3,489 research outputs found
A Trust Based Fuzzy Algorithm for Congestion Control in Wireless Multimedia Sensor Networks (TFCC)
Network congestion has become a critical issue for resource constrained
Wireless Sensor Networks (WSNs), especially for Wireless Multimedia Sensor
Networks (WMSNs)where large volume of multimedia data is transmitted through
the network. If the traffic load is greater than the available capacity of the
sensor network, congestion occurs and it causes buffer overflow, packet drop,
deterioration of network throughput and quality of service (QoS). Again, the
faulty nodes of the network also aggravate congestion by diffusing useless
packets or retransmitting the same packet several times. This results in the
wastage of energy and decrease in network lifetime. To address this challenge,
a new congestion control algorithm is proposed in which the faulty nodes are
identified and blocked from data communication by using the concept of trust.
The trust metric of all the nodes in the WMSN is derived by using a two-stage
Fuzzy inferencing scheme. The traffic flow from source to sink is optimized by
implementing the Link State Routing Protocol. The congestion of the sensor
nodes is controlled by regulating the rate of traffic flow on the basis of the
priority of the traffic. Finally we compare our protocol with other existing
congestion control protocols to show the merit of the work.Comment: 6 pages, 5 figures, conference pape
Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data
As the reliance on wireless sensor networks (WSNs) rises in numerous sectors,
cyberattack prevention and data transmission integrity become essential
problems. This study provides a complete framework to handle these difficulties
by integrating a cognitive intelligence (CI) framework, an information
processing protocol, and sophisticated artificial intelligence (AI) and big
data analytics approaches. The CI architecture is intended to improve WSN
security by dynamically reacting to an evolving threat scenario. It employs
artificial intelligence algorithms to continuously monitor and analyze network
behavior, identifying and mitigating any intrusions in real time. Anomaly
detection algorithms are also included in the framework to identify packet drop
instances caused by attacks or network congestion. To support the CI
architecture, an information processing protocol focusing on efficient and
secure data transfer within the WSN is introduced. To protect data integrity
and prevent unwanted access, this protocol includes encryption and
authentication techniques. Furthermore, it enhances the routing process with
the use of AI and big data approaches, providing reliable and timely packet
delivery. Extensive simulations and tests are carried out to assess the
efficiency of the suggested framework. The findings show that it is capable of
detecting and preventing several forms of assaults, including as
denial-of-service (DoS) attacks, node compromise, and data tampering.
Furthermore, the framework is highly resilient to packet drop occurrences,
which improves the WSN's overall reliability and performanc
Intelligent Transportation Systems: Fusing Computer Vision and Sensor Networks for Traffic Management
Intelligent Transportation Systems (ITS) represent a pivotal approach to addressing the complex challenges posed by modern-day urban mobility. By seamlessly integrating computer vision and sensor networks, ITS offer a comprehensive solution for traffic management, safety enhancement, and environmental sustainability. This paper delves into the synergistic fusion of computer vision and sensor networks within the framework of ITS, emphasizing their collective role in optimizing traffic flow, mitigating congestion, and enhancing overall road safety. Leveraging cutting-edge technologies such as machine learning, image processing, and Internet of Things (IoT), ITS harness real-time data acquisition and analytics capabilities to facilitate informed decision-making by transportation authorities. Through a comprehensive review of recent advancements, challenges, and opportunities, this paper illuminates the transformative potential of integrating computer vision and sensor networks in ITS. Furthermore, it presents compelling case studies and exemplary applications, showcasing the tangible benefits of this fusion across diverse traffic management scenarios. Ultimately, this paper advocates for the widespread adoption of integrated ITS solutions as a means to usher in a new era of smarter, safer, and more sustainable urban transportation systems
KFOA: K-mean clustering, Firefly based data rate Optimization and ACO routing for Congestion Control in WSN
Wireless sensor network (WSN) is assortment of sensor nodes proficient in environmental information sensing, refining it and transmitting it to base station in sovereign manner. The minute sensors communicate themselves to sense and monitor the environment. The main challenges are limited power, short communication range, low bandwidth and limited processing. The power source of these sensor nodes are the main hurdle in design of energy efficient network. The main objective of the proposed clustering and data transmission algorithm is to augment network performance by using swarm intelligence approach. This technique is based on K-mean based clustering, data rate optimization using firefly optimization algorithm and Ant colony optimization based data forwarding. The KFOA is divided in three parts: (1) Clustering of sensor nodes using K-mean technique and (2) data rate optimization for controlling congestion and (3) using shortest path for data transmission based on Ant colony optimization (ACO) technique. The performance is analyzed based on two scenarios as with rate optimization and without rate optimization. The first scenario consists of two operations as k- mean clustering and ACO based routing. The second scenario consists of three operations as mentioned in KFOA. The performance is evaluated in terms of throughput, packet delivery ratio, energy dissipation and residual energy analysis. The simulation results show improvement in performance by using with rate optimization technique
KFOA: K-mean clustering, Firefly based data rate Optimization and ACO routing for Congestion Control in WSN
Wireless sensor network (WSN) is assortment of sensor nodes proficient in environmental information sensing, refining it and transmitting it to base station in sovereign manner. The minute sensors communicate themselves to sense and monitor the environment. The main challenges are limited power, short communication range, low bandwidth and limited processing. The power source of these sensor nodes are the main hurdle in design of energy efficient network. The main objective of the proposed clustering and data transmission algorithm is to augment network performance by using swarm intelligence approach. This technique is based on K-mean based clustering, data rate optimization using firefly optimization algorithm and Ant colony optimization based data forwarding. The KFOA is divided in three parts: (1) Clustering of sensor nodes using K-mean technique and (2) data rate optimization for controlling congestion and (3) using shortest path for data transmission based on Ant colony optimization (ACO) technique. The performance is analyzed based on two scenarios as with rate optimization and without rate optimization. The first scenario consists of two operations as k- mean clustering and ACO based routing. The second scenario consists of three operations as mentioned in KFOA. The performance is evaluated in terms of throughput, packet delivery ratio, energy dissipation and residual energy analysis. The simulation results show improvement in performance by using with rate optimization technique
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