Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by $25.8\%$ and data throughput by $26.86\%$. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

In this work, SDN has been utilized to alleviate and eliminate the problem of ARP poisoning attack. This attack is the underlying infrastructure for many other network attacks, such as, man in the middle, denial of service and session hijacking. In this paper we propose a new algorithm to resolve the problem of ARP spoofing. The algorithm can be applied in two different scenarios. The two scenarios are based on whether a network host will be assigned a dynamic or a static IP address. We call the first scenario SDN_DYN; the second scenario is called SDN_STA. For the evaluation process, a physical SDN-enabled switch has been utilized with Ryu controller. Our results show that the new algorithm can prevent ARP spoofing and other attacks exploiting it.

The cooling of PV models is an important process that enhances the generated electricity from these models, especially in hot areas. In this work, a new, active cooling algorithm is proposed based on active fan cooling and an artificial neural network, which is named the artificial dynamic neural network Fan cooling algorithm (DNNFC). The proposed system attaches five fans to the back of a PV model. Subsequently, only two fans work at any given time to circulate the air under the PV model in order to cool it down. Five different patterns of working fans have been experimented with in this work. To select the optimal pattern for any given time, a back propagation neural network model was trained. The algorithm is a dynamic algorithm since it re-trains the model with new recorded surface temperatures over time. In this way, the model automatically adapts to any weather and environmental conditions. The model was trained with an indoor dataset and tested with an outdoor dataset. An accuracy of more than 97% has been recorded, with a mean square error of approximately 0.02.

This paper introduces singular value decomposition (SVD), a major matrix decomposition technique. SVD serves as the underlining computational engine of many other techniques such as principal component analysis (PCA), eigen decomposition, matrix decomposition, Cholesky decomposition and others. SVD is utilized in many applications such as data analysis and dimensionality reduction, image compression, Google's PageRank algorithm, Netflix's recommender system and many more. This paper overviews the mathematics behind SVD in a simple way. It also applies SVD technique in image compression and in dimensionality reduction as the underlining technique of the PCA and data analysis.

In this paper an analytical model of the optimal number of clusters and the corresponding optimal probability are derived for different types of three-dimensional(3D) WSN settings utilizing single-hop homogeneous LEACH protocol. Three types of 3D network configurations are analyzed. These include cube, cylinder and sphere network configurations. Energy analysis of these network configurations is done in noise-prone communication environment. The noise is modelled utilizing the probability of reception pr. A small value of pr indicates more packets are dropped, which is a sign of large noise. It was shown through analysis and simulation that optimal number of clusters, optimal probability and energy consumption are affected by the geometry of the 3D WSN, noise, and transmission amplification model used.