A Wireless Sensor Network (WSN) is a network of sensor nodes using low-power wireless technology to collect data in a region of interest (ROI). Due to their low energy, locating sensor nodes in large outdoor areas is challenging, which precludes GPS integration. WSNs typically comprise a small number of beacon nodes (BN) whose locations are known in advance, with most nodes deployed at unknown coordinates within the ROI. Endeavours to determine the locations of such unknown WSN nodes are largely based on the impractical assumption that every unknown node (UN) is within the communication range of BNs. Subsequently, these approaches utilise at least two BNs to determine the position of one UN. The Received Signal Strength Indicator (RSSI) or Angle of Arrival (AoA) values of the signals from the BNs form the basis for such localisation. This article suggests an iterative hybrid approach incorporating AoA and RSSI techniques, achieving accurate localisation with just one BN. The iterative method gradually covers the region, avoiding the unrealistic assumption of having all UNs within range. It also presents an innovative use of a unipolar stepper motor for AoA measurements. Experiments in a simulated environment and a real-world prototype validate the approach’s effectiveness.

A Wireless Sensor Network (WSN) comprises an ad-hoc network of nodes laden with sensors that are used to monitor a region mostly in the outdoors and often not easily accessible. Despite exceptions, several deployments of WSN continue to grapple with the limitation of finite energy derived through batteries. Thus, it is imperative that the energy of a WSN be conserved and its life prolonged. An important direction of work to this end is towards the transmission of data between nodes in a manner that minimum energy is expended. One approach to doing this is cluster-based routing, wherein nodes in a WSN are organised into clusters, and transmission of data from the node is through a representative node called a cluster-head. Forming optimal clusters and choosing an optimal cluster-head is an NP-Hard problem. Significant work is done towards devising mechanisms to form clusters and choosing cluster heads to reduce the transmission overhead to a minimum. In this article, an approach is proposed to create clusters and identify cluster heads that are near optimal. The approach involves two-stage clustering, with the clustering algorithm for each stage chosen through an exhaustive search. Furthermore, unlike existing approaches that choose a cluster-head on the basis of the residual energy of nodes, the proposed approach utilises three factors in addition to the residual energy, namely the distance of a node from the cluster centroid, the distance of a node from the final destination (base-station), and the connectivity of the node. The approach is shown to be effective and economical through extensive validation via simulations and through a real-world prototypical implementation.

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

Edge computing technology has gained popularity due to its ability to process data near the source or collection device, benefiting from low bandwidth utilization and enhanced security. Edge devices are typically equipped with multiple devices that employ asymmetric multi-cores for efficient data processing. To ensure optimal performance, it is crucial to carefully assign tasks to the appropriate cores in asymmetric multi-core processors. However, the current Linux scheduler needs to consider the capabilities of individual cores when assigning tasks. Consequently, high-priority tasks may be assigned to energy-efficient cores, while low-priority tasks end up on high-performance cores. This sub-optimal task assignment negatively impacts the overall system performance. To address this issue, a new algorithm has been proposed. This algorithm considers both the core’s capabilities and the task’s priority. However, due to the asymmetric nature of the cores, prior knowledge of each core’s speed is necessary. The algorithm fetches the priorities of the tasks and classifies them into high, medium, and low-priority categories. High-priority tasks are scheduled on high-performance cores, while medium and low-priority tasks are allocated to energy-efficient cores. The proposed algorithm demonstrates superior performance for high-priority tasks compared to the existing Linux task scheduling algorithm. It significantly improves task scheduling time by up to 16%, thereby enhancing the system’s overall efficiency.