Research Article Power Profiling of Context Aware Systems: A Contemporary Analysis and Framework for Power Conservation Umar Mahmud , 1 Shariq Hussain , 1 and Shunkun Yang 2 1 Department of Sofware Engineering, Foundation University Islamabad, Pakistan 2 School of Reliability and Systems Engineering, Beihang University, Beijing, China Correspondence should be addressed to Shunkun Yang; ysk@buaa.edu.cn Received 14 March 2018; Revised 18 July 2018; Accepted 29 August 2018; Published 16 September 2018 Academic Editor: Mubashir H. Rehmani Copyright © 2018 Umar Mahmud et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the advent of smart, inexpensive devices and a highly connected world, a need for smart service discovery, delivery, and adaptation has appeared. Tis 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. Te devices are composed of small sensors that consume power in the order of a few mW. However, their consumptions increase manifold during data processing. Tere is a need to conserve power while delivering the requisite functionality of the context aware system. Terefore, this feature is termed as ‘power awareness.’ In this paper, we describe diferent power awareness techniques and compare them in terms of their conservation efectiveness. In addition, based on the investigations and comparison of the results, a power aware framework is proposed for a context aware system. 1. Introduction Context awareness is termed as the awareness of a system to external and internal stimuli gathered through internal and external sensors. Such a system classifes this as an activity to adapt the services present within an environment. A context aware system is typically found in a handheld device, such as a smart phone [1–3]. Context is simply the description of the current situation as bound by the environment, while context awareness utilizes the context to adapt and change the ser- vices present in the environment. Te environment becomes smart and delivers better services to the end-user [4]. A sim- ple example is the change in orientation of a handheld device from portrait to landscape, based on how a person holds the phone. Among various issues and challenges of context aware- ness is the management of battery power [5]. Battery power is used by diferent sensors that generate data. For instance, a device orientation sensor generates orientation data, whereas an accelerometer generates acceleration data. Tis data is then processed by the context aware system to classify the current context or situation. Tese sensors typically consume low power in the order of 1-2mW. However, they consume 180– 300 mW when processing is carried out [6, 7]. Tis signif- cant increase in consumption requires sufcient power con- servation such that the functionality or efectiveness of a con- text aware system is not reduced. Te term ‘power conserva- tion’ is interchangeably used as ‘energy conservation’ in the literature. Tis paper uses the former terminology. Power is related to energy, wherein the use of power over a period of time is considered energy consumption. Te reduction in the use of energy for the same task is considered as power conservation. Tis paper presents a literature review of diferent tech- niques of power conservation that have been proposed by re- searchers over the years. Te literature, in general, considers power and energy to be synonymous. However, this paper follows the convention of power conservation. Furthermore, we discuss the efectiveness of these techniques and pre- sent a framework for power conservation. We describe two Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 1347967, 15 pages https://doi.org/10.1155/2018/1347967