Abstract In the current era of technology and development, component‐based software development (CBSD) has been progressively implemented. Components are used in large products for multiple users with diverse viewpoints and are highly configurable to provide higher satisfaction. Components are reused because of their similar functionality to reduce complexity and ensure the correct interaction between interfaces during product development. However, implementation of component functionalities creates complications due to improper specification, prioritization of components requirements, encapsulated functionalities, and more human interaction. Furthermore, due to configurability and the involvement of multiple stakeholders, ambiguity and semantic issues arise in the behaviour of reusable components. To overcome semantic‐based specification and prioritization components' related issues, we propose a framework that uses text mining and case‐based reasoning (CBR) techniques. Results of our empirical evaluation demonstrate that the proposed framework significantly outperforms the conventional technique.

Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affect fleet productivity and performance. In this article, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle's health. Data from sensors are received on the driver's smartphone through a microcontroller and, after processing, useful information is displayed on the driver's mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.

With the advent of smart, inexpensive devices and a highly connected world, a need for smart service discovery, delivery, and adaptation has appeared. This interconnection is composed of sensors within devices or placed externally in the surrounding environment. Our research addresses this need through a context aware system, which adapts to the users’ context. Given that the devices are mobile and battery operated, the main challenge in a context awareness approach is power conservation. The devices are composed of small sensors that consume power in the order of a few mW. However, their consumptions increase manifold during data processing. There is a need to conserve power while delivering the requisite functionality of the context aware system. Therefore, this feature is termed as ‘power awareness.’ In this paper, we describe different power awareness techniques and compare them in terms of their conservation effectiveness. In addition, based on the investigations and comparison of the results, a power aware framework is proposed for a context aware system.