563 research outputs found
FlexiChain 2.0: NodeChain Assisting Integrated Decentralized Vault for Effective Data Authentication and Device Integrity in Complex Cyber-Physical Systems
Distributed Ledger Technology (DLT) has been introduced using the most common
consensus algorithm either for an electronic cash system or a decentralized
programmable assets platform which provides general services. Most established
reliable networks are unsuitable for all applications such as smart cities
applications, and, in particular, Internet of Things (IoT) and Cyber Physical
Systems (CPS) applications. The purpose of this paper is to provide a suitable
DLT for IoT and CPS that could satisfy their requirements. The proposed work
has been designed based on the requirements of Cyber Physical Systems.
FlexiChain is proposed as a layer zero network that could be formed from
independent blockchains. Also, NodeChain has been introduced to be a
distributed (Unique ID) UID aggregation vault to secure all nodes' UIDs.
Moreover, NodeChain is proposed to serve mainly FlexiChain for all node
security requirements. NodeChain targets the security and integrity of each
node. Also, the linked UIDs create a chain of narration that keeps track not
merely for assets but also for who authenticated the assets. The security
results present a higher resistance against four types of attacks. Furthermore,
the strength of the network is presented from the early stages compared to
blockchain and central authority. FlexiChain technology has been introduced to
be a layer zero network for all CPS decentralized applications taking into
accounts their requirements. FlexiChain relies on lightweight processing
mechanisms and creates other methods to increase security
Intelligence at the Extreme Edge: A Survey on Reformable TinyML
The rapid miniaturization of Machine Learning (ML) for low powered processing
has opened gateways to provide cognition at the extreme edge (E.g., sensors and
actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research
field proposes to democratize the use of Machine Learning (ML) and Deep
Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly
energy-efficient pervasive devices capable of operating with less than a few
Milliwatts of power. Nevertheless, many solutions assume that TinyML can only
run inference. Despite this, growing interest in TinyML has led to work that
makes them reformable, i.e., work that permits TinyML to improve once deployed.
In line with this, roadblocks in MCU based solutions in general, such as
reduced physical access and long deployment periods of MCUs, deem reformable
TinyML to play a significant part in more effective solutions. In this work, we
present a survey on reformable TinyML solutions with the proposal of a novel
taxonomy for ease of separation. Here, we also discuss the suitability of each
hierarchical layer in the taxonomy for allowing reformability. In addition to
these, we explore the workflow of TinyML and analyze the identified deployment
schemes and the scarcely available benchmarking tools. Furthermore, we discuss
how reformable TinyML can impact a few selected industrial areas and discuss
the challenges and future directions
A Survey of Recent Developments in Testability, Safety and Security of RISC-V Processors
With the continued success of the open RISC-V architecture, practical deployment of RISC-V processors necessitates an in-depth consideration of their testability, safety and security aspects. This survey provides an overview of recent developments in this quickly-evolving field. We start with discussing the application of state-of-the-art functional and system-level test solutions to RISC-V processors. Then, we discuss the use of RISC-V processors for safety-related applications; to this end, we outline the essential techniques necessary to obtain safety both in the functional and in the timing domain and review recent processor designs with safety features. Finally, we survey the different aspects of security with respect to RISC-V implementations and discuss the relationship between cryptographic protocols and primitives on the one hand and the RISC-V processor architecture and hardware implementation on the other. We also comment on the role of a RISC-V processor for system security and its resilience against side-channel attacks
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ENABLING IOT AUTHENTICATION, PRIVACY AND SECURITY VIA BLOCKCHAIN
Although low-power and Internet-connected gadgets and sensors are increasingly integrated into our lives, the optimal design of these systems remains an issue. In particular, authentication, privacy, security, and performance are critical success factors. Furthermore, with emerging research areas such as autonomous cars, advanced manufacturing, smart cities, and building, usage of the Internet of Things (IoT) devices is expected to skyrocket. A single compromised node can be turned into a malicious one that brings down whole systems or causes disasters in safety-critical applications. This dissertation addresses the critical problems of (i) device management, (ii) data management, and (iii) service management in IoT systems. In particular, we propose an integrated platform solution for IoT device authentication, data privacy, and service security via blockchain-based smart contracts. We ensure IoT device authentication by blockchain-based IC traceability system, from its fabrication to its end-of-life, allowing both the supplier and a potential customer to verify an IC’s provenance. Results show that our proposed consortium blockchain framework implementation in Hyperledger Fabric for IC traceability achieves a throughput of 35 transactions per second (tps). To corroborate the blockchain information, we authenticate the IC securely and uniquely with an embedded Physically Unclonable Function (PUF). For reliable Weak PUF-based authentication, our proposed accelerated aging technique reduces the cumulative burn-in cost by ∼ 56%. We also propose a blockchain-based solution to integrate the privacy of data generated from the IoT devices by giving users control of their privacy. The smart contract controlled trust-base ensures that the users have private access to their IoT devices and data. We then propose a remote configuration of IC features via smart contracts, where an IC can be programmed repeatedly and securely. This programmability will enable users to upgrade IC features or rent upgraded IC features for a fixed period after users have purchased the IC. We tailor the hardware to meet the blockchain performance. Our on-die hardware module design enforces the hardware configuration’s secure execution and uses only 2,844 slices in the Xilinx Zedboard Zynq Evaluation board. The blockchain framework facilitates decentralized IoT, where interacting devices are empowered to execute digital contracts autonomously
Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
The most essential component of every Distributed Ledger Technology (DLT) is
the Consensus Algorithm (CA), which enables users to reach a consensus in a
decentralized and distributed manner. Numerous CA exist, but their viability
for particular applications varies, making their trade-offs a crucial factor to
consider when implementing DLT in a specific field. This article provided a
comprehensive analysis of the various consensus algorithms used in distributed
ledger technologies (DLT) and blockchain networks. We cover an extensive array
of thirty consensus algorithms. Eleven attributes including hardware
requirements, pre-trust level, tolerance level, and more, were used to generate
a series of comparison tables evaluating these consensus algorithms. In
addition, we discuss DLT classifications, the categories of certain consensus
algorithms, and provide examples of authentication-focused and
data-storage-focused DLTs. In addition, we analyze the pros and cons of
particular consensus algorithms, such as Nominated Proof of Stake (NPoS),
Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the
applicability of these consensus algorithms to various Cyber Physical System
(CPS) use cases, including supply chain management, intelligent transportation
systems, and smart healthcare.Comment: 50 pages, 20 figure
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