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Scope and Application of VANET
Published in Sonali P. Botkar, Sachin P. Godse, Parikshit N. Mahalle, Gitanjali R. Shinde, VANET, 2021
Sonali P. Botkar, Sachin P. Godse, Parikshit N. Mahalle, Gitanjali R. Shinde
Random number generator: Random number generator (RNG) is used to generate randomness in a simulation model. It is generated by sequentially taking numbers from a deterministic sequence of pseudo-random number. Number selected from the sequence is appeared to be random. In some cases, pseudo-random sequence is predefined and used by every RNG. In some cases, RNG takes number from different locations of pseudo-random sequence. Location is called as seed. The actual implementation of RNG is initialized with seed. A seed identifies the starting location in a pseudo-random sequence from which RNG starts to pick numbers. In different simulations, seeds are different and thus generate different results. Consider an ideal example as computer network simulation, where packet arrival process, waiting process, and service process are usually modeled as random processes. A random process is expressed by sequences of random variables. These random processes are usually implemented with the aid of an RNG. for a comprehensive treatment on random process implementation (e.g., those having the uniform, exponential, Gaussian, Poisson, binomial distribution functions).
Random Number Generators and Random Processes
Published in E. Mikhailov Eugeniy, Programming with MATLAB for Scientists, 2017
The best we can do is to generate a sequence of pseudo random numbers. By “ pseudo,” we mean that starting from the same initial conditions, the computer will generate exactly the same sequence of numbers (very handy for debugging ). But otherwise, the sequence will look like random numbers and will have the statistical properties of random numbers. In other words, if we do not know the RNG algorithm, we cannot predict the next generated number based on a known sequence of already produced numbers, while the numbers obey the required probability distribution.
On the use of common random numbers in activity-based travel demand modeling for scenario comparison
Published in Transportation Planning and Technology, 2023
H. Zhou, J. L. Dorsman, M. Mandjes, M. Snelder
To apply CRN in this additional ABM component, one needs to make sure that across scenarios the same error samples are used for every traveler/mode combination and every traveler/mode/trip combination respectively. To make this happen, we use the notion of initial seeds. Every time the same initial seed is set in a random number generator (RNG), it will generate the same sequence of random numbers. Therefore, incorporating CRN for traveler/mode combinations errors can be done by associating with each traveler a seed. Then, every time a different scenario is considered, the RNG will still generate the same traveler/mode errors, independent of the actual scenario. For the traveler/mode/trip errors, this can be done at a trip level: we associate with each trip a seed, so that each time the trip is considered, the same traveler/model/trip errors are computed. This way, the errors between scenarios are maximally synchronized. Furthermore, this strategy has the additional computational advantage that, when a trip is not undertaken in a certain scenario, the required traveler/mode/trip errors will not be generated either, saving computation time.
Efficient Key Generation Techniques for Securing IoT Communication Protocols
Published in IETE Technical Review, 2021
Amol K. Boke, Sangeeta Nakhate, Arvind Rajawat
In this category, a dedicated circuit gives the random string of bits. All the bits generated are also needed to store in memory for further operation. This Random Number Generator (RNG) often gives an initial seed or start bit pattern so that it could generate the same pattern when the circuit is used in different devices. Such RNGs are called Pseudo-random number generators (PRNGs). The security of the initial seed becomes a problem while transferring from source to destination devices. Hence to remove this imminent threat it is better to use True random number generators (TRNG). In TRNGs there is no initial seed provided to start the process. The circuit itself starts generating random bits as per clock input. These random bits were then processed and stored in memory for further operations [35]. These storage operations are quite costly in terms of area and execution time; however, storage of key is needed as the same key used for both encryption and decryption operation. Although there are some methods which may not require memory to store the bits generated, in one such type of method Physically Unclonable Functions (PUF) the randomness depends on uncontrolled variations of IC fabrication process [36]. In this section, categorization of RNGs is done on the basis memory requirement by the system. Categories under which these are to be discussed are TRNGs and PUF-based RNGs.
FPGA Implementation of True Random Number Generator Architecture Using All Digital Phase-Locked Loop
Published in IETE Journal of Research, 2022
Huirem Bharat Meitei, Manoj Kumar
In this fast and rapidly changing environment of communication network where the use of the internet has no boundary, securing the internet has become a paramount task for today’s data-driven world. A new generation of fast computing and escalating is used for consumer electronics, which focuses on mobile computing, the internet of things (IoT) and even the internet of everything (IoE). People find embedded and glued themselves in various electronic devices such as laptop, smartphones, computers, smart devices, sensors and actuators. Such electronic devices are interlinked through a local network and Fog or connected in a wider space through the internet and cloud. Considering ever-increasing data dependency on different applications and various smart devices and sensors, the security of the communication link becomes the prime concern. The security and privacy of the end-user must be well protected with a secure yet strong method of Random Number Generator (RNG) like True Random Number Generator (TRNG). To make TRNG highly secure, a stringent security protocol must be laid down while generating unpredictable random key [1]. Highly secure TRNG is the ultimate result of a higher degree of random bitstreams that are generated from different sources of hardware entropy. This hardware-based design is more secure, more strong and more easy to enact on TRNG-based design and also competent for many applications in the field of the financial market, banking security, cyber security, and the most recent emerging field of Internet of Technology (IoT) and Industrial Internet of Thing Technology (IIoT) [2]. Nowadays, implantable sensing devices for the health sector and other wearable devices in consumer electronics are considered to be one of the prominent IoT applications that have the tremendous potential to transform the whole world in future [3]. Moreover, the constraints of Pseudo-Random number generator (PRNG) using different types of macroscopic and microscopic aspects like the electrical disturbance as a root of randomness which may produce predictable results [4,5] that can be easily overcome by our proposed ADPLL-based TRNG. Considering the crucial contribution of True Random number on various smart devices connected with multiple sensors, new opportunity and challenges arise in designing more versatile random generator, which can fit with the present demand of more secure data protection.