In 1935, Edwin Armstrong introduced his employers at RCA to his new radio broadcasting technology, Frequency Modulation. His employers saw FM as a legitimately groundbreaking technology, a massive improvement to existing broadcast systems, but also a disruptive innovation to their existing business models and their status quo. For the next 19 years, they lobbied government and fought successfully in the courts against the technology, driving Armstrong to poverty and extreme hardship.

A new technological ecosystem provides both opportunities and challenges for a society and to those interested in retaining the incumbent technological infrastructures. Those challenges are often legal in nature, partly because the law is a powerful mechanism of control and prevention of change – but also because by disrupting the ways in which we communicate, behave and make use of information, we often create case scenarios that lie outside those imagined as possible at the time the relevant laws are written. As a result, these challenges need to be identified, negotiated and managed if the disruptive technologies are to be harnessed for the good of society.

Legislation frameworks need to support innovation for the greater good. However, in order for innovation to take place, transgression of the letter of the law is often inevitable. That does not mean that ethical issues such as privacy, safety, fairness and the agency of individuals can be ignored – quite the opposite. Where legislation does not reflect the realities of the new technological environment, fairness and the interests of the greater good are often set at odds against the legal infrastructure of the status quo.

Innovations should be tested in terms of their capacity for emancipatory potential – not simply for economic stakeholders but for the participation of all stakeholders and citizens. The Swedish concept of ‘lagom’ (just enough) provides a useful guiding principle for business enterprise in the field. While there is clear urgency to innovate, invest and exploit in the field of IoT, a rapacious ‘gold rush’ mentality will do more harm than good.

Experiments with IoT need to consider perennial ethical principles – in terms of privacy, security, equality, labour exploitation, protection of the vulnerable, and so on – but it’s important to understand that the legal aspects and normative values have to be considered and reflected upon at a very early stage in the design and implementation cycle. IoT innovation is currently in its early experimental stage, but already it is challenging existing frameworks and regulatory systems that were designed to operate within a different ecosystem.

Dialogue between innovators and legislators needs to be ongoing, and focus on the ethical ‘first principles’ from which the laws arise, rather than from the rules themselves. Disruptive innovation will often be transgressive by nature, but it need not be at odds with what is good for society, culture and the economy.

Once again, Uber provides us with a very good case in point. The service is actively breaking new ground and as a result new legislations are already needed. London cab drivers traditionally require years of training and testing in “the knowledge” but that registration and testing process is seemingly made redundant by technological advances that use GPS. Arguably, the principle (safe passage, good service and fair prices to customers) still applies, but the mandated mechanism that ensures that principle (the knowledge) is no longer strictly required.

As a case study Uber is useful from an ethical and social perspective within the context of European policy. While Uber is massively disruptive, it has also been shown to be open to misuse of information and unethical practice by staff of the service. This is problematic because it adheres to a logic of capital that, like the industry it seeks to disrupt, prioritises the maximisation of shareholder return over social good.

It’s important for IoT innovation to begin from a moral, ethical and legal standpoint, as information carries legal, moral and ethical values and affordances – and especially because IoT technologies provide for communication without the immediate mediation of a human actor – even though that information may be used in a way that directly affects human experience.

Within Europe, we have, right at this moment, a unique opportunity at a time of significant change to engineer significant technological disruption in the interest of the greater societal good, and balance that interest with the need to incentivise innovation and investment in the IoT space. To do so requires that we favour ethics and the social good over the specific requirements of legislation that may no longer be entirely fit for purpose. In the case of Uber, while providing ethical and legal challenges, which need to be addressed, its model is also predicated on the idea that the company makes money if the drivers make money. In this respect, as a profit sharing participatory system, it also provides a case study in economic innovation.

As much as any social, political and economic factor, the standardisation of railway tracks throughout America in the mid 1800s contributed to the creation of a coherent multi-state nation. Communication, mobility and seamless transition were made possible with the advent of trans-national rail. Not just viable businesses, but entire towns and cities were built on the back of the single, consistent gauge railway and the Pacific Railway Act of 1863 that ensured it.

Likewise today, for the creation of successful and innovative business ecosystems within the frontier of the Allternet, it is necessary to build cohesive, interoperable protocols. These allow for creative, useful and experiential devices and services to be developed to run on them.

Protocols must be centred on easy to understand, layman-level classifications of network types and capabilities. Allternet protocols require clear and simple interfacing through APIs, graphical and/or tangible user interfaces (GUIs and TUIs) that give a high degree of flexibility and freedom. Certification can happen in a modular fashion. As with open source technologies, we can certify an element, people can develop it, and we pass that certification on through the system.

These protocols should not be locked to a particular operating system or proprietary environment. It’s crucial to preserve creative possibilities as well as incorporate open frameworks in the design process. Certification and licensing provides attribution for design inheritance (as with Open Product Licences). This degree of openness and simplicity provides for a variety of new business models and services that can be made available to potential content creators and participants.

Data provides transparency. For example, when using Uber, the passenger requesting a ride knows exactly where the cab is, has a clear idea how much the journey will cost and knows the make, model and licence plate of the car as well as what the driver looks like. For drivers, Uber provides data that reports traffic information, the best routes, highlights busy periods and ways in which drivers can maximise their revenue so they have greater agency as well as a clear basis for decision making about their own work.

Stakeholders provide choice. The creators of simple protocols are, metaphorically, laying railway tracks. The stakeholders and content creators build a wide variety of trains and ancillary services. They may create a luxury passenger car or a goods train. However, standardisation of the tracks is important, because otherwise there is no connectivity.

Sharing provides trust. If the track providers do not take too much revenue (if, for instance, they demand less than a quarter of profits generated by their use), then there is room for stakeholders to make money. This not only incentivises creativity and innovation in new uses of those tracks, but also establishes the necessary trust required to invest in building upon that infrastructure.

Transparency, choice and trust encourage participation. The people who take passengers down the tracks are the service providers. If it is made easy to build on those tracks, then anyone can use them. That in turn creates employment – or further entrepreneurship – that also contributes to that ecosystem. In other words, there is an opportunity for monetisation by contributing to the platform.

If, on the other hand, the contributor is not given the opportunity to make a profit or that profit is too small or risky to incentivise participation, then the platform itself will not make a profit. The Allternet provides a context for the creation of a non-exploitative service to both clients and contributors. Stakeholders view their contribution and involvement as a partnership with the platform.

Just as they did with the establishment of the standardised railway, new and unimagined types of businesses can flourish, and new communities can emerge and thrive, enabled by Allternet protocols.

In their 1977 short film Powers of 10, Charles and Ray Eames demonstrate that different kinds of understanding are possible at different orders of magnitude. When designing data-driven systems, it is crucial to analyse data at the human scale as well as at the mass aggregate scale.

Today we have a model for understanding data at different levels of magnitude: Google Maps. We can zoom in and out of geography, and are able to distinguish and analyse continents with a level of detail appropriate for the scale. That does not include minutiae such as streets, houses and parks. In that frame, we can identify data that is grouped at the level of Europe, Africa and so on – and we do not require more complex insights at that level. We zoom in to distinguish features such as cities and their geospatial relationships. We are able to orient ourselves with more and more levels of granularity, or we can zoom out again to get a sense of the overall picture.

At the human scale, there is a very different requirement of data mapping than there is at a global scale. As such, the notion of ‘zooming in’ provides a very good metaphor for how we should design intelligent data systems.

Data is not only stored in the cloud, it is also analysed in the cloud. Smart IoT systems use Big Data filtering, create ontologies and classifications in order to make sense of that data, consider the context of data usage and use AI to train systems to recognise patterns in data. The wealth of aggregate data accumulated by the Allternet provides, in itself, opportunities for understanding at a high level of analysis, but that analysis may not be relevant at the human scale.

In order for ‘Internet of Things’ projects to be validated, it is essential to run pilots deploying agent-driven applications. In this way, it will be possible to test, for instance, a ‘System of Systems’ in physical space, in relation to a scale comprehensible and useful to the people using the devices within that system. In this way, these projects and systems are contextualised and understood within the broader Allternet space.

There are those who advocate creating a system of systems in abstraction. There is another school of thought that believes we should start from the users. Neither of those two is better nor more important than the other. Instead, it’s about the rules or assumptions that can be made at each level. At the level of reconnaissance, there is a more abstract relationship with data which is about describing contours. At the level of the individual, the relationship is with the person and their specific needs and requirements. Not only are both valid, but there are multiple layers of understanding that can be reached at different levels of magnification.

It’s important not to make assumptions about somebody sitting on the street based on data that is mapped from the perspective of an altitude of 10,000 feet. When you’re sitting next to that person, you will have a very different understanding of what data is useful to you.

Designing data-driven systems is about creating truly intelligent systems that understand and appropriately respond to scale (as with the Powers of 10) – as well as to time, since data takes on different kinds of meaning over time. If you create one set of descriptors at a particular time, you will inevitably need to renew those descriptors when conditions change. The Allternet is, in this respect, like a living ecosystem.

As with data – so with ideas. An idea is always a result of particular affordances and parameters that are on offer at that particular point in time. In the case of EU-funded projects, this is usually mapped up front, rather than allowed to evolve. Any intelligences that we can draw from these projects change too. Because EU projects are locked in the first moment, they struggle to create a good business model. The project is cemented in the past before it has begun. Good business is always a living ecosystem. It needs to continually innovate in order to survive, keep ahead of competition, and reinvent itself.

Understanding data at scale (and over time) reflects the fact that the Allternet acts as a living ecosystem. From that adaptive, reactive and context-aware starting point, novel and disruptive IoT business models can be supported.