PPNC: Privacy Preserving Scheme for Random Linear Network Coding in Smart Grid

He, Shiming; Zeng, Weini; Xie, Kun; Yang, Hongming; Lai, Mingyong; Su, Xin

doi:10.3837/tiis.2017.03.015

Cited by 13 publications

(3 citation statements)

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“…Such as user's age and expertise, context's information, for instance, location, movement and usage, and system's information essentially device type, security, and delay. This information includes private individual data that need to be secured and protected [83] are collected locally in the relevant Blockchain ledger and sent to the Blockchaincloud layer where it will be organized and stored for the AI learning module. As an example of how AI can learn from these data to improve the network is explained in section 3.…”

Section: Device Layermentioning

confidence: 99%

Block5GIntell: Blockchain for AI-Enabled 5G Networks

et al. 2020

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Nowadays, 5G network is considered to be one of the main pillars of various industries, including the Internet of Things (IoT), smart cities, virtual reality, and many more. Unlike previous network generations, 5G utilizes complex digital technologies such as massive Multiple Input Multiple Output (mMIMO) and runs over higher radio frequencies. The introduction of new technologies and advanced features in the 5G network raises new challenges for network operators, and merging Artificial Intelligence (AI) is one of the effective solutions to address these complexities. However, AI-enabled 5G network engenders security concerns and requires improvement to meet the standardization and qualification of the new network generation. To mitigate these dilemmas, Blockchain must be integrated. Blockchain, as a decentralized methodology provides a secure sharing of information and resources among various nodes of 5G environments. Blockchain can support other technologies, such as AI-based 5G, to create smarter, more efficient, and secure cellular networks. In this paper, we present a comprehensive intelligence and secure data analytics framework for 5G networks based on the convergence of Blockchain and AI named "Block5GIntell". We depict the applications of Blockchain and AI on 5G networks separately and we argue on the support that Blockchain can provide for AI to create smart and secure 5G networks relying on our proposed framework. To support our proposition, we present an energy-saving case study using Blockchain for AI-enabled 5G. The simulation shows an overall 20% decrease in energy consumption at the RAN level.

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Section: Device Layermentioning

confidence: 99%

Block5GIntell: Blockchain for AI-Enabled 5G Networks

et al. 2020

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“…The data collection of a sensor-cloud is limited by the communication capability of WSNs, which can hardly satisfy the real-time data transmission requirements between WSNs and a cloud [44,45,46]. We proposed a new framework based on mobile edge computing where numerous mobile sink nodes serve as the edge layer to eliminate the communication gap between WSNs and a cloud, whose main objective is to achieve the best collaboration among mobile edge nodes and to minimize the transfer delay [47].…”

Section: State Of the Art Of Mobile Edge-based Sensor-cloudsmentioning

confidence: 99%

When Sensor-Cloud Meets Mobile Edge Computing

Wang

Cao

et al. 2019

Sensors

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Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds.

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“…Although the robots in current scenarios are using advanced sensors and advanced vision techniques to make actions [25][26][27][28][29][30][31][32], still the techniques are so erroneous. Somewhere, we need body object integration along with the advanced vision and sensors for the smooth functioning and it may have many applications in machine learning, smart grids, and the energy sector [33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment

Shrivastava

Kumar

Tripathi³

et al. 2020

Applied Sciences

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Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interacting with the working environment. To overcome this problem, in this work, an agent is computationally devised that mimics the grid and place neuron functionality to learn cognitive maps from the input spatial data of an environment or an object. A novel quadrant-based approach is proposed to model the behavior of the grid neuron, which, like the real grid neuron, is capable of generating periodic hexagonal grid-like output patterns from the input body movement. Furthermore, a cognitive map formation and their learning mechanism are proposed using the place–grid neuron interaction system, which is meant for making predictions of environmental sensations from the body movement. A place sequence learning system is also introduced, which is like an episodic memory of a trip that is forgettable based on their usage frequency and helps in reducing the accumulation of error during a visit to distant places. The model has been deployed and validated in two different spatial data learning applications, one being the 2D object detection by touch, and another is the navigation in an environment. The result analysis shows that the proposed model is significantly associated with the expected outcomes.

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PPNC: Privacy Preserving Scheme for Random Linear Network Coding in Smart Grid

Cited by 13 publications

References 0 publications

Block5GIntell: Blockchain for AI-Enabled 5G Networks

Block5GIntell: Blockchain for AI-Enabled 5G Networks

When Sensor-Cloud Meets Mobile Edge Computing

A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment

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