Principles of Cognitive Radio
More space for new technologies — New technologies can be developed for these unused bands. Cost would also be minimal when this unutilized spectrum is used by the unlicensed user.
Ability to utilize multiple channels — In a traditional wireless sensor network, a single channel is used for communication. In this, when an event is detected, the sensor nodes generate packets. In a dense network, sensor nodes try to occupy the single communication channel at the same time.
This increases the possibility of collisions and the communication quality is decreased. Moreover, there is more power consumption and packet delay. Cognitive radio gives the opportunity to utilize multiple channels thereby reducing the chances of collision and also increases the communication quality. Energy Efficiency — In traditional wireless sensor network, the power wastage is more due to increased packet retransmission due to packet loss. This energy consumption due to packet retransmission is overcome by cognitive radio.
Global Operability — There are certain spectrum regulation rules for each country. Each country is allotted different spectrum band which is not available in another country. Cognitive radio gives the capability to be operated in any country of the world. Utilization of spectrum band that is application specific — There is an increase in wireless sensors that are deployed for different applications. The wireless sensor network generates packets when an event is triggered. The data traffic in wireless sensor network is correlated temporally and spatially which cause design challenge of the communication protocol.
There are intelligent communication protocols in Cognitive radio wireless sensor network that can overcome the design challenge of protocols. Financial Advantage — Those users who cannot obtain a license for spectrum due to financial problem can obtain this on lease from license holder at a low cost.
This will be beneficial to both users. Attacks are avoided — Cognitive radio uses a wide range of spectrum, therefore, several types of attacks can be unlike other wireless sensor network that works on a particular band of frequency. Military and Security — Cognitive Radio is used in various military and public services like chemical, biological and nuclear radiation detection, war surveillance etc.
Traditional wireless sensor networks face the problem of signal jam. But this problem is no longer exist if we use cognitive radio wireless sensor network. Cognitive Radio can handoff a wide range of frequencies.
Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
It is also useful when the applications need large bandwidth. Healthcare — Cognitive Radio also find its applications in the healthcare. It find its use in wearable body wireless sensors. Using this, critical data and information from patients can be acquired by doctors sitting at a distant location. These wireless sensor nodes are also reliable.
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Home appliances — Wireless sensor network is used in certain indoor applications. But there are some challenges in those indoor applications. Cognitive Radio overcome these challenges. The examples of these applications are intelligent buildings, home monitoring system and personal entertainment.
Real-Time applications — In multihop wireless sensor network, there are chances of link failure which can cause delay in communication. Moreover, the nodes move to another channel if they find another channel idle. Cognitive Radio wireless sensor network increases the channel bandwidth due to which channel aggregation and use of multiple channels are possible. Transportation — Cognitive Radio find its applications in transport system as well as vehicular system.
A lot of research work is going on in this field with various protocols proposed for highway safety. These are also likely to be more reliable and convenient than the traditional wireless sensor network.
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Diverse Sensing — Cognitive Radio wireless sensor network can be used in the same area but with a different motive. Cognitive Radio can use different channels for different applications using medium access protocol MAC. Cognitive Radio wireless sensor network are different from traditional wireless sensor network in certain aspects. Although they are way better than these conventional wireless sensor networks but they also face some challenges.
Some of these challenges are:. Miss-Detection Probability and False Alarm — These are the two metrics used in cognitive radio wireless sensor network. These two metrics can cause problems like long waiting delay, frequent channel switching and degradation in output. Hardware Issues — There are certain constraints applied on hardware of the system like computational power, storage and energy.
To design an intelligent hardware for CR is a laborious task. The hardware should be such that it should be able to respon d with the changes in the environment. The basic principles of cognitive radio can be fulfilled using certain artificial intelligence techniques like artificial neural network, M arkov models, case-based systems etc.
More or less it can be said that the design of the hardware is a very challenging issue. Changes in Topology — The topologies in cognitive radio are more prone to change than the traditional wireless sensor network. This can cause hardware malfunctioning and depletion of energy. Following are the topologies employed in cognitive radio wireless network:. The protocols designed for cognitive radio do not have the capacity to tolerate fault. This affects the overall performance of the wireless sensor network. High Manufacturing Costs — High manufacturing cost is another challenge for cognitive radio wireless network.
Therefore algorithms need to be designed that can lower the manufacturing cost and production. This can cause non-cooperation between the nodes. This challenge can be overcome by designing some AI algorithms. Power Consumption — In this network, more energy is consumed for spectrum sensing, channel selection, route discovery, transmission and reception than the traditional network.
Quality of Service — Quality of Service is a challenging issue due to certain constraints on power, memory and source. Junfeng Fan. Francisco Rodrigo Porto Cavalcanti. Yong Shi. Advanced Hybrid Information Processing. Guanglu Sun. Enqing Chen. Image and Graphics. Yao Zhao. Shang-Hong Lai. Graph-Based Representations in Pattern Recognition.
Pasquale Foggia. Artificial Intelligence and Soft Computing. Leszek Rutkowski. Image and Video Technology. Manoranjan Paul.
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