Publications

Papers

  • Nailah Alhassoun, Md Yusuf Sarwar Uddin, and Nalini Venkatasubramanian. “SAFER: An IoT-Based Perpetual Safe Community Awareness and Alerting Network”. In 2017 Eighth International Green and Sustainable Computing Conference (IGSC), 2017.[PDF]
  • M. Y. S. Uddin, A. Nelson, K. Benson, G. Wang, Q. Zhu, Q. Han, N. Alhassoun, P. Chakravarthi, J. Stamatakis, D. Hoffman, S. Almomen, L. DArcy, and N. Venkatasubramanian, “The SCALE2 Multi-network Architecture for IoT-based Resilient Communities, ” In 2016 IEEE International Conference on Smart Computing (SMARTCOMP), 2016.[PDF]
  • M.B. Menai and N. Al-Hassoun. “Similarity Detection in Java Programming Assignments”. Proceedings of the IEEE 2010 5th International Conference on Computer Science and Education (ICCSE), 2010. [PDF]

Posters

  • Optimizing Energy in Heterogeneous Perpetual IoT Platforms. N. Alhassoun, In ACM/IFIP/USENIX Middleware 2017, ICGS PhD Forum 2017.[PDF]
  • Ambient Multi-sensors Based Fall Detection System for Assisted Living. N. Alhassoun, N. Venkatasubramanian, In CWIC SoCal 2016.[PDF]
  • Elderly Assisted Living System. N. Alhassoun, In NASA DIRECT-STEM Annual Research Symposium 2016 at CalState, LA.
  • CAPlag : Code-base Plagiarism Detection ToolN. Alhassoun, M.B. Menai. In IECHE 2010. [PDF]

Projects

  • Enabling Personal Sensing in SCALE

SCALE (Safe Community Awareness and Alerting Network) is a cyber-physical system (CPS) leveraging the pervasive Internet of Things (IoT) to extend a smarter, safer home to all residents at a low incremental cost. SCALE has been successfully deployed in the Victory Senior Housing facility in Montgomery County, MD for a wide variety of sensing applications, such as personal safety, building/space safety, seismic activity, fire events and environmental monitoring (air quality).

Our work in the personal sensing direction uses fall detection in senior populations as a driving scenario ­- we developed an ambient IoT system leveraging SCALE that observes the status of multi­-connected sensors (in situ and on person).  The deployed sensors were used  for detecting different activities of daily living (ADLs) and then contacting and sending alerts to healthcare providers when a fall is detected for immediate assistance.  We implemented a prototype testbed that includes (a) smart pressure pad that is a 4×6 matrix consisting of 24 Square Force-Sensitive Resistor sensors; (b) wearable sensor (CC2541 Ti SensorTag); (c) mobile sensor (accelerometer); (d) local broker (Raspberry Pi B) that supports the interconnections of multiple networks and sensors, and publishes data to the back-end SCALE server using the MQTT protocol.

 

  • SAFER: IoT-Based Perpetual Safe Community Awareness and Alerting Network

Perpetual awareness systems are sensing systems characterized by continuous monitoring and ubiquitous sensing; they are essential to many safety and mission-critical applications, e.g. assisted living, healthcare and public safety. In this paper, we present SAFER, a perpetual heterogeneous IoT system; deployed in homes to detect critical events (injury, hazardous-environment) that must trigger immediate action and response. A key challenge here is the energy consumption associated with perpetual operations. We propose a novel energy-aware perpetual home IoT system where battery-operated and wall-powered IoT devices co-execute to ensure safety of occupants. We use a semantic approach that extracts activities-of-daily-living from device data to drive energy-optimized sensor activations. To validate our approach, we developed an elderly fall detection system using multi-personal and in-situ sensing de- vices. Using initial measurements to drive larger simulations, we show that our Cost-Function-Gradient algorithm can achieve greater than 4X reductions in energy dissipation without loss of sensing accuracy.

  • CAPlag: Code-base Plagiarism Detection

In the age of information technologies, plagiarism has become more actual and turned into a serious problem especially in university courses with programming assignments. Detecting plagiarism manually is a challenging task because it is difficult and time- consuming. Hence, automated search tools are particularly helpful in detecting similarity between pairs of programs. Plagiarism detection systems are generally grouped into two classes: Attribute-counting systems, and structure-based systems. We propose a new plagiarism detection system, called CAPlag “Computing Assignment Plagiarism”, for Java programming assignments, in which we exploit both attribute-counting and structure-based properties in order to identify lexical and structural changes. Our contribution consists in using a Java program profiling to extract the main program characteristics and a comparison method inspired by DNA sequencing. Experimental results show that CAPlag can outperform JPlag, a well-known plagiarism detection system, especially on small program instances.

  • OSM: Operating System Magnifier 

OS Magnifier software focuses on the two most important parts in the operating system: process management “scheduling” and memory management “paging”. We implemented OS Magnifier application to help the college-level operating system learners.

We designed OS Magnifier to be simple, clear, and organized. The software and its user manual can be downloaded.