Research Article
a
April
2018
Special Issue: National Conference on Emerging Trends in Engineering 2018
Conference Held at Sri Venkatesa Perumal College of Engineering & Technology, Puttur, A.P., India
Computer Science and Software Engineering
ISSN: 2277-128X (Volume-8, Issue-4)
Secure Data Sharing and Searching at the Edge of
Cloud-Assisted Internet of Things
C. Pravallika
Associate Professor, SVPCET, Puttur, Andhra Pradesh, India
I. Bhanu Prakash
Mtech-CSE, SVPCET, Puttur, Andhra Pradesh, India
P. Sukanya
Assistant Professor, SVPCET, Puttur, Andhra Pradesh, India
Abstract: The Internet of things (Iot) is considered as a future internet that extends the connection of the internet to all kinds of real-world physical smart devices. Generally, the smart devices have limited resources. On the other hand, cloud resources have virtually unlimited storage and processing capabilities with scalability and on-demand accessibility anywhere. Thus with the help of the cloud, the IoT smart devices can relieve the burden of limited resources. For IoT applications, smart devices require low latency, high data rate, fast data access, and real-time data analytics/processing with decision-making and mobility support.
I. INTRODUCTION
This article proposes an efficient data-sharing scheme that allows smart devices to share securely data with others at the edge of cloud-assisted Internet of Things (IoT). We also propose a secure searching scheme to search desired data within own/shared data on storage. The Internet of things (Iot)1 is considered as a future internet that extends the connection of the internet to all kinds of real-world physical smart devices. A report by Cisco estimates that by 2020 around 50 billion of such smart devices will be connected to the Internet. By connecting these billions of smart devices to the Internet, the IoT will provide developed smart and autonomous cyber-physical environments in the area of smart grids, smart cities, smart homes, smart medical and healthcare systems, wearable technologies, transportation systems, etc. However, the majority of these devices are part of a large platform, hence, a huge amount of data are generated that requires high computational capabilities for storage, processing, and analyzing purposes in a secure and effi client manner. Generally, the smart devices have limited resources. On the other hand, cloud resources have virtually unlimited storage and processing capabilities with scalability and on-demand accessibility anywhere. Thus with the help of the cloud, the IoT smartdevices can relieve the burden of limited resources.For IoT applications, smart devices require low latency, high data rate, fast data access, and real-time data analytics/processing with decision-making and mobility support. Due to several drawbacks, the cloud cannot fulfi ll the aforesaid requirements. However, edge computing adds many benefi ts to cloud-assisted IoT and supports aforesaid requirements by keeping data processing, communications, and storage operationon edge servers that are close to the devices atthe edge of the networks. Moreover, due to smart devices’limited range of connectivity, the edge servers can serve as intermediaries for communications over long distances. These edge servers are any personal device or mobile device, stand-alone servers, or network devices that are hosted within one hop far from the end devices. In addition, the edge servers also cooperate and connect strongly with cloud servers. With the increasing number and availability of smart devices, data sharing is offered within cloud assisted IoT applications
Characteristics and Services Models The salient characteristics of cloud computing based on the definitions provided by the National Institute of Standards and Terminology (NIST) are outlined below:
On-demand self-service: A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service’s provider.
Broad network access: Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: The provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location-independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction
(e.g., country, state, or data center). Examples of resources include storage, processing, memory, network bandwidth, and virtual machines.
Rapid elasticity: Capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
ISSN(E): 2277-128X, ISBN: 978-93-87396-07-4, pp. 328-332
Services Model: Cloud Computing comprises three different service models, namely Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). The three service models or layer are completed by an end user layer that encapsulates the end user perspective on cloud services. The model is shown in figure below. If a cloud user accesses services on the infrastructure layer, for instance, she can run her own applications on the resources of a cloud infrastructure and remain responsible for the support, maintenance, and security of these applications herself. If she accesses a service on the application layer, these tasks are normally taken care of by the cloud service provider.
Benefits of cloud computing:
1. Achieve economies of scale – increase volume output or productivity with fewer people. Your cost per unit, project or product plummets.
2. Reduce spending on technology infrastructure. Maintain easy access to your information with minimal upfront spending. Pay as you go (weekly, quarterly or yearly), based on demand.
3. Globalize your workforce on the cheap. People worldwide can access the cloud, provided they have an Internet connection.
4. Streamline processes. Get more work done in less time with less people.
5. Reduce capital costs. There’s no need to spend big money on hardware, software or licensing fees. 6. Improve accessibility. You have access anytime, anywhere, making your life so much easier! 7. Monitor projects more effectively. Stay within budget and ahead of completion cycle times.
8. Less personnel training is needed. It takes fewer people to do more work on a cloud, with a minimal learning curve on hardware and software issues.
9. Minimize licensing new software. Stretch and grow without the need to buy expensive software licenses or programs.
10. Improve flexibility. You can change direction without serious ―people‖ or ―financial‖ issues at stake.
Advantages:
1. Price: Pay for only the resources used.
2. Security: Cloud instances are isolated in the network from other instances for improved security.
3. Performance: Instances can be added instantly for improved performance. Clients have access to the total resources of the Cloud’s core hardware.
4. Scalability: Auto-deploy cloud instances when needed.
5. Uptime: Uses multiple servers for maximum redundancies. In case of server failure, instances can be automatically created on another server.
6. Control: Able to login from any location. Server snapshot and a software library lets you deploy custom instances.
7. Traffic: Deals with spike in traffic with quick deployment of additional instances to handle the load.
II. IMPLEMENTATION
MODULES:
Uploader Authority
User
MODULES DESCRIPTION: Uploader:
The Main Responsibility of the Uploader is To upload a Document to the cloud storage. And view the files what the different uploaders uploaded. To download that document uploader have to get a key from the Authority.
Authority:
The Authority people is able to view the list of uploader, users in this case he has the another option if he need to add the uploader he need to add otherwise delete and also he is able to give the keys for the requests from the user and uploader
User:
The user can able to viewthe files if he wants to download the file he need to send the request to Authority after receiving the key he need to download
III. INPUT DESIGN AND OUTPUT DESIGN
INPUT DESIGN:
ISSN(E): 2277-128X, ISBN: 978-93-87396-07-4, pp. 328-332
What data should be given as input?
How the data should be arranged or coded?
The dialog to guide the operating personnel in providing input.
Methods for preparing input validations and steps to follow when error occur.
OBJECTIVES
1. Input Design is the process of converting a user-oriented description of the input into a computer-based system. This design is important to avoid errors in the data input process and show the correct direction to the management for getting correct information from the computerized system.
2. It is achieved by creating user-friendly screens for the data entry to handle large volume of data. The goal of designing input is to make data entry easier and to be free from errors. The data entry screen is designed in such a way that all the data manipulates can be performed. It also provides record viewing facilities.
3. When the data is entered it will check for its validity. Data can be entered with the help of screens. Appropriate messages are provided as when needed so that the user will not be in maize of instant. Thus, the objective of input design is to create an input layout that is easy to follow.
OUTPUT DESIGN
A quality output is one, which meets the requirements of the end user and presents the information clearly. In any system results of processing are communicated to the users and to other system through outputs. In output design it is determined how the information is to be displaced for immediate need and also the hard copy output. It is the most important and direct source information to the user. Efficient and intelligent output design improves the system’s relationship to help user decision-making.
1. Designing computer output should proceed in an organized, well thought out manner; the right output must be developed while ensuring that each output element is designed so that people will find the system can use easily and effectively. When analysis design computer output, they should Identify the specific output that is needed to meet the requirements.
2. Select methods for presenting information.
3. Create document, report, or other formats that contain information produced by the system.
The output form of an information system should accomplish one or more of the following objectives.
Convey information about past activities, current status or projections of the
Future.
Signal important events, opportunities, problems, or warnings.
Trigger an action.
Confirm an action.
IV. LITERATURE SURVEY
1) Internet of things in 5G era: enablers,Archictecture and Business models:
AUTHORS: Maria Rita Palattella, Member, IEEE, Mischa Dohler, Fellow Member, IEEE, Alfredo Grieco Senior
Member, IEEE, Gianluca Rizzo, Member, IEEE, Johan Torsner, Thomas Engel, Member, IEEE, and Latif Ladid
The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem. Index Terms—Internet of Things, IoT, 5G, cellular, Low-Power Wifi, Standardization
Cloud Computing Archictectutre
AUTHORS: H. Kumarage, I. Khalil, A. Alabdulatif, Z. Tari,and X. Yi.Wan,M
As the cloud computing market continues to mature, a variety of cloud deployment models have emerged. Cloud models are typically categorized by where the cloud environment is deployed (the basis of distinction between public cloud, private cloud, community cloud, and hybrid cloud), and by which part of the IT service and application stack the cloud provides (the distinction between IaaS, PaaS, and SaaS clouds). Regardless of the cloud model that an organization may embrace, clouds tend to have certain cloud computing architecture elements in common. It's these core cloud computing architecture components that make a "cloud".
3) 3)Processing Distributed Internet of Things Data in Clouds AUTHORS. Wang and R.Ranjan
ISSN(E): 2277-128X, ISBN: 978-93-87396-07-4, pp. 328-332 generated from Internet of Things (IoT) devices and sensors. IoT comprises billions of Internet-connected devices (ICDs) or ―things,‖ each of which can sense, communicate, compute, and potentially actuate, and can have intelligence, multimodal interfaces, physical/virtual identities, and attributes. ICDs can be sensors, RFIDs, social media, click streams, business transactions, actuators (such as machines/equipment fitted with sensors and deployed for mining, oil exploration, or manufacturing operations), lab instruments (such as a high energy physics synchrotron), and smart consumer appliances (TV, phone, and so on)
4) Convey Intelligence to Edge Aggregation Analytics AUTHORS: Natascha Harth, Kostas Delakouridis
In Internet of Things (IoT) environments, networks of sensors, actuators, and computing devices are responsible to locally process contextual data, reason and collaboratively support aggregation analytics tasks. We rest on the edge computing paradigm where pushing processing and inference to the edge of the IoT network allows the complexity of analytics to be distributed into many smaller and more manageable pieces and to be physically located at the source of the contextual information it needs to work on. This enables a huge amount of rich contextual data to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized cloud/back-end processing system. We propose a lightweight, distributed, predictive intelligence mechanism that supports communication efficient aggregation analytics within the edge network. Our idea is based on the capability of the edge nodes to perform sensing and locally determine (through prediction) whether to disseminate contextual data in the edge network or to locally re-construct undelivered contextual data in light of minimizing the required communication interaction at the expense of accurate analytics tasks. Based on this decision making, we eliminate data transfer at the edge of the network, thus saving network resources for sensing and receiving data, by exploiting the nature of the captured contextual data.
5)Cross-tenant trust models in cloud computing AUTHORS: Tang, B. and Sandhu, R.
Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures. Each tenant essentially operates in an individual silo without interacting with other tenants. As cloud computing evolves we anticipate there will be increased need for tenants to collaborate across tenant boundaries. This will require cross -tenant trust models supported and enforced by the cloud service provider. Considering the on-demand self-service feature intrinsic to cloud computing, we propose a formal cross -tenant trust model (CTTM) and its role-based extension (RB-CTTM) integrating various types of trust relations into cross -tenant access control models which can be enforced by the multi-tenant authorization as a service (MTAaaS) platform in the cloud
V. CONCLUSION
In this paper, we present a proposed data-sharing and -searching scheme to share and search data securely by IoT smart devices at the edge of cloud-assisted IoT. The performance analysis demonstrates that our scheme can achieve better efficiency in terms of processing time compared with existing cloud-based systems. In future work, we plan on authenticating and accessing control challenges in this area. We hope that our proposed scheme is practical to be deployed and opens a new door in edge-oriented security research for cloud assisted IoT applications.
Future work will include a comparative analysis of the proposed CTAC model with other state-of-the-art cross domain access control protocols using real-world evaluations. For example ,one could implement
REFERENCES
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[16] MUHAMMAD BAQER MOLLAH is a MSc student in the department of computer science and engineering at the Jahangirnagar University, Bangladesh. His research interests include advanced communication and security techniques for future wireless networks. He has a BSc in electrical and electronic engineering from International Islamic University Chittagong, Bangladesh. He is a Student Member of IEEE.
[17] MD. ABUL KALAM AZAD is an associate professor in the department of computerscience and engineering at the Jahangirnagar University, Bangladesh, and a PhD candidate in Ubiquitous Computing Lab at the University of Ulsan, Korea. His research interests include cloud computing, wireless sensor networks and information security. He has a BSc in computer science and engineering from Jahangirnagar University, Bangladesh, and MSc in computer science from KTH Royal Institute of Technology, Sweden. He is a Member of IEEE
