Cell Life

The rise of the mobile device is upon us, even if it’s arriving a little late.

The reason why mobile devices will become so important - they’ll help us (and our friends, and maybe everyone) know where we are at all times, driving social, advertising and other applications. Being location aware is the single most important feature in a mobile device that otherwise suffers in comparison with desktop and laptop Internet devices - terrible screen size and resolution, poor battery life, slow connectivity and less than satisfying data input choices.

But being location aware more than makes up for those limitations. Social networking will fundamentally change as your device knows who’s around you, for example. Startups like Loopt, Brightekite and Limbo are all offering social networking products that leverage location awareness.

Other types of killer applications will also be built on the back of location aware technologies, and the advertising possibilities are substantial (care to stop by that Starbucks you are driving by for a free latte?).
The carriers generally know where we are, all the time (or can easily find out), but they guard this information jealously and rarely disclose it to third parties. But that isn’t stopping Google, Apple and a host of other startups from finding ways to get that information anyway.

Eric Carr, the VP of Location Technologies at Loopt, is the guy we go to when we have questions about new location technologies. Instead of just calling him every day, we asked him to write a primer on the core technologies being used to solve the location problem. Luckily for all of us, he agreed.

His post is below:

Mobile location-based services (LBS) are generating renewed interest as both the market and technology mature to support the growing set of innovative services being released and in development today. This is being driven by new Location APIs and new location technologies, which are making it easier for mobile location developers to gain access to location information and develop innovative new LBS apps.
This article is a brief primer on the key location technologies that are emerging.

Positioning Technologies
This section will review the primary positioning technologies in use today.

GPS (Global Positioning System)
GPS is a system of 32 satellites orbiting 12,600 miles above the earth. The system was designed such that 6 (and usually more) satellites should be visible from any vantage point on earth. GPS works by making extremely precise distance estimates from timing the delay time of signals (1 ns ~ 1ft) sent from the GPS satellites to earth. Trialateration (triangulation) is used to determine the device’s location given at least 3 or more satellites. When a GPS receiver is turned on, it looks for and locks on to visible GPS satellites to begin receiving and decoding the GPS broadcast signal. The GPS almanac data provides a list of visible satellites. The GPS ephemeris data provides precise timing and location information for each satellite. Latitude, longitude, time and altitude are the 4 dimensions that must be solved for (usually the first 3). Position acquisition time is driven by previously collected GPS information and GPS signal quality. GPS is accurate to 5-20m depending on environmental conditions.

Assisted GPS or A-GPS
Assisted GPS is an improvement on GPS, primarily for mobile devices with network connectivity. Since mobile devices are connected to a wireless network on a known wireless base station, the location of the base station can be used to aid the GPS calculation. Assisted GPS takes the known location of the base station and provides synthetic or seed data to the mobile device’s GPS chip to improve the speed of GPS signal acquisition. For instance, if the GPS chip knows that it should “see” a specific set of 4-5 GPS satellites at it’s coarse location, the GPS chip is able to narrow the search of signals to acquire. This reduces the amount of time required for the GPS chip in collecting ephemeris data and improves the sensitivity of the GPS chip in processing noisier GPS signals. This allows for a faster location fix time and makes possible GPS use in some indoor environments.

Assisted GPS does require device chipset support and network communication. The A-GPS server has traditionally resided in the operator’s network. However, there is trending towards device manufacturers offering similar services for their devices specifically. For A-GPS to work, an initial position estimate is required. This has traditionally been via the operator’s Cell-ID database. Additional initial position estimate options are being made available with 3rd party Cell-ID and WiFi databases.

Network Base Station Database
Since mobile devices (both cellular and WiFi) are associated with a wireless access point / base station, it is possible to use the position of the base station as a proxy for the location of the device. In a simple case, the base station, the device is associated with, is returned as the device’s location. Mobile operators view their base station databases as proprietary network information and have not generally shared it with 3rd parties. There are a number of 3rd parties who have begun to accumulate detailed Cell-ID and WiFi databases that can then be used to locate devices.

Network database collection approaches, fall into two camps: 3rd party driven or user-generated. SkyHook is the best example of a 3rd party driven approach where SkyHook employs a fleet of drivers that drive in cities collecting WiFi access point information. Google and Navizon are companies that employ user-generated approaches where data is fed back from mobile devices with the appropriate client software installed. To build a database, these user-generated approaches can either leverage the collection of both GPS and base station signals, or leverage the collection of base station signals with existing known reference base station locations. It is likely, a mix of fleet and user-generated approaches will be necessary to ensure high accuracy, rapid updating intervals, and low acquisition costs going forward for both Cell-ID and WiFi database collection.

3rd party Cell-ID databases have focused primarily on GSM/WCDMA networks since GSM/WCDMA base station identifiers have been relatively static over time. In contrast, CDMA networks change base station identifiers much more frequently, making it harder to build an accurate base station database without carrier involvement. The accuracy of a Cell-ID position will depend on the density of the wireless network. Urban areas will lead to more precise estimates, rural areas to much less precise estimates. Accuracy should be in the 100-5000m range depending on the network density of base stations. However, Cell-ID will at best provide a coarse level estimate of location. (WiMAX networks use a higher frequency than cellular network, requiring denser base stations, resulting in higher accuracy Cell-ID location.)

3rd party WiFi databases have propagated in the last few years. These take advantage of WiFi standard beacons that broadcast both the SSID and MAC address of the access point constantly. The SSID is used to create the list of available WiFi access points seen on many WiFi laptop connections. The MAC address is a unique identifier that can be collected and used to roughly identify where the receiving device is located. Since most WiFi signals propagate a maximum distance of 100-150m, if a WiFi access point is observed, a relatively precise position determination can be made.

Both Cell-ID and WiFi approaches can benefit from observing multiple base stations and incorporating radio strength information to calculate more accurate Cell-ID derived location.

Network Triangulation (signal strength-based)
The majority of positioning determination innovation is occurring in the area of leveraging the RF signal strength of signals received by the device and incorporating network information (known position of base stations). The algorithms range in complexity and depend on the availability of modeled or true RF signal propagation characteristics. RF fingerprinting takes the locations of known base stations, either empirically or analytically calculates the propagation of RF signals, and uses the resulting RF base map and signals received at the device to estimate the device’s position. There are also a number of client-side solutions that are leveraging the availability of multiple cell-ids on the device to more accurately determine device location. RF based approaches are also being incorporated as a fall-back techniques when A-GPS is not available.

Network Triangulation (time-based)
There are some position technologies that make use of signal timing information from the network to make a position determination (vs. GPS which uses timing information from satellites). Time Difference of Arrival (TDOA) and Advanced Forward Link Triangulation (AFLT) are two of the more well known methods. The primary difference in methods is where the timing difference is determined: in-network or on-device.

TDOA was the positioning technology selected by GSM/ WCDMA operators in the US for E911 purposes. Unfortunately, the amount of signaling traffic required to make a position estimate, is not trivial. This has made the TDOA technology not a viable option for commercial LBS services.

AFLT is a CDMA specific technology that uses the signaling characteristics of a CDMA to make a positioning determination. This acts as a fall-back method for CDMA devices if A-GPS is not successful and the network was requested to make a location determination.

Enhanced Cell-ID is an approach that uses signal strength and timing received from the GSM signal to make an in-network calculation of location which is more accurate than Cell-ID alone. Enhanced Cell-ID also can make use of sector information since many cellular base stations are directional in nature (IE 3 sectors with each sector covering 120 degrees from the base station).

Network triangulation is accurate to 40-400m depending on environmental conditions.

Hybrid approaches
From the descriptions above, it should be clear no one position technology is best for all use cases. The positioning technology market is pursuing hybrid approaches where the strengths of each technique are leveraged where appropriate. Since A-GPS requires a location estimate to start with, other less accurate location technologies can be leveraged to seed the A-GPS algorithm. Each location technique also is more successful in different environments (namely urban vs. rural and in-doors vs. out-doors). Luckily, the strengths and weaknesses of many of these position technologies are complementary, motivating further exploration of hybrid location approaches.

Evaluating Positioning Technologies
After a brief understanding of position determination methodologies, it is valuable to define how each positioning method should be evaluated. A few key categories are listed below and described. There are of course others that could be relevant depending on the location use case. Each position determination technique has strengths and weaknesses across each dimension that will be discussed in the following section.

Positioning Accuracy and Uncertainty
Accuracy will vary depending on environmental conditions (indoors, urban environment, signal quality, etc.). Accuracy can vary from 5-20m (GPS) to 50-5000m+ (Cell-ID). Each positioning technology will also have varying error ranges depending, again, on a broad set of environmental conditions.

Generally, the positioning industry has been driven by GPS chipset manufacturers who are motivated to provide the highest accuracy, lowest uncertainty possible location solution. GPS-level accuracy is necessary for navigation and other turn-by-turn level accuracy user cases. However, there is a wide range of LBS apps that can make do with “lower” levels of accuracy which can be provided by WiFi, Cell-ID or other hybrid approaches. Accuracy is important, but it is only one of the factors that should be considered in weighing available positioning options.

Positioning Latency or Time To First Fix (TTFF)
Positioning latency is a critical factor in driving the usability and responsiveness of a location-aware app. In the mobile app space, acquiring a location quickly is paramount to offering a compelling user experience.

Positioning latency or TTFF is most commonly associated with portable GPS receivers or Personal Navigation Devices (PNDs). Without network assistance, GPS receivers can take on the order of 5-10 minutes before a first fix. This is a function of the time required to lock on to and receive enough information from the GPS satellites in space. Assisted GPS is able to improve this fix time to 10-30 seconds by synthetically seeding the GPS receiver with network data and accelerating the GPS acquisition and position determination process. Assisted GPS still requires data exchange and computation on both a network server and on the device, making it challenging to reduce TTFF further. Other non-GPS based technologies are able to determine a location estimate in below 10 seconds (IE WiFi or Cell-ID looking up a location in a database is all that is required).

Positioning Determination Ubiquity
A positioning technology is not useful if it is not available in the area where your mobile device is. GPS-based methods have a strong advantage here since it is a globally accessible method and will work even if the mobile device is not associated with a mobile network (there are clear disadvantages in that situation, but it could work). WiFi methods are constrained to areas where there are wireless WiFi Access Points. This is generally not a problem in urban or suburban areas (where most interesting LBS Apps are targeting).
Indoors vs. outdoors is another factor to consider. GPS, generally, does not work well inside buildings. Whereas, WiFi is most likely best suited for indoor positioning determinations.

Positioning Fallback Options
Related to positioning ubiquity and hybrid approaches, devices are expected to make the best location calculation given available information. IE if GPS fails, a network-based method or Cell-ID location is desired to be returned vs. a message of “failed, try again later.” The challenge is, since many methods rely on base station databases, the device must optimize how then a LBS App location request is handled. If the network is asked to make the location calculation (IE A-GPS MS-Assist), then all available network information can be used. Hybrid approaches on-device and off-device must take into consideration returning the best known position estimate in a timely manner, even if the preferred method fails.

There are also a number of device-specific dimensions that are critical to keep in mind which drive position determination implementation on device.

Device Impact (battery, CPU drain)
Assisted GPS chip performance has improved considerably over the last few years. However, receiving and processing signals from space still takes a large amount of energy. GPS antenna placement is also critical as more devices add additional RF technologies (WiFi, Bluetooth, additional cellular bands). GPS integration continues to be an important art for device manufacturers.

Most of these positioning technologies involve some level of network data connectivity that can also impact device battery life. There are a number of on-device caching solutions that are evolving for both GPS and WiFi/ Cell-ID approaches that minimize the amount of network traffic required to make a position fix. However, given most LBS Apps are making use of network connections, location becomes a limited portion of App traffic.

Device Prevalence / Support
Assisted GPS was adopted by CDMA carriers to support the FCC’s E911 mandate. Qualcomm has added GPS chips to it’s line of core chips. This has enabled CDMA operators worldwide to deploy innovative mobile LBS Apps leveraging A-GPS. The GSM/ WCDMA world has been slow in adopting A-GPS, with forecasts of 2009-2010 being the year of broader device support.

WiFi has seen relatively limited support in mobile devices to date. There are a growing set of high-end smart phones that are integrating WiFi and driving the market (namely the Apple iPhone, HTC, Nokia, RIM and others). WiFi could be viewed as a competitor to 3G data services, operators are interested in up-selling users with. WiFi also creates additional technical challenges (antenna placement, additional battery draw, etc.) which continue to improve, but remains important consideration for device manufacturers.

Device Location APIs
The availability and accessibility of Location APIs on mobile devices has been spotty at best to date. RIM, Nokia and Motorola iDEN have provided device specific Location APIs that have motivated strong initial interest from developers. The Apple iPhone and Google Android Location APIs will spur additional interest ongoing. Existing J2ME (JSR-179) and BREW (IPOSDET) API exist today for a wide set of feature phones.
However, operator involvement has made it challenging for some developers to gain easy access to location. It is clear, device vendors are using location as a competitive differentiator to better position their device platforms relative to others. However, universal Location APIs still remain a challenge with the fragmentation of different APIs, devices and operator policies.

VS

Apple Inc.’s new iPhone went on sale on Friday 8 a.m. and that’s when nightmare for many new iPhone enthusiasts started. Since the new iPhone has been subsidized by the exclusive carrier if bought with their contracts, Apple and the carrier had planned to activate the phone right at the point of purchase i.e. the stores.

Result…huge queues of people were seen outside the store ahead of 8 a.m., many even camped out overnight to be first. What happened then???

First…It took the store half an hour to get the phone activated. This itself caused, the enthusiasm of many in queue behind the first person, die down at that very instant.

Second…there was a global problem with Apple Inc.’s iTunes servers that prevented the phones from being fully activated in-store.

Third…The problem extended to owners of the previous iPhone model. A software update released for that phone on Friday morning required the phone to be reactivated through iTunes.

Fourth…”It’s a mess,” said some, who updated their first-generation iPhone only to find it unusable.

Yesterday, I read an article in International Herald Tribune about Reuter’s trial in India for generating business out of rural areas…quiet an impressive one indeed. I thought, I will share the same with all the readers here as well…

Whether it is for a Wall Street trader or a farmer in India, the right information at the right time is a necessity for success.

For 157 years, since signing a contract in 1851 to supply stock prices from exchanges in Continental Europe to the London Stock Exchange, Reuters has served up numbers to the finance set. The International Herald Tribune has a partnership with Reuters, under which the two companies jointly publish the Business with Reuters section of the paper.

Now, Reuters, part of Thomson Reuters, is trying to provide analogous services to farmers in India, where price information is stubbornly hard to compare. If successful, the program could become a model for economists and international agencies for the use of technology - in particular, the mobile phone - to burnish economic growth in places like India and sub-Saharan Africa.

To that end, the company has been testing a program called Reuters Market Light for several months in Maharashtra, an Indian state about the size of Italy. The state is one of India’s prominent agricultural centers, with farmers growing onions, oranges, corn, soybeans, wheat and bananas. But the farmers’ business suffers from the difficulty of comparing prices from one market to another.

“We kind of saw that there was a clear market inefficiency,” said Mans Olof-Ors, a Reuters employee who had the idea for Market Light three years ago. “The farmer would decide which market to travel to, then would just sell to that market. So there was no competition between markets.”

Reuters has dispatched about 60 market reporters to the region to report on the going price for, say, oranges or onions, and to package the data into a text message that is sent to subscribers.

The service is signing up about 220 subscribers a day at a price of 175 rupees, or about $4.10, for three months at post offices throughout Maharashtra. The average monthly income of a farm household is about $50, according to the Indian government. The service has about 40,000 customers so far - a tiny portion of India’s farm population, which is in the hundreds of millions, but it proves that many farmers are hungry for more information.

Reuters has collected anecdotal evidence from farmers about how the service has influenced their decisions about crop sales. One farmer, according to Reuters, held back the sale of 30 quintals of soybeans - one quintal equals 100 kilograms, or 220 pounds - for 15 days after noticing that prices had been rising for several days. He was able to get 400 extra rupees a quintal.

Amit Mehra, managing director of Market Light, said early data showed that most subscribers were making more money from their crops.

“We’ve seen that about 70 percent have benefited and changed their behavior about when to sell and when to harvest and where to sell,” he said.

Some academic research has shown that mobile phones can have a stark effect on economic growth in rural areas. Robert Jensen, an economist at the Watson Institute for International Studies at Brown University, has studied the impact of putting mobile phones in the hands of fishermen in Kerala, a southern region of India. His study found that both fishermen and consumers benefited: profits rose 8 percent while prices of fish fell 4 percent.

VS

I have been pondering over ever increasing hue and cry about mobile marketing, for a while.

I have been going around to people from US to Europe to India to Australia etc. etc. etc. asking about their views on top 10 points to consider in Mobile Marketing area. Received numerous responses.

To give out the results of this survey, I am putting my views and the results together in a white paper on the same…which will be out soon…

If any of you would like to get the same…please get in touch with us and we will send it promptly. Till then…keep reading and sharing your views/ comments…

VS

The new 3G iPhone is creating substantial buzz. Check out Om Malik’s interview with Ralph de la Vega, CEO of AT&T Mobility.

http://gigaom.com/2008/06/09/att-mobility-ceo-new-3g-iphone-game-changer/

One of the concerns is that increase in mobile data usage for social networking, LBS or streaming video apps is going to cause a lot of network congestion on AT&T wireless network. Mr de la Vega is confident that AT&T network can handle it. For more power users of mobile data, the hope is that they are smart enough to use WiFi connection on the iPhone to download large data files instead of congesting the wireless network. A future filled with FMC devices like Femtocells only makes sense here. Femtocells will allow bandwidth hogging home wireless to pass on broadband wireline connections allowing the wireless network to remain somewhat available.

AT&T is taking some hits on its earnings estimates due to the lowered iPhone price and Apple is taking a hit as well as AT&T will no longer share the subscription revenue. But overall sales volume is expected to increase dramatically with the lower price and that is expected to more than compensate for the lower price on the device.

The new iPhone is loaded with good stuff. The App Store will allow for a slew of cool consumer and enterprise apps. The enterprise support is great with MS-Exchange integration. GPS is cool for LBS accuracy.

Nokia and RIM need not fear however. iPhone will only increase smartphone penetration increasing the size of the market for everybody. We feel the smartphones will cannibalize the non-smartphone cellphone market to some extent.

MT

Steve Jobs of Apple announced today the availability and details of 3G iPhone. It is available in the US from July 11 for $199. Not sure if if AT&T is discounting it or Apple is willing to sell it at a lower price (compared to $399 for the previous iPhone version). The new iPhone is a 3G device with GPS Mapping, a loaded App Store which houses some cool iPhone apps, MS-Exchange integration (attack on RIM turf - the enterprise?), and GPS. Apparently it is a 8GB device at $199. There is a $16 GB version at $299.

GPS is cool - it will drive Location Based Services and related applications like advertising, finder services and social networking.

MS-Exchange support is cool too as it will help in more enterprise adoption and cause some headaches to the folks at Research in Motion (Blackberry manufacturer).

The price is great too for cost-conscious customers.

Apparently 3G iPhone will be released in 20 countries.

We are excited and cannot wait to lay our hands on the new 3G iPhone.

MT

Our article “Mobile Strategy - The New Corporate Imperative” has been published in the most recent editions of The Smart Techie magazine and Silicon India magazines. The links are below :

http://www.thesmarttechie.com/magazine/fullstory.php/NOBH841638463

http://www.siliconindia.com/magazine/pay.php/DMKZ462464795

Happy Reading. The Smart Techie magazine (www.thesmarttechie.com) is the leading Indian magazine targeting technology professionals and executives. Silicon India (www.siliconindia.com) is the corresponding US magazine.

MT

ComScore, the online usage measurement firm, acquired M:Metrics, the mobile specialist in usage measurements for $44.3 million. This amounts to a market consolidation in mobile measurement industry and also a convergence of mobile usage reporting with general online measurements.

This makes ComScore a top firm in mobile measurement space - an area which has seen huge growth rates in recent times with advertisers desire to track and report on user adoption of mobile data services and advertising.

The Nielsen Co. is the other top player in this space.

MT

We just published a rich white paper on Mobile Advertising and Marketing on CellStrat website. Check it out under the link :

http://www.cellstrat.com/whitepapers.html

Let us know any comments.

Thanks

MT

I came across this term used to measure the revenue from ad spending. eCPM is “Effective Cost Per thousand impressions”. In layman terms, it means revenue per thousand impressions. Even more simply, it measures which ad formats or ad models lead to higher ad revenue. This metric creates a unit to measure ad revenue impact.

eCPM = (total earnings / impressions) x 1000

This metric assumes that 1000 impressions is a large enough sample to measure the impact accurately. 

Higher the eCPM, better the ad revenue from an ad format or ad model. This metric is relevant to firms developing ad platforms in print, online or mobile world. It helps them figure out what kind of ad platform, format and technology will lead to higher ad spending by advertisers for minimum no of impressions.

MT