Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks.

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are low-cost, special-purpose networks introduced to resolve various daily life domestic, industrial, and strategic problems. These networks are deployed in such places where the repairments, in most cases, become difficult. The nodes in WSNs, due to their vulnerable nature, are always prone to various potential threats. The deployed environment of WSNs is noncentral, unattended, and administrativeless; therefore, malicious attacks such as distributed denial of service (DDoS) attacks can easily be commenced by the attackers. Most of the DDoS detection systems rely on the analysis of the flow of traffic, ultimately with a conclusion that high traffic may be due to the DDoS attack. On the other hand, legitimate users may produce a larger amount of traffic known, as the flash crowd (FC). Both DDOS and FC are considered abnormal traffic in communication networks. The detection of such abnormal traffic and then separation of DDoS attacks from FC is also a focused challenge. This paper introduces a novel mechanism based on a Bayesian model to detect abnormal data traffic and discriminate DDoS attacks from FC in it. The simulation results prove the effectiveness of the proposed mechanism, compared with the existing systems.

Academic use of social networking sites in learners' engagement in underdeveloped countries' schools.

Students' engagement has been a hot topic since the origin of teaching and learning; and is developing rapidly with time and technology. With the recent advances in Information and Communication Technology (e.g, Internet of Things, Artificial Intelligence and 5G), it is a need of the hour to revive its smart use in academia. In underdeveloped countries, parents are offended by financial burdens and educating children is not a priority, resulting students are not effectively engaged in learning. Smartphones are mostly used for fun and entertainment, why not for teaching, learning and monitoring to reshape pedagogy. This study investigated the role of social media in learners' engagement (l = 734) by making a productive relationship among the parents (p = 400), teachers (t = 21) and Principal in underdeveloped countries' schools. The results of the study are promising. The statistics for 2018-2019 (i.e, without social media), shows only 3% to 4 % parental participation in meetings and scarce teachers interest in schooling, resulting in the learner disengagement. However, the statistics from 2019-2020 (i.e, use of social media), shows improvements in the parental engagement up to 20% and teachers engagement up to 70%, resulting in a productive learners engagement. It is worth mentioning here that the school (located in the village), learner average attendance increased to 95% (dropped the truancy to almost zero), which got higher authorities admiration.