In this paper, we review systematically the role of problem-based learning (PBL) in developing soft skills in Technical and Vocational Education and Training (TVET) and other fields of studies. The Systematic Literature Review (SLR) includes the most recent empirical, review, and conceptual studies from TVET and other multiple fields of studies including medicine, humanities, and engineering between the years of 2001 and 2016 collected from four databases. A qualitative method was used to accomplish the systematic review. After the collection of articles, the selected studies were analyzed through thematic analysis. From this review, we concluded that PBL as an instructional approach has a significant role in the development of soft skills among students of various disciplines including TVET; empirical evidence is predominantly conclusive in identifying the acquisitions of various soft skills including communication skills, conflict resolution skills, leadership skills, and interpersonal skills, and finally, several factors might influence the relationship of PBL and soft skills such as duration and process of PBL instruction, role of facilitator, and awareness and training of learners. Moreover, there are fewer number of empirical studies on the role and effects of PBL approach to developing soft skills in TVET.

Sensor networks have gained tremendous appreciation in recent years and have been successfully tested in various application scenarios such as disaster management, pipeline monitoring, and environmental monitoring, as well as different military and security-based applications. In order to protect pipelines from leakage and corrosion, various conventional methods and techniques are used for monitoring. Functioning only in a specific environment, these conventional techniques require special infrastructure and higher maintenance. These challenges could, however, be overcome efficiently by using wireless sensors and robots. Most previous surveys mainly focused on the hardware design of the sensors, basic sensing techniques, and transmission media, but overlooked conducting an exclusive survey of pipeline monitoring schemes using linearly deployed wireless sensor networks or robots. This study attempts to carry out an extensive survey on the factors affecting pipeline monitoring techniques and provides novel classification in terms of classifying them into different strategies, sensor types, sensing coverage, communication methods, and monitoring types. In addition, a survey of comparative analysis of existing pipeline monitoring techniques is also presented, and general pipeline monitoring methods are also compared and summarized. Moreover, the need to explore pipeline monitoring schemes that incorporate wireless sensor networks (WSNs) is highlighted because WSNs are easy to deploy and flexible enough to be installed in any environment. This study discovers that most existing pipeline monitoring schemes that do not deploy sensors in a linear direction, which ultimately increases the cost and reduces overall network performance. Finally, several unresolved issues occurring in the development of pipeline monitoring techniques are highlighted, and future directions are provided accordingly.

The importance of body area sensor networks (BASNs) is increasing day by day because of their increasing use in Internet of things (IoT)-enabled healthcare application services. They help humans in improving their quality of life by continuously monitoring various vital signs through biosensors strategically placed on the human body. However, BASNs face serious challenges, in terms of the short life span of their batteries and unreliable data transmission, because of the highly unstable and unpredictable channel conditions of tiny biosensors located on the human body. These factors may result in poor data gathering quality in BASNs. Therefore, a more reliable data transmission mechanism is greatly needed in order to gather quality data in BASN-based healthcare applications. Therefore, this study proposes a novel, multiobjective, lion mating optimization inspired routing protocol, called self-organizing multiobjective routing protocol (SARP), for BASN-based IoT healthcare applications. The proposed routing scheme significantly reduces local search problems and finds the best dynamic cluster-based routing solutions between the source and destination in BASNs. Thus, it significantly improves the overall packet delivery rate, residual energy, and throughput with reduced latency and packet error rates in BASNs. Extensive simulation results validate the performance of our proposed SARP scheme against the existing routing protocols in terms of the packet delivery ratio, latency, packet error rate, throughput, and energy efficiency for BASN-based health monitoring applications.