Quantum Key Distribution: Modeling and Simulation through BB84 Protocol Using Python3

Autonomous “Things” is becoming the future trend as the role, and responsibility of IoT keep diversifying. Its applicability and deployment need to re-stand technological advancement. The versatile security interaction between IoTs in human-to-machine and machine-to-machine must also endure mathematical and computational cryptographic attack intricacies. Quantum cryptography uses the laws of quantum mechanics to generate a secure key by manipulating light properties for secure end-to-end communication. We present a proof-of-principle via a communication architecture model and implementation to simulate these laws of nature. The model relies on the BB84 quantum key distribution (QKD) protocol with two scenarios, without and with the presence of an eavesdropper via the interception-resend attack model from a theoretical, methodological, and practical perspective. The proposed simulation initiates communication over a quantum channel for polarized photon transmission after a pre-agreed configuration over a Classic Channel with parameters. Simulation implementation results confirm that the presence of an eavesdropper is detectable during key generation due to Heisenberg’s uncertainty and no-cloning principles. An eavesdropper has a 0.5 probability of guessing transmission qubit and 0.25 for the polarization state. During simulation re-iterations, a base-mismatch process discarded about 50 percent of the total initial key bits with an Error threshold of 0.11 percent.</p

Development and experimental validation of high performance embedded intelligence and fail-operational urban surround perception solutions of the PRYSTINE project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.</p

Enhancing Security of IoT through Quantum Cryptography

As the role and responsibility of IoT keeps diversifying, its applicability, deployment, and communication processes needs to re-stand technological advancement. The versatile interaction between IoTs in human-to-machine and machine-to-machine communications also needs to endure cryptographic attacks that relies on mathematical and computational complexities. Security is important in IoT as the vision of autonomous ``Things" is becoming the future trend. Quantum cryptography uses the laws of quantum mechanics for generating a secure key through the manipulation of light properties to secure an end-to-end communication. An analyses on how the advantages of quantum cryptography over classic cryptography can improve IoT communications is approached from theoretical, methodological, and practical perspective. A simulation model designed with CrypTool 2 is implemented using the Python language to simulate quantum cryptographic principles ( quantum mechanics laws) with the BB84 protocol. The statistical analysis from the simulation confirms the presence of an eavesdropper is detectable in the key generation and distribution process. Due to Heisenberg's uncertainty and no-cloning principles, an eavesdropper has a probability of 0.5 chances of guessing the current quantum states and $1/4$ for the four quantum states. For this reason, an average of 50 percent bits from the total initial key is discarded through as base-mismatch process. Error detection and amplification process follows to eliminate all errors as well as strengthening the final shared key which does not match at the end when an eavesdropper is enabled during the simulation. The findings from the simulation also shows that IoT devices can get access to the final shared key through series of network configurations. The QKD nodes at the end of both communication takes care of the initial key transactions for the devices to either query directly or through sub queries. This then becomes an advantage in IoT communicational processes when coupled with classic cryptographic algorithms.Siirretty Doriast

Quantum Key Distribution: Modeling and Simulation through BB84 Protocol Using Python3

Autonomous &ldquo;Things&rdquo; is becoming the future trend as the role, and responsibility of IoT keep diversifying. Its applicability and deployment need to re-stand technological advancement. The versatile security interaction between IoTs in human-to-machine and machine-to-machine must also endure mathematical and computational cryptographic attack intricacies. Quantum cryptography uses the laws of quantum mechanics to generate a secure key by manipulating light properties for secure end-to-end communication. We present a proof-of-principle via a communication architecture model and implementation to simulate these laws of nature. The model relies on the BB84 quantum key distribution (QKD) protocol with two scenarios, without and with the presence of an eavesdropper via the interception-resend attack model from a theoretical, methodological, and practical perspective. The proposed simulation initiates communication over a quantum channel for polarized photon transmission after a pre-agreed configuration over a Classic Channel with parameters. Simulation implementation results confirm that the presence of an eavesdropper is detectable during key generation due to Heisenberg&rsquo;s uncertainty and no-cloning principles. An eavesdropper has a 0.5 probability of guessing transmission qubit and 0.25 for the polarization state. During simulation re-iterations, a base-mismatch process discarded about 50 percent of the total initial key bits with an Error threshold of 0.11 percent

Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project&mdash;PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck