<commit_before>\section{Conclusions}
\label{s:conc}
Context survey conclusions \\
	-critically reviewed three most important areas for the project:
		-phone sensing, energy measurement methods(results), energy-efficient sensing
	-in each of them the possible enhancement was identified
	-(big data set, hybrid approached to energy-efficient phone sensing)
	-most importantly, the energy method was chosen suitable for the research
		-plus the improvement over it

Sensor Energy measurement's method:\\
	-design novel way of investigating energy efficiency\\
	-checked it in practice, identify its problems\\
	-the method is validated since the results are compared with other research\\
	-specific characteristics of the batteries:
		-wrong samples
		-energy efficiency levels
	-cons:\\
		-the method is instable for older batteries \\
			-and thus cannot be used as online measurement, as it is not practical (too many samples )\\
	
	
Sensor Energy measurement's results:\\
	-complete set of results across three different phones including all sensors\\
		-it is believed that the first one like this\\
	-different energy characteristics across different devices\\
		->reason for online measurement, invalidate power models\\
	-the evolution of sensors for localization services\\
	-cons:\\
		-limited information on nature of energy consumption:\\
			"combined sensors" -> how they are going to perform?\\
			-no different parameters for phones are checked:\\
				-no duty cycling checked, no different periods/sleeping intervals checked\\
	-those results are used to design the energy-efficient algorithms for the library:\\
		-sensor substitution as cheaper sensor may be leveraged to replace heavy-duty sensors\\
	
Locy, the energy-efficient sensing library the design:\\
	-leverage sensors
		-moving detection algorithm
			-standard deviation, three windows
	-duty-cycling in order sensing to be more energy-efficient
		-differentiate it depending on battery level
	-easily plugged to exsisting applications
		behave as Android APi, requires just one single line change
		example ESM.
	
Locy, the evaluation: \\
	compare against baseline implementation
	two scenarios, three different devices
	Locy is more energy-efficient
	should convince a reader that it is worth incorporating

Future work as well\\
	extensions of the library:
		inLocy
		Library as a service could be compared
		+ evaluation
	other idea for optimization:
		leveraging the history for sensing<commit_msg>conclusions done<commit_after>\section{Conclusions}
\label{s:conc}
Three areas which are relevant to this project has been reviewed\ (phone sensing, energy measurements methods and energy-efficient sensing). In each of those areas, a possible enhancement has been identified. It was concluded that phone sensing could be further progressed by building a large sensor data set of different user activities. Furthermore, measuring 1\% of battery life was proposed as the suitable method of energy measurements for the project. Lastly, the hybrid approaches of different energy-efficient techniques were suggested to achieve additional energy savings. 

Measuring 1\% of battery life is a novel way of investigating the energy efficiency of a mobile phone. The method was tested in the series of the experiments and turned out to be valid. It also revealed specific characteristics of battery live\ (wrong samples and energy efficiency levels). However, the method is not stable for older batteries, and thus, it cannot be used for online energy measurements. 		

The series of the experiments provided the energy efficiency of all sensors available across three different devices. It is believed to be the first study of this type. There were different sensors' energy demands depending on a device, which justifies why online energy measurement is important for the energy-efficient sensing. The energy efficiency of the sensors utilized for localization were analyzed in more details. Those results explain how localization technologies evolve. Finally, physical sensors turned out be more energy-efficient than standard localization sensors\ (GPS and IEEE 802.11 scanning). This observation was used for the design of the energy-efficient sensing library, Locy.

Locy provides GPS localization service in an energy-efficient manner. It leverages energy-efficient accelerometer to infer whether a user is moving. If a user is not moving, high-power GPS can be switched off to save energy.  For further energy optimization, it utilizes duty-cycling accelerometer sampling. The ratio of duty-cycling\ (sampling time over sleeping interval) changes depending on the level of battery life. As a result, the library uses less energy if the battery life is low. Locy can be easily plugged to existing applications since it exposes the same API as LocalizationManager provided by Android. The replacement of line was needed to plug Locy into Tristan's ESM.

The energy efficiency of Locy was evaluated in the separate series of the experiments. Its performance was compared against the naive GPS implementation. The experiments involved two real-life scenarios and was conducted over three different devices. Locy turned out to have energy demands than the naive GPS implementation. 

Two extensions for Locy were proposed: inLocy\ (Wi-Fi based localization) and Locy working as a service . In future work, those extensions could be further developed and evaluated in real-life scenarios. Also, Locy could leverage other ideas for energy-efficient sensing. The regular patterns in human behaviour may help in optimizing duty-cycling sampling\ (by changing duty-cycling ratio accordinglt). The next research project is planned to further investigate that idea.
		
The project extended knowledge in energy-efficient phone sensing. It revealed novel characteristics of battery life and provided the complete set of sensor's energy efficiency across different phones. In the project, the energy-efficient sensing sensing library was created. The library was evaluated and outperforms the standard Android implementation. 
	
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