Error Correction Schemes (ECSs) significantly contribute to enhancing reliability and energy efficiency of Wireless Sensor Networks (WSNs). This review paper offers an overview of the different types of ECS used in communication systems and a synopsis of the standards for WSN. We also discuss channels and network models for WSN as they are crucial for efficient ECS design and implementation. The literature review conducted on the proposed energy consumption and efficiency models for WSN indicates that existing research work has not considered Single Hop Asymmetric Structure (SHAS) with high performing Error Correcting Codes (ECCs). We present a review on proposed ECS for WSN based on three criteria: Forward Error Correction (FEC), adaptive error correction techniques, and other techniques. Based on our review work, we found that there are limited works on ECS design on a realistic network model i.e., a modified multi-hop WSN model. Finally, we offer future research challenges and opportunities on ECS design and implementation for WSN.

As mobile devices have become a part of our everyday life, there is a lot of research going on how to make this technology more productive for the students. Most of the research studies have indicated that, mobile devices are very useful for students while a few research studies negates its effectiveness. In this paper, we have presented the literature survey conducted on the use of mobile technologies in the field of Teaching and Learning (T&L). These studies are broadly classified into three areas: Impact and learning environment; specific applications; and mobile learning framework. We will be exploring all these areas in this paper. Our review in impact and environment of mobile learning indicates that the use of mobile technology is nourishing the knowledge and learning of students in most cases. Our review on specific applications of mobile learning reveals positive impacts on learning in various fields but the key concern raised was difficulties in implementing them and not having proper control guideline. Moreover, our review on frameworks for mobile learning indicates that there are different frameworks developed for different applications. Most of the applications are limited to non-technical areas. However, we can observed that there is a lack of connectivity between course development and framework for mobile learning. Therefore, the future course developers need to address the gap by taking into account of learners’ behavior in using mobile devices and their cultures to enrich student engagement and learning.

Location prediction in an indoor environment is a challenge, and this has been a research trend for recent years, with many potential applications. In this paper, machine-learning-based regression algorithms and Received Signal Strength Indicator (RSSI) fingerprint data from Wireless Access Points (WAPs) with dual Service set IDentifiers (SSIDs) are used, and positioning prediction and location accuracy are compared with single SSIDs. It is found that using Wi-Fi RSSI data from dual-frequency SSIDs improves the location prediction accuracy by up to 19%. It is also found that Support Vector Regression (SVR) gives the best prediction among classical machine-learning algorithms, followed by K-Nearest Neighbour (KNN) and Linear Regression (LR). Moreover, we analyse the effect of fingerprint grid size, coverage of the Reference Points (RPs) and location of the Test Points (TPs) on the positioning prediction and location accuracy using these three best algorithms. It is found that the prediction accuracy depends upon the fingerprint grid size and the boundary of the RPs. Experimental results demonstrates that reducing fingerprint grid size improves the positioning prediction and location accuracy. Further, the result also shows that when all the TPs are inside the boundary of RPs, the prediction accuracy increases.