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Indoor positioning (IP) has attracted significant demand in diverse smart indoor consumer electronics applications like domotics appliances, automated energy management, patient tracking in hospitals, indoor navigation industries, etc. Most of these applications use Wi-Fi access points and a centralized server to create an IP network. The location of target nodes in these systems is disclosed, compromising user privacy. To overcome this, the current work proposes a federated KNN-based privacy-enforcing technique where the location coordinates of each target node are secured through discrete coordinate encryption. Hence the exact location coordinates are known only to the target node. However, real time consumer applications show that the privacy preserving technique increases localization error. But, efficient deployment of access points can improve localization accuracy. Therefore, we use Hausdorff distance to deploy dynamic access points based on the movements of the target nodes within convex hull regions. Experiments reveal that the proposed Hausdorff distance-based deployment model incorporated with a federated KNN results in better localization accuracy. Moreover, the proposed deployment technique does not require any additional hardware, making the system cost efficient.

The strategic deployment of nodes in underwater acoustic sensor networks (UASNs) is pivotal for enabling critical network functions such as localization and efficient monitoring. Achieving optimal deployment necessitates balancing two vital parameters: area coverage and network lifetime. Improving one parameter in isolation may adversely affect the other, emphasizing the need for joint optimization of area coverage and energy utilization to enhance overall UASN efficiency. This article introduces a novel node deployment scheme leveraging Voronoi-fuzzy C-means (FCM) and salp swarm optimization (SSO) to address challenges associated with random node deployment, such as uneven distribution, coverage overlaps, and holes. First, FCM computes initial centroids for optimal node placement. Subsequently, Voronoi polygons around these centroids address issues of spatial overlaps and indistinct boundaries. Additionally, Voronoi facilitates the determination of an optimum sensing radius. SSO refines node locations within each polygon by converging the nodes toward centroids. Furthermore, a sleep-wake mechanism for sensor nodes further extends the network lifetime. Performance evaluation reveals that our proposed scheme outperforms existing methods in diverse network scenarios. The proposed method achieved a remarkable 99.31% area coverage, extended network lifetime, and reduced energy consumption across diverse scenarios, all accomplished with a high convergence rate.

Security is the utmost importance for the safe operation of the Internet of Things (IoT) devices. Behind IoT, a sensor network operates. Hence, security and lifetime extensions of the sensor networks are the primary concern. Node clustering is a technique to lengthen the lifetime of sensor networks. In a clustering technique, Cluster Heads (CH) are chosen. To provide the security to the sensor network, reliable CHs are elected. In this article, a protocol is proposed which elects reliable CHs based on node features such as residual energy, distance to the sink and node behavior such as the number of packets transmitted and received successfully, data consistency factor. The simulation results obtained prove that the proposed protocol performs better.

A hybrid method is proposed to balance the energy consumption of the deployed sensors and that reduces energy holes near the sink by balancing the communication load in this paper. Hilbert Curve have been considered to optimize the network lifetime, a pre-clustering, clustering and sink mobility for fixed sensing points. The cluster heads are selected on the basis of link cost mainly consisting of the residual energy and distance to the sink parameters. An extensive simulations were performed with different network scenarios of the proposed work to enhance the network lifetime. In comparison with the static scheme, the load-balanced energy with sink mobility achieves a uniform distribution of clusters, extends the lifetime of the network and avoids the energy hole problem.

Wireless Sensor Networks (WSN) has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS). Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.

Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nodes face many hurdles and are unable to cover the required region during a natural disaster such as tsunami, flood, earthquakes, and environmental interference. It creates a coverage hole and consumes high energy with bad network quality. This problem considered as an NP-complete problem where a set of sensor nodes is required to identify the k-coverage hole and m-connectivity. In the literature, researchers have not focused on k-coverage hole restoration and m-connectivity issues during natural disasters and environmental interference. To mitigate this problem, we proposed priority-based coverage hole restoration and -connection using a whale optimization scheme to restore coverage holes and extract relevant information for the construction of undersea oilfield reservoirs, minerals, and mines. In this scheme, we identified the list of k-coverage holes and addressed autonomous underwater vehicles (AUVs) to place the additional mobile nodes in an appropriate coverage hole. A novel multi-objective function is formulated to obtain the optimal path for AUVs. Furthermore, while restoring coverage holes, we checked the connectivity of nodes. In the network, each node coordinated sleep scheduling with neighbor nodes to maintain energy efficiency. Performance evaluation of the proposed scheme shows better results than the existing schemes under different network scenarios which provide maximum coverage and connectivity, less energy consumption with a high convergence rate.