William Leal

Kansei: a high-fidelity sensing testbed

Hardware and software testbeds are becoming the preferred basis for experimenting with embedded wireless sensor network applications. The Kansei testbed at the Ohio State University features a heterogeneous hardware infrastructure, with dedicated node resources for local computation, storage, data retrieval, and back-channel communication. Kansei includes a time-accurate hybrid simulation engine that uses testbed hardware resources to simulate large arrays. It supports high-fidelity sensor data generation as well as real-time data and event injection. The testbed also includes software components and an associated job-control language for complex multi-tier experiments.

Scalable self-stabilization via composition

Objections to the practical use of stabilization have centered around problems of scale. Because of potential interferences between actions, global reasoning over the entire system is in general necessary. The complexity of this task increases dramatically as systems grow in size. Alternatives to dealing with this complexity focus on reset and composition. For reset, the problem is that any fault, no matter how minor, will cause a complete system reset with potentially significant lack of availability. For existing compositional alternatives, including compositional reset, severe restrictions on candidate systems are imposed. To address these issues, we give a framework for composition in which global reasoning and detailed system knowledge are not necessary, and which apply to a significantly wider range of systems than has hitherto been possible. We explicitly identify for each component which other components it can corrupt. Additionally, the correction of one component often depends on the prior correction of one or more other components, constraining the order in which correction can take place. Given appropriate component stabilizers such as detectors and correctors, we offer several ways to coordinate system correction, depending on what is actually known about the corruption and correction relations. By reducing the design of and reasoning about stabilization to local activities involving each component and the neighbors with which it interacts, the framework is scalable. Reset is generally avoided by using the correction relation to check and correct only where necessary. By including both correction and corruption relations, the framework subsumes and extends other compositional approaches. Though not directly a part of this work, we mention tools and techniques that can be used to help calculate the dependency and corruption relations and to help create the necessary stabilizers. To illustrate the theory, we show how this framework has been applied in our work in sensor networks.

DESAL alpha: An Implementation of the Dynamic Embedded Sensor-Actuator Language

We present DESALalpha, a realization of the dynamic embedded sensor-actuator language for Telos-based devices. The platform provides native support for: (i) rule-based programming; (ii) synchronized action scheduling; (iii) neighborhood management; and (iv) distributed state sharing. We describe the design and implementation of DESALalpha, present examples that illustrate its use, and summarize the resource requirements of compiled applications. Finally, we present lessons learned based on our use of DESALalpha during the past year.

A state-based language for sensor-actuator networks

This paper introduces a language design for sensor-actuator networks. The main features are communication by a soft-state abstraction and behavior control by periodic rule evaluation. These features enable a state-based, rather than event-based, style of programming. Dynamic changes to network configuration and failures of components are automatically handled by this approach. The design choices target applications which experience low-to-moderate rates of sensor input and which do not require extreme, low-latency sensor processing and actuation. Coordinated actuation is concisely expressed in this language.

Next-Generation Internet Architectures and Protocols: KanseiGenie: software infrastructure for resource management and programmability of wireless sensor network fabrics

This chapter describes an architecture for slicing, virtualizing, and federating wireless sensor network (WSN) resources. The architecture, which we call KanseiGenie, allows users—be they sensing/networking researchers or application developers—to specify and acquire node and network resources as well as sensor data resources within one or more facilities for launching their programs. It also includes server side measurement and management support for user programs, as well as client side support for experiment composition and control. We illustrate KanseiGenie architectural concepts in terms of a current realization of KanseiGenie that serves WSN testbeds and application-centric fabrics at The Ohio State University and at Wayne State University.

Chowkidar: A Health Monitor for Wireless Sensor Network Testbeds

Wireless sensor network (WSN) testbeds are useful because they provide a way to test applications in an environment that makes it easy to deploy experiments, configure them statically or dynamically, and gather performance information. Sensor data collected in the field can be replayed on nodes, and new ways to process the data can be tested easily. Testbeds are rapidly growing in size, with hundreds or thousands of devices, and testbed services are also becoming richer and more complex. Due to their size and complexity, faults can (and do) occur in these testbeds, affecting the outcomes of experiments. Awareness of testbed health status is important to both testbed administrators charged with maintaining functional services, and users who prefer to use healthy devices and like to know if there are any failures during their experiments. Based on our experience with Kansei, a large WSN testbed at Ohio State, we identify use cases that motivate the design of Chowkidar, a health monitoring facility. Key among these are: monitoring as a service that operates independently of users to provide up-to-date testbed status information; monitoring of heterogeneous testbed devices and networks; distinguishing between node and interface failures; and diagnosing common-mode failures such as power supply or Ethernet hub failure. We present in this paper, a centralized and a distributed Chowkidar protocol that reliably monitor the health of large, heterogenous WSN testbeds. We present experimentally measured Chowkidar performance as well as real experiences and lessons learnt from the integration of Chowkidar with Kansei, including feedback from both testbed users and administrators who have found Chowkidar to be a useful tool for improving the accuracy and efficiency of testbed experimentation and maintenance.

From Kansei to KanseiGenie: Architecture of Federated, Programmable Wireless Sensor Fabrics

This paper deals with challenges in federating wireless sensing fabrics. Federations of this sort are currently being developed in next generation global end-to-end experimentation infrastructures, such as GENI, to support rapid prototyping and hi-fidelity validation of protocols and applications. On one hand, federation should support access to diverse (and potentially provider-specific) wireless sensor resources and, on the other, it should enable users to uniformly task these resources. Instead of more simply basing federation upon a standard description of resources, we propose an architecture where the ontology of resource description can vary across providers, and a mapping of user needs to resources is performed to achieve uniform tasking. We illustrate one realization of this architecture, in terms of our refactoring the Kansei testbed to become the KanseiGenie federated fabric manager, which has full support for programmability, sliceability and federated experimentation over heterogeneous sensing fabrics.

Chowkidar: Reliable and scalable health monitoring for wireless sensor network testbeds

Wireless sensor network (WSN) testbeds are useful because they provide a way to test applications in an environment that makes it easy to deploy experiments, configure them statically or dynamically, and gather performance information. However, WSNs are typically composed of low-cost devices and tend to be unreliable, with failures a common phenomenon. Accurate knowledge of network health status, including nodes and links of each type, is critical for correctly configuring applications on WSN testbeds and for interpreting the data collected from them. In this article we present a stabilizing protocol, Chowkidar, that provides accurate and efficient network health monitoring in WSNs. Our approach adapts the well-known problem of message-passing rooted spanning tree construction and its use in propagation of information with feedback (PIF) for the case of a WSN. The Chowkidar protocol is initiated upon demand; that is, it does not involve ongoing maintenance, and it terminates with accurate results, including detection of failure and restart during the monitoring process. Chowkidar is distinguished from others in two important ways. Given the resource constraints of WSNs, it is message-efficient in that it uses only a few messages per node. Also, it tolerates ongoing node and link failure and node restart, in contrast to requiring that faults stop during convergence. We have implemented the Chowkidar protocol as part of enabling a network health status service that is tightly integrated with a remotely accessible wireless sensor network testbed, Kansei, at The Ohio State University. We present experimental results from this testbed that validate the correctness and performance of Chowkidar. We also report on initial experiences and lessons learnt from the integration of Chowkidar with Kansei, including feedback from both testbed users and administrators who have found Chowkidar to be a useful tool for improving the accuracy and efficiency of testbed experimentation and maintenance, and the need for well-defined policies to address issues such as minimizing interference with concurrently running experiments. Finally, we discuss extensions that enhance the functionality and usability of Chowkidar.

Stabilizing health monitoring for wireless sensor networks

Wireless sensor networks (WSNs) comprised of low-cost devices tend to be unreliable, with failures a common phenomenon. Being able to accurately observe the network health status -- of nodes of each type and links of each type -- is essential to properly configure applications on WSN fabrics and to interpret the information collected from them. In this paper we study accurate network health monitoring in WSNs. Specifically, we reconsider the well-known problem of message-passing rooted spanning tree construction and its use in PIF (propagation of information with feedback) for the case of a WSN. We present a stabilizing protocol, Chowkidar, that is initiated upon demand; that is, it does not involve ongoing maintenance, and it terminates with accurate results, including detection of failure and restart during the monitoring process. Our protocol is distinguished from others in two important ways. Given the resource constraints of WSNs, it is message-efficient in that it uses only a few messages per node. And it tolerates ongoing node and link failure and node restart, in contrast to requiring that faults stop during convergence. We have implemented the protocol as part of enabling a network health status service that is tightly integrated with a remotely accessible wireless sensor network testbed, Kansei, at The Ohio State University. We report on experimental results.

The OCRC fuel cell lab safety system: a self-stabilizing safety-critical system

We describe the practical application of self-stabilization to a safety-critical system. The Ohio Coal Research Center (OCRC) at Ohio University has a fuel-cell laboratory that uses explosive and poisonous gases. The lab is located in and uses the ventilation system of a large campus building that houses offices, other labs, and classrooms. The OCRC fuel cell lab safety system seeks to protect lab and other building personnel in the event of a gas leak. We present the system and the use of self-stabilization to ensure that, in the presence of actual or potential hazards, the lab converges to as safe a state as possible. It is responds to environmental conditions such as gas leaks and is tolerant to faults that affect the systems sensors and actuators.