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This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language "Solidity" is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes' historical data fromthe Internet Archive and theWaybackMachine project. The collected data has been compared with two other services that harvest the number of Ethereumnodes. Ourmethod has collectedmore than 30%more than the other services. The data trained a neural network model regarding time series to predict the number of online nodes in the future.Our findings show that there are less than 20%of the same nodes daily, indicating thatmost nodes in the network change frequently. It poses a question of the stability of the network. Furthermore, historical data shows that the top ten countries with Ethereum clients have not changed since 2016. The popular operating system of the underlying nodes has shifted from Windows to Linux over time, increasing node security. The results have also shown that the number of Middle East and North Africa (MENA) Ethereum nodes is neglected compared with nodes recorded from other regions. It opens the door for developing new mechanisms to encourage users from these regions to contribute to this technology. Finally, the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network. [ABSTRACT FROM AUTHOR]

IEEE802.11, known as WiFi has proliferated in the last decade. It can be found in smartphones, laptops, smart TVs and surveillance cameras. This popularity has revealed many issues in health, data privacy and security. In this work, a WiFi measurement study has been conducted in Amman, the capital city of Jordan. An Android App has been written to harvest WiFi information of the transmitted frames of any surrounding Access points (APs). More than 240,000 APs information has been harvested in this work. The harvested data have been analyzed to find statistics ofWiFi devices in this city. Moreover, three power distribution models have been derived from the data for three different areas, closed, open and hybrid areas. In addition, the collected data revealed that the SSID can be leveraged as a landmark for the access points (APs). To this end, SSIDtrack algorithm is proposed to track shoppers/walkers in closed areas, such as malls to find their walking route utilizing only the SSID information collected from the surrounding area. The algorithm has been tested in two different malls that consist of four different floors. The accuracy recorded for the algorithm acceded 95%. [ABSTRACT FROM AUTHOR]