Engineering Full Stack IoT Systems with Distributed Processing Architecture—Software Engineering Challenges, Architectures and Tools

2020 ◽  
pp. 71-87
Author(s):  
S. Thiruchadai Pandeeswari ◽  
S. Padmavathi ◽  
N. Hemamalini
Keyword(s):  
Software Engineering ◽  
Distributed Processing ◽  
Processing Architecture

Remote E-Voting Using the Smart Card Web Server

2014 ◽  
Vol 5 (1) ◽  
pp. 39-60 ◽  
Author(s):  
Sheila Cobourne ◽  
Lazaros Kyrillidis ◽  
Keith Mayes ◽  
Konstantinos Markantonakis
Keyword(s):  
Mobile Devices ◽  
Smart Card ◽  
Distributed Processing ◽  
Web Server ◽  
Mobile Network ◽  
Generic Model ◽  
Voting Systems ◽  
Network Operator ◽  
Management Procedures ◽  
Processing Architecture

Voting in elections is the basis of democracy, but voting at polling stations may not be possible for all citizens. Remote (Internet) e-voting uses the voter's own equipment to cast votes, but is potentially vulnerable to many common attacks, which affect the election's integrity. Security can be improved by distributing vote processing over many web servers installed in tamper-resistant, secure environments, using the Smart Card Web Server (SCWS) on a mobile phone Subscriber Identity Module (SIM). A generic voting model is proposed, using a SIM/SCWS voting application with standardised Mobile Network Operator (MNO) management procedures to process the votes cast. E-voting systems Prêt à Voter and Estonian I-voting are used to illustrate the generic model. As the SCWS voting application is used in a distributed processing architecture, e-voting security is enhanced: to compromise an election, an attacker must target many individual mobile devices, rather than a centralised web server.

Analysis of Distributed Computing Architecture Search Principle Based on Hadoop

2014 ◽  
Vol 519-520 ◽  
pp. 54-57
Author(s):  
Ai Ling Duan ◽  
Dan Cao ◽  
Hai Fang Si
Keyword(s):  
Distributed Computing ◽  
Search Engine ◽  
Distributed Processing ◽  
Data Handling ◽  
Distributed Search ◽  
Time And Space ◽  
Positive Role ◽  
Computing Architecture ◽  
Engine System ◽  
Processing Architecture

Distributed search techniques of Hadoop are researched and analyzed. Combined with Lucene indexing objects, a search engine system IS successfully built. Efficiency of the system in both time and space is investigated. Merit of distributed processing architecture for a single architecture in data handling is verified. The access and update of file information in distributed search technology are further explored. The research plays a positive role in promoting study of related fields

scholarly journals Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a δ-GLMB Filter

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2308 ◽  
Author(s):  
Feng Lian ◽  
Liming Hou ◽  
Jing Liu ◽  
Chongzhao Han
Keyword(s):  
Distributed Processing ◽  
Computation Time ◽  
Complex Method ◽  
Tracking Accuracy ◽  
Posterior Density ◽  
Generalized Covariance ◽  
Multi Target Tracking ◽  
Sensor Control ◽  
Finite Set ◽  
Processing Architecture

The existing multi-sensor control algorithms for multi-target tracking (MTT) within the random finite set (RFS) framework are all based on the distributed processing architecture, so the rule of generalized covariance intersection (GCI) has to be used to obtain the multi-sensor posterior density. However, there has still been no reliable basis for setting the normalized fusion weight of each sensor in GCI until now. Therefore, to avoid the GCI rule, the paper proposes a new constrained multi-sensor control algorithm based on the centralized processing architecture. A multi-target mean-square error (MSE) bound defined in our paper is served as cost function and the multi-sensor control commands are just the solutions that minimize the bound. In order to derive the bound by using the generalized information inequality to RFS observation, the error between state set and its estimation is measured by the second-order optimal sub-pattern assignment metric while the multi-target Bayes recursion is performed by using a δ-generalized labeled multi-Bernoulli filter. An additional benefit of our method is that the proposed bound can provide an online indication of the achievable limit for MTT precision after the sensor control. Two suboptimal algorithms, which are mixed penalty function (MPF) method and complex method, are used to reduce the computation cost of solving the constrained optimization problem. Simulation results show that for the constrained multi-sensor control system with different observation performance, our method significantly outperforms the GCI-based Cauchy-Schwarz divergence method in MTT precision. Besides, when the number of sensors is relatively large, the computation time of the MPF and complex methods is much shorter than that of the exhaustive search method at the expense of completely acceptable loss of tracking accuracy.

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