Jasmeet Chhabra

Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea

Sensing technology is a cornerstone for many industrial applications. Manufacturing plants and engineering facilities, such as shipboard engine rooms, require sensors to ensure product quality and efficient and safe operation. We focus on one representative application, preventative equipment maintenance, in which vibration signatures are gathered to predict equipment failure. Based on application requirements and site surveys, we develop a general architecture for this class of industrial applications. This architecture meets the applications data fidelity needs through careful state preservation and over-sampling. We describe the impact of implementing the architecture on two sensing platforms with differing processor and communication capabilities. We present a systematic performance comparison between these platforms in the context of the application. We also describe our experience and lessons learned in two settings: in a semiconductor fabrication plant and onboard an oil tanker in the North Sea. Finally, we establish design guidelines for an ideal platform and architecture for industrial applications. This paper includes several unique contributions: a study of the impact of platform on architecture, a comparison of two deployments in the same application class, and a demonstration of application return on investment.

Real-world experiences with an interactive ad hoc sensor network

While it is often suggested that moderate-scale ad hoc sensor networks are a promising approach to solving real-world problems, most evaluations of sensor network protocols have focused on simulation, rather than realworld, experiments. In addition, most experimental results have been obtained in limited scale. This paper describes a practical application of moderate-scale ad hoc sensor networks. We explore several techniques for reducing packet loss, including quality-based routing and passive acknowledgment, and present an empirical evaluation of the effect of these techniques on packet loss and data freshness.

A stream-oriented power management protocol for low duty cycle sensor network applications

Most power management protocols are packet-based and optimized for applications with mostly asynchronous (i.e. unexpected) traffic. We present AppSleep, a stream-oriented power management protocol for latency tolerant sensor network applications. For this class of applications, AppSleep demonstrates an over 3/spl times/ lifetime gain over B-MAC and SMAC. AppSleep leverages application characteristics in order to take advantage of periods of high latency tolerance to put the network to sleep for extended periods of time, while still facilitating low latency responses when required. AppSleep also gives applications the flexibility to efficiently and effectively trade latency for energy when desired, and enables energy efficient multi-fragment unicast communication by only keeping the active route awake. We also present Adaptive AppSleep, an application driven addition to AppSleep which supports varying latency requirements while still maximizing energy efficiency. Our evaluation demonstrates that for an overlooked class of applications, stream-oriented power management protocols such as AppSleep outperform packet-based protocols such as B-MAC and S-MAC.

Experimental evaluation of synchronization and topology control for in-building sensor network applications

While multi-hop networks consisting of 100s or 1000s of inexpensive embedded sensors are emerging as a means of mining data from the environment, inadequate network lifetime remains a major impediment to real-world deployment. This paper describes several applications deployed throughout our building that monitor conference room occupancy and environmental statistics and provide access to room reservation status. Because it is often infeasible to locate sensors and display devices near power outlets, we designed two protocols that allow energy conservation in a large class of sensor network applications. The first protocol, Relay Organization (ReOrg), is a topology control protocol which systematically shifts the networks routing burden to energy-rich nodes, exploiting heterogeneity. The second protocol, Relay Synchronization (ReSync), is a MAC protocol that extends network lifetime by allowing nodes to sleep most of the time, yet wake to receive packets. When combined, ReOrg and ReSync lower the duty cycle of the nodes, extending network lifetime. To our knowledge, this paper presents the first experimental testbed evaluation of energy-aware topology control integrated with energy-saving synchronization. Using a 54-node testbed, we demonstrate an 82-92% reduction in energy consumption, depending on traffic load. By rotating the burden of routing, our protocols can extend network lifetime by 5-10 times. Finally, we demonstrate that a small number of wall-powered nodes can significantly improve the lifetime of a battery-powered network.

Experimental evaluation of topology control and synchronization for in-building sensor network applications

While multi-hop networks consisting of 100s or 1000s of inexpensive embedded sensors are emerging as a means of mining data from the environment, inadequate network lifetime remains a major impediment to real-world deployment. This paper describes several applications deployed throughout our building that monitor conference room occupancy and environmental statistics and provide access to room reservation status. Because it is often infeasible to locate sensors and display devices near power outlets, we designed two protocols that allow energy conservation in a large class of sensor network applications. The first protocol, Relay Organization (ReOrg), is a topology control protocol which systematically shifts the network’s routing burden to energy-rich nodes, exploiting heterogeneity. The second protocol, Relay Synchronization (ReSync), is a MAC protocol that extends network lifetime by allowing nodes to sleep most of the time, yet wake to receive packets. When combined, ReOrg and ReSync lower the duty cycle of the nodes, extending network lifetime. To our knowledge, this research provides the first experimental testbed evaluation of energy-aware topology control integrated with energy-saving synchronization. Using a 54-node testbed, we demonstrate an 82–92% reduction in energy consumption, depending on traffic load. By rotating the burden of routing, our protocols can extend network lifetime by 5–10 times. Finally, we demonstrate that a small number of wall-powered nodes can significantly improve the lifetime of a battery-powered network.

Sensor networks in Intel fabrication plants

The deployment of large-scale sensor networks in industrial environments presents technical challenges in achieving ease of deployment, flexibility in operation, and overall commercial viability. Sensor deployments are characterized by non-uniform placement of nodes, intermittent node connectivity, and the aggregation and reliable transfer of a large amount of data as networks scale to larger and larger sizes. Operation challenges include efficiently utilizing battery powered nodes, dynamically selecting sample periods and equipment clusters of interest, integrating with existing sensing and analysis infrastructure and easily correlating faults identified by the sensor network back to key factory operations and equipment. Commercial aspects involve returning value to the organization with hardened network nodes and reliable network operation that easily justifies the sensor network deployment and operating costs. We will demonstrate a sensor network in the ultra-pure water facility of an Intel fabrication plant, including details on the fab vibration application, the hardware nodes, and the heterogeneous wireless network. The fab vibration application uses vibration analysis to predict and correct equipment failures before fab operations are impacted. The ultra-pure water facility has a diversity of pumps and metal infrastructure both in a gymnasium size room and in an outside open air area that require 51 nodes with 201 vibration sensors. Data samples of 6K bytes are taken periodically from each vibration sensor, forwarded to a central server, measured for velocity, spike energy, acceleration, and displacement, analyzed with FFTs and trend analysis, and compared against expected profiles. When the analysis detects vibration variations outside of normal operating parameters, the affected equipment is scheduled for preventive maintenance, and repairs are made during normal down times. The hardware nodes for the fab vibration application include battery-powered motes (Figure 1(a)) and line-powered gateways. The motes are powered by four C Cell batteries, use a 900 MHz transceiver, and support an RPM (Revolutions per Minute) sensor and up to six vibration sensors. The gateways contain both a 900 MHz transceiver and a 2.4 GHz transceiver running 802.11b to bridge the sensor radios to the 802.11 network. Both motes and access points use heavy metal housings and are hardened for industrial use. Motes and gateways self configure into a heterogeneous wireless network on power up. Motes form an ad hoc network and cluster to gateways. Gateways form an ad hoc 802.11b overlay network and provide high-capacity data transport allowing us to scale the network. One of the gateways is connected to the corporate network through Ethernet. The motes are cycled through planned sleep and wake periods to alternately conserve power and perform vibration data acquisitions. Motes send the data to the gateways where it is aggregated and ultimately forwarded to a server for analysis and storage. Our demonstration includes the display of actual vibration data with real time updates from an Intel fab, together with hands-on samples of gateways, motes, vibration sensors, and RPM sensors used in the deployment. Overview of the network topology and physical deployment is also shown along with the key network technologies developed and used to run the vibration application. We also discuss current learnings from the deployment and future work.

Heavy industry applications of sensornets

Sensors are a cornerstone of heavy industrial operations. Manufacturing plants and general engineering facilities, such as power plants or shipboard engine rooms, require a high degree of sensing to ensure product quality and/or efficient and safe operation. Wireless sensor networks are a natural fit to meeting the demands of scale, data access and cost. However, the nature and environment of industrial applications presents unique requirements for sensornets. To better understand these challenges, we conducted deployments in two settings: a semiconductor fabrication plant and an oil tanker. The context for the deployments was a condition based maintenance application which monitors machinery vibration to detect/preempt failures.