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In general, a network that possesses numerous free or autonomous nodes is proffered as a Mobile Ad hoc Network (MANET). In this, to send along receive data, every single node acts as a router. The entire network's performance is degraded with the existence of faulty Sensor Nodes (SNs); thus, to obtain better Quality of Service (QoS), the detection of faulty SNs is highly significant. Therefore, an Efficient Node Localization and Failure Node Detection in a MANET environment is proposed here. Here, first, the nodes are initialized. Next, distance estimation, position computation, and optimal localization are the '3' steps utilizing which the SNs are localized. After that, via the network, the data packets are broadcasted. The best path is regarded for transmitting the data packet efficiently. It includes '2' steps for this; first, Failure Probability (FP) calculation; second, estimation of error calculation. Lastly, by employing the Soft Taxicab Poisson Binomial-Reference Point Group Mobility Model (STPB-RPGM), the Faulty Nodes (FNs) are detected. In this, the Taxicab Distance (TD)-Fuzzy C-Means (TD-FCM) is utilized to detect the group members along with by utilizing the Poisson Binomial Distribution (PBD)-Emperor Penguin Optimization (PB-EPO), the group leader is selected. Consequently, the failure along with the non-failure node is detected effectually by the STPB-RPGM. Lastly, the proposed STPB-RPGM's outcomes are analogized with the prevailing algorithms. The experiential outcomes displayed that the faulty SNs are detected with higher accurateness in the proposed methodology; thus, it surpassed the other state-of-the-art methodologies. The findings indicate that we were able to get 96.8% accuracy, 96.46% sensitivity, 96.88% precision, 97.12% specificity, 96.64% F-measure, and 96.46% recall using the STPB-RPGM. In 0.619 s, the TD-FCM finishes the clustering operation. The benefit of this investigation is that, compared to other clustering algorithms, the localization strategy decreases the number of dead nodes in data transmission, and the TD-FCM completes the clustering process quickly. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users' minimum data rate. The proposed solution to the problem considers the users' movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of $\simeq ~1/60$ and $\simeq ~1/9$ compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of $\simeq ~1/7$ compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.

Abstract: In this paper, we propose a protocol for dynamic reconfiguration of ad-hoc wireless networks with movable base stations in presence of obstacles. Hosts are assigned to base stations according to a probabilistic throughput function based on both the quality of the signal and the base station load. In order to optimize space coverage, base stations cluster hosts using a distributed clustering algorithm. Obstacles may interfere with transmission and obstruct base stations and hosts movement. To overcome this problem, we perform base stations repositioning making use of a motion planning algorithm on the visibility graph based on an extension of the bottleneck matching technique. We implemented the protocol on top of the NS2 simulator as an extension of the AODV. We tested it using both Random Way Point and Reference Point Group mobility models properly adapted to deal with obstacles. Experimental analysis shows that the protocol ensures the total space coverage together with a good throughput on the realistic model (Reference Point Group) outperforming both the standard AODV and DSR. [ABSTRACT FROM AUTHOR]