optimal solution. In addition, we propose a novel SCA-SPCA-based iterative algorithm while its convergence property is proved. The simulation shows that our SCA-SPCA-based method outperforms the conventional methods.

Connectivity Estimation Approaches for Internet of Things-Enabled Wireless Sensor Networks

2022 ◽  
pp. 104-122
Author(s):  
Zuleyha Akusta Dagdeviren ◽  
Vahid Akram
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Exact Algorithms ◽  
Estimation Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Estimation Algorithms ◽  
Communication Layer ◽  
Main Ideas

Internet of things (IoT) envisions a network of billions of devices having various hardware and software capabilities communicating through internet infrastructure to achieve common goals. Wireless sensor networks (WSNs) having hundreds or even thousands of sensor nodes are positioned at the communication layer of IoT. In this study, the authors work on the connectivity estimation approaches for IoT-enabled WSNs. They describe the main ideas and explain the operations of connectivity estimation algorithms in this chapter. They categorize the studied algorithms into two divisions as 1-connectivity estimation algorithms (special case for k=1) and k-connectivity estimation algorithms (the generalized version of the connectivity estimation problem). Within the scope of 1-connectivity estimation algorithms, they dissect the exact algorithms for bridge and cut vertex detection. They investigate various algorithmic ideas for k connectivity estimation approaches by illustrating their operations on sample networks. They also discuss possible future studies related to the connectivity estimation problem in IoT.

scholarly journals Similarity Measure Learning in Closed-Form Solution for Image Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Jing Chen ◽  
Yuan Yan Tang ◽  
C. L. Philip Chen ◽  
Bin Fang ◽  
Zhaowei Shang ◽  
...  
Keyword(s):  
Closed Form ◽  
Similarity Measure ◽  
Dimensional Space ◽  
Closed Form Solution ◽  
Optimal Solution ◽  
Research Problem ◽  
Form Solution ◽  
Similarity Learning ◽  
Learning Tasks ◽  
Active Research

Adopting a measure is essential in many multimedia applications. Recently, distance learning is becoming an active research problem. In fact, the distance is the natural measure for dissimilarity. Generally, a pairwise relationship between two objects in learning tasks includes two aspects: similarity and dissimilarity. The similarity measure provides different information for pairwise relationships. However, similarity learning has been paid less attention in learning problems. In this work, firstly, we propose a general framework for similarity measure learning (SML). Additionally, we define a generalized type of correlation as a similarity measure. By a set of parameters, generalized correlation provides flexibility for learning tasks. Based on this similarity measure, we present a specific algorithm under the SML framework, called correlation similarity measure learning (CSML), to learn a parameterized similarity measure over input space. A nonlinear extension version of CSML, kernel CSML, is also proposed. Particularly, we give a closed-form solution avoiding iterative search for a local optimal solution in the high-dimensional space as the previous work did. Finally, classification experiments have been performed on face databases and a handwritten digits database to demonstrate the efficiency and reliability of CSML and KCSML.

scholarly journals An Optimized and Decentralized Energy Provision System for Smart Cities

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1451
Author(s):  
Ayusee Swain ◽  
Surender Reddy Salkuti ◽  
Kaliprasanna Swain
Keyword(s):  
Smart Grids ◽  
Communication Systems ◽  
Smart Cities ◽  
Optimal Solution ◽  
Clean Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Blockchain Technology ◽  
Advanced Metering

Energy efficiency and data security of smart grids are one of the major concerns in the context of implementing modern approaches in smart cities. For the intelligent management of energy systems, wireless sensor networks and advanced metering infrastructures have played an essential role in the transformation of traditional cities into smart communities. In this paper, a smart city energy model is proposed in which prosumer communities were built by interconnecting energy self-sufficient households to generate, consume and share clean energy on a decentralized trading platform by integrating blockchain technology with a smart microgrid. The efficiency and stability of the grid network were improved by using several wireless sensor nodes that manage a massive amount of data in the network. However, long communication distances between sensor nodes and the base station can greatly consume the energy of sensors and decrease the network lifespan. Therefore, bio-inspired algorithm approaches were proposed to improve routing by obtaining the shortest path for traversing the entire network and increasing the system performance in terms of the efficient selection of cluster heads, reduced energy consumption, and extended network lifetime. This was carried out by studying the properties and mechanisms of biological systems and applying them in the communication systems in order to obtain the best results for a specific problem. In this comprehensive model, particle swarm optimization and a genetic algorithm are used to search for the optimal solution in any problem space in less processing time.

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