Node localisation is one of the significant concerns in Wireless Sensor Networks (WSNs). It is a process in which we estimate the coordinates of the unknown nodes using sensors with known coordinates called anchor nodes. Several bio-inspired algorithms have been proposed for accurate estimation of the unknown nodes. However, use of bio-inspired algorithms is a highly time-consuming process. Hence, finding optimal network parameters for node localisation during the network set-up process with the desired accuracy in a short time is still a challenging task. In this article, we have proposed an efficient way to evaluate the optimal network parameters that result in low Average Localisation Error (ALE) using a machine learning approach based on Support Vector Regression (SVR) model. We have proposed three methods (S-SVR, Z-SVR and R-SVR) based on feature standardisation for fast and accurate prediction of ALE. We have considered the anchor ratio, transmission range, node density and iterations as features for training and prediction of ALE. These feature values are extracted from the modified Cuckoo Search (CS) simulations. In doing so, we found that all the methods perform exceptionally well with method R-SVR outperforming the other two methods with a correlation coefficient (R = 0.82) and Root Mean Square Error (RMSE = 0.147m).

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an $80\%$ decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

The social IoMT-based activity-monitoring system comprises several devices with different datasets. It faces challenges like a collection of a global activity dataset which comprises a myriad of activities. In this article, we propose a federated Siamese network-based data-independent group activity segregator—Skipper—which aims to identify anomalies in an activity-monitoring social IoMT system. The novelty of this work is that Skipper does not require any dataset before its deployment, which removes the need for any prior training of the model for activity monitoring. As a proof of concept, we select activities pertaining to school environments to identify low-performing students in a classroom, who would require teachers’ close attention to ensure balanced growth and proper health. Skipper monitors the students independently for their motion signatures through a wearable device that consists of an accelerometer. A federated Siamese network calculates indices that signify the degree of similarity among the students’ activities. Skipper identifies the students who do not perform the same activity. With real-world implementations, we observe that Skipper requires network rates of 10 Kb/s, making it suitable for low bandwidth networks while we achieve just 20% CPU and 10 MB memory utilization on constrained edge devices. Further, with an increasing number of students up to 100, the time delay for final results is limited to 80 s. Hence, Skipper is a fast, easy, and accurate solution for recognizing outliers in IoMT social systems.