| Recent advances in microelectronic mechanical systems (MEMS) and wireless communication technologies have fostered the rapid development of networked embedded systems like wireless sensor networks (WSNs). WSNs consist of a large amount of wireless sensor nodes and can be used for a wide range of applications such as ecological surveillance, habitat monitoring, and infrastructure protection, etc. As the complexity of WSNs applications are increasingly growing, it is a huge challenge to ensure the reliability of the applications.The bug fixing techniques for WSNs are studied extensively in this thesis. First, a dynamic logging facility Dylog is proposed to localize potential bugs. Second, a network reboot mechanism NR is proposed for the recovery of certain non-deterministic errors. Third, we propose a long-term energy efficiency model.To ensure the reliable operation, we present Dylog, a dynamic logging facility for networked embedded systems. Dylog employs several techniques to enable lightweight and interactive logging. First, Dylog uses binary instrumentation for dynamically inserting or removing logging statements, enabling interactive debugging at the runtime. Second, Dylog incorporates an efficient storage system and log collection protocol for recording and transferring the logging messages. In particular, Dylog significantly reduces the communication cost by storing string identifiers and restoring them back to corresponding strings at the PC. Third, Dylog employs MAC layer time stamping and a linear clock model for reconstructing the synchronized time of the logging messages with a very high precision.To facilitate software error recovery, we propose NR, a network reboot mechanism for WSNs. NR enables reliable and efficient network reboot in WSNs. NR ensures100%reliability by incorporating an eventual consistency dissemination protocol. It avoids flash I/Os by storing data items on persistent RAM.To guide operators to efficiently manage the network, we provide a long-term energy efficiency model for WSNs. This energy model fully considers the energy consumption during reprogramming. Moreover, we analyze the relationship between network lifetime and reprogramming intervals for the three reprogramming approaches. |
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