ABSTRACT Wireless Sensor Networks (WSNs) have numerous applications, one of which is localization. Localization is crucial for determining the position of unknown sensor nodes in the deployment environment. Localization techniques are broadly classified into two categories: range based and range‐free. Range‐free localization techniques are gaining popularity as they are easy to implement and do not require external hardware. In this proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. Ten nearest beacon nodes are considered to determine the coordinates of a single unknown node. The hop value and hop size of that unknown node are calculated from the 10 nearest beacon nodes, and by using the hop value and hop size, the weight is calculated. After the weight estimation, the weighted sum is calculated, and using the weighted sum, the coordinates of an unknown node are determined. Simulation results indicate that the proposed Weighted Centroid Amorphous method achieves superior accuracy, with rates of 90.41%, 89.57%, and 86.10% compared to the traditional Amorphous, improved Amorphous, and Ensemble approach, respectively.

Summary In the modern era, unmanned aerial vehicle (UAV) based wireless sensor networks (WSN) are rising technologies in wireless communication. Through UAV, the sensed data can be forwarded to the base station. However, the increase in network users leads to several malicious attacks on UAVs. Hence, it affects the performance of a WSN platform while transmitting private information through UAVs. Therefore, the proposed study intends to develop an effective malicious UAV detection approach using a machine‐learning algorithm. Initially, the deployed sensor nodes in WSN are utilized to collect the environmental data. These sensor nodes transmit the collected data to the UAV. During data transmission, the sensor nodes generate a feed packet (authentication parameter) and forward it to the UAV along with the sensed information. The feedback packet is encrypted through a proxy re‐encryption scheme to secure the input data. These encrypted packets with the sensed input data are then transmitted to the base station. Finally, the feedback packet is decrypted and attains the actual input information. From the received data, the classification is performed using a proposed support vector machine with a shuffled frog leap (SVM‐SFL) approach. The proposed approach is implemented with the NS3 Python tool, and the results are analyzed by evaluating several performance matrices. Compared with other existing methods, the proposed study obtained improved results in terms of accuracy (98.61%), precision (98.5%), sensitivity (98.63%), and F‐measure (98.62%).

Summary The process of node identification is referred to as localization, and it is rapidly gaining popularity in the field of WSN. Different node identification processes have different findings, benefits, challenges, costs, effectiveness, and applications. In this work, the position error of the Amorphous algorithm is minimized by optimizing the hop size. For optimization of the hop size of the Amorphous algorithm, two different optimization algorithms, such as ALO and GWO, are considered. Proposed Amorphous‐ALO and Amorphous‐GWO provide higher accuracy rates of 33.89% and 4.22% than traditional Amorphous as well as ensemble approaches. Amorphous‐ALO and Amorphous‐GWO provide position errors 2.9161 and 2.9164 respectively, which are very similar. Therefore, to determine the suitable optimization algorithm for Amorphous, the minimum, average, and maximum execution times of Amorphous‐ALO and Amorphous‐GWO are considered. The approach that has less execution time is considered as most suitable for Amorphous. Amorphous‐ALO takes 67.76, 69.60 and 84.24 s for minimum, average and maximum execution whereas Amorphous‐GWO takes 65.17, 65.46 and 65.66 s for minimum, average and maximum execution respectively. As Amorphous‐GWO takes less execution time than Amorphous‐ALO; therefore, GWO is more suitable for optimization in Amorphous algorithm.

Abstract In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.

Connected cars are equipped with a rich set of sensors, such as GPS, accelerometer, video cameras, and pollution detectors. The information generated by these sensors can be used to offer a wide range of on-demand services, such as congestion notification, parking lots, and video surveillance. These services need a reliable and low latency unicast communication scheme in order to efficiently deliver the information requested by drivers. In this paper, a local traffic aware unicast routing scheme is proposed. To overcome network fragmentation, the proposed scheme relies on base stations and virtual base stations to transmit information from source car to the destination car using backhaul link. In this model, as base stations are sparsely deployed in the junction areas, a car moving in the junction area acts as a virtual base station node to support the routing process in the absence of a base station. Moreover, it avoids the impact of unreliable channel on information delivery. In the proposed scheme, each base station and virtual base station uses the short status messages (beacons) exchanged by the cars to form a local database of car locations. The stored information is used to find a base station or virtual base station that offers a minimum delay path to the destination car. The simulation results show that the proposed scheme outperforms the existing routing schemes in terms of end-to-end delay and packet delivery ratio. The proposed scheme is also validated by a connected car prototype built in an indoor laboratory environment.