This document provides an analysis of different pseudorandom and orthogonal spreading sequences used in direct sequence code division multiple access (DS-CDMA). It begins with an introduction to CDMA transmission and reception and an example of direct sequence spread spectrum. It then discusses various pseudorandom sequences like maximal length sequences, Gold sequences, Gold-like sequences, Barker sequences, and Kasami sequences. It also covers orthogonal sequences including Walsh-Hadamard codes, modified Walsh-Hadamard codes, and orthogonal variable spreading factor codes. The document concludes by comparing the performance of these different sequences based on their correlation properties and suitability for use in CDMA networks.
Seven step model of migration into the cloudRaj Raj
The document describes a seven-step model for migrating applications to the cloud: 1) conduct assessments, 2) isolate dependencies, 3) map messaging and environment, 4) re-architect lost functionalities, 5) leverage cloud features, 6) test the migration, and 7) iterate and optimize. The model involves assessing costs and benefits, isolating on-premise dependencies, mapping components, redesigning for the cloud, leveraging cloud features, extensive testing, and iterating to optimize and ensure a robust migration. Key risks are identified in testing and addressed through optimization iterations.
Cloud computing allows users to access virtual hardware, software, platforms, and services on an as-needed basis without large upfront costs or commitments. This transforms computing into a utility that can be easily provisioned and composed. The long-term vision is for an open global marketplace where IT services are freely traded like utilities, lowering barriers and allowing flexible access to resources and software for all users.
The document is a question bank for the cloud computing course CS8791. It contains 26 multiple choice or short answer questions related to key concepts in cloud computing including definitions of cloud computing, characteristics of clouds, deployment models, service models, elasticity, horizontal and vertical scaling, live migration techniques, and dynamic resource provisioning.
Mac protocols for ad hoc wireless networks Divya Tiwari
The document discusses MAC protocols for ad hoc wireless networks. It addresses key issues in designing MAC protocols including limited bandwidth, quality of service support, synchronization, hidden and exposed terminal problems, error-prone shared channels, distributed coordination without centralized control, and node mobility. Common MAC protocol classifications and examples are also presented, such as contention-based protocols, sender-initiated versus receiver-initiated protocols, and protocols using techniques like reservation, scheduling, and directional antennas.
This document provides an analysis of different pseudorandom and orthogonal spreading sequences used in direct sequence code division multiple access (DS-CDMA). It begins with an introduction to CDMA transmission and reception and an example of direct sequence spread spectrum. It then discusses various pseudorandom sequences like maximal length sequences, Gold sequences, Gold-like sequences, Barker sequences, and Kasami sequences. It also covers orthogonal sequences including Walsh-Hadamard codes, modified Walsh-Hadamard codes, and orthogonal variable spreading factor codes. The document concludes by comparing the performance of these different sequences based on their correlation properties and suitability for use in CDMA networks.
Seven step model of migration into the cloudRaj Raj
The document describes a seven-step model for migrating applications to the cloud: 1) conduct assessments, 2) isolate dependencies, 3) map messaging and environment, 4) re-architect lost functionalities, 5) leverage cloud features, 6) test the migration, and 7) iterate and optimize. The model involves assessing costs and benefits, isolating on-premise dependencies, mapping components, redesigning for the cloud, leveraging cloud features, extensive testing, and iterating to optimize and ensure a robust migration. Key risks are identified in testing and addressed through optimization iterations.
Cloud computing allows users to access virtual hardware, software, platforms, and services on an as-needed basis without large upfront costs or commitments. This transforms computing into a utility that can be easily provisioned and composed. The long-term vision is for an open global marketplace where IT services are freely traded like utilities, lowering barriers and allowing flexible access to resources and software for all users.
The document is a question bank for the cloud computing course CS8791. It contains 26 multiple choice or short answer questions related to key concepts in cloud computing including definitions of cloud computing, characteristics of clouds, deployment models, service models, elasticity, horizontal and vertical scaling, live migration techniques, and dynamic resource provisioning.
Mac protocols for ad hoc wireless networks Divya Tiwari
The document discusses MAC protocols for ad hoc wireless networks. It addresses key issues in designing MAC protocols including limited bandwidth, quality of service support, synchronization, hidden and exposed terminal problems, error-prone shared channels, distributed coordination without centralized control, and node mobility. Common MAC protocol classifications and examples are also presented, such as contention-based protocols, sender-initiated versus receiver-initiated protocols, and protocols using techniques like reservation, scheduling, and directional antennas.
Cloud Computing - Technologies and TrendsMarcelo Sávio
This document provides an overview of cloud computing, including definitions of cloud service models (IaaS, PaaS, SaaS), deployment options (private, public, hybrid clouds), characteristics of cloud computing, major factors driving adoption of cloud computing, and trends in cloud adoption among organizations. Key trends discussed include the growth of cloud services, increasing utilization of cloud technologies by enterprises, and different motivations for cloud adoption between IT and business users.
Load Balancing In Cloud Computing newpptUtshab Saha
The document discusses various load balancing algorithms for cloud computing including round robin, first come first serve (FCFS), and simulated annealing. It provides implementations of each algorithm in CloudSim and compares the results. Round robin and FCFS showed similar overall response times, data center processing times, and maximum/minimum values. Simulated annealing had slightly lower average overall response time. The document proposes using a genetic algorithm for host-side optimization to select the best host for virtual machine requests.
EMC is a leading cloud service provider that offers IaaS, PaaS and SaaS through its IT division. EMC IT provides virtual infrastructure, application platforms and business solutions as services. It uses virtualization to allocate resources on demand and increase efficiency. EMC also offers the Captiva Cloud Toolkit to help developers quickly build scan-enabled web applications.
Google is another major cloud provider that offers services like Cloud Platform, Cloud Storage, Cloud Connect, Cloud Print and App Engine. Google Cloud Platform provides scalable infrastructure for application development and testing. Cloud Storage provides secure file storage and sharing capabilities. Cloud Connect integrates cloud storage with Microsoft Office applications. Cloud Print allows printing from any device via the internet.
Cloud architectures can be thought of in layers, with each layer providing services to the next. There are three main layers: virtualization of resources, services layer, and server management processes. Virtualization abstracts hardware and provides flexibility. The services layer provides OS and application services. Management processes support service delivery through image management, deployment, scheduling, reporting, etc. When providing compute and storage services, considerations include hardware selection, virtualization, failover/redundancy, and reporting. Network services require capacity planning, redundancy, and reporting.
This chapter discusses various Python tools and cloud offerings for IoT applications. It provides examples of using the Python boto library to interface with Amazon Web Services like EC2, AutoScaling, S3, RDS, DynamoDB. It also discusses using Python for MapReduce programming and introduces web frameworks like Django. Python packages for JSON, XML, HTTP, email and machine learning are also covered. The chapter concludes with pointers to further reading on Python cloud libraries and services.
Advanced topics in artificial neural networksswapnac12
The document discusses various advanced topics in artificial neural networks including alternative error functions, error minimization procedures, recurrent networks, and dynamically modifying network structure. It describes adding penalty terms to the error function to reduce weights and overfitting, using line search and conjugate gradient methods for error minimization, how recurrent networks can capture dependencies over time, and algorithms for growing or pruning network complexity like cascade correlation.
The document discusses various protocols and security aspects related to IoT. It provides details on protocols such as IEEE 802.15.4, BACnet, Modbus, KNX, Zigbee etc. It also outlines vulnerabilities in IoT like unauthorized access, information corruption, DoS attacks. Key elements of IoT security discussed are identity establishment, access control, data security, non-repudiation and availability. Security requirements and models for IoT are also mentioned.
With the increasing in the number of anti-social activates that have been taking place, security has been given utmost importance lately. Many Organizations have installed CCTVs for constant Monitoring of people and their interactions. For a developed Country with a population of 64 million, every person is captured by a camera 30 times a day. A lot of video data generated and stored for a certain time duration. A 704x576 resolution image recorded at 25fps will generate roughly 20GB per day. Constant Monitoring of data by humans to judge if the events are abnormal is near impossible task as requires a workforce and their constant attention. This creates a need to automate the same. Also , there is need to show in which frame and which part of it contain the unusual activity which aid the faster judgment of the unusual activity being abnormal. This is done by converting video into frames and analyzing the persons and their activates from the processed frame .Machine learning and Deep Learning Algorithms and techniques support us in a wide accept to make Possible.
This document discusses various domain-specific Internet of Things (IoT) applications. It outlines IoT applications for homes, cities, the environment, energy systems, retail, logistics, industry, agriculture, and health and lifestyle. It then provides more details on specific IoT applications for homes (smart lighting, smart appliances, intrusion detection, smoke/gas detectors), cities (smart parking, smart road lighting, smart roads, structural health monitoring, surveillance, emergency response) and the environment (weather monitoring, air pollution monitoring, noise pollution monitoring, forest fire detection, river flood detection).
This document summarizes an Internet of Things (IoT) meetup that covered various topics:
- Introduction to IoT and how objects can transfer data over networks.
- Introduction to cloud computing and how resources are shared over the internet.
- IoT architecture including things, gateways, and networks/cloud.
- IoT gateways like Raspberry Pi that interface devices and cloud.
- Sensor interfaces like XBee and RS-485 that connect to gateways.
- Network interfaces like WiFi and GPRS to connect gateways to cloud.
- Cloud architecture models from various sources.
- Data acquisition from devices using open-source Ponte software.
- Data storage
This document discusses different types of interconnection network topologies for parallel machines. It provides details on:
1) Linear array networks have nodes connected in a line, with diameter of n-1, node degree of 2, and bisection width of 1.
2) Mesh networks connect nodes in a grid, with diameter of 2(n-1), node degree of 4, and bisection width of n for an nXn mesh.
3) Hypercube networks have nodes connected by log2N routing functions, with diameter and node degree of log2N and bisection width of 2n-1 for a network with N nodes.
Neuromorphic computing is an emerging interdisciplinary field that takes inspiration from biology to design hardware models of neural systems. Specifically, it uses very-large-scale integrated circuits containing analog electronic circuits to mimic the neurobiological architectures in the nervous system, as conceived by Carver Mead in the late 1980s. Two examples are Neurogrid, a mixed-analog-digital multichip system emulating a million neurons and billion connections using subthreshold analog logic, and IBM's TrueNorth, which contains 16 neuromorphic cores and is completely digital. Both aim to achieve the scale and low power operation of the biological brain through novel computing architectures.
UNIT IV WIRELESS SENSOR NETWORKS (WSNS) AND MAC PROTOCOLS 9 Single node architecture: hardware and software components of a sensor node - WSN Network architecture: typical network architectures-data relaying and aggregation strategies -MAC layer protocols: self-organizing, Hybrid TDMA/FDMA and CSMA based MAC- IEEE 802.15.4.
Memory virtualization uses a portion of a disk drive as an extension of main memory. It divides both main memory and virtual memory into pages of equal size. Only needed pages reside in memory at a time, while unnecessary pages are stored on disk. When memory runs low, pages are written to the disk swap file as virtual memory. This allows larger programs to run in less physical RAM by swapping pages between disk and memory as needed.
This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters
ECG analysis in the cloud allows for remote monitoring of patients' heartbeats without visiting the hospital. Sensors attached to patients measure their ECG and transmit the data via Bluetooth to mobile devices and the cloud for analysis. This analysis is done as a cloud service across infrastructure, platform, and software layers. The cloud provides elastic resources and near real-time analysis, allowing doctors to monitor more patients without large local computing infrastructures.
Federated Cloud Computing - The OpenNebula Experience v1.0sIgnacio M. Llorente
The talk mostly focuses on private cloud computing to support Science and High Performance Computing environments, the different architectures to federate cloud infrastructures, the existing challenges for cloud interoperability, and the OpenNebula's vision for the future of existing Grid infrastructures.
The document discusses different types of virtualization including hardware, network, storage, memory, software, data, and desktop virtualization. Hardware virtualization includes full, para, and partial virtualization. Network virtualization includes internal and external virtualization. Storage virtualization includes block and file virtualization. Memory virtualization enhances performance through shared, distributed, or networked memory that acts as an extension of main memory. Software virtualization allows guest operating systems to run virtually. Data virtualization manipulates data without technical details. Desktop virtualization provides remote access to work from any location for flexibility and data security.
The document discusses common standards in cloud computing. It describes organizations like the Open Cloud Consortium and Distributed Management Task Force that develop standards. It then summarizes standards for application developers, messaging, and security including XML, JSON, LAMP, SMTP, OAuth, and SSL/TLS.
The document discusses sensor cloud, which integrates wireless sensor networks with cloud computing. It allows for the powerful analysis of sensor data through massive cloud infrastructure. The key benefits of sensor cloud include scalability, increased data storage and processing power, dynamic provisioning of services, and automation. Some challenges are implementation costs and maintaining continuous connectivity between sensors and the cloud. The document outlines the general architecture and components of a sensor cloud system and provides examples of applications in transportation monitoring, military use, weather forecasting, and healthcare.
Cloud computing for mobile users can offloading computation save energyIEEEFINALYEARPROJECTS
The document discusses using cloud computing to offload computation from mobile devices to save energy. It describes how cloud computing works and how offloading computation can reduce the energy usage of mobile apps. The proposed system would determine whether to offload parts of an app's computation to the cloud based on factors like bandwidth, amount of computation, and data transfer size. Challenges include privacy, security since data is stored remotely, and reliability depending on the network and cloud service.
Identifying parameters for Code Offloading as a practical solution to optimiz...Anindya Duti Dhar
The blessing mobile cloud has explored the wisdom of application development and deployment with techniques such as code offloading. While code offloading has been widely considered for reducing energy consumption and increasing responsiveness of mobile devices, the technique still faces many challenges pertaining to practical usage. Advances in mobile hardware and operating systems have made mobile a first-class development platform. However, developers are still constrained by the inherent resource limitations of mobile devices. And they are facing a hesitation with which way is the best to develop an application to reduce the energy consumption of a smartphone. Deciding to offload requires a careful consideration of the costs and benefits of a range of possible program partitions. This cost-benefit analysis depends on external factors, such as network conditions and the resources availability, as well as internal app properties, such as component dependencies, data representations, and code complexity. Thus, benefiting from offload requires some assistance from developers. In this article, I adopt a systemic approach for analysing the components of a generic code offloading architecture. Based on theoretical analysis, I identify the parameters to describe the key limitations for code offloading in practice and then propose solutions to mitigate these limitations. In this paper I characterize the architecture of Google App Engine to reach a solution that reduces the amount of developer effort required to improve the performance as well as reduce the power consumption of smartphone.
Cloud Computing - Technologies and TrendsMarcelo Sávio
This document provides an overview of cloud computing, including definitions of cloud service models (IaaS, PaaS, SaaS), deployment options (private, public, hybrid clouds), characteristics of cloud computing, major factors driving adoption of cloud computing, and trends in cloud adoption among organizations. Key trends discussed include the growth of cloud services, increasing utilization of cloud technologies by enterprises, and different motivations for cloud adoption between IT and business users.
Load Balancing In Cloud Computing newpptUtshab Saha
The document discusses various load balancing algorithms for cloud computing including round robin, first come first serve (FCFS), and simulated annealing. It provides implementations of each algorithm in CloudSim and compares the results. Round robin and FCFS showed similar overall response times, data center processing times, and maximum/minimum values. Simulated annealing had slightly lower average overall response time. The document proposes using a genetic algorithm for host-side optimization to select the best host for virtual machine requests.
EMC is a leading cloud service provider that offers IaaS, PaaS and SaaS through its IT division. EMC IT provides virtual infrastructure, application platforms and business solutions as services. It uses virtualization to allocate resources on demand and increase efficiency. EMC also offers the Captiva Cloud Toolkit to help developers quickly build scan-enabled web applications.
Google is another major cloud provider that offers services like Cloud Platform, Cloud Storage, Cloud Connect, Cloud Print and App Engine. Google Cloud Platform provides scalable infrastructure for application development and testing. Cloud Storage provides secure file storage and sharing capabilities. Cloud Connect integrates cloud storage with Microsoft Office applications. Cloud Print allows printing from any device via the internet.
Cloud architectures can be thought of in layers, with each layer providing services to the next. There are three main layers: virtualization of resources, services layer, and server management processes. Virtualization abstracts hardware and provides flexibility. The services layer provides OS and application services. Management processes support service delivery through image management, deployment, scheduling, reporting, etc. When providing compute and storage services, considerations include hardware selection, virtualization, failover/redundancy, and reporting. Network services require capacity planning, redundancy, and reporting.
This chapter discusses various Python tools and cloud offerings for IoT applications. It provides examples of using the Python boto library to interface with Amazon Web Services like EC2, AutoScaling, S3, RDS, DynamoDB. It also discusses using Python for MapReduce programming and introduces web frameworks like Django. Python packages for JSON, XML, HTTP, email and machine learning are also covered. The chapter concludes with pointers to further reading on Python cloud libraries and services.
Advanced topics in artificial neural networksswapnac12
The document discusses various advanced topics in artificial neural networks including alternative error functions, error minimization procedures, recurrent networks, and dynamically modifying network structure. It describes adding penalty terms to the error function to reduce weights and overfitting, using line search and conjugate gradient methods for error minimization, how recurrent networks can capture dependencies over time, and algorithms for growing or pruning network complexity like cascade correlation.
The document discusses various protocols and security aspects related to IoT. It provides details on protocols such as IEEE 802.15.4, BACnet, Modbus, KNX, Zigbee etc. It also outlines vulnerabilities in IoT like unauthorized access, information corruption, DoS attacks. Key elements of IoT security discussed are identity establishment, access control, data security, non-repudiation and availability. Security requirements and models for IoT are also mentioned.
With the increasing in the number of anti-social activates that have been taking place, security has been given utmost importance lately. Many Organizations have installed CCTVs for constant Monitoring of people and their interactions. For a developed Country with a population of 64 million, every person is captured by a camera 30 times a day. A lot of video data generated and stored for a certain time duration. A 704x576 resolution image recorded at 25fps will generate roughly 20GB per day. Constant Monitoring of data by humans to judge if the events are abnormal is near impossible task as requires a workforce and their constant attention. This creates a need to automate the same. Also , there is need to show in which frame and which part of it contain the unusual activity which aid the faster judgment of the unusual activity being abnormal. This is done by converting video into frames and analyzing the persons and their activates from the processed frame .Machine learning and Deep Learning Algorithms and techniques support us in a wide accept to make Possible.
This document discusses various domain-specific Internet of Things (IoT) applications. It outlines IoT applications for homes, cities, the environment, energy systems, retail, logistics, industry, agriculture, and health and lifestyle. It then provides more details on specific IoT applications for homes (smart lighting, smart appliances, intrusion detection, smoke/gas detectors), cities (smart parking, smart road lighting, smart roads, structural health monitoring, surveillance, emergency response) and the environment (weather monitoring, air pollution monitoring, noise pollution monitoring, forest fire detection, river flood detection).
This document summarizes an Internet of Things (IoT) meetup that covered various topics:
- Introduction to IoT and how objects can transfer data over networks.
- Introduction to cloud computing and how resources are shared over the internet.
- IoT architecture including things, gateways, and networks/cloud.
- IoT gateways like Raspberry Pi that interface devices and cloud.
- Sensor interfaces like XBee and RS-485 that connect to gateways.
- Network interfaces like WiFi and GPRS to connect gateways to cloud.
- Cloud architecture models from various sources.
- Data acquisition from devices using open-source Ponte software.
- Data storage
This document discusses different types of interconnection network topologies for parallel machines. It provides details on:
1) Linear array networks have nodes connected in a line, with diameter of n-1, node degree of 2, and bisection width of 1.
2) Mesh networks connect nodes in a grid, with diameter of 2(n-1), node degree of 4, and bisection width of n for an nXn mesh.
3) Hypercube networks have nodes connected by log2N routing functions, with diameter and node degree of log2N and bisection width of 2n-1 for a network with N nodes.
Neuromorphic computing is an emerging interdisciplinary field that takes inspiration from biology to design hardware models of neural systems. Specifically, it uses very-large-scale integrated circuits containing analog electronic circuits to mimic the neurobiological architectures in the nervous system, as conceived by Carver Mead in the late 1980s. Two examples are Neurogrid, a mixed-analog-digital multichip system emulating a million neurons and billion connections using subthreshold analog logic, and IBM's TrueNorth, which contains 16 neuromorphic cores and is completely digital. Both aim to achieve the scale and low power operation of the biological brain through novel computing architectures.
UNIT IV WIRELESS SENSOR NETWORKS (WSNS) AND MAC PROTOCOLS 9 Single node architecture: hardware and software components of a sensor node - WSN Network architecture: typical network architectures-data relaying and aggregation strategies -MAC layer protocols: self-organizing, Hybrid TDMA/FDMA and CSMA based MAC- IEEE 802.15.4.
Memory virtualization uses a portion of a disk drive as an extension of main memory. It divides both main memory and virtual memory into pages of equal size. Only needed pages reside in memory at a time, while unnecessary pages are stored on disk. When memory runs low, pages are written to the disk swap file as virtual memory. This allows larger programs to run in less physical RAM by swapping pages between disk and memory as needed.
This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters
ECG analysis in the cloud allows for remote monitoring of patients' heartbeats without visiting the hospital. Sensors attached to patients measure their ECG and transmit the data via Bluetooth to mobile devices and the cloud for analysis. This analysis is done as a cloud service across infrastructure, platform, and software layers. The cloud provides elastic resources and near real-time analysis, allowing doctors to monitor more patients without large local computing infrastructures.
Federated Cloud Computing - The OpenNebula Experience v1.0sIgnacio M. Llorente
The talk mostly focuses on private cloud computing to support Science and High Performance Computing environments, the different architectures to federate cloud infrastructures, the existing challenges for cloud interoperability, and the OpenNebula's vision for the future of existing Grid infrastructures.
The document discusses different types of virtualization including hardware, network, storage, memory, software, data, and desktop virtualization. Hardware virtualization includes full, para, and partial virtualization. Network virtualization includes internal and external virtualization. Storage virtualization includes block and file virtualization. Memory virtualization enhances performance through shared, distributed, or networked memory that acts as an extension of main memory. Software virtualization allows guest operating systems to run virtually. Data virtualization manipulates data without technical details. Desktop virtualization provides remote access to work from any location for flexibility and data security.
The document discusses common standards in cloud computing. It describes organizations like the Open Cloud Consortium and Distributed Management Task Force that develop standards. It then summarizes standards for application developers, messaging, and security including XML, JSON, LAMP, SMTP, OAuth, and SSL/TLS.
The document discusses sensor cloud, which integrates wireless sensor networks with cloud computing. It allows for the powerful analysis of sensor data through massive cloud infrastructure. The key benefits of sensor cloud include scalability, increased data storage and processing power, dynamic provisioning of services, and automation. Some challenges are implementation costs and maintaining continuous connectivity between sensors and the cloud. The document outlines the general architecture and components of a sensor cloud system and provides examples of applications in transportation monitoring, military use, weather forecasting, and healthcare.
Cloud computing for mobile users can offloading computation save energyIEEEFINALYEARPROJECTS
The document discusses using cloud computing to offload computation from mobile devices to save energy. It describes how cloud computing works and how offloading computation can reduce the energy usage of mobile apps. The proposed system would determine whether to offload parts of an app's computation to the cloud based on factors like bandwidth, amount of computation, and data transfer size. Challenges include privacy, security since data is stored remotely, and reliability depending on the network and cloud service.
Identifying parameters for Code Offloading as a practical solution to optimiz...Anindya Duti Dhar
The blessing mobile cloud has explored the wisdom of application development and deployment with techniques such as code offloading. While code offloading has been widely considered for reducing energy consumption and increasing responsiveness of mobile devices, the technique still faces many challenges pertaining to practical usage. Advances in mobile hardware and operating systems have made mobile a first-class development platform. However, developers are still constrained by the inherent resource limitations of mobile devices. And they are facing a hesitation with which way is the best to develop an application to reduce the energy consumption of a smartphone. Deciding to offload requires a careful consideration of the costs and benefits of a range of possible program partitions. This cost-benefit analysis depends on external factors, such as network conditions and the resources availability, as well as internal app properties, such as component dependencies, data representations, and code complexity. Thus, benefiting from offload requires some assistance from developers. In this article, I adopt a systemic approach for analysing the components of a generic code offloading architecture. Based on theoretical analysis, I identify the parameters to describe the key limitations for code offloading in practice and then propose solutions to mitigate these limitations. In this paper I characterize the architecture of Google App Engine to reach a solution that reduces the amount of developer effort required to improve the performance as well as reduce the power consumption of smartphone.
The document discusses prospects and risks of cloud-based modeling and simulation services. It explores how cloud computing can be used in the area of computer-aided engineering (CAE) through two research projects funded by the German government and European Union. The projects examine technical foundations, security aspects, potential applications, and viable business models for offering CAE modeling and simulation services in the cloud. While the cloud provides benefits like flexibility, accessibility, and reduced costs, security, reliability, and usability must be ensured for long-term success of cloud-based engineering applications.
This document contains a summary of Ameya Kasbekar's experience and qualifications. He currently works as a Senior Software Engineer at Qualcomm Technologies Inc, where he has designed and developed modem algorithms and supported commercialization of new chipsets over 9 years. Previously, he worked as a Software Engineer at Infosys Technologies Ltd and in system administration at Clemson University. He has a Master's degree in Computer Science from Clemson University and a Bachelor's degree in Computer Engineering.
Cloud Computing for hand-held Devices:Enhancing Smart phones viability with C...IOSR Journals
This document discusses computation offloading from mobile devices to the cloud in order to save energy and extend battery life. It begins by introducing cloud computing and how it can provide shared resources to devices like smartphones. Computation offloading involves moving intensive processes from mobile devices to more powerful servers in the cloud. This reduces the computation done on the mobile device, saving energy. The document analyzes several research papers on computation offloading and mobile cloud computing. It discusses the benefits of offloading like extended battery life and improved reliability. It also examines challenges like low bandwidth, availability issues, and security concerns. Overall, the document argues that computation offloading to the cloud can help minimize mobile energy usage and increase battery life.
Secured Way Of Offloading Mobile Cloud Process For Smart PhoneIRJET Journal
This document proposes a secured way of offloading mobile cloud processes to reduce computation power usage on smartphones. It introduces a ternary decision making (TDM) framework that determines whether a task should be performed on the smartphone or offloaded to the cloud based on estimated execution time and energy consumption. The tasks are offloaded to the cloud for remote execution using an encryption technique for security. This results in reduced computation power usage and battery consumption on smartphones by utilizing cloud resources instead.
CloneCloud proposes a flexible architecture that seamlessly uses cloud resources to augment mobile applications in an energy-efficient manner. It clones a mobile app and partitions it at runtime to migrate computation-heavy threads to the clone in the cloud. This allows threads to leverage faster CPUs and hardware accelerations remotely while keeping other functionality local. A static analyzer identifies legal partitions, a dynamic profiler collects execution data to build cost models, and an optimization solver picks optimal partitions to minimize time and energy. The prototype delivers significant speedups and energy reductions without requiring programmer involvement in partitioning. However, it does not give programmers flexibility over partitioning and may not cover all parameter combinations.
IRJET - Positioning and Tracking of a Person using Embedded Controller in a D...IRJET Journal
This document proposes a system to track and monitor the location of individuals within a defined area using GPS. The system uses an ESP8266 microcontroller interfaced with GPS modules to acquire location data and update it to a cloud database. An administrator can then monitor locations in real-time through a mobile app or web interface by requesting location coordinates from the cloud. The system aims to provide easier tracking of individuals compared to conventional camera-based methods while eliminating the need for continuous human monitoring.
A secure sharing control framework supporting elastic mobile cloud computing IJECEIAES
In elastic mobile cloud computing (EMCC), mobile devices migrate some computing tasks to the cloud for execution according to current needs and seamlessly and transparently use cloud resources to enhance their functions. First, based on the summary of existing EMCC schemes, a generic EMCC framework is abstracted; it is pointed out that the migration of sensitive modules in the EMCC program can bring security risks such as privacy leakage and information flow hijacking to EMCC; then, a generic framework of elastic mobile cloud computing that incorporates risk management is designed, which regards security risks as a cost of EMCC and ensures that the use of EMCC is. Finally, it is pointed out that the difficulty of risk management lies in risk quantification and sensitive module labeling. In this regard, risk quantification algorithms are designed, an automatic annotation tool for sensitive modules of Android programs is implemented, and the accuracy of the automatic annotation is demonstrated through experiments.
Enhancing Data Security in Cloud Computation Using Addition-Composition Fully...Dr. Richard Otieno
The document discusses enhancing data security in cloud computing using an addition-composition fully homomorphic encryption scheme. It begins by reviewing prior work on homomorphic encryption schemes, including Gentry's seminal work constructing the first fully homomorphic encryption scheme based on ideal lattices. It notes limitations of prior schemes in terms of computational strain. The document then proposes a new encryption scheme that uses both addition and composition operations to lessen computational strain while supporting faster encryption, larger ciphertexts, and versatility. The scheme is implemented in Java and tested on a basic hardware configuration, showing it enhances data security in cloud computing.
IRJET - Data Security in Cloud Computing using Homomorphic AlgorithamIRJET Journal
This document discusses using homomorphic encryption to securely store and process data in the cloud. It begins with an introduction to cloud computing and data security challenges. The proposed system would encrypt user data before transferring it to the cloud server using homomorphic encryption. This allows computations to be performed on the encrypted data without decrypting it first, protecting data privacy. The document reviews related work on authentication schemes and secure file storage using encryption. It presents the proposed system architecture and concludes that homomorphic encryption can help address cloud computing security issues by allowing operations on encrypted user data.
HOMOGENEOUS MULTISTAGE ARCHITECTURE FOR REAL-TIME IMAGE PROCESSINGcscpconf
The document describes a homogeneous multistage architecture for real-time image processing. It proposes a parallel architecture using multiple identical processing elements connected by different communication links. As an example application, it discusses a multi-hypothesis approach for road recognition, which uses multiple hypotheses to detect and track road edges in video in real-time. Experimental results using a FPGA demonstrate the architecture can detect roadsides in images within 60 milliseconds.
Harnessing the cloud for securely outsourcing large scale systems of linear e...JPINFOTECH JAYAPRAKASH
The document proposes a secure mechanism for outsourcing the solving of large-scale systems of linear equations to the cloud. It uses an iterative method rather than direct methods like Gaussian elimination, as iterative methods only require simpler matrix-vector operations. The mechanism enables a customer to securely outsource the iterative computation while keeping the input and output private. It also includes an efficient batch result verification mechanism that allows the customer to verify all answers from previous iterations in one batch, ensuring efficiency and robustness. Experiments show the method can provide computational savings for customers solving large-scale linear equations in the cloud.
The document discusses how cloud implementation can maximize ROI for laboratories. It explains that adopting a thin-client architecture hosted on the cloud provides benefits like high storage capacity, cost effectiveness, strong data security, and the ability for multiple simultaneous users. The cloud's pay-as-you-go model allows laboratories to access laboratory informatics software without large upfront hardware costs. Overall, the cloud enables laboratories to streamline operations while minimizing total cost of ownership.
Dynamic Framework Design for Offloading Mobile Applications to Cloudiosrjce
Mobile Cloud Computing (MCC) is an infrastructure where the data and the processing of data are
outsourced. MCC integrates cloud computing into the mobile environment and executes the applications in the
mobile device effectively by partitioning and offloading the computation intensive task to external resources
(e.g. Public Clouds). The effective offloading is mainly focused on the decision maker which tells “when to
offload” during execution time. Though prior decision making techniques has its own pros and cons, it doesn’t
support dynamic changing environment and also consumes more time and energy for training the input
instances. Also while offloading there is no security for the information to be transmitted. So, the proposed
dynamic framework is designed with efficient intelligent classifier for offloading mobile application in
dynamically changing by estimating the applications on-device and on-server performance. And to ensure
security, Steganography technique is additionally used within the proposed framework to conceal the
information
This document discusses various computing paradigms such as fog computing, cloud computing, edge computing, mobile cloud computing, and fog-based computing. It provides an overview of fog computing, describing its layered architecture and comparing it to similar paradigms like cloud and edge computing. Some key points discussed include:
- Fog computing enhances cloud computing by extending services and resources to the network edge, supporting low-latency applications.
- It has a 3-layer architecture with end devices, fog nodes, and cloud layers, placing resources closer to end users than the cloud.
- Characteristics of fog computing include low latency, mobility support, location awareness, and decentralized storage and analytics.
- Challen
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We are connecting the GSM modem to the system using any supportable port like serial or USB port then develop software to control the functionality of the system.
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2. ABSTRACT
Cloud computing is a very useful solution to many individual users and
organizations.
It can provide many services based on different needs and requirements.
However, there are many issues related to the user data that need to be
addressed when using cloud computing.
Among the most important issues are: data ownership, data privacy, and
storage.
In other words, more important data will be encrypted with more secure
encryption algorithm and larger key sizes, while less important data might even
not be encrypted.
The results of applying the proposed framework show improvement and
efficiency over other existing frameworks.
3. INTRODUCTION
An Efficient and Secured Framework for Mobile Cloud Computing
Smartphones provide a wide range of applications, such as face detection,
augmented reality, image and video processing, and video gaming and
speech recognition.
These applications are complex and there is an increasing demand for
computing resources.
Despite advances in smartphones, however, the extent of battery life
remained one of the main challenges in improving computational
requirements through battery upgrading.
4. FEATURES
The framework also adds a new security layer, which in the off-607
loading case uses an AES technique to protect the data methods before
transferring to the cloud.
5. Frame work architecture and content
The framework design consists of six modules, namely, estimator, profile,
network and bandwidth monitor, decision maker, mobile manager, and cloud
manager.
First, the framework works at the method level, wherever the developers have to
be compelled to add AN annotation (Remote) specifically intensive strategy at
the developing step.
These strategies should need further computation and might be offloaded to the
cloud for remote execution. These strategies should not
a) depend upon the user interface or
b) use any I/O mobile device like GPS, camera, or measuring device.
6. FRAME WORK MODULES
1. Estimator
The estimator module is responsible for identifying these strategies for
native execution on the mobile device and remote execution on the cloud
with totally different input sizes at the installation step.
Then, the module obtains the values of execution time, memory usage,
CPU utilization, and energy consumption for every annotated technique
for these totally different input sizes
Finally, the values are communicated and sent to the profile module.
7. FRAME WORK MODULE
2. Profile
The profile module obtains the values of execution time, memory usage,
CPU utilization, and energy consumption from figurer module for every
annotated technique.
Then, the module creates a brand new file for every technique and stores
these values into the file.
These files are updated when every running method and utilized by the
decision maker module as a history-based go into the offloading decision.
8. FRAME WORK MODULE
3. Network and Bandwidth Monitor
This module only monitors this standing of the network and gathers cell
connection state and its information measure, Wi-Fi association state and
its information measure, and signal strength of cell and Wi-Fi connection.
Then, this data is shipped to {the call ,the choice} maker module to
support the determination the offloading decision.
9. FRAME WORK MODULE
4. Decision Maker
The choice build, that is, the core module of the planned framework,
contains associate whole number linear programming model and
decision-making rule that predicts at runtime wherever the annotated
strategies are executed.
The goal of the model is to search out associate application partitioning
strategy that minimizes the energy consumption, transfer knowledge,
memory usage, and electronic equipment utilization, in smartphones,
subject to bound constraints.
10. FRAME WORK MODULES
5. Mobile Manager
The mobile manager module is responsible for causing a computer file
containing the method code and its needed libraries at the installation step.
The mobile manager handles the execution of the method supported the model
decision.
If the tactic is dead regionally on the mobile device, the files are updated with
new values through the profile module.
AES technique and communicates with the cloud manager module to transfer
this knowledge with the method name.
Finally, the mobile manager receives and delivers the results to the appliance.
11. FRAME WORK MODULE
6. Cloud Manager
The cloud manager module is that the solely module deployed on the cloud side.
This module is written strictly in Java.
Therefore, any application will take pleasure in the projected framework to offload its
computation to any resource that runs the Java Virtual Machine (JVM).
Communication between the cloud manager and also the mobile manager modules is
managed by wading bird communication middleware within the 1st communication.
Then, the cloud manager receives the ways data and decrypts them within the following
run.
Then, the manager executes the tactic remotely and sends the result back to the mobile
manager module with the new values to be updated by the profile module.
12. FRAME WORK EXECUTION FLOW
ALGORITHM
Framework execution flow Input: Input size,
memory usage, CPU utilization and energy
consumption for each annotated method.
Output: Execution place and result for each method.
Read annotated methods name.
Check the current network status using Network &
Bandwidth monitor Module.
if there isn’t connection then
Execute the method locally on the mobile device.
else
for each method i do
Read C transfer, C memory, and CCPU and C power
through the profile module.
Solve the optimization model in and determine the
offloading decision.
if the decision is offloading then 10. Encrypt
the method data using AES Algorithm.
Send it to the cloud for remote execution.
Return Result back to the mobile device
(communication managed using Mobile
Manager & Cloud Manger Modules).
else
Execute the method locally on the mobile
device.
end if
end for
end if
Update the profile file with new values.
14. CONCLUSION
This framework will offload only the appliance strategies that consume
substantial mobile resources.
The offloading decision is formed using a developed 0–1 number applied
mathematics model.
This call is formed dynamically at runtime supported four constraints,
namely, memory usage, C.P.U. utilization, energy consumption, and
execution time.
The framework also adds a replacement security layer that uses associate
AES technique to shield the strategies knowledge before transferring to
the cloud within the offloading case.