Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing and workload processing latencies associated with fog computing, ignori
ng the fact that wireless access latency can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month measurement study of the wireless access latency between connected vehicles and a fog/cloud computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement a fog/cloud infrastructure. AdaptiveFogs main objective is to maximize the service confidence level, defined as the probability that the latency of a given service type is below some threshold. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two scenarios based on finite- and infinite-horizon optimization of short-term and long-term confidence are investigated. For each scenario, a simple threshold policy based on weighted K-R distance is proposed and proved to maximize the latency confidence for smart vehicles. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30% to 50% improvement in the confidence levels of fog and cloud latencies, respectively.
It has been known for many years that the leading correction to the black hole entropy is a logarithmic term, which is universal and closely related to conformal anomaly. A fully consistent analysis of this issue has to take quantum backreactions to
the black hole geometry into account. However, it was always unclear how to naturally derive the modified black hole metric especially from an effective action, because the problem refers to the elusive non-locality of quantum gravity. In this paper, we show that this problem can be resolved within an effective field theory (EFT) framework of quantum gravity. Our work suggests that the EFT approach provides a powerful and self-consistent tool for studying the quantum gravitational corrections to black hole geometries and thermodynamics.
With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges. This paper introduces a novel framewo
rk, Federated Generative Adversarial Networks and Automatic Classification (FGAN-AC), which integrates decentralized data synthesizing with traffic classification. FGAN-AC is able to synthesize and classify multiple types of service data traffic from decentralized local datasets without requiring a large volume of manually labeled dataset or causing any data leakage. Two types of data synthesizing approaches have been proposed and compared: computation-efficient FGAN (FGAN-uppercaseexpandafter{romannumeral1}) and communication-efficient FGAN (FGAN-uppercaseexpandafter{romannumeral2}). The former only implements a single CNN model for processing each local dataset and the later only requires coordination of intermediate model training parameters. An automatic data classification and model updating framework has been proposed to automatically identify unknown traffic from the synthesized data samples and create new pseudo-labels for model training. Numerical results show that our proposed framework has the ability to synthesize highly mixed service data traffic and can significantly improve the traffic classification performance compared to existing solutions.
The low in-plane symmetry in layered 1T-ReS$_2$ results in strong band anisotropy, while its manifestation in the electronic properties is challenging to resolve due to the lack of effective approaches for controlling the local current path. In this
work, we reveal the giant transport anisotropy in monolayer to four-layer ReS$_2$ by creating directional conducting paths via nanoscale ferroelectric control. By reversing the polarization of a ferroelectric polymer top layer, we induce conductivity switching ratio of >1.5x10$^8$ in the ReS$_2$ channel at 300 K. Characterizing the domain-defined conducting nanowires in an insulating background shows that the conductivity ratio between the directions along and perpendicular to the Re-chain can exceed 7.9x10$^4$. Theoretical modeling points to the band origin of the transport anomaly, and further reveals the emergence of a flat band in few-layer ReS$_2$. Our work paves the path for implementing the highly anisotropic 2D materials for designing novel collective phenomena and electron lensing applications.
Network slicing has been considered as one of the key enablers for 5G to support diversified IoT services and application scenarios. This paper studies the distributed network slicing for a massive scale IoT network supported by 5G with fog computing
. Multiple services with various requirements need to be supported by both spectrum resource offered by 5G network and computational resourc of the fog computing network. We propose a novel distributed framework based on a new control plane entity, federated-orchestrator , which can coordinate the spectrum and computational resources without requiring any exchange of the local data and resource information from BSs. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting . We prove DistADMM-PVS minimizes the average service response time of the entire network with guaranteed worst-case performance for all supported types of services when the coordination between the F-orchestrator and BSs is perfectly synchronized. Motivated by the observation that coordination synchronization may result in high coordination delay that can be intolerable when the network is large in scale, we propose a novel asynchronized ADMM algorithm. We prove that AsynADMM can converge to the global optimal solution with improved scalability and negligible coordination delay. We evaluate the performance of our proposed framework using two-month of traffic data collected in a in-campus smart transportation system supported by a 5G network. Extensive simulation has been conducted for both pedestrian and vehicular-related services during peak and non-peak hours. Our results show that the proposed framework offers significant reduction on service response time for both supported services, especially compared to network slicing with only a single resource.
Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network
and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator (RO), which can be deployed between base stations (BSs) and fog nodes to coordinate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that the proposed algorithm can minimize the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that the proposed algorithm converges much faster than some other existing algorithms. The joint network slicing with both bandwidth and computational resources can offer around 15% overall latency reduction compared to network slicing with only a single resource.
The epitaxial growth of multifunctional oxides on semiconductors has opened a pathway to introduce new functionalities to semiconductor device technologies. In particular, ferroelectric materials integrated on semiconductors could lead to low-power f
ield-effect devices that can be used for logic and memory. Essential to realizing such field-effect devices is the development of ferroelectric metal-oxide-semiconductor (MOS) capacitors, in which the polarization of a ferroelectric gate is coupled to the surface potential of a semiconducting channel. Here we demonstrate that ferroelectric MOS capacitors can be realized using single crystalline SrZrxTi1-xO3 (x = 0.7) that has been epitaxially grown on Ge. We find that the ferroelectric properties of SrZrxTi1-xO3 are exceptionally robust, as gate layers as thin as 5 nm corresponding to an equivalent-oxide-thickness of just 1.0 nm exhibit a ~ 2 V hysteretic window in the capacitance-voltage characteristics. The development of ferroelectric MOS capacitors with nanoscale gate thicknesses opens new vistas for nanoelectronic devices.
Fog computing is a promising architecture to provide economic and low latency data services for future Internet of things (IoT)-based network systems. It relies on a set of low-power fog nodes that are close to the end users to offload the services o
riginally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of fog nodes to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of fog nodes (FNs) to all the DSSs to achieve an optimal and stable performance is an important problem. In this paper, we propose a joint optimization framework for all FNs, DSOs and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.
This paper studies the resource allocation problem for a heterogeneous network (HetNet) in which the spectrum owned by a macro-cell operator (MCO) can be shared by both unlicensed users (UUs) and licensed users (LUs). We formulate a novel hierarchica
l game theoretic framework to jointly optimize the transmit powers and sub-band allocations of the UUs as well as the pricing strategies of the MCO. In our framework, an overlapping coalition formation (OCF) game has been introduced to model the cooperative behaviors of the UUs. We then integrate this OCF game into a Stackelberg game-based hierarchical framework. We prove that the core of our proposed OCF game is non-empty and introduce an optimal sub-band allocation scheme for UUs. A simple distributed algorithm is proposed for UUs to autonomously form optimal coalition formation structure. The Stackelberg Equilibrium (SE) of the proposed hierarchical game is derived and its uniqueness and optimality have been proved. A distributed joint optimization algorithm is also proposed to approach the SE of the game with limited information exchanges between the MCO and the UU.
We investigated the entropy bounds of the three types of statistics: para-Bose, para-Fermi and infinite statistics. We showed that the entropy bounds of the conventional Bose, Fermi statistics and their generalizations to parastatistics obey the $A^{
3/4}$ law, while the entropy bound of infinite statistics obeys the area law. This suggests a close relationship between infinite statistics and quantum gravity.
