Predictive Estimation of Optimal Signal Strength from Drones over IoT Frameworks in Smart Cities
ABSTARCT :
The integration of drones, IoT, and AI domains can produce exceptional solutions to today's complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. In this paper, an intelligent technique is proposed to predict the signal strength from a drone to IoT devices in smart cities in order to maintain the network connectivity, provision the desired QoS, and identify the drone coverage area. An ANN based efficient and accurate solution is presented to predict the signal strength from a drone based on several pertinent factors like drone altitude, path loss, etc. Furthermore, the signal strength estimates are then used to predict the drone flying path. The findings show that the proposed ANN technique has achieved good agreement with the validation data generated via simulations, yielding determination coefficient R2 to be 0.96 and 0.98, for variation in drone height and distance from a drone, respectively. Therefore, the proposed ANN technique is reliable, useful, and fast to estimate the signal strength, determine the optimal drone flying path and predict the next location based on received signal strength.
EXISTING SYSTEM :
? Drones are attractive for emergency communication because of the possibility of rapid deployment and users operating them from their existing mobile handsets in disaster zones.
? In particular, when flying drones are used, they can support the connectivity of existing terrestrial wireless networks such as cellular and broadband networks.
? A variety of research work addresses the wide range of IoT technologies existing or even under standardization that would need to be integrated into the future communication network.
? Instead of inventing new energy resources for UAVs, new research has been shifting towards better utilization of existing energy resources.
DISADVANTAGE :
? The UAV can enable the communication services, while the wireless communication networks are damaged during disaster.
? The impacts of environment will destroy the signal by reflection, diffraction, and scattering, while as make signal weak.
? During transmission signal, the signal strength suffers from dynamics of the atmosphere and the environment impacts such as scattering, diffraction, reflection, and shadowing of electromagnetic waves, which can powerfully distort the signals.
? Therefore, we investigate the impact of a limited number of signal strength measurements on the accuracy of coverage prediction and estimation of propagation parameters.
PROPOSED SYSTEM :
• The proposed network architecture, that is, the integration of drones, IoT and smart wearable devices, offers numerous services like supporting disaster relief team to save human lives, long-distance communication, greening communication, etc.
• A mobile ad hoc network (MANET) is proposed to connect the drones and responders to perform tasks efficiently.
• Recommendations for WSN and UAV have been made based on the proposed classification of three stages of disaster management, i.e., pre-disaster preparedness, disaster assessment, disaster response and recovery.
• Effective iterative lowcomplexity algorithms have been proposed to solve the optimization problems associated with these types of relay networks .
ADVANTAGE :
? Therefore, the performance of communication link among the drones and other robots on the ground get more degradation and attenuation.
? Regarding indoor environments neuro-fuzzy is proposed to simulate the propagation model for predicting RSS and comparing the performance with empirical models of channels.
? The path loss information may be used as a controlling factor for system performance or coverage.
? The primary application of analytically of the channel is the ability to optimize the performance of the smart UAV rapidly.
? They are already beginning to efficiently replace that connected sensors at stationary with one device which has most essential features such as easy to deploy, flexible payloads, reprogrammable, measure anything at any time in anywhere.
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