Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Wireless Sensor Networks (WSN) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing Stable Election Protocol (SEP) that implements a threshold-based cluster head selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and cluster head nodes. The sensor nodes are also categorized into three different types called normal, intermediate and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput.

Recent developments in low-power communication and signal processing technologies have led to the extensive implementation of wireless sensor networks (WSNs). In a WSN environment, cluster formation and cluster head (CH) selection consume significant energy. Typically, the CH is chosen probabilistically, without considering the real-time factors such as the remaining energy, number of clusters, distance, location, and number of functional nodes to boost network lifetime. Based on the real-time issues, different strategies must be incorporated to design a generic protocol suited for applications such as environment and health monitoring, animal tracking, and home automation. Elementary protocols such as LEACH and centralized-LEACH are well proven, but gradually limitations evolved due to increasing desire and need for proper modification over time. Since the selection of CHs has always been an important criterion for clustered networks, this paper overviews the modifications in the threshold value of CH selection in the network. With the evolution of bio-inspired algorithms, the CH selection has also been enhanced considering the behavior of the network. This paper includes a brief description of LEACH-based and bio-inspired protocols, their pros and cons, assumptions, and the criteria of CH selection. Finally, the performance factors such as longevity, scalability, and packet delivery ratio of various protocols are compared and discussed.

A terahertz metamaterial absorber, capable of detecting cancerous cells is proposed in this paper. The proposed design consists of several merged Circular Ring Resonators (CRRs) (CRRs) over a Gallium Arsenide substrate. It achieves an absorption of 99% at 3.71 THz and a high Quality factor (Q-factor) of 92.75. The resonance frequency shifts with alterations in refractive index of the encompassing medium and this is used for detecting cancerous cells. The refractive index has been varied in the range of 1.35 to 1.40 on account of the fact that many of the cancer cells are in this range and its sensitivity is found to be 1447 GHz/RIU. The sensor's high Q factor and a Figure of Merit (FoM) of 36.175 has several biomedical sensing applications.

Increased impact of technology leads numerous researches to conduct research on the effect of social media on performance. Though, very limited of such studies have tried to observe and record the use of social media for collaborative learning as a way to improve collaborative authoring. This study tries to validation of Technology Acceptance Model (TAM) on social media use for collaborative learning to enhance collaborative authoring among research students. To achieve the study objective, the study employed a questionnaire as the main data collection method and distributed it to 1118 responses of various researchers from Universiti Teknologi Malaysia (UTM) all of whom use social media. The findings were obtained via a quantitative research method, Structural Equation Modeling (SEM-AMOS). The findings from our empirical study indicate that all the hypotheses were supported; the authors of the said study demonstrate that the use of social media positively and significantly related to collaborative authoring through collaborative learning between researchers in higher education. Therefore, the proposed model will help academic and decision-makers formulate strategies that can encourage students and researchers to use social media to improving collaborative authoring through collaborative learning.