scholarly journals Design and Implementation of Virtual Private Storage Framework Using Internet of Things Local Networks

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 489 ◽  
Author(s):  
Hwi-Ho Lee ◽  
Jung-Hyok Kwon ◽  
Eui-Jik Kim
Keyword(s):  
Internet Of Things ◽  
Original Data ◽  
Residual Energy ◽  
Local Network ◽  
Communication Overhead ◽  
Storage Space ◽  
Local Networks ◽  
Request Message ◽  
Sensor Devices ◽  
User Data

This paper presents a virtual private storage framework (VPSF) using Internet of Things (IoT) local networks. The VPSF uses the extra storage space of sensor devices in an IoT local network to store users’ private data, while guaranteeing expected network lifetime, by partitioning the storage space of a sensor device into data and system volumes and, if necessary, logically integrating the extra data volumes of the multiple sensor devices to virtually build a single storage space. When user data need to be stored, the VPSF gateway divides the original data into several blocks and selects the sensor devices in which the blocks will be stored based on their residual energy. The blocks are transmitted to the selected devices using the modified speedy block-wise transfer (BlockS) option of the constrained application protocol (CoAP), which reduces communication overhead by retransmitting lost blocks without a retransmission request message. To verify the feasibility of the VPSF, an experimental implementation was conducted using the open-source software libcoap. The results demonstrate that the VPSF is an energy-efficient solution for virtual private storage because it averages the residual energy amounts for sensor devices within an IoT local network and reduces their communication overhead.

Data Management in Internet of Things

2017 ◽  
pp. 80-97
Author(s):  
Ashok V. Sutagundar ◽  
Daneshwari Hatti
Keyword(s):  
Internet Of Things ◽  
Data Management ◽  
Communication Overhead ◽  
Storage Space ◽  
Data Archiving ◽  
Heterogeneous Devices ◽  
Compression Data ◽  
Processing Techniques ◽  
And Control

This chapter gives overview of Internet of Things (IoT), various issues in IoT and describes data management in IoT. IoT is emerging technology which interconnects things through the Internet. Things present in the surrounding are communicated and control the objects without human intervention. IoT helps in performing two way communications among various heterogeneous devices by using cloud storage and cloud computing. IoT mainly concentrates on communication, so the vast amount of data generated from plenty of devices is to be managed as it consumes lot of memory. Data management includes data processing techniques such as data filtering, aggregation, compression, data archiving. Various processing techniques eliminate the irrelevant data, reduce communication overhead and enhance bandwidth, storage space and Quality of service.

scholarly journals Organization of management and structure in local networks internet of things

2020 ◽  
pp. 11-16
Author(s):  
U. A. Vishniakou ◽  
A. H. Al-Masri ◽  
S. K. Al-Haji
Keyword(s):  
Internet Of Things ◽  
Cloud Service ◽  
Production Quality ◽  
Milk Quality ◽  
Local Network ◽  
Smart Sensors ◽  
Local Networks ◽  
Multi Agent ◽  
Four Levels ◽  
Symbolic Formula

 Internet of Things (IoT) symbolic formula is given. The analysis of management technologies both in the network structures of infocommunications, based on the NSMP, and on local networks of the Io T. Two approaches for implementing the management process in infocommunication networks are shown: one is based on creating special software tools, the second is based on the working with data describing the network device. The basic operations of SNMP are given. Four levels of IoT in local network structure are described: smart sensors, network vehicles, services, and applications. Structure of local network of IoT which includes smart sensors, transport environment, services and applications information representation in network use semantic web are considered.The structure of multi-agent system (MAS) of milk farms analyzing in Leban (MASMFA) for monitoring of production quality. MASMFA structure has many agents such as quality milk sensors, agents of communications, data base, analysis of the information received from sensor agents, decision-making. This system implements the functions to ensure the required class of milk quality and based on IoT local network construction. The information algorithm processing in such IoT is proposed. Milk sensor shell be periodically queried, their values will be recorded in the server database. The decisionmaking subsystem will issue data on milk quality to the farm administrator on a mobile device. The server structure will be implemented using a cloud service. Implementation this Internet of things network is being developed using LTE technology.

Data Management in Internet of Things

2017 ◽  
pp. 365-382 ◽  
Author(s):  
Ashok V. Sutagundar ◽  
Daneshwari Hatti
Keyword(s):  
Internet Of Things ◽  
Data Management ◽  
Communication Overhead ◽  
Storage Space ◽  
Data Archiving ◽  
Heterogeneous Devices ◽  
Compression Data ◽  
Processing Techniques ◽  
And Control

This chapter gives overview of Internet of Things (IoT), various issues in IoT and describes data management in IoT. IoT is emerging technology which interconnects things through the Internet. Things present in the surrounding are communicated and control the objects without human intervention. IoT helps in performing two way communications among various heterogeneous devices by using cloud storage and cloud computing. IoT mainly concentrates on communication, so the vast amount of data generated from plenty of devices is to be managed as it consumes lot of memory. Data management includes data processing techniques such as data filtering, aggregation, compression, data archiving. Various processing techniques eliminate the irrelevant data, reduce communication overhead and enhance bandwidth, storage space and Quality of service.

A New User-controlled and Efficient Encrypted Data Sharing Model in Cloud Storage

2019 ◽  
Vol 13 (4) ◽  
pp. 356-363
Author(s):  
Yuezhong Wu ◽  
Wei Chen ◽  
Shuhong Chen ◽  
Guojun Wang ◽  
Changyun Li
Keyword(s):  
Data Sharing ◽  
Data Security ◽  
Cloud Storage ◽  
Screening Program ◽  
Original Data ◽  
Third Party ◽  
Active System ◽  
Encrypted Data ◽  
Active User ◽  
User Data

Background: Cloud storage is generally used to provide on-demand services with sufficient scalability in an efficient network environment, and various encryption algorithms are typically applied to protect the data in the cloud. However, it is non-trivial to obtain the original data after encryption and efficient methods are needed to access the original data. Methods: In this paper, we propose a new user-controlled and efficient encrypted data sharing model in cloud storage. It preprocesses user data to ensure the confidentiality and integrity based on triple encryption scheme of CP-ABE ciphertext access control mechanism and integrity verification. Moreover, it adopts secondary screening program to achieve efficient ciphertext retrieval by using distributed Lucene technology and fine-grained decision tree. In this way, when a trustworthy third party is introduced, the security and reliability of data sharing can be guaranteed. To provide data security and efficient retrieval, we also combine active user with active system. Results: Experimental results show that the proposed model can ensure data security in cloud storage services platform as well as enhance the operational performance of data sharing. Conclusion: The proposed security sharing mechanism works well in an actual cloud storage environment.

Coding Algorithms and Network Plan for Context-Aware Data Collection Based on Internet of Things in Large Marine Ships

2014 ◽  
Vol 701-702 ◽  
pp. 957-960
Author(s):  
Feng Xie
Keyword(s):  
Internet Of Things ◽  
Formal Analysis ◽  
Cluster Head ◽  
Communication Overhead ◽  
Context Aware ◽  
Sensing Data ◽  
Communication Efficiency ◽  
Data Volume ◽  
Using Data ◽  
Data Context

The equipment maintenance in large marine ships may rely on Internet of Things to provide monitoring of equipment status instantly. The data volume of sensing data is huge as the number of equipments is large. It is critical to decrease the communication overhead of uploading sensing data for efficiently and timely monitoring. In this paper, we propose several coding algorithms by using data context that is modeled by our normal forms on the base of our observations. The communication efficiency is improved, which is justified by formal analysis and rigorous proof. We also propose several network plan policies for further improvement of the communication efficiency by using data context and cluster head deployment.

Towards the Protection and Security in Fog Computing for Industrial Internet of Things

2021 ◽  
pp. 17-32
Author(s):  
G. Rama Subba Reddy ◽  
K. Rangaswamy ◽  
Malla Sudhakara ◽  
Pole Anjaiah ◽  
K. Reddy Madhavi
Keyword(s):  
Internet Of Things ◽  
Distributed Computing ◽  
Fog Computing ◽  
Security And Privacy ◽  
Safety Measures ◽  
Iot Applications ◽  
Industrial Internet ◽  
Sensor Devices ◽  
Key Enabling Technologies ◽  
Privacy Issues

Internet of things (IoT) has given a promising chance to construct amazing industrial frameworks and applications by utilizing wireless and sensor devices. To support IIoT benefits efficiently, fog computing is typically considered as one of the potential solutions. Be that as it may, IIoT services still experience issues such as high-latency and unreliable connections between cloud and terminals of IIoT. In addition to this, numerous security and privacy issues are raised and affect the users of the distributed computing environment. With an end goal to understand the improvement of IoT in industries, this chapter presents the current research of IoT along with the key enabling technologies. Further, the architecture and features of fog computing towards the fog-assisted IoT applications are presented. In addition to this, security and protection threats along with safety measures towards the IIoT applications are discussed.

KDSR

2018 ◽  
Vol 14 (1) ◽  
pp. 27-45
Author(s):  
Abderrahmen Guermazi ◽  
Abdelfettah Belghith ◽  
Mohamed Abid
Keyword(s):  
Large Scale ◽  
Secure Routing ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Storage Space ◽  
Computation Complexity ◽  
Replay Attacks ◽  
Node Capture ◽  
Secure Network ◽  
Global Broadcast

This article deals with a key distribution protocol to secure routing in large-scale Wireless Sensor Networks (WSNs) and proposes a new protocol called KDSR. The authors' protocol has two originalities: to provide a secure network structure for large-scale WSNs, and to use lightweight local process to share efficiently the Local Broadcast Keys, the Pairwise Keys and the Global Broadcast Key. These keys are useful to secure several communication patterns in WSNs: one-to-many, one-to-one and one-to-all. Security analyses show that KDSR can withstand several attacks against WSNs. Through fast node revocation process, KDSR offers a good resilience against node capture. Immunity against MiM and replay attacks are well checked with the AVISPA tools. The experimentations are done on real TelosB motes and through the TOSSIM simulator. Simulation results confirm that KDSR is scalable, provides a good key connectivity and a good resilience. Comparison to earlier work shows that KDSR causes less computation complexity, less communication overhead and much less storage space even for large-scale WSNs.

scholarly journals ARS: Adaptive Robust Synchronization for Underground Coal Wireless Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4981
Author(s):  
Kuiyuan Zhang ◽  
Mingzhi Pang ◽  
Yuqing Yin ◽  
Shouwan Gao ◽  
Pengpeng Chen
Keyword(s):  
Internet Of Things ◽  
Clock Synchronization ◽  
Sampling Period ◽  
Upper And Lower Bounds ◽  
Communication Overhead ◽  
Wireless Internet ◽  
Robust Synchronization ◽  
Time Stamps ◽  
Communication Links ◽  
Incomplete Measurement

Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.

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