A multi-hop routing protocol for an energy-efficient in wireless sensor network

The low-energy adaptive clustering hierarchy (LEACH) protocol has been developed to be implemented in wireless sensor networks (WSNs) systems such as healthcare and military systems. LEACH protocol depends on clustering the employed sensors and electing one cluster head (CH) for each cluster. The CH nodes are changed periodically to evenly distribute the energy load among sensors. Updating the CH node requires electing different CH and re-clustering sensors. This process consumes sensors’ energy due to sending and receiving many broadcast and unicast messages thus reduces the network lifetime, which is regarded as a significant issue in LEACH. This research develops a new approach based on modifying the LEACH protocol to minimize the need of updating the cluster head. The proposal aims to extend the WSN’s lifetime by maintaining the sensor nodes’ energy. The suggested approach has been evaluated and shown remarkable efficiency in comparison with basic LEACH protocol and not-clustered protocol in terms of extending network lifetime and reducing the required sent messages in the network reflected by 15%, and, in addition, reducing the need to reformatting the clusters frequently and saving network resources

ANALYZING AND EVALUATING THE SECURITY STANDARDS IN WIRELESS NETWORK: A REVIEW STUDY

Wireless networks used widely in office, home, and public places so security is one of the significant issues to keep the transmitted information safe. The applied security standards have been developed in response to the demand of high security and the developed hardware with software. Currently, the available security standards are (WEP, WPA, WPA2 and under development WPA3). These security standards are different in the offered security level base on the employed authentication method and encryption algorithms. The major objective of this paper is studying security standards and analyzing them based on their features. In addition to presenting a detailed review about WPA3 and its improvements over the older security standards. The conducted evaluations explained the differences among the Wi-Fi security standards in term of the offered security level, software and hardware requirements

The effect of gamma value on support vector machine performance with different kernels

Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed