The world is consuming large amounts of energy in various forms like electric energy and mechanical energy. Since the electrical energy is an important factor for the development of the world, many researchers tried to generate electricity from renewable energy sources collected by sensors in order to overcome the shortage of electrical energy for household appliances and industrial areas. In this paper, we develop Internet-of-Things (IoT) based system to generate electrical energy from multiple sensors for household appliances and industrial areas. Different sensors namely piezoelectric sensor, body heat to electric converter and solar panel are utilized and connected to the power storage circuit for generation of electrical energy. Two different Artificial Intelligence (AI) models such as Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) are utilized for the total power generated from renewable energy resources. Validation is done through the statistical parameters such as Root Mean Square Error (RMSE) and R2 coefficient of correlation. Result outcome from the models shows that ANN performance is better than ANFIS.

With the wide applications of Wireless sensor networks (WSNs) in various fields such as environment monitoring, battlefield surveillance, healthcare, and intrusion detection, trust establishment among sensor nodes becomes vital requirement to improve security, reliability and successful cooperation. The existing trust management approaches for large scale WSN are failed due to their low dependability (i.e. cooperation), higher communication and memory overheads (i.e. resource inefficient). In this paper, we propose a novel and comprehensive trust estimation approach (LTS) for large scale WSN that employs clustering to improve cooperation, trustworthiness, and security by detecting malicious (faulty or selfish) sensor nodes with reduced resource (memory, power) consumption. The proposed scheme (LTS) operates on two levels namely, intra-cluster and inter-cluster along with distributed approach and centralized approach respectively to make accurate trust decision of sensor nodes with minimum overheads. LTS consists of unique features like robust trust estimation function, attack resistant and efficient trust aggregation at cluster head to obtain global feedback trust value. Data trust along with communication trust plays a significant role to cope with malicious nodes. In LTS, punishment and trust severity can be tuned according to the application requirement makes it an innovative trust estimation approach. Moreover, dishonest recommendations (outliers) are eliminated before aggregation at the base station by observing statistical dispersion. Theoretical and mathematical validation along with simulation results exhibit great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes as well as communication overhead.

Software maintainability predicts changes or failures that may occur in software after it has been deployed. Since it deals with the degree to which an application may be understood, repaired or enhanced, it also takes into account the overall cost of the project. In the past several measures have been taken into account for predicting metrics that influence software maintainability. However, deep learning is yet to be explored for the same. In this paper, we perform Deep Learning for Software Maintainability Metrics Prediction on a large number of datasets. Unlike the previous research works, we have relied on large datasets from 299 software’s and subsequently applied various metrics and functions to the same. Twenty-nine object-oriented metrics have been considered along with their impact on software maintainability of open source software. Five Machine Learning algorithms namely Ridge Regression with Variable Selection, Decision Tree, Quantile Regression Forest, Support Vector Machine and Principal Component Analysis have been applied to the original datasets, as well as to the refined datasets. It was found that our study provides results in form of metrics that may be used in the prediction of software maintenance and the proposed Deep Learning model outperforms all of the other methods that were considered. Further, the results of experiment affirm the efficiency of the proposed Deep Learning model for software maintainability prediction.