Biblio

The major energy-hungry components in today's battery-operated embedded devices are mostly peripheral modules like LTE, WiFi, GPS, etc. Inefficient use of these modules causes energy hotspots, namely segments of the embedded software in which the module wastes energy. We study two such hotspots in the current paper, and provide the corresponding detection and removal algorithms based on static analysis techniques. The program code hotspots occur due to unnecessary releasing and re-acquiring of a module (which puts the module in power saving mode for a while) and misplaced acquiring of the module (which makes the module or processor to waste energy in idle mode). The detections are performed according to some relation between extreme (worst-case/best-case) execution times of some program segments and time/energy specifications of the module. The experimental results on our benchmarks show about 28 percent of energy reduction after the hotspot removals.

Cyber-Physical Systems (CPS) are growing with added complexity and functionality. Multidisciplinary interactions with physical systems are the major keys to CPS. However, sensors, actuators, controllers, and wireless communications are prone to attacks that compromise the system. Machine learning models have been utilized in controllers of automotive to learn, estimate, and provide the required intelligence in the control process. However, their estimation is also vulnerable to the attacks from physical or cyber domains. They have shown unreliable predictions against unknown biases resulted from the modeling. In this paper, we propose a novel control design using conditional generative adversarial networks that will enable a self-secured controller to capture the normal behavior of the control loop and the physical system, detect the anomaly, and recover from them. We experimented our novel control design on a self-secured BMS by driving a Nissan Leaf S on standard driving cycles while under various attacks. The performance of the design has been compared to the state-of-the-art; the self-secured BMS could detect the attacks with 83% accuracy and the recovery estimation error of 21% on average, which have improved by 28% and 8%, respectively.

In this paper we propose a framework for automating feedback control to balance hard-to-predict wind power variations. The power imbalance is a result of non-zero mean error around the wind power forecast. Our proposed framework is aimed at achieving the objective of frequency stabilization and regulation through one control action. A case-study for a real-world system on Flores island in Portugal is provided. Using a battery-based storage on the island, we illustrate the proposed control framework.
 

Electric vehicle is the automobile that powered by electrical energy stored in batteries. Due to the frequent recharging, vehicles need to be connected to the recharging infrastructure while they are parked. This may disclose drivers' privacy, such as their location that drivers may want to keep secret. In this paper, we propose a scheme to enhance the privacy of the drivers using anonymous credential technique and Trusted Platform Module(TPM). We use anonymous credential technique to achieve the anonymity of vehicles such that drivers can anonymously and unlinkably recharge their vehicles. We add some attributes to the credential such as the type of the battery in the vehicle in case that the prices of different batteries are different. We use TPM to omit a blacklist such that the company that offer the recharging service(Energy Provider Company, EPC) does not need to conduct a double spending detection.