Dependence-based source level tracing and replay for networked embedded systems
Error detection and diagnosis for networked embedded systems remain challenging and tedious due to issues such as a large number of computing entities, hardware resource constraints, and non-deterministic behaviors. The run-time checking is often necessitated by the fact that the static verification fails whenever there exist conditions unknown prior to execution. Complexities in hardware, software and even the operating environments can also defeat the static analysis and simulations. Record-and-replay has long been proposed for distributed systems error diagnosis. Under this method, assertions are inserted in the target program for run-time error detection. At run-time, the violation of any asserted property triggers actions for reporting an error and saving an execution trace for error replay. This dissertation takes wireless sensor networks, a special but representative type of networked embedded systems, as an example to propose a dependence-based source-level tracing-and-replay methodology for detecting and reproducing errors. This work makes three main contributions towards making error detection and replay automatic. First, SensorC, a domain-specific language for wireless sensor networks, is proposed to specify properties at a high level. This property specification approach can be not only used in our record-replay methodology but also integrated with other verification analysis approaches, such as model checking. Second, a greedy heuristic method is developed to decompose global properties into a set of local ones with the goal of minimizing the communication traffic for state information exchanges. Each local property is checked by a certain sensor node. Third, a dependence-based multi-level method for memory-efficient tracing and replay is proposed. In the interest of portability across different hardware platforms, this method is implemented as a source-level tracing and replaying tool. To test our methodology, we have built different wireless sensor networks by using TelosB motes and Zolertia Z1 motes separately. The experiments' results show that our work has made it possible to instrument several test programs on wireless sensor networks under the stringent program memory constraint, reduce the data transferring required for error detection, and find and diagnose realistic errors.
Li, Purdue University.
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