Current state
The
notion of an Internet of Things is at once both old and new. From the
earliest days of the World Wide Web, devices were connected so people
could see the view out a window, traffic or ski conditions, a coffee pot
at the University of Cambridge, or a Coke machine at Carnegie Mellon
University. The more recent excitement dates to 2010 or thereabouts, and
builds on a number of developments: many new Internet Protocol (IP)
addresses have become available, the prices of sensors are dropping, new
data and data-processing models are emerging to handle the scale of
billions of device "chirps," and wireless bandwidth is getting more and
more available. At a deeper level, however, the same criteria -- sense,
think, act -- that define a robot for many working in the field also
characterize large-scale Internet of Things systems: they are
essentially meta-robots, if you will. The GE Industrial Internet model
discussed below includes sensors on all manner of industrial
infrastructure, a data analytics platform, and humans to make presumably
better decisions based on the massive numbers from the first domain
crunched by algorithms and computational resources in the second.
Building Blocks
The
current sensor landscape can be understood more clearly by contrasting
it to the old state of affairs. Most important, sensor networks mimicked
analog communications: radios couldn't display still pictures (or
broadcast them), record players couldn't record video, newspapers could
not facilitate two- or multi-way dialog in real time. For centuries,
sensors in increasing precision and sophistication were invented to
augment human senses: thermometers, telescopes, microscopes, ear
trumpets, hearing aids, etc. With the 19th century advances in
electro-optics and electro-mechanical devices, new sensors could be
developed to extend the human senses into different parts of the
spectrum (e.g., infrared, radio frequencies, measurement of vibration,
underwater acoustics, etc.).
Where they were available, electromechanical sensors and later sensor networks
*stood alone
*measured one and only one thing
*cost a lot to develop and implement
*had inflexible architectures: they did not adapt well to changing circumstances.
Sensors
traditionally stood alone because networking them together was
expensive and difficult. Given the lack of shared technical standards,
to build a network of offshore data buoys for example, the
interconnection techniques and protocols would be uniquely engineered to
a particular domain, in his case, salt water, heavy waves, known
portions of the magnetic spectrum, and so on. An agency seeking to
connect sensors of a different sort (such as surveillance cameras) would
have to start from scratch, as would a third agency monitoring road
traffic.
In part because of their mechanical componentry, sensors
rarely measured across multiple yardsticks. Oven thermometers measured
only oven temperature, and displayed the information locally, if at all
(given that perhaps a majority of sensor traffic informs systems rather
than persons, the oven temperature might only drive the thermostat
rather than a human-readable display). Electric meters only counted
watt-hours in aggregate. Fast forward to today: a consumer Global
Positioning Satellite (GPS) unit or smartphone will tell location,
altitude, compass heading, and temperature, along with providing weather
radio.
Electromechanical sensors were not usually mass-produced,
with the exception of common items such as thermometers. Because supply
was limited, particularly for specialized designs, the combination of
monopoly supply and small order quantities kept prices high.
The
rigid architecture was a function of mechanical devices’ specificity. A
vibration sensor was different from a camera was different from a
humidistat. Humidity data, in turn, was designed to be moved and managed
in a particular analog domain (a range of zero to 100 per cent), while
image recognition in the camera’s information chain typically featured
extensive use of human eyes rather than automated processing.
Ubiquity
Changes
in each of these facets combine to help create today’s emerging sensor
networks, which are growing in scope and capability every year. The many
examples of sensor capability accessible to (or surveilling) the
everyday citizen illustrate the limits of the former regime: today there
are more sensors recording more data to be accessed by more end points.
Furthermore, the traffic increasingly originates and transits
exclusively in the digital domain.
*Computers, which sense their own temperature, location, user patterns, number of printer pages generated, etc.
*Thermostats, which are networked within buildings and now remotely controlled and readable
*Telephones,
the wireless variety of which can be understood as beacons, bar-code
scanners, pattern-matchers (the Shazam application names songs from a
brief audio sample), and network nodes
*Motor and other industrial
controllers: many cars no longer have mechanical throttle linkages, so
people step on a sensor every day without thinking as they drive by
wire. Automated tire-pressure monitoring is also standard on many new
cars. Airbags rely on a sophisticated system of accelerometers and
high-speed actuators to deploy the proper reaction for collision
involving a small child versus a lamp strapped into the front passenger
seat.
*Vehicles: the OBD II diagnostics module, the toll pass,
satellite devices on heavy trucks, and theft recovery services such as
Lojack, not to mention the inevitable mobile phone, make vehicle
tracking both powerful and relatively painless
*Surveillance cameras (of which there are over 10,000 in Chicago alone, and more than 500,000 in London)
*Most
hotel door handles and many minibars are instrumented and generate
electronic records of people’s and vodka bottles’ comings and goings.
*Sensors,
whether embedded in animals (RFID chips in both household pets and race
horses) or gardens (the EasyBloom plant moisture sensor connects to a
computer via USB and costs only $50), or affixed to pharmaceutical
packaging.
Note the migration from heavily capital-intensive or
national-security applications down-market. A company called Vitality
has developed a pill-bottle monitoring system: if the cap is not removed
when medicine is due, an audible alert is triggered, or a text message
could be sent.
A relatively innovative industrial deployment of
vibration sensors illustrates the state of the traditional field. In
2006, BP instrumented an oil tanker with "motes," which integrated a
processor, solid-state memory, a radio, and an input/output board on a
single 2" square chip. Each mote could receive vibration data from up to
ten accelerometers, which were mounted on pumps and motors in the
ship’s engine room. The goal was to determine if vibration data could
predict mechanical failure, thus turning estimates—a motor teardown
every 2,000 hours, to take a hypothetical example—into concrete evidence
of an impending need for service.
The motes had a decided
advantage over traditional sensor deployments in that they operated over
wireless spectrum. While this introduced engineering challenges arising
from the steel environment as well as the need for batteries and
associated issues (such as lithium’s being a hazardous material), the
motes and their associated sensors were much more flexible and
cost-effective to implement compared to hard-wired solutions. The motes
also communicate with each other in a mesh topology: each mote looks for
nearby motes, which then serve as repeaters en route to the data’s
ultimate destination. Mesh networks are usually dynamic: if a mote
fails, signal is routed to other nearby devices, making the system
fault-tolerant in a harsh environment. Finally, the motes could perform
signal processing on the chip, reducing the volume of data that had to
be transmitted to the computer where analysis and predictive modeling
was conducted. This blurring of the lines between sensing, processing,
and networking elements is occurring in many other domains as well.
All
told, there are dozens of billions of items that can connect and
combine in new ways. The Internet has become a common ground for many of
these devices, enabling multiple sensor feeds—traffic camera,
temperature, weather map, social media reports, for example—to combine
into more useful, and usable, applications. Hence the intuitive appeal
of "the Internet of Things." As we saw earlier, network effects and
positive feedback loops mean that considerable momentum can develop as
more and more instances converge on shared standards. While we will not
discuss them in detail here, it can be helpful to think of three
categories of sensor interaction:
*Sensor to people: the
thermostat at the ski house tells the occupants that the furnace is
broken the day before they arrive, or a dashboard light alerting the
driver that the tire pressure on their car is low
*Sensor to sensor:
the rain sensor in the automobile windshield alerts the antilock brakes
of wet road conditions and the need for different traction-control
algorithms
*Sensor to computer/aggregator: dozens of cell phones on a
freeway can serve as beacons for a traffic-notification site, at much
lower cost than helicopters or "smart highways."
An "Internet of
Things" is an attractive phrase that at once both conveys expansive
possibility and glosses over substantial technical challenges. Given 20+
years of experience with the World Wide Web, people have long
experience with hyperlinks, reliable inter-network connections, search
engines to navigate documents, and wi-fi access everywhere from
McDonalds to mid-Atlantic in flight. None of these essential pieces of
scaffolding has an analog in the Internet of Things, however:
garage-door openers and moisture sensors aren't able to read; naming,
numbering, and navigation conventions do not yet exist; low-power
networking standards are still unsettled; and radio-frequency issues
remain problematic. In short, as we will see, "the Internet" may not be
the best metaphor for the coming stage of device-to-device
communications, whatever its potential utility.
Beyond the Web metaphor
Given
that "the Internet" as most people experience it is global, searchable,
and anchored by content or, increasingly, social connections, the
"Internet of Things" will in many ways be precisely the opposite. Having
smartphone access to my house's thermostat is a private transaction,
highly localized and preferably NOT searchable by anyone else. While
sensors will generate volumes of data that are impossible for most
humans to comprehend, that data is not content of the sort that Google
indexed as the foundation of its advertising-driven business. Thus while
an "Internet of Things" may feel like a transition from a known world
to a new one, the actual benefits of networked devices separate from
people will probably be more foreign than saying "I can connect to my
appliances remotely."
Consumer applications
The notion of
networked sensors and actuators can usefully be subdivided into
industrial, military/security, or business-to-business versus consumer
categories. Let us consider the latter first. Using the smartphone or a
web browser, it is already possible to remotely control and/or monitor a
number of household items:
• slow cooker
• garage-door opener
• blood-pressure cuff
• exercise tracker (by mileage, heart rate, elevation gain, etc)
• bathroom scale
• thermostat
• home security system
• smoke detector
• television
• refrigerator.
These
devices fall into some readily identifiable categories: personal health
and fitness, household security and operations, entertainment. While
the data logging of body weight, blood pressure, and caloric
expenditures would seem to be highly relevant to overall physical
wellness, few physicians, personal trainers, or health insurance
companies have built business processes to manage the collection,
security, or analysis of these measurements. Privacy, liability,
information overload, and, perhaps most centrally, outcome-predicting
algorithms have yet to be developed or codified. If I send a signal to
my physician indicating a physical abnormality, she could bear legal
liability if her practice does not act on the signal and I subsequently
suffer a medical event that could have been predicted or prevented.
People
are gradually becoming more aware of the digital "bread crumbs" our
devices leave behind. Progressive Insurance's Snapshot campaign has had
good response to a sensor that tracks driving behavior as the basis for
rate-setting: drivers who drive frequently, or brake especially hard, or
drive a lot at night, or whatever could be judged worse risks and be
charged higher rates. Daytime or infrequent drivers, those with a light
pedal, or people who religiously buckle seat belts might get better
rates. This example, however, illustrates some of the drawbacks of
networked sensors: few sensors can account for all potentially causal
factors. Snapshot doesn't know how many people are in the car (a major
accident factor for teenage drivers), if the radio is playing, if the
driver is texting, or when alcohol might be impairing the driver's
judgment. Geographic factors are delicate: some intersections have high
rates of fraudulent claims, but the history of racial redlining is also
still a sensitive topic, so data that might be sufficiently predictive
(ZIP codes traversed) might not be used out of fear it could be abused.
The
"smart car" applications excepted, most of the personal Internet of
Things use cases are to date essentially remote controls or intuitively
useful data collection plays. One notable exception lies in
pattern-cognition engines that are grouped under the heading of
"augmented reality." Whether on a smartphone/tablet or through special
headsets such as Google Glass, a person can see both the physical world
and an information overlay. This could be a real-time translation of a
road sign in a foreign country, a direction-finding aid, or a tourist
application: look through the device at the Eiffel Tower and see how
tall it is, when it was built, how long the queue is to go to the top,
or any other information that could be attached to the structure,
attraction, or venue.
While there is value to the consumer in
such innovations, these connected devices will not drive the data
volumes, expenditures, or changes in everyday life that will emerge from
industrial, military, civic, and business implementations.
The Internet(s) of [infrastructure] Things
Because
so few of us see behind the scenes to understand how public water
mains, jet engines, industrial gases, or even nuclear deterrence work,
there is less intuitive ground to be captured by the people working on
large-scale sensor networking. Yet these are the kinds of situations
where networked instrumentation will find its broadest application, so
it is important to dig into these domains.
In many cases, sensors
are in place to make people (or automated systems) aware of exceptions:
is the ranch gate open or closed? Is there a fire, or just an
overheated wok? Is the pipeline leaking? Has anyone climbed the fence
and entered a secure area? In many cases, a sensor could be in place for
years and never note a condition that requires action. As the prices of
sensors and their deployment drop, however, more and more of them can
be deployed in this manner, if the risks to be detected are high enough.
Thus one of the big questions in security -- in Bruce Schneier's
insight, not "Does the security measure work?" but "Are the gains in
security worth the costs?" -- gets difficult to answer: the costs of
IP-based sensor networks are dropping rapidly, making cost-benefit-risk
calculations a matter of moving targets.
In some ways, the
Internet of Things business-to-business vision is a replay of the RFID
wave of the mid-aughts. Late in 2003, Wal-Mart mandated that all
suppliers would use radio-frequency tags on their incoming pallets (and
sometimes cases) beginning with the top 100 suppliers, heavyweight
consumer packaged goods companies like Unilever, Procter & Gamble,
Gillette, Nabisco, and Johnson & Johnson. The payback to Wal-Mart
was obvious: supply chain transparency. Rather than manually counting
pallets in a warehouse or on a truck, radio-powered scanners could
quickly determine inventory levels without workers having to get
line-of-sight reads on every bar code. While the 2008 recession
contributed to the scaled-back expectations, so too did two powerful
forces: business logic, and physics.
To take the latter first,
RFID turned out to be substantially easier in labs than in warehouses.
RF coverage was rarely strong and uniform, particularly in retrofitted
facilities. Noise -- in the form of everything from microwave ovens to
portable phones to forklift-guidance systems -- made reader accuracy an
issue. Warehouses involve lots of metal surfaces, some large and flat
(bay doors and ramps), others heavy and in motion (forklifts and carts):
all of these reflect radio signals, often problematically. Finally, the
actual product being tagged changes radio performance: aluminum cans of
soda, plastic bottles of water, and cases of tissue paper each
introduce different performance effects. Given the speed of assembly
lines and warehouse operations, any slowdowns or errors introduced by a
new tracking system could be a showstopper.
The business logic
issue played out away from the shop floor. Retail and CPG profit margins
can be very thin, and the cost of the RFID tagging systems for
manufacturers that had negotiated challenging pricing schedules with
Wal-Mart was protested far and wide. The business case for total supply
chain transparency was stronger for the end seller than for the
suppliers, manufacturers, and truckers required to implement it for
Wal-Mart's benefit. Given that the systems delivered little value to the
companies implementing them, and given that the technology didn't work
as advertised, the quiet recalibration of the project was inevitable.
RFID
is still around. It is a great solution to fraud detection, and
everything from sports memorabilia to dogs to ski lift tickets can be
easily tested for authenticity. These are high-value items, some of them
scanned no more than once or twice in a lifetime rather than thousands
of times per hour, as on an assembly line. Database performance,
industry-wide naming and sharing protocols, and multi-party security
practices are much less of an issue.
While it's useful to
recall the wave of hype for RFID circa 2005, the Internet of Things will
be many things. The sensors, to take only one example, will be
incredibly varied, as a rapidly growing online repository makes clear.
Laboratory instruments are shifting to shared networking protocols
rather than proprietary ones. This means it's quicker to set up or
reconfigure an experiment, not that the lab tech can see the viscometer
or Geiger counter from her smart phone or that the lab will "put the
device on the Internet" like a webcam.
Every one of the billions
of smartphones on the planet is regularly charged by its human operator,
carriers a powerful suite of sensors -- accelerometer, temperature
sensor, still and video cameras/bar-code readers, microphone, GPS
receiver -- and operates on multiple radio frequencies: Bluetooth,
several cellular, WiFi. There are ample possibilities for crowdsourcing
news coverage, fugitive hunting, global climate research (already,
amateur birders help show differences in species' habitat choices), and
more using this one platform.
Going forward, we will see more
instrumentation of infrastructure, whether bridges, the power grid,
water mains, dams, railroad tracks, or even sidewalks. While states and
other authorities will gain visibility into security threats, potential
outages, maintenance requirements, or usage patterns, it's already
becoming clear that there will be multiple paths by which to come to the
same insight. The state of Oregon was trying to enhance the experience
of bicyclists, particularly commuters. While traffic counters for cars
are well established, bicycle data is harder to gather. Rather than
instrumenting bike paths and roadways, or paying a third party to do so,
Oregon bought aggregated user data from Strava, a fitness-tracking
smartphone app. While not every rider, particularly commuters, tracks
his mileage, enough do that the bike-lane planners could see cyclist
speeds and traffic volumes by time of day, identify choke points, and
map previously untracked behaviors.
Strava was careful to
anonymise user data, and in this instance, cyclists were the
beneficiaries. Furthermore, cyclists compete on Strava and have joined
with the expectation that their accomplishments can show up on leader
boards. In many other scenarios, however, the Internet of Things'
ability to "map previously untracked behaviors" will be problematic, for
reasons we will discuss later.
Industrial scenarios
GE
announced its Industrial Internet initiative in 2013. The goal is to
instrument more and more of the company's capital goods -- jet engines
are old news, but also locomotives, turbines, undersea drilling rigs,
MRI machines, and other products -- with the goal of improving power
consumption and reliability for existing units, and to improve the
design of future products. Given how big the company's footprint is in
these industrial markets, 1% improvements turn out to yield
multi-billion-dollar opportunities. Of course, instrumenting the
devices, while not trivial, is only the beginning: operational data must
be analyzed, often using completely new statistical techniques, and
then people must make decisions and put them into effect.
This
holistic vision is far-sighted on GE's part and transcends the frequent
technology-centric marketing messages that often characterize Silicon
Valley rhetoric. That is, GE's end-to-end insistence on sensors AND
software AND algorithms AND people is considerably more nuanced and
realistic than, for example, Qualcomm's vision:
“the Internet of
Everything (IoE) is changing our world, but its effect on daily life will be most profound. We will move through our days and nights
surrounded by connectivity that intelligently responds to what we need
and want—what we call the Digital Sixth Sense. Dynamic and intuitive,
this experience will feel like a natural extension of our own abilities.
We will be able to discover, accomplish and enjoy more. Qualcomm is
creating the fabric of IoE for everyone everywhere to enable this
Digital Sixth Sense.”
Not surprisingly, Cisco portrays the Internet
of Things in similar terms; what Qualcomm calls "fabric" Cisco names
"connectivity," appropriately for a networking company:
“These
objects contain embedded technology to interact with internal states or
the external environment. In other words, when objects can sense and
communicate, it changes how and where decisions are made, and who makes
them.
The IoT is connecting new places–such as manufacturing floors,
energy grids, healthcare facilities, and transportation systems–to
the Internet. When an object can represent itself digitally, it can be
controlled from anywhere. This connectivity means more data, gathered
from more places, with more ways to increase efficiency and improve
safety and security.”
The other striking advantage of the GE approach
is financial focus: 1% savings in a variety of industrial process areas
yields legitimately huge cost savings opportunities. This approach has
the simultaneous merits of being tangible, bounded, and motivational.
Just 1% savings in aviation fuel over 15 years would generate more than
$30 billion, for example.
But to get there, the GE vision is
notably realistic about the many connected investments that must precede
the harvesting of these benefits.
1) The technology doesn't
exist yet. Sensors, instrumentation, and user interfaces need to be made
more physically robust, usable by a global work force, and standardized
to the appropriate degree.
2) Information security has to
protect assets that don't yet exist, containing value that has yet to be
measured, from threats that have yet to materialize.
3) Data
literacy and related capabilities need to be cultivated in a global
workforce that already has many skills shortfalls, language and cultural
barriers, and competing educational agendas. Traditional engineering
disciplines, computer science, and statistics will merge into new
configurations.
Despite a lot of vague marketing rhetoric, the
good news is that engineers, financial analysts, and others are
recognizing the practical hurdles that have yet to be cleared. Among
these are the following:
1) Power consumption
If all of those
billions of sensors require either hard-wired power or batteries, the
toxic waste impact alone could be daunting. Add to this requirement the
growing pressure of the electric-car industry on the worldwide battery
supply, and the need for new power management, storage, and disposal
approaches becomes clear.
2) Network engineering
It's easy to
point to all those sensors, each with its own IP address, and make
comparisons to the original Internet. It's quite another matter,
however, to make networks work when the sensor might "wake up" only once
a day -- or once a month -- to report status. Other sensors, as we saw
with jet engines, have the opposite effect, that of a firehose. Some
kind of transitional device will likely emerge, either collecting
infrequent heterogeneous "chirps" or consolidating, error-checking,
compressing, and/or pre-processing heavy sensor volumes at the edge of a
conventional network. Power management, security, and data integrity
might also be in some of these devices' job description.
3)
Security
As the Stuxnet virus illustrated, the Internet of Things will
be attacked by both amateur and highly trained people writing a wide
variety of exploits. Given that Internet security is already something
of a contradiction in terms, and given widespread suspicion that the NSA
has engineered back doors into U.S. firms' technology products, market
opportunities for EU and other IoT vendors might increase as a result.
In any event, the challenge of making lightweight, distributed systems
robustly secure without undue costs in selling price, operational
overhead, interoperability, or performance has yet to be solved at a
large scale. In 2014 the security firm Symantec announced that all
exercise monitors tested were found to be insecure.
4) Data
processing
The art and science of data fusion is far from standardized
in fields that have been practicing it for decades. Context, for
instance, is often essential for interpretation but difficult to
guarantee during collection. Add to the mix humans as sensor platforms,
intermittent and hybrid network connectivity, information security
requirements outside a defense/intelligence cultural matrix, and unclear
missions -- many organizations quite reasonably do not know why they
are measuring what they are measuring until after they try to analyze
the numbers -- and the path of readings off the sensors and into
decision-making becomes complicated indeed.
5) Cost
effectiveness
The RFID experiment foundered in part on the price of the
sensors, which even when measured in dimes became an issue when the
volumes of items to be tracked ranged into the millions. With past
hardware investments in memory, for example, still stinging some
investors, the path to profitability for ultra-low-power, ultra-low-cost
now will be considerably different from the high-complexity,
high-margin world that Intel so successfully mastered in the PC era.
6)
Protocols
The process by which the actual day-to-day workings of
complex systems get negotiated makes for good business-school case
studies, but challenging investment and decision-making. The USB
standard, for example, had substantial industry "convening power" being
exercised by Intel, and the benefits have been widely shared. For the
IoT, it's less clear which companies will have a similar combination of
engineering know-how, intellectual property (and a management mandate to
form a profitless patent pool), industry fear and respect, and so on.
As the VHS/Betamax, high-resolution audio CD, and high-resolution DVD
standards wars have taught many people, it's highly undesirable to be
stranded on the wrong side of an industry protocol. Hence, many players
may sit out pending identifiable winners in the various standards
negotiations.
7) APIs and middleware
The process by which
device chirps become management insights requires multiple handoffs
between sensors and PCs or other user devices. Relatively high up the
stack are a variety of means by which processed, analyzed data can be
connected to and queried by human decision makers, and so far,
enterprise software vendors have yet to make a serious commitment to
integrating these new kinds of data streams (or trickles, or floods)
into management applications.
8) System management
The IoT will
need to generate usage logs, integrity checks, and all manner of tools
for managing these new kinds of networks. Once again, data center and
desktop PC systems management tools simply are not designed to handle
tasks at this new level of granularity and scale. What will an audit of a
network of "motes" look like? Who will conduct it? Who will require it?
Conclusion
As
this note has hinted, the label "Internet of Things" could well steer
thinking in unproductive directions. Looking at the World Wide Web as a
prototype has many shortcomings: privacy, security, network engineering,
human-in-the-loop advantages that may not carry over, and even the
basic use case. At the same time, thinking of sensor networks in the
same proprietary, single-purpose terms that have dictated generations of
designs is also overdue.
Beyond the level of the device, data
processing is being faced with new challenges -- in both scope and kind
-- as agencies, companies, and NGOs (to name but three interested
parties) try to figure out how to handle billions of cellphone chirps,
remote-control clicks, or GPS traces. What information can and should be
collected? By what entity? With what safeguards? For how long? At what
level of aggregation, anonymization, and detail? With devices and people
opting in or opting out? Who can see what data at what stage in the
analysis life cycle?
Once information is collected, the
statistical and computer science disciplines are challenged to find
patterns that are not coincidence, predictions that can be validated,
and insights available in no other way. Numbers rarely speak for
themselves, and the context for Internet of Things data is often
difficult to obtain or manage given the wide variety of data types in
play. The more inclusive the model, however, the more noise is
introduced and must be managed. And the scale of this information is
nearly impossible to fathom: according to IBM Chief Scientist Jeff
Jonas, mobile devices in the United States alone generated 600 billion
geo-tagged transactions every day -- as of 2010.
In addition to
the basic design criteria, the privacy issues cannot be ignored. Here,
the history of Google Glass might be instructive: whatever the benefits
that accrue to the user, the rights of those being scanned, identified,
recorded, or searched matter in ways that Google has yet to acknowledge.
Magnify Glass to the city or nation-state level (recall that England
has an estimated 6 million video cameras, but nobody knows exactly how
many), as the NSA revelations appear to do, and it's clear that
technological capability has far outrun the formal and informal rules
that govern social life in civil society.
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