Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works.

With the continuous prevalence of wireless sensor network (WSN) applications in the recent days, localization of sensor nodes became an important aspect in research in terms of its accuracy, communication overhead and computational complexity. Localization plays an important role in location sensitive applications like object tracking, nuclear attacks, biological attacks, fire detection, traffic monitoring systems, intruder detections, and finding survivors in post-disasters, etc. The objective of localization is to identify the coordinates of target nodes using information provided by anchor nodes. Precision improvement of the sensor node positions is a key issue for an effective data transmission between sensor nodes and save the node’s energy as well as enhance the network lifetime. In this article, a cost-effective localization algorithm with minimal number of anchor nodes is proposed that uses nature inspired optimization techniques to enhance the localization accuracy compared to the state-of-the-art localization algorithms. The performance metrics considered for simulations and comparison with the existing algorithms include average localization accuracy, communication range, and the number of anchor nodes. The simulation results prove that the proposed gaussian-newton localization through multilateration algorithm (GNLMA) enhances the mean localization accuracy to 92.8% and the range measurement error is limited to 1.22meters. Depending on the communication range of sensor nodes, the average localization accuracy is achieved up to 94.4% using the proposed GNLMA.