National Science Foundation (NSF) Award
Successfully tackling many urgent challenges in socio-economically critical domains (such as sustainability, public health, and biology) requires obtaining a deeper understanding of complex relationships and interactions among a diverse spectrum of entities in different contexts. In complex systems, (a) it is critical to discover how one object influences others within specific contexts, rather than seeking an overall measure of impact, and (b) the context-aware understanding of impact has the potential to transform the way people explore, search, and make decisions in complex systems.
This project establishes the foundations of big data driven Context-Sensitive Impact Discovery (CSID) in complex systems and fills an important hole in big data driven decision making in many critical application domains, including epidemic preparedness, biological pathway analysis, climate, and resilient water/energy infrastructures.
Department of Defense (DoD) Award
Large-scale wireless networks are central to the DoD’s vision of net-centric warfare. Past research has shown that the capacity of multi-hop wireless networks decreases as the number of nodes in the network increases. To achieve linear capacity scaling with increase in network size several significant limitations, such as long latency, high technical complexity, restricted traffic pattern, or infrastructure requirement, need to be addressed.
The objectives of the project are to: 1) investigate the fundamental capacity limits of wireless networks with DE links, and the conditions under which close-to-linear capacity scaling is achievable; 2) investigate the practical network algorithms and protocols that can achieve close-to-linear capacity scaling.
There is a growing need for optimization methods to support decentralized decision-making in complex multi-agent systems including target tracking in sensor networks, mission planning of unmanned autonomous vehicles, coordination of rescue robots in disaster scenarios, and scheduling of intelligent devices in smart homes within smart grids.
This project will make the necessary foundational contributions to the field of multi-agent systems to improve the scope and applicability of such systems, especially those that utilize automated planning and constraint optimization techniques, in the real world. More specifically, this project will result in (i) novel ways to more accurately model a large class of multi-agent planning problems using decentralized constraint-based models; (ii) new scalable algorithms with theoretical guarantees suitable for solving large-scale decentralized planning problems; and (iii) effective ways of improving computational thinking in high-school students via the use of constraint-based representations.
REU Site: BIGDatA – Big Data Analytics for Cyber-Physical Systems
PI: Dr. Huiping Cao
Co-PI: Dr. Jay Misra
The goal of this REU site initiative is to inspire and prepare undergraduate students to pursue careers in STEM with a focus on big data analytics for Cyber-physical Systems (CPS). PI’s propose research projects to explore big data analytics for CPS in three intertwined layers: (L1) systems and architecture, (L2) models and algorithms, and (L3) visualization, spanning across four CPS application areas including smart grids, wireless sensor networks, smart homes for elderly and disabled, and disaster response. The planned student activities include: (i) team-based research activities on focused research projects, (ii) creation of cohorts, which will engage in training workshops, to develop research skills and to prepare for graduate schools, (iii) field trips to companies, and local and national labs to broaden students’ research horizon, (iv) workshop and conference participation to present research findings, and (v) mentoring by faculty members and interaction with other student researchers.
Robust Solutions for Distributed Constraint Optimization Problems
PI: Dr. William Yeoh
Distributed constraint optimization problems (DCOPs) have been shown to be useful in modeling various distributed combinatorial optimization problems, including meeting scheduling, sensor network, and power management problems. However, many of these problems are not only distributed in nature but dynamic as well. For example, a disaster rescue scenario can include dynamic events like the collapse of buildings, detection of new survivors, and spread of fires. Previous attempts to cope with dynamism in DCOPs have focused on reactively finding a new solution when an event occurs.
In this project, the PI will take a proactive approach by taking possible future events into consideration when searching for solutions. This research will result in (1) a newly designed Robust DCOP (R_DCOP) model that will include a probabilistic scheme representing the likelihood of dynamic events; and (2) R_DCOP algorithms that will address the stochastic elements of the problem. The broader impacts of this research project are two fold: (1) Through this research, the PI will build the foundations for a general robust DCOP model that can be applied in dynamic environments and spur deployment of DCOP algorithms in the real world; and (2) This project will support broadening participation of underrepresented students at NMSU, a minority- and Hispanic-serving institution.
NASA Award – Collaboration with UTEP
Minority University Research and Education Project (MUREP)
PI: Mr. Nate Robinson, with iCREDIT’s Dr. Susan Brown
In collaboration with UTEP’s Nate Robinson, Dr. Susan Brown is assisting to integrate the iCREDITS after-school curriculum to build programs that connect students from underrepresented and underserved communities with NASA, giving students the strong foundation they need to pursue and excel in STEM fields. Projects include providing narratives that link to the learning experiences in the iCREDITS lessons. Staff create stories with characters that are designed by an artist with Disney studios. Characters encounter problems that students solve in group-settings, using the STEM concepts learned during the iCREDITS lessons. This is the first semester of this pilot, and we are gathering the results at this time.
“The story-centric approach in this model and its use and reuse of existing STEM content, particularly government resources, is novel. Users can take existing curricula, lessons and other media and integrate those materials into narratives with characters, storylines and varying plots that can be customized for each educator, instructional program or learner. They are able to select different components of a story (like chapters) and combine them into their own story though each of the chapters contains lessons, video, experiments, etc. that feed the story.”
iCREDITS researchers and collaborators, Reza Tourani, Satyajayant Misra, Joerg Kliewer, Scott Ortegel, and Travis Mick, published “Catch Me If You Can: A Practical Framework to Evade Censorship in Information-Centric Networks” at the 2nd ACM Conference on Information-Centric Networking (ICN 2015) in San Francisco, CA.
Conference Information: ACM ICN is a single track conference focusing on current ICN research topics, featuring paper presentations and demonstrations. The fundamental concept in Information-Centric Networking (ICN) is to provide accessing named data as a principal network service, evolving the Internet from today’s host based packet delivery towards directly retrieving information objects by names in a secure, reliable, scalable, and efficient way. These architectural design efforts aim to directly address the challenges that arise from the increasing demands for highly scalable content distribution, from accelerated growths of mobile devices, from wide deployment of Internet-of-things (IoT), and from the need to secure the global Internet.
Paper Abstract: Internet traffic is growing rapidly, driven by the fast-growing mobile user base that is more interested in the content rather than its origin. These trends have motivated proposals for a new Internet networking paradigm–information-centric networking (ICN). This paradigm requires unique names for packets to leverage pervasive in-network caching, name-based routing, and named-data provenance. However named-data routing makes user censorship easy. Hence an anti-censorship mechanism is imperative to help users mask their named queries to prevent censorship and identification. This masking mechanism should not adversely affect request rates.
In this paper, we propose such an anti-censorship framework, which is lightweight and specifically targets low compute-power devices. We analyze our framework’s information-theoretic secrecy and present perfect secrecy thresholds under different scenarios. We also analyze its breakability and computational security. Experimental results prove the framework’s effectiveness: for requests it adds between 1.3–1.8 times in latency overhead over baseline ICN; significantly lesser than the overhead of the state of the art Tor (up to 38 times over TCP).