S^3 LQA:A Link Quality Assessment Metric for WSNs Based on Symbol Error and Received Signal Strengt

With the rapid evolution of WSNs technology,it is very important to evaluate link quality quickly and accurately,so that the routing protocols can take relevant strategies in time to keep the entire network working steadily and efficiently. However,the issue of improving link quality assessment methods on physical layer is still open to research. To tackle this issue,a novel link quality assessment metric called S 3 LQA is proposed,which estimates the link quality of wireless sensor networks by CC2420 wireless radio frequency transceiver principles and free space propagation theory. The metric adopts both complete and incomplete packages to improve the evaluation performance effectively based on IEEE802. 15. 4 frame format and DSSS-O-QPSK mechanism. The experimental results show that the proposed method can improve energy cost and achieves better real-timing performance than traditional counting-based ( PRR) link quality assessment metric.

CCI-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss

This paper proposes a chip correlation indicator （CCI）-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss. On the basis of analyzing all related factors, it can be concluded that signal-to-noise rate （SNR） is the main factor causing the non-perceived packet loss. In this paper, the relationship model between CCI and non-perceived packet loss rate （NPLR） is established from related models such as SNR versus packet success rate （PSR）, CCI versus SNR and CCI-NPLR. Due to the large fluctuating range of the raw CCI, Kalman filter is introduced to do de-noising of the raw CCI. The cubic model and the least squares method are employed to fit the relationship between CCI and SNR. In the experiments, many groups of comparison have been conducted and the results show that the proposed mechanism can achieve more accurate measurement of the non-perceived packet loss than existing approaches. Moreover, it has the advantage of decreasing extra energy consumption caused by sending large number of probe packets.

Routing metric of interference-aware link quality: an improved ETX in wireless mesh networks

This article addresses the problem of route selection in wireless mesh networks （WMNs）. The traditional routing metrics adopt packet delivery ratio （PDR） as a representative metric of wireless link quality assessment. However, PDR measured by the broadcast-based probe method is affected by the size, number and transmission rate of probe packets, which influences the metric accuracy. In this paper, improved expected transmission count （iETX）, a new routing metric of interference-aware link quality, is proposed for WMNs. Dispensing with traditional broadcast-based probing method, the iETX uses regional physical interference model to obtain PDR. Regional physical interference model is built upon the relationship between signal to interference plus noise ratio （SINR） and PDR, which contributes to the improvement of metric accuracy. The iETX comprehensively considers the effects of interference and link quality and minimizes the expected number of packet transmissions required for successful delivery, which helps find a path with minimum interference and high throughput. Simulation shows that the proposed metric can significantly improve the network performance.

A Rendezvous Mechanism for Energy Balance and Link Quality in WSNs

Quorum System is a good method to assist the design of sensors of the wake-up scheduling mechanism. However, there are some problems about idle rendezvous and poor link quality when the sensor performs a sensing task according to the Quorum System. In order to improve the efficiency of the sensors and reduce energy consumption, a new rendezvous mechanism is proposed. The use of energy and the link quality between sensors are considered from a global perspective. It can not only ensure the rendezvous opportunity among sensors, but also balance the residual energy of the sensors, so it can improve the energy-saving efficiency of traditional Quorum System. According to the simulation, compared with the traditional Quorum System scheduling mechanism, the rendezvous mechanism proposed in this paper can effectively reduce the number of time-slots when the sensor is idle. Moreover, it also realizes the balance of the residual energy of the sensor and the link quality, which prolongs the life cycle of the wireless sensor network and improves the communication quality.

Secure and Optimal LOADng Routing for IoT with Composite RoutingMetric

Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things(IoT)network.To overcome this challenge,a secure Lightweight On-demand Ad hoc Distancevector—Next Generation(LOADng)Routing Protocol is proposed in this paper.As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector(AODV)protocol,it retains most of the basic functionality and characteristics of AODV.During the route discovery process,the cyclic shift transposition algorithm(CSTA)is used to encrypt the control packets of the LOADng protocol to improve its security.CSTA approach only derives transposition and substitution without product cipher with respect to input data.Besides this,for choosing the best probable path between the source and destination,routing metrics such as link quality Indicator(LQI),hop count(HC)and queue length(QL)are included in the control packets.The data is then securely sent using CSTA using the optimal secure path selected.Experimental Results depict that the proposed secure and optimal LOADng(SO-LOADng)using CSTA encryption obtains better throughput,delivery ratio encryption time and decryption time than the existing state-ofart approaches.

Fuzzy Based Reliable Load Balanced Routing Approach for Ad hoc Sensor Networks

Energy management and packet delivery rate are the important factors in ad hoc networks.It is the major network where nodes share the information without administration.Due to the mobility of nodes,maximum energy is spent on transmission of packets.Mostly energy is wasted on packet dropping and false route discovery.In this research work,Fuzzy Based Reliable Load Balanced Routing Approach(RLRA)is proposed to provide high energy efficiency and more network lifetime using optimal multicast route discovery mechanism.It contains three phases.In first phase,optimal multicast route discovery is initiated to resolve the link failures.In second phase,the link quality is estimated and set to threshold value to meet the requirements of high energy efficiency.In third phase,energy model is shown to obtain total energy of network after transmission of packets.A multicast routing is established Based on path reliability and fault tolerant calculation is done and integrated with multicast routing.The routes can withstand the malicious issues.Fuzzy decision model is integrated with propose protocol to decide the performance of network lifetime.The network simulation tool is used for evaluating the RLRA with existing schemes and performance of RLRA is good compared to others.

Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, networkconnectivity is facilitated between smart devices from anyplace and anytime.IoT-based health monitoring systems are gaining popularity and acceptance forcontinuous monitoring and detect health abnormalities from the data collected.Electrocardiographic (ECG) signals are widely used for heart diseases detection.A novel method has been proposed in this work for ECG monitoring using IoTtechniques. In this work, a two-stage approach is employed. In the first stage, arouting protocol based on Dynamic Source Routing (DSR) and Routing byEnergy and Link quality (REL) for IoT healthcare platform is proposed for effi-cient data collection, and in the second stage, classification of ECG for Arrhythmia. Furthermore, this work has evaluated Support Vector Machine (SVM),Artificial Neural Network (ANN), and Convolution Neural Networks (CNNs)-based approach for ECG signals classification. Deep-ECG will use a deep CNNto extract critical features and then compare through evaluation of simple and fastdistance functions in order to obtain an efficient classification of heart abnormalities. For the identification of abnormal data, this work has proposed techniquesfor the classification of ECG data, which has been obtained from mobile watchusers. For experimental verification of the proposed methods, the Beth Israel Hospital (MIT/BIH) Arrhythmia and Massachusetts Institute of Technology (MIT)Database was used for evaluation. Results confirm the presented method’s superior performance with regards to the accuracy of classification. The CNN achievedan accuracy of 91.92% and has a higher accuracy of 4.98% for the SVM and2.68% for the ANN.

Status adaptive routing with delayed rebroadcast scheme in AODV-based MANETs

An efficient solution is proposed in this article to determine the best reliable route and to prolong the lifetime of the mobile Ad-hoc networks （MANETs）. In the proposed solution, the route discovery process of the Ad-hoc on-demand distance vector routing protocol （AODV） has been modified using a novel delayed rebroadcast scheme. It combines the shortest route selection criterion of AODV with the real network status including the wireless link quality, the remaining power capacity, as well as the traffic load at each node. Simulation results show that the proposed scheme can significantly extend the network lifetime and provide fewer packet losses than the conventional AODV protocol.

Integrated mutual selection based code dissemination for reprogramming wireless sensor networks

Code dissemination is an important building block of wireless reprogramming. We notice that current code dissemination protocols fails to consider page selection and energy balancing. In this paper, we present an integrated mutual selection based code dissemination protocol （MSD）. The key idea of MSD is mutual selection comprising sender selection and page selection. By exploiting link quality information in mutual selection, MSD enhances the propagation. Further, MSD takes energy level information in the sender selection, thus avoiding low power nodes＇ over-consumption. We simulate MSD by settings of Mica-Z motes. Simulation results show that MSD can reduce the completion time and average transmission, and help low power nodes avoid unnecessary transmission and energy consumption.