Abstract — The proliferation of wireless and mobile devices has fostered the demand of context aware applications, in which location is often viewed as one of the most significant contexts. Classically, trilateration is widely employed for testing network localizability; even in many cases it wrongly recognizes a localizable graph as non-localizable. In this study, we analyze the limitation of trilateration based approaches and propose a novel approach which inherits the simplicity and efficiency of trilateration, while at the same time improves the performance by identifying more localizable nodes. We prove the correctness and optimality of this design by showing that it is able to locally recognize all 1-hop localizable nodes. To validate this approach, a prototype system with 19 wireless sensors is deployed. Intensive and large-scale simulations are further conducted to evaluate the scalability and efficiency of our design. I.

Complex interactions and the distributed nature of wireless sensor networks make automated testing and debugging before deployment a necessity. A main challenge is to detect bugs that occur due to non-deterministic events, such as node reboots or packet duplicates. Often, these events have the potential to drive a sensor network and its applications into corner-case situations, exhibiting bugs that are hard to detect using existing testing and debugging techniques. In this paper, we present KleeNet, a debugging environment that effectively discovers such bugs before deployment. KleeNet executes unmodified sensor network applications on symbolic input and automatically injects non-deterministic failures. As a result, KleeNet generates distributed execution paths at high-coverage, including low-probability cornercase situations. As a case study, we integrated KleeNet into the Contiki OS and show its effectiveness by detecting four insidious bugs in the µIP TCP/IP protocol stack. One of these bugs is critical and lead to refusal of further connections.

Abstract—Wireless sensor networks enable large amount of surveillance applications especially for critical and even hostile environments, for example, the sea monitoring. In OceanSense, we make the pioneer attempt to explore the possibility of deploying networked sensors on the sea surface, monitoring the sea depth, temperature, as well as other valuable environmental parameters. Sea depth monitoring is a critical task to ensure the safe operation of harbors. Traditional schemes largely rely on labor-intensive work and expensive hardware. We present a new solution for measuring the sea depth with Restricted Floating Sensors. Data without location is meaningless, to address the problem of node localization on the critical sea environment, we propose Perpendicular Intersection (PI), a novel mobile-assisted localization scheme. Network diagnosis is of great importance for an operational sensor networks, in OceanSense project, we propose the concept of passive diagnosis as well as the PAD approach which is both light-weight and adaptive to network dynamics. The OceanSense system has been working for over 16 months and provides large amounts of valuable data of the sea environment. Keywords-component; OceanSense; wireless sensor networks; environment surveillance; I.

Abstract—Existing approaches to diagnosing sensor networks are generally sink-based, which rely on actively pulling state information from all sensor nodes so as to conduct centralized analysis. However, the sink-based diagnosis tools incur huge communication overhead to the traffic sensitive sensor networks. Also, due to the unreliable wireless communications, sink often obtains incomplete and sometimes suspicious information, leading to highly inaccurate judgments. Even worse, we observe that it is always more difficult to obtain state information from the problematic or critical regions. To address the above issues, we present the concept of self-diagnosis, which encourages each single sensor to join the fault decision process. We design a series of novel fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. The fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the fault diagnosis by transiting the detector’s current state to a new one based on local evidences and then pass the fault detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs the final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100 nodes testbed. I.

Wireless sensor networks (WSNs) are hard to program due to unconventional programming models used to satisfy stringent resource constraints. The common event-driven concurrent programming model and lack of kernel protection in these systems introduce the possibility of several subtle faults such as race conditions. These faults are often triggered by unexpected interleavings of events in the real world, and can occur long after their causes. Reproducing a fault from the trace of the past events can play a crucial role in debugging such faults. The same tight constraints that motivate the specific programming model however make tracing challenging. This paper proposes an efficient intra-procedural and inter-procedural control-flow tracing algorithm that generates the traces of all interleaving concurrent events. Our approach enables reproducing faults at a later stage, allowing the programmer to identify them effectively. We argue for the accuracy of our approach through case studies, and illustrate its low overhead through measurements and simulations.

We address the problem of analysing performance anomalies in sensor networks. In this paper, we propose an approach that uses the local flash storage of the motes for logging system data, in combination with online statistical analysis. Our results show not only that this is a feasible method but that the overhead is significantly lower than that of communication-centric methods, and that interesting patterns can be revealed when calculating the correlation of large data sets of separate event types.

Wireless Sensor Networks(WSNs) are being increasingly deployed in the real world to monitor the environment and large industrial infrastructures. The extreme resource constraints inherent to WSNs, the in situ deployment in harsh environments and the lack of run-time support tools make debugging and maintaining WSN applications very challenging. In particular, run-time debugging tools are required to detect and diagnose complex run-time faults such as raceconditions, which occur due to unexpected interaction with the real-world environment.The ability to repeatedly reproduce the failure by replaying the execution from the trace of events that took place can play a crucial role in debugging such faults. Obtaining such a trace is made difficult due to tight resource constraints. In this paper, we propose a lightweight tracing tool for WSNs which uses a novel control flow tracing and encoding scheme to generate a highly compressed control-flow trace. In addition to the construction of the trace, our tracing tool supports storing the trace in non-volatile memory and querying interface that allows base station to retrieve the trace when needed. We show the effectiveness of our tracing tool through a case-study and illustrate its low overhead through measurements. Categories and Subject Descriptors D.2.8 [Software Engineering]: Testing and Debugging— tracing; C.3 [Special-Purpose and Application-Based

Troubleshooting interactive complexity bugs in

Abstract. Call traces can provide detailed insight into the operation of distributed embedded systems. Developers inspect traces to understand and debug systems using manual and automatic techniques such as data mining. Correlation of traces between nodes provides a network level view of system. These traces are typically gathered by logging a globally unique identifier for each called function. Unfortunately, this naive call trace gathering technique results in excessive consumption of the limited memory, bandwidth, and energy resources available in wireless sensor networks. This paper proposes three new call trace gathering techniques that are designed specifically for the computing platforms with extreme resource constraints. The first technique uses local name spaces and caller side logging to significantly reduce the bit size of function identifiers. The second technique reconstructs call traces from a log of the runtime control flow decisions made by a program. The third technique performs a novel reduction over a program’s control flow graph to limit logging to control flow nodes effecting runtime call decisions. Our work automates the insertion of logging statements into source code for all the techniques described above. Our experimental results show promising outlook where two of the techniques reduced the size of the log to less than 15 % of traces produced by traditional methods. These savings make the new call trace capturing techniques attractive additions to the toolbox employed by developers and users of wireless sensor networks. 1

Troubleshooting unresponsive sensor nodes is a significant challenge in remote sensor network deployments. This paper introduces the tele-diagnostic powertracer, an in-situ troubleshooting tool that uses external power measurements to determine the internal health condition of an unresponsive host and the most likely cause of its failure. We developed our own low-cost power meter with low-bandwidth radio to report power measurements and findings, hence allowing remote (i.e., tele-) diagnosis. The tool was deployed and tested in a remote solar-powered sensing network for acoustic and visual environmental monitoring. It was shown to successfully distinguish between several categories of failures that cause unresponsive behavior including energy depletion, antenna damage, radio disconnection, system crashes, and anomalous reboots. It was also able to determine the internal health conditions of an unresponsive node, such as the presence or absence of sensing and data storage activities (for each of multiple sensors). The paper explores the feasibility of building such a remote diagnostic tool from the standpoint of economy, scale and diagnostic accuracy. To the authors ’ knowledge, this is the first paper that presents a remote diagnostic tool that uses power measurements to diagnose sensor system failures.