Standardized software architectures, such as AUTomotive Open System ARchitecture (AUTOSAR), are being pursued within the automotive industry in order to reduce the cost of developing new vehicle features. Many of these features will need to be highly dependable. Fault injection plays an important role during the dependability analysis of such software. This work evaluates the feasibility of leveraging the CANoe simulation environment to develop software-based methods for injecting faults into AUTOSAR applications. We describe a proof-of-concept fault-injection framework with example fault-injection scenarios, as well as implementation issues faced and addressed, lessons learned, and the suitability of using CANoe as a fault-injection environment.

Existing network reprogramming protocols target the efficient, reliable, multi-hop dissemination of application updates in sensor networks, but assume correct or fail-stop behavior from participating sensors. Compromised nodes can subvert such protocols to result in the propagation and remote installation of malicious code. Sluice aims for the progressive, resource-sensitive verification of updates in sensor networks to ensure that malicious updates are not disseminated or installed, while trusted updates continue to be efficiently disseminated. Our verification mechanism provides authenticity and integrity through a hash-chain construction that amortizes the cost of a single digital signature over an entire update. We integrate Sluice with an existing network reprogramming protocol and empirically evaluate its effectiveness both in a real sensor testbed and through simulation.

Existing code update protocols target efficiency and assume correct behavior from participating sensor nodes. This work aims for the progressive, resource sensitive verification of code updates in sensor networks to ensure that unauthorized updates from malicious nodes are not propagated, while correct updates continue to be efficiently disseminated.

Wireless networks have a constraint on their functional lifetime. This is due to the limited energy capacity of batteries powering the wireless nodes. For extending the lifetime of such battery-operated networks, we present a scheme for dynamically selecting the transmission rate for each node in the network. The transmission rate is based on the available energy budget in each node's battery. The goal is to increase the network capability of delivering more packets. The rate selection for each node is subject to satisfying a QoS timing constraint on the packet delivery time. Through adaptively varying each node's rate, we extended the lifetime 10 times on average more transmitting at a maximum rate and delivered on average 7.5 times more data packets. When compared with a scheme that transmits data at a lower rates independent of the battery levels, our scheme delivers up to 12% more packets for the same available total energy.