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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]

Internet of Things (IoT) is steadily revolutionizing people's lives, and accurate location sensing is crucial in achieving this. Global positioning system (GPS) is being widely used outdoors, but its accuracy decreases in indoor environments due to signal attenuation and multipath effect. Simultaneously, Wi-Fi fingerprint-based techniques that use signal strengths from Wi-Fi access points in a building have become more popular for performing indoor positioning. However, location-based services also result in smartphone's battery life consumption because of frequent access point scanning. There are very few studies that focus on the energy conservation of localization systems, despite the fact that it is a significant factor in real-world applications. This paper proposes an intelligent scanning period dilation (ISPD) technique that uses a semi-centralized architecture and schedules Wi-Fi scans by allocating dynamic time intervals for each user. Experimental results show that the proposal saves 7.56% energy while reducing the location accuracy only by 1.35%. [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]

Localization is the fundamental problem for event detecting and monitoring due to the mobility of nodes UWSNs . In the literature, researchers have examined the localization problem through optimization schemes for evaluating the accurate location of unknown target nodes. However, the existing schemes have not considered the failure of anchor nodes during obstacles, natural disasters and environmental interferences. Due to this, the network leads to high localization error and energy depletion. Therefore, in this paper, we considered anchor node failure and bad distance computational between anchor-target pair for better localization. For this, fault-resilient localization using FL and NSGA I I -based metaheuristic scheme is proposed for UWSNs . In this scheme, first, we refined the correction value of an anchor node to improve the distance between anchor-target pair. Afterward, FL and NSGA II are applied to identify the set of optimal anchor nodes for the fault-resilient localization process. Furthermore, we have also incorporated fitness value with the three parameters such as residual energy, modified anchor-target distance and a number of the connected nodes as the input parameters, whereas probability is the output for the selection of optimal anchor nodes. Performance evaluation of the proposed scheme outperforms the existing scheme in terms of average localization error, better coverage positioning, localized and bad node proportion with a fast convergence rate under different network scenarios. [ABSTRACT FROM AUTHOR]

Fault resilient routing is a typical issue for underwater wireless sensor networks ( UWSNs ) due to contact of underwater creatures, mobility of nodes and natural disaster. In the existing studies, most of the researchers introduced routing scheme for routing the packets towards the base station through a cluster head. However, these schemes use static or mobile nodes for deployment, but nodes move from its original position to another position and get stuck in a particular region due to ocean current, natural disaster and environmental interference. Therefore, this leads to high energy depletion, link failure, disjoint path and overloaded data. To resolve this issue, we have proposed fault resilient routing based on moth flame optimization ( MFO ) scheme for transferring the packets towards the base station through autonomous underwater vehicles ( AUVs ). In this scheme, AUVs is used instead of cluster head to avoid the reclustering, overloading problem and it also feasible for large scale networks. There may be a disjoint path issue, so we have deployed additional mobile nodes with the help of AUVs within the network. Further, a novel fitness function is integrated with the MFO scheme to overcome the link failure problem. The proposed scheme is applied to select the best forwarding node for transmitting the packets towards the nearest AUVs using multi-hop acoustic links. In UWSNs , AUVs is placed to move the packets towards the BS from lower level to upper level. Performance evaluation of proposed scheme shows better result in terms of fault resilient, residual energy, network lifetime, packet delivery ratio and convergence rate than the existing scheme under the different network scenario. [ABSTRACT FROM AUTHOR]