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Published 2023
An Emulation-Based Performance Evaluation Methodology for Edge Computing and Latency Sensitive Applications
Author: Manuel Olguín Muñoz
Type and Venue: PhD Thesis
Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1758413&dswid=-3445
Abstract: Cloud Computing, with its globally-accessible nature and virtually unlimited scalability, has revolutionized our daily lives since its widespread adoption in the early 2000s. It allows us to access our documents anywhere, keep in touch with friends, back up photos, and even remotely control our appliances. Despite this, Cloud Computing has limitations when it comes to novel appli- cations requiring real-time processing or low-latencies. Applications such as Cyber-Physical Systems (CPSs) or mobile eXtended Reality (XR), which in turn also have great transformative potential, are unable to run on the Cloud.
Edge Computing is emerging as a potential solution to these limitations by bringing computation closer to the edge of the network, thereby reducing latency and enabling real-time decision making. The combination of Edge Computing and modern mobile network technologies such as 5G offers potential for massive deployments of latency-sensitive applications. However, scaling and understanding these deployments poses important challenges such the optimization of latency through multiple processing steps and trade-offs in wireless system choice, protocols, hardware, and algorithms. Existing approaches have so far been unsuccessful in capturing the complex effects arising from the interplay between network and compute in these systems.
This dissertation addresses the challenge of performance evaluation of Edge Computing and the applications enabled by this paradigm with two key contributions to literature. First, a methodological approach to experimentally studying the trade-offs between system responsiveness and resource consumption in latency-sensitive applications such as CPSs and XR is introduced. These applications and systems feature characteristics, such as tight interaction with the physical world and the involvement of humans, that make them challenging to study through simulated approaches or analytical modeling. The approach presented in this thesis involves therefore the emulation of the client-side workload while maintaining the real server-side process and network stack to retain the realism of network and compute effects. […See link for full abstract…]
Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages
Author: Daniel Lundén
Type and Venue: PhD Thesis
Link: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1741049
Abstract: Probabilistic programming languages (PPLs) allow users to express statistical inference problems that the PPL implementation then, ideally, solves automatically. In particular, PPL users can focus on encoding their inference problems, and need not concern themselves with the intricacies of inference. Universal PPLs are PPLs with great expressive power, meaning that users can express essentially any inference problem. Consequently, universal PPL implementations often use general-purpose inference algorithms that are compatible with all such inference problems. A problem, however, is that general-purpose inference algorithms can often not efficiently solve complex inference problems. Furthermore, for certain inference algorithms, there are no formal correctness proofs in the context of universal PPLs.
This dissertation considers research problems related to Monte Carlo inference algorithms—sampling-based general-purpose inference algorithms that universal PPL implementations often apply. The first research problem concerns the correctness of sequential Monte Carlo (SMC) inference algorithms. A contribution in the dissertation is a proof of correctness for SMC algorithms in the context of universal PPLs. The second research problem concerns execution time improvements when suspending executions—a requirement in many Monte Carlo inference algorithms. The dissertation addresses the problem through two separate approaches. The first approach is a compilation technique targeting high-performance platforms. The second approach is a static suspension analysis guiding a selective continuation-passing style (CPS) transformation, reducing overhead compared to a full CPS transformation. The third research problem concerns inference improvements through alignment—a useful and often overlooked property in PPLs. The dissertation contributions are a formal definition of alignment, a static analysis technique that automatically aligns programs, and aligned versions of SMC and Markov chain Monte Carlo (MCMC) inference algorithms. The final research problem is more practical, and concerns the effective implementation of PPLs. Specifically, the contribution is the Miking CorePPL universal PPL and its compiler. Overall, the contributions in the dissertation significantly improve the efficiency of Monte Carlo algorithms as applied in universal PPLs.
Deep Learning Side-Channel Attacks on Advanced Encryption Standard
Author: Huanyu Wang
Type and Venue: PhD Thesis
Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2:1735246
Abstract: Side-channel attacks (SCAs) have become one of the most realistic threats to implementations of cryptographic algorithms. By exploiting the nonprime, unintentional physical leakage, such as different amount of power consumed by the device during the execution of the cryptographic algorithm, SCAs are able to bypass the theoretical strength of cryptography and extract the secret key. A compromised cryptographic implementation can definitely lead to a complete loss of information security.
Recently, with advances in deep learning, SCAs found a powerful ally. A well-trained deep-learning model is feasible to make the attack several fold more efficient than traditional SCAs. Therefore, it is important to understand the capabilities and limitations of deep-learning side-channel attacks (DLSCAs) to design trustworthy countermeasures in the future.
To that end, we investigate to which extent DLSCAs can compromise implementations of Advanced Encryption Standard (AES) in different attack scenarios, as AES is the most widely used symmetric encryption algorithm. The demonstrated attacks in this dissertation focus on two side channels: power consumption and far field electromagnetic (EM) emissions, as the power consumption is one of the most widely exploited side channels and far field EM SCAs are one of the most threatening attacks. […See link for full abstract…]
Optimal Service Caching and Pricing in Edge Computing: a Bayesian Gaussian Process Bandit Approach
Author: Feridun Tütüncüoglu and György Dán
Type and Venue: Journal paper, IEEE/ACM Transaction on Mobile Computing
DOI: 10.1109/TMC.2022.3221465, Link: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1767411
Abstract: Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading in a dynamic environment where (WDs) can be active or inactive at any point in time. We model the problem as a single leader multiple-follower Stackelberg game, where the service operator is the leader and decides what applications to cache and how much to charge for their use, while the WDs are the followers and decide whether or not to offload their computations. We show that the WDs’ interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that under perfect and complete information the equilibrium price of the service operator can be computed in polynomial time for any cache placement. For the incomplete information case, we propose a Bayesian Gaussian Process Bandit algorithm for learning an optimal price for a cache placement and provide a bound on its asymptotic regret. We then propose a Gaussian process approximation-based greedy heuristic for computing the cache placement. We use extensive simulations to evaluate the proposed learning scheme, and show that it outperforms state of the art algorithms by up to 50% at little computational overhead.
Aligning Human Preferences with Baseline Objectives in Reinforcement Learning
Author: Daniel Marta, Simon Holk, Christian Pek, Jana Tumova, Iolanda Leite
Type and Venue: Conference on Robot Learning (CoRL 2022)
Link: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1744884
Abstract: Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an amplitude of factors, such as designing reward functions that cover every possible interaction. To address the heavy burden of robot reward engineering, we aim to leverage subjective human preferences gathered in the context of human-robot interaction, while taking advantage of a baseline reward function when available. By considering baseline objectives to be designed beforehand, we are able to narrow down the policy space, solely requesting human attention when their input matters the most. To allow for control over the optimization of different objectives, our approach contemplates a multi-objective setting. We achieve human-compliant policies by sequentially training an optimal policy from a baseline specification and collecting queries on pairs of trajectories. These policies are obtained by training a reward estimator to generate Pareto optimal policies that include human preferred behaviours. Our approach ensures sample efficiency and we conducted a user study to collect real human preferences, which we utilized to obtain a policy on a social navigation environment.
Towards 5G-Aware Robot Planning for Industrial Applications
Nils Jörgensen, Ajay Kattepur, Swarup Mohalik, Aneta Vulgarakis and Elena Fersman
Type and Venue: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2022)
Link: https://ieeexplore.ieee.org/document/9921449
Abstract: With the emergence of Industry 4.0, comes an increasing need for multi-robot coordination and communication to efficiently complete joint tasks. A critical technology is the fifth generation (5G) mobile network, which enables multiple robots to execute control tasks with differentiated quality-of-service (QoS) features. However, there has been limited analysis of the impact of real 5G capabilities on multi-agent robot planning problems. In this paper, we provide a review of robot planning algorithms suitable for industrial use-cases, which consider communication aspects in the planning formulation. The paper is further positioned to identify gaps in existing state of the art within communication-aware planning. This is followed by an analysis of key challenges to be targeted at the intersection of 5G, Industry 4.0 and multi-agent robot planning. This analysis is strategically important and would prove useful to academic researchers and industry experts focusing on deployment of robots in industrial settings.
Automatic Alignment in Higher-Order Probabilistic Programming Languages
Author: Daniel Lundén, Gizem Çaylak, Fredrik Ronquist, David Broman
Type and Venue: 32nd European Symposium on Programming (ESOP 2023)
Link: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1739445
Abstract: Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints—relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run—they are aligned. We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC. We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC.
Trends, drivers and strategic directions for trustworthy edge-computing in industrial applications
Author: James Gross, Martin Törngren, György Dan, David Broman, Erik Herzog, Iolanda Leite, Raksha Ramakrishna, Rebecca Stower and Haydn Thompson
Type and Venue: Magazine article, Incose Insight
Link: https://doi.org/10.1002/inst.12408
Abstract: TECoSA – a university-based research center in collaboration with industry – was established early in 2020, focusing on Trustworthy Edge Computing Systems and Applications. This article summarizes and assesses the current trends and drivers regarding edge computing. In our analysis, edge computing provided by mobile network operators will be the initial dominating form of this new computing paradigm for the coming decade. These insights form the basis for the research agenda of the TECoSA center, highlighting more advanced use cases, including AR/VR/Cognitive Assistance, cyber-physical systems, and distributed machine learning. The article further elaborates on the identified strategic directions given these trends, emphasizing testbeds and collaborative multidisciplinary research.
NeuroRAN: Rethinking Virtualization for AI-native Radio Access Networks in 6G
Author: Paris Carbone, György Dán, James Gross, Bo Göransson and Marina Petrova
Type and Venue: Magazine article, Incose Insight
Link: https://doi.org/10.1002/inst.12416
Abstract: Network softwarization has revolutionized the architecture of cellular wireless networks. State-of-the-art container based virtual radio access networks (vRAN) provide enormous flexibility and reduced life-cycle management costs, but they also come with prohibitive energy consumption. We argue that for future AI-native wireless networks to be flexible and energy efficient, there is a need for a new abstraction in network softwarization that caters for neural network type of workloads and allows a large degree of service composability. In this paper we present the NeuroRAN architecture, which leverages stateful function as a user facing execution model, and is complemented with virtualized resources and decentralized resource management. We show that neural network based implementations of common transceiver functional blocks fit the proposed architecture, and we discuss key research challenges related to compilation and code generation, resource management, reliability and security.
Optimal Service Caching and Pricing in Edge Computing: a Bayesian Gaussian Process Bandit Approach
Author: Feridun Tütüncüoglu and György Dán
Type and Venue: Journal paper, IEEE/ACM Transaction on Mobile Computing
DOI: 10.1109/TMC.2022.3221465, Link: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1767411
Abstract: Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading in a dynamic environment where (WDs) can be active or inactive at any point in time. We model the problem as a single leader multiple-follower Stackelberg game, where the service operator is the leader and decides what applications to cache and how much to charge for their use, while the WDs are the followers and decide whether or not to offload their computations. We show that the WDs’ interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that under perfect and complete information the equilibrium price of the service operator can be computed in polynomial time for any cache placement. For the incomplete information case, we propose a Bayesian Gaussian Process Bandit algorithm for learning an optimal price for a cache placement and provide a bound on its asymptotic regret. We then propose a Gaussian process approximation-based greedy heuristic for computing the cache placement. We use extensive simulations to evaluate the proposed learning scheme, and show that it outperforms state of the art algorithms by up to 50% at little computational overhead.
Adding Cyberphysical Systems to the Engineering Education “Pi”
Authors: Claudia Andruetto, Rafia Inam, Martin Törngren
Type and Venue: (Journal) IEEE Explore
DOI: 10.1109/MC.2022.3226917
Abstract: Because many systems are evolving into cyberphysical systems, it is essential to examine their impact on society. This article introduces a multidisciplinary course that provides an overview of how these systems contribute to sociotechnical change.
Published 2022
Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack
Author: Raksha Ramakrishna and György Dán
Type and Venue: Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022 (TSRML 2022)
Link: https://openreview.net/forum?id=j_e-8lVzTD3
Abstract: Property inference attacks against machine learning (ML) models aim to infer properties of the training data that are unrelated to the primary task of the model, and have so far been formulated as binary decision problems, i.e., whether or not the training data have a certain property. However, in industrial and healthcare applications, the proportion of labels in the training data is quite often also considered sensitive information. In this paper we introduce a new type of property inference attack that unlike binary decision problems in literature, aim at inferring the class label distribution of the training data from parameters of ML classifier models. We propose a method based on shadow training and a meta-classifier trained on the parameters of the shadow classifiers augmented with the accuracy of the classifiers on auxiliary data. We evaluate the proposed approach for ML classifiers with fully connected neural network architectures. We find that the proposed meta-classifier attack provides a maximum relative improvement of 52% over state of the art.
Inferring Class-Label Distribution in Federated Learning
Author: Raksha Ramakrishna and György Dán
Type and Venue: ACM Workshop on Artificial Intelligence and Security (AISec2022)
DOI Link: https://doi.org/10.1145/3560830.3563725
Abstract: Federated Learning (FL) has become a popular distributed learning method for training classifiers by using data that are private to individual clients. The clients´ data are typically assumed to be confidential, but their heterogeneity and potential class-imbalance adversely impact the accuracy of the trained model. The class-imbalance may not be common knowledge or may even be confidential information itself. Thus, the inference of the class-label distribution of the training data is important both from a performance and from a privacy perspective. In this paper, we study the problem of class-label distribution inference from an adversarial perspective, based on model parameter updates sent to the parameter server. Firstly, we present conditions under which exact inference is possible. We then introduce four new methods to estimate class-label distribution in the general FL setting. We evaluate the proposed inference methods on four different datasets and our results show that they significantly outperform state of the art methods.
Joint Wireless and Edge Computing Resource Management with Dynamic Network Slice Selection
Author: Sladana Josilo and György Dán
Type and Venue: (Journal) IEEE ACM Transactions on Networking
DOI Link: https://doi.org/10.1109/TNET.2022.3156178
Abstract: Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint dynamic assignment of computational tasks to slices, management of radio resources across slices and management of radio and computing resources within slices. We formulate the Joint Slice Selection and Edge Resource Management (JSS-ERM) problem as a mixed-integer problem with the objective to minimize the completion time of computational tasks. We show that the JSS-ERM problem is NP-hard and develop an approximation algorithm with bounded approximation ratio based on a game theoretic treatment of the problem. We use extensive simulations to provide insight into the performance of the proposed solution from the perspective of the whole system and from the perspective of individual slices. Our results show that the proposed slicing policy can achieve significant gains compared to the equal slicing policy, and that the computational complexity of the proposed task placement algorithm is approximately linear in the number of devices.
Large-Scale Scenario Generation for Robotic Manipulation via Conditioned Generative Models
Author: Sanne van Waveren, Christian Pek, Iolanda Leite, Jana Tumova and Danika Kragic
Type and Venue: Conference on Robot Learning (CoRL 2022)
Link: http://www.diva-portal.org/smash/record.jsf?pid=diva2:1708670
Abstract: Data-driven robotic manipulation has been gaining traction. However, creating synthetic large-scale datasets for training, validation and benchmarks often relies on random sampling or perturbations, and the resulting scenarios do not necessarily reflect the desired task goals or spatial constraints on the manipulated objects, i.e., they are not spatially structured. We leverage spatial logics and generative models to automatically create spatially-structured manipulation scenarios from high-level specifications. We condition the models on such specifications to impose diverse spatial object relations on the data, e.g., the mug should be left of the plate. This approach enables users to define custom specifications and generate millions of scenarios within minutes, which specifically satisfy or violate the specifications to a desired extent.
Decentralized Multi-agent Coordination under MITL Specifications and Communication Constraints
Author: Wei Wang, Georg Friedrich Schuppe and Jana Tumova
Type and Venue: (Journal) IEEE Robotics and Automation Letters (RA-L)
Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2:1701199
Abstract: We propose a decentralized solution for high-level multi-agent task planning problems in environments with acommunication network failure. In particular, we consider that the agents can only sense each other and communicate only within a limited radius, yet, they may need to collaborateto accomplish their tasks. These are given in Metric Interval Temporal Logic (MITL) which enables to capture complex task specifications involving explicit time constraints. To substitute for the lacking communication network, we propose to deployan agile robot that transfers information between the heavy-duty robots executing the tasks. We propose an algorithm to decompose each MITL formula into an independent promise for the respective agent and an independent request for others.The agile robot systematically pursues heavy-duty robots to exchange requests. The heavy-duty robots use formal methods-based algorithms to compute path plans satisfying the independent promises and the received requests. While the plan computation for the agents is fully decentralized, the satisfaction of all tasks is guaranteed (if such plans are found). We presenta series of illustrative simulation examples motivated by searchand rescue scenarios.
Online Learning for Rate-Adaptive Task Offloading under Latency Constraints in Serverless Edge Computing
Author: Feridun Tütüncüoglu Sladana Josilo and György Dán
Type and Venue: (Journal) IEEE/ACM Transactions on Networking
DOI Link: https://doi.org/10.1109/TNET.2022.3197669
Abstract: We consider the interplay between latency constrained applications and function-level resource management in a serverless edge computing environment. We develop a game theoretic model of the interaction between rate adaptive applications and a load balancing operator under a function-oriented pay-as-you-go pricing model. We show that under perfect information, the strategic interaction between the applications can be formulated as a generalized Nash equilibrium problem, and use variational inequality theory to prove that the game admits an equilibrium. For the case of imperfect information, we propose an online learning algorithm for applications to maximize their utility through rate adaptation and resource reservation. We show that the proposed algorithm can converge to equilibria and achieves zero regret asymptotically, and our simulation results show that the algorithm achieves good system performance at equilibrium, ensures fast convergence, and enables applications to meet their latency constraints.
Shape Estimation of a 3D Printed Soft Sensor Using Multi-hypothesis Extended Kalman Filter
Author: Kaige Tan, Qinglei Ji, Lei Feng and Martin Törngren
Type and Venue: (Journal) IEEE Robotics and Automation Letters (RA-L)
DOI: 10.1109/lra.2022.3187832, Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2:1683147
Abstract: This study develops a multi-hypothesis extended Kalman filter (MH-EKF) for the online estimation of the bending angle of a 3D printed soft sensor attached to soft actuators. Despite the advantage of compliance and low interference, the 3D printed soft sensor is susceptible to the hysteresis property and nonlinear effects. Improving measurement accuracy for sensors with hysteresis is a common challenge. Current studies mainly apply complex models and highly nonlinear functions to characterize the hysteresis, requiring a complicated parameter identification process and challenging real-time applications. This study enhances the model simplicity and the real-time performance for the hysteresis characterization. We identify the hysteresis by combining multiple polynomial functions and improving the sensor estimation with the proposed MH-EKF. We examine the performance of the filter in the real-time closed-loop control system. Compared with the baseline methods, the proposed approach shows improvements in the estimation accuracy with low computational complexity.
Energy Efficient Sampling Policies for Edge Computing Feedback Systems
Authors: Vishnu Moothedath, Jaya Champati, James Gross
Type and Venue: (Journal) IEEE Transactions on Mobile Computing
DOI: 10.1109/ICCWorkshops50388.2021.9473894
Abstract: We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to the communication and processing of redundant samples. On the other hand, lower sampling frequency results in higher delay in detecting the event, thus increasing the idle energy usage and degrading the quality of experience. We quantify this trade-off as a weighted function between the number of samples and the sampling interval. We solve the minimisation problem for exponential and Rayleigh distributions, for the random time to the event of interest. We prove the convexity of the objective functions by using novel techniques, which can be of independent interest elsewhere. We argue that adding an initial offset to the periodic sampling can further reduce the energy consumption and jointly compute the optimum offset and sampling interval. We apply our framework to two practically relevant applications and show energy savings of up to 36% when compared to an existing periodic scheme.
Scheduling of Wireless Edge Networks for Feedback-Based Interactive Applications
Authors: Samuele Zoppi, Jaya Prakash Champati, James Gross, Wolfgang Kellerer
Type and Venue: (Journal) IEEE Transactions on Communications
DOI: https://ieeexplore.ieee.org/document/9745620
Abstract: Interactive applications with automated feedback will largely influence the design of future networked infrastructures. In such applications, status information about an environment of interest is captured and forwarded to a compute node, which analyzes the information and generates a feedback message. Timely processing and forwarding must ensure the feedback information to be still applicable; thus, the quality-of-service parameter for such applications is the end-to-end latency over the entire loop. By modelling the communication of a feedback loop as a two-hop network, we address the problem of allocating network resources in order to minimize the delay violation probability (DVP), i.e. the probability of the end-to-end latency exceeding a target value. We investigate the influence of the network queue states along the network path on the performance of semi-static and dynamic scheduling policies. The former determine the schedule prior to the transmission of the packet, while the latter benefit from feedback on the queue states as time evolves and reallocate time slots depending on the queue’s evolution. The performance of the proposed policies is evaluated for variations in several system parameters and comparison baselines. Results show that the proposed semi-static policy achieves close-to-optimal DVP and
Energy Minimization of Mobile Edge Computing Networks with HARQ in the Finite Blocklength Regime
Authors: Yao Zhu, Yulin Hu, Anke Schmeink, James Gross
Type and Venue: (Journal) IEEE Transactions on Wireless Communications
DOI: https://ieeexplore.ieee.org/document/9729105
Abstract: We consider a mobile edge computing (MEC) network supporting low-latency, critical offloading workloads. The task offloading from the user to the server is operated under a truncated Hybrid Automatic Repeat reQuest (HARQ) process, i.e., we consider finite retransmission attempts. Both the HARQ type-I and type-II schemes are studied. For each scheme, we first characterize the total error probability and the total energy cost, while the impact of finite blocklength (FBL) on the stochastic retransmission behavior is considered. Following the characterizations, we are interested in optimal frameworks for each considered HARQ type, where the number of potential retransmission attempts is optimized together with the duration of each transmission, while the CPU frequency at the edge node is adjusted via voltage scaling. The objective is to minimize the total energy cost with error probability threshold. We show that the resulting stochastic optimization problems can be solved by means of convex optimization. We furthermore demonstrate that sharp minima exist among the energy consumption, underlying the importance of near-optimal parameter choice in the studied scenarios. Our results underline the importance of trading off communication and computational characteristics in delay-critical MEC setups with FBL codes.
Foresee the Unseen: Evaluating Sequential Reasoning about Hidden Objects in Traffic
Authors: José Manuel Gaspar Sánchez, Truls Nyberg, Christian Pek, Jana Tumova
Type and Venue: (Conference) 33rd IEEE Intelligent Vehicles Symposium (IV22)
Download: http://kth.diva-portal.org/smash/get/diva2:1635726/FULLTEXT01.pdf
Abstract: Safe driving requires autonomous vehicles to anticipate potential hidden traffic participants and other unseen objects, such as a cyclist hidden behind a large vehicle, or an object on the road hidden behind a building. Existing methods are usually unable to consider all possible shapes and orientations of such obstacles. They also typically do not reason about observations of hidden obstacles over time, leading to conservative anticipations. We overcome these limitations by (1) modeling possible hidden obstacles as a set of states of a point mass model and (2) sequential reasoning based on reachability analysis and previous observations. Based on (1), our method is safer, since we anticipate obstacles of arbitrary unknown shapes and orientations. In addition, (2) increases the available drivable space when planning trajectories for autonomous vehicles. In our experiments, we demonstrate that our method, at no expense of safety, gives rise to significant reductions in time to traverse various intersection scenarios from the CommonRoad Benchmark Suite.
Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review
Authors: Xinhai Zhang, Jianbo Tao, Kaige Tan, Martin Törngren, José Manuel Gaspar Sánchez, Muhammad Rusyadi Ramli, Xin Tao, Magnus Gyllenhammar, Franz Wotawa, Naveen Mohan, Mihai Nica, Hermann Felbinger
Type and Venue: (Journal) IEEE Transactions on Software Engineering
Download: http://kth.diva-portal.org/smash/get/diva2:1595026/FULLTEXT02.pdf
Abstract: Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.
Optimal Pricing for Service Caching and Task Offloading in Edge Computing
Author: Feridun Tütüncüoglu and György Dán
Type and Venue: IEEE, IFIP Wireless on Demand Networks and Systems (WONS) 2022
DOI: 10.23919/WONS54113.2022.9764593, Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2:1691820
Abstract: Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading. We model the problem as a multiplefollower Stackelberg game, where the operator is the leader and decides what applications to cache and how much to charge for their use, while the wireless devices (WDs) are the followers and decide whether or not to offload their computations. We show that the WDs’ interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that the equilibrium price of the operator can be computed in polynomial time for any cache placement, and propose a greedy algorithm for computing the applications to be cached. We use extensive simulations to show that the proposed heuristic performs close to optimal at negligible computational overhead.
Evaluating Sequential Reasoning about Hidden Objects in Traffic
Author: Truls Nyberg José Manuel Gaspar Sánchez, Christian Pek, Jana Tumova, Martin Törngren
Type and Venue: ICCPS ’22: Proceedings of the 13th ACM/IEEE International Conference on Cyber-Physical Systems, Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/ICCPS54341.2022.00044, Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2:1648442
Abstract: Hidden traffic participants pose a great challenge for autonomous vehicles. Previous methods typically do not use previous observations, leading to over-conservative behavior. In this paper, we present a continuation of our work on reasoning about objects outside the current sensor view. We aim to demonstrate our recently proposed method on an autonomous platform and evaluate its reliability and real-time feasibility when using real sensor data. Showing a significant driving performance increase on a real platform, without compromising safety, would be a significant contribution to the field of autonomous driving.
Correct Me If I’m Wrong: Using Non-Experts to Repair Reinforcement Learning Policies
Authors: Sanne van Waveren, Christian Pek, Jana Tumova, Iolanda Leite
Type and Venue: (Conference) 8th ACM Cyber-Physical System Security Workshop (CPSS2022)
DOI: 10.1109/HRI53351.2022.9889604, Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2:1635509
Abstract: Reinforcement learning has shown great potential for learning sequential decision-making tasks. Yet, it is difficult to anticipate all possible real-world scenarios during training, causing robots to inevitably fail in the long run. Many of these failures are due to variations in the robot’s environment. Usually experts are called to correct the robot’s behavior; however, some of these failures do not necessarily require an expert to solve them. In this work, we query non-experts online for help and explore 1) if/how non-experts can provide feedback to the robot after a failure and 2) how the robot can use this feedback to avoid such failures in the future by generating shields that restrict or correct its high-level actions. We demonstrate our approach on common daily scenarios of a simulated kitchen robot. The results indicate that non-experts can indeed understand and repair robot failures. Our generated shields accelerate learning and improve data-efficiency during retraining.
Industrial Edge-based Cyber-Physical Systems – application needs and concerns for realization
Authors: Martin Törngren, Haydn Thompson, Rafia Inam, James Gross, György Dán
Type and Venue: (Conference) TEC2021 Worskhop at 6th ACM/IEEE Symposium on Edge Computing (SEC 2021)
Download: https://people.kth.se/~gyuri/Pub/TorngrenTHIGD_SECTEC_Edge21.pdf
Abstract: Industry is moving towards advanced cyber-physical systems, with trends towards smartness, automation, connectivity and collaboration. We examine the drivers and requirements for the use of edge computing in critical industrial applications. Our purpose is to provide a better understanding of industrial needs and to initiate a discussion on what role edge computing could take, complementing current industrial and embedded systems, and the cloud. Four domains are chosen for analysis with representative use-cases; manufacturing, transportation, the energy sector and networked applications in the defense domain. We further discuss challenges, open issues and suggested directions that are needed to pave the way the use of edge computing in industrial CPS.
Towards Safer and Risk-aware Motion Planning and Control for Robotic Systems
Author: Fernando S. Barbosa
Type and Venue: PhD Thesis
Link: https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1627303&dswid=-9345
Abstract: Safety and risk-awareness are important properties for robotic systems, be it for protecting them from potentially dangerous internal states, or for avoiding collisions with obstacles and environmental hazards in disaster scenarios. Ensuring safety may be the role of more than one algorithmic layer in a system, each with varying assumptions and guarantees. This thesis investigates how to provide safety and risk-awareness in a robotic system by leveraging temporal logics, motion planning algorithms, and control theory.
Traditional control theory approaches interpret the collision avoidance safety task as a `stay-away’ task; obstacles are abstracted as collections of geometric shapes, and controllers are designed to avoid each shape individually. We propose interpreting the collision avoidance problem as a `stay-within’ task: the obstacle-free space is abstracted into safe regions. We propose control laws based on Control Barrier functions that guarantee that the system remains within such safe regions throughout its mission. Our results demonstrate that our controller indirectly avoids obstacles while providing the system the freedom to move within the safe regions, without the necessity to plan and track a safe trajectory. Furthermore, by extending our idea with Metric Interval Temporal Logic, we are able to consider missions with explicit time bounds. […See link for full abstract…]
Published 2021
Risk-Aware Motion Planning in Partially Known Environments
Authors: Fernando S. Barbosa, Bruno Lacerdo, Paul Duckworth, Jana Tumova, Nick Hawes
Type and Venue: (Conference) IEEE 60th Conference on Decision and Control (CDC2021)
Download: http://kth.diva-portal.org/smash/get/diva2:1626320/FULLTEXT01.pdf
Abstract: Recent trends envisage robots being deployed inareas deemed dangerous to humans, such as buildings with gasand radiation leaks. In such situations, the model of the underlying hazardous process might be unknown to the agent a priori, giving rise to the problem of planning for safe behaviour inpartially known environments. We employ Gaussian Process regression to create a probabilistic model of the hazardous process from local noisy samples. The result of this regression is then used by a risk metric, such as the Conditional Value-at-Risk, to reason about the safety at a certain state. The outcome is a risk function that can be employed in optimal motion planning problems. We demonstrate the use of the proposed function in two approaches. First is a sampling-based motion planning algorithm with an event-based trigger for online replanning. Second is an adaptation to the incremental Gaussian Process motion planner (iGPMP2), allowing it to quickly react and adapt to the environment. Both algorithms are evaluated in representative simulation scenarios, where they demonstrate the ability of avoiding high-risk areas.
Advanced Far Field EM Side-Channel Attack on AE
Authors: Ruize Wang, Huanyu Wang, Elena Dubrova, Martin Brisfors
Type and Venue: (Conference) 7th ACM Cyber-Physical System Security Workshop (CPSS)
DOI: https://dl.acm.org/doi/10.1145/3457339.3457982
Abstract: Several attacks on AES using far field electromagnetic (EM) emission as a side channel have been recently presented. Unlike power analysis or near filed EM analysis, far field EM attacks do not require a close physical proximity to the device under attack. However, in all previous attacks traces for the profiling stage are also captured at a distance (fixed or variable) from the profiling devices. This degenerates the quality of profiling traces due to noise and interference. In this paper, we train deep learning models on “clean” traces, captured through a coaxial cable. Our experiments show that the resulting models can extract the AES key from less than 500 traces on average captured at 15 m from the victim device without repeating each encryption more than once. This is a 20-fold improvement over the previous attacks which require about 10K traces for the same conditions.
Can Deep Learning Break a True Random Number Generator?
Authors: Yang Yu, Michail Moraitis, Elena Dubrova
Type and Venue: (Journal) IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II)
DOI: 10.1109/TCSII.2021.3066338
Abstract: True Random Number Generators (TRNGs) create a hardware-based, non-deterministic noise that is used for generating keys, initialization vectors, and nonces in a variety of applications requiring cryptographic protection. A compromised TRNG may lead to a system-wide loss of security. In this brief, we show that an attack combining power analysis with bitstream modification is capable of classifying the output bits of a TRNG implemented in FPGAs from a single power measurement. We demonstrate the attack on the example of an open source AIS-20/31 compliant ring oscillator-based TRNG implemented in Xilinx Artix-7 28nm FPGAs. The combined attack opens a new attack vector which makes possible what is not achievable with pure bitstream modification or side-channel analysis.
Energy-Optimal Sampling and Processing of Edge-Based Feedback Systems
Authors: Vishnu Moothedath, Jaya Champati, James Gross
Type and Venue: (Conference) IEEE International Conference on Communications Workshops
DOI: 10.1109/ICCWorkshops50388.2021.9473894
Abstract: We study a problem of optimizing the sampling interval in an edge-based feedback system, where sensor samples are offloaded to a back-end server which process them and generates a feedback that is fed-back to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to the communication and processing of some redundant samples. On the other hand, lower sampling frequency results in a higher delay in detecting an event of interest thus increasing the idle energy usage and degrading the quality of experience. We propose a method to quantify this trade-off and compute the optimal sampling interval, and use simulation to demonstrate the energy savings.
Joint Resource Dimensioning and Placement for Dependable Virtualized Services in Mobile Edge Clouds
Authors: Peiyue Zhao, György Dán
Type and Venue: (Journal) IEEE Trans. on Mobile Computing
DOI: https://doi.ieeecomputersociety.org/10.1109/TMC.2021.3060118
Abstract: Mobile edge computing (MEC) is an emerging architecture for accommodating latency sensitive virtualized services (VSs). Many of these VSs are expected to be safety critical, and will have some form of reliability requirements. In order to support provisioning reliability to such VSs in MEC in an efficient and confidentiality preserving manner, in this paper we consider the joint resource dimensioning and placement problem for VSs with diverse reliability requirements, with the objective of minimizing the energy consumption. We formulate the problem as an integer programming problem, and prove that it is NP-hard. We propose a two-step approximation algorithm with bounded approximation ratio based on Lagrangian relaxation. We benchmark our algorithm against two greedy algorithms in realistic scenarios. The results show that the proposed solution is computationally efficient, scalable and can provide up to 30% reduction in energy consumption compared to greedy algorithms.
Resilient Resource Allocation for Service Placement in Mobile Edge Clouds
Author: Peiyue Zhao
Type and Venue: PhD thesis
Read more or download as PDF here: http://kth.diva-portal.org/smash/get/diva2:1538664/FULLTEXT01.pdf
Abstract: Mobile edge computing makes available distributed computation and storage resources in close proximity to end users and allows to provide low-latency and high-capacity services within mobile networks. Therefore, mobile edge computing is emerging as a promising architecture for hosting critical services with stringent latency and performance requirements, which otherwise are challenging to be addressed in conventional cloud computing architectures. Notable use cases of mobile edge computing include real-time data analytic services, industrial process control, and computation offloading for Internet of things devices. However, those services rely on efficient resource management, including resource dimensioning and service placement, and require to be resilient to cyber-attacks, to faulty components and to operation mistakes. The work in this thesis proposes models of resilient resource management that support rapid response to incidents in mobile edge computing and develops efficient algorithms for the resulting resource management problems.
Nordic Industrial IoT Roadmap: Research and Innovation for the Green Transition
Editors: Paul Pop, Martin Törngren
Type and Venue: Nordic Industrial IoT Roadmap
Read more or download as PDF here: http://www.nordic-iot.org/roadmap/
Abstract: Five Nordic universities have developed a roadmap on Industrial IoT (IIoT). IIoT is a key enabling technology for the green transition, bringing together several technological paradigms, from smart electronic components, 5G technologies, to AI and Edge Computing. The roadmap supplements the existing European roadmaps released recently, however, since the Nordic countries are far ahead on digitalization compared to the rest of Europe it calls for specific Nordic measures. According to the EU’s digital DESI index the Nordic countries are ranked at numbers one, two, three and four within the EU. Therefore, the Nordic countries are years ahead of their EU counterparts in the digital roll out of implementing digital services and infrastructure. The Nordic roadmap suggests several measures to be pursued in the next decade.
Cyber-Physical Systems have Far-reaching Implications
Author: Martin Törngren
Type and Venue: HIPEAC Roadmap
Read more or download as PDF here: https://www.hipeac.net/vision/2021/
Abstract: Our world is evolving very rapidly, both from the technological point of view – with impressive advances in articial intelligence and new hardware challenging longstanding PC hardware traditions, for example – and as a result of unexpected events. e year 2020 was quite exceptional, an annus horribilis, according to some. It is hard to disagree with this statement, but every dark cloud has a silver lining. 2020 was also the year that accelerated digital transformation beyond what could have been imagined in 2019. Vaccine development happened faster than would ever have been conceivable a year ago, digital payment became the norm for many people and e-commerce and online sales threatened brick and mortar shops. Employees were encouraged to work from home – with its advantages and disadvantages, videoconferencing became the de facto way to interact with both family and colleagues, schools were forced to experiment with distance learning. e list goes on. Aer living for over a year in an online world, most people will not return completely to the “old normal”. ey will go for a combination of the “old normal” and things they discovered and experimented with in the circumstances forced upon us by COVID-19; they might keep their home oce on some days, and be in the workplace on other days. Higher education will certainly also continue to offer online teaching. The rapidly evolving digital world has also had an impact on the HiPEAC Vision: updating it every two years no longer seems quite in keeping with the speed of the evolution of computing systems. erefore, we decided to move from producing a large roadmap document every other year, to an agile, rapidly evolving electronic magazine-like set of articles.
An FPGA Implementation of 4 x 4 Arbiter PUF
Authors: Can Aknesil, Elena Dubrova
Type and Venue: (Conference) 51st IEEE International Symposium on Multiple-Valued Logic (ISMVL’2021)
Links to DOI (if available) and/or PDF:
Also a full Master thesis (2020.08), downloadable as a PDF: http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1460662&dswid=-5569
Abstract: The need of protecting data and bitstreams increasesin computation environments such as FPGA as a Service (FaaS). Physically Unclonable Functions (PUFs) have been proposedas a solution to this problem. In this paper, we present animplementation of Arbiter PUF with 4×4 switch blocksin Xilinx Series 7 FPGA, perform its statistical analysis, andcompare it to other Arbiter PUF variants. We show that thepresented implementation utilizes five times less area than 2×2 Arbiter PUF-based implementations. It is suitable for manyreal-world applications, including identification, authentication,key provisioning, and random number generation.
Published 2020
Caching Policies over Unreliable Channels
Authors: Paulo Sena, Igor Carvalho, Antonio Abelem, György Dán, Daniel Menasche, Don Towsley
Type and Venue: (Conference) WiOpt 2020 Workshop on Content Caching and Data Delivery over Wireless Networks (CCDWN)
DOI: https://ieeexplore.ieee.org/document/9155273
Read more or download as PDF here: https://people.kth.se/~gyuri/Pub/SenaCADMT_CCDWN2020_UnreliableCaching.pdf
Abstract: Recently, there has been substantial progress in the formal understanding of how caching resources should be allocated when multiple caches each deploy the common LRU policy. Nonetheless, the role played by caching policies beyond LRU in a networked setting where content may be replicated across multiple caches and where channels are unreliable is still poorly understood. In this paper, we investigate this issue by first analyzing the cache miss rate in a system with two caches of unit size each, for the LRU, and the LFU caching policies, and their combination. Our analytical results show that joint use of the two policies outperforms LRU, while LFU outperforms all these policies whenever resource pooling is not optimal. We provide empirical results with larger caches to show that simple alternative policies, such as LFU, provide superior performance compared to LRU even if the space allocation is not fine tuned. We envision that fine tuning the cache space used by such policies may lead to promising additional gains.
Federated Learning in Side-Channel Analysis
Authors: Elena Dubrova, Huanyu Wang
Type and Venue: (Conference) International Conference on Information Security and Cryptology 2020
Link to DOI: https://doi.org/10.1007/978-3-030-68890-5_14
Abstract: Recently introduced federated learning is an attractive framework for the distributed training of deep learning models with thousands of participants. However, it can potentially be used with malicious intent. For example, adversaries can use their smartphones to jointly train a classifier for extracting secret keys from the smartphones’ SIM cards without sharing their side-channel measurements with each other. With federated learning, each participant might be able to create a strong model in the absence of sufficient training data. Furthermore, they preserve their anonymity. In this paper, we investigate this new attack vector in the context of side-channel attacks. We compare the federated learning, which aggregates model updates submitted by N participants, with two other aggregating approaches: (1) training on combined side-channel data from N devices, and (2) using an ensemble of N individually trained models. Our first experiments on 8-bit Atmel ATxmega128D4 microcontroller implementation of AES show that federated learning is capable of outperforming the other approaches.
Tandem Deep Learning Side-Channel Attack on FPGA Implementation of AES
Authors: Huanyu Wang, Elena Dubrova
Type and Venue: (Conference) IEEE International Symposium on Smart Electronic Systems (iSES 2020)
Link to DOI: 10.1109/iSES50453.2020.00041
Abstract: The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to recover the key. The potential benefits of combining multiple classifiers with ensemble learning method have not been fully explored in the side-channel attack’s context. In this paper, we show that, by combining several CNN classifiers which use different attack points, it is possible to considerably reduce (more than 40% on average) the number of traces required to recover the key from an FPGA implementation of AES by power analysis. We also show that not all combinations of classifiers improve the attack efficiency.
A Permissioned Blockchain based Feature Management System for Assembly Devices
Authors: Lifei Tang, Martin Törngren, Lihui Wang
Type and Venue: (Journal) IEEE Access
Links to DOI (if available) and/or PDF: DOI: 10.1109/ACCESS.2020.3028606
Abstract: With the increasing spread and adoption of electronics and software as integral parts of all kinds of physical devices, such devices are becoming controlled by their embedded software. Correspondingly, the manufacturing business has started the transition from selling hardware to selling features (e.g. “insane mode” and “ludicrous mode” in Tesla Model S). Consequently, a trustworthy system to automate such a process becomes essential. This article introduces a permissioned blockchain-based feature management system for assembly devices. Firstly, it leverages software licensing technology to control assembly devices’ features. Secondly, by recording the license ownership transaction data in a permissioned blockchain, the approach (1) takes advantage of blockchain’s trust mechanism and its distributed nature to improve the trustworthiness of the feature management system, and (2) adopts the permissioned blockchain technology to ensure that the license transactions are only visible and applicable to authenticated actors. We further describe an implementation, a proof-of-concept evaluation focusing on functionality and performance, as well as a security analysis.
Bitstream Modification with Interconnect In Mind
Authors: Michail Moraitis, Elena Dubrova
Type and Venue: (Conference) Hardware and Architectural Support for Security and Privacy Workshop (HASP’2020)
Links to DOI (if available) and/or PDF: https://caslab.csl.yale.edu/workshops/hasp2020/
Abstract: Bitstream reverse engineering is traditionally associated with Intellectual Property (IP) theft. Another, less known, threat deriving from that is bitstream modification attacks. It has been shown that the secret key can be extracted from FPGA implementations of cryp-tographic algorithms by injecting faults directly into the bitstream. Such bitstream modification attacks rely on changing the content of Look Up Tables (LUTs). Therefore, related counter measures aim to make the task of identifying a LUT more difficult (e.g. by masking LUT content). However, recent advances in FPGA reverse engineering revealed information on how interconnects are encoded in the bitstream of Xilinx 7 series FPGAs. In this paper, we show that this knowledge can be used to break or weaken existing counter measures, as well as improve existing attacks. Furthermore, a straight-forward attack that re-routes the key to an output pin becomes possible. We demonstrate our claims on an FPGA implementationof SNOW 3G stream cipher, a core algorithm for confidentiality and integrity used in several 3GPP wireless communication standards, including the new Next Generation 5G.