This thesis evaluates the battery performance of two widely known game engines and compares the results to find the most battery friendly choice. Unity and Unreal Engine 4 are the two engines being compared; it aims to fill the need for research around mobile games battery performance. Smartphones battery life issues becomes more and more common with the powerful smartphones we use in today’s society. They are becoming faster every year, but the batteries stay the same. Power hungry games are not uncommon to find on the biggest game market that has surpassed the combined income of PC and Console games. Tests were performed on an Android system where multiple applications were tested while the battery consumption was measured. The applications are testing how different 2D or 3D components will impact the battery consumption compared to their baseline idling consumption. This thesis provides instructions on how the test applications were developed and which components were included. The findings show that the components had very little impact on the battery consumption for both Unity and Unreal Engine 4. Unity consumed between 1,1 – 1,4 % (36-42mAh) battery for all applications, Unreal Engine 4 consumed between 1,3 – 1,6% (39-48mAh) battery. This thesis conclusion is that Unreal Engine 4 game components require more battery power than the corresponding components of Unity. Therefore, Unity is according to the findings of this thesis a more battery friendly game engine for android games. These results mean that those who looks to create a smaller 2D or 3D game would get a more battery friendly application if they chose to use Unity. However, the results are limited because of how features like gyro, GPS and bigger applications were not included in the performed test cases.

Partially Observable Markov Decision Process (POMDP) is widely used to model sequential decision making process under uncertainty and incomplete knowledge of the environment. It requires strong computation capability and is thus usually deployed on powerful machine. However, as mobile platforms become more advanced and more popular, the potential has been studied to combine POMDP and mobile in order to provide a broader range of services. And yet a question comes with this trend: how should we implement POMDP on mobile platform so that we can take advantages of mobile features while at the same time avoid being restricted by mobile limitations, such as short battery life, weak CPU, unstable networking connection, and other limited resources. In response to the above question, we first point out that the cases vary by problem nature, accuracy requirements and mobile device models. Rather than pure mathematical analysis, our approach is to run experiments on a mobile device and concentrate on a more specific question: which POMDP implementation is the ``best'' for a particular problem on a particular kind of device. Second, we propose and justify a POMDP implementation criterion mainly based on battery consumption that quantifies ``goodness'' of POMDP implementations in terms of mobile battery depletion rate. Then, we present a mobile battery consumption model that translates CPU and WIFI usage into part of the battery depletion rate in order to greatly accelerate the experiment process. With our mobile battery consumption model, we combine a set of simple benchmark experiments with CPU and WIFI usage data from each POMDP implementation candidate to generate estimated battery depletion rates, as opposed to conducting hours of real battery experiments for each implementation individually. The final result is a ranking of POMDP implementations based on their estimated battery depletion rates. It serves as a guidance for on POMDP implementation selection for mobile developers. We develop a mobile software toolkit to automate the above process. Given basic POMDP problem specifications, a set of POMDP implementation candidates and a simple press on the ``start'' button, the toolkit automatically performs benchmark experiments on the target device on which it is installed, and records CPU and WIFI statistics for each POMDP implementation candidate. It then feeds the data to its embedded mobile battery consumption model and produces an estimated battery depletion rate for each candidate. Finally, the toolkit visualizes the ranking of POMDP implementations for mobile developers' reference. Evaluation is assessed through comparsion between the ranking from estimated battery depletion rate and that from real experimental battery depletion rate. We observe the same ranking out of both, which is also our expectation. What's more, the similarity between estimated battery depletion rate and experimental battery depletion rate measured by cosine-similarity is almost 0.999 where 1 indicates they are exactly the same.

Denna rapport redogör dels för utvecklingen av två applikationer för Android och dels för jämförelsen mellan de två. Dessa applikationer skulle regelbundet samla in och skicka sin position till en server för spårning. Tanken var att en administratör för till exempel ett orienteringsevent skulle kunna följa deltagarna i realtid. I slutet av rapporten återfinns en utvärdering av de två applikationerna som utvecklades som försöker avgöra vilken av de två applikationerna som verkar effektivast med avseende på energikonsumtion och dataöverföringshastighet.   Arbetet utfördes åt Progmera i Linköping som tidigare haft en del kunder inom bl a orienteringsbranchen. / This report details both the development of two separate applications for Android and a comparative evaluation between the two. The applications where supposed to regularly collect and transmit the device position to a server for tracking. The applications where meant to be used at orientation events to maintain locations of all participants in real-time. At the end of this report the evaluation tries to determine the most effective solution based on energy consumption and data transfer time.   The project was performed for Progmera in Linköping, Sweden. The company had clients in the orientation business.

Philosophiae Doctor - PhD / Community wireless mesh initiatives are a pioneering option to cheap ‘last-mile’ access to network services for rural low-income regions primarily located in Sub-Saharan Africa and Developing Asia. However, researchers have criticized wireless mesh networks for their poor scalability; and scalability quantification research has mostly consisted of modularization of per-node throughput capacity behaviour. A scalability quantification model to design wireless mesh networks to provide adequate quality of service is lacking. However, scalability quantification of community mesh networks alone is inadequate because rural users need affordable devices for access; and they need to know how best to use them. Low-cost low-end smartphones offer handset affordability solutions but require smart management of their small capacity battery. Related work supports the usage of Wi-Fi for communication because it is shown to consume less battery than 2G, 3G or Bluetooth. However, a model to compare Wi-Fi battery consumption amongst different low-end smartphones is missing, as is a comparison of different over-the-top communication applications.

The Hybrid approach of development has for a long time been the dominating way to develop cross-platform applications targeting both the web and mobile. In recent years, a new combination of technology has appeared called Progressive Web Application (PWA) which aims to combine Native capabilities with best practices of the web to deliver a new Native-like experience to users without the need of Native wrappers. So far PWAs have proven to be the inferior choice when it came to performance and platform support. The purpose of this study is to compare the two technologies based on a literature review and evaluate the current performance across three parameters in an experiment - battery consumption, CPU utilization and time to first activity. Two applications were developed using each respective technique, with the Apache Cordova framework being used for the Hybrid approach and the React framework being used to implement PWA features. The results showed that the Hybrid approach is better in the majority of tests, offering more in terms of platform API access and providing better performance while only being slower when it came to time it took to first activity; but something to consider is that the PWA approach was not far behind. The conclusion this study arrived at was that PWAs have developed significantly since previous studies and is almost able to match Hybrid apps in terms of APIs and performance, but that Hybrid apps are still the preferred choice when it comes to performance. Further development and a wider adaptation of the PWA specification could very well change the way developers choose to approach mobile app development in the future as well as a potential for bringing the web closer to the mobile platform.

Climate change is one of the most pressing issues today and companies are looking for ways to lower their carbon emissions. Software companies that develop mobile applications have a carbon footprint through several sources. The application uses networking which consumes energy through data transmission across the internet and data centers. The app consumes energy from the device's battery, which needs to be recharged with part carbon electricity. In this thesis, a literature study was initially conducted in order to investigate and gain knowledge about the different sources of energy consumption associated with a mobile application. An energy savings technique for networking in apps called batching was then tested on a real device. Additionally, different periodic intervals of sending network requests were tested and evaluated in terms of energy consumption. An iOS experiment app was built and run on an iPhone 7. The tests in the app was performed on both WiFi and 4G. The iPhone was altered and connected to a power monitor that was able to sample the energy consumption of the device with high accuracy. The results showed that sending 1000 HTTP GET requests periodically over 5 minutes, consumed 130\% more energy than batching them. On 4G the periodic requests consumed 250\% more energy than batching. The results from testing the request intervals showed that the energy consumption per request was higher for the test with larger intervals.

Under de senaste åren har användningen av mobilapplikationer i smartphones ökat och kommer förmodligen fortsätta att göra det. Smartphoneanvändare ställer ofta höga krav och vill nästan alltid ha bättre prestanda i smartphones vilket driver utvecklingen framåt. Dock har många smartphoneanvändare klagat på att batteriet inte räcker till för deras användning. Enligt olika forskare är inte batteriet dåligt utan det är mobilapplikationer som förbrukar för mycket energi. De flesta studier som handlar om energiförbrukningen i mobilapplikationer fokuserar mycket på de tekniska aspekterna och det finns relativt lite kunskap om vad användare tycker om energiförbrukningen. Detta har skapat ett behov av att forska mer om användningen av mobilapplikationer utifrån användarens perspektiv. Syftet med denna studie är att ta reda på om mobilapplikationers energiförbrukning påverkar hur smartphoneanvändare använder mobilapplikationer. För att samla in kunskap om detta har sammanlagt åtta smartphoneanvändare intervjuats med hjälp av semistrukturerade intervjuer. Studiens resultat visar bland annat att användningen påverkas på olika sätt och det har även visat sig att vissa applikationer påverkar användningen mer än andra. Studien presenterar också vilka inställningar som användare brukar ändra på för att minska energiförbrukningen i mobilapplikationer.