Many routing protocols have been proposed for wireless sensor networks. These routing protocols are almost always based on energy efficiency. However, recent advances in complementary metal-oxide semiconductor (CMOS) cameras and small microphones have led to the development of Wireless Multimedia Sensor Networks (WMSN) as a class of wireless sensor networks which pose additional challenges. The transmission of imaging and video data needs routing protocols with both energy efficiency and Quality of Service (QoS) characteristics in order to guarantee the efficient use of the sensor nodes and effective access to the collected data. Also, with integration of real time applications in Wireless Senor Networks (WSNs), the use of QoS routing protocols is not only becoming a significant topic, but is also gaining the attention of researchers. In designing an efficient QoS routing protocol, the reliability and guarantee of end-to-end delay are critical events while conserving energy. Thus, considerable research has been focused on designing energy efficient and robust QoS routing protocols. In this paper, we present a state of the art research work based on real-time QoS routing protocols for WMSNs that have already been proposed. This paper categorizes the real-time QoS routing protocols into probabilistic and deterministic protocols. In addition, both categories are classified into soft and hard real time protocols by highlighting the QoS issues including the limitations and features of each protocol. Furthermore, we have compared the performance of mobility-aware query based real-time QoS routing protocols from each category using Network Simulator-2 (NS2). This paper also focuses on the design challenges and future research directions as well as highlights the characteristics of each QoS routing protocol.
In this paper, an analytical model of a proposed low-cost high efficiency NPN silicon-based solar cell structure is presented. The structure is based on using low cost heavily doped commercially available silicon wafers and proposed to be fabricated by the same steps as the conventional solar cells except an extra deep trench etch step. Moreover, the cell has been engineered to react to the UV spectrum, resulting in a greater conversion performance. The presented analytical model takes the electrical and optical characteristics into account. Thus, the influence of both physical and technological parameters on the structure performance could be easily examined. Consequently, the optimization of the structure performance becomes visible. To inspect the validity of the analytical model, a comparison of the main performance parameters resulting from the model results with TCAD simulations is carried out showing good agreement.
Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.
In this work, we report on the effect of substituting the active intrinsic i-layer on a conventional pin structure of lead-free perovskite solar cell (PSC) by a homo p-n junction, keeping the thickness of the active layer constant. It is expected that when the active i-layer is substituted by a p-n homo junction, one can increase the collection efficiency of the photo-generated electrons and holes due to the built-in electric field of the homo junction. The impact of the technological and physical device parameters on the performance parameters of the solar cell have been worked out. It was found that p-side thickness must be wider than the n-side, while its acceptor concentration should be slightly lower than the donor concentration of the n-side to achieve maximum efficiency. In addition, different absorber types, namely, i-absorber, n-absorber and p-absorber, are compared to the proposed pn-absorber, showing a performance-boosting effect when using the latter. Moreover, the proposed structure is made without a hole transport layer (HTL) to avoid the organic issues of the HTL materials. The back metal work function, bulk trap density and ETL material are optimized for best performance of the HTL-free structure, giving Jsc = 26.48, Voc = 0.948 V, FF = 77.20 and PCE = 19.37% for AM1.5 solar spectra. Such results highlight the prospective of the proposed structure and emphasize the importance of using HTL-free solar cells without deteriorating the efficiency. The solar cell is investigated by using SCAPS simulator.
Abstract-Recently wireless sensor networks (WSN) became an interesting topic because of its increasing usage in many fields; medical systems, environment monitoring, military applications and video surveillance. Usually sensors are placed in the desired locations to gather information frequently and then transfer it to the observers. WSN consists of a collection of application specific sensors, a wireless transceiver and a simple general purpose processor. In heterogeneous wireless sensor network, researchers found many challenging issues including the limited energy, the efficient usage of the energy, and the problem with the hierarchy of the network as imbalance network. Many studies indicated that the node clustering is a promising solution for such issues. Clustering has been shown to increase the efficacy of the energy consumption where clusters are formed dynamically with neighboring sensors and the power is assumed to be distributed equally among nodes. One of the nodes is considered as the cluster head that is responsible for transferring data among the neighboring sensors. In this work, we propose a modification based on SEP protocol. EM-SEP aims to prolong the stable period of the sensor network by maintaining balanced energy consumption. This means that we choose the advanced nodes to become cluster heads more often than the normal nodes as the case with SEP protocol. Furthermore, EM-SEP takes in account the number of nodes that are associated with each cluster head. Another important enhancement of EM-SEP protocol that if there are more than one sensor available to be a cluster head at certain round, we choose the sensor with highest energy.
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