Background Perspective on Wireless Micro-networking


§        Ultra-low Power

§        Short-range RF wireless

§        Low-cost

§        Self-healing ad-hoc networking

§        Multi-hop, peer-to-peer topology

§        Multi-year operation


The emerging field of wireless sensor networks combines sensing, computation, and communication into a single tiny device.  Through advanced mesh networking protocols, these devices form a sea of connectivity that extends the reach of cyberspace out into the physical world.  As water flows to fill every room of a submerged ship, the mesh networking connectivity will seek out and exploit any possible communication path by hopping data from node to node in search of its destination.  While the capabilities of any single device are minimal, the composition of hundreds of devices offers radical new technological possibilities.  

The power of wireless sensor networks lies in the ability to deploy large numbers of tiny nodes that assemble and configure themselves.   Usage scenarios for these devices range from real-time tracking, to monitoring of environmental conditions, to ubiquitous computing environments, to in situ monitoring of the health of structures or equipment.   While often referred to as wireless sensor networks, they can also control actuators that extend control from cyberspace into the physical world.

The most straightforward application of wireless sensor network technology is to monitor remote environments for low frequency data trends.   For example, a chemical plant could be easily monitored for leaks by hundreds of sensors that automatically form a wireless interconnection network and immediately report the detection of any chemical leaks.   Unlike traditional wired systems, deployment costs would be minimal.  Instead of having to deploy thousands of feet of wire routed through protective conduit, installers simply have to place quarter-sized device, such as the one pictured in below, at each sensing point.   The network could be incrementally extended by simply adding more devices – no rework or complex configuration. With the devices used by JLH Labs, the system would be capable of monitoring for anomalies for several years on a single set of batteries.

In addition to drastically reducing the installation costs, wireless sensor networks have the ability to dynamically adapt to changing environments.   Adaptation mechanisms can respond to changes in network topologies or can cause the network to shift between drastically different modes of operation.   For example, the same embedded network performing leak monitoring in a chemical factory might be reconfigured into a network designed to localize the source of a leak and track the diffusion of poisonous gases.    The network could then direct workers to the safest path for emergency evacuation.

Other wireless systems only scratch the surface of possibilities emerging from the integration of low-power communication, sensing, energy storage, and computation. Generally, when people consider wireless devices they think of items such as cell phones, personal digital assistants, or laptops with 802.11.   These items costs hundreds of dollars, target specialized applications, and rely on the pre-deployment of extensive infrastructure support.   In contrast, wireless sensor networks use small, low-cost embedded devices for a wide range of applications and do not rely on any pre-existing infrastructure.   The vision is that these devise will cost less that $1 by 2005. 

Unlike traditional wireless devices, wireless sensor nodes do not need to communicate directly with the nearest high-power control tower or base station, but only with their local peers.  Instead, of relying on a pre-deployed infrastructure, each individual sensor or actuator becomes part of the overall infrastructure.  Peer-to-peer networking protocols provide a mesh-like interconnect to shuttle data between the thousands of tiny embedded devices in a multi-hop fashion.  The flexible mesh architectures envisioned dynamically adapt to support introduction of new nodes or expand to cover a larger geographic region.   Additionally, the system can automatically adapt to compensate for node failures.

The vision of mesh networking is based on strength in numbers.  Unlike cell phone systems that deny service when too many phones are active in a small area, the interconnection of a wireless sensor network only grows stronger as nodes are added.  As long as there is sufficient density, a single network of nodes can grow to cover limitless area.  With each node having a communication range of 50 meters and costing less that $1 a sensor network that encircled the equator of the earth will cost less that $1M.

 An example network is shown in above.  It depicts a precision agriculture deployment—an active area of application research.   Hundreds of nodes scattered throughout a field assemble together, establish a routing topology, and transmit data back to a collection point.  The application demands for robust, scalable, low-cost and easy to deploy networks are perfectly met by a wireless sensor network.  If one of the nodes should fail, a new topology would be selected and the overall network would continue to deliver data.  If more nodes are placed in the field, they only create more potential routing opportunities.

There is extensive research in the development of new algorithms for data aggregation, ad hoc routing, and distributed signal processing in the context of wireless sensor networks.  JLH Labs has strong ties into the research community in order to track the latest algorithmic advances.

In developing the micro-networking technology, the most difficult resource constraint to meet is power consumption.  As physical size decreases, so does energy capacity.    Underlying energy constraints end up creating computational and storage limitations that lead to a new set of architectural issues.    Many devices, such as cell phones and pagers, reduce their power consumption through the use specialized communication hardware in ASICs that provide low-power implementations of the necessary communication protocols and by relying on high-power infrastructure.   However, the strength of wireless sensor networks is their flexibility and universality.   The wide range of applications being targeted makes it difficult to develop a single protocol, and in turn, an ASIC, that is efficient for all applications.  A wireless sensor network platform must provide support for a suite of application-specific protocols that drastically reduce node size, cost, and power consumption for their target application.  

The wireless sensor network architecture used by JLH Labs includes both a hardware platform and an operating system designed specifically to address the needs of wireless sensor networks.  TinyOS is a component based operating system designed to run in resource constrained wireless devices.  It provides highly efficient communication primitives and fine-grained concurrency mechanisms to application and protocol developers. A key concept in TinyOS is the use of event based programming in conjunction with a highly efficient component model.   TinyOS enables system-wide optimization by providing a tight coupling between hardware and software, as well as flexible mechanisms for building application specific modules.

TinyOS has been designed to run on a generalized architecture where a single CPU is shared between application and protocol processing.  We detail three generations of wireless nodes and a host of application deployments that have proven the capabilities of our general system architecture.   Below is a picture and timeline of several “mote” generations.  The Mica platform has been produced in the largest quantities – over 5000 Mica nodes have been produced and distributed to over 250 companies and r  esearch organizations from around the country.   The Mica platform includes a low power transceiver, a power management subsystem, extended storage and an embedded microcontroller.

The most advanced hardware platform we present is a single-chip CMOS device that integrates the processing, storage and communication capabilities to form a complete system node.  This single chip node – called Spec – measures just 2.5 mm x 2.5 mm, contains a microcontroller, transmitter, ADC, general purpose I/O ports, UART, memory and encryption engine.   The tiny chip only needs to be supported by a 32 KHz watch crystal, an off-chip inductor and a power supply, a battery and a 4 MHz clock.   The Spec node represents the coming generation of wireless sensor nodes that will be manufactured for pennies and deployed in the millions.

Design lineage of Mote Technology.  COTS (Common off the shelf) prototypes lead to the weC platform.  Rene then evolved to allow sensor expansion and enabled hundreds of compelling applications.  The Dot node was architecturally the same as Rene but shrunk into a quarter-sized device.  Mica – discussed in depth in this thesis – made significant architectural improvements in order to increase performance and efficiency.  Spec represents the complete integrated CMOS vision.

Both the Mica and Spec node are used to substantiate our claim that optimal system architecture for wireless sensor networks is to have a single central controller directly connected to a low-power radio transceiver through a rich interface that supports hardware assistance for communication primitives.   In contrast to having a hierarchical partition of hardware resources dedicated to specific functions, a single shared controller performs all processing.   This allows for the dynamic allocation of computation resources to the set of computational tasks demanded by the system.   The layers of abstraction typically achieved through hardware partitioning can instead be achieved through the use of a highly efficient software-based component model.   Software abstractions allow for a wider scope of cross-layer optimizations that can achieve orders of magnitude improvements is system performance.  The power and viability of this architecture is demonstrated through a collection benchmarks performed on real-world hardware and in application level deployments.