Many applications in wireless sensor networks require communication performance that is both consistent and of high quality. Unfortunately, performance of current network protocols can vary significantly because of various interferences and environmental changes. Current protocols estimate link quality based on the reception of probe packets over a short time period. This method is neither efficient nor accurate enough to capture the dramatic variations of link quality. Therefore, we propose a link metric called competence that characterizes links over a longer period of time. We combine competence with current short-term estimations in routing algorithm designs. To further improve network performance, we have designed a distributed route maintenance framework based on feedback control solutions. This framework allows every link along an end-to-end (E2E) path to adjust its link protocol parameters, such as transmission power and number of retransmissions, to ensure specified E2E reliability and latency under dynamic link qualities. Our solutions are evaluated in both extensive simulations and real system experiments. In real system evaluations with 48 T-Motes, our overall solution improves E2E packet delivery ratio over existing solutions by up to 40% while reducing transmission energy consumption by up to 22%. Importantly, our solution also achieves more stable and better transient performance than current approaches.

Background: Diabetes and hypertension are two of the commonest diseases in the world. As they unfavorably affect people of different age groups, they have become a cause of concern and must be predicted and diagnosed well in advance. Objective: This research aims to determine the effectiveness of artificial neural networks (ANNs) in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases. Sample: This work used two online datasets which consist of data collected from 768 individuals. We applied neural network algorithms to predict if the individuals have those two diseases based on some factors. Diabetes prediction is based on five factors: age, weight, fat-ratio, glucose, and insulin, while blood pressure prediction is based on six factors: age, weight, fat-ratio, blood pressure, alcohol, and smoking. Method: A model based on the Multi-Layer Perceptron Neural Network (MLP) was implemented. The inputs of the network were the factors for each disease, while the output was the prediction of the disease’s occurrence. The model performance was compared with other classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). We used performance metrics measures to assess the accuracy and performance of MLP. Also, a tool was implemented to help diagnose the diseases and to understand the results. Result: The model predicted the two diseases with correct classification rate (CCR) of 77.6% for diabetes and 68.7% for hypertension. The results indicate that MLP correctly predicts the probability of being diseased or not, and the performance can be significantly increased compared with both SVM and KNN. This shows MLPs effectiveness in early disease prediction.

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Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.