Special Issue on Digital Ecologies

Can intelligence optimise Digital Ecosystems? How could a distributed intelligence interact with the ecosystem dynamics? Can the software components that are part of genetic selection be intelligent in themselves, as in an adaptive technology? We consider the effect of a distributed intelligence mechanism on the evolutionary and ecological dynamics of our Digital Ecosystem, which is the digital counterpart of a biological ecosystem for evolving software services in a distributed network. We investigate Neural Networks (NNs) and Support Vector Machines (SVM) for the learning based pattern recognition functionality of our distributed intelligence. Simulation results imply that the Digital Ecosystem performs better with the application of a distributed intelligence, marginally more effectively when powered by SVM than NNs. These results suggest that a distributed intelligence can contribute to optimising the operation of our Digital Ecosystem.

Systems

Cyberspace is a new frontier, not just for hackers, but for engineers. It is a digital ecosystem, the next generation of Internet and network applications, promising a whole new world of distributed and open systems that can interact, self-organize, evolve, and adapt. These ecosystems transcend traditional collaborative environments, such as client-server, peer-to-peer, or hybrid models (e.g., web services), to become a self-organized, evolving, interactive environment. Understanding cyberspace as a system is critical if we are to properly design systems to exist within it. Considering it to be a digital ecosystem, where systems can adapt and evolve, will enable systems engineering to become more effective in the future of networks and the Internet. While most systems engineers have only anecdotal experience with large segments of this ecosystem, in today’s world all of them must come to understand it. Engineering any system, or portion of a system, begins with an understanding of t...

International Journal of Parallel, Emergent and Distributed Systems, 2020

2012

After Weiner [1], this paper considers the Cyber-as a 'system whole' representing two 'networks in being': one to do with collaborative, social influence; the other with coordination, rule and control. We identify the need to establish the collaborative trusts and assuranceswe call sûrétenecessary for institutionalising good governance (S2 † ). We then posit an alternative model for enabling innovation and adaptation within the internet. We contend that it is these social-trusts that will underpin future successful economies (S3); so providing both for security (S4) and the classification necessary for standardisation (S7) and sûréte. We further consider the Internet not simply as the Cyber-but as a part of what may be considered the wider being of Cyber-. After Foucault's 'cogito-unthought duality [2]' (which considers bi-polarity (he calls duality) both as an experiential subject and the almost implausible understanding of the object of that experience) and Badiou [3] (who talks of 'multiplebeings' representing 'all the common traits of the collective in question' and: 'the truth of the collective's being') we consider 'Networks-in-Being' [4]. Networks we identify as 'being' two different, entangled system ecologies.

2014

We describe a diffuse control system for household appliances rooted in an Internet of Thing network empowered by a cognitive system. The key idea is that these appliances constitute an ecosystem populated by a plenty of devices with common features, yet called to satisfy in an almost repetitive way needs that may be very diversified, depending on the user preferences. This calls for a network putting them in connection and a cognitive system that is capable to interpret the user requests and translate them into instructions to be transmitted to the appliances. This in turn requires a proper architecture and efficient protocols for connecting the appliances to the network, as well as robust algorithms that concretely challenge cognitive and connectionist theories to produce the instructions ruling the appliances. We discuss both aspects from a design perspective and exhibit a mockup where connections and algorithms are implemented.

Cybernetics and Systems, 2018

Cyber-physical systems-of-systems (CPSoS) are networks that interconnect cyber-physical systems and people. Intercommunications and interaction between a wide range of separate and autonomous components make a cyber-physical system-of-systems a highly complex entity. This complex entity works based on self-organization that is made possible by the exchange and sharing of information among the separate components. Information produces a temporary form of entanglement between the separate components of the new entity. Entanglement enables the new entity to develop emergent properties that are responsible for the functioning of this new entity. New scientific knowledge is required to be able to analyze and design this entity.

Communications of the ACM, 2009

, and other technologies are being used to capture the information in data while machine learning, entity extraction, neural networks, clustering, and latent semantics are approaches to extracting information from that data and help reason about it. The fi eld is an active area of research and experimentation and is still rapidly evolving (see the sidebar "Semantic Computing" vs. "Semantic Web"). Data mesh At the center of our discussion is the concept of a "data mesh," a term we use to refer to the various information and knowledge representation techniques/technologies that have been developed over the years (see Figure 2). In its simplest form, a data mesh looks like a directed graph in which the nodes represent data/information captured in well-known formats T he WeB hAS emerged as the largest distributed information repository on the planet. Human knowledge is captured on the Web in various digital forms: Web pages, news articles, blog posts, digitized books, scanned paintings, videos, podcasts, lyrics, speech transcripts, and so forth. Over the years, services have emerged to aggregate, index, and enable the rapid searching of all this digital data but the full meaning of that data may only be interpretable by humans. In the common case, machines are incapable of understanding or reasoning about the vast amounts of data available on the Web. They are not able to interpret or infer new information from the data and this has been a topic of active research interest for decades within the artifi cial intelligence community. While the dream of artifi cial intelligence-machines capable of human-level reasoning and understanding-may still not be within our grasp, we believe semantic technologies hold the promise that machines will be able to meaningfully process, combine, and infer information from the world's data in the not-too-distant future (see Figure 1). The Web ecosystem of simple formats and protocols is an example of how we can effectively manage, share, access, and represent large amounts of data. Companies like Microsoft and Google are building large-scale services (such as search and cloud services) leveraging the existing hardware and Tony hey

Journal of Social and Evolutionary Systems, 2000

Natural Computing, 2011

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures (EOA) where the word ecosystem is more than just a metaphor.