Gregory J. Pottie

Wireless integrated network sensors

W ireless integrated network sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors deeply embedded in equipment, facilities, and the environment. The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security. WINS combine microsensor technology and low-power signal processing, computation, and low-cost wireless networking in a compact system. Recent advances in integrated circuit technology have enabled construction of far more capable yet inexpensive sensors, radios, and processors, allowing mass production of sophisticated systems linking the physical world to digital data networks [2–5]. Scales range from local to global for applications in medicine, security, factory automation, environmental monitoring, and condition-based maintenance. Compact geometry and low cost allow WINS to be embedded and distributed at a fraction of the cost of conventional wireline sensor and actuator systems. WINS opportunities depend on development of a scalable, low-cost, sensor-network architecture. Such applications require delivery of sensor information to the user at a low bit rate through low-power transceivers. Continuous sensor signal processing enables the constant monitoring of events in an environment in which short message packets would suffice. Future applications of distributed embedded processors and sensors will require vast numbers of devices. Conventional methods of sensor networking represent an impractical demand on cable installation and network bandwidth. Processing at the source would drastically reduce the financial, computational, and management burden on communication system

Protocols for self-organization of a wireless sensor network

We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes.

Instrumenting the world with wireless sensor networks

Pervasive micro-sensing and actuation may revolutionize the way in which we understand and manage complex physical systems: from airplane wings to complex ecosystems. The capabilities for detailed physical monitoring and manipulation offer enormous opportunities for almost every scientific discipline, and it will alter the feasible granularity of engineering. We identify opportunities and challenges for distributed signal processing in networks of these sensing elements and investigate some of the architectural challenges posed by systems that are massively distributed, physically-coupled, wirelessly networked, and energy limited.

Wireless integrated network sensors: Low power systems on a chip

Wireless Integrated Network Sensors (WINS) now provide a new monitoring and control capability for transportation, manufacturing, health care, environmental monitoring, and safety and security. WINS combine sensing, signal processing, decision capability, and wireless networking capability in a compact, low power system. WINS systems combine microsensor technology with low power sensor interface, signal processing, and RF communication circuits. The need for low cost presents engineering challenges for implementation of these systems in conventional digital CMOS technology. This paper describes micropower data converter, digital signal processing systems, and weak inversion CMOS RF circuits. The digital signal processing system relies on a continuously operating spectrum analyzer. Finally, the weak inversion CMOS RF systems are designed to exploit the properties of high-Q inductors to enable low power operation. This paper reviews system architecture and low power circuits for WINS.

Channel access algorithms with active link protection for wireless communication networks with power control

A distributed power-control algorithm with active link protection (DPC/ALP) is studied in this paper. It maintains the quality of service of operational (active) links above given thresholds at all times (link quality protection). As network congestion builds up, established links sustain their quality, while incoming ones may be blocked and rejected. A suite of admission control algorithms, based on the DPC/ALP one, is also studied. They are distributed/autonomous and operate using local interference measurements. A primarily networking approach to power control is taken here, based on the concept of active link protection, which naturally supports the implementation of admission control. Extensive simulation experiments are used to explore the network dynamics and investigate basic operational effects/tradeoffs related to system performance.

Self-organizing distributed sensor networks

Advances in CMOS IC and micro electrical-mechanical systems (MEMS) technologies are enabling construction of low-cost building blocks each of which incorporates sensing, signal processing, and wireless communications. Collections of these integrated microsensor nodes may be formed into sensor networks in a wide variety of ways, with characteristics that depend on the specific application--the total number of nodes, the spatial density, the geometric configuration (e.g., linear vs. areal), topographic aspects (e.g., smooth vs. rough terrain), and proximity and proportion of user/sink points. The power of these distributed sensor networks will be unleashed by means of their ability to self-organize, i.e., to bootstrap and dynamically maintain organizational structure befitting the purpose and situation that is presented, without the need for human assistance. A prototype sensor system and networking protocols are being developed under the DARPA/TTO AWAIRS Program and are described.

Multilevel codes based on partitioning

Following V.V. Ginzburg (1984), a hierarchy of codes is proposed to match the geometric partitioning of a signal set. The authors show that coset codes (including Ungerboeck, lattice, and binary codes) and indeed any codes which rely on partitioning of the signal set are all subclasses of the proposed coding scheme. The combination of such codes in a multilevel scheme often leads to reduced complexity in comparison with previously published schemes. A variety of decoder structures is discussed. >

Radio link admission algorithms for wireless networks with power control and active link quality protection

Presents a distributed power control scheme, which maintains the signal/interference ratios (SIRs) of operational (active) links above their required thresholds at all times (link quality protection), while new users are being admitted; furthermore, when new users cannot be successfully admitted, existing ones do not suffer fluctuations of their SIRs below their required thresholds values. The authors also present two admission/rejection control algorithms, which exercise voluntary drop-out of links inadmissible to the network so as to reduce interference and possibly facilitate the admission of other links.

Performance of a novel self-organization protocol for wireless ad-hoc sensor networks

The paper presents an ad-hoc architecture for wireless sensor networks and other wireless systems similar to them. In this class of wireless system the physical resource at premium is energy. Bandwidth available to the system is in excess of system requirements. The approach to solve the problem of ad-hoc network formation here is to use available bandwidth in order to save energy. The method introduced solves the problem of connecting an ad-hoc network. This algorithm gives procedures for the joint formation of a time schedule (similar to a TDMA schedule) and activation of links therein for random network topologies. This self-organization method is energy-sensitive, distributed, scalable, and able to form a connected network rapidly.

Sensor network data fault types

This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scientific deployments to develop a set of commonly used features useful in detecting and diagnosing sensor faults. We use this feature set to systematically define commonly observed faults, and provide examples of each of these faults from sensor data collected at recent deployments.