Evaluation of the Effectiveness of ACK Filtering and ACK Congestion Control in Mitigating the Effects of Bandwidth Asymmetry

The user demand for high speed and ubiquitous connectivity has led to the development and deployment of many new technologies, such as DSL and satellite-based networks, for accessing the Internet network. The goal of these technologies is to mitigate the bottleneck. Other technologies, such as wireless and packet radio networks aimed at providing the user with unrestricted access to their mobile devices and the Internet. Given that these networks are increasingly being deployed as high-speed access networks, it is highly desirable to achieve good network performance over such networks. These technologies show different characteristics (asymmetry) in uplink and downlink directions. Network asymmetry (uneven bandwidth) can negatively affect the performance of feedback-based transport protocol such as Transmission Control Protocol (TCP). This is because that congestion in any direction can affect the flow of feedback in the other direction. ACK Filtering and ACK Congestion Control techniques are used to diminish the congestion on the upstream link. These techniques suffer from sender burstiness and a slowdown in congestion window growth problems. This project addresses the TCP performance problems caused by network asymmetry and discuss the reasons for the inapplicability between TCP and asymmetric networks. It studies the effectiveness of these techniques in mitigating the effects of bandwidth asymmetry in TCP/IP networks and provides suggestions to overcome the problems associated with these techniques. Based on the performance model presented in this project, achieving optimum TCP performance under different asymmetric conditions is described

Energy efficient chain based routing protocol for deterministic node deployment in wireless sensor networks

Wireless Sensor Network (WSN) consists of small sensor devices, which are connected wirelessly for sensing and delivering specific data to Base Station (BS). Routing protocols in WSN becomes an active area for both researchers and industrial, due to its responsibility for delivering data, extending network lifetime, reducing the delay and saving the node’s energy. According to hierarchical approach, chain base routing protocol is a promising type that can prolong the network lifetime and decrease the energy consumption. However, it is still suffering from long/single chain impacts such as delay, data redundancy, distance between the neighbors, chain head (CH) energy consumption and bottleneck. This research proposes a Deterministic Chain-Based Routing Protocol (DCBRP) for uniform nodes deployment, which consists of Backbone Construction Mechanism (BCM), Chain Heads Selection mechanism (CHS) and Next Hop Connection mechanism (NHC). BCM is responsible for chain construction by using multi chain concept, so it will divide the network to specific number of clusters depending on the number of columns. While, CHS is answerable on the number of chain heads and CH nodes selection based on their ability for data delivery. On the other hand, NHC is responsible for next hop connection in each row based on the energy and distance between the nodes to eliminate the weak nodes to be in the main chain. Network Simulator 3 (ns-3) is used to simulate DCBRP and it is evaluated with the closest routing protocols in the deterministic deployment in WSN, which are Chain-Cluster Mixed protocol (CCM) and Two Stage Chain based Protocol (TSCP). The results show that DCBRP outperforms CCM and TSCP in terms of end to end delay, CH energy consumption, overall energy consumption, network lifetime and energy*delay metrics. DCBRP or one of its mechanisms helps WSN applications by extending the sensor nodes lifetime and saving the energy for sensing purposes as long as possible

DCBRP: a deterministic chain-based routing protocol for wireless sensor networks

Background: Wireless sensor networks (WSNs) are a promising area for both researchers and industry because of their various applications The sensor node expends the majority of its energy on communication with other nodes. Therefore, the routing protocol plays an important role in delivering network data while minimizing energy consumption as much as possible. The chain-based routing approach is superior to other approaches. However, chain-based routing protocols still expend substantial energy in the Chain Head (CH) node. In addition, these protocols also have the bottleneck issues.Methods:A novel routing protocol which is Deterministic Chain-Based Routing Protocol (DCBRP). DCBRP consists of three mechanisms: Backbone Construction Mechanism, Chain Head Selection (CHS), and the Next Hop Connection Mechanism. The CHS mechanism is presented in detail, and it is evaluated through comparison with the CCM and TSCP using an ns-3 simulator. Results:It show that DCBRP outperforms both CCM and TSCP in terms of end-to-end delay by 19.3 and 65%, respectively, CH energy consumption by 18.3 and 23.0%, respectively, overall energy consumption by 23.7 and 31.4%, respectively, network lifetime by 22 and 38%, respectively, and the energy*delay metric by 44.85 and 77.54%, respectively.Conclusion:DCBRP can be used in any deterministic node deployment applications, such as smart cities or smart agriculture, to reduce energy depletion and prolong the lifetimes of WSNs

Chain-based routing protocols in wireless sensor networks: A survey

In the last few years, wireless sensor networks )WSN( have become an active area for researchers due to its broad and growing application. However, routing is a critical issue that needs consideration as it directly impacts the performance of WSN.Several protocols have been proposed to address this issue as well as reducing energy consumption and prolong a lifetime of the sensor nodes in WSN.The chain-based is one approach from Hierarchical routing protocols which reduces the energy consumption in WSN. However, a problem arises when the chain has long-link (LL) from the base station (BS). This paper presents a comprehensive survey on chain-base hierarchical routing protocols, in terms of details, who to work, Phases, figures, and the main advantage and disadvantage for each protocol. Furthermore, the characteristics of chain-based routing protocols and the performance metrics that are used in WSN are discussed. Finally, this paper presents open challenges for researchers

Performance evaluation of CCM and TSCP routing protocols within/without data fusing in WSNs

Wireless Sensor Network (WSN) is a large number of small sensor devices that can connect each other wirelessly.WSNs applications are rapidly growing in last decades, furthermore, in WSN research, energy is one of the important issues that must consider when designing a new protocol. Due to the fact, almost all of nodes' energy deplete in the communication part, and the data fusing directly impact the performance of routing protocol.This paper studies the impact of data fusing for chain based routing protocols.In this study, ns-3 simulator used to evaluate Chain-Cluster Mixed (CCM) and Two Stage Chain Protocol (TSCP) routing protocols with deterministic nodes deployment. The experiments show that TSCP overcomes CCM in network lifetime when data fusing applied while CCM overcomes TSCP in the same metric with non-fusing of data for First Node Die (FND), 10%, 25%, 50% and Last node (LND). Furthermore, CCM is still playing a good behavior in delay for both approaches. The main conclusion for this paper is non-fusing of data must be applied when design new routing protocol to study all the packets traffic behaviors in the path from source to destination

Moderating role of compassion in the link between fear of Coronavirus disease and mental health among undergraduate students

"Background: The societal challenges presented by fear related to the coronavirus disease (COVID-19) pandemic may present unique challenges for an individual's mental health. However, the moderating role of compassion in the relationship between fear of COVID-19 and mental health has not been well-studied. The present study aimed to explore the association between fear of COVID-19 and mental health, as well as test the buffering role of compassion in this relationship. Methods: The participants in this study were 325 Iranian undergraduate students (228 females), aged 18–25 years, who completed questionnaires posted on social networks via a web-based platform. Results: The results showed that fear of COVID-19 was positively related with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The results also showed that compassion was negatively associated with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The interaction-moderation analysis revealed that compassion moderated the relationship between fear of COVID-19 and subscale of mental health. Conclusion: Results highlight the important role of compassion in diminishing the effect of fear of COVID-19 on the mental health (physical symptoms, social function, depressive symptoms, and anxiety symptoms) of undergraduate students.

The impact of data fusing vs non-fusing for routing protocols in wireless sensor networks

Wireless Sensor Network (WSN) is a large number of small sensor devices that can connect to each other wirelessly.WSNs applications are rapidly growing in last decades.In WSN research, energy is one of the very important things that must be considered.Due to the fact that almost all of nodes’ energy is depleted in the communication part, this paper studies the impact of fusing vs non-fusing of data for chain based routing protocols, where the routing protocol designer should consider it.In this study, NS3 simulator used to evaluate Chain-Cluster Mixed (CCM) and Two Stage Chain Protocol (TSCP) routing protocols with deterministic nodes deployment.The experiments show that TSCP overcomes CCM in network life time when data fusing applied while CCM overcomes TSCP in the same metric with non-fusing of data for First Node Die (FND), 10%, 25%, 50% and Last node (LND).Furthermore, CCM is still playing a good behavior in delay for both approaches. The main conclusion for this paper is non-fusing of data must be apply when design new routing protocol to study all the packets traffic behaviors in the path from source to destination

A novel Alcoholic EEG signals Classification Approach Based on AdaBoost k-means Coupled with Statistical Model

Identification of alcoholism is an important task because it affects the operation of the brain. Alcohol consumption, particularly heavier drinking is identified as an essential factor to develop health issues, such as high blood pressure, immune disorders, and heart diseases. To support health professionals in diagnosis disorders related with alcoholism with a high rate of accuracy, there is an urgent demand to develop an automated expert systems for identification of alcoholism. In this study, an expert system is proposed to identify alcoholism from multi-channel EEG signals. EEG signals are partitioned into small epochs, with each epoch is further divided into sub-segments. A covariance matrix method with its eigenvalues is utilised to extract representative features from each sub-segment. To select most relevant features, a statistic approach named Kolmogorov–Smirnov test is adopted to select the final features set. Finally, in the classification part, a robust algorithm called AdaBoost k-means (AB-k-means) is designed to classify EEG features into two categories alcoholic and non-alcoholic EEG segments. The results in this study show that the proposed model is more efficient than the previous models, and it yielded a high classification rate of 99%. In comparison with well-known classification algorithms such as K-nearest k-means and SVM on the same databases, our proposed model showed a promising result compared with the others. Our findings showed that the proposed model has a potential to implement in automated alcoholism detection systems to be used by experts to provide an accurate and reliable decisions related to alcoholism

An Intelligence Approach for Blood Pressure Estimation from Photoplethysmography Signal

Commercial cuff-based Blood pressure (BP) devices are mainly not suitable or portable. To ease the measurement of BP devices, we proposed a new model for BP estimation based on photoplethysmography (PPG) signal. PPG signals are segmented into cycles using an improved peak detection algorithm. Then, each segment is mapped into a graph. Graph wavelets transform (GWT) is applied to each segment. The spectral graph features are extracted and tested to assess the BP. A ridge regression is employed to evaluated the BP with the reference of PPG. A publica dataset is used to evaluate the proposed model. The proposed model achieved good results and the obtained results are promising in improving the accuracy of BP estimation

Optimization and design of machine learning computational technique for prediction of physical separation process

Machine learning (ML) methods were developed and optimized for description and understanding a physical separation process. Indeed, this work indicates application of machine learning technique for a real physical system and optimization of process parameters to achieve the target. A bunch of datasets were extracted from resources for physical adsorption process in removal of impurities from water as a case study to test the developed machine learning model. The case study process is adsorption process which has extensive application in science and engineering. The machine learning (ML) method was developed, and the parameters were optimized in order to get the best simulation’s performance for adsorption process. The data are used to correlate the adsorption capacity of the material to the adsorption parameters including dosage and solution pH. Randomized training and validation were performed to predict the process’s output, and great agreement was obtained between the predicted values and the observed values with R2 values greater than 0.9 for all cases of training and validation at the optimum conditions. Three different machine learning techniques including Random Forest (RF), Extra Tree (ET), and Gradient Boosting (GB) were employed for the adsorption data. Quantitatively, R2 scores of 0.958, 0.998, and 0.999 were obtained for RF, GB, and ET, respectively. It was indicated that GB and ET models performed almost the same and better than RF in prediction of adsorption data