Browse
Submit Data
Databases
API
Help

Dataset Information

29 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Improved Object Localization Using Accurate Distance Estimation in Wireless Multimedia Sensor Networks.

ABSTRACT: Object localization plays a key role in many popular applications of Wireless Multimedia Sensor Networks (WMSN) and as a result, it has acquired a significant status for the research community. A significant body of research performs this task without considering node orientation, object geometry and environmental variations. As a result, the localized object does not reflect the real world scenarios. In this paper, a novel object localization scheme for WMSN has been proposed that utilizes range free localization, computer vision, and principle component analysis based algorithms. The proposed approach provides the best possible approximation of distance between a wmsn sink and an object, and the orientation of the object using image based information. Simulation results report 99% efficiency and an error ratio of 0.01 (around 1 ft) when compared to other popular techniques.

SUBMITTER: Ur Rehman YA

PROVIDER: S-EPMC4631602 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Adaptive redundant speech transmission over wireless multimedia sensor networks based on estimation of perceived speech quality.

Project description:An adaptive redundant speech transmission (ARST) approach to improve the perceived speech quality (PSQ) of speech streaming applications over wireless multimedia sensor networks (WMSNs) is proposed in this paper. The proposed approach estimates the PSQ as well as the packet loss rate (PLR) from the received speech data. Subsequently, it decides whether the transmission of redundant speech data (RSD) is required in order to assist a speech decoder to reconstruct lost speech signals for high PLRs. According to the decision, the proposed ARST approach controls the RSD transmission, then it optimizes the bitrate of speech coding to encode the current speech data (CSD) and RSD bitstream in order to maintain the speech quality under packet loss conditions. The effectiveness of the proposed ARST approach is then demonstrated using the adaptive multirate-narrowband (AMR-NB) speech codec and ITU-T Recommendation P.563 as a scalable speech codec and the PSQ estimation, respectively. It is shown from the experiments that a speech streaming application employing the proposed ARST approach significantly improves speech quality under packet loss conditions in WMSNs.

| S-EPMC3231469 | biostudies-other

Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks.

Project description:A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results.

| S-EPMC5033373 | biostudies-literature

Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks.

Project description:Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate.

| S-EPMC4610506 | biostudies-literature

Parametric Loop Division for 3D Localization in Wireless Sensor Networks.

Project description:Localization in Wireless Sensor Networks (WSNs) has been an active topic for more than two decades. A variety of algorithms were proposed to improve the localization accuracy. However, they are either limited to two-dimensional (2D) space, or require specific sensor deployment for proper operations. In this paper, we proposed a three-dimensional (3D) localization scheme for WSNs based on the well-known parametric Loop division (PLD) algorithm. The proposed scheme localizes a sensor node in a region bounded by a network of anchor nodes. By iteratively shrinking that region towards its center point, the proposed scheme provides better localization accuracy as compared to existing schemes. Furthermore, it is cost-effective and independent of environmental irregularity. We provide an analytical framework for the proposed scheme and find its lower bound accuracy. Simulation results shows that the proposed algorithm provides an average localization accuracy of 0.89 m with a standard deviation of 1.2 m.

| S-EPMC5539484 | biostudies-other

Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm.

Project description:Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

| S-EPMC11323089 | biostudies-literature

An improved pairing-free certificateless aggregate signature scheme for healthcare wireless medical sensor networks.

Project description:In healthcare wireless medical sensor networks (HWMSNs), the medical sensor nodes are employed to collect medical data which is transmitted to doctors for diagnosis and treatment. In HWMSNs, medical data is vulnerable to various attacks through public channels. In addition, leakage of patients' information happens frequently. Hence, secure communication and privacy preservation are major concerns in HWMSNs. To solve the above issues, Zhan et al. put forward a pairing-free certificateless aggregate signature (PF-CLAS) scheme. However, according to our cryptanalysis, the malicious medical sensor node (MSNi) can generate the forged signature by replacing the public key in the PF-CLAS scheme. Hence, to address this security flaw, we design the improved PF-CLAS scheme that can achieve unforgeability, anonymity, and traceability. Since we have changed the construction of the partial private key, the improved PF-CLAS scheme can resist Type I and Type II attacks under the Elliptic Curve Discrete Logarithm assumption. In terms of the performance evaluation, the proposed scheme outperforms related CLAS schemes, which is more suitable for HWMSNs environments.

| S-EPMC9273098 | biostudies-literature

Integration and analysis of neighbor discovery and link quality estimation in wireless sensor networks.

Project description:Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

| S-EPMC3942343 | biostudies-other

Geographic wormhole detection in wireless sensor networks.

Project description:Wireless sensor networks (WSNs) are ubiquitous and pervasive, and therefore; highly susceptible to a number of security attacks. Denial of Service (DoS) attack is considered the most dominant and a major threat to WSNs. Moreover, the wormhole attack represents one of the potential forms of the Denial of Service (DoS) attack. Besides, crafting the wormhole attack is comparatively simple; though, its detection is nontrivial. On the contrary, the extant wormhole defense methods need both specialized hardware and strong assumptions to defend against static and dynamic wormhole attack. The ensuing paper introduces a novel scheme to detect wormhole attacks in a geographic routing protocol (DWGRP). The main contribution of this paper is to detect malicious nodes and select the best and the most reliable neighbors based on pairwise key pre-distribution technique and the beacon packet. Moreover, this novel technique is not subject to any specific assumption, requirement, or specialized hardware, such as a precise synchronized clock. The proposed detection method is validated by comparisons with several related techniques in the literature, such as Received Signal Strength (RSS), Authentication of Nodes Scheme (ANS), Wormhole Detection uses Hound Packet (WHOP), and Wormhole Detection with Neighborhood Information (WDI) using the NS-2 simulator. The analysis of the simulations shows promising results with low False Detection Rate (FDR) in the geographic routing protocols.

| S-EPMC4300191 | biostudies-literature

Sensor Localization from Distance and Orientation Constraints.

Project description:The sensor localization problem can be formalized using distance and orientation constraints, typically in 3D. Local methods can be used to refine an initial location estimation, but in many cases such estimation is not available and a method able to determine all the feasible solutions from scratch is necessary. Unfortunately, existing methods able to find all the solutions in distance space can not take into account orientations, or they can only deal with one- or two-dimensional problems and their extension to 3D is troublesome. This paper presents a method that addresses these issues. The proposed approach iteratively projects the problem to decrease its dimension, then reduces the ranges of the variable distances, and back-projects the result to the original dimension, to obtain a tighter approximation of the feasible sensor locations. This paper extends previous works introducing accurate range reduction procedures which effectively integrate the orientation constraints. The mutual localization of a fleet of robots carrying sensors and the position analysis of a sensor moved by a parallel manipulator are used to validate the approach.

| S-EPMC4969840 | biostudies-literature

The Action Constraints of an Object Increase Distance Estimation in Extrapersonal Space.

Project description:This study investigated the role of action constraints related to an object as regards allocentric distance estimation in extrapersonal space. In two experiments conducted in both real and virtual environments, participants intending to push a trolley had to estimate its distance from a target situated in front of them. The trolley was either empty (i.e., light) or loaded with books (i.e., heavy). The results showed that the estimated distances were larger for the heavy trolley than for the light one, and that the actual distance between the participants and the trolley moderated this effect. This data suggests that the potential mobility of an object used as a reference affects distance estimation in extrapersonal space. According to embodied perception theories, our results show that people perceive space in terms of constraints related to their potential actions.

| S-EPMC6414791 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data