Search

We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications, including military, environment monitoring, and surveillance and body area sensor networks. A wireless sensor network builds the core part for IoT. Besides this, lifetime maximization is the biggest challenge in the wireless sensor network. Also, In a wireless sensor network, it is difficult to find an optimal node deployment approach that would minimize costs, be robust to node failures, decrease computing overhead and communication, and maintain a high degree of coverage and network connectivity. There is numerous literature addressed this challenge which is discussed in this paper; still there are lot many challenges yet to be addressed. Considering this scenario, in this paper, we propose a scheduled-based node deployment algorithm using Firefly Optimization (FA) to offer a circumstance where we have a group of target points that satisfy p-coverage and sensor nodes that satisfy q-connectivity, with subject to the selection of the optimal number of a sensor node that has the highest energy and minimum distance. The multiple parameters as no. of sensor nodes, distance, survivability factors, coverage, and connectivity of the sensor nodes are considered for designing the fitness function. A comprehensive statistical analysis is done using the simulation results to prove the proposed scheme's efficiency with other existing state-of-the-art methods under various p-coverage and q-connectivity configurations. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are the substantial parts of any Internet of Things (IoT)-based applications. A WSN is deployed to collect essential data from a specific application field in the IoT. A WSN can fail for various reasons, which can disrupt the operation of numerous IoT applications. The main threat to network failure is topology changes, node failure due to battery drain, or breakdown of the wireless nodes communication module. An effective fault tolerance mechanism for WSN can guarantee reliable data acquisition and transmission in IoT applications. This paper proposes a fault-tolerant cluster-based routing protocol for WSNs using a hybrid algorithm called FAGWO-H, which combines Firefly Optimization (FA) and Grey Wolf Optimization (GWO). FA is employed for optimal clustering, and GWO chooses the best route between the cluster head (CH) and the base station. The proposed method uses two novel fitness functions for FA and GWO. The algorithm considers the energy efficiency and fault-tolerant aspect of the sensor nodes and CHs to improve network performance while meeting QoS criteria. We assessed the effectiveness of the techniques using several WSN scenarios and compared them to the existing state of art methods. [ABSTRACT FROM AUTHOR]