Mobility in Wireless Sensor Networks
- Author(s): Mehta, Ankur Mukesh
- Advisor(s): Pister, Kristofer S. J.
- et al.
The combination of mobility with wireless networks greatly expands the application space of both robots and distributed sensor networks; such a pervasive system can enable seamless integration between the digital and physical worlds. However, there are a number of issues in both robotic and wireless sensor network (WSN) fields that demand research, and their integration generates further challenges.
A fundamental open problem in robotic systems is the issue of self-contained localization. Especially difficult when considering small scale flying robots, the ability to determine one's position using only on-board sensing is necessary for autonomous robots. GINA, a small wireless inertial measurement unit weighing only 1.6~g was designed to calculate the 6 degree of freedom position of a rigid body. Together with necessary software and hardware, the resulting WARPWING platform served as a highly capable and versatile flight controller for micro air vehicles (MAVs). As an open source hardware project, WARPWING further enabled other unrelated research projects by abstracting away the electronic system design.
As designed, the WARPWING platform was used to control small flying robots. Rocket systems can be used to deliver microelectronic sensor nodes into low earth orbit (LEO) as tiny satellites; analysis of the mechanical parameters demonstrates the feasibility of using a small scale multistage solid fuel guided chemical rocket to deliver a small payload into an orbital trajectory given a suitable controller. Helicopters, similar to rockets, employ attitude control to effect stability and guidance, and so share similar control requirements. Off the shelf toy helicopters can be used as a mechanical airframe; replacing the control electronics with the GINA board enables the design of autonomous MAVs. Purely inertial operation of the GINA board provided stability control, but accumulated drift inhibited guidance control. To calculate position, the state estimator was augmented with additional vision-based sensors such as the VICON motion capture system or an on-board smart camera aimed at an infrared beacon.
The GINA board, containing a wireless enabled processor, was also a platform for WSN research. The key design parameter in WSN systems is power consumption; minimizing energy requirements extends node and system lifetime or lowers required battery mass. A time synchronized, channel hopping (TSCH) medium access control (MAC) protocol, standardized as the IEEE 802.15.4e specification, combines time division multiple access with frequency diversity to ensure reliable, robust low power communication across environmental conditions. This TSCH protocol can be augmented with a variable data rate coding scheme at the physical (PHY) layer to further improve power saving and scalability. The environmental conditions that enable higher data rates also allow wireless communication with imprecise frequency references. A modified PHY layer with frequency offset compensation can be used to implement crystal-free radios with on-board LC oscillators.
Enabling multi-hop networking to mobile MAVs required combining the previous two research thrusts. A helicopter augmented with a payload bay could deploy GINA nodes as wireless repeaters along a flight path, and communicate along them to a base station acting as its controller. The base station can be further connected to the internet; a mobile phone application was used to interface to a remote helicopter over a hybrid multi-hop path, passing downstream control commands and receiving upstream video images. To maintain the performance and reliability benefits of TSCH mesh networks in the presence of such MAV elements, the protocols designed for stationary networks were redesigned with extensions optimized for mobile nodes.
This work on an integrated system as well as the separate subsystems paves the path towards networked robots. Future work can focus on system-level solutions to fully implement the vision of smart pervasive mobile swarms.