Jason Hallstrom is director of FAU's I-SENSE program, and a professor in the Department of Computer and Electrical Engineering and Computer Science at FAU.
Areas of Expertise (7)
The Internet of Things
Embedded Network Systems
Internet-scale Sensing Infrastructure
Ohio State University: Ph.D.
Selected Media Appearances (1)
New device to change how Florida monitors sea level rise, water quality and hurricanes
Science X network online
Small wireless computing devices, ranging from the size of a matchbox to the size of a dime are going to change the way Florida monitors its water quality, sea level rise, hurricanes, agriculture, aquaculture, and even its aging senior population. The types of sensing devices developed by computer scientist Jason Hallstrom, Ph.D., who recently joined Florida Atlantic University, can collect information about the surrounding environment and transmit that information to cloud-based computing systems that store, analyze and present that information to educators, researchers and decision-makers. Deployable at massive scales, the technology represents a paradigm shift in how our world is observed and managed.
Selected Articles (3)
Christopher J. Post, Michael P. Cope, Patrick D. Gerard, Nicholas M. Masto, Joshua R. Vine, Roxanne Y. Stiglitz, Jason O. Hallstrom, Jillian C. Newman, Elena A. Mikhailova
Dissolved oxygen is a critical component of river water quality. This study investigated average weekly dissolved oxygen (AWDO) and average weekly water temperature (AWT) in the Savannah River during 2015 and 2016 using data from the Intelligent River® sensor network. Weekly data and seasonal summary statistics revealed distinct seasonal patterns that impact both AWDO and AWT regardless of location along the river. Within seasons, spatial patterns of AWDO and AWT along the river are also evident. Linear mixed effects models indicate that AWT and low and high river flow conditions had a significant impact on AWDO, but added little predictive information to the models. Low and high river flow conditions had a significant impact on AWT, but also added little predictive information to the models. Spatial linear mixed effects models yielded parameter estimates that were effectively the same as non-spatial linear mixed effects models. However, components of variance from spatial linear mixed effects models indicate that 23–32% of the total variance in AWDO and that 12–18% of total variance in AWT can be apportioned to the effect of spatial covariance. These results indicate that location, week, and flow-directional spatial relationships are critically important considerations for investigating relationships between space- and time-varying water quality metrics.
Jinwei Liu, Haiying Shen, Lei Yu, Husnu Saner Narman, Jiannan Zhai, Jason O. Hallstrom, Yangyang He
As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce n-guaranteed delivery which means that the ratio of successful data deliveries is not less than n, and study the relationship between the transmission power of sensors and the probability of achieving n-guaranteed delivery. Furthermore, with considering the effect of network congestion, link collision, and holes (e.g., those caused by physical obstacles such as a lake), we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving n-guaranteed delivery.
Neelam Soundarajan, Jason O. Hallstrom
Design patterns provide guidance to system designers onhow to structure individual classes or groups of classes, aswell as constraints on the interactions among these classes,to enable them to implement flexible and reliable systems.Patterns are usually described informally. While such informaldescriptions are useful and even essential, if we wantto be sure that designers precisely and unambiguously understandthe requirements that must be met when applyinga given pattern, and be able to reliably predict the behaviorsthe resulting system will exhibit, we also need formalcharacterizations of the patterns.In this paper, we develop an approach to formalizing designpatterns. The requirements that a designer must meetwith respect to the structures of the classes, as well as withrespect to the behaviors exhibited by the relevant methods,are captured in the responsibilities component of the patternýsspecification; the benefits that will result by applyingthe pattern, in terms of specific behaviors that the resultingsystem will be guaranteed to exhibit, are captured in therewards component. One important aspect of many designpatterns is their flexibility; our approach is designed to ensurethat this flexibility is retained in the formalization ofthe pattern. We illustrate the approach by applying it to astandard design pattern.