Tag Archives: iot

Supporting (& paying for) the network segments that support IoT Systems

Network segmentation is often promoted as the answer to IoT device and systems management and risk mitigation for an institution, city, or corporation. While segmenting networks is important, a subtle problematic aspect is that:

  1. segmenting networks takes work, energy, and resources in the form of initial investment and ongoing management and oversight
  2. numbers of network segments growth may well track with IoT device count growth – which, at least for the next few years, appears exponential
  3. cities and institutions may not be planning for increased network management resources to support IoT Systems deployments

Success criteria for an IoT System implementation

I use a two overarching component criteria to define a successful IoT System implementation for a city or institution —

1. ROI – does the system perform as expected for the actual (vs projected) costs of deployment and subsequent management

2. Cyber risk – did the implementation of the IoT System make the city or institution worse off in the course of deploying and operating the system?

A key aspect to both of these criteria is system manageability. An unmanageable or difficult to manage system costs more in terms of staffing, rework, repair/updating, and operational disruption. At the same time, a difficult to manage system can create cybersecurity vulnerabilities — both seen and unseen — and divert limited institutional resources from existing operational, cybersecurity and risk mitigation activities.

Similarly, as IoT Systems need to be manageable to positively (at least not negatively) affect ROI and institutional risk profile, the network segments supporting IoT Systems also need to be manageable.

Managing the network segments that support IoT Systems

Networks are no longer, “make this thing talk to that thing,” or “make these things talk to those things.” Network management requires a robust set of supporting system services that support consistent connectivity, resilience, real-time health reporting capabilities, and rapid network diagnostic capabilities.

In addition to these core network support services, for IoT Systems deployments, there is another overarching criteria needed to successfully support and manage networks that support IoT Systems —

The IoT System owner should be able to measure, monitor, and determine performance of the IoT System(s) at any particular point in time. This supports both the effort of determining ROI as well as providing visibility for cyber risk mitigation.

In the case of a city, the IoT System owner might be the city’s transportation department while the network provision is provided by or contracted through the city’s central IT organization. In the case of an institution, such as a research university, the IoT System owner might be an academic department purchasing and deploying an IoT System to support a research grant while the underlying supporting network segment is supported by the university’s central IT organization.

The IoT System owner should not be relying on the network provider to provide IoT application/system management and diagnostic services. IoT Systems are evolving so rapidly, it is very unrealistic to expect the network services provider to have the resources or wherewithal to keep up with the nuances of each new IoT System deployment, much less manage the performance expectations for a rapidly growing number of different IoT Systems.

Examples of IoT System/application-specific network services include:

  • enumeration – how many things/devices are on this network?
    • Is this count different from yesterday? By how much?
  • identification – how many of these devices belong to my IoT System? Have I seen them before? Yesterday? a month ago? How rapidly is this changing?
    • Is what I am seeing different from what I was expecting?
      • (Did I know what I was expecting?)
  • Application-specific network device awareness and health
    • Device heartbeat – are you there?
    • Device performance specifics –
      • Is device characteristic 1 returning a result within acceptable constraints? e.g. voltage level
      • Is device characteristic 2 returning a result within acceptable constraints? e.g. device temperature,outside air temperature (OAT)
      • Is device characteristic 3 returning a result within acceptable constraints? e.g. response time
      • Is device characteristic n returning a result within acceptable constraints?

Accomplishing these requirements is not free. Some technology investment is needed, but more importantly, an organizational framework that supports this activity is necessary.

Growth of devices & growth of network segments

While we don’t know what the rate of growth of network segments is or will be, we can be pretty sure that it will continue to grow for the foreseeable future. This growth in network segments stems from at least two reasons, 1) network segments whether VRF’s, VLAN’s or other are easier to implement than they used to be, and 2) network segmentation is currently a popular strategy for addressing IoT Systems risk mitigation. (Regarding the latter, I believe that this is in part because we don’t know what else to do — when all you have is a hammer, everything looks like a nail).

There are multiple projections that IoT device count is growing at an exponential rate, such as this Ericsson Mobility Report that suggests a 23% annual rate of growth between 2015 and 2021 and this McKinsey report that suggests 15% – 20% annual growth by 2020. Related projections on IoT market growth can be even higher with annual growth over of 50%.

This ongoing steady (or more) growth year after year appears to be exponential growth. While IoT devices don’t compound each other like dollars do, the growth count curve still appears exponential. To borrow from the idea of compounding (exponential) growth in finance — we add 1 to the rate of growth and raise that sum to the number of years out that we want to project and then multiple that whole thing by the starting count —

(Wikipedia)

So, let’s say that we start with 10,000 IoT devices in a hypothetical city or institution and that the growth rate in IoT device count over the next few years is 20% . That growth curve looks something like this —

exponential device count growth @ 20%

(As a quick side note, we can see the Rule of 72 providing a rough estimate of the time it takes to double, about 3 1/2 years, at work here).

Now let’s say that there are currently 1000 network segments (VLANs, VRFs, etc) in that city or institution and consider two hypothetical growth trajectories — one exponential at the same rate as the device count growth rate and one linear growing at 1000 network segments per year.

Hypothetical exponential growth at 20% and hypothetical linear growth at 1000 segments per year

While don’t know exactly what the network segment growth rate or trajectory will be for cities and institutions in particular or in aggregate, we can expect that count to continue to grow for the next several years. With that growth comes an increased demand on institutional and city resources. The question is, are we planning to resource that increase in demand?

Network segmentation management — impacts on ROI & institutional cyber risk

As mentioned earlier, to manage network segments for successful IoT systems implementations, at least two components are required — manageability of the network segment itself and manageability of the specific IoT System(s) on that network. Without both of these, the likelihood of an IoT System’s success for the city or institution is low. Without both of these, the city or institution can expect to feel negative impacts to ROI, the city or institution’s cyber risk profile, or more likely both.

Because both of these success components require current resourcing (eg staffing and tool investment) and planning for future resourcing, success is not guaranteed. If we’re not thoughtful about implementation, we could end up with broad portfolios of a rapidly growing number of unmanaged or under-managed networks. And that’s not good for any of us.

 

Developing counter-drone technologies not so easy

It’s not easy countering drones.

As fast as the drone industry is growing with hobbyist and commercial drone counts in the low millions by 2021, some anticipate that the counter-drone industry will be even larger with a combined annual growth rate of almost 26% between 2017 and 2021.  Increasing the challenge, because of the multitude of factors involved in drone deployments and the many places of interest where drones can be deployed, whether individually or en masse, there are a surprisingly large number of scenarios to consider when developing and deploying counter-drone technologies and that in turn makes development and deployment of counter-drone technology particularly difficult.

There are obvious concerns amongst commercial, private, and government entities around risk stemming from drone use (to include property loss, privacy, injury, and potentially death) — such as drones over flying nuclear plants in France, drones snooping in the Olympics in Rio, drones with cameras outside 26th floor high rise apartments in Seattle, and many others. The challenge is that there are so many factors and so many interdependencies between those factors, that it can be hard to establish a shared conceptual framework around counter-drone approaches to even have a discussion about the concerns.

Already, many different approaches to counter-drone strategies

The broad and varied approaches of counter-drone technologies already on the market speak to the diversity of potential drone deployments and the areas in which they will operate – and which ostensibly there is a desire to be countered. For example, DroneShield’s DroneGun uses a rifle-like radio frequency jammer. Skywall uses a shoulder-fired drone-capturing net. The US Air Force is trying out net-filled shotgun shells by SkyNet and Malou Tech offers drone-nets-drone technology. And the US Navy and Marine Corps bring it on home with laser, aka directed energy, anti-drone approaches. The approaches are wide-ranging and I think we’ll continue to see even more evolution and many more companies in the space.

One approach for considering counter-drone systems

An approach discussed here splits out variables/attributes in drone deployments and operation from the variables/attributes of the operating area and what it contains into two separate sets of factors. This provides a broad base for conceptualizing counter-drone considerations. These two sets of factors are:

1. drone deployment attributes and
2. drone operating area attributes

In turn, each set of factors also has its own multiplicity of factors and interdependencies between them.

counter-drone sets of factors071017

many factors to consider in counter-drone systems development

 

Drone deployment attributes

When considering how to counter drones, there are many factors that contribute to any particular drone deployment. A few of them are:

  • Mass – How much does the drone weigh?
  • Altitude – Is there a characteristic altitude of the deployment?
    • < 50 feet?
    • 50 – 200 feet?
    • above 200 feet?
    • varies?
  • Potential Energy (PE)
    • derives from mass & altitude
  • Velocity
    • Is there a characteristic speed of the drone(s)
      • 5 mph?
      • 5 – 20 mph?
      • > 20 mph?
      • Varies?
  • Kinetic Energy (KE)
    • derives from velocity and mass of drone
    • rotor rotation also contributes to KE
  • Volume – What is the size of the drone?
    • cross-section?
    • diameter across opposing rotor tips?
  • Cardinality
    • Is it just one drone?
    • 2 – 25 drones?
    • 25 or more drones?
  • Behavior >1 drone
    • is the behavior coordinated?
    • are they operating independently?
    • sporadic?
  • Intent – Is the operator intent:
    • malicious?
    • benign?
    • negligent?
    • operator-impaired?
  • What is the payload of the drone?
    • nothing?
    • camera?
    • explosive
    • radiological?/chemical?
    • combination?

Effects of factor interdependencies (aka Potential Energy can become Kinetic Energy)

As discussed, these factors can have substantial interdependencies between them as well. One of the interdependencies that is the most basic and seems to be often forgotten in conversation is that any drone with any altitude has Potential Energy. Regardless of motors, batteries, wind, payload, etc — just the fact that a mass (drone) has been raised to an altitude, it now has Potential Energy. The larger the mass and the higher the altitude, the more Potential Energy is converted to downward directed Kinetic Energy if a drone stops flying – for whatever reason. This may be important to anyone in the path of the dead drone’s earth-seeking trajectory.

city2

Drone operating area attributes

The other part of the equation is what is the drone’s operating area? What are those things on the ground (or in the air) that have value? This could be the value that one puts on their individual privacy, to the value a corporation puts on a production facility, the value a city puts on a power substation, or the value of 50 people watching a little league game or 50,000 people watching an NFL game. Some operating area attributes/variables include:

stadium2

 

  • Does/do the drone(s) fly over a population?
    • Population size – 10’s, 100’s, 1000’s, more?
    • Density
      • rural
      • city
      • high density congregation (eg professional sports game, parade, concert, ..)
      • does the population density change, eg high when ball games & low when not
    • Does/do drone(s) fly over/near critical infrastructure?
      • power
      • water –  dams, reservoirs, …
      • gas
      • sewer
      • nuclear
    • Does/do drone(s) fly over/near schools, churches, hospitals …
    • Does/do drone(s) fly in area of critical radio spectrum?
      • broadcast
      • police, fire
      • aviation, marine
      • military

    ci2

    Counter-drone Combinatorics

    So let’s take a combination and create a constraint domain to illustrate the point.  Where there is a broad range or spectrum of possibilities for a particular attribute, I break them down into three arbitrarily chosen sub-ranges. This will allow us to make the numbers smaller and a little more manageable and yet still see how the number of possibilities to consider can become quite large. You can, of course, break them down into whatever sub-ranges you would like and into as many sub-ranges as you would like.

    the number of scenarios can scale quickly

    the number of scenarios can scale quickly

    Some counter-drone systems will naturally address multiple scenarios. However, to be effective and competitive in a rapidly growing market, the counter-drone systems developer has many flight profiles and many operating area profiles to consider.

    Another factor in counter-drone technology development is the legal aspect. Examples include laws regarding national aviation system navigational system protection, individual state law, and civil lawsuits. This will likely be a substantially evolving landscape as drone technologies evolve and damaging events occur with real loss.

    Dynamic landscape

    When considering the factors involved in developing or evaluating counter-drone technologies, one approach that can be helpful is to think about both the characteristics of the drone (or drones) potential flight as well as the area in which the flight will occur and what’s valuable in that space.

    With rapidly evolving and distribution of drone technologies, rapidly evolving counter-drone technological approaches, increased public awareness, and changes in local, state, and federal laws, I think investment, development, and deployment of counter-drone technology will continue to be a very dynamic realm.

In IoT ecosystem evolution, constraints = opportunities for IoT innovators

What are our opportunities for guiding the rapidly evolving IoT ecosystem? The Internet of Things, with its explosive growth, unprecedented variety of device & system types, lack of broadly shared language and conceptual frameworks to discuss and plan, lack of precedence for implementation, and the organizationally complex consumer systems — i.e. cities and institutions — required to implement and manage these IoT systems — all make for a challenging space. It can be difficult to even know where to start. One way to add structure and framework to the conversation is to introduce some constraints — and good news! There are constraints already there! They’re just not broadly seen or talked about yet.

What does a successful IoT system implementation look like ?

A natural source for constraints is from those things that define a successful IoT System implementation in an institution or city. I use two primary components to define IoT System implementation success:

  1. Return on Investment (ROI)
  2. Cyber risk profile

Regarding the first — ROI, does the system do what we thought it would do at the costs/investment that we thought would be incurred? As discussed in a recent post on IoT System costing, determining costs of IoT Systems implementation is different from traditional enterprise systems. Most institutions and cities have little experience at it and are generally not very good at it. Further, other subtleties such as expectations of the data created from deployed IoT systems across a spectrum of populations, demographics, & constituencies directly impact perceptions of system (and investment) success.

Regarding the second — cyber risk profile, did the IoT System implementation make things worse for the institution or city? Cyber risk profile degradation can come from poorly configured devices/endpoints, insufficient management resources (skill, capacity) for endpoints and data aggregators/controllers, inadequate vendor management, and others.

Constraints drive opportunities in the IoT ecosystem

These same two analysis requirements of a city’s or institution’s success, aka constraints, can also be used by innovators and providers of IoT systems. Knowledge of these constraints by IoT systems providers, these requirements for city/institution implementation success, creates opportunity for the IoT systems innovator and provider by identifying where they can help address organizational complexities in the course of pursuing ROI and cyber risk posture/profile objectives.

IoT systems are different

As discussed in other articles and posts, IoT Systems are different. The process of selecting, procuring, implementing, & managing IoT systems is different from doing the same for traditional enterprise systems such as email, calendaring, resource and customer management, etc. At least six aspects of IoT Systems contribute to this difference:

  1. High number and growth rate of IoT devices
  2. High degree of variability of device types & variability of multiple hardware/software components within a device
  3. Lack of language and frameworks to discuss IoT opportunities, risks
  4. IoT Systems span multiple organizations within an institution or city
  5. IoT endpoint/devices tend to be out of sight out of mind
  6. Lack of precedent for successfully implementing these systems, few examples, few patterns to follow

Of these differences, #4 – the organizational spanning aspect of IoT Systems — presents a subtle but substantial challenge. Deploying IoT Systems in a city or institution is not like deploying an enterprise application in a data center or SaaS in the cloud and then providing for end-user training and support. This, of course, does not mean that deploying large enterprise systems is easy by any stretch, but rather that there are more and different organizations required in the technical, operational, and management aspects of the system.  Because of this, new levels of inter-organizational cooperation and collaboration are sought. And, as we all have experienced, collaboration and cooperation is frequently touted but successful collaboration and cooperation is often not achieved — “the discrepancy between the promise of collaboration and the reality of persistent failure” (Koschmann).

Cities and institutions are complex multi-component organizations that offer a complex substrate for IoT System implementation. These complex IoT product and service consuming organizations are not blank slate, clean whiteboard, or powerpoint deck solution organizations. There is little homogeneity here.

IoT Systems innovators and providers that recognize these constraints brought on by these complex consumer systems, that seek to learn the institutional organizational challenges in detail, and get in the dirt at the outset with the city or institution will ultimately be IoT Systems ecosystem drivers.

“I built it in my garage, it works there, it’ll be awesome in your city!”

Because of the seemingly unbounded potential of IoT Systems solutions, there’s also room for undifferentiated, poorly provisioned, and poorly serviced garbage in this space.

Because of the newness of IoT Systems, often there are many technologies and many vendors without particularly long track records. There are some big names in the game of course — Cisco, Microsoft, Intel, Siemens for example. But there are many providers in that long tail, both proven and unproven, and some of them will offer great innovation and value. Some of them will not. The challenge for institutions and cities is to work to separate the wheat from the chaff as they select, procure, implement, and manage IoT Systems.

Going by name brand alone is not sufficient because there will be many new innovators and providers that do indeed offer promising and solid solutions that give a reasonable likelihood of ROI and an approach that does not degrade the existing cyber risk profile of the institution. Further, sometimes large companies can be problematic because they are used to throwing their weight around, possibly invested heavily in particular approaches, and may not be open to new or alternative approaches. This may or may not be with whom a city or institution wants to work.

Eyes off the bling for a moment

So how can a city or institution begin to separate the wheat from the chaff in choosing IoT systems? An initial step can be to take one’s eyes off of the ‘bling’ for a moment. The bling is all of the feature sets and bells and whistles that most think of when they think of IoT systems. So, a three step process would be:

  1. Take eyes off of the bling (feature sets, bells & whistles) for a moment
  2. Review implementation challenges internal to the institution
    • organizational spanning complexity
    • calculating IoT system support costs across all organizations
    • analyzing internally available skill sets and capacity
    • consider what criteria different demographics will use to assess success or failure
    • seek input in estimating cyber risk to which an  institution or city is already exposed to provide an estimated baseline
  3. Seek and prioritize IoT Systems innovators/providers that help address some of these internal organizational challenges and shortcomings

insideoutoutsidein

Cities and institutions look inside out — Some of their internal challenges include:

  • organizational complexity (spanning)
  • IoT system support staffing capacity
  • appropriate skill sets
  • IoT system support tools availability & experience
  • what ROI will a particular provider’s IoT system bring?
  • will implementing this IoT system make my cyber risk picture worse? how do I know?

IoT innovators & providers can look outside in —  and use these constraints to create market differentiators for their organizations, such as:

  • can I help city/institution address internal challenges?
  • can I provide tools to help them manage their system?
  • can I help them reach the ROI they were expecting?
  • can I help them mitigate their cyber risk from this implementation?

Not just one IoT System

We’ve been talking about just one prototypical IoT System for an institution or city. In practice, institutions and cities will have many IoT Systems. Many of these  IoT Systems will:

  • use shared technical resources of the city or institution, eg network and supporting systems
  • have interdependency with other systems
    • at device level
    • at data level
    • to include co-existing with legacy systems & new systems
  • dip into the same limited pool of skill sets and capacity for systems support

This further deepens the IoT Systems management challenge within the city or institution. Implementation challenges for these complex city and institutional consumers will only continue to grow. They won’t diminish.

IoT Systems innovators and providers that recognize and speak to this additional level of complexity — this ecosystem with multiple providers and vendors within an institution —  and provide options, services, and support to help cities and institutions manage this complexity will set themselves apart from the competition and develop longer lasting relationships.

In this seemingly open-ended space of IoT systems possibilities, identifying and developing solutions for organic complex consumer constraints and challenges can be a differentiator for IoT product innovators and service providers.

FAA drone activity timeline

The FAA has found itself in a bold new role over the past few years regarding drone, aka Unmanned Aircraft System (UAS) policy, registration services, guidance, detection, establishment of permanent and temporary no drone zones, studies on drone/human collision risk and impact, and related areas. We can expect that this will continue to be a very active space over the next several years.

A timeline of some FAA drone activity since early 2015 is depicted below. The graph shows the total number of FAA News/Updates and Press Releases for that month. I used the FAA’s website news search engine to get these numbers. (I used the dates from search engine as the basis even though the actual event date might have differed by a few days in a couple of cases.) Some representative links to FAA News/Updates and Press Releases follow the graphic.

FAAdronenewsprtimeline2

Super Bowl XLIX
Super Bowl 50
Super Bowl LI

Other No Drone Zone announcements —

DC kicks off No Drone Zone
Papal visit No Drone Zone press release

Airport drone detection system updates —

Denver International Airport & FAA partner on drone safety
Testing FBI drone detection systems @ JFK
Evaluating drone detection systems in Denver area
Testing drone detection system @ DFW

Other notable updates —

Drone registration site launched
San Francisco 49ers launch drone safety effort
Registration requirements go into effect
FAA responds to Court of Appeals reversal of registration requirements

 

Clearly, drone policy development and enforcement, drone detection and management systems, other drone-related tech, and messaging the same to the public is likely to a substantial growth area and effort for the FAA over (at least) the next several years.

Costing IoT Systems in cities & institutions

Determining the total cost of ownership, operation, and stewardship of IoT Systems for an institution or city has a number of considerations. Some of these considerations are shared with traditional enterprise systems and some are unique to IoT Systems. Lack of realization and acknowledgement of these costs can lead to disappointing and costly IoT Systems outcomes. These disappointing outcomes manifest themselves in the lost ROI of an IoT investment as well as negative impacts to the cyber-risk profile to an institution.

Listing from least complex/nuanced to more complex/nuanced …

  1. Costs to host servers, databases, & redundancy — whether under desks (hopefully not), in local data centers, or in the cloud
  2. Costs to support large numbers of geographically distributed ‘Things’ & devices (the T in IoT) & the different institutional organizations that may be involved (some of which may not see or realize the potential benefit to the institution but may bear at least some of its support costs)
  3. Costs stemming from the natural friction, sometimes small & sometimes large, between multiple historically disparate organizations within an institution as they attempt to coordinate, collaborate, and address problems of understanding to support the system

1. The more familiar part — application server, database server, & redundancy costing

This costing is more closely aligned with traditional enterprise application costing than other IoT systems costs. Application licensing and support agreements are a part of this aspect. Important additional costs to include, though, are: what are the costs of hosting those applications and databases? And what are the costs of supporting that hosting (e.g. who manages the relationships, the tickets, the problems, etc).

The applications servers or virtual machines (VM’s), database servers/VM’s, and redundancy servers/VM’s can be hosted in a local data center, shared data center, the cloud, or similar. Hosting or otherwise servicing these applications and supporting databases have their own costs. In addition to fees for the hosting, there’s also some cost to managing the vendor relationship and agreement/contract.

2. A guy, a truck, and a ladder – a less obvious part of IoT System costing

 

this is not cheap

this is not cheap

Imagine an institution or city that implements 500 smart street lights that provide lighting, sense movement, maybe samples air quality, and possibly monitors and reports street or underground vibration. There will be some failure rate amongst the components in any single device/endpoint and some failure rate across the total number of installed devices/sensors – the T’s in IoT.

In this hypothetical scenario, troubleshooting and/or repair means:

  • deploying 1 or more skilled tradespeople
    • 1 or more for the work & possibly another as a safety observer
  • rolling a truck or trucks
    • with associated vehicle/fleet/fuel costs
  • spending 1-2 hours just to get to the point of troubleshooting/repair
  • 2 – 4 hours troubleshooting/repair
  • 1-2 hours wrap up and return

For this hypothetical example, let’s say the skilled tradespeople involved make $60/hour and their benefit load (expense to institution or city) is 25% for a total $75/hour expense. So, disregarding fleet and related costs, one estimate might be:

(2 hrs prep + 4 hrs on site troubleshooting/repair + 2 hrs recovery) x 2 tradespeople x $75/hr = $1200 per support event

Continuing on the thread, let’s say there’s a 10% / year failure rate (or required hardware/software update rate) of at least some component on a single device or Thing (T in IoT). That would be:

500 devices x 10% repair or troubleshoot rate/year x $1200/event = $60,000 / year

That dollar figure starts to become non-trivial. And that’s just one IoT System. Cities and institutions will have many, a portfolio, of well-managed or less-well-managed IoT Systems. Another hypothetical example is in the example  below —

IoTSysCostingExample050917

multiple costs involved

3. The insidious part — loss from organizational friction

The least apparent and possibly most costly aspect is the loss that occurs from the coordination and collaboration required between organizations and the oversight needed across all of them for a successful implementation. In the idealized scenario of institutional capacity, there is a homogeneous set of resources that include components such as available staffing levels (FTE), requisite skill sets (technical, operational, and interpersonal), support funding, political/institutional will to support the implementation and operation of the IoT System, vendor relationships, and other.

idealizedcapacity

idealized view of IoT System management capacity

In practice, however, this institutional capacity is comprised of many different organizations and the interrelationships between them.  While collaboration and inter-organizational cooperation is typically universally lauded, we all know from personal and professional experience that often collaboration between institutional organizations does not in fact work so well. Research has also been done on this phenomena where, “the discrepancy between the promise of collaboration and the reality of persistent failure” is studied (Koschmann).

Wherever two or more organizations interact with each other, some sort of friction or system loss is present. Metaphorically similar to the 2nd law of thermodynamics, not all of the time, energy, resources put into ostensible institutional IoT System management capacity will be used, or can be used, to do the expected work. In the organizational friction example, losses can come from a multitude of sources where the number of friction sources and the intensity of each can vary from organizational relationship to organizational relationship. Examples of this sort of friction/system loss and resultant loss in expected institutional capacity include:

real capacity stems from multiple organizations with multiple relationships and the friction between

real capacity stems from multiple organizations with multiple relationships and the friction between

The insidious part is that while the friction between any two organizations, may be small and possibly not obvious, these small instances of organizational friction aggregate across the whole institutional and IoT System implementation effort. Further, not all relationships are one-to-one. There are often many relationships where many organizations are involved. Likening to Newton’s Three Body Problem, adding a third planet, billiard ball, or organization can significantly increase the complexity of analysis, prediction/forecasting, and the ability to get work done.

capacityloss

 

Costing differently

In practice, capturing all of these costs can be challenging. #1 — application and database licensing and hosting costs is relatively the most straightforward and for which we generally have the most experience, but that doesn’t mean it’s effortless. We have less experience with #2 for IoT Systems — the guy, truck, & ladder costs — but that support cost can be estimated and the failure rates of devices and device components can be estimated. Costing #3 — aggregated inter-organizational friction is, by far, the most difficult and possibly the most impactful — in part because of its magnitude and in part because of the uncertainty it introduces.

The import thing, I believe, is to acknowledge all of these components, compute and estimate what we can, and work to allow for and hopefully prepare for the unique uncertainty that selecting, procuring, implementing, and managing IoT Systems brings. If we do this work, we have our best chance of reaching anticipated ROI and not degrading (possibly even enhancing) the risk profiles of our cities and institutions.

 

 

Can we manage what we own? — IoT in smart cities & institutions

The rate of growth of IoT devices and systems is rapidly outpacing the ability of an institution or city to manage those same devices and systems. The tools, capacities, and skill sets in institutions and cities that are currently in place were built and staffed for different information systems and technologies — centralized mail servers, file sharing, business applications, network infrastructure support, and similar. Some of these systems still exist within the enterprise and still need robust, effective support while others have moved to the cloud. The important consideration is to not assume that toolsets developed for traditional enterprise implementations are appropriate or sufficient for IoT Systems implementations.

What's manageable- 032217

things are increasing faster than the ability to manage those things

Working from the outside in

Starting with the outer ring, the number of ‘things’ — the T in IoT — is rapidly growing within institutions and cities. From my perspective, an IoT ‘thing’ is a device that computes in some way, is networked, and interacts with its local environment in some way. Further, these systems may be acquired via non-traditional methods. For example, a city’s transportation department may seek and acquire a sensor, data aggregation, and analysis system for predictive maintenance for a particular roadway. This system might have been selected, procured, implemented, and subsequently managed independently of the organization’s traditional central IT organization & processes. Complex and high data producing systems are entering the institution/city from a variety of sources and with little formal vetting or analysis.

Can we even count them?

Because of the rapid growth of IoT devices and systems in concert with alternative entry points into the city/institution, even counting (enumerating)  — these devices — which can compute with growing ability and are networked — is increasingly difficult. This lack of countability in itself is not so bad, it’s just a fact of life – the trouble comes when we base our management systems on the assumption that we can count, inventory, much less manage all of our devices.

What do we know about the devices?

Do we have documentation and clarity of support for the tens, hundreds, thousands (or more) of devices. What do they do? How are they configured? Have we set a standard for configuration? How do we know that that standard is being met? What services do we think should be running on the devices? Are those services indeed running on them? Are there more services than those required running? Are there processes for sampling and auditing those device services over the next 12 – 36 months?  Or did we install them, or have them installed, and simply move onto the next thing?

We can borrow from the construction industry and ask for as-built documentation. What actually got installed? What are the documents that we have to work with to support this system? Drawings? IP addresses? Configuration documents for logins, passwords, open ports/services?

What is manageable?

If we are in the fortunate position to be able to actually count these computing/networked/sensing devices with reasonable accuracy and we know some (enough) things about the devices, then the next question is — do we have the resources — staffing, time, skill sets, opportunity cost, etc — to actually support the devices? Suddenly in smart cities, smart institutions, smart campuses, we’re installing things, endpoints, in the field that may require regular updating (yearly, monthly, …) — and this occurs between the customer network with its protocols/processes and the vendor system that is proposed. Not all (possibly substantial) device updating can be accomplished effectively remotely.

Another challenge is that often the organizations that are charged with staffing, installing, and supporting these deployed IoT devices, such as smart energy meters or environmental monitoring systems, are more accustomed to supporting machines that last for years or decades. Such facilities management organizations have naturally built their planning, repair, and preventative maintenance cycles around longer periods. For example, a centrifugal fan in a building might have a projected lifespan of approximately 25 years, soft start electric motors 25 years, and variable air volume (VAV) boxes with expectancies of 25 years.

Similarly, central IT organizations generally are not accustomed to running out into the field with trucks and ladders to support 100’s, 1000’s, or more of computing, networked devices in a city or institution. So the question of who’s going to do the actual support work in the field is not clear in terms of capacity, skill sets, and costs.

device count vs mgmt ability 032217-3

Actually managing the things

So, if we have all of the above — and that subset gets smaller and smaller — have the decisions been made and priorities established to actually manage the devices? That is, to prioritize, risk manage, and develop process to manage the devices in practice? There’s a good chance that manageable things won’t actually be managed due to lack of knowledge of owned things, competing priorities, and other.

On not managing the things

It is my opinion that we will not be able to manage all of the ‘things’ in the manner that we have historically managed networked, computing things. While that’s a change, that’s not all bad either. However we do have to realize, acknowledge, and adjust for the fact that we’re not managing all of these things like we thought we could. Thinking we’re managing something we’re not is the biggest risk.

We’re moving into a world of potentially greater benefit to the populace via technology and information systems. However, we will have to do the hard work of being thoughtful about it across multiple populations and realize that we’re bringing in new risks with some known — and unknown — consequences.

Creating IoT Systems Manageability – A Risk-Managed Set of Networked Things

To achieve IoT Systems ROI and to ensure non-degradation of an institution’s existing cyber-risk profile, IoT Systems must be manageable. In turn, in order to build IoT Systems manageability, institutions will need to manage their IoT Systems risk with non- traditional approaches that includes assigning IoT endpoints (the ‘things’ in IoT) to risk categories that can be independent of the underlying technologies and vendors.

IoT Systems are increasingly complex to implement, manage, and to establish system ownership in institutions, whether cities, Higher Education institutions, or corporate campuses. In turn, an institution’s IoT Systems Portfolio – a systems of systems – rapidly deepens the complexity. We will need to tackle the problems and challenges in new ways and with new organizational concepts if we are to have an opportunity for well-managed and reasonably risk-mitigated systems. This includes thoughtful inter-organizational planning, partnerships, and development of a more common language between central IT, distributed operational organizations and departments, and vendors. Further, this will be required to establish system ownership and management plans between organizations such as facilities organizations, central IT, research groups, vendors, and others. One step toward this objective is identifying things to be managed independently of the technologies and vendors implementing them — a Risk-Managed Set of Networked Things.

Central IT won’t own all of the IoT Systems

Traditional enterprise network and system management tools, staffing models, and even language are ill-equipped to address this rapidly changing technology. Historically, network and system management tools have all been within the purview of central IT. Central IT will not be able to keep up with the accelerating growth of IoT Systems across an institution. Just like central IT organizations cannot manage every user/academic/business application on their networks (or even many of them), central IT will not be able to support all of the IoT Systems either. Business owners — operational (academic in the Higher Ed case) and administrative — will have to share that load. That’s better for them too — they are closer to the problem and have a truer understanding of desired outcomes from the system. Implementing this coordination across two or more organizations in the institution is new work though. There are not great precedents for this. Institutions, particularly Higher Education institutions, are known for their bureaucracies within bureaucracies, entrenched ways, and “cylinders of excellence..” (aka silos) .

system of system of systems ...

system of system of systems …

In a similar fashion facilities management organizations have substantial skill sets in building in and integrating equipment into built environments whether they are buildings or spaces. However, facilities management organizations don’t have network design, implementation, network management, and traditional server management skill sets. Finally, while operational departments, whether acting independently or in collective partnerships with other operational departments, know what they want systems to do and comment on performance, they do not have the required skill sets that facilities management and central IT groups bring to the table.

This organizational-spanning nature of IoT Systems in institutions make establishing ownership and a post-implementation management plan particularly challenging.

Designing for & building IoT System manageability

The growth in institutional system count, system complexity, and system interdependency makes for rapidly evolving systems management and owner environments for Higher Ed institutions. We have to take definitive steps to make things more manageable. That is, we have to design for system manageability. Applying historical and traditional tools and organizational approaches to this rapidly changing environment will no longer be sufficient.

A core component of any framework to facilitate manageability is a language, or at least shared concepts, that support it. In turn, a substantial objective of that shared language development (shared, for example, between central IT, facilities, and operational users) is to develop structures that make the systems more manageable. This sounds obvious, but in our complex environments and with our dwindling availability of time and cognitive bandwidth, it is easy to lose sight of this objective.

Agreeing on what is being managed

Before different organizations within an institution can establish those manageability- facilitating-structures and figure out how to partner, establish ownership, and mitigate risk to institutional systems, they have to be able to mutually identify and agree upon what is being managed. What is the set of things — devices, systems, spaces, buildings, infrastructure, etc — that we care about managing, from both operational and risk mitigation standpoints?

In days of relatively simpler systems, sets of networked things/devices to be managed were often defined by the network itself and/or systems on the network and/or the particular brand of technology supporting the network. Further, these networked things/devices were typically run by central IT organizations and these organizations were comfortable with using locally understood network terminology and concepts to define that set of things. Examples include — devices/things on a particular subnet or set of subnets, devices/things behind a particular firewall, on a particular VLAN or VRF, etc.

These examples above don’t mean much to potential system owners that are business organizations and/or academic organizations. The terms used are way too abstract, jargon-y, and/or colloquial. The cross-organizational planning and coordination needed for IoT Systems implementations and subsequent management cannot occur if participating groups can’t mutually identify what is to be managed.

Also problematic in trying to apply these old approaches of identifying things and systems to be owned, operationally managed, and risk-managed is that it is easy to slip into the high-granularity/high-entropy of technical details when the initial conversation is simply identifying and agreeing upon what is to be managed. Because these new and rapidly evolving technologies are increasingly complex, requiring increasingly deep technical skill sets, conversations in technical detail can be challenging even for technical professionals and effectively useless for potential academic and business partners and systems owners.

Finally, sets of things/devices to be managed might involve multiple technologies — eg maybe partially wired, partially wireless/near-field, on a VRF, behind a firewall, etc. So using a technology as a defining mechanism is further unhelpful. While a particular technology or network might happen to align well with a business need for defining a group of assets to be managed, we don’t want to start with that assumption.

IoT System Manageability Groups – A Risk-Managed Set of Networked Things

To address these shortcomings, we can consider a Risk-Managed Set of Networked Things (RMSONT). In this approach, we work to establish sets of networked things based on what best enhances manageability of the system. This is independent of underlying implementing technologies, particular vendors, and existing organizational charts.

What constitutes IoT System manageability?

A managed IoT System will have at least these attributes:

  • the IoT System was selected methodically and with purpose
  • the IoT System is named & known
    • the system has a common name that is known, shared, & published to participating parties (eg central IT, facilities management, operational departments, etc)
  • devices/things of the IoT System are enumerable
  • that is, via network process the device can be known and named
  • IoT System owners identified
  • IoT System component owners identified
  • satisfactory system performance is defined
  • system performance is measured
  • system performance is reviewed by business owners and systems support providers
  • estimates of total costs are established and shared — includes IT and Operational Technology (OT) costs
  • other

What are the qualities/attributes of a thing/device?

Things/devices in IoT Systems have at least these qualities or attributes —

  • a location
  • a function (what is it supposed to do)
  • an IP address; a MAC address
  • a power requirement
  • an associated data aggregator or controller
  • supports a user, users, or population (department, organization, constituency, etc)
  • rate of failure (estimated or known)
  • other

Creating a Risk-Managed Set of Networked Things

The #1 goal is to build and enhance IoT Systems manageability. A risk-managed set of networked things is established to create a manageable group. This could be a group managed by the business consumer or an institutional service organization such as facilities management or central IT — whatever best facilitates system ownership and management.

Multiple sets of risk-managed things can be created to facilitate overall system manageability. One example of a set of risk-managed sets might be:

  • law enforcement/security office owns and manages a set of networked video cameras (possibly with support from central IT, facilities management, local IT, vendor, etc)
  • in an academic setting, a researcher that uses a specialized HVAC IoT System with sensors, actuators, and data aggregation to provide tight environmental control of their research environment might be a logical choice to own that system
  • a metering system might best be managed by the institution’s energy management office
  • a building manager might have a risk-managed set of networked things local to their particular building, but of different types of things— surveillance cameras, energy management system, etc

In general, getting those most familiar with the IoT System’s performance expectations and actual performance into a system management role is probably a good idea.

As we talk about sets of things, and particular kinds of sets of things, we can borrow lightly from the mathematical idea of groups.

As I understand it, mathematical groups are:

  1. Sets of things
  2. These things abide by or participate in some set of rules
mathematicalgroup

A selected set of things abiding by a certain set of conditions & operations …

Similarly, a Risk-Managed Set of Networked Things, can be:

1. A set of networked and computing things/devices (that interact with the environment)

2. These things/devices participate in or are governed by some sort of network management processes and human management processes — eg automated network device enumeration/inventory, device health/responsiveness, etc

iotsystemsgroupimage

RMSONT – A Risk-Managed Set of Networked Things

 

You gotta keep ‘em separated (or not)

To borrow from Offspring’s social commentary (and popular song) on gang membership, colors, and violence in Come Out and Play, the theme of IoT Systems and network segmentation seems to be, “you gotta keep ‘em separated.” The problem is that that is not as easy as it seems.

offspringlive

tie your own rope, tie your own rope, tie your own rope (hey!)

Network segmentation has been all the rage as an answer to IoT Security and risk mitigation. However, as we’ve seen, network segmentation alone is not sufficient. Risk-managed sets of things need to be thoughtfully chosen, the rules and operations supporting that set of things needs to be thorough, and systems owners thoughtfully coupled with systems in order to achieve manageability.

We can manage IoT Systems within our institutions. And we can manage portfolios of IoT Systems within our institutions. However, we need to acknowledge that these are different kinds of systems and that our existing traditional IT systems operational and risk management approaches are likely insufficient. From that point we can sculpt and evolve new management approaches that facilitate successful, well-managed IoT Systems portfolios.

Organizational-spanning characteristics of IoT Systems

Gaps between institutional organizations implementing & supporting IoT Systems create challenges

Gaps between institutional organizations implementing & supporting IoT Systems create challenges

One of the unique characteristics of IoT Systems, and one that adds to the complexity of a system’s deployment, is that they tend to span many organizations and entities within the institution. This is particularly true in Higher Education institutions with their city-like aspects, multiple service lines, and wide variety of activities in their buildings and spaces. While traditional enterprise systems, such as e-mail or calendaring, are likely to be owned and operated by one or two institutional organizations, IoT Systems involve many and are deployed in the ‘complex and material manifestations’ that characterize buildings and spaces.

A Higher Education institution example might be a research lab that incorporates an automation and environmental control system that involves the facilities organization, the central IT organization, maybe a local/distributed IT organization, the lead researcher (aka Principal Investigator or PI), her lab team, at least one vendor/contractor and probably several other vendors. Between each of these, a gap forms where system ownership and accountability can fall. Everyone sees their piece, but not much of the others. There’s no one monitoring the greater Gestalt of the IoT System. And that’s where the wild things are.

Traditional enterprise systems tend to fall within the domain of central IT with use of the system being distributed around the institution. So with traditional enterprise systems, use is distributed but ownership and operation is largely with one organization. IoT Systems, on the other hand, tend to have multiple parties/organizations involved in the implementation and management, but the ownership is unclear.

IoT Systems are systems within systems within systems ...

IoT Systems are systems within systems within systems …

This lack of ownership can lead to unfortunate assumptions. For example, the end user/researcher in the Higher Ed case is probably thinking, “central IT and the Chief Information Security Officer are ensuring my system is safe and secure.” The central IT group is thinking, “I’ve got no idea what they’re plugging into the network down there … I didn’t even know they bought a new system. Where did that come from? “ The facilities people might be thinking, “Okay I’ll install these 100 sensors and 50 actuators around this building and these two computers in the closet that the vendor said I had to install. The research people and central IT people will make sure it’s all configured properly.” No one is seeing the whole picture or managing the whole system to desired outcomes.

This implementation and management of IoT Systems is a part of what is being explored within Internet2’s IoT Systems Risk Management Task Force in support of  Internet2’s  Smart Campus Initiative.

Technology adoption in other aspects of the building industry

Research has been done in other areas of technology implementation in the building, space, and campus realms that might help shed some light on the multi-organizational challenge that IoT system implementations can bring to institutions.

Research in the Building Information Modeling (BIM) field suggests that buildings have a ‘complex social and material manifestation … [that requires] a shared frame of reference to create.’ Building Information Modeling seeks to codify or digitize the physical aspects of a space or place such that its attributes can be stored, transmitted, exchanged in a way that supports decision-making and analysis.

In their paper, “Organizational Divisions in BIM-Enabled Commercial Construction”, researchers Carrie Dossick and Gina Neff identify competing obligations within supporting/contributing organizations that limit technology adoption.  They point out that BIM-enabled projects are “often tightly coupled technologically, but divided organizationally.” I believe that their observations regarding BIM projects also share common aspects of deploying and managing IoT Systems.

The Dossick/Neff research suggests that mechanical, electrical, plumbing, and fire life safety systems can be as much as 40% of the commercial construction project scope.It is likely that this number will only increase as our buildings and spaces become more alive, aware, and aggregating of information of what goes on in and around these spaces.

For the BIM implementations studied, the research suggests that there are three obligations of the people and groups contributing to the effort and that these can be in conflict with each other.  The obligations are: scope, project, and company.

As I interpret the paper, scope obligation is what a person or group is supposed to accomplish for the effort – what are they tasked to do. That mission is not overarching organization and coordination of the project, but rather specific, often local, tasks that must be done in support of the effort. In the course of that work, participants in the work are naturally “advocating for their particular system.“

Projects bring together temporary teams for a particular purpose. These time boundaries and purpose boundaries create the environment for the project obligation. Specific timelines and milestones can drive the project obligation. This area can be particularly challenging as it can involve negotiations among different providers, the interests of owners, and design requirements.

Finally, obligations to company “emphasize the financial, legal, and logistical requirements” where ownership and management provide the environment and context in which work is done. This also makes sense intuitively as company is whence one’s paycheck comes. Performance today can influence today’s paycheck as well as paychecks down the road.

While not exactly the same, I think there are some parallels with IoT Systems implementation and management.

BIM implementation/adoption -> IoT Systems implementation & management

  • scope -> scope
  • project -> project
  • company -> department/organization

To me, organizational aspects of scope and project are very similar between IoT Systems implementation/management and those observed and analyzed by the research in BIM implementation.

For IoT Systems implementation and management within an institution, internal departments and internal organizations can closely parallel that of the company that the research addresses. A person’s or group’s directives, performance expectations, and paycheck approval ultimately comes from that department or organization so there will be natural alignment there.

Leadership ‘glue’ has its limits

Finally, the paper also points out the role of leadership in an effort where there is new technology to potentially be adopted or leveraged. While strong leadership is clearly desirable, the research suggests that even good leaders often cannot overcome structural organizational problems with great efficiency or effectiveness. The authors also note that research does not yet understand what “organizational resources to be in place for effective collaboration to occur after new technologies are introduced.”

IoT System ownership for implementation & management

Like BIM and related technology adoption, I believe successful IoT Systems implementations have similar institutional organizational challenges. IoT Systems implementations are themselves a part of larger system of institution, organizations, and people. IoT System implementation and management success will, I believe, also require learning to work with these multiple organizations that have inherent competing obligations. While there may be other approaches to evolve to solve these organizational challenges, a reasonable place to start in the near term might be to establish organizational-spanning system ownership and accountability.

Internet2 Chief Innovation Office launches IoT Systems Risk Management Task Force

Internet2 has launched a national Task Force to study risk management needs around IoT Systems in Higher Education and research institutions. The Task Force is composed of Higher Education and research IT and Information Management leaders across the country and will explore the areas of IoT Systems selection, procurement, implementation, and management. At the end of 12 months, the IoT Systems Risk Management Task Force will deliver a set of recommendations for 3 – 5 areas of further in-depth work. (And in the interest of full disclosure, I am Chairing the IoT Systems Risk Management Task Force.)

Internet of Things Systems or IoT Systems offer great potential value to higher education, research, government, and corporate institutions. From energy management, to research automation systems, to systems that enhance student, faculty, staff, and public safety, to academic learning systems, IoT Systems offer great promise. However, these systems need to be implemented thoughtfully and thoroughly or the investment value won’t be realized. Further, because of the distributed computing and networking capabilities of IoT devices, poor IoT Systems implementations can even make things worse for institutions, corporations, or governments.

Internet2 Chief Innovation Office

i2logoThe mission of the Internet 2 Chief Innovation Office, led by Florence Hudson,  is to work with Internet2 members to define and develop new innovations around the Internet. The Innovation Program has three core working groups —

Internet2’s core offerings are its 100 gbps network and their NET+ services.  Their membership includes 300 Higher Education institutions and over 150 industry, lab, and national agency organizations.

Many IoT systems risk topics

Examples of topics that the Task Force will cover include IoT systems vendor management issues, network segmentation strategies and approaches, cost estimating tools and approaches for IoT systems, potential tool development and/or partnering with organizations that perform Internet-wide scanning for IoT-related systems, and the organizational and cultural issues encountered in transitioning to a data-centric organization.

IoT systems vendor management approaches

Organizations and institutions need to raise the bar with IoT systems vendors regarding what constitutes a successfully delivered product or service. For example, has the vendor delivered documentation showing the final installation architecture, have default logins & passwords been change on all devices (how is this demonstrated), have all unnecessary services been deactivated on all devices and systems and how is this demonstrated?

Development of common ‘backends’ for IoT systems

Current IoT systems (to include utility distribution, building automation systems, many others) vendor approaches require that institutions invest in separate and proprietary ‘backend’ architectures consisting of application servers, databases, etc for each different vendor. This is an approach that does not lend itself to manageability, extensibility, or scalability.  In this space, perhaps newer container and container management technologies offer solutions as well as other possibilities.

1200px-Internet_of_things_wilgengebroedDevelopment of network segmentation/micro-segmentation strategies and approaches for IoT Systems

Network segmentation seems to offer great promise for mitigating risk around IoT Systems implementations. However, without appropriate guidance for IoT network segmentation implementation and operation, institutions can end up with a full portfolio of poorly managed network segments. Exploration and development of institutional network segmentation best practices can serve to lower an organization’s risk profile.

Development of cost estimating tools and approaches for IoT Systems

There is little in the way of precedent for cost models for the rapidly evolving IoT systems space and, as such, planning for IoT Systems and trying to estimate Total Cost of Ownership is difficult and nuanced. Exploration of and development of IoT Systems cost models can be of real value to institutions making planning and resourcing decisions.

Development of risk language & risk categories around IoT systems

Currently it is difficult to discuss new risk brought on by IoT systems with enterprise risk managers because IoT systems themselves are difficult to describe and discuss.  Development and socializing IoT risk language, that incorporates existing familiar institutional risk language, would enhance the ability to discuss IoT systems risk at the enterprise level. This Task Force will explore this nuanced space as well.

Analysis tool development and partnering

The Task Force will explore tool development and/or partnerships with organizations that scan the Internet for industrial control systems and IoT systems and publish these results online. Exploring internal tool development of the same is also a possibility. Development of benchmarks and baselines of Internet-scanning results across different industries and market sectors will also be considered.

Organizational cultural barriers to successful implementation of IoT Systems

Changing from a traditional organization to a data centric organization is a non-trivial transition and not addressing these issues can be a barrier to successful implementations of IoT Systems in institutions, organizations, and cities. The Task Force will study this important space as well.

Early Task Force work will also include identifying and enumerating other independent and overlapping risk areas (operational, cyber, cultural, and others). Over the year, Task Force members will participate in phone conferences, listen to subject matter expert presentations, and identify, discuss, and prioritize IoT Systems issues. Finally, recommendations will be made for further focused work on the highest priority areas.  If you have questions, comments, further interest, please contact me ChuckBenson@longtailrisk.com or the Internet2 Chief Innovation Office at CINO@internet2.edu.

 

[IoT image above: By Wilgengebroed on Flickr – https://www.flickr.com/photos/wilgengebroed/8249565455/, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=32745541]