Autonomic and embedded wireless sensor protocols for critical infrastructures

The tragic events of September 11th ushered a new era of unprecedented challenges. Our nation has to be protected from the alarming threats of adversaries. These threats exploit the nation's critical infrastructures affecting all sectors of the economy. There is the need for pervasive monitoring and decentralized control of the nation's critical infrastructures. The communications needs of monitoring and control of critical infrastructures was traditionally catered for by wired communication systems. These technologies ensured high reliability and bandwidth but are however very expensive, inflexible and do not support mobility and pervasive monitoring. The communication protocols are Ethernet-based that used contention access protocols which results in high unsuccessful transmission and delay. An emerging class of wireless networks, named embedded wireless sensor and actuator networks has potential benefits for real-time monitoring and control of critical infrastructures. The use of embedded wireless networks for monitoring and control of critical infrastructures requires secure, reliable and timely exchange of information among controllers, distributed sensors and actuators. The exchange of information is over shared wireless media. However, wireless media is highly unpredictable due to path loss, shadow fading and ambient noise. Monitoring and control applications have stringent requirements on reliability, delay and security. The primary issue addressed in this dissertation is the impact of wireless media in harsh industrial environment on the reliable and timely delivery of critical data. In the first part of the dissertation, a combined networking and information theoretic approach was adopted to determine the transmit power required to maintain a minimum wireless channel capacity for reliable data transmission. The second part described a channel-aware scheduling scheme that ensured efficient utilization of the wireless link and guaranteed delay. Various analytical evaluations and simulations are used to evaluate and validate the feasibility of the methodologies and demonstrate that the protocols achieved reliable and real-time data delivery in wireless industrial networks.

High School Influences on the Selection of Athletic Training as a Career

Context: Research suggests internships, mentorship, and specialized school programs positively influence career selection; however, little data exists specific to athletic training. Objective: We identified high school (HS) experiences influencing career choice in college athletic training students (ATS). Design: Our survey included 35 Likert-type close-ended questions, which were reviewed by a panel of faculty and peers to establish content and construct validity. Setting: Participants completed an online questionnaire at their convenience. Participants: 217 college ATS (153 female, 64 male) from a random selection of accredited programs on the east coast. We excluded minors, freshmen, and undecided majors from the study. Informed consent was implied by proceeding to the questionnaire. Data Collection and Analysis: We used descriptive statistics to analyze the data collected via a secure website. Results: Mentors were most influential in the decision of career path (62.4%;n=131/210) with 85.2% (n=138/162) reporting mentors were readily available to answer questions regarding career options and 53.1% (n=86/162) counseled them regarding HS electives. Of participants involved in an internship (41.0%;n=86/210), most developed such opportunities independently (66.3%;n=57/86). Respondents who attended traditional HS suggested providing diverse electives (71.9%;n=133/185), additional internship (53.5%;n=99/185), and mentorship (33.0%;n=61/185) opportunities to effectively educate students regarding career options. Conclusions: College ATS that gained internship experience during HS report the opportunity positively influenced their career selection. Mentors support HS students by offering insight and expertise in guiding students’ career choices. Participants suggested HS afford diverse electives with internship and mentorship opportunities to positively influence interested students towards pursuing a career in athletic training.

Novel online data cleaning protocols for data streams in trajectory, Wireless Sensor Networks

The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or "dirty" sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.