In the future, being around other people may prolong the lifetime of your smartphone’s battery

KNOWLEDGE @ KRISTIANIA: Technolgy 

Today, smartphones are used to aid and improve all kinds of tasks in almost all aspects of daily life. Smartphones, however, are generally considered fairly resource constrained and their available power is the most constrained resource of all— without power the smartphone won’t function.  

Not surprisingly, battery life has been identified as one of the most desired features of mobile devices.    

A possible solution 

For the purpose of improving battery life in settings where users are co-located, we conducted a study to measure the impact on the consumed smartphone energy when data were collected in a distributed manner. This means that every device shared the same data as opposed to every device collecting its own sensor data.  

The results indicated that a shared distributed collection imposed less energy consumption than traditional approaches. 

By opportunistically connecting to and sharing common computations with nearby smartphones, co-located users can compute tasks as ‘one entity’. The result of the computations done collectively is then available for all nearby devices and ensures that no computation is done more than once.  

A more seamless travel experience 

Let’s take an example of a near-future public transport application, taking advantage of the Be-in/Be-out paradigm. Be-in/Be-out is about enabling user’s of public transport to enter and leave vehicles without having to do any explicit action— a seamless, ticket free travel experience. In order to achieve this, heavy local computations are needed in order to estimate or verify if a user is actually on a public transport vehicle, which vehicle and how long the journey is, in order to achieve accurate ticketing.  

The Be-in/Be-out scenario in public transport, is a very relevant scenario which we are currently researching through a joint research effort between fluxLoop, Kristiania University College and other public transport operators such as Nordland Fylkeskommune and Ruter.  

Devices sharing the same data 

In scenarios such as this, the smartphones of embarking and disembarking travelers can collaborate as one entity in order to calculate whether travelers are embarking, disembarking and which mode of transport the travelers are in.   

Near-future public transport solutions are examples where opportunistic collaboration could prove beneficial. However, this approach could also be employed in any kind of scenario where devices are co-located and ambient sensor information needs to be collected and processed. 

Cloud solutions are not necessarily the best solutions 

The last couple of years, in order to combat the resource scarceness of mobile phones, many applications send heavy computation to the cloud for processing and the result is then returned to the device. This can significantly lower the resource consumption of mobile phones and contribute to a longer lasting battery. 

At the same time, the concept of the Internet of Things (IoT) has emerged which has led to a drastic increase in connected devices. This, in turn, leads to elevated network traffic which potentially can cause latency and a heightened load on servers worldwide.  

Not only does this affect the internet backbone and the service providers, but an increase in network usage from mobile devices might also incur added cost for the end-user. In addition, by sending data to the cloud it brings an added risk to security and introduces new attack vectors. Data can be intercepted on the way, or malicious employees working at the service provider can access and steal your data.  

More processing on device may increase the security of mobile solutions 

Security concerns, in combination with better hardware and more efficient algorithms has led to the computations again being moved back to the users’ devices. However, the issue of available power on mobile devices still persists. This is quite the predicament!  

On one hand you have a significant reduction in energy consumption on the user’s device and on the other you have a significant increase in network load and additional security concerns. It is currently hard to deal with all these issues, however, on-device processing and opportunistic collaboration can help mitigate security issues to some extent.   

A way to reduce energy consumption 

It is difficult to say exactly which calculations are needed for different solutions in the future. However, for scenarios where multiple devices need to do the same calculations within the same context, collaborating on the computation is a viable approach. Through opportunistic collaboration it is possible to reduce energy consumption, while simultaneously keeping network traffic at a minimum.  

In the future this solution might contribute to a more seamless travel experience with fairer pricing schemes and hopefully— once implemented, the travelers won’t notice a significant change in their device’s power consumption due to distributed work collaboration.  

Referanser: 
Skretting and T.-M. Grønli, "Distributed Sensor Data Collection Using Mobile Clouds for Public Transportation," in IEEE 17th International Conference on Intelligent Computer Communication and Processing, 2021.  

Ma, Y. Zhao, L. Zhang, H. Wang and L. Peng, "When mobile terminals meet the cloud: computation offloading as the bridge," in IEEE Network, vol. 27, no. 5, pp. 28-33, September-October 2013, doi: 10.1109/MNET.2013.6616112.

Text: PHD-candidate Anders Skretting, School of Economics, Innovation and Technology. Anders.Skretting@kristiania.no