ICTworks by Inveneo

I am Michael Benedict and while working with SMS-based applications I’ve noticed an air of mystery around the issue of reliability. I hear colleagues say ambiguous things like “carriers consider SMS to be lower priority than voice”, or “SMS delivery isn’t guaranteed”.

My personal experience has been that messages are almost always delivered quickly and correctly, but I’ve heard stories of hours-long delays, corrupt data, and occasionally messages that never arrive. Since I am working on two projects that depend on reliable SMS service — one involves field-based data collection and another employs SMS as a transport layer between computers — I’m curious about how factors such as network, location, and time of day impact message transmission.

I found myself in Mwanza, Tanzania last week with two GPRS modems and a local partner who was unenthusiastic about the work I was actually there to do, so I tried a little experiment. I bought SIM cards and airtime for three of the major TZ networks, put two at a time in the modems, and wrote a simple python script using Adam Mckaig’s excellent pygsm library.

The script sent messages in all permutations at an arbitrarily chosen interval of about 20 minutes. This means that four messages were sent at a time: each SIM to itself and to the other SIM, repeated about every 20 minutes for periods of up to 27 hours. Outgoing messages noted the origin and destination networks, send time, and a sequential message number to make it easier to spot lost messages. Incoming SMS were stamped with the time received, and the delay in seconds between sending and delivery. Messages were sent two seconds apart to reduce errors from the modems trying to send and receive at the same time, and results were recorded in a CSV file.

a) Airtel and Zantel

Zantel is one of the smaller networks in TZ, but I was having technical problems with my Tigo SIM so started with Zantel and Airtel. Airtel was known as Zain until recently when Zain was acquired by Baharti Airtel, based in India. The script ran from about 1AM on January 14 to just before 5AM on January 15. Here are the results:

Delivery times and dropped messages between Airtel and Zantel Tanzania. Click to see scale. Note that the Airtel modem stopped sending and receiving at about 7AM on Jan 15, and the script hung until I woke up shortly after 8 and reset the modem. I do not know what caused the problem.

331 messages were sent and 10 (3%) were dropped. Delivery times fell into three bands. Airtel-Airtel (i.e. the Airtel SIM sending to itself) was easily the fastest, with messages typically delivered in 6 or 7 seconds. Messages in either direction between Airtel and Zantel took roughly twice as long, typically between 13 and 16 seconds. One outlier, an Airtel to Zantel message sent between 2 and 3 AM on Jan 15, took 24 seconds to arrive, and the only dropped Airtel-Airtel message occurred around the same time. Delivery times for Zantel-Zantel messages were split unevenly: one cluster near 6 seconds, another around 15 seconds, and a third around 32 seconds. 9 messages sent from Zantel-Airtel were not received.

Some aspects of this plot seem reasonable. Airtel is a larger company with wider network coverage, so it’s not too far fetched to imagine that messages sent from an Airtel SIM to itself would be the fastest to arrive. By that reasoning I’d expect messages sent between the two networks to take somewhat longer, and they do. The Zantel-Zantel delivery times are less intuitive. I don’t know what would account for the three distinct time ranges for delivery. It is also unexpected to see that 9 Zantel-Airtel messages were never delivered but no Airtel-Zantel messages experienced this problem.

b) Airtel and Tigo

SMS delivery times between Airtel and Tigo SIMs on January 14, 2011. Click image to see scale. *Messages marked unreadable appear to have reached the modems but could not be read, and resulted in an (unfortunately unlogged) exception in pygsm.

192 messages were sent and 3 (1.6%) were not readable by the modem. Four distinct delivery time ranges emerged. I eliminated one outlier from the plot to make it easier to view: on January 15 at 11:47, a Tigo-Airtel message took 129 seconds to arrive. I believe that the 3 unreadable Tigo-Tigo messages reached the modem, but the script raised an exception and the messages were not read. Unfortunately I was not logging pygsm’s debugging output so I am marking the missing messages as “unreadable” instead of dropped. I will have to log output in future runs and see if I can reproduce the problem. I didn’t expect Tigo-Airtel messages to arrive slower than Airtel-Tigo. The difference is small but it would be interesting to know why it’s there.

c) Two Tigo SIMs.

The purpose of having the modems send messages to themselves in the previous two runs was to test delivery times within a network. It occurred to me that sending from a SIM to itself may not be equivalent to sending between two different SIMs on the same network, so I bought a second Tigo SIM:

SMS delivery times between two Tigo SIMs. Click to scale.

164 messages were sent and 1 (0.6%) was not received. The data shown excludes one outlier: the first Tigo A-Tigo B message took 457 seconds to arrive. So: is SIM to SIM within a network equivalent to sending from a SIM to itself? Sometimes. I don’t know why it varies, but note that the number for Tigo A is +255716379091 while Tigo B is +255717435798.

In particular, after the 255 country code the numbers have different prefixes, 716 for A and 717 for B. Typically Tanzanian SIMs on the same network have the same first 3 digits, and the difference here may indicate that Tigo’s servers treat the two numbers differently. The Airtel/Tigo data above used the SIM that is labeled as “Tigo B” here.

Thoughts and next steps

Unexpected patterns emerged, including asymmetric delivery times between networks, that I would like to understand better. The few dropped messages do not seem to follow a clear pattern, which is a concern for both human and computer-focused SMS applications where missing messages can cause confusion and frustration.

It seems possible that having the modems send all 4 messages with only a 2 second delay between sending could be impacting the results. It would be interesting to revise the script to randomly offset the message send times by a few minutes and see if that increases reliability.

This is not a scientific experiment since there are variables such as time of day, day of the week, network traffic, modem location, weather, and likely many others that are not being controlled for. Still I think the data gives a representative snapshot of network performance. Overall SMS delivery was quite good. With the exception of a few of outliers most messages were delivered in under a minute, and Airtel consistently delivered on-network SMS in less than 10 seconds.

Occasional dropped messages show that safeguards need to be in place if SMS is used for critical applications. Some preliminary timing data I took in Uganda suggests that the networks there are not nearly as reliable. I’m looking forward to looking more carefully at Ugandan networks when I’m back.

If you like this sort of thing and also have too many modems and not enough social life, feel free to try out the script yourself. I’d be interested to see what the results look like in other countries.

A special thanks to Henry Corrigan-Gibbs and Matt Berg for interesting discussions about the timing script and SMS reliability.

This was posted first as SMSpecially Reliable? Part 1 and is repulblished here with permission

The first SMS, or “text” message, was sent over Vodafone’s network in December 1992. By most accounts, it read simply “Merry Christmas.” Using just fifteen of the permitted 160 characters, it was an appropriately succinct beginning to what is today by far the most widely used data service worldwide. By one estimate as many as 7 trillion SMS messages will be sent globally in 2011.

Smart Connect sensor

SMS’s reach and reliability, combined with its popularity among users, make it particularly attractive to those working on applications for the developing world, where Internet and smart phones are not yet widely available or affordable.

Open source SMS messaging platforms – RapidSMS, FrontlineSMS and others – and rapidly falling prices are fueling an SMS innovation explosion and fostering new thinking about how SMS can be leveraged for non-mobile applications.

Enter Smart Connect, a “communication appliance” developed by PATH and Inveneo which uses SMS to improve the reliability and performance of one of the most important systems in all of global health: the medical “cold chain.” First, though, a little bit on why the cold chain matters.

The Cold Chain

Few if any interventions compare with vaccination as a way to improve human health.

Famously, in the decade from 1967 to 1977, the World Health Organization (WHO) lead a global vaccination campaign against smallpox and, in 1979, officially declared that the disease had been eradicated. The Polio vaccine has come close to this mark, reducing the global incidence of this scourge by over 99% worldwide, and new vaccines for Malaria, HIV and more obscure diseases are in the works.

All told, the WHO estimates that, in 2003 alone, global immunization initiatives saved 2 million lives and UNICEF concludes that by 2015, immunization could be preventing 4 to 5 million child deaths each year.

Yet none of this would have been possible without the cold chain: the logistical processes and refrigeration systems that keep vaccines within strict temperature parameters from manufacture through administration. But with over 200,000 vaccine refrigerators in use in the developing world alone (according to PATH), most of them in harsh and remote environments, keeping the cold chain up and running is a major challenge.

Smart Connect

We developed the Smart Connect device to confront communication barriers and address the cold chain challenge by bringing a “digital dial tone” to remote health posts in the developing world.

Smart Connect device at Pacayita Health Post

We started in Nicaragua. During our research we visited rural health clinics in places like Yolaina, El Serrano, and La Fonseca bordering the remote Caribbean lowlands and Pueblo Nuevo, Las Cruces, and Zompopera in the Central Highlands. Each clinic serves a population of about 1,000 people with a staff of one to three.

During visits to these remote posts, we learned that it often takes many hours of travel by foot, horse or bus to reach the nearest town. As a result, health workers face long delays in picking up diagnostic test results and submitting reports. Also, problems such as running out of supplies or trouble with equipment go unreported.

It turns out that much of the necessary communication requires just a small amount of data. For example, a nurse in Nicaragua said she would like to know when the clinic lost power, so that she could check the vaccines, and she would like to know when pay was available so she could plan her visit to town. Based on these and other examples, we determined that SMS messages could be used by Smart Connect to send information.

In Nicaragua, we also discovered that – even though cell phones were widespread – health care workers would not consistently have cell phones. Cell phone coverage is often marginal – so some phones would work and others wouldn’t, or one had to find a very special place to stand to make a call. With most cell phone users relying on prepaid usage, it is common to have phones run out of credit.

We decided to make Smart Connect a facility based device. Even though it has many parts in common with a cell phone, it is constructed to be secured in place. We did this to improve security of the device, to ensure that the device was associated with the health facility, to allow it to connect with external sensors and to make it possible to connect to an external antenna for improved reception.

One of the first applications for Smart Connect is temperature monitoring of vaccine refrigerators. Refrigerators which regularly drop below freezing are quite common – so it is important to bring these to the attention of cold chain managers. Temporary power disruptions and breakdowns are also a problem since they lead to vaccines getting too hot.

Previously, refrigerator temperatures were tracked and recorded by hand with long delays in collecting the records. Now Smart Connect records the refrigerator temperature and sends out alert messages when there is a problem. Messages are sent to a web site and then automatically relayed to service technicians. A daily summary of refrigerator temperatures is also sent to the web site so that the manager can understand how well the equipment is functioning.

Beyond temperature monitoring, Smart Connect has the capacity to run a range of additional applications. For example, the Smart Connect deployment in Vietnam includes an application to track the use of vaccines so that that “stock outs” can be avoided. In the future, we plan for Smart Connect to be used with a bar code scanner to be able to read tags on vaccines when they arrive, and a printer to be able to provide receipts of test results to patients.

With Smart Connect we have seen that a small amount of communication delivered by SMS can have a big impact. By “thinking outside of the phone” we have created a custom communication device that meets the specific needs of rural health facilities and improves healthcare services in communities in Nicaragua, Vietnam and beyond.

This was first published as Thinking outside the phone by National Geographic

In April I am travelling to Cameroon to visit the African Center for Technology, Innovations and Ventures (ActivSpaces), otherwise the leading technology incubator in the country. ActivSpaces is actually a project that has been in development since January 2009. The founders Valery Colong, Bill Zimmerman and Fua Tse unknowingly built what is likely the first technology incubator in Sub-Sahara Africa. Appfrica Labs based in Kampala Uganda, founded by Jon Gosier in March of the same year, being the other.

Like Appfrica Labs, ActivSpaces is building a model organization that serves to facilitate socially-responsible investment and the incubation of African small and medium sized enterprises (SMEs). This is a response to the generally accepted idea that SMEs in African countries have greater difficulty in obtaining the necessary financial support and mentorship services they need to effectively scale up and grow their businesses. Access to traditional growth capital, including debt and equity, is not only limited in developing countries but often prohibitively costly due to various factors such as insufficient legal and regulatory policies, and inadequate financial markets. Labs like this serve as a platform for the local tech community, helping to pool resources and generally serve as a launchpad.

Recently business incubation in Africa has emerged as a means of accelerating the growth of technologies, industries and business skills in otherwise challenging conditions. Combined with an incubator, ActivSpaces is also a coworking space with professional office services and income generating opportunities. The running costs are covered with a 10% on the invoiced commercial projects, giving members a chance to upgrade their skills and generate an income they can use to further develop their own concepts. All activities serve to foster collaboration between members. By using a flexible model, a qualitative approach and on-the- ground experience, ActivSpaces seeks to fulfill a broad spectrum of financing, business development and socio-economic needs.

The ActivSpaces vision builds upon the following core objectives:

- Support for SMEs with high growth potential
- Assessing a company’s risk profile
- Promoting innovation
- Developing products & services for the local market
- Employment & wealth creation
- Technology & skills transfer
- Enhancing links between universities, research institutions & the business community
- Adherence to and advocacy of the highest standards of business ethics
- Reputation as a socially-responsible and innovative firm

I asked Bill (my business partner in Sanaga Ventures – a combined effort that supports initiatives such as ActivSpaces, Afrilabs and VC4Africa) why he believes so strongly in ActivSpaces and he responded, ‘Based on my current analysis, observations and personal relationships, I increasingly believe the preconditions exist for rapid growth in the Sub-Saharan African technology sector. ICTs have many of the same features (i.e., they may be produced, bought and exchanged) as traditional commodity-based industries, but have a far lower barrier to entry and carry lesser capital requirements for young companies.’

Asked how some of the challenges in Cameroon could be translated into strengths he says, ‘Africa is a continent renowned for technical innovations conceived and built from limited resources. Countless examples exist of indigenous technologies borne from constraints (lack of infrastructure, poor governance, poverty, etc.) that have created sustainable SMEs. Among them, mobile money (M-PESA, Safaricom) is a profitable service created in Kenya whose model has only recently been prototyped in the West. Likewise, research and development in fields such as physics, chemistry, GIS & mapping, electronics, security, embedded systems, alternative energy and so on have good potential to be productized and spawn sustainable SMEs.’

He goes on to say, ‘for these reasons, ActivSpaces seeks to foster the growth of physical spaces for innovation, creation, research, development and collaboration. Popularly known as ‘Hacker Spaces’ there are 96 known active hacker spaces worldwide, with 29 in the United States, according to Hackerspaces.org, with another 27 U.S. spaces in the planning or building stage. Local demand exists for a Cameroon- based hacker space. This is integral with our vision and will be financed with a combination of member dues and profits spun off from ActivSpaces.’

As in many African countries, the population in Cameroon is young: an estimated 40.9% are under 15, and 96.7% are under 65. Outlets such as ActivSpaces are critical in leveraging the emergence of technology and the opportunities it presents local entrepreneurs. I am excited to see the space, to take part in celebrating their 2 year milestone, host two VC4Africa meetups, and to spend time interviewing and getting to know the team. It’s exciting times when you see projects emerging from ActivSpaces that carry the mission to ‘disrupt the SMS industry’ as Fritz plans to do with iYam.mobi. I look forward to working with ActivSpaces and the entrepreneurs part of its network.

Recently at an mHealth meeting, I overheard an interesting comment. One of the speakers mentioned that Interactive Voice Response (IVR) is popular in South Asia but has little uptake in Africa, yet Short Message Service (SMS) is used widely in Africa but not much in Asia.

I tweeted this observation and got a few interesting replies:

Ajay Kumar beleives its from a lack of literacy, but I don't agree. Literacy rates are very low in both areas. In fact, I'd argue that literacy rates are higher in South Asia, which should mean that text would be more acceptable than voice for conversations.

Emer Beamer says the lack of SMS in South Asia is due to the many different alphabets. This could be a real issue. At the mHealth meeting we laughed at the folly of new Android phones for sale in India that did not support Hindi script.

I think Prabhas Pokharel said it the best. The IVR/SMS difference is not from any one facotr, but many, like more languages, more illiteracy (or concern for illiteracy), more oral culture, and cheaper voice or SMS costs.

Now, what do you think? Why is IVR popular in South Asia yet SMS is popular in Africa? Is it language, culture, or just mobile phone services pricing?

According to the Daily Independent, the Nigerian Federal Government has introduced RapidSMS for data collection in malaria control and immunization.

Minister of Health, Oyebuchi Chukwu says the goal of RapidSMS deployment is to use SMS in the National Malaria Control Programme (NMCP) at the facility level in 774 LGAs for:

1. routine data management
2. track changes over time in service delivery
3. inform service and resource planning

So far, RapidSMS has been deployed in stand-alone campaigns in Kano, Anambra, Rivers and Akwa Ibom states. It has also been used to track and coordinate delivery of 70 million ‘long lasting insecticide-treated nets’ distribution campaign.

As a pilot activity, RapidSMS was used in Kano state to capture data of commodities from state stores to the Local Government Area (LGA) and distribution points using logisticians in 21 selected LGAs and 4 pilot LGAs for mobilization and distribution of nets. Distributors who were not reporting were able to be identified and contacted immediately to follow up on distribution problems.