M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs

In this research work, we advise gateway based energy-efficient routing protocol (M-GEAR) for Wireless Sensor Networks (WSNs). We divide the sensor nodes into four logical regions on the basis of their location in the sensing field. We install Base Station (BS) out of the sensing area and a gateway node at the centre of the sensing area. If the distance of a sensor node from BS or gateway is less than predefined distance threshold, the node uses direct communication. We divide the rest of nodes into two equal regions whose distance is beyond the threshold distance. We select cluster heads (CHs)in each region which are independent of the other region. These CHs are selected on the basis of a probability. We compare performance of our protocol with LEACH (Low Energy Adaptive Clustering Hierarchy). Performance analysis and compared statistic results show that our proposed protocol perform well in terms of energy consumption and network lifetime.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Energy-aware Theft Detection based on IoT Energy Consumption Data

With the advent of modern smart grid networks, advanced metering infrastructure provides real-time information from smart meters (SM) and sensors to energy companies and consumers. The smart grid is indeed a paradigm that is enabled by the Internet of Things (IoT) and in which the SM acts as an IoT device that collects and transmits data over the Internet to enable intelligent applications. However, IoT data communicated over the smart grid could however be maliciously altered, resulting in energy theft due to unbilled energy consumption. Machine learning (ML) techniques for energy theft detection (ETD) based on IoT data are promising but are nonetheless constrained by the poor quality of data and particularly its imbalanced nature (which emerges from the dominant representation of honest users and poor representation of the rare theft cases). Leading ML-based ETD methods employ synthetic data generation to balance the training the dataset. However, these are trained to maximise average correct detection instead of ETD. In this work, we formulate an energy-aware evaluation framework that guides the model training to maximise ETD and minimise the revenue loss due to mis-classification. We propose a convolution neural network with positive bias (CNN-B) and another with focal loss CNN (CNN-FL) to mitigate the data imbalance impact. These outperform the state of the art and the CNN-B achieves the highest ETD and the minimum revenue loss with a loss reduction of 30.4% compared to the highest loss incurred by these methods

Frequency of Clinical Symptoms of Gastroesophageal Reflux Disease in Asthmatic Patients

Background: Gastroesophageal reflex is known as an acid reflex, is long term condition where stomach contents back into the oesophagus resulting in either symptoms or complications. GERD disease is caused by weakness or failure of the lower oesophageal sphincter. Symptoms include the acidic taste behind the mouth, heart burn, chest pain, difficult breathing and vomiting. Complication includes esophagitis, oesophageal strictures and barrettes oesophagus. Objective: The aim of this research was to introduce the symptoms of GERD disease in asthmatic patients and how these symptoms worsen the symptoms of asthma disease and what clinical pictures present with the asthmatic disease. Methodology: A designed performa was used to collect the data and after filling the performa, results were drawn and conclusion through the facts and the information given by patients. Results: In the present study among all 164 asthmatic patients, 70 (42.7%) patients showed dyspepsia, 58 (35.4%) were with chest burning, 23 (14%) were asking about chest pain, with acidic mouth taste were 39 (23.8%), 22 (13.4%) were feeling sore throat and 44 (26.8%) showed regurgitation reflex. Among these 164 patients 16 (9.8%) were smokers and 148 (90.2 %) were non-smokers. 47 (28.7%) were males and 117 (71.3%) were females. Conclusion: It is concluded that gastroesophageal reflux disease in asthmatic patients present symptoms of acidic mouth taste, chest burning, chest pain, dyspepsia, regurgitation reflex and sore throat