Smartphones have evolved in leaps and bounds during the last decade and this evolution is continuing with new software and hardware capabilities as well as new application domains, such as augmented reality and the Internet of Things (IoT). In this chapter we briefly examine future smartphone trends from the perspective of energy consumption. We conclude that radical disruptions are not likely in the near future, keeping energy a first-class resource for mobile devices and warranting new solutions to maintain and improve device battery life.

Future smartphone

The smartphone has become the essential instrument for our daily lives, supporting a plethora of usage scenarios from the workplace to the home and leisure. Today's smartphone is a small-factor, high-performance computer that can offer graphics resolution and power on a par with the previous generation of game consoles. The communications capabilities of smartphones have evolved tremendously over the past decade and now high-speed, low-latency 4G and beyond networks, Wi-Fi, and local area networks are ubiquitously supported. The sensing capabilities have improved dramatically with GPS, acceleration, light, and temperature to give some examples of onboard sensors and the sensing capabilities include complex gestures and voice.

In addition to the hardware capabilities, the mobile-application ecosystem has had stellar success in recent years since iOS, Android, and Windows Phone were launched. Application developers have used the mobile platform APIs and the hardware capabilities of smartphones to create new innovative applications that in many cases combine sensors and communications capabilities in unexpected ways.

In this chapter, we continue to look at the different ways to reduce the energy consumed by wireless communication. Our focus is on how to take advantage of the fact that modern smartphones include many different wireless technologies integrated under the hood and can even switch seamlessly between some of them.

How using multiple WNIs saves energy

Smartphones today contain many different radio technologies including Wi-Fi, Bluetooth, BLE, and cellular radios. As we learned in Section 7.3, using different WNIs can cause quite different amounts of energy to be consumed. More importantly, the energy utility of the different technologies vary substantially. For this reason, opportunities to save energy arise by using the WNIs wisely.

Recall from Section 7.3 that energy is consumed in non-connected and connected modes. The former essentially means discovering an AP or another device in order to establish communication, while the actual data transfer happens in the latter mode. The amount of energy spent in such a discovery process can account for a large part of the total amount of energy consumed. Fortunately, the differences in the energy consumption between the different kinds of radio can be used in that process. In addition, keeping multiple radios continuously powered on in the smartphone is usually unnecessary. In most cases, it is enough to have one radio active so that the phone remains reachable at all times to be able to receive phone calls or incoming messages pushed by mobile services.

In this chapter we give an overview of smartphone batteries and their modeling. Battery models are important when the remaining battery capacity needs to be estimated. In this chapter, we give an overview of smartphone Li-Po batteries, their static and dynamic parameters and characteristics, and charging technology, and then consider techniques for assessing and modeling a battery SOC.

Overview

Figure 3.1 presents an overview of the Li-Ion battery charging and discharging process. Current is carried by lithium ions (Li+) that move from negative (anode) to positive (cathode) electrodes during discharging. The lithium ions move in the reverse direction when charging the battery. The ions move through the non-aqueous electrolyte and the separator. Applying a charge places the battery in a closed-circuit voltage, in which the voltage behavior is set by the internal battery resistance. Charging and discharging distorts the battery and it can take up to 24 hours for the battery to stabilize.

Battery life is proportional to the active reaction sites across the cathode. When the discharge current is low, inactive reaction sites are uniformly distributed over the volume of the cathode. When the discharge current is high, the volume of inactive sites at the outer surface of the cathode is large, causing active sites to become unreachable. As a consequence, the battery capacity is reduced at high discharge rates. When a high current is drawn from the battery, the diffusion rate cannot match the rate at which ions are being absorbed at the cathode.

We now turn our attention from individual optimization techniques to applications. We investigate a few different cases where the principles and techniques we have learned earlier can be applied in mobile applications.

We first look at a specific application, namely video streaming, which is one of the most important internet applications today, from the mobile internet's perspective. We first study the way that video streaming consumes energy and illustrate that through measurement results from real systems. We then cover different strategies that can be used to save energy in video streaming. It turns out that there are a few things that need to be taken into account when applying generic energy-saving techniques to mobile video streaming.

The next two examples are not really specific applications but rather integral parts of many applications and, therefore, they represent extremely important cross-application scenarios. The first of those is sensing. Sensing is a hot research topic at the moment and it is expected to become a very important part of smartphone applications. We study ways to reduce energy consumption with applications that require different kinds of sensing by exploring separately each category of sensors included in modern smartphones. We focus on two well-established techniques: sensor selection and duty cycling.

The second cross-application energy optimization scenario is security. We look at the energy overhead caused by security protocols and algorithms, based on measurement studies. Then, we discuss whether and when it is possible to find a tradeoff between the level of security and energy consumption.

This chapter is not about standalone positioning as such, but rather about all the enhancements to standalone positioning, which we have discussed in the previous chapter, but achieved without data link. Here we use the term BGPS (BGNSS) to describe technology that achieves results similar to AGPS (AGNSS), but without immediate corrections data.

Advantages of positioning without a data link

In a 1996 article in GPS World magazine [1], the author stated that GPS is calmly but rapidly penetrating mass markets, and finds itself in cellular phones, cars, watches, cameras, and golf carts. By 2013, this process is almost complete. The GNSS is the essential part in all these devices. This has increased the number of GPS users drastically. With the many navigation satellites in the sky, and a handful of GNSS-enabled gadgets, a user would expect to experience a seamless positioning service – instant positioning at any time and at any place. And that is without becoming a specialist in satellite navigation. Technology in general is trying to meet this new requirement, sometimes quite drastically, as in the Windows 8 interface.

Testing procedures are an essential part of the development, manufacture, and integration of GNSS receivers into mobile devices. The core instrument for testing mobile device GNSS functionality is a GNSS simulator, and this chapter discusses their use. Readers who may be interested in in-depth information about simulators and the principles of their operation and design, should consult [1], which is also accompanied by a bundled DIF signal simulator. This chapter describes GNSS testing equipment and procedures mostly following [2]–[5].

Multi-channel simulator

Almost everything in the business surrounding GNSS can be related to either signal generation or signal processing. Simulators are used in the GNSS field mostly for testing. The methodological and research values of GNSS signal simulators are often overlooked. In [1], Petrovski and Tsujii looked at a simulator as a model of a real GNSS in order to understand better how a GNSS signal is generated and affected by various factors. Here we look at the simulator from a different perspective, as testing equipment only. Quite often, live signals from satellites are used to test GNSS devices. However, with live satellites a user has no access to the true data and no control over the signal. This leaves the simulator as an essential piece of test equipment.

Merging mobile and geodetic technologies

Positioning with data link is not the same as referenced positioning (a method that allows measurements from more than one receiver to be combined and processed together in order to enhance accuracy), which we have discussed in Chapter 4. In this chapter, we consider all possible external information that can be used to enhance receiver specification. This external information includes measurements from other receivers, but it also includes other information which can be used to improve not only accuracy, but also other parameters in the specification, such as TTFF and sensitivity.

It is very important for many applications to be able to provide instant positioning, i.e. to avoid the necessity of tracking a satellite signal and reading a navigation message. It takes up to 36 s to read a complete navigation message for a GPS L1 signal to ensure the decoding necessary for positioning data. If navigation message data are available through some other data link, it is still necessary to decode a time mark from the navigation message, which may require up to 6 s. BGPS (and AGPS before that) are very important for many applications because they allow instant positioning using just a snapshot of data.

Global Navigation Satellite Systems (GNSS) at the time of writing comprise four systems, two of which are fully operational and two of which are on their way (see Table 1.1). A brief history of GNSS is given in Chapter 10 along with a timeline of application development and prospects for this development, especially concerning mobile applications.

Each GNSS comprises a constellation of satellites, called a space segment, and a ground segment (Figure 1.1). The main idea behind GNSS is to measure distances between a satellite and a user located on the surface of the Earth or in a lower atmosphere. Satellite coordinates can be calculated at any moment of time. The information that allows the calculation of satellite position is uploaded to and then broadcast from satellites to the user. The ground segment is responsible for determining satellite orbits, which it then uploads to the satellites, and also for defining the coordinate frame and time frame in which satellite and user positions are estimated.

Having received the information on satellite orbits and measured distances to the satellites, a user can calculate receiver position as an intersection of four spheres in a four-dimensional space-time continuum. If the receiver clock is perfectly synchronized with the satellite time frame, only three satellites would be required to determine receiver position in three-dimensional space (Figure 1.2).