Research Projects at WiSe Lab

Opportunistic Networks (Oppnets)

We propose a new paradigm and a new technology of opportunistic networks or oppnets to enable an integration of the diverse communication, computation, sensing, storage and other resources that surround us more and more. For more details of the project, visit: Project Oppnet

Project Members Involved: Leszek Lilien, Ajay Gupta, Ala Al-Fuqaha, James Yang, Zille Huma Kamal, Vijay Bhuse and Mark Jochum

Smart Occupancy Monitoring System

An application to determine the occupancy of a room using a network of motes (tiny self-contined, battery-powered devices with radio links) with sensor boards attached to them. Occupancy of a room is determined by taking into consideration varoius environmental factors such as light, temperature, and sound in a room. Other features of the application include getting raw information of the same readings from the network, checking the battery power of the devices in the network, activate a fire monitor that informs the user of any rapid temperature changes in a room, reset the devices on the network if the motes do not behave correctly. One device is connected to a base station (usually a pc/laptop) which sends out queries into the network to obtain the above readings. All the devices in the network work together to respond back to the base station and thus the user gets the results back.

Project Members Involved: Siva Desaraju, Vivek Kinra, Eric Boromisa, Ajay Gupta

Collaborative Signal Processing

Collaborative Signal Processing is important to wireless sensor devices, especially small devices since they have constraints on their power/energy consumption. This study attempts to utilize Collaborative Signal Processing in order to achieve more accurate results without consuming excessive energy. Collaborative Signal Processing can be achieved in two ways - via Decision Fusion or Data Fusion. In Decision Fusion, all nodes in an environment make decisions, say the presence or absence of a certain gas or object (for which these sensors have already been trained) based on observed data, and then communicate these decisions to a designated manager node. This manager node integrates these various decisions and make a collective decision. In Data Fusion, the observed data of each node is communicated to the manager node, which statistically combines the data to make a collective decision. This project studies the training and deployment phases of collaborative signal processing and the focus for this semester would be to develop an application indicating practical implications of collaborative signal processing.

Project Members Involved: Mohammad Ali Salahuddin, Zille Huma Kamal, Ajay Gupta

Location Tracking of Mobile Objects

Finding the location of a mobile object has applications ranging from trying to remember where you put your keys to pinpointing the location of an enemy tank. Our location tracking system uses sensor nodes with known fixed locations. A query is sent from a base station to locate a mobile node. When the mobile node receives the query, it begins sending out signals of increasing radio strength. When it receives a response from three of the fixed nodes, it forwards its data (the ID of the three nodes and the distance from each node) back to the base station. The base station uses the network topology and the incoming data to locate the mobile node. Our current work concentrates on improving the reliability of the system.

Project Members Involved: Mark Terwilliger, Junaith Shahabdeen, Ajay Gupta

Mobile and Self-Calibrating Irrigation System

The system consists of small mobile nodes which are placed in the soil around plants. When appropriate, the nodes sample the environment and waters the plants accordingly. This method, if successful, will allow crops to be grown without human monitoring or contact for long periods of time, even in adverse conditions. These nodes can also be attached to climate control applications used within a greenhouse allowing for a completely automated environmental control system Current work involves making the system more robust and modeling it against existing systems used on farms and in greenhouses. When completed, the system will have the ability to irrigate many plants in multiple stages of life.

Project Members Involved: Benjamin Beckmann, Ajay Gupta

On Incorporating Mobile-ware In Parallel Computation

This project is to integrate mobile devices into computational grid to benefit from untouched mobile power. In our project, we consider the aggregate mobile power, feasibility of such integration, ways to convince device owners, intermittent buffering etc. in addition to important issues such as communication overhead, energy consumption, etc. Goals: In the original proposal, we distribute workload from computation grid to mobile devices. We are investigating issues related to the distribution of workload from mobile devices to the computational grid as well. Propose a model to relate all the parameters. Use ns2/OPNET to simulate the model developed and to analyze feasibility of integration. Implement a client-side agent for communication with the grid controller. Adding mobile communication/device support into MPI library. Solving/reducing security problems during the connection. Information gathering about a. lam, lamtrace, lamboot, mpirun b. integration of mobile devices other than laptops like PDAs, cell phones, etc. c. learning ns2 and OPNET when simulations are required d. security issues with AP, SSH, MPI

Project Members Involved: Sathya Priya Durairju, Mustafa Manver, Ajay Gupta

Dynamic Sensor Networks

The title of the research idea is somewhat vague, but the idea is rather simple, which is nice. It essentially builds upon standard sensor networks by making the individual sensors mobile, and moving them in an intelligent manner. The purpose of this movement is to arrange the sensors in such a way that the network has the clearest possible picture of its environment; fewer sensors will be located in areas of relatively low interest, while more sensors will be located in areas of high interest in order to increase the data available on those areas. Hopefully, the result of this sensor network will be to gather more information on an environment using fewer sensors, and allowing the network itself to do the rather tricky work of arranging the sensors. A fundamental aspect of the network will be its ability to move its sensors dynamically as the environment changes, thus making it adaptable to an ever-changing environment. The goals of this research effort are as follows: To discover if this concept has been attempted elsewhere, and if so evaluate the techniques used. Develop a model to simulate the environment the sensor network exists in to a degree sufficient for testing the network. Develop a technique for implementing the dynamic sensor network. Implement the network and evaluate its performance. Uniform sensor distribution ------------ 'Smart’ sensor distribution Here’s a pair of diagrams to show how data can be lost in an environment, and how the dynamic network could supposedly rearrange the sensors to concentrate on the interesting area. Note that the network would start with a uniform distribution and then move the sensors to their ideal locations.

Project Members Involved: Evan Rubin, Ajay Gupta