A Kind of New Multicast Routing Algorithm for Application of Internet of Things

Wireless Sensor Networks (WSN) is widely used as an effective medium to integrate physical world and information world of Internet of Things (IOT). While keeping energy consumption at a minimal level, WSN requires reliable communication. Multicasting is a general operation performed by the Base Station, where data is to be transmitted to a set of destination nodes. Generally, the packets are routed in a multi-hop approach, where some intermediate nodes are also used for packet forwarding. This problem can be reduced to the well-known Steiner tree problem, which has proven to be NP-complete for deterministic link descriptors and cost functions. In this paper, we propose a novel multicast protocol, named heuristic algorithms for the solution of the Quality of Service (QoS) constrained multicast routing problem, with incomplete information in Wireless Sensor Networks (WSN). As information aggregation or randomly fluctuating traffic loads, link measures are considered to be random variables. Simulation results show that the Hop Neural Networks (HNN) based heuristics with a properly chosen additive measures can yield to a good solution for this traditionally NP complex problem, when compared to the best multicast algorithms known

The impact of different processing techniques on foot parameters in adults

This study presents data on the application of two different processing techniques of foot parameters and the comparison of their validity and reliability in adults. Twenty-four healthy participants volunteered took part in the study. Three digital scans were obtained from each participant by one experienced investigator. The foot parameters were: Clarke index, ‘KY’ index of Sztriter-Godunov, heel angle and Wejsflog index. The parameters were identified into two ways: manually using software SolidWorks and by a new computer-aided system. The results of the Spearman's rank correlation suggest a strong positive relationship between parameters obtained from the computer-aided system and manually counted (R> 0.9). The current study suggests that computer-aided system is a practical tool for foot type assessment in adults and could be recommended for both research and clinical applications

Novel fusion computing method for bio-medical image of WSN based on spherical coordinate

In bio-medical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing. Data from many different sources can be collected into massive data sets via localized sensor networks. Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment. The application of bio-medical image fusion for big-data computing has strong development momentum, big-data bio-medical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing. The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for big-data bio-medical image of WSN is proposed in this paper. In this method, the three high-frequency coefficients in wavelet domain of bio-medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data bio-medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on multi-scale edge of bio-medical image, it can be fused and reconstructed. Experimental results indicate the novel method is effective and very useful for transmission of big-data bio-medical image, which can solve the problem of data redundancy, more energy cost and low quality

New mixed adaptive detection algorithm for moving target with big data

Aiming at the troubles (such as complex background, illumination changes, shadows and others on traditional methods) for detecting of a walking person, we put forward a new adaptive detection algorithm through mixing Gaussian Mixture Model (GMM), edge detection algorithm and continuous frame difference algorithm in this paper. In time domain, the new algorithm uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection algorithm which mixes the edge detection algorithm, continuous frame difference algorithm and GMM to get the initial contour of moving target with big data, and gets the ultimate moving target with big data. This algorithm not only can adapt to the illumination gradients and background disturbance occurred on scene, but also can solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional algorithm. As experimental result showing, this algorithm holds better real-time and robustness. It is not only easily implemented, but also can accurately detect the moving target with big data

Research on dynamic characteristics of spiral basilar membrane after replacing artificial auditory ossicle based on the reconstructed human ear model

In this paper, PATRAN software was used to establish a complete 3D finite element model of human ears, and it was then combined with NASTRAN software to analyze frequency responses. This paper conducted a detailed analysis on the dynamic parameters including umbo and stapes displacements of normal human ears under sound pressures 90 dB and 105 dB. The numerically computational results were compared with experimental data. When the analyzed frequency was less than 1000 Hz, the computational result of numerical simulation was well consistent with the upper limit. When the analyzed frequency was more than 1000 Hz, the computational result of numerical simulation was well consistent with the lower limit. Therefore, the numerically computational model was reliable. In addition, based on the verified model, this paper studied vibration characteristics of spiral basilar membrane after replacing artificial auditory ossicle based on the whole hearing system, and found that vibration characteristics of spiral basilar membrane had an obvious change at low and high frequencies after replacing artificial auditory ossicle TORP. Using finite element method to analyze vibration characteristics of spiral basilar membrane can well predict the hearing recovery effect after replacing artificial auditory ossicle. Compared with normal ears, the vibration level of spiral basilar membrane after replacing artificial auditory ossicle has slowed down in 100 Hz-600 Hz, 2000 Hz-4000 Hz and 7000 Hz-10000 Hz, and has been strengthened in 600 Hz-2000 Hz and 4000 Hz-7000 Hz, which provided some help for the hearing recovery at the high-frequency band

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

Biomechanical assessment of brain dynamic responses due to blast-induced wave propagation

Traumatic brain injuries (TBI) due to blast-induced wave propagation are not well studied owing to limited published literatures on the subject. This study demonstrates the utilization of a head-helmet model and investigates the effect of using a faceshield with different configurations of laminate composites of polycarbonate and aerogel materials. The model validation is performed against studies published in the literature. The processes of blast wave propagation in the air and blast interaction with the head are modeled by a Coupled Eulerian-Lagrangian (CEL) multi-material finite element method (FEM) formulation, together with a fluid-structure dynamic interaction algorithm. The effectiveness of the different faceshield configurations when exposed to a frontal blast wave with one atmosphere (atm) peak overpressure is evaluated. Results show that the helmet with faceshield can delay the transmission of blast waves to the face and lower the skull stresses and intracranial pressures (ICP) at the frontal and parietal lobes in the first 1.7 ms. Faceshields with a combination of polycarbonate and aerogel layers perform better than the fully polycarbonate ones. It is also revealed that the single 0.6 mm thick aerogel layer in the 3-layer configuration and two layers of 0.6 mm thick aerogel in the 5-layer configuration are the most effective. The paper provides insights into the interaction mechanics between the biological head model and the blast wave

Novel fusion computing method for bio-medical image of WSN based on spherical coordinate

In bio-medical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing. Data from many different sources can be collected into massive data sets via localized sensor networks. Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment. The application of bio-medical image fusion for big-data computing has strong development momentum, big-data bio-medical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing. The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for big-data bio-medical image of WSN is proposed in this paper. In this method, the three high-frequency coefficients in wavelet domain of bio-medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data bio-medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on multi-scale edge of bio-medical image, it can be fused and reconstructed. Experimental results indicate the novel method is effective and very useful for transmission of big-data bio-medical image, which can solve the problem of data redundancy, more energy cost and low quality

Performance Comparison of A New Non-RSSI Based Wireless Transmission Power Control Protocol with RSSI Based Methods:Experimentation with Real World Data

In this paper, simulations with MATLAB are used to compare the performance of a RSSI-based output power control with non-RSSI based adaptive power in terms of saving energy and extending the lifetime of battery powered wireless sensor nodes. This non-RSSI (received signal strength indicator) based adaptive power control algorithm does not use RSSI side information to estimate the link quality. The non-RSSI based approach has a unique methodology to choose the appropriate power level. It has drop-off algorithm that enables it to come back from a higher to a lower power level when deemed necessary. The performance parameters are compared with the RSSI-based adaptive power control algorithm and fixed power transmission. In order to evaluate the protocols in the real world scenarios, RSSI data from different indoor radio environments are collected. In simulation, these RSSI values are used as an input to the RSSI based power control algorithm to calculate the packet success rates and the energy expenditures. In this paper we present extensive analysis of the simulation results to find out the advantages and limitations of the non-RSSI based adaptive power control algorithm under different channel conditions