In this paper we present a model for coordinating distributed long running and multi-service transactions in Digital Business EcoSystems. The model supports various forms of service composition, which are translated into a tuples-based behavioural description that allows to reason about the required behaviour in terms of ordering, dependencies and alternative execution. The compensation mechanism warranties consistency, including omitted results, without breaking local autonomy. The proposed model is considered at the deployment level of SOA, rather than the realisation level, and is targeted to business transactions between collaborating SMEs as it respects the loose-coupling of the underlying services. © 2007 IEEE.
In this paper, we describe a true-concurrent hierarchical logic interpreted over concurrent automata. Concurrent automata constitute a special kind of asynchronous transition system (ATS) used for modelling the behaviour of components as understood in component-based software development. Here, a component-based system consists of several interacting components whereby each component manages calls to and from the component using ports to ensure encapsulation. Further, a component can be complex and made of several simpler interacting components. When a complex component receives a request through one of its ports, the port delegates the request to an internal component. Our logic allows us to describe the different views we can have on the system. For example, the overall component interactions, whether they occur sequentially, simultaneously or in parallel, and how each component internally manages the received requests (possibly expressed at different levels of detail). Using concurrent automata as an underlying formalism we guarantee that the expressiveness of the logic is preserved in the model. In future work, we plan to integrate our truly-concurrent approach into the Edinburgh Concurrency Workbench. © 2007 Elsevier B.V. All rights reserved.
In this paper we describe a formal model for the distributed coordination of long-running transactions in a Digital Ecosystem for business, involving Small and Medium Enterprises (SMEs). The proposed non-interleaving model of interaction-based service composition allows for communication between internal activities of transactions. The formal semantics of the various modes of service composition are represented by standard xml schemas. The current implementation framework uses suitable asynchronous message passing techniques and reflects the design decisions of the proposed model for distributed transactions in digital ecosystems.
Razavi A, Krause Paul, Moschoyiannis Sotiris (2010) Digital Ecosystems: challenges and proposed solutions, In: Antonopoulos N, Exarchakos G, Li M, Liotta A (eds.), Handbook of research on P2P and grid systems for service-oriented computing: Models, Methodologies and Applications pp. 1003-1031
Information Science Reference - Imprint of: IGI Global Publishing
With REST becoming the dominant architectural paradigm for web services in distributed systems, more and more use cases are applied to it, including use cases that require transactional guarantees. We propose a RESTful transaction model that satisfies both the constraints of transactions and those of the REST architectural style. We then apply the isolation theorems to prove the robustness of its properties on a formal level.
We propose the use of structured natural language (English) in specifying service choreographies, focusing on the what rather than the how of the required coordination of participant services in realising a business application scenario. The declarative approach we propose uses the OMG standard Semantics of Business Vocabulary and Rules (SBVR) as a modelling language. The service choreography approach has been proposed for describing the global orderings of the invocations on interfaces of participant services. We therefore extend SBVR with a notion of time which can capture the coordination of the participant services, in terms of the observable message exchanges between them. The extension is done using existing modelling constructs in SBVR, and hence respects the standard specification. The idea is that users - domain specialists rather than implementation specialists - can verify the requested service composition by directly reading the structured English used by SBVR. At the same time, the SBVR model can be represented in formal logic so it can be parsed and executed by a machine.
Steering a complex system towards a desired outcome is a challenging task. The lack of clarity on the system?s exact architecture and the often scarce scientific data upon which to base the op- erationalisation of the dynamic rules that underpin the interactions between participant entities are two contributing factors. We describe an analytical approach that builds on Fuzzy Cognitive Map- ping (FCM) to address the latter and represent the system as a complex network. We apply results from network controllability to address the former and determine minimal control configurations - subsets of factors, or system levers, which comprise points for strategic intervention in steering the system. We have implemented the combination of these techniques in an analytical tool that runs in the browser, and generates all minimal control configurations of a complex network. We demonstrate our approach by reporting on our experience of working alongside industrial, local-government, and NGO stakeholders in the Humber region, UK. Our results are applied to the decision-making process involved in the transition of the region to a bio-based economy.
Modern software systems become increasingly complex as they are expected to support a large variety of different functions. We need to create more software in a shorter time, and without compromising the quality of the software. In order to build such systems efficiently, a compositional approach is required. This entails some formal technique for analysis and reasoning on local component properties as well as on properties of the composite. In this paper, we present a mathematical framework for the composition of software components, at a semantic modelling level. We describe a mathematical concept of a component and identify properties that ensure its potential behaviour can be captured. Based on that, we give a formal definition of composition and examine its effect on the individual components. We argue that properties of the individual components can, under certain conditions, be preserved in the composite. The proposed framework can be used for guiding the composition of components as it advocates formal reasoning about the composite before the actual composition takes place.
Concurrency control mechanisms such as turn-taking, locking, serialization, transactional locking mechanism, and operational transformation try to provide data consistency when concurrent activities are permitted in a reactive system. Locks are typically used in transactional models for assurance of data consistency and integrity in a concurrent environment. In addition, recovery management is used to preserve atomicity and durability in transaction models. Unfortunately, conventional lock mechanisms severely (and intentionally) limit concurrency in a transactional environment. Such lock mechanisms also limit recovery capabilities. Finally, existing recovery mechanisms themselves afford a considerable overhead to concurrency. This paper describes a new transaction model that supports release of early results inside and outside of a transaction, decreasing the severe limitations of conventional lock mechanisms, yet still warranties consistency and recoverability of released resources (results). This is achieved through use of a more flexible locking mechanism and by using two types of consistency graph. This provides an integrated solution for transaction management, recovery management and concurrency control. We argue that these are necessary features for management of long-term transactions within "digital ecosystems" of small to medium enterprises.
The concept of a digital ecosystem (DE) has been used to explore scenarios in which multiple online services and resources can be accessed by users without there being a single point of control. In previous work we have described how the so-called transaction languages can express concurrent and distributed interactions between online services in a transactional environment. In this paper we outline how transaction languages capture the history of a long-running transaction and highlight the benefits of our true-concurrent approach in the context of DEs. This includes support for the recovery of a long-running transaction whenever some failure is encountered. We introduce an animation tool that has been developed to explore the behaviours of long-running transactions within our modelling environment. Further, we discuss how this work supports the declarative approach to the development of open distributed applications. © 2012 IEEE.
The aim of this paper is to facilitate e-business transactions between small and medium enterprises (SMEs), in a way that respects their local autonomy, within a digital ecosystem. For this purpose, we distinguish transactions from services (and service providers) by considering virtual private transaction networks (VPTNs) and virtual service networks (VSNs). These two virtual levels are optimised individually and in respect to each other. The effect of one on the other, can supply us with stability, failure resistance and small-world characteristics on one hand and durability, consistency and sustainability on the other hand. The proposed network design has a dynamic topology that adapts itself to changes in business models and availability of SMEs, and reflects the highly dynamic nature of a digital ecosystem.
With REST becoming a dominant architectural paradigm for web services in distributed systems, more and more use cases are applied to it, including use cases that require transactional guarantees. We believe that the loose coupling that is supported by RESTful transactions, makes this currently our preferred interaction style for digital ecosystems (DEs). To further expand its value to DEs, we propose a RESTful transaction model that satisfies both the constraints of recoverable transactions and those of the REST architectural style. We then show the correctness and applicability of the model.
© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
We describe a true-concurrent approach for managing dependencies between distributed and concurrent coordinator components of a long-running transaction. In previous work we have described how interactions specified in a scenario can be translated into a tuples-based behavioural description, namely vector languages. In this paper we show how reasoning against order-theoretic properties of such languages can reveal missing behaviours which are not explicitly described in the scenario but are still possible. Our approach supports the gradual refinement of scenarios of interaction into a complete set of behaviours that includes all desirable orderings of execution and prohibits emergent behaviour of the transaction. Crown Copyright © 2010.
We describe a formal approach to protocol design for dialogues between autonomous agents in a digital ecosystem that involve the exchange of arguments between the participants. We introduce a vector language-based representation of argumentation protocols, which captures the interplay between different agentspsila moves in a dialogue in a way that (a) determines the legal moves that are available to each participant, in each step, and (b) records the dialogue history. We use UML protocol state machines (PSMs) to model a negotiation dialogue protocol at both the individual participant level (autonomous agent viewpoint) and the dialogue level (overall interaction viewpoint). The underlying vector semantics is used to verify that a given dialogue was played out in compliance with the corresponding protocol.
?Dynamic wireless charging of electric vehicles (EVs)
is becoming a preferred method since it enables power exchange
between the vehicle and the grid while the vehicle is moving.
In this article, we present mobile energy disseminators (MED),
a new concept, that can facilitate EVs to extend their range in
a typical urban scenario. Our proposed method exploits InterVehicle
(IVC) communications in order to eco-route electric
vehicles taking advantage of the existence of MEDs. Combining
modern communications between vehicles and state of the art
technologies on energy transfer, vehicles can extend their travel
time without the need for large batteries or extremely costly
infrastructure. Furthermore, by applying intelligent decision
mechanisms we can further improve the performance of the
The distinct feature of this volume is its focus on mathematical models that identify the "core" concepts as first class modeling elements, and its providing of techniques for integrating and relating them.
This paper presents a true-concurrent approach to formalising integration of Small-to-Medium Enterprises (SMEs) with Web services. Our approach formalises common notions in service-oriented computing such as conversations (interactions between clients and web services), multi-party conversations (interactions between multiple web services) and coordination protocols, which are central in a transactional environment. In particular, we capture long-running transactions with recovery and compensation mechanisms for the underlying services in order to ensure that a transaction either commits or is successfully compensated for. © 2008 Springer-Verlag Berlin Heidelberg.
We describe a translation of scenarios given in UML 2.0 sequence diagrams into a tuples-based behavioural model that considers multiple access points for a participating instance and exhibits true-concurrency. This is important in a component setting since different access points are connected to different instances, which have no knowledge of each other. Interactions specified in a scenario are modelled using tuples of sequences, one sequence for each access point. The proposed unfolding of the sequence diagram involves mapping each location (graphical position) onto the so-called component vectors. The various modes of interaction (sequential, alternative, concurrent) manifest themselves in the order structure of the resulting set of component vectors, which captures the dependencies between participating instances. In previous work, we have described how (sets of) vectors generate concurrent automata. The extension to our model with sequence diagrams in this paper provides a way to verify the diagram against the state-based model.
This paper presents the implementation of a compiler of SBVR Structured English to SQL data models and queries, with SBVR Logical Formulation as an intermediate step. The compiler is implemented in OMeta/JS and targets browsers that support Web SQL Databases. We discuss each stage of our compiler as well as the optimizations and necessary tradeoffs.
In this paper we explore the concept of ldquoecosystemrdquo as a metaphor in the development of the digital economy. We argue that the modelling of social ecosystems as self-organising systems is also relevant to the study of digital ecosystems. Specifically, that centralised control structures in digital ecosystems militate against emergence of innovation and adaptive response to pressures or shocks that may impact the ecosystem. We hope the paper will stimulate a more holistic approach to gaining empirical and theoretical understanding of digital ecosystems.
Moschoyiannis S, Krause P, Shields MW (2009) A True-Concurrent Interpretation of Behavioural Scenarios, Electronic Notes in Theoretical Computer Science 203 (7) pp. 3-22
In this paper we present a prototype of a tool that demonstrates how existing limitations in ensuring an agent?s compliance to an argumentation-based dialogue protocol can be overcome. We also present the implementation of compliance enforcement components for a deliberation dialogue protocol, and an application that enables two human participants to engage in an efficiently moderated dialogue, where all inappropriate utterances attempted by an agent are blocked and prevented from inclusion within the dialogue.
Declarative technologies have made great strides in expressivity between SQL and SBVR. SBVR models are more expressive that SQL schemas, but not as imminently executable yet. In this paper, we complete the architecture of a system that can execute SBVR models. We do this by describing how SBVR rules can be transformed into SQL DML so that they can be automatically checked against the database using a standard SQL query. In particular, we describe a formalization of the basic structure of an SQL query which includes aggregate functions, arithmetic operations, grouping, and grouping on condition. We do this while staying within a predicate calculus semantics which can be related to the standard SBVR-LF specification and equip it with a concrete semantics for expressing business rules formally. Our approach to transforming SBVR rules into standard SQL queries is thus generic, and the resulting queries can be readily executed on a relational schema generated from the SBVR model.
Razavi A, Moschoyiannis S, Krause P (2008) A self-organising environment for evolving business activities, Proceedings of the 3rd International Multi-Conference Computing in the Global Information Technology pp. 277-283 IEEE
In this paper we are concerned with providing support for business activities in moving from value chains to value networks. We describe a fully distributed P2P architecture which reflects the dynamics of business processes that are not governed by a single organisation. The temporary virtual networks of long-term business transactions are used as the building block of the overall scale-free business network. The design is based on dynamically formed permanent clusters resulting in a topology that is highly resilient to failures (and attacks) and is capable of reconfiguring itself to adapt to changes in business models and respond to global failures of conceptual hubs. This fosters an environment where business communities can evolve to meet emerging business opportunities and achieve sustainable growth within a digital ecosystem.
Complexity theory has been used to study a wide range of systems in biology and nature
but also business and socio-technical systems, e.g., see . The ultimate objective is to
develop the capability of steering a complex system towards a desired outcome. Recent
developments in network controllability  concerning the reworking of the problem of
finding minimal control configurations allow the use of the polynomial time Hopcroft-
Karp algorithm instead of exponential time solutions. Subsequent approaches build on
this result to determine the precise control nodes, or drivers, in each minimal control
configuration , . A browser-based analytical tool, CCTool1, for identifying such
drivers automatically in a complex network has been developed in .
One key characteristic of a complex system is that it continuously evolves, e.g.,
due to dynamic changes in the roles, states and behaviours of the entities involved.
This means that in addition to determining driver nodes it is appropriate to consider
an evolving topology of the underlying complex network, and investigate the effect of
removing nodes (and edges) on the corresponding minimal control configurations. The
work presented here focuses on arriving at a classification of the nodes based on the
effect their removal has on controllability of the network.
Manaf N, Antoniades A, Moschoyiannis S (2018) SBVR2Alloy: an SBVR to Alloy compiler, Proceedings of 10th IEEE International Conference on Service Oriented Computing and Applications (IEEE SOCA 2017)
We present a compilation tool SBVR2Alloy which
is used to automatically generate as well as validate service
choreographies specified in structured natural language. The
proposed approach builds on a model transformation between
Semantics of Business Vocabulary and Rules (SBVR), an OMG
standard for specifying business models in structured English,
and the Alloy Analyzer which is a SAT based constraint solver. In
this way, declarative specifications can be enacted via a standard
constraint solver and verified for realisability and conformance.
One of the barriers for the adoption of Electric Vehicles (EVs) is the anxiety around the
limited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless
charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In
this article, we focus on the intelligent routing of EVs in need of charging so that they can make most
efficient use of the so-called Mobile Energy Disseminators (MEDs) which operate as mobile charging
stations. We present a method for routing EVs around MEDs on the road network, which is based on
constraint logic programming and optimization using a graph-based shortest path algorithm. The proposed
method exploits Inter-Vehicle (IVC) communications in order to eco-route electric vehicles. We argue that
combining modern communications between vehicles and state of the art technologies on energy transfer,
the driving range of EVs can be extended without the need for larger batteries or overtly costly infrastructure.
We present extensive simulations in city conditions that show the driving range and consequently the overall
travel time of electric vehicles is improved with intelligent routing in the presence of MEDs.
Current advances in software engineering practice involve the adoption of a component- based approach in developing large-scale, complex systems. The component-based paradigm provides better structuring of systems and facilitates systematic software reuse. However, complex interactions between components, especially in concurrent and distributed applications, pose greater challenges. This thesis provides a formal framework for managing the dependencies between components, in terms of their interactions in a concurrent setting. In our approach, composites and single components are represented by a component signature, which identifies a component, and a vector language, also called component language, which describes the behaviour of a component. This language-based representation of component behaviour makes it possible to capture concurrency at both the individual component level and the composition level. The interpretation of concurrency is that of a non-interleaving model, with the notion of causal independence lifted to vectors. We describe how component languages are obtained from scenario-based specifications, typically used in an industrial context. Based on the order structure of a component language, we identify implicit or missing interactions which represent potentially faulty or simply unthought scenarios. This excludes pathological behaviour, the source of which can be traced back to inconsistencies in the sequence diagrams of the scenario specification such as race conditions, and this gives a characterisation of well-behaved components. Components are put together in our approach by matching required and provided interfaces in terms of the respective sequences of events. This builds on the concept of parallel composition in process algebras. We show that the properties that define well-behaved components are preserved under composition in the resulting composite. Well-behaved components give rise to discrete behavioural presentations which can capture concurrency and simultaneity between event occurrences on component interfaces. Well-behaved components are also associated with automata whose transition structure reflects the concurrency in the corresponding component language. This state-based description of component behaviour is graphically represented using state diagrams. This formal framework for components has been related to more conventional approaches to software design, as exemplified by strong connections to UML. It can aid designers in determining the complete set of intended behaviours before generating state models of the scenario-based specifications.
We apply a learning classifier system, XCSI, to the task of
providing personalised suggestions for passenger onward journeys. Learn-
ing classifier systems combine evolutionary computation with rule-based
machine learning, altering a population of rules to achieve a goal through
interaction with the environment. Here XCSI interacts with a simulated
environment of passengers travelling around the London Underground
network, subject to disruption. We show that XCSI successfully learns
individual passenger preferences and can be used to suggest personalised
adjustments to the onward journey in the event of disruption.
In this paper we describe the application of a learning classifier system (LCS) variant known as the eXtended classifier system (XCS) to evolve a set of ?control rules? for a number of Boolean network instances. We show that (1) it is possible to take the system to an attractor, from any given state, by applying a set of ?control rules? consisting of ternary conditions strings (i.e. each condition component in the rule has three possible states; 0, 1 or #) with associated bit-flip actions, and (2) that it is possible to discover such rules using an evolutionary approach via the application of a learning classifier system. The proposed approach builds on learning (reinforcement learning) and discovery (a genetic algorithm) and therefore the series of interventions for controlling the network are determined but are not fixed. System control rules evolve in such a way that they mirror both the structure and dynamics of the system, without having ?direct? access to either.
Karlsen Matthew R., Moschoyiannis Sotiris K. (2018) Optimal control rules for random Boolean networks, Complex Networks and their Applications: Proceedings of Complex Networks 2018 (The 7th International Conference on Complex Networks and Their Applications) 812 pp. 828-840
Springer International Publishing
A random Boolean network (RBN) may be controlled through the use
of a learning classifier system (LCS) ? an eXtended Classifier System (XCS) can
evolve a rule set that directs an RBN from any state to a target state. However,
the rules evolved may not be optimal, in terms of minimising the total cost of the
paths used to direct the network from any state to a specified attractor. Here we
uncover the optimal set of control rules via an exhaustive algorithm. The performance
of an LCS (XCS) on the RBN control problem is assessed in light of the
newly uncovered optimal rule set.
We present a Peer-to-Peer network design which aims to support business activities conducted through a network of collaborations that generate value in different, mutually beneficial, ways for the participating organisations. The temporary virtual networks formed by long-term business transactions that involve the execution of multiple services from different providers are used as the building block of the underlying scale-free business network. We show how these local interactions, which are not governed by a single organisation, give rise to a fully distributed P2P architecture that reflects the dynamics of business activities. The design is based on dynamically formed permanent clusters of nodes, the so-called Virtual Super Peers (VSPs), and this results in a topology that is highly resilient to certain types of failure (and attacks). Furthermore, the proposed P2P architecture is capable of reconfiguring itself to adapt to the usage that is being made of it and respond to global failures of conceptual hubs. This fosters an environment where business communities can evolve to meet emerging business opportunities and achieve sustainable growth within a digital ecosystem.
Moschoyiannis Sotiris, Maglaras Leandros, Manaf Nurulhuda A (2019) Trace-based Verification of Rule-based Service Choreographies, Proceedings of the 2018 IEEE 11th International Conference on Service-Oriented Computing and Applications (IEEE SOCA 2018) pp. 185-193
Institute of Electrical and Electronics Engineers (IEEE)
The service choreography approach has been proposed
for describing the global ordering constraints on the
observable message exchanges between participant services in
service oriented architectures. Recent work advocates the use
of structured natural language, in the form of Semantics of
Business Vocabulary and Rules (SBVR), for specifying and validating
choreographies. This paper addresses the verification of
choreographies - whether the local behaviours of the individual
participants conform to the global protocol prescribed by the
choreography. We describe how declarative specifications of
service choreographies can be verified using a trace-based
model, namely an adaptation of Shields? vector languages. We
also use the so-called blackboard rules, which draw upon the
Bach coordination language, as a middleware that adds reactiveness
to this declarative setting. Vector languages are to trace
languages what matrices are to linear transformations; they
afford a more concrete representation which has advantages
when it comes to computation or manipulation.
Technical Report SCOMP-TC-02-01
Rule-based machine learning focuses on learning or evolving
a set of rules that represents the knowledge captured by the system.
Due to its inherent complexity, a certain amount of fine tuning is required before it can be applied to a particular problem. However, there
is limited information available to researchers when it comes to setting
the corresponding run parameter values. In this paper, we investigate
the run parameters of Learning Classifier Systems (LCSs) as applied to
single-step problems. In particular, we study two LCS variants, XCS for
reinforcement learning and UCS for supervised learning, and examine
the effect that different parameter values have on enhancing the model
prediction, increasing accuracy and reducing the resulting rule set size.
In this paper we describe the application of a Deep Reinforcement
Learning agent to the problem of control of Gene Regulatory Networks
(GRNs). The proposed approach is applied to Random Boolean Networks
(RBNs) which have extensively been used as a computational model for GRNs.
The ability to control GRNs is central to therapeutic interventions for diseases
such as cancer. That is, learning to make such interventions as to direct the
GRN from some initial state towards a desired attractor, by allowing at most
one intervention per time step. Our agent interacts directly with the environment;
being an RBN, without any knowledge of the underlying dynamics, structure
or connectivity of the network. We have implemented a Deep Q Network
with Double Q Learning that is trained by sampling experiences from the environment
using Prioritized Experience Replay. We show that the proposed novel
approach develops a policy that successfully learns how to control RBNs significantly
larger than previous learning implementations. We also discuss why
learning to control an RBN with zero knowledge of its underlying dynamics is
important and argue that the agent is encouraged to discover and perform optimal
control interventions in regard to cost and number of interventions.
Deployed AI platforms typically ship
with bulky system architectures which present
bottlenecks and a high risk of failure. A serverless
deployment can mitigate these factors and provide
a cost-effective, automatically scalable (up or
down) and elastic real-time on-demand AI solution.
However, deploying high complexity production
workloads into serverless environments is far from
trivial, e.g., due to factors such as minimal
allowance for physical codebase size, low amount of
runtime memory, lack of GPU support and a
maximum runtime before termination via timeout.
In this paper we propose a set of optimization
techniques and show how these transform a
codebase which was previously incompatible with a
serverless deployment into one that can be
successfully deployed in a serverless environment;
without compromising capability or performance.
The techniques are illustrated via worked examples
that have been deployed live on rail data and realtime
predictions on train movements on the UK rail
network. The similarities of a serverless
environment to other resource constrained
environments (IoT, Mobile) means the techniques
can be applied to a range of use cases.
In this paper, we present a survey of deep learning approaches for cybersecurity intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.
The Electric Vehicles (EVs) market has seen rapid growth recently despite the anxiety about driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging that enables power exchange between the vehicle and the grid while the vehicle is moving. Specifically, part of the literature focuses on the intelligent routing of EVs in need of charging. Inter-Vehicle communications (IVC) play an integral role in intelligent routing of EVs around a static charging station or dynamic charging on the road network. However, IVC is vulnerable to a variety of cyber attacks such as spoofing. In this paper, a probabilistic cross-layer Intrusion Detection System (IDS), based on Machine Learning (ML) techniques, is introduced. The proposed IDS is capable of detecting spoofing attacks with more than 90% accuracy. The IDS uses a new metric, Position Verification using Relative Speed (PVRS), which seems to have a significant effect in classification results. PVRS compares the distance between two communicating nodes that is observed by On-Board Units (OBU) and their estimated distance using the relative speed value that is calculated using interchanged signals in the Physical (PHY) layer.
Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system (LCS) ? an eXtended Classifier System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state (attractor). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and ?natural? steps. We then compare the performance of this optimal rule calculator algorithm (ORC) and the XCS variant of learning classifier systems. We find that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.
We propose a set of optimization techniques for transforming a generic AI codebase so that it can be successfully deployed to a restricted serverless environment, without compromising capability or performance. These involve (1) slimming the libraries and frameworks (e.g., pytorch) used, down to pieces pertaining to the solution; (2) dynamically loading pre-trained AI/ML models into local temporary storage, during serverless function invocation; (3) using separate frameworks for training and inference, with ONNX model formatting; and, (4) performance-oriented tuning for data storage and lookup. The techniques are illustrated via worked examples that have been deployed live on geospatial data from the transportation domain. This draws upon a real-world case study in intelligent transportation looking at on-demand, realtime predictions of flows of train movements across the UK rail network. Evaluation of the proposed techniques shows the response time, for varying volumes of queries involving prediction, to remain almost constant (at 50 ms), even as the database scales up to the 250M entries. The query response time is important in this context as the target is predicting train delays. It is even more important in a serverless environment due to the stringent constraints on serverless functions? runtime before timeout. The similarities of a serverless environment to other resource constrained environments (e.g., IoT, telecoms) means the techniques can be applied to a range of use cases.