Here are a list of research projects we have been involed in:

  1. Free Space Optical Communication

  2. Cognitive Networks

  3. Cross-layer QoS/QoE Optimization

  4. DistressNet: Wireless Networks for First Responders

  5. Mobile Indoor Self-Localization

Free Space Optical Communication

Radio Frequency (RF) spectrum congestion, as well as an increase in mobile data traffic has led to a search for alternate PHY layers. Over the last few decades, systems which use free space optics (FSO) operating in the 352-384 and 187-197 THz ranges have been developed. Recently, the NASA LLCD project demonstrated a 622 Mbps optical link from Earth to Moon. The spate of recent advances in visible light communication has reinforced this trend. However, FSO is highly directional, and suffers from drawbacks such as requiring near perfect alignment of both transmitter and receiver, as well as susceptibility to absorption in the atmosphere.


  1. M. Atakora, H. Chenji, “Optimal Multicasting in Hybrid RF/FSO DTNs”,
    in IEEE Global Communications Conference (Globecom), 2016

Cognitive Networks


  1. H. Chenji, G. Stewart, Z. Wu, A. Javaid, V. Devabhaktuni, K. Bhasin, B. Wang,
    “An architecture concept for cognitive space communication networks,”
    in 34th AIAA International Communications Satellite Systems Conference and Exhibition (ICSSC), 2016

  2. Z. Wu, H. Chenji, G. Stewart, A. Javaid, V. Devabhaktuni, K. Bhasin and B. Wang,
    “Intelligent Channel Sensing based Secure Cross Layer Cognitive Networking for Resilient Space Communication,”
    in IEEE National Aerospace and Electronics Conference (NAECON), 2016

Quality of Service/Quality of Experience

Bandwidth is a valuable spectral resource; this project aims to improve bandwidth utilization in wireless content delivery networks. The metric of interest is the Quality of Experience (QoE), a subjective user-centric metric, instead of a network-centric metric like the Quality of Service (QoS). The key research thrusts are fairness, stability, complexity, and feasibility.


  1. H. Chenji, Z. J. Haas, P.Xue,
    “Low Complexity QoE-aware Bandwidth Allocation for Wireless Content Delivery,”
    in IEEE Military Communications Conference (MILCOM), 2015
    pdf doi

  2. H. Chenji, Z. J. Haas,
    “Enhancement of Wireless Bandwidth Utilization through User's QoE,”
    in IEEE Wireless Communications and Networking Conference (WCNC), 2015
    pdf doi

Disaster Response Networks

Imagine a large scale disaster that has occurred over a large scale geographical area, such as the earthquake and tsunami in Japan. One of the key disaster response functions is Urban Search & Rescue, which involves “the location, rescue (extrication), and initial medical stabilization of victims trapped in confined spaces”. The design of DistressNet began based on our interaction with members of Texas Task Force 1 headquartered in College Station. We identified a set of areas for improving disaster response times: victim detection in collapsed buildings, information storage and collection about buildings, detection of first responder team separation and lost tools, and throughput and latency of data delivered to first responders. DistressNet discusses the design (i.e., software/hardware architectures, and the guiding design principles), implementation and realistic evaluation of a system that targets the aforementioned areas for reducing the Urban Search & Rescue response time. It is built using battery powered COTS hardware and with open standards and protocols, pushing the complexity that the very diverse Urban Search & Rescue scenarios pose, to user level applications (apps). Apps in DistressNet run on unmodified hardware ranging from smartphones, to low power ZigBee motes and wireless routers.

Click here for the DistressNet micro-site


  1. H. Chenji, W. Zhang, R. Stoleru, C. Arnett,
    “DistressNet: A Disaster Response System Providing Constant Availability Cloud-like Services,”
    in Ad Hoc Networks (Elsevier), Nov. 2013, Vol. 11, No. 8
    pdf doi

  2. S. M. George, W. Zhou, H. Chenji, M. Won, Y.-Oh Lee, A. Pazarloglou, R. Stoleru, P. Barooah,
    “A Wireless AdHoc and Sensor Network Architecture for Situation Management in Disaster Response,”
    in IEEE Communications Magazine, Mar. 2010, Vol. 48, No. 3
    pdf doi

  3. H. Chenji, R. Stoleru,
    “Pareto Optimal Cross Layer Lifetime Optimization for Disaster Response Networks,”
    in 6th International Conference on Communication Systems and Networks (COMSNETS), 2014 (17.6% acceptance rate)
    pdf doi

  4. H. Chenji, L. Smith, R. Stoleru, E. Nikolova,
    “Raven: Energy Aware QoS Control for DRNs,”
    in 9th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2013 (29% acceptance rate)
    pdf doi

  5. H. Chenji, W. Zhang, M. Won, R. Stoleru, C. Arnett,
    “A Wireless System for Reducing Response Time in Urban Search & Rescue,”
    in 31st IEEE International Performance Computing and Communications Conference (IPCCC), 2012 (27% acceptance rate)
    pdf doi


FuzLoc is a distributed indoor self-localization protocol that the aforementioned low power motes can use. By tagging sensor data with a location, first responders’ response time can be reduced. The node localization problem in indoor mobile sensor networks is not new. Particle filters adapted from robotics have produced good localization accuracies in conventional settings. In spite of these successes, state of the art solutions suffer significantly when used in challenging indoor and mobile environments characterized by a high degree of radio signal irregularity (such as a collapsed building with concrete blocks and iron bars). This is because the radio range of a node is no longer the same in all directions. A particle filter becomes “polluted” by incorrectly considering far away nodes as neighbors, successively corrupting the localization accuracy, and leaving the node unable to recover. This is known as the kidnapping problem in robotics. Range-based multi-lateration solutions suffer since they are unable to correctly infer the distance between two nodes owing to phenomenon like interference which affect the received signal strength. FuzLoc uses a fuzzy logic-based approach for mobile node localization in challenging environments. The imprecision present in ranging is used to compute a node's location as an area and not a two dimensional point. Localization is formulated as a fuzzy multi-lateration problem, and is solved using a nonlinear system of equations where the variables are not scalars but are fuzzy numbers with a range and membership function. For sparse networks with few available anchors, a fuzzy grid-prediction scheme is proposed. The fuzzy logic-based localization scheme has been implemented in a simulator and compared to state of the art solutions. Extensive simulation results demonstrate improvements in the localization accuracy from 20% to 40% when the radio irregularity is high. A hardware implementation running on Epic motes and transported by iRobot mobile hosts confirms simulation results and extends them to the real world. FuzLoc was the subject of my M.S. thesis, completed in 2009 under the guidance of Dr. Radu Stoleru.


  1. H. Chenji, R. Stoleru,
    “Towards Accurate Mobile Sensor Network Localization in Noisy Environments,”
    in IEEE Transactions on Mobile Computing, Jun. 2013, Vol. 12, No. 6
    pdf doi

  2. H. Chenji, R. Stoleru,
    “Mobile Sensor Network Localization in Harsh Environments,”
    in 6th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), 2010
    pdf doi