ICE: An Integrated Configuration Engine for Interference Mitigation in Cloud Services

2015 ◽  
Author(s):  
Amiya K. Maji ◽  
Subrata Mitra ◽  
Saurabh Bagchi
Keyword(s):  
Interference Mitigation ◽  
Cloud Services

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

A Survey on Utilizing Cloud Services in the E-learning Process

2015 ◽  
Vol 6 (9) ◽  
pp. 1606-1612
Author(s):  
Zaydoon Mohammad Hatamleh ◽  
Eslam Najim Badran ◽  
Bilal Mohammad Hatamleh
Keyword(s):  
Learning Process ◽  
Cloud Services ◽  
E Learning

Adaptive Interference Mitigation for Multilevel Flash Memory Devices

2011 ◽  
Vol E94-A (11) ◽  
pp. 2453-2457
Author(s):  
Myeongwoon JEON ◽  
Kyungchul KIM ◽  
Sungkyu CHUNG ◽  
Seungjae CHUNG ◽  
Beomju SHIN ◽  
...  
Keyword(s):  
Flash Memory ◽  
Interference Mitigation ◽  
Memory Devices

Implementaon of digital economy elements in electric power industry

2018 ◽  
Vol 11 (2) ◽  
pp. 94-102 ◽  
Author(s):  
A. G. Filimonov ◽  
N. D. Chichirova ◽  
A. A. Chichirov ◽  
A. A. Filimonovа
Keyword(s):  
Electric Power ◽  
Energy Production ◽  
Predictive Analytics ◽  
Digital Economy ◽  
Digital Transformation ◽  
Cloud Services ◽  
Small Scale ◽  
Economic Activities ◽  
Industrial Enterprises ◽  
Automation Systems

Energy generation, along with other sectors of Russia’s economy, is on the cusp of the era of digital transformation. Modern IT solutions ensure the transition of industrial enterprises from automation and computerization, which used to be the targets of the second half of the last century, to digital enterprise concept 4.0. The international record of technological and structural solutions in digitization may be used in Russia’s energy sector to the full extent. Specifics of implementation of such systems in different countries are only determined by the level of economic development of each particular state and the attitude of public authorities as related to the necessity of creating conditions for implementation of the same. It is shown that a strong legislative framework is created in Russia for transition to the digital economy, with research and applied developments available that are up to the international level. The following digital economy elements may be used today at enterprises for production of electrical and thermal energy: — dealing with large amounts of data (including operations exercised via cloud services and distributed data bases); — development of small scale distributed generation and its dispatching; — implementation of smart elements in both electric power and heat supply networks; — development of production process automation systems, remote monitoring and predictive analytics; 3D-modeling of parts and elements; real time mathematic simulation with feedback in the form of control actions; — creating centres for analytical processing of statistic data and accounting in financial and economic activities with business analytics functions, with expansion of communication networks and computing capacities. Examples are presented for implementation of smart systems in energy production and distribution. It is stated in the paper that state-of art information technologies are currently being implemented in Russia, new unique digital transformation projects are being launched in major energy companies. Yet, what is required is large-scale and thorough digitization and controllable energy production system as a multi-factor business process will provide the optimum combination of efficient economic activities, reliability and safety of power supply.

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