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Vehicle technology has developed rapidly these years, however, the security measures for in-vehicle network does not keep up with the trend. Controller area network(CAN) is the most used protocol in the in-vehicle network. With the characteristic of CAN, there exists many vulnerabilities including lacks of integrity and confidentiality, and hence CAN is vulnerable to various attacks such as impersonation attack, replay attack, etc. In order to implement the authentication and encryption, secret key derivation is necessary. In this work, we proposed an efficient key management scheme for in-vehicle network. In particular, the scheme has five phases. In the first and second phase, we utilize elliptic curve cryptography-based key encapsulation mechanism(KEM) to derive a pairwise secret between each ECU and a central secure ECU in the same group. Then in the third phase, we design secure communication to derive group shared secret among all ECU in a group. In the last two phases, SECU is not needed, regular ECU can derive session key on their own. We presented a possible attack analysis(chosen-ciphertext attack as the main threat) and a security property analysis for our scheme. Our scheme is evaluated based on a hardware-based experiment of three different microcontrollers and a software-based simulation of IVNS. We argue that based on our estimation and the experiment result, our scheme performs better in communication and computation overhead than similar works.

We propose a new paradigm for designing efficient p-adaptive arbitrary high order methods. We consider arbitrary high order iterative schemes that gain one order of accuracy at each iteration and we modify them in order to match the accuracy achieved in a specific iteration with the discretization accuracy of the same iteration. Apart from the computational advantage, the new modified methods allow to naturally perform p-adaptivity, stopping the iterations when appropriate conditions are met. Moreover, the modification is very easy to be included in an existing implementation of an arbitrary high order iterative scheme and it does not ruin the possibility of parallelization, if this was achievable by the original method. An application to the Arbitrary DERivative (ADER) method for hyperbolic Partial Differential Equations (PDEs) is presented here. We explain how such framework can be interpreted as an arbitrary high order iterative scheme, by recasting it as a Deferred Correction (DeC) method, and how to easily modify it to obtain a more efficient formulation, in which a local a posteriori limiter can be naturally integrated leading to p-adaptivity and structure preserving properties. Finally, the novel approach is extensively tested against classical benchmarks for compressible gas dynamics to show the robustness and the computational efficiency.

Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse sparsity degrees. To offer a good trade-off between accuracy and hardware performance, an ideal DNN accelerator should have high flexibility to efficiently translate DNN sparsity into reductions in energy and/or latency without incurring significant complexity overhead. This paper introduces hierarchical structured sparsity (HSS), with the key insight that we can systematically represent diverse sparsity degrees by having them hierarchically composed from multiple simple sparsity patterns. As a result, HSS simplifies the underlying hardware since it only needs to support simple sparsity patterns; this significantly reduces the sparsity acceleration overhead, which improves efficiency. Motivated by such opportunities, we propose a simultaneously efficient and flexible accelerator, named HighLight, to accelerate DNNs that have diverse sparsity degrees (including dense). Due to the flexibility of HSS, different HSS patterns can be introduced to DNNs to meet different applications' accuracy requirements. Compared to existing works, HighLight achieves a geomean of up to 6.4x better energy-delay product (EDP) across workloads with diverse sparsity degrees, and always sits on the EDP-accuracy Pareto frontier for representative DNNs.

Recently, intermittent computing (IC) has received tremendous attention due to its high potential in perpetual sensing for Internet-of-Things (IoT). By harvesting ambient energy, battery-free devices can perform sensing intermittently without maintenance, thus significantly improving IoT sustainability. To build a practical intermittently-powered sensing system, efficient routing across battery-free devices for data delivery is essential. However, the intermittency of these devices brings new challenges, rendering existing routing protocols inapplicable. In this paper, we propose RICS, the first-of-its-kind routing scheme tailored for intermittently-powered sensing systems. RICS features two major designs, with the goal of achieving low-latency data delivery on a network built with battery-free devices. First, RICS incorporates a fast topology construction protocol for each IC node to establish a path towards the sink node with the least hop count. Second, RICS employs a low-latency message forwarding protocol, which incorporates an efficient synchronization mechanism and a novel technique called pendulum-sync to avoid the time-consuming repeated node synchronization. Our evaluation based on an implementation in OMNeT++ and comprehensive experiments with varying system settings show that RICS can achieve orders of magnitude latency reduction in data delivery compared with the baselines.

Vehicle-to-vehicle (V2V) communications is a part of next-generation wireless networks to create smart cities with the connectivity of intelligent transportation systems. Besides, green communications is considered in V2V communication systems for energy sustainability and carbon neutrality. In this scope, radio-frequency (RF) energy harvesting (EH) provides a battery-free energy source as a solution for the future of V2V communications. Herein, the employment of RF-EH in V2V communications is considered where the bit error probability (BEP) of a dual-hop decode-and-forward relaying system is obtained depending on the utilization of antennas at the relay. The multiple antenna power-constraint relay harvests its power by applying dedicated antenna (DA)/power splitting (PS) EH modes and linear (L)/nonlinear (NL) EH models. Moreover, the links between nodes are exposed to double-Rayleigh fading. Finally, the performance of different system parameters is compared using theoretical derivations of BEP. The results provide a comprehensive analysis of the proposed system considering PS/DA-EH modes and L/NL-EH models, as well as deterministic/uniformly distributed placement of nodes. It is observed that PS-EH outperforms DA-EH assuming a placement of an equal number of antennas and distances. Moreover, optimal performance of PS/DA-EH is achieved by allocating more power and increasing the number of antennas for EH, respectively.

The effectiveness of Recurrent Neural Networks (RNNs) for tasks such as Automatic Speech Recognition has fostered interest in RNN inference acceleration. Due to the recurrent nature and data dependencies of RNN computations, prior work has designed customized architectures specifically tailored to the computation pattern of RNN, getting high computation efficiency for certain chosen model sizes. However, given that the dimensionality of RNNs varies a lot for different tasks, it is crucial to generalize this efficiency to diverse configurations. In this work, we identify adaptiveness as a key feature that is missing from today's RNN accelerators. In particular, we first show the problem of low resource-utilization and low adaptiveness for the state-of-the-art RNN implementations on GPU, FPGA and ASIC architectures. To solve these issues, we propose an intelligent tiled-based dispatching mechanism for increasing the adaptiveness of RNN computation, in order to efficiently handle the data dependencies. To do so, we propose Sharp as a hardware accelerator, which pipelines RNN computation using an effective scheduling scheme to hide most of the dependent serialization. Furthermore, Sharp employs dynamic reconfigurable architecture to adapt to the model's characteristics. Sharp achieves 2x, 2.8x, and 82x speedups on average, considering different RNN models and resource budgets, compared to the state-of-the-art ASIC, FPGA, and GPU implementations, respectively. Furthermore, we provide significant energy-reduction with respect to the previous solutions, due to the low power dissipation of Sharp (321 GFLOPS/Watt).

The food supply chain has a number of challenges, including a lack of transparency and disengagement among stakeholders. By providing a transparent and traceable digital ledger of transactions and movements for all supply chain actors, blockchain technology can provide a resolution to these problems. We propose a blockchain-based system for tracking a product's full path, from its raw components to the finished item in the store. Many advantages of the offered system include improved quality assessment, increased product transparency and traceability, and sophisticated fraud detection capabilities. By reinventing the way transactions are carried out and enabling stakeholders to obtain a complete record of each product's journey, the system has the potential to completely alter the food supply chain. Also, by minimising inefficiencies, waste, and fraudulent activities that have a negative influence on the supply chain, the deployment of this system can remove limits imposed by the current supply chain. Overall, the suggested blockchain-based system has the potential to significantly increase the efficiency, transparency, and traceability of the food supply chain.

Partial client participation has been widely adopted in Federated Learning (FL) to reduce the communication burden efficiently. However, an inadequate client sampling scheme can lead to the selection of unrepresentative subsets, resulting in significant variance in model updates and slowed convergence. Existing sampling methods are either biased or can be further optimized for faster convergence.In this paper, we present DELTA, an unbiased sampling scheme designed to alleviate these issues. DELTA characterizes the effects of client diversity and local variance, and samples representative clients with valuable information for global model updates. In addition, DELTA is a proven optimal unbiased sampling scheme that minimizes variance caused by partial client participation and outperforms other unbiased sampling schemes in terms of convergence. Furthermore, to address full-client gradient dependence,we provide a practical version of DELTA depending on the available clients' information, and also analyze its convergence. Our results are validated through experiments on both synthetic and real-world datasets.

Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of CNNs (e.g. local prior, spatially shared parameters) limit the performance in the face of broken images with diverse and complex forms. Recently, a class of attention-based network architectures, called transformer, has shown significant performance on natural language processing fields and high-level vision tasks. Compared with CNNs, attention operators are better at long-range modeling and have dynamic weights, but their computational complexity is quadratic in spatial resolution, and thus less suitable for applications involving higher resolution images, such as image inpainting. In this paper, we design a novel attention linearly related to the resolution according to Taylor expansion. And based on this attention, a network called $T$-former is designed for image inpainting. Experiments on several benchmark datasets demonstrate that our proposed method achieves state-of-the-art accuracy while maintaining a relatively low number of parameters and computational complexity. The code can be found at \href{//github.com/dengyecode/T-former_image_inpainting}{github.com/dengyecode/T-former\_image\_inpainting}

Nakamoto consensus (NC) powers major proof-of-work (PoW) and proof-of-stake (PoS) blockchains such as Bitcoin or Cardano. Given a network of nodes with certain communication and computation capacities, against what fraction of adversarial power (the resilience) is Nakamoto consensus secure for a given block production rate? Prior security analyses of NC used a bounded delay model which does not capture network congestion resulting from high block production rates, bursty release of adversarial blocks, and in PoS, spamming due to equivocations. For PoW, we find a new attack, called teasing attack, that exploits congestion to increase the time taken to download and verify blocks, thereby succeeding at lower adversarial power than the private attack which was deemed to be the worst-case attack in prior analysis. By adopting a bounded bandwidth model to capture congestion, and through an improved analysis method, we identify the resilience of PoW NC for a given block production rate. In PoS, we augment our attack with equivocations to further increase congestion, making the vanilla PoS NC protocol insecure against any adversarial power except at very low block production rates. To counter equivocation spamming in PoS, we present a new NC-style protocol Sanitizing PoS (SaPoS) which achieves the same resilience as PoW NC.