Abstract — Wireless sensor networks are deployed today to monitor the environment, but their own health status is relatively opaque to network administrators, in most cases. Our system, Memento, provides failure detection and symptom alerts, while being frugal in the use of energy and bandwidth. Memento has two parts: an energy-efficient protocol to deliver state summaries, and a distributed failure detector module. The failure detector is robust to packet losses, and attempts to ensure that reports of failure will not exceed a specified false positive rate. We show that distributed monitoring of a subset of wellconnected neighbors using a variance-bound based failure detector achieves the lowest rate of false positives, suitable for use in practice. We evaluate our findings using an implementation for the TinyOS platform on the Mica2 motes on a 55-node network, and find that Memento achieves a 80-90 % reduction in bandwidth use compared to standard data collection methods. I.

Effective debugging usually involves watching program state to diagnose bugs. When debugging sensor network applications, this approach is often time-consuming and errorprone, not only because of the lack of visibility into system state, but also because of the difficulty to watch the right variables at the right time. In this paper, we present declarative tracepoints, a debugging system that allows the user to insert a group of action-associated checkpoints, or tracepoints, to applications being debugged at runtime. Tracepoints do not require modifying application source code. Instead, they are written in a declarative, SQL-like language called TraceSQL independently. By triggering the associated actions when these checkpoints are reached, this system automates the debugging process by removing the human from the loop. We show that declarative tracepoints are able to express the core functionality of a range of previously isolated debugging techniques, such as EnviroLog, NodeMD, Sympathy, and StackGuard. We describe the design and implementation of the declarative tracepoints system, evaluate its overhead in terms of CPU slowdown, illustrate its expressiveness through the aforementioned debugging techniques, and finally demonstrate that it can be used to detect real bugs using case studies of three bugs based on the development of the LiteOS operating system.

Recent gains in energy-efficiency of new-generation NAND flash storage have strengthened the case for in-network storage by data-centric sensor network applications. This paper argues that a simple file system abstraction is inadequate for realizing the full benefits of high-capacity low-power NAND flash storage in data-centric applications. Instead we advocate a rich object storage abstraction to support flexible use of the storage system for a variety of application needs and one that is specifically optimized for memory and energy-constrained sensor platforms. We propose Capsule, an energy-optimized log-structured object storage system for flash memories that enables sensor applications to exploit storage resources in a multitude of ways. Capsule employs a hardware abstraction layer that hides the vagaries of flash memories for the application and supports energy-optimized implementations of commonly used storage objects such as streams, files, arrays, queues and lists. Further, Capsule supports checkpointing and rollback of object states to tolerate software faults in sensor applications running on inexpensive, unreliable hardware. Our experiments demonstrate that Capsule provides platform-independence, greater functionality, more tunability, and greater energy-efficiency than existing sensor storage solutions, while operating even within the memory constraints of the Mica2 Mote. Our experiments not only demonstrate the energy and memory-efficiency of I/O operations in Capsule but also shows that Capsule consumes less than 15% of the total energy cost in a typical sensor application.

Software failures in wireless sensor systems are notoriously difficult to debug. Resource constraints in wireless deployments substantially restrict visibility into the root causes of node-level system and application faults. At the same time, the high costs of deployment of wireless sensor systems often far exceed the cumulative costs of all other sensor hardware, so that software failures that completely disable a node are prohibitively expensive to repair in real world applications, e.g. by on-site visits to replace or reset nodes. We describe NodeMD, a deployment management system that successfully implements lightweight run-time detection, logging, and notification of software faults on wireless mote-class devices. NodeMD introduces a debug mode that catches a failure before it completely disables a node and drops the node into a stable state that enables further diagnosis and correction, thus avoiding on-site redeployment. We analyze the performance of NodeMD on a real world application of wireless sensor systems.

This paper proposes a new sensornet protocol design goal: visibility. Visibility into behaviors at the network level will simplify debugging and ease the development process. We argue that increasing visibility is the responsibility of the network protocols themselves, and not solely the responsibility of existing debugging tools. We describe a quantitative visibility metric to evaluate and compare protocols, where visibility is defined as the energy cost of diagnosing the cause of a behavior in a protocol. The design and evaluation of Pull Collection Protocol, a novel multi-hop collection protocol, is an example of how to design for visibility without sacrificing throughput or node-level fairness. We also describe our optimizations for an existing protocol, Deluge, to increase its visibility and efficiency. 1

Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach. 1.

Understanding the behavior of deployed sensor networks is difficult as they become more sophisticated and larger in scale. Much of the difficulty comes from the lack of tools to provide a global view on the network dynamics. This paper describes LiveNet, a set of tools and techniques for reconstructing complex dynamics of live sensor network deployments. LiveNet is based on the use of passive sniffers co-deployed with the network. We address several challenges: merging multiple sniffer traces, determining coverage of sniffers, inference of missing information for path reconstruction and high-level analyses with application-specific knowledge. To validate LiveNet’s accuracy, we conduct controlled experiments on an indoor testbed. Finally, we present data from a real deployment using LiveNet. The results show that LiveNet is able to to reconstruct network topology, bandwidth usage, routing paths, identify hot-spot nodes, and disambiguate failures observed at application level without instrumenting application code.

Abstract. Deployment of sensor networks in real-world settings is a labor-intensive and cumbersome task: environmental influences often trigger problems that are difficult to track down due to limited visibility of the network state. In this paper we present a framework for passive inspection (i.e., no instrumentation of sensor nodes required) of deployed sensor networks and show how this framework can be used to inspect data gathering applications. The basic approach is to temporarily install a distributed network sniffer alongside the inspected sensor network, with overheard messages being analyzed by a data stream processor and network state being displayed in a graphical user interface. Our tool can be flexibly applied to different sensor network operating systems and protocol stacks, and can deal well with incomplete information. 1

Typical sensor nodes are resource constrained microcontrollers containing user level applications, operating system components, and device drivers in a single address space, with no form of memory protection. A programming error in an application can easily corrupt the state of the operating system and other software components on the node. In this paper, we propose a memory protection scheme that prevents the corruption of operating system state by buggy applications. We use sandboxing to restrict application memory accesses within the address space. Severe resource constraints on the sensor node present interesting challenges in designing a sandbox for user applications. We have implemented and tested our scheme on the SOS operating system. Our experiments were able to detect a memory corruption bug in an application module that had been in use for several months. 1

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