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Article Citation - WoS: 5Citation - Scopus: 8Optimal Operation Mode Selection for Energy-Efficient Light-Weight Multi-Hop Time Synchronization in Linear Wireless Sensor Networks(Springer, 2020) Al Imran, Md Abdullah; Dalveren, Yaser; Tavli, Bulent; Kara, AliWe explored the joint effect of synchronization window and offset/drift mode selection on the time synchronization of linear wireless sensor networks (LWSNs). Recent advances in the field along with the availability of capable hardware led to adoption of LWSNs in diverse areas like monitoring of roads, pipelines, and tunnels. The linear topology applications are susceptible to single point of failure; therefore, energy efficient operation of LWSNs is even more important than the traditional WSNs. To address the challenge, we investigate the time synchronization mode selection for the optimum operation of a multi-hop and low-overhead LWSN. We investigate two modes of synchronization: synchronization by using only offset and synchronization by using offset in addition to the clock drift. Furthermore, we investigate the effects of synchronization window size. Our experimental results reveal that computation of offset alone for smaller window sizes and resynchronization periods is sufficient in achieving acceptable degree of synchronization.Article Citation - WoS: 18Citation - Scopus: 19Performance Analysis of Modular Rf Front End for Rf Fingerprinting of Bluetooth Devices(Springer, 2020) Uzundurukan, Emre; Ali, Aysha M.; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) could provide an efficient solution to address the security issues in wireless networks. The data acquisition system constitutes an important part of RFF. In this context, this paper presents an implementation of a modular RF front end system to be used in data acquisition for RFF. Modularity of the system provides flexible implementation options to suit diverse frequency bands with different applications. Moreover, the system is able to collect data by means of any digitizer, and enable to record the data at lower frequencies. Therefore, proposed RF front end system becomes a low-cost alternative to existing devices used in data acquisition. In its implementation, Bluetooth (BT) signals were used. Initially, transients of BT signals were detected by utilizing a large number of BT devices (smartphones). From the detected transients, distinctive signal features were extracted. Then, support vector machine (SVM) and neural networks (NN) classifiers were implemented to the extracted features for evaluating the feasibility of proposed system in RFF. As a result, 96.9% and 96.5% classification accuracies on BT devices have been demonstrated for SVM and NN classifiers respectively.
