Web and Wireless Computing & Pervasive and Mobile Computing Laboratory



The W2C lab is designed to carry out cutting edge research in different aspects of data management (security, compression, replication, caching, query processing, aggregation, fusion) in wireless networks and cloud computing environment. Our focus is on scientific research to advance the state of art in these areas. The current projects are supported by NSF, DOE, ARL, ARO, AFRL, NIST, UM System, etc. The current researchers in the lab are pursuing their PhD/MS degree in different areas of interest. The lab is well-equipped with over 50 3.2 Ghz PCs, 5 Dell Server, linux machines, laptops etc. The lab also has sensor network test-beds consists of Crossbow sensor motes like Telosb, Mica2 and Missouri S&T motes. Lab has also developed a DTN testbed for disseminating information securely for battlefield environment.  The lab has graduated 5 PhD and 22 MS thesis students, with 7  PhD and 5 MS thesis in progress.

Current Lab Faculty Researchers

Current Projects:

  • Data Compression and Management in Wireless Sensor Networks
  • Secure Data Aggregation and Trust Management in Wireless Sensor Networks
  • Secure Sensor Cloud
  • Information Fusion in Networked Controlled Systems
  • Social Network based Routing and Security in Delay Tolerant Networks
  • Exploring Query Optimization and Caching in Programming Codes
  • Mobile Cloud Computing
  • Data Management in Mobile Networks
  • Secure and Privacy Preserving Smart-grids
  • Secure Intelligent Transportation
  • Resilient Sensor Networks
  • Assessing Human behavior from Internet Usage

Current Funding Sources:

Research Highlights:

Data Compression in Wireless Sensor Networks
Sanjay Madria

Wireless sensor networks possess significant limitations in storage, bandwidth, processing, and energy. Additionally, real-time sensor network applications such as monitoring poisonous gas leaks cannot tolerate high latency. While some good data compression algorithms exist specific to sensor networks, in this project we have developed TinyPack, a suite of energy-efficient methods with high- compression ratios that reduce latency, storage, and bandwidth usage further in comparison with some other recently proposed algorithms. Our Huffman style compression schemes exploit temporal locality and delta compression to provide better bandwidth utilization important in the wireless sensor network, thus reducing latency for real time sensor-based monitoring applications. We have shown performance evaluations over many different real data sets (over 10) using a simulation platform as well as a hardware implementation show comparable compression ratios and energy savings with a significant decrease in latency compared to some other existing approaches. We have also discussed robust error correction and recovery methods to address packet loss and corruption common in sensor network environments. This paper has won the best paper award in 12th IEEE International Conference on Mobile Data Management (MDM2011) in Sweden and an extended version has now appeared in Distributed and Parallel Database Journal. The algorithm has also been implemented in AFRL application during one of the summer to suite their data sets.

  1. Tommy Szalapski and Sanjay K Madria, "On Compressing Data in Wireless Sensor Networks For Energy Efficiency and Real Time Delivery", to appear in  Distributed and Parallel Databases, 2012.
  2. Tommy Szalapski and Sanjay K Madria, "Energy-efficient Real-Time Data Compression in Wireless Sensor Networks", in proceedings of 12th IEEE International Conference on Mobile Data Management, MDM 2011, Sweden (Best Paper Award).
  3. Tommy Szalapski, Sanjay K Madria and Mark Linderman, "TinyPack XML: Real Time XML Compression for Wireless Sensor Networks", in proceedings of IEEE Wireless Communications and Networking Conference (WCNC), 2012, France.