Hierarchal Fuzzy Logic Controller and Internet of Things (IoT) Information

With the advances of networks and sensing technologies, it is possible to benefit from the surrounding environment's data in enhancing peoples' life. Currently, we have different types of networks such as Wireless Sensor Networks (WSNs), Vehicle Ad Hoc Networks (VANETs), Cellular Networks (CNs), and Social Networks (SNs) along with underlying computing such as Cloud computing. These types of networks provide huge data about the surrounding environments including weather information, peoples' relations, peoples' interest, and location information. This paper examines the suitability of hierarchal fuzzy logic controller in classifying the IoT data. The paper also tries to answer “if-else “questions about the effect of each of the input parameters. The authors' test case in this paper is related to the disease spreading prediction problem. This test case is highly important to the health care organizations. Different case studies are generated to examine the efficiency of the proposed framework and methodologies.

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Modified fuzzy-based greedy routing protocol for VANETs

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189154 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8357-8364

Author(s):

Thompson Stephan ◽

Ananthnarayan Rajappa ◽

K.S. Sendhil Kumar ◽

Shivang Gupta ◽

Achyut Shankar ◽

...

Keyword(s):

Fuzzy Logic ◽

Routing Protocol ◽

Intelligent Transportation Systems ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Research Area ◽

Transportation Systems ◽

Delivery Ratio ◽

Greedy Routing ◽

Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

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Performance Comparison of MANET, VANET and FANET

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i5.692 ◽

2018 ◽

Vol 8 (5) ◽

pp. 132

Author(s):

Aarti Sahu ◽

Laxmi Shrivastava

Keyword(s):

Fuzzy Logic ◽

Wireless Network ◽

Packet Loss ◽

Ad Hoc Network ◽

Fuzzy Logic Controller ◽

Ad Hoc ◽

Mobile Ad Hoc Network ◽

Performance Comparison ◽

Fuzzy Rules ◽

Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

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