First-principles DFT study for the structural stability of Hydrogen passivated graphene (H-graphene) and atomic adsorption of oxygen on H-graphene with different schemes

First-principles DFT levels of calculations have been carried out in order to study the structural stability and electronic properties of hydrogen passivated graphene (H-graphene) clusters. Two different shaped clusters, rectangular and circular, consisting of 6 to 160 carbon atoms and hydrogen termination at the zigzag boundary edges have been studied. The relative stability of rectangular shaped cluster consisting 96 C-atoms found to be 1.5% greater than that of circular shape cluster consisting same number of C-atoms. In comparing circular and rectangular cluster containing same number of C-atoms, the HOMO-LUMO gap of former have been predicted to be 2.159 eV and that of later just 0.346 eV. Adsorption of oxygen atom on H-graphene with different schemes including single sided, both sided and high concentration adsorption, was also studied systematically through first-principles DFT calculations by taking four different H-graphene clusters.Comment: 21 pages, 17 figures, 2 table

Study of sensitivity of parameters of Bernstein-Stancu operators

This paper is aimed at studying sensitivity of parameters \alpha and \beta appearing in the operators introduced by D.D. Stancu [11] in 1969. Results are established on the behavior the nodes used in Bernstein-Stancu polynomials and the nodes used in Bernstein polynomials and graphical presentations of them are generated. Alternate proof of uniform convergence of Bernstein-Stancu operators and an upper bound estimation are derived. It is also established that the parameters \alpha and \beta in Bernstein-Stancu polynomials can be used to get better approximation at a point x = (\alpha)/(\beta) in [0,1] to the Bernstein polynomials

Local and global results for modified Sz\'{a}sz - Mirakjan operators

In this paper, we study a natural modification of Sz\'{a}sz - Mirakjan operators. It is shown by discussing many important established results for Sz\'{a}sz - Mirakjan operators. The results do hold for this modification as well, be they local in nature or global, be they qualitative or quantitative. It is also shown that this generalization is meaningful by means of examples and graphical representations.Comment: 20 pages, 13 graph

Learning-based Resource Optimization in Ultra Reliable Low Latency HetNets

In this paper, the problems of user offloading and resource optimization are jointly addressed to support ultra-reliable and low latency communications (URLLC) in HetNets. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. In the considered scheme, SBSs bid to serve URLLC users under their coverage at a given price, and the MBS decides whether to serve each user locally or to offload it to one of the overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low complexity learning-based heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM)-based solution to the CRO problem. Simulation results show that using LHM, the MBS significantly decreases the spectrum access delay for users (by $\sim$ 93\%) as compared to JUR, while also reducing its bandwidth and power costs in serving users (by $\sim$ 33\%) as compared to directly serving users without offloading.Comment: Submitted to IEEE Globecom 201

Colonel Blotto Game for Secure State Estimation in Interdependent Critical Infrastructure

Securing the physical components of a city's interdependent critical infrastructure (ICI) such as power, natural gas, and water systems is a challenging task due to their interdependence and a large number of involved sensors. In this paper, using a novel integrated state-space model that captures the interdependence, a two-stage cyber attack on an ICI is studied in which the attacker first compromises the ICI's sensors by decoding their messages, and, subsequently, it alters the compromised sensors' data to cause state estimation errors. To thwart such attacks, the administrator of each critical infrastructure (CI) must assign protection levels to the sensors based on their importance in the state estimation process. To capture the interdependence between the attacker and the ICI administrator's actions and analyze their interactions, a Colonel Blotto game framework is proposed. The mixed-strategy Nash equilibrium of this game is derived analytically. At this equilibrium, it is shown that the administrator can strategically randomize between the protection levels of the sensors to deceive the attacker. Simulation results coupled with theoretical analysis show that, using the proposed game, the administrator can reduce the state estimation error by at least $50\%$ compared to a non-strategic approach that assigns protection levels proportional to sensor values.Comment: 30 pages, 6 figure

Dynamic Radio Resource Management for Random Network Coding: Power Control and CSMA Backoff Control

Resource allocation in wireless networks typically occurs at PHY/MAC layers, while random network coding (RNC) is a network layer strategy. An interesting question is how resource allocation mechanisms can be tuned to improve RNC performance. By means of a differential equation framework which models RNC throughput in terms of lower layer parameters, we propose a gradient based approach that can dynamically allocate MAC and PHY layer resources with the goal of maximizing the minimum network coding throughput among all the destination nodes in a RNC multicast. We exemplify this general approach with two resource allocation problems: (i) power control to improve network coding throughput, and (ii) CSMA mean backoff delay control to improve network coding throughput. We design both centralized algorithms and online algorithms for power control and CSMA backoff control. Our evaluations, including numerically solving the differential equations in the centralized algorithm and an event-driven simulation for the online algorithm, show that such gradient based dynamic resource allocation yields significant throughput improvement of the destination nodes in RNC. Further, our numerical results reveal that network coding aware power control can regain the broadcast advantage of wireless transmissions to improve the throughput.Comment: 28 pages, 9 figures. Submitted to IEEE Transactions on Wireless Communication

On the Sum-Capacity of Degraded Gaussian Multiaccess Relay Channels

The sum-capacity is studied for a K-user degraded Gaussian multiaccess relay channel (MARC) where the multiaccess signal received at the destination from the K sources and relay is a degraded version of the signal received at the relay from all sources, given the transmit signal at the relay. An outer bound on the capacity region is developed using cutset bounds. An achievable rate region is obtained for the decode-and-forward (DF) strategy. It is shown that for every choice of input distribution, the rate regions for the inner (DF) and outer bounds are given by the intersection of two K-dimensional polymatroids, one resulting from the multiaccess link at the relay and the other from that at the destination. Although the inner and outer bound rate regions are not identical in general, for both cases, a classical result on the intersection of two polymatroids is used to show that the intersection belongs to either the set of active cases or inactive cases, where the two bounds on the K-user sum-rate are active or inactive, respectively. It is shown that DF achieves the capacity region for a class of degraded Gaussian MARCs in which the relay has a high SNR link to the destination relative to the multiaccess link from the sources to the relay. Otherwise, DF is shown to achieve the sum-capacity for an active class of degraded Gaussian MARCs for which the DF sum-rate is maximized by a polymatroid intersection belonging to the set of active cases. This class is shown to include the class of symmetric Gaussian MARCs where all users transmit at the same power.Comment: 50 pages, 4 figures, submitted to the IEEE IT Transaction

Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning

Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication to operate effectively. This reliance on cyber components exposes ACVs to cyber and physical attacks in which an adversary can manipulate sensor readings and physically take control of an ACV. In this paper, a comprehensive framework is proposed to thwart cyber and physical attacks on ACV networks. First, an optimal safe controller for ACVs is derived to maximize the street traffic flow while minimizing the risk of accidents by optimizing ACV speed and inter-ACV spacing. It is proven that the proposed controller is robust to physical attacks which aim at making ACV systems instable. To improve the cyber-physical security of ACV systems, next, data injection attack (DIA) detection approaches are proposed to address cyber attacks on sensors and their physical impact on the ACV system. To comprehensively design the DIA detection approaches, ACV sensors are characterized in two subsets based on the availability of a-priori information about their data. For sensors having a prior information, a DIA detection approach is proposed and an optimal threshold level is derived for the difference between the actual and estimated values of sensors data which enables ACV to stay robust against cyber attacks. For sensors having no prior information, a novel multi-armed bandit (MAB) algorithm is proposed to enable ACV to securely control its motion. Simulation results show that the proposed optimal safe controller outperforms current state of the art controllers by maximizing the robustness of ACVs to physical attacks. The results also show that the proposed DIA detection approaches, compared to Kalman filtering, can improve the security of ACV sensors against cyber attacks and ultimately improve the physical robustness of an ACV system.Comment: 30 pages, 11 figure

Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems

To operate effectively in tomorrow's smart cities, autonomous vehicles (AVs) must rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication. Such dependence on sensors and communication links exposes AVs to cyber-physical (CP) attacks by adversaries that seek to take control of the AVs by manipulating their data. Thus, to ensure safe and optimal AV dynamics control, the data processing functions at AVs must be robust to such CP attacks. To this end, in this paper, the state estimation process for monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel adversarial deep reinforcement learning (RL) algorithm is proposed to maximize the robustness of AV dynamics control to CP attacks. The attacker's action and the AV's reaction to CP attacks are studied in a game-theoretic framework. In the formulated game, the attacker seeks to inject faulty data to AV sensor readings so as to manipulate the inter-vehicle optimal safe spacing and potentially increase the risk of AV accidents or reduce the vehicle flow on the roads. Meanwhile, the AV, acting as a defender, seeks to minimize the deviations of spacing so as to ensure robustness to the attacker's actions. Since the AV has no information about the attacker's action and due to the infinite possibilities for data value manipulations, the outcome of the players' past interactions are fed to long-short term memory (LSTM) blocks. Each player's LSTM block learns the expected spacing deviation resulting from its own action and feeds it to its RL algorithm. Then, the the attacker's RL algorithm chooses the action which maximizes the spacing deviation, while the AV's RL algorithm tries to find the optimal action that minimizes such deviation.Comment: 8 pages, 4 figure

Hartman-Wintner-type inequality for fractional differential equation with Prabhakar derivative

In this paper, we consider a nonlocal fractional boundary value problem with Prabhakar derivative and obtained a Hartman-Wintner type inequality for it