Mohammad Abdullah Al Faruque

TAPE: thermal-aware agent-based power economy for multi/many-core architectures

A growing challenge in embedded system design is coping with increasing power densities resulting from packing more and more transistors onto a small die area, which in turn transform into thermal hotspots. In the current late silicon era silicon structures have become more susceptible to transient faults and aging effects resulting from these thermal hotspots. In this paper we present an agent-based power distribution approach (TAPE) which aims to balance the power consumption of a multi/many-core architecture in a pro-active manner. By further taking the systems thermal state into consideration when distributing the power throughout the chip, TAPE is able to noticeably reduce the peak temperature. In our simulation we provide a fair comparison with the state-of-the-art approaches HRTM [19] and PDTM [9] using the MiBench benchmark suite [18]. When running multiple applications simultaneously on a multi/many-core architecture, we are able to achieve an 11.23% decrease in peak temperature compared to the approach that uses no thermal management [14]. At the same time we reduce the execution time (i.e. we increase the performance of the applications) by 44.2% and reduce the energy consumption by 44.4% compared to PDTM [9]. We also show that our approach exhibits higher scalability, requiring 11.9 times less communication overhead in an architecture with 96 cores compared to the state-of-the-art approaches.

Energy Management-as-a-Service Over Fog Computing Platform

By introducing microgrids, energy management is required to control the power generation and consumption for residential, industrial, and commercial domains, e.g., in residential microgrids and homes. Energy management may also help us to reach zero net energy (ZNE) for the residential domain. Improvement in technology, cost, and feature size has enabled devices everywhere, to be connected and interactive, as it is called Internet of Things (IoT). The increasing complexity and data, due to the growing number of devices like sensors and actuators, require powerful computing resources, which may be provided by cloud computing. However, scalability has become the potential issue in cloud computing. In this paper, fog computing is introduced as a novel platform for energy management. The scalability, adaptability, and open source software/hardware featured in the proposed platform enable the user to implement the energy management with the customized control-as-services, while minimizing the implementation cost and time-to-market. To demonstrate the energy management-as-a-service over fog computing platform in different domains, two prototypes of home energy management (HEM) and microgrid-level energy management have been implemented and experimented.

Configurable links for runtime adaptive on-chip communication

Reliability concerns associated with upcoming technology nodes coupled with unpredictable system scenarios resulting from increasingly complex systems require considering runtime adaptivity in all possible parts of future on-chip systems. We are presenting a novel configurable link which can change its supported bandwidth on-demand at runtime (2X-Links) for an adaptive on-chip communication architecture. We have evaluated our results using real-time multi-media and the E3S application benchmark suits. Our 2X-Links provide a higher throughput of up to 36%, with an average throughput increase of 21.3%, compared to the Normal-Full-Duplex-Links [12], [14], [17], [20] and keep performance-related guarantees with as low as 50% of the Normal-Full-Duplex-Links capacity. Our simulation shows when some links fail, the NoC with 2X-Links can recover from these faults with an average probability of 82.2% whereas these faults would be fatal for the Normal-Full-Duplex-Links.

A model-based design of Cyber-Physical Energy Systems

Cyber-Physical Energy Systems (CPES) are an amalgamation of both power gird technology, and the intelligent communication and co-ordination between the supply and the demand side through distributed embedded computing. Through this combination, CPES are intended to deliver power efficiently, reliably, and economically. The design and development work needed to either implement a new power grid network or upgrade a traditional power grid to a CPES-compliant one is both challenging and time consuming due to the heterogeneous nature of the associated components/subsystems. The Model Based Design (MBD) methodology has been widely seen as a promising solution to address the associated design challenges of creating a CPES. In this paper, we demonstrate a MBD method and its associated tool for the purpose of designing and validating various control algorithms for a residential microgrid. Our presented co-simulation engine GridMat is a MATLAB/Simulink toolbox; the purpose of it is to co-simulate the power systems modeled in GridLAB-D as well as the control algorithms that are modeled in Simulink. We have presented various use cases to demonstrate how different levels of control algorithms may be developed, simulated, debugged, and analyzed by using our GridMat toolbox for a residential mi-crogrid.

Acoustic side-channel attacks on additive manufacturing systems

Additive manufacturing systems, such as 3D printers, emit sounds while creating objects. Our work demonstrates that these sounds carry process information that can be used to indirectly reconstruct the objects being printed, without requiring access to the original design. This is an example of a physical-to-cyber domain attack, where information gathered from the physical domain, such as acoustic side-channel, can be used to reveal information about the cyber domain. Our novel attack model consists of a pipeline of audio signal processing, machine learning algorithms, and context-based post-processing to improve the accuracy of the object reconstruction. In our experiments, we have successfully reconstructed the test objects (designed to test the attack model under various benchmark parameters) and their corresponding G-codes with an average accuracy for axis prediction of 78.35% and an average length prediction error of 17.82% on a Fused Deposition Modeling (FDM) based additive manufacturing system. Our work exposes a serious vulnerability in FDM based additive manufacturing systems exploitable by physical-to-cyber attacks that may lead to theft of Intellectual Property (IP) and trade secrets. To the best of our knowledge this kind of attack has not yet been explored in additive manufacturing systems.

Runtime Thermal Management Using Software Agents for Multi- and Many-Core Architectures

System-level runtime approaches provide a new dimension of variation tolerance in multi- and many-core systems. This article looks into a scalable system-level, dynamic thermal management solution using an agent-based, distributed-application-mapping approach.

Battery lifetime-aware automotive climate control for electric vehicles

Electric Vehicle (EV) optimization involves stringent constraints on driving range and battery lifetime. Sophisticated embedded systems and huge number of computing resources have enabled researchers to implement advanced Battery Management Systems (BMS) for optimizing the driving range and battery lifetime. However, the Heating, Ventilation, and Air Conditioning (HVAC) control and BMS have not been considered together in this optimization. This paper presents a novel automotive climate control methodology that manages the HVAC power consumption to improve the battery lifetime and driving range. Our experiments demonstrate that the HVAC consumption is considerable and flexible in an EV which significantly influences the driving range and battery lifetime. Hence, this influence on the above-mentioned constraints has been modeled and analyzed precisely, then it has been considered thoroughly in the EV optimization process. Our methodology provides significant improvement in battery lifetime (on average 14%) and average power consumption (on average 39% reduction) compared to the state-of-the-art methodologies.

KCAD: kinetic cyber-attack detection method for cyber-physical additive manufacturing systems

Additive Manufacturing (AM) uses Cyber-Physical Systems (CPS) (e.g., 3D Printers) that are vulnerable to kinetic cyber-attacks. Kinetic cyber-attacks cause physical damage to the system from the cyber domain. In AM, kinetic cyber-attacks are realized by introducing flaws in the design of the 3D objects. These flaws may eventually compromise the structural integrity of the printed objects. In CPS, researchers have designed various attack detection method to detect the attacks on the integrity of the system. However, in AM, attack detection method is in its infancy. Moreover, analog emissions (such as acoustics, electromagnetic emissions, etc.) from the side-channels of AM have not been fully considered as a parameter for attack detection. To aid the security research in AM, this paper presents a novel attack detection method that is able to detect zero-day kinetic cyber-attacks on AM by identifying anomalous analog emissions which arise as an outcome of the attack. This is achieved by statistically estimating functions that map the relation between the analog emissions and the corresponding cyber domain data (such as G-code) to model the behavior of the system. Our method has been tested to detect potential zero-day kinetic cyber-attacks in fused deposition modeling based AM. These attacks can physically manifest to change various parameters of the 3D object, such as speed, dimension, and movement axis. Accuracy, defined as the capability of our method to detect the range of variations introduced to these parameters as a result of kinetic cyber-attacks, is 77.45%.

Automotive Cyber–Physical Systems: A Tutorial Introduction

This tutorial gives an introduction to novices in CPS and particularly highlights the basics of control theory with respect to automotive applications. The authors furthermore describe the “semantic gap” between control models and their implementation and conclude that a new CPS-oriented design approach is required.

GridMat: Matlab toolbox for GridLAB-D to analyze grid impact and validate residential microgrid level energy management algorithms

Residential microgrid has the capability to participate in the distribution grid as a very flexible and dynamic component for demand side energy management (energy efficiency, peak-load reduction, and demand response). Various hierarchical (appliance, home, and neighborhood level) advanced control algorithms need to be developed and validated for such residential microgrids. GridLAB-D is the most promising tool for power system modeling of a microgrid. However, it is limited in supporting advanced control algorithm development with debugging support and does not provide a user friendly interface for modeling the structural and behavioral aspects of a residential microgrid. Therefore, in this paper, we present a new Matlab toolbox (GridMat) to integrate the capabilities of domain-specific modeling & simulation tools from power system (GridLAB-D) and control (Matlab). The GridMat tool supports user friendly model creation, robust debugging, and intelligent grid impact analysis utilities. To demonstrate the capability of GridMat, we have implemented three different levels of energy management controllers (including direct load control) for a residential microgrid using this tool to reduce and shift peak load according to Time-Of-Use (TOU) electricity rate.