Salih Safa Bacanli

Bio: Salih Safa Bacanli is an academic researcher from University of Central Florida. The author has contributed to research in topics: DBSCAN & Home automation. The author has an hindex of 3, co-authored 17 publications receiving 38 citations.

Opportunistic Message Broadcasting in Campus Environments

Abstract: In this paper, we propose an infrastructure- independent opportunistic mobile social networking strategy for efficient message broadcasting in campus environments. Specifically, we focus on the application scenario of university campuses. In our model, the students' smart-phones forward messages to each other. The messages are created spontaneously as independent events in various places of the campus. The events can be either urgent security alerts or private announcements to the students currently on the campus. Our proposed state- based campus routing (SCR) protocol is based on the idle and active states of the students in indoor and outdoor places. The proposed model is analyzed through extensive network simulations using mobility datasets collected from students on University of Milano and University of Cambridge campuses. The opportunistic network model and the SCR protocol are compared with epidemic, epidemic with TTS (Times To Send), PROPHET, and random routing protocols. The message delivery performance of SCR is close to Epidemic and PROPHET while SCR reduces the amount of message transmissions.

A Smart Walker for People with Both Visual and Mobility Impairment.

Abstract: In recent years, significant work has been done in technological enhancements for mobility aids (smart walkers). However, most of this work does not cover the millions of people who have both mobility and visual impairments. In this paper, we design and study four different configurations of smart walkers that are specifically targeted to the needs of this population. We investigated different sensing technologies (ultrasound-based, infrared depth cameras and RGB cameras with advanced computer vision processing), software configurations, and user interface modalities (haptic and audio signal based). Our experiments show that there are several engineering choices that can be used in the design of such assistive devices. Furthermore, we found that a holistic evaluation of the end-to-end performance of the systems is necessary, as the quality of the user interface often has a larger impact on the overall performance than increases in the sensing accuracy beyond a certain point.

Predicting the Temperature Dynamics of Scaled Model and Real-World IoT-Enabled Smart Homes

Abstract: Recent advances in IoT sensors and actuators and smart home controllers allow us to collect real-time information about the state of the home and take intelligent actions that maximize the user's goals with respect to comfort, convenience, environmental awareness and cost. While thermal comfort is one of the primary concerns of many users, many homes use a very simple, energy inefficient approach that blankets the home with constant temperature air conditioning. Such systems do not take advantage of more energy efficient and environment friendly natural ways to manage the temperature, such as opening and closing windows, window shades and interior doors. In this paper we develop a deep neural network based model that predicts the temperature in various rooms of the home function of the state of the actuators. We also describe a scaled model of a four room home which allows us to control the doors and windows and collect data using IoT devices. We train and validate our temperature models on both data collected from the scaled model as well as from publicly available datasets from two real-world smart homes.

Exposing Vulnerabilities in Mobile Networks: A Mobile Data Consumption Attack

Abstract: Smartphone carrier companies rely on mobile networks for keeping an accurate record of customer data usage for billing purposes. In this paper, we present a vulnerability that allows an attacker to force the victim's smartphone to consume data through the cellular network by starting the data download on the victim's cell phone without the victim's knowledge. The attack is based on switching the victim's smartphones from the Wi-Fi network to the cellular network while downloading a large data file. This attack has been implemented in real-life scenarios where the test's outcomes demonstrate that the attack is feasible and that mobile networks do not record customer data usage accurately.

Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project

Abstract: The smart home concept can significantly benefit from predictive models that take proactive management operations on home actuators, based on users' behavior evaluation. In this paper, we use a small-scale physical model, the ScaledHome-2 testbed, to experiment with the evolution of measurements in a suburban home under different environmental scenarios. We start from the observation that, for a home to become smart, in addition to IoT sensors and actuators, we also need a predictive model of how actions taken by inhabitants and home actuators affect the internal environment of the home, reflected in the sensor readings. In this paper, we propose a technique to create such a predictive model through machine learning in various simulated weather scenarios. This paper also contributes to the literature in the field by quantitatively comparing several machine learning algorithms (K-nearest neighbor, regression trees, Support Vector Machine regression, and Long Short Term Memory deep neural networks) in their ability to create accurate and generalizable predictive models for smart homes.

A Survey of Human Mobility Models

Abstract: Human mobility models are key components of various research fields including transportation, mobile networks, disaster management, urban planning, and epidemic modeling. Understanding human mobility has a major role in the realistic evaluation of new approaches to challenges in these fields. For the perspective of networked systems, simulations of the networks with human participants such as opportunistic social networks are highly dependent on human mobility. In this article, we summarize the state of the art for scientific research on human mobility and survey the currently used human mobility models. We discuss the commonly used metrics and data collection techniques. Furthermore, we include a taxonomy of the mobility models according to their main characteristics and classify them. We lastly discuss the general trends, applicability, further research directions and open problems of human mobility modeling.

An Exact and Fast Computation of Discrete Fourier Transform for Polar and Spherical Grid

Abstract: Numerous applied problems of two-dimensional (2-D) and 3-D imaging are formulated in continuous domain. They place great emphasis on obtaining and manipulating the Fourier transform in polar and spherical coordinates. However, the translation of continuum ideas with the discrete sampled data on a Cartesian grid is problematic. There exists no exact and fast solution to the problem of obtaining discrete Fourier transform for polar and spherical grids in the literature. In this paper, we develop exact algorithms to the above problem for 2-D and 3-D, which involve only 1-D equispaced fast Fourier transform with no interpolation or approximation at any stage. The result of the proposed approach leads to a fast solution with very high accuracy. We describe the computational procedure to obtain the solution in both 2-D and 3-D, which includes fast forward and inverse transforms. We find the nested multilevel matrix structure of the inverse process, and we propose a hybrid grid and use a preconditioned conjugate gradient method that exhibits a drastic improvement in the condition number.

A Basic Probability Assignment Methodology for Unsupervised Wireless Intrusion Detection

Abstract: The broadcast nature of wireless local area networks has made them prone to several types of wireless injection attacks, such as Man-in-the-Middle (MitM) at the physical layer, deauthentication, and rogue access point attacks. The implementation of novel intrusion detection systems (IDSs) is fundamental to provide stronger protection against these wireless injection attacks. Since most attacks manifest themselves through different metrics, current IDSs should leverage a cross-layer approach to help toward improving the detection accuracy. The data fusion technique based on the Dempster–Shafer (D-S) theory has been proven to be an efficient technique to implement the cross-layer metric approach. However, the dynamic generation of the basic probability assignment (BPA) values used by D-S is still an open research problem. In this paper, we propose a novel unsupervised methodology to dynamically generate the BPA values, based on both the Gaussian and exponential probability density functions, the categorical probability mass function, and the local reachability density. Then, D-S is used to fuse the BPA values to classify whether the Wi-Fi frame is normal (i.e., non-malicious) or malicious. The proposed methodology provides 100% true positive rate (TPR) and 4.23% false positive rate (FPR) for the MitM attack and 100% TPR and 2.44% FPR for the deauthentication attack, which confirm the efficiency of the dynamic BPA generation methodology.

The study of grit in engineering education research: a systematic literature review

Abstract: Research on the role of grit – defined as both perseverance and passion for long-term goals – on human performance has been conducted for the past decade. It has been suggested that this non-cognit...