Wireless Sensor Network
S. Shams Shamsabad Farahani
Abstract
Background and Objectives: Reliable data transmission and congestion control are considered as the transport layer primary functions in Wireless Sensor Networks (WSNs). WSNs are a specific category of wireless ad-hoc networks where their performance is highly affected by their characteristics and limitations. ...
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Background and Objectives: Reliable data transmission and congestion control are considered as the transport layer primary functions in Wireless Sensor Networks (WSNs). WSNs are a specific category of wireless ad-hoc networks where their performance is highly affected by their characteristics and limitations. These limitations necessitate an effective data transport control in WSNs which considers quality of service (QoS), energy efficiency, and congestion control.Methods: Congestion affects normal data transmission and ends in packet loss. Furthermore, wireless channels introduce packet loss because of high bit-error rate which wastes energy and affects reliability. The major problems regarding transport protocols in WSNs are congestion and reliability where the latter is classified and reviewed in the current paper.Results: In this paper, reliable data transport protocols are classified as the traffic direction, the parameter the reliability focuses on, and loss detection, notification, and recovery. Traffic direction-based reliable data transport protocols can be upstream, downstream or bidirectional, however, the parameter-based ones can be packet-based, event-based or destination-based, the loss detection and notification-based ones can be ACK-based, NACK-based, ACK and NACK-based or SACK-based, and the loss recovery-based reliable data transport protocols can be E-2-E or H-by-H. Thereafter, a comprehensive review of different reliable data transport protocols in wireless sensor networks is presented. Also, different performance metrics are used to compare these schemes.Conclusion: In this paper, reliable data transport protocols in WSNs are classified, reviewed and compared using different performance metrics. Finally, the current work attempts to provide specific directives to design and develop novel reliable data transport protocols in wireless sensor networks.
Wireless Sensor Network
S. Ashraf; T. Ahmed; Z. Aslam; D. Muhammad; A. Yahya; M. Shuaeeb
Abstract
Background and Objectives: The quick response time and the coverage range are the crucial factors by which the quality service of a wireless sensor network can be acknowledged. In some cases, even networks possess sufficient available bandwidth but due to coverage tribulations, the customer ...
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Background and Objectives: The quick response time and the coverage range are the crucial factors by which the quality service of a wireless sensor network can be acknowledged. In some cases, even networks possess sufficient available bandwidth but due to coverage tribulations, the customer satisfaction gets down suddenly. The increasing number of nodes directly is neither a canny solution to overcome the coverage problem nor a cost-effective. In fact, by changing the positions of the deployed node sagaciously can resolve the coverage issue and seems a cost-effective solution. Therefore, keeping all circumstances, a Depuration based Efficient Coverage Mechanism (DECM) has been developed. This algorithm suggests the new shifting positions for previously deployed sensor nodes to fill the coverage gap.Methods: It is a redeployment process and accomplished in two rounds. The first round avails the Dissimilitude Enhancement Scheme (DES), which searches the node to be shifted at new positions. The second round controls the unnecessary movement of the sensor nodes by the Depuration mechanism thereby the distance between previous and new positions is reduced. Results: The factors like loudness, pulse emission rate, maximum frequency, and sensing radius are meticulously explored during simulation rounds conducted by MATLAB. The performance of DECM has been compared with superlative algorithms i.e., Fruit Fly Optimization Algorithm (FOA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) in terms of mean coverage range, computation time, standard deviation, and network energy diminution.Conclusion: According to the simulation results, the DECM has achieved more than 98% coverage range, with a trivial computation time of nearly 0.016 seconds as compared to FOA, PSO, and ACO.
