Energy and Trust Aware Optimal Cluster-Based Routing in WSN using Lyrebird-Red Panda Optimization
This paper proposes an Energy and Trust Aware Optimal Cluster-Based Routing protocol for Wireless Sensor Networks (OCR-WSN) that utilizes the Lyrebird Assisted Red Panda Optimization Algorithm (LAROA) to optimize Cluster Head selection and routing paths, thereby enhancing network longevity and Quality of Service by balancing factors such as delay, distance, energy, and trust.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine a vast, silent field dotted with hundreds of tiny, battery-powered devices, each acting as a pair of eyes and ears for a larger system. These are wireless sensor networks, the invisible nervous systems of modern technology, used to monitor everything from forest fires and crop health to border security and patient vitals. The challenge with these networks is that the devices are small, cheap, and often placed in remote locations where changing a battery is impossible. They must survive on a single charge for as long as possible. To make this work, the devices cannot simply shout their data to a central station; that would drain their power too quickly. Instead, they organize themselves into small groups. Within each group, one device is chosen to act as a leader, gathering information from its neighbors and sending a single, consolidated report to the central station. This method saves energy, but it relies on picking the right leader and finding the safest, most efficient path for the data to travel. If the wrong leader is chosen, or if the data takes a long, winding route, the network can fail prematurely.
Researchers Vikram Sadashiv Gawali and Sonali Kshirsagar from the Government College of Engineering in India have proposed a new way to solve this problem, aiming to make these networks last longer and work more reliably. They developed a system called the Optimal Cluster-based Routing protocol, which uses a clever computer algorithm to decide which device should lead a group and how the data should move through the network. Their approach is unique because it does not just look at how much energy a device has left; it also considers how far the device is from others, how long it takes for a message to arrive, and whether the device can be trusted to behave honestly. To find the best possible arrangement, they combined two different strategies inspired by nature: the hunting behavior of a lyrebird and the foraging habits of a red panda.
The researchers simulated a network of one hundred and two hundred sensor nodes to test their idea. In their simulation, the new algorithm, which they named the Lyrebird-Red Panda Optimization, had to constantly evaluate the network to pick the best leaders and routes. The results showed that this hybrid method outperformed several other existing techniques. When the network had one hundred nodes, the new system managed to send data with a delay of just 0.0000001976 seconds, a remarkably fast speed that ensures information arrives almost instantly. The distance the data had to travel was optimized to an average of 40.078 meters, and the energy consumed for the task was kept low at 0.398 joules. Perhaps most importantly, the system maintained a high level of trust, scoring 0.673 on a scale that measures reliability, ensuring that the network was not being compromised by faulty or malicious nodes.
Beyond just speed and distance, the study looked at how well the network could deliver its messages. In a test with one hundred nodes, the new method achieved a packet delivery ratio of 0.876, meaning that nearly 88 percent of the data sent actually reached its destination. This was higher than the results from other popular methods, which ranged from 0.764 to 0.849. The researchers also measured the overall quality of service, a metric that combines speed, reliability, and efficiency. Their system scored 0.865, again beating the other methods. The study also examined how quickly the algorithm could find the best solution. In a process called convergence, where the computer searches for the perfect setup, the new method found a highly efficient solution in just 25 steps, with a cost value of 0.968 for the smaller network. This was faster and more precise than the other algorithms tested, which took longer to settle on a good answer and often ended up with less efficient configurations.
The researchers also looked at the practical side of running these calculations, such as how much computer memory the system needed and how long it took to run. The new method was the most efficient in both categories. It required only 53.568 megabytes of memory, the least of all the methods tested, and it completed its calculations in 1,564.56 seconds. In contrast, some of the older methods required nearly double the memory and took over 3,000 seconds to finish. This suggests that the new approach is not only smarter but also lighter and faster to run on the computers that manage these networks. The study confirmed these findings using statistical tests, showing that the improvements were consistent and not just a lucky result of a single run.
While the results are promising, the researchers note that these findings come from computer simulations rather than a physical deployment in the real world. The work demonstrates that combining the search strategies of a lyrebird and a red panda creates a robust system for managing wireless sensor networks. By carefully balancing energy use, distance, speed, and trust, this method offers a way to keep these critical networks alive and functioning for longer periods. The authors suggest that as these networks grow to include thousands of nodes or move into more complex environments, this type of intelligent routing will become essential. Future work could involve testing the system in real-world scenarios, such as industrial monitoring or healthcare, and exploring ways to integrate energy harvesting to further extend the life of these tiny, tireless sentinels.
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