Biosensing in the Kampung

Marc Böhlen, Ziyan Yin Department of Media Study Department of Biostatistics

University at Buffalo New York, USA

Ilya Maharika, Luqman Hakim Department of Architecture

Department of Environmental Engineering Universitas Islam Indonesia

Yogyakarta, Indonesia

Abstract — this paper describes the technical and organizational infrastructure designed to integrate a biosensing based water quality evaluation system into a nascent water well monitoring and response program in the Terban district of Yogyakarta, Indonesia. The system AirKami (Bahasa Indonesia for 'our water'), combines a desktop bioincubator, flexible and scalable IT data management, local ecological knowledge and community support to create a new community-technology effort that includes a novel water filtration and distribution system. The project materially improves the drinking water supply for Terban residents where government efforts have been insufficient.

Keywords - biosensing, water, urban computing, smart kampung, architecture and planning, technology in emerging economies, postcolonial computing.

I. INTRODUCTION This paper describes an attempt to create an IT enabled

water monitoring system commensurate with the needs and constraints of the Indonesian kampung. We describe in detail a bioincubator enabled water quality evaluation system that can be combined with other cheap sensor modalities to evaluate fluctuations in the quality of drinking water wells in the Terban district of Yogyakarta. Our approach combines state of the art technology together with local ecological knowledge and community engagement. We consider in particular drinking water in the (smart) kampung in the Indonesian context where villages and towns are being urbanized in the wake of rapid and often inadequately planned economic expansion.

Networked environmental monitoring technologies can play a role in countering some of the negative side effects of the dynamics of rapid urbanization. Previous research has emphasized the potential for the combined role of targeted sensing technologies and tailored control measures for risk mitigation of potable water from microbial environmental contamination [1], for example. This is especially important in data-poor areas with short histories of urban planning.

We begin with an overview of the kampung and the smart kampung concept, and discuss the water quality monitoring system as well as the data it has produced. We also discuss the limits of the existing local ecological knowledge and how our approach to biosensing has added to this knowledge base. We then describe an urban intervention as an informal health care service that materially improves the quality of drinking water for Terban residents.

II. THE KAMPUNG AND THE SMART KAMPUNG

The kampung has become part of a critical discourse on informal urbanism with global relevance; it is no longer considered a third world phenomenon. Roy [20], McFarlane [14], Porter et al [17], and Dovey & Raharjo [8], for instance, have developed an extensive critical discourse on the informal urbanism of the kampung with its intricate relations of spaces (physical, social, power). While most of the current debate focuses on the importance of the potential dynamics of this informal urbanism, less attention has been paid to the actually existing dynamics of the kampung, and these dynamics include uncontrolled consequences from rapid economic development. From the Indonesian perspective, it is now desirable to intervene into the informal urban environment, to record its dynamics, and to create a basis for informed consensus amongst the kampung actors. Deeper knowledge of environmental conditions can act against the uncertainty that has hampered urban planning efforts on multiple levels. This problem is not only one of procedural methodology, but of imagination. It addresses a potential for the development of generative processes in general, including new trajectories for the future [2].

The kampung offers some benefits large cities lack, namely a robust level of social cohesion as well as community-level forces that can perform tasks administrative actors fail to address (more on this below). However, the kampung lacks the planning and maintenance resources that large cities often have access to.

In this context, the efficiencies of intelligent monitoring technologies have a particular potential to meaningfully alter living conditions in the kampung. Real time data collection and processing as well as open access to data on issues of shared concern make explicitly public what is public. Indonesia hopes for a better future, and such a sensor-enabled ‘smart kampung’ is perceived to be one viable path to a better future with increased economic competitiveness and improved quality of life [5]. However, precisely because of the confluence of urgency and hope, one must carefully consider the promise of technological intervention. One of the goals of this project is to more clearly understand the limitations and side effects created through automated sensing technologies in the kampung context.

2014 International Conference on Intelligent Environments

978-1-4799-2947-4/14 $31.00 © 2014 IEEE

DOI 10.1109/IE.2014.11

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A. Variations on smartness While there is a certain enthusiasm for the potential

benefits from the kind of smartness sensing and computing technologies are capable of, there is no consensus on how, in practice, to integrate abstracting intelligence into kampung life. For example, which degree of complexity should smart solutions entail, and how can they operate under quotidian institutional dynamics? What flavor of smartness would allow one to accommodate the complex social organization forms active in the kampung? What kind of smartness might emerge from the combination of synthetic sensing and existing ecological knowledge? Or less optimistically: What kind of power structures and new economic dependencies will a state-of-our-art environmental monitoring system impose?

The last question in particular has been discussed within the HCI community where the idea of postcolonial computing [12] has been suggested as a context-sensitive approach to various aspects of deploying computing technologies into emerging economies. While the critique of corporate interests underlying ‘development efforts’ must be acknowledged, this line of inquiry has been rather weak at providing actionable insights beyond position statements.

Specifically in the area of water infrastructure, the idea of a portfolio of context-aware approaches has been suggested [13]. Recent research in holistic systems design suggests that modelling techniques might be able to address technical needs, infrastructure limitations and institutional constraints. This approach has been formalized in a capacity factor analysis and applied in a study in Cimahi, Indonesia [11]. However, the model serves more as an analytical tool and does not, in our view, provide affordances for existing environmental practices, nor does it suggest practical counter-measures to recognized deficiencies.

Multiple forces oppose high complexity technology-driven smartness in the kampung context. One such force is the multifaceted Indonesian concept of public and communal space that can be impractical to ’monitor’ with sensing systems. There are, for example well-defined procedures for using waters from public water wells that are informally enforced with trust mechanisms. Terban residents know which kinds of activities are acceptable for each well in the area; which wells to use for bathing and which to use for drinking. Other opposing forces include compromised infrastructure such as electric power and network connectivity.

The AirKami Project (www.AirKami.org) seeks to address these complex conditions while considering the potential of networked environmental sensing on the kampung level in Yogyakarta. AirKami attempts to bring the benefits of precise and rapid detection of bacterial contamination of drinking water to Terban residents, and to integrate the new knowledge into existing, though imperfect, environmental monitoring and response routines.

III. WATER IN THE TERBAN DISTRICT OF YOGYAKARTA

We began our inquiry with a survey of water wells in the Terban district. Over the course of 7 months we monitored 14 water sites: 13 water wells and the Code River that runs from north to south through Yogyakarta. Of the 14 test sites, 8 are private and 6 are public. The oldest wells are more than 15 years old and over 5m deep. Each well has its own history and history of adaptation to environmental challenges. Efforts to ensure access to good water sources include increasing the distance between wells and seepage tanks, digging deeper into the ground (based only on guess work, assuming that deeper is better, which is not necessarily the case) and capping a well with a cement cover.

Environmental monitoring has a patchy track record in Indonesia. Government efforts in collecting and processing environmental data in Indonesia are notoriously inadequate. Some of the public water wells in the Terban district are tested periodically, but so infrequently (once annually) as to be of no analytic use. Furthermore, the Terban residents are not informed of the results, nor are actions taken to ameliorate the dire situation the measurements more often than not describe.

IV. LOCAL ECOLOGICAL KNOWLEDGE AND ITS LIMITATIONS

Terban residents have developed a keen sense of water

quality without sophisticated sensing equipment. Wells with particularly good water are renowned. As in other newly urbanized societies, local ecological knowledge (LEK) remains an important reference source. But it is

Figure 1: Map of Yogyakarta and the 14 test sites.

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largely tuned to local history and perceptual cues, making it more often than not ill-suited to address industrial conditions such as chemical and biological contamination. Many water-borne bacteria and pathogens, for example, are odorless and tasteless, remaining imperceptible to human senses. Because of their ‘invisibility’, the presence or absence of some bacteria (such as E. coli) has not entered the Terban LEK framework.

There is, however, a culture of water treatment for drinking water, enshrined as ritual and operating as a mostly adequate disinfection process. Boiling water is a custom the Javanese have practiced for hundreds of years, preceding the knowledge of bacterial contamination introduced much later. So while LEK does not associate seepage tank leaks directly with disinfection techniques of boiling water, water use rituals established themselves through trial and error, and ensure contextually sufficient hygiene standards.

Over half of the families living in the Terban district must make do with as little as $5/day in combined family income1. While bottled water does offer respite from the traditional but now compromised water sources, the economic reality these families face makes this option impossible.

Given the compromised water conditions and the lack of viable alternative water sources, Terban inhabitants have developed an operational relationship with ‘dirty waters', and there is considerable improvisation in the use of the available water resources. Terban inhabitants use shallow wells close to the river for bathing and laundry, and deeper wells further up the incline and away from the river for drinking. While rain water is a viable drinking water source, there is never enough to last through the dry season and efforts to collect rain water are haphazard at best. Government supplied piped drinking water is hardly used even when it is available, due to the high content of chlorine added to disinfect the water.

V. A NETWORKED BIOINCUBATOR IN THE TROPICS The AirKami system includes an industry-grade bench top optical bioincubator and sensing system [9] that detects the florescent enzyme activity of target bacteria in a custom-designed growth medium (see figures 2 and 3). This desktop bioincubator is an example of a new class of miniaturized biosensor systems that deliver laboratory grade results without the need for traditional laboratory infrastructure. Handling and use of the system has been organized such that laypeople without formal lab training can operate the device. As such, this biosensor system is part of a new wave of non-expert devices that are bringing previously cost-prohibitive biosensing technologies to locations and situations that large scale laboratories never reached. It is also placing powerful detection technologies into the hands of more people, opening doors to new areas of DIY, DIWA and street science [6]. While our project used only one particular kind of biological test agent for the water quality testing, the need to keep live media in cool and dry storage is a general

1 Private communication with Pak Ulun, Head of the RW in

Terban, Yogyakarta (April 2013).

problem many environmental biosensing technologies face. In our case, we had to supply the health care center with a refrigerator (the only one in the building) and keep it powered 24/7 while the health care staff sweltered in the heat. This 'technology babysitting' made for some curious comments, but the health care staff made the best of the situation and decided to use the spare space in the fridge to cool their own drinking supplies.

The biosensor system we have employed is based on prevailing water quality diagnostic approach by which the quality of drinking water is associated with the concentration of coliforms present in a water sample. Coliforms are defined in operational terms, meaning that the media and incubation conditions used for their isolation and quantitation define the category as far as water quality evaluation is concerned. In the past, the term coliform included all lactose-fermenting species of the family Enterobacteriaceae, and these are usually found in mammalian (including human) feces. From the perspective of drinking water evaluation, E. coli are of interest as indicator bacteria; their presence is understood as an indicator of the presence of other bacteria, several of which can be very harmful to human beings [19]. Moreover, E. coli are absent when other harmful bacteria are absent in water. However, some E. coli can be harmful. The E. coli strain 0157:H7 can produce a powerful toxin [19].

Recent biosensing techniques of quantifying bacterial contamination replace the traditional membrane filtration technique with enzyme-substrate detection methods which target β-D-galactosidase and β-D-glucuronidase for total coliforms and E. coli, respectively [10]. The system is capable of processing up to 16 samples simultaneously and detecting coliforms down to the single colony level within 16 hours. The duration of the analysis depends on the concentration of contamination, with higher levels leading to much quicker responses. This class of biosensing system produces more precise, faster (and more expensive) results than currently established laboratory procedures. AirKami collects the results from the bio-incubator through a postfix mail-server, stores them on an old laptop under Xubuntu and sends them via a router through a wired network to a local server (the house of public data, see below) whenever connectivity allows for the data transfer.

Figure 2: The local health care center with the bioincubator, laptop, router, and weather station console (desk level) as well as voltage converter and power backup system (below desk).

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Because of intermittent connectivity, an algorithm keeps track of the state of datasets, ensuring that all the measurements eventually find their way to the local server.

B. A weather station in the tropics

AirKami includes a prosumer grade weather observation system [7] that collects wind, wind gust, wind direction, rain, rain rate and humidity data at 30 minute intervals. Our equipment choice was guided by the desire to have a system that is compatible with open source software efforts [22] and for which replacement parts are available in South East Asia at competitive prices. The weather station is solar powered and maintenance free once installed. While ash from local volcanoes occasionally covers the rain collector, the torrential rains that occur during the rainy season clean the system out sufficiently. The data collected from the weather station is sent via radio signal to the weather console at the Puskesmas (Pusat Kesehatan Masyarakat, the community health care center). The console, in turn, is connected to a laptop computer that maintains a Postgresql database with the complete weather recording history. From this database system the data are sent to the local server and then copied over to a cloud network for analysis and backup.

C. The house of public data

AirKami’s data is collected locally, and stored on a rack server at the Universitas Islam Indonesia. These are Indonesian data, and Indonesia manages the collection. As such, AirKami is not a ‘development project’ that parachutes a foreign improvement onto unassuming local residents. While the Indonesian server was setup by our research team, it is now serviced and maintained by the Indonesian IT team and is available to the public via a website that reports the latest results as soon as they are available. We have named this server and its data policies the house of public data.

Figure 3: Diagram of the Airkami technical components.

The data is uploaded from the house of public data to a global cloud service provider. We established this framework for several reasons, including the desire for redundancy. While the connection to an ‘outsider’ server may seem paternalistic, it has proven itself useful during

blackouts and local server updates, allowing us to ferry the increasingly large data collection out of the local server and back again. But the combination has an additional benefit. Coupling the local to the global adds to the modest kampung an international, scalable IT context that opens this data-poor area to the world and new communities with similar challenges. It makes the project accessible to other researchers for data sharing as well as the development of new projects within one single IT framework. This allows AirKami to operate not only as a local water project, but to be a future part of a global effort in organizing responses to water crises [23]. More on this topic in the discussion section (V).

II. DATA COLLECTION AND ANALYSIS

The biosensor-supported results from the 7 month observation period describe a sad state of affairs. In general, the drinking water sources in Terban are contaminated with fecal bacteria despite state mandated zero coliform content for public drinking sources. There are strong variations amongst the wells, and there are strong variations across the dry and wet seasons. Table 1 summarizes the 187 E. coli measurements collected from May to December 2013 at the 14 test sites.

Each site was tested approximately twice a month. Sites 2 and 7 have the mandated zero coliform levels due to treatment with chlorine. These two sites are used for cleaning and bathing purposes only as residents strongly dislike the taste of highly chlorinated water. Site 11 is filtered through a reverse osmosis system and serves as a test reference.

.

Table 1: Statistical summary of the E. coli measurements [cfu/100ml].

Our weather monitoring system collected data automatically in 30 minute intervals, while our water samples were collected manually and transferred for overnight analysis to the health care center. Our original plan was to sample each water test site once per week, but organizational problems prevented this. In order to address the sparse data situation we expanded our recorded E. coli data with polynomial fits of that data, balancing carefully the minimization of errors (via least squares) against slavish over-fitting to the sparse data. The assumption this data expansion exercise implies is that the fitted data actually represents the (physical dynamics of

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the) data we would have collected. We assume this is true, because we assume that the measurements are correct, but the substantial variance we find in within-well data, as well as across-well data, are a reason for caution.

Assuming that the level of E. coli contamination might vary with rainfall, we performed several statistical tests on our weather and biosensor data sets. Sites 2, 7 and 11 have no significance for this test as their waters are treated or filtered as described above. We first applied a Pearson test to check for a linear correlation and found none. We then applied a two sample T-test to the rainfall and E. coli data (A and B below). The T test statistic is the number of standard errors by which the two sample means are separated, where XA and XB denote the means of samples A and B, and s2

A and s2B denote the standard variances of

samples A and B, and nA and nB denote the sample sizes of samples A and B.

(1)

Our null hypothesis is that the two sample means are the same. We set type I error to 0.01. The polynomial fit as described above represents the E. coli data. If the p-value we derive from the test is less than 0.01, we reject the null hypothesis, which leads to the conclusion that there exists a significant difference between the two samples.

Table 2: Results from the two-sample T-test (p = 0.01).

As Table 2 shows, we found statistical evidence suggesting that the our water wells fall into two basic categories: those that show increased E. coli levels during the wet season and those that show increased E. coli levels during the dry season. Wells 5 and 10 however, seem to have some features of both properties.

The older and shallower wells are somewhat more prone to vary with strong rainfalls. The reasons for the variety of behaviors are probably to be found in the varying saturation of the ground soils, the varying depth of the wells that access different parts of aquifer system, and the dynamics of the upper Merapi aquifer system [18] that feed the wells. Broken seepage tanks and decrepit sewage piping contribute to the chaotic mix as well.

Examples of the two prominent well categories are shown in figures 4 and 5, with the original, recorded (not the polynomial fit) data depicted.

Figure 4: Rain, humidity and E .coli measurements for water well #1 from May to early December 2013.

Figure 5: Rain, humidity and E. coli measurements for water well #3 from May to early December 2013.

We have not modeled the hydro-geology of Terban

and do not know how quickly the aquifers replenish the wells. It is possible that each well has its own refresh rate. But since we are not concerned with reconstructing the water flow physics, but only establishing a trend in this analysis, we believe our data analysis approach is justified.

The dependency between rainfall across the dry and wet seasons and fecal contamination as well as the substantial variations across wells is new knowledge produced by our experiments.

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III. PUBLIC NOTIFICATION

The results generated from the AirKami system are available to the public along two distinct vectors. We keep a running tally of the newest results online, available to the public through a web interface2. Usually such datasets are kept under lock, and only government officials have access to the data in Indonesia. The second level of public notification is a messaging system that delivers a summary of all tests completed overnight by the bioincubator system, together with a water use recommendation, to local health care providers via email formatted for mobile phones. Combined, these two procedures form a new approach to environmental data dissemination in Yogyakarta.

A. Creating a context for new biosensor knowledge

The new biosensing system is effective at detecting bacterial contamination, but an uncomfortable fit for existing water care practices in Terban. Airkami attempts to integrate the new knowledge into existing daily habits of Terban residents.

In Javanese, kemrengseng describes the onset of the 'rolling boil'3. A large majority of waterborne bacteria die when exposed to temperatures above 100C [21], and so boiling beyond kemrengseng is a practical and effective disinfectant. Our tests (see table 3) showed that even highly contaminated water samples collected from the Code River were E. coli and total coliform free after at least one minute of exposure to a rolling boil after kemrengseng. In kitchen practice, the water preparation process can result in a 10 to 15 minute boiling effort as the small gas stoves are under-dimensioned for the rather large water pots Terban residents use.

Table 3: Results from the kemrengseng tests.

Nonetheless, the detection of the kemrengseng moment is part of Terban LEK. It allows residents to identify this important event via visual inspection without the aid of a thermometer. For this reason we decided to integrate the kemrengseng moment into a water treatment recommendation algorithm that evaluates the results from

2 http://54.235.133.52/hasil_air_25.php 3 The rolling boil occurs at 0m elevation at 100C. Yogyakarta

sits at about 125m above ocean level. Since the boiling point decreases approximately 1 °C linearly for every 285 m of elevation, the kemrengseng point in Yogyakarta is about 99.56 C, very close to the nominal boiling point of 100C.

the biosensing system. This simple algorithm checks the newest lab results (E. coli and total coliforms) and suggests the use of the established water boiling practice to achieve kemrengseng and maintain this state for at least one minute on water taken from any well with E. coli levels greater than zero.

Originally we intended to send the messages with test results and recommendations via SMS directly to Terban residents. However, mobile data plan providers offering savings and rebates entice residents to continuously change their data plans (and phone numbers), and keeping track of these changes proved impractical. So we decided to send the recommendations not to the residents themselves, but to the doctors and lab staff at the Puskesmas via email messaging from the server. We leave communication of the 'last meter' to the human professionals in the know. Since the health care staff is intimately familiar with Terban residents and territory, they can contextualize the results and recommendation as no clever algorithm can. This approach supports what the residents of Terban value: personal contact and advice from a healthcare professional who can explain the significance of the results.

From our data analysis we know that the Merapi aquifer, rainfall and water well quantity are intricately interwoven factors that have an effect on the bacterial contamination dynamics of the water wells. We have not formally included the rainfall dynamics into the water treatment recommendations as more research is needed to solidify this new knowledge.

IV. INFORMAL HEALTH CARE INTERVENTIONS Despite the success of AirKami's test and notification

system, it became clear that the energies surrounding the biosensing effort created an opportunity for an urban level response to the analytical insights. It became clear that the new knowledge really needed to generate new actions, and that these actions should address the water problem 'at the source'. Where drinking water is a luxury, a water monitoring system that only generates more data (and more bad news) is simply not good enough. This is the motivation for the WaterBank project.

A. Searching for the Belik Ayu

Three different locations were under consideration for WaterBank. Hydro-geologically, all three sites are water springs. After long debates amongst the Terban residents, the choice fell on the Belik Ayu (Javanese for beautiful spring) since it is, based on Terban LEK, believed to carry the largest amounts of water of the best available quality. The Belik Ayu had the additional advantage of being accessible with simple building techniques, offering sufficient public space for the WaterBank construction itself. However, Belik Ayu required remediation as it was covered with debris originating from the 2006 Mount Merapi eruption. In the process of this remediation, a fish pond was built adjacent to Belik Ayu and placed under the care of a group of young Terban residents.

Sadly, even the Belik Ayu is no longer a source of pure drinking water. Our biosensing system found the

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water from the Belik Ayu to be contaminated with fecal bacteria in concentrations far beyond drinkable limits. The traditional LEK and its practice of identifying drinking water sources had reached its limits, making an additional intervention necessary.

B. Treating the Belik Ayu

WaterBank is a small water filtration plant and water distribution plan. It appears as a two story structure in the heart of Terban, one public walking path away from the Code River. The filter room and reservoir tank are in upper story reachable directly from the neighborhood alley that connects to the Code river walk way4. There is a small playground underneath this upper floor with access to the fish pond. The structure is made of reinforced concrete, and the foundation and retaining walls are made of natural stone obtained from the banks of the river Code. Construction was performed by local masons and supervised by a civil engineer who is also member of the Terban community. Terban residents donated various forms of construction materials for WaterBank, and these were put to clever use by the workers. The floor of the top story is finished with a mosaic of donated ceramic shards. The walls were made with donated panes of glass set into an aluminum support structure.

WaterBank processes water from the Belik Ayu through a multi-stage filter system. The first filter step removes solids such as sand, slime and rust. The second step passes the through a reverse osmosis system. Thereafter the water flows through a two-stage ultraviolet filter and ozone infusion to neutralize any remaining bacteria. This clean water is then crafted into a re-mineralized water through a post-filter containing Basalt from Mount Merapi, Gneiss, Feldspar and Quartz from the mountains surrounding the Glacier du Mont Miné in Switzerland. Terban residents have the cleanest, freshest and fanciest water in the vicinity.

Figure 6: Formal and informal elements of the Indonesia health care system.

4 www.airkami.org has visual documentation of the WaterBank

project.

WaterBank can produce up to 400 liters of filtered water per day. A group of Terban residents, the PKK (Perbinaan Kesejahteraan Keluarga) who manage community resources and conflicts, have taken charge of the day to day operation of WaterBank. They have set the cost of one canister (19 liters) of WaterBank water at 4000 Rupiah, about $0.4. The water is free of fecal contamination and considered by Terban inhabitants to be very tasty, a revitalized Belik Ayu. About 10 canisters of WaterBank water are sold daily, and proceeds from sales now cover the cost of electricity and filter replacements. WaterBank has regular customers, and a new snack shop, hoping to make use of WaterBank water for specialty drinks, has sprung up a few doors down the road.

While AirKami is integrated into the formal health care structure via the Puskesmas, WaterBank is integrated into the informal structures of Terban through the RW (Rukun Warga, see figure 6). The RW structure organizes the daily administration of neighborhood affairs. By placing WaterBank into the RW network, we enabled Terban residents, as opposed to government officials, to be in charge of the project.

V. DISCUSSION

Related research in wetland ecology has shown how LEK can be used to augment (remote) sensing applications [16]. Our experiments suggest the possibility of the reverse mechanism with biosensing: detecting events outside of LEK. Our experiments found dynamic relationships between water well quality and rainfall events that are not a formal part of the Terban LEK.

Combining simple and cheap sensing modalities with high fidelity but expensive biosensor data might allow for a useful expansion of biosensor-enabled environmental test regimes in R+D compromised contexts. Furthermore, such an approach has the advantage of supporting existing environmental observation practices while deploying expensive biosensing technologies selectively, where and when they are most effective as analytical anchor points.

Figure 7: A Terban resident filling a canister with filtered and fresh WaterBank water.

Terban residents are invested in the success of Airkami and WaterBank, and for good reason. Government efforts

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in improving even the most basic needs of daily life are often insufficient, leaving Terban residents to fend for themselves. AirKami and WaterBank support the stakeholders outside of official structures in organizing practical alternatives to the status quo. But the future of the AirKami project remains uncertain. The ministry of health has yet to decide on an institutional framework by which to support the new biosensing technology. Integrating this new technology into the existing health care framework requires yet to be determined funding. The ministry of health is considering the option of offering ‘premium’ water testing services for private clients based on the efficiencies demonstrated by our prototype system. The privatization of public services can be a highly undesirable side-effect of new investments into public infrastructure. It is likely that the new monitoring regime of the public wells in Terban will suffer through this ‘creative’ private service model.

The framework described in this paper has facilitated a new form of caring for water resources in Terban. But it is also introducing alien concepts and new responsibilities. Now water care requires not just the shared appreciation of a Belik Ayu, but sensor maintenance, sample collection and computer updates. And the medical waste disposal procedure suggested by the biosensor equipment manufacturer for used water sample containers requires an infrastructure that hardly exists in Indonesia. The management of data through global server networks offers undisputable data management efficiencies for emerging economies. But it also introduces new dependencies on essential infrastructure operating in foreign countries. Moreover, our system does not address the root problems of shared resource deterioration. Uncontrolled urban growth cannot be ‘sensed’ into reasonable patterns; policies of excess cannot be remedied with technology alone; informal adhoc solutions do not address long-term structural insufficiencies.

While this project is situated in a rapidly expanding economy in South East Asia, it holds lessons for biosensing applications in general. Biosensing for personal fitness, for example, has become wildly popular. When biosensing enters the public realm and focuses its analytical potential on shared resources, the stakes change. Could this new knowledge be too disruptive to integrate into existing environmental response and urban planning mechanisms? Fundamental changes to the way we organize the care of the commons are necessary.

ACKNOWLEDGMENTS This research is supported in part by a grant from Intel

Research Labs, assistance from an Amazon AWS education grant, the University at Buffalo and the Universitas Islam Indonesia.

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