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TinyEVM: Off-Chain smart Contracts on Low-Power IoT Devices

TinyEVM: Off-Chain Smart Contracts on Low-Power IoT Devices

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international conference on Distributed computing Systems

作者： Christos Profentzas Magnus Almgren Olaf Landsiedel Chalmers University of Technology Gothenburg Sweden Kiel University Kiel Germany

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

With the rise of the Internet of Things (IoT), billions of devices ranging from simple sensors to smart-phones will participate in billions of micropayments. However, current centralized solutions are unable to handle a massive number of micropayments from untrusted devices. Blockchains are promising technologies suitable for solving some of these challenges. Particularly, permissionless blockchains such as Ethereum and Bitcoin have drawn the attention of the research community. However, the increasingly large-scale deployments of blockchain reveal some of their scalability limitations. Prominent proposals to scale the payment system include off-chain protocols such as payment channels. However, the leading proposals assume powerful nodes with an always-on connection and frequent synchronization. These assumptions require in practice significant communication, memory, and computation capacity, whereas IoT devices face substantial constraints in these areas. Existing approaches also do not capture the logic and process of IoT, where applications need to process locally collected sensor data to allow for full use of IoT micro-payments. In this paper, we present TinyEVM, a novel system to generate and execute off-chain smart contracts based on sensor data. TinyEVM's goal is to enable IoT devices to perform micro-payments and, at the same time, address the device constraints. We investigate the trade-offs of executing smart contracts on low-power IoT devices using TinyEVM. We test our system with 7,000 publicly verified smart contracts, where TinyEVM achieves to deploy 93% of them without any modification. Finally, we evaluate the execution of off-chain smart contracts in terms of run-time performance, energy, and memory requirements on IoT devices. Notably, we find that low-power devices can deploy a smart contract in 215 ms on average, and they can complete an off-chain payment in 584 ms on average.

关键词： Performance evaluation Scalability smart contracts Memory management Blockchain Sensor systems and applications intelligent sensors

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A new concept of smart shopping platform based on IoT solutions

A new concept of smart shopping platform based on IoT soluti...

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international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： George SUCIU Cristian BALANEAN Adrian PASAT Cristiana ISTRATE Hussain IJAZ Rafaela MATEI R&D Department BEIA Consult International Bucharest Romania

ISBN: (数字)9781728168432

ISBN: (纸本)9781728168449

In order to create innovative operational models within the retail industry, a consensus has been reached on the use of Big data, Cloud computing, and Artificial Intelligence (AI) technologies. The recent advancement of IoT technologies is the basis on which the new solutions for customer-oriented marketing are based. This paper presents an architecture for developing smart shopping solutions covering the image processing techniques used to generate digital maps, the internal location of the smartphone using BLE beacons, and internal routing techniques for finding stores in a mall. We discuss the disadvantages and advantages of using BLE technology for indoor location and results after experimenting with SDKs and multiple BLE devices for mobile applications. Our goal was to advance an innovative solution based on computer vision techniques designed to facilitate the creation of interior hand maps. We also focused on developing the best practice guide for using mobile apps in the retail and consumer education industry, including an interactive questionnaire for feedback on improving Android app design. Moreover, the solution can be used to guide and educate customers during shopping activities for the use of smart mobile applications. Thus, our smart shopping platform is based on an enriched mobile application. Our smart shopping solution also advances the integration of smart parking sensors.

关键词： Mobile applications Research and development Servers Computer architecture intelligent sensors Tools Systems architecture

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A Blockchain-based Conditional Privacy-Preserving Traffic data Sharing in Cloud

A Blockchain-based Conditional Privacy-Preserving Traffic Da...

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IEEE international conference on Communications (ICC)

作者： Jingwei Liu Gaojian Zhang Rong Sun Xiaojiang Du Mohsen Guizani Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi’an China State Key Lab of ISN Xidian University Xi’an China Department of Computer and Information Sciences Temple University Philadelphia PA USA Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

As a typical application scenarios of Internet of Things (IoT), Internet of Vehicles (IoV) is playing an important role in the field of intelligent transportation. In such a system, vehicles are allowed to collect ambient sensing data and communicate with other vehicles, roadside infrastructure, cloud server, even personal mobile smart devices in order to exchange the relevant traffic information that usually includes the owners' sensitive information, such as geographical location or personally identifiable information. However, in open network environment, different wireless communication technologies make all these information vulnerable to various attacks and compromises. The users' private information is put at the risk of leaking at any time. To address this issue, a blockchain-based privacy-preserving traffic data sharing protocol with cloud storage is proposed based on a novel heterogeneous signcryption scheme between Public Key Infrastructure (PKI) and Certificateless Cryptosystem (CLC). Through theoretical and simulation analysis in terms of security, complexity and time consumption, the proposed traffic data sharing protocol is proved to meet the security requirements of data tamper-proofing, anonymity and traceability with higher efficiency.

关键词： Cloud computing Public key Servers Transportation Protocols

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Image Classification using Convolution Neural Network Based Hash Encoding and Particle Swarm Optimization

Image Classification using Convolution Neural Network Based ...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Sameer I. Ali Al-Janabi Sufyan T. Faraj Al-Janabi Belal Al-Khateeb College of Computer Science and IT University of Anbar Ramadi Iraq

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Image Retrieval (IR) has become one of the main problems facing computer society recently. To increase computing similarities between images, hashing approaches have become the focus of many programmers. Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using Convolutional Neural Networks (CNNs). This paper aims to design and implement a high-performance image classifier that can be used in several applications such as intelligent vehicles, face recognition, marketing, and many others. This work considers experimentation to find the sequential model's best configuration for classifying images. The best performance has been obtained from two layers' architecture; the first layer consists of 128 nodes, and the second layer is composed of 32 nodes, where the accuracy reached up to 0.9012. The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, which are called the Transfer Values (TRVs). Indeed, the Particle Swarm Optimization (PSO) algorithm is used to reduce the TRVs. In this respect, the work focus is to reduce the TRVs to obtain high-performance image classifier models. Indeed, the PSO algorithm has been enhanced by using the crossover technique from genetic algorithms. This led to a reduction of the complexity of models in terms of the number of parameters used and the execution time.

关键词： Training Predictive models Image retrieval data models Visualization Classification algorithms TV

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Collaborative Computation Offloading for smart Cities in Mobile Edge computing

Collaborative Computation Offloading for Smart Cities in Mob...

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IEEE international conference on Cloud computing, CLOUD

作者： Hualong Huang Kai Peng Xiaolong Xu College of Engneering Huaqiao university Quanzhou China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

With the emergence of the Internet of things (IOT), smart cities have changed from concept to reality. Meanwhile, those countless IOT devices are scattered in every corner of the city which generate a mass of sensing big data and IoT services every minute. However, the computing capability of IoT devices is so constrained that IoT devices fail to process computational-intensive services. Mobile edge computing (MEC) is an emergent architecture for reinforcing the computing capabilities of IoT devices to cope with the resource-hungry IoT services. In the MEC system, IoT devices are capable of offloading part of IoT services to the cloudlet for execution. Although offloading can prominently mitigate the computing burden on IoT devices, it may result in enormous transmission cost and consuming resource of cloudlets. Therefore, it poses major challenges to how to achieve trade-offs in terms of time cost, energy cost of IOT devices and resource utilization of cloudlets. In consideration of the challenge, we devise a collaborative computation offloading approach to acquire the above trade-off in the collaboration of IoT device-cloudlet-cloud three ends. Firstly, the balancing strategies of the trade-off are obtained by leveraging the multi-objective evolutionary algorithm based on decomposition (MOEA/D). We then employ a multi-criterion decision-making method that combines the entropy weight (EW) method and technique for order preference by similarity to an ideal solution (TOPSIS), is known as (EW-TOPSIS), to acquire the optimum offloading decision in the acquired balancing strategies. Finally, extensive experiments and theoretical analysis validate the effectiveness of the proposed method.

关键词： Cloud computing smart cities Collaboration Sensors Internet of Things Resource management Edge computing

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Wireless Sensor Network for Traffic Monitoring

Wireless Sensor Network for Traffic Monitoring

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international conference on Promising Electronic Technologies (ICPET)

作者： Mohammd Fadi AbdelHaq Adnan Salman Advanced Computing AnNajah National University Nablus Palestine AnNajah National University Nablus Palestine

ISBN: (数字)9780738111391

ISBN: (纸本)9781665403818

intelligent Traffic Systems (ITS) management plays an important role in modern transportation systems. An important area in ITS research is the development of traffic monitoring systems that collect traffic information such as vehicle counting, speed measurements, and vehicle classification. This information allows for developing smart transportation systems that take into consideration the current traffic state in order to generate smart decisions. In this paper, we developed a Wireless Sensor Network (WSN) application for vehicle counting, vehicle classification, and speed measurement based on roadside magnetic sensors. Magnetic sensors can be deployed on the roadside to collect traffic data. Then, the data can be communicated using wireless channels to a central computer to be processed and analyzed. The traffic data will be saved in a database and can be shared with other transportation applications through the use of web services. This approach has many advantages includes real-time traffic monitoring, ensures proper monitoring of the roads, and requires less human intervention. The focus of this paper is on the development of the wireless sensor network and traffic data management. Simulated data is used to evaluate the system instead of deploying magnetic sensors. The software implementation of this paper is available at https://***/FadiHaq/WSN_TrafficMonitoring.

关键词： Wireless sensor networks Roads Base stations Wireless communication Magnetic domains Perpendicular magnetic anisotropy Magnetic sensors

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A Predictive-Trend-Aware and Critical-Path-Estimation-Based Method for Workflow Scheduling Upon Cloud Services

A Predictive-Trend-Aware and Critical-Path-Estimation-Based ...

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IEEE international conference on Services computing (SCC)

作者： Yi Pan Xiaoning Sun Yunni Xia Wanbo Zheng Xin Luo College of Computer Science Chongqing University Chongqing China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.

关键词： Cloud computing Processor scheduling Computational modeling Quality of service Predictive models Dynamic scheduling Real-time systems

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A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

A Diverse Biases Non-negative Latent Factorization of Tensor...

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IEEE international conference on Networking, Sensing and Control

作者： Xuke Wu Hang Gou Hao Wu Juan Wang Minzhi Chen Siyu Lai Computer School China West Normal University Nanchong China Computer School of China West Normal University Nanchong Sichuan China University of Chinese Academy of Sciences Beijing China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China North Sichuan Medical College Nanchong China Medical College Nanchong Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy.

关键词： Tensors conferences Predictive models data models Sensors data mining

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Demand Response Implementation in an Optimization Based SCADA Model Under Real-Time Pricing Schemes 15th

Demand Response Implementation in an Optimization Based SCAD...

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15th international conference on Distributed computing and Artificial Intelligence

作者： Khorram, Mahsa Faria, Pedro Abrishambaf, Omid Vale, Zita Polytech Porto ISEP IPP Inst Engn GECAD Res Grp Intelligent Engn & Comp Adv Innovat Porto Portugal

ISBN: (纸本)9783319996080

Advancement of renewable energy resources, development of smart grids, and the effectiveness of demand response programs, can be considered as solutions to deal with the rising of energy consumption. However, there is no benefit if the consumers do not have enough automation infrastructure to use the facilities. Since the entire kinds of buildings have a massive portion in electricity usage, equipping them with optimization-based systems can be very effective. For this purpose, this paper proposes an optimization-based model implemented in a Supervisory Control and data Acquisition, and Multi Agent System. This optimization model is based on power reduction of air conditioners and lighting systems of an office building with respect to the price-based demand response programs, such as real-time pricing. The proposed system utilizes several agents associated with the different distributed based controller devices in order to perform decision making locally and communicate with other agents to fulfill the overall system's goal. In the case study of the paper, the proposed system is used in order to show the cost reduction in the energy bill of the building, while it respects the user preferences and comfort level.

关键词： Optimization SCADA Multi-agent system

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Improving the quality of education system using data Science Technologies: Survey

Improving the quality of education system using Data Science...

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IEEE conference on Innovative Technologies in intelligent Systems and Industrial Applications (CITISIA)

作者： Pattinige Ravindra R Fernando School of Computing and Mathematics Charles Sturt University Study Centre Melbourne Australia

ISBN: (数字)9781728194370

ISBN: (纸本)9781728194387

Education is the most important and silent weapon in a country for both individual and country's economy. However lower level of adoption in the education system, poor decision making with less accuracy levels, adoption to new curriculums or subjects, teaching and learning styles are the main issues in education systems. These factors also have further long-term consequences for a country such as unemployment rates rises high, lack of suitable workforce for the demanding fields, individual dissatisfaction while being unemployed as well as in the community and socially. Unemployment rates are risen in Australia from past few years and this as a factor will be an ongoing issue if the government does not take any further actions to overcome these issues will definitely be direct hit to their economy in relation to work force in the present and future. Therefore the right technology should be implemented in order to obtain extract insights, obtain accurate decisions and high level adoption in education sector, as an example technologies such as data warehousing, big data, data mining, business intelligence and data analytics are in the peak of other industries such as aviation, retail, banking and other financial institutions. The main objective of this project is to facilitate a guide or a review for having data science technologies implemented in education sector in order to accomplish better education, as well as emphasis potential advantages of data technologies if it has been implemented in and around education systems.

关键词： Education Warehousing Decision making data science data warehouses data mining Unemployment

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