Secure Mobile Sensor Data Transfer using Asymmetric Cryptography Algorithms

2020 ◽  
Author(s):  
Nouman Kabir ◽  
Shaharyar Kamal
Keyword(s):  
Data Transfer ◽  
Sensor Data ◽  
Mobile Sensor ◽  
Asymmetric Cryptography

scholarly journals Implementation of Digital Signature Using Hybrid Cryptosystem

2018 ◽  
Vol 7 (3.4) ◽  
pp. 34
Author(s):  
Leela K ◽  
Smitha Vinod
Keyword(s):  
Digital Signature ◽  
Hash Function ◽  
Data Transfer ◽  
Security Level ◽  
Electronic Data ◽  
Hybrid Encryption ◽  
Asymmetric Cryptography

Security is a major concern when it comes to electronic data transfer. Digital signature uses hash function and asymmetric algorithms to uniquely identify the sender of the data and it also ensures integrity of the data transferred. Hybrid encryption uses both symmetric and asymmetric cryptography to enhance the security of the data. Digital Signature is used to identify the owner of the document but it does not hide the information while transferring the document. Anyone can read the message. To avoid this, data sent along with the signature should be secured. In this paper, Digital signature is combined with hybrid encryption to enhance the security level. Security of the data or the document sent is achieved by using hybrid encryption technique along with digital signature. 

A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections

2018 ◽  
Vol 2672 (45) ◽  
pp. 253-264 ◽  
Author(s):  
Juyuan Yin ◽  
Jian Sun ◽  
Keshuang Tang
Keyword(s):  
Kalman Filter ◽  
Queue Length ◽  
Estimation Method ◽  
Mobile Internet ◽  
Internet Technology ◽  
Sensor Data ◽  
Linear Regression Method ◽  
Mobile Sensor ◽  
Length Estimation ◽  
Queue Lengths

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

scholarly journals Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)

2017 ◽  
Vol 16 (2) ◽  
pp. 18-22 ◽  
Author(s):  
Santosh Kumar ◽  
Gregory Abowd ◽  
William T. Abraham ◽  
Mustafa al'Absi ◽  
Duen Horng Chau ◽  
...  
Keyword(s):  
Sensor Data ◽  
Mobile Sensor ◽  
Center Of Excellence

Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System

2018 ◽  
pp. 141-154 ◽  
Author(s):  
Chandu Thota ◽  
Revathi Sundarasekar ◽  
Gunasekaran Manogaran ◽  
Varatharajan R ◽  
Priyan M. K.
Keyword(s):  
Healthcare System ◽  
Data Transfer ◽  
Fog Computing ◽  
Asynchronous Communication ◽  
Security And Privacy ◽  
Sensor Data ◽  
Cloud Environment ◽  
Authentication And Authorization ◽  
Remote Healthcare ◽  
Secure Authentication

This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients' medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.

The SWARMs Approach to Integration of Underwater and Overwater Communication Sub-Networks and Integration of Heterogeneous Underwater Communication Systems

2018 ◽  
Author(s):  
Francesco Pacini ◽  
Giacomo Paoli ◽  
Iván Cayón ◽  
Tamara Rivera ◽  
Beatriz Sarmiento ◽  
...  
Keyword(s):  
Communication Network ◽  
Communication Systems ◽  
Ad Hoc ◽  
Data Transfer ◽  
Underwater Vehicles ◽  
Variable Number ◽  
Sensor Data ◽  
Radio Communication ◽  
Transfer Rates ◽  
And Control

The management of a heterogeneous mix of underwater vehicles needs a robust and reliable communication network, able to connect the remote command and control station (typically ashore or on board of a support ship) with nodes and vehicles in the deep sea. On the basis of this scenario, the infrastructure shall satisfy requirements such as: medium to extremely long distances between the control room and the area of operation; management of a variable number and type of nodes and vehicles (mobile, fixed, underwater, surfaced); a guaranteed bandwidth to send commands and receive platform status and tasks execution information with minimum latency; a high bitrate to transfer sensor data, pictures and videos in “near real time”; etc. Compared to the available solutions used nowadays for radio communication systems, the underwater environment imposes several constraints on the maximum achievable bandwidth and distance, drastically reducing data transfer rates. This means that the full communication network is a trade-off between different requirements and performances. The SWARMs project approach to this problem is to select, combine and integrate different and heterogeneous communication technologies, components and solutions, in order to obtain the best performances for the management and control of underwater vehicles during the execution of different missions and tasks. The network is mainly based on commercial components, but specific adaptations were made in order to fulfil the requirements of ad hoc underwater and overwater sub networks in maritime specific scenarios. Several experiments and sea trials have allowed the verification of the performance of the full network and the optimization of its configuration according to the mission needs.

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