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DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things
Author(s) -
Arulanantham Dhandapani,
Palanisamy Chenniappan
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4672
Subject(s) - computer science , computer network , channel (broadcasting) , routing protocol , routing (electronic design automation) , default gateway , ant colony optimization algorithms , real time computing , artificial intelligence
Summary The Internet of Things (IoT) is a structure with sensors, base station, gateway, and network servers. Users can access the information contained in it and use it as they need. These sensors are embedded in several data collection locations in a uniform or random manner to collect data. The users in many parts of the world obtain such data that fit their requirements. For example, sensors are used in remote areas to monitor various areas such as forests, wildlife sanctuaries, fields, industrial needs, human condition, and changes in the ocean. The changes like these that take place in the open spaces require the selection of the best routing method and seamless delay reduction in data collection helps to immediately reach those involved in it. In the proposed protocol, dynamic ant routing‐based channel accessing cognitive sensor network (DACASN) in the IoT, cognitive sensors are used. It is made with the knowledge of choosing a channel for communication as well as selecting the parameters for path selection and setting the most accurate path with the channel knowledge and communicating with the internet server. The sensors set the paths for this route selection using the centrifugal method and the anticolonial method. As a result, it can be seen in the results and discussion section that the quality of the routing information is high because it is delivered to the destination in the shortest possible time.

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