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"Efficient Non-Interactive Polynomial Commitment Scheme in the Discrete" by Peiheng Zhang, Min Tang et al.

Polynomial commitment schemes (PCS) are fundamental components that can effectively solve the problems arising from the combination of IoT and blockchain. These allow a committer to commit to a polynomial and then later evaluate the committed polynomial at an arbitrary challenge point along with a proof of valid, without revealing any additional information about the polynomial. Recent works have presented polynomial commitment schemes based on the discrete logarithm assumption. Their schemes do not require a trusted setup, and the verifier uses homomorphism to check the polynomial evaluation proofs. However, these schemes require two-party interactions and satisfy only special soundness and special honest verifier zero-knowledge, which are infeasible for some non-simultaneous online or decentralized applications. In this paper, we propose a novel polynomial commitment scheme inspired by the idea of the Fiat-Shamir heuristic. Our scheme is non-interactive between the committer and the ....

Complexity Theory , Iscrete Logarithm Assumption , Hash Functions , Nternet Of Things , On Interactivity , Pedersen Commitment , Olynomial Commitment , Robabilistic Logic , Rapdoor Commitment Scheme ,

"Optimizing Sample Delivery in RF-Charging Multi-Hop IoT Networks" by Muchen Jiang and Kwan Wu Chin

This paper studies sample delivery in a multi-hop network where a power beacon charges devices via radio frequency (RF) signals. Devices forward samples with a deadline from a source to a sink. The goal is to minimize the power beacon’s transmit power and guarantee that samples arrive at the sink with probability (1-) by their deadline, where is a given probability of failure. A key challenge is that the power beacon does not have instantaneous channel gains information to devices and also between devices; i.e., it does not know the energy level of devices. To this end, we formulate a chance-constrained stochastic program for the problem at hand, and employ the sample-average approximation (SAA) method to compute a solution. We also outline two novel approximation methods: Sampling based Probabilistic Optimal Power Allocation (S-POPA) and Bayesian Optimization based Probabilistic Optimal Power Allocation (BO-POPA). Briefly, S-POPA generates a set of candidate solutions and iterativel ....

Probabilistic Optimal Power Allocation , Bayesian Optimization , Array Signal Processing , Energy Transfer , Imperfect Knowledge , Nternet Of Things , Monte Carlo , Robabilistic Logic , Radio Frequency , Resource Management , Stochastic Optimization ,

"Charging RF-Energy Harvesting Devices in IoT Networks with Imperfect C" by Hang Yu, Kwan Wu Chin et al.

This paper considers energy delivery by a Hybrid Access Point (HAP) to one or more Radio Frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal Channel State Information (CSI), and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a Mixed Integer Linear Program (MILP) coupled with a Sample Average Approximation (SAA) method. The second is a Model Predictive Control (MPC) solution that utilizes Gaussian Mixture Model (GMM) and a so called backoff that is used to tighten probabilistic constraints. The results show that the performance of the MPC based solution is within 8% of the optimal solution with a probability of 90.8%. ....

Integer Linear Program , Hybrid Access Point , Radio Frequency , Channel State Information , Mixed Integer Linear Program , Sample Average Approximation , Model Predictive Control , Gaussian Mixture Model , Internet Of Things , Power System Reliability , Robabilistic Logic , Eceding Horizon , Resource Management , Stochastic Optimization , Task Analysis , Wireless Charging ,

"Short-Term Lateral Behavior Reasoning for Target Vehicles Considering " by Zhisong Zhou, Yafei Wang et al.

A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting mul ....

Autonomous Vehicles , Ehavior Reasoning , River Preview Model , Hidden Markov Models , Ateral Behavior , Predictive Models , Robabilistic Logic ,