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To mitigate the threat of malware intrusions on networked embedded systems, it is desirable to provide remote attestation assurances for them. Embedded systems have special limitations concerning cost, power efficiency, computation, and memory that influence how this goal can be achieved. Moreover, many types of applications require integrity guarantees for the system over an interval of time rather than just at a given instant. We propose a Cumulative Attestation Kernel (CAK) that addresses these concerns. We demonstrate the value of CAKs for Advanced Metering Infrastructure (AMI) and show how to implement a CAK in less than one quarter of the memory available on low end flash MCUs similar to those used in AMI deployments. Regarding this prototype, we present the first formal proof we are aware of that a system is tolerant to power supply interruptions. We also discuss how to provide cumulative attestation for devices with tighter memory constraints by offloading computation and storage onto a Cumulative Attestation Coprocessor (CAC).

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.