Erol Gelenbe

Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the meantime , Neuromorphic Computing platforms have recently achieved remarkable performance running more bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network (RNN), a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of a spiking neural network. This is demonstrated on a number of real-world classification datasets.

The Internet of Things (IoT) was born in the mid 2010's, when the threshold of connecting more objects than people to the Inter-net, was crossed. Thus, attacks and threats on the content and quality of service of the IoT platforms can have economic, energetic and physical security consequences that go way beyond the traditional Internet's lack of security, and way beyond the threats posed by attacks to mobile tele-phony. Thus, this paper describes the H2020 project "Secure and Safe Internet of Things" (SerIoT) which will optimize the information security in IoT platforms and networks in a holistic, cross-layered manner (i.e. IoT platforms and devices, honeypots, SDN routers and operator's controller) in order to offer a secure SerIoT platform that can be used to implement secure IoT platforms and networks anywhere and everywhere.

Storage nodes are expected to be placed as an intermediate tier of large scale sensor networks for caching the collected sensor readings and responding to queries with benefits of power and storage saving for ordinary sensors. Nevertheless, an important issue is that the compromised storage node may not only cause the privacy problem, but also return fake/incomplete query results. We propose a simple yet effective dummy reading-based anonymization framework, under which the query result integrity can be guaranteed by our proposed verifiable top-k query (VQ) schemes. Compared with existing works, the VQ schemes have a fundamentally different design philosophy and achieve the lower communication complexity at the cost of slight detection capability degradation. Analytical studies, numerical simulations, and prototype implementations are conducted to demonstrate the practicality of our proposed methods.

In this paper, we analyze the network attacks that can be launched against Internet of Things (IoT) gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We then present the principles and design of a deep learning-based approach using dense random neural networks (RNN) for the online detection of network attacks. Empirical validation results on packet captures in which attacks are inserted show that the Dense RNN correctly detects attacks. However our experiments show that a simple threshold detector also provides results of comparable accuracy on the same data set.

Artificial Neural Networks (ANNs) based techniques have dominated state-of-the-art 1 results in most problems related to computer vision, audio recognition, and natural language 2 processing in the past few years, resulting in strong industrial adoption from all leading technology 3 companies worldwide. One of the major obstacles that have historically delayed large scale adoption 4 of ANNs is the huge computational and power costs associated with training and testing (deploying) 5 them. In the meantime , Neuromorphic Computing platforms have recently achieved remarkable 6 performance running the bio-realistic Spiking Neural Networks at high throughput and very low 7 power consumption making them a natural alternative to ANNs if they could match their classification 8 performance. Here, we propose using the Random Neural Network (RNN), a spiking neural network 9 with both theoretical and practical appealing properties, as a general purpose classifier that can match 10 the classification power of ANNs on a number of tasks while enjoying all the features of being a 11 spiking neural network. This is demonstrated on a number of real-world classification datasets. 12

Software security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.

The Internet of Things (IoT) was born in the mid 2010's, when the threshold of connecting more objects than people to the Internet, was crossed. Thus, attacks and threats on the content and quality of service of the IoT platforms can have economic, energetic and physical security consequences that go way beyond the traditional Internet's lack of security, and way beyond the threats posed by attacks to mobile telephony. Thus, this paper describes the H2020 project "Secure and Safe Internet of Things"(SerIoT) which will optimize the information security in IoT platforms and networks in a holistic, cross-layered manner (i.e. IoT platforms and devices, honeypots, SDN routers and operator's controller) in order to offer a secure SerIoT platform that can be used to implement secure IoT platforms and networks anywhere and everywhere.

This paper briefly reviews some recent research in Cybersecurity in Europe funded by the European Commission in areas such as mobile telephony, networked health systems, the Internet of Things. We then outline the objectives of the SerIoT Project which started in 2018 to address the security needs of fields such as Smart Cities, Smart Transportation Systems, Supply Chains and Industrial Informatics.

We consider auction mechanism design and performance analysis for data transactions in mobile social networks. Existing mobile network plans can result in some users ending a monthly plan with excess data, while others may have to pay a costly fee to buy more data. Thus we suggest data auctions with a single seller, or a multiple-seller networked data auction, that operate in mobile social networks, to deal with the asymmetry between extra unused data resources and urgent data demands. Based on earlier work on the analysis of auctions, we design the data transaction mechanism, and summarise the analysis on state transmission, stationary probabilities of the system, and the expected income for data sellers. To improve the efficiency and performance of the system, a socially-aware mobility model is also proposed. The proposed data auction mechanisms and friendship-based mobility model are then simulated as operating on Flickr, a real-world online social network database. Results show that the number of data bidders in different auctions can be balanced through the proposed mobility model, and also increase the income per unit time of sellers in the networked data auction.

We present a performance model for an energy harvesting wireless sensor node in which data gathering and harvesting are slow random processes as compared to fast wireless communications. We assume that the system will use stored energy when collecting data in standby, and that energy will leak from capacitors and batteries. In the presence of these imperfections we derive the system's packet transmission capacity when its packet storage buffer and its energy storage unit have a finite capacity that may lead to both data packet overflows, and the loss of incoming energy in addition to standby losses. We also consider an infinite capacity model which operates in the presence of transmission errors due to channel noise and interference.

We propose the Random Neural Network with Deep Learning Clusters that emulates the way the brain takes decisions. We apply our model to measure and evaluate Web result relevance by associating each Deep Learning Cluster with a different Web Search Engine; in addition, we include a Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from the Deep Learning clusters. Our algorithm reorders the Web results obtained from different Web Search Engines after a user introduces a query. We evaluate the performance of the Management Cluster when included as an additional layer to the Deep Learning Clusters. On average; our Management Cluster improves result relevance; its inclusion increments the overall result relevance quality.

We demonstrate experimentally how an Autonomic Network based on the CPN protocol can provide the Quality of Service (QoS) required by voice communications. The implementation uses Reinforcement Learning to dynamically seek paths that meet the quality requirements of voice communications. Measurements of packet delay, jitter, and loss illustrate the performance obtained from the system.

Smart network users can use sensing and intelligence to optimise various parameters for their communications as in CSMA channels. Though this paper fo-cuses on energy consumption per successfully transmitted packet as the primary QoS metric, we also examine how the different QoS metrics, such as throughput and delay, also interact with energy consumption, and show how trade-offs can be effected among them. The approach combines a queueing theoretic analysis with the sensing and error control effects in the channel in the presence of interfering communications.

We introduce a probability model for gene regulatory networks, based on a system of Chapman-Kolmogorov equations that represent the dynamics of the concentration levels of each agent in the network. This unifying approach includes the representation of excitatory and inhibitory interactions between agents, second-order interactions which allow any two agents to jointly act on other agents, and Boolean dependencies between agents. The probability model represents the concentration or quantity of each agent, and we obtain the equilibrium solution for the joint probability distribution of each of the concentrations. The result is an exact solution in "product form," where the joint equilibrium probability distribution of the concentration for each gene is the product of the marginal distribution for each of the concentrations. The analysis we present yields the probability distribution of the concentration or quantity of all of the agents in a network that includes both logical dependencies and excitatory-inhibitory relationships between agents. I. GENE REGULATORY NETWORKS Kaufman 1 pioneered models of gene regulatory networks 2 that have been extended 3,4 in order to include logical dependencies between agents 5-8, as well as sto-chastic dynamics 9-11. In this paper we develop a unifying approach to model the noisy behavior of regulatory networks that includes i exci-tatory and ii inhibitory interactions between agents, and iii second-order interactions which allow any two agents to jointly act on other agents. We also show that Boolean dependencies between agents can be modeled with our approach by using second-order interactions. The model studied in this paper represents the concentration levels or quantity of each agent in the network. All transitions in the model are probabilistic. Time is represented via random transition times whose average value depends on which agents are involved in each transition. The work in 12,13 is a precursor of the approach that we develop here. The present paper extends our prior work on G networks 14,15, so as to compute the probability of activation of the agents in the presence of complex interactions. The model in this paper differs from probabilistic Boolean networks 10 in that we propose an integer valued concentration level for each agent i, denoted by K i t, and we study the stochastic dynamical behavior of the vector Kt whose elements are the K i t, with the probability distribution Pk , t = PKt = k. On the other hand, we define a mapping of the variables K i t into binary variables B i t such that B i t =1 if K i t 0, and B i t =0 if K i t = 0, and also compute the steady-state probabilities of the B i t from the corresponding distribution for the Pk , t. However, we do not deal directly with the dynamics of the vector Bt whose elements are the B i t. In our model Kt is a Markov chain in continuous time, but Bt is not a Markov chain. In 16 a deterministic population model is considered; it uses nonlinear ordinary differential equations Eq. 1 in 16 to represent the concentration or quantity of different genes. The approach in 16 is similar to the use of the general mass equations GMA of chemistry, and variability due to biology and measurement noise is represented in 16 by modifying the parameter values in the data sets. Our approach uses a probabilistic model similar to the chemical master equations CME of chemistry, and noise is intrinsi-cally part of the model. In chemistry, the GMA are a "mac-roscopic" deterministic approximation of the "microscopic" probabilistic representation provided by the CME. The work of Ribeiro et al. 11 also considers the latter approach, and in 11 the system is analyzed using Monte Carlo simulations , while our work pursues an analytic approach. Also, 11 describes the logical dependencies between agents via rate equations, while here we present both the probabilistic CME and derive explicit Boolean dependencies between these equations. Since practical measurements with microarrays will deal with large populations of cells each of whose individual instantaneous behavior may not be synchronized, in 17 the effect of the variation in the number of cells which have a given gene expression at a given measurement instant is studied; signal processing techniques are used to derive the correct gene expression for cyclic gene expression from a large number of cells with specific reference to a single gene. This paper presents a model based on a single cell and multiple agents, and includes the time-dependent probabilistic dynamics as presented in the differential equation 5. II. REGULATORY NETWORKS AND G NETWORKS We will first begin by presenting the model that we propose , which is based on G networks 14,18,15,19, which are probabilistic dynamical models with an unbounded discrete state space, operating in continuous time. The model is composed of the following: i Agents, which are the primary objects of interest; they represent genes or other active biochemical or living objects whose levels of activity we wish to represent. ii Gates, which represent the interactions between *e.gelenbe@imperial.ac.uk

Large scale distributed systems, such as natural neuronal and artificial systems, have many local interconnections, but often also have the ability to propagate information very fast over relatively large distances. Mechanisms that enable such behaviour include very long physical signalling paths, and possibly also saccades of synchronous behaviour that may propagate across a network. This paper studies the modeling of such behaviours in neuronal networks, and develops a related learning algorithm. This is done in the context of the random neural network (RNN), a probabilistic model with a well developed mathematical theory, which was inspired by the apparently stochastic spiking behaviour of certain natural neuronal systems. Thus we develop an extension of the RNN to the case when synchronous interactions can occur, leading to synchronous firing by large ensembles of cells. We also present an O(N 3) gradient descent learning algorithm for an N-cell recurrent network having both conventional excitatory-inhibitory interactions and synchronous interactions. Finally, the model and its learning algorithm are applied to a resource allocation problem which is N P-hard and requires fast approximate decisions.

We investigate the assignment of assets to tasks where each asset can potentially execute any of the tasks, but assets execute tasks with a probabilistic outcome of success. There is a cost associated with each possible assignment of an asset to a task, and if a task is not executed there is also a cost associated with the non-execution of the task. Thus any assignment of assets to tasks will result in an expected overall cost which we wish to minimise. We formulate the allocation of assets to tasks in order to minimise this expected cost, as a nonlinear combinatorial optimisation problem. A neural network approach for its approximate solution is proposed based on selecting parameters of a Random Neural Network (RNN), solving the network in equilibrium, and then identifying the assignment by selecting the neurons whose probability of being active is highest. Evaluations of the proposed approach are conducted by comparison with the optimum (enumerative) solution as well as with a greedy approach over a large number of randomly generated test cases. The evaluation indicates that the proposed RNN based algorithm is better in terms of performance than the greedy heuristic, consistently achieving on average results within 5% of the cost obtained by the optimal solution for all problem cases considered. The RNN based approach is fast and is of low polynomial complexity in the size of the problem, while it can be used for decentralised decision making.

Large scale distributed systems, such as natural neuronal and artificial systems, have many local interconnections, but often also have the ability to propagate information very fast over relatively large distances. Mechanisms that enable such behaviour include very long physical signalling paths, and possibly also saccades of synchronous behaviour that may propagate across a network. This paper studies the modeling of such behaviours in neuronal networks, and develops a related learning algorithm. This is done in the context of the random neural network (RNN), a probabilistic model with a well developed mathematical theory, which was inspired by the apparently stochastic spiking behaviour of certain natural neuronal systems. Thus we develop an extension of the RNN to the case when synchronous interactions can occur, leading to synchronous firing by large ensembles of cells. We also present an O(N 3) gradient descent learning algorithm for an N-cell recurrent network having both conventional excitatory-inhibitory interactions and synchronous interactions. Finally, the model and its learning algorithm are applied to a resource allocation problem which is N P-hard and requires fast approximate decisions.

We investigate the assignment of assets to tasks where each asset can potentially execute any of the tasks, but assets execute tasks with a probabilistic outcome of success. There is a cost associated with each possible assignment of an asset to a task, and if a task is not executed there is also a cost associated with the non-execution of the task. Thus any assignment of assets to tasks will result in an expected overall cost which we wish to minimise. We formulate the allocation of assets to tasks in order to minimise this expected cost, as a nonlinear combinatorial optimisation problem. A neural network approach for its approximate solution is proposed based on selecting parameters of a Random Neural Network (RNN), solving the network in equilibrium, and then identifying the assignment by selecting the neurons whose probability of being active is highest. Evaluations of the proposed approach are conducted by comparison with the optimum (enumerative) solution as well as with a greedy approach over a large number of randomly generated test cases. The evaluation indicates that the proposed RNN based algorithm is better in terms of performance than the greedy heuristic, consistently achieving on average results within 5% of the cost obtained by the optimal solution for all problem cases considered. The RNN based approach is fast and is of low polynomial complexity in the size of the problem, while it can be used for decentralised decision making.

We consider a wireless sensor node that gathers energy through harvesting and reaps data through sensing. The node has a wireless transmitter that sends out a data packet whenever there is at least one "energy packet" and one "data packet", where an energy packet represents the amount of accumulated energy at the node that can allow the transmission of a data packet. We show that such a system is unstable when both the energy storage space and the data backlog buffer approach infinity, and we obtain the stable stationary solution when both buffers are finite. We then show that if a single energy packet is not sufficient to transmit a data packet, there are conditions under which the system is stable, and we provide the explicit expression for the joint probability distribution of the number of energy and data packets in the system. Since the two flows of energy and data can be viewed as flows that are instantaneously synchronised, this paper also provides a mathematical analysis of a fundamental problem in computer science related to the stability of the "join" synchronisation primitive.

The power needs of digital devices, their installation in locations where it is difficult to connect them to the power grid and the difficulty of frequently replacing batteries, create the need to operate digital systems with harvested energy. In such cases, local storage batteries must overcome the intermittent nature of the energy supply. System performance then depends on the intermittent energy supply, possible energy leakage, and system workload. Queueing networks with product-form solution (PFS) are standard tools for analyzing the performance of interconnected systems, and predicting relevant performance metrics including job queue lengths, throughput, and system turnaround times and delays. However, existing queueing network models assume unlimited energy availability, whereas intermittently harvested energy can affect system performance due to insufficient energy supply. Thus, this paper develops a new PFS for the joint probability distribution of energy availability, and job queue length for an N-node tandem system. Such models can represent production lines in manufacturing systems, supply chains, cascaded repeaters for optical links, or a data link with multiple input data ports that feeds into a switch or server. Our result enables the rigorous computation of the relevant performance metrics of such systems operating with intermittent energy. Index Terms-Energy packet (EP) network, energy harvesting, multihop networks, product-form solution (PFS), simultaneous state transitions.

In the presence of limitations in the availability of energy for data centres, especially in dense urban areas, a novel system that we call an Energy Packet Network is discussed as a means to provide energy on demand to Cloud Computing servers. This approach can be useful in the presence of renewable energy sources, and if scarce sources of energy must be shared by multiple computational units whose peak to average power consumption ratio is high. Such a system will use energy storage units to best match and smooth the intermittent supply and the intermittent demand. The analysis of such systems based on queueing networks is suggested and applied to a special case for illustration.

Recently, wireless streaming of on-demand videos of 1 mobile users (MUs) has become the major form of data traffic 2 over cellular networks. As a response, caching popular videos 3 in the storage of small base stations (SBSs) has been regarded 4 as an efficient approach to reduce the transmission latency and 5 alleviate the data traffic loaded over backhaul channels. This 6 paper considers a small-cell based caching market composed of 7 one mobile network operator (MNO) and multiple video service 8 providers (VSPs). In this system, the MNO manages and operates 9 its SBSs, and assigns these SBSs' storage to different VSPs, 10 who have caching requirements. However, videos have different 11 popularities and MUs present different preferences to these VSPs 12 when they request videos. In addition, the caching service brings 13 different utilities to different VSPs as well as that providing 14 caching service to different VSPs causes distinct costs to the 15 MNO. Such privacy information cannot be aware of among VSPs 16 and the MNO. Therefore, to elicit this hidden information, this 17 paper designs a double auction-based caching mechanism, which 18 ensures the efficient operation of the market by maximizing the 19 social welfare, i.e., the gap between VSPs' caching utilities and 20 MNO's caching costs. Moreover, this paper demonstrates the 21 economic properties of the designed caching mechanism, which 22 are also validated by the simulation results. 23 Index Terms-Video caching, double auction, heterogeneous 24 networks, economic property, information hidden. 25

Cloud computing enables the accommodation of an increasing number of applications in shared infrastructures. The routing for the incoming jobs in the cloud has become a real challenge due to the heterogeneity in both workload and machine hardware and the changes of load conditions over time. The present paper design and investigate the adaptive dynamic allocation algorithms that take decisions based on on-line and up-to-date measurements, and make fast online decisions to achieve both desirable QoS levels and high resource utilization. The Task allocation platform(TAP) is implemented as a practical system to accommodate the allocation algorithms and perform online measurement. The paper studies the potential of our proposed algorithms to deal with multi-class tasks in heterogeneous cloud environments and the experimental evaluations are also presented.

This article is a summary description of the Cognitive Packet Network (CPN) which is an early example of a completely software defined network (SDN), and of a fully implemented self-aware computer network (SAN). CPN has been completely implemented and is used in numerous experiments. CPN is able to observe its own internal performance as well as the interfaces of the external systems that it interacts with, in order to modify its behaviour so as to adaptively achieve objectives, such as discovering services for its users, improving their Quality of Service (QoS), reduce its own energy consumption, compensate for components which fail or malfunction, detect and react to intrusions, and defend itself against attacks.

Mobile Networks are subject to signaling storms launched by misbehaving applications or malware, which result in bandwidth overload at the cell level and excessive signaling within the mobile operator, and may also deplete the battery power of mobile devices. This paper reviews the causes of signaling storms and proposes a novel technique for storm detection and mitigation. The approach is based on counting the number of successive signaling transitions that do not utilize allocated bandwidth, and temporarily blocking mobile devices that exceed a certain threshold to avoid overloading the network. Through a mathematical analysis, we derive the optimum value of the counter's threshold, which minimizes both the number of misbehaving mobiles and the signal-ing overload in the network. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

This paper proposes an anomaly detection framework that utilizes key performance indicators (KPIs) and traffic measurements to identify in real-time misbehaving mobile devices that contribute to sig-naling overloads in cellular networks. The detection algorithm selects the devices to monitor and adjusts its own parameters based on KPIs, then computes various features from Internet traffic that capture both sudden and long term changes in behavior, and finally combines the information gathered from the individual features using a random neural network in order to detect anomalous users. The approach is validated using data generated by a detailed mobile network simulator.

Signaling storms are becoming prevalent in mobile networks due to the proliferation of smartphone applications and new network uses, such as machine-to-machine communication, which are designed without due consideration to the signaling overheads associated with the de/allocation of radio resources to User Equipment (UE). In this work, we conduct a set of experiments on a 3G operational mobile network to validate previous claims in literature that it is possible to significantly change the signaling behavior of a normal UE so that the UE has an adverse impact on the mobile network. Our early results show that it is possible to increase by 0.330 signaling messages/s the signaling rate of a normal 3G UE loaded with popular applications when it is not in active use by the owner. In addition, we explore the different factors which can either increase or decrease the effectiveness of signaling attacks on mobile networks.

This paper studies interconnected wireless sensors with the paradigm of Energy Packet Networks (EPN) which were previously introduced. In the EPN model, both data transmissions and the flow of energy are discretized, so that an energy packet (EP) is the minimum amount of energy (say in microjules) that is needed to process and transmit a data packet (DP) or to process a job. Previous work has modeled such systems to determine the relation between energy flow and DP transmission, or to study the balance between energy and the processing of jobs in Cloud Servers. The lack of energy, in addition to processing times, is the main source of latency in networks of sensor nodes. Thus this paper models this phenomenon, and shows that under some reasonable conditions, assuming feedforward flow of data packets and local consumption and leakage of energy, such networks have product form solutions. Abstract. This paper studies interconnected wireless sensors with the paradigm of Energy Packet Networks (EPN) which were previously introduced. In the EPN model, both data transmissions and the flow of energy are discretized, so that an energy packet (EP) is the minimum amount of energy (say in microjules) that is needed to process and transmit a data packet (DP) or to process a job. Previous work has modeled such systems to determine the relation between energy flow and DP transmission , or to study the balance between energy and the processing of jobs in Cloud Servers. The lack of energy, in addition to processing times, is the main source of latency in networks of sensor nodes. Thus this paper models this phenomenon, and shows that under some reasonable conditions , assuming feedforward flow of data packets and local consumption and leakage of energy, such networks have product form solutions.

We consider a wireless sensor node that gathers energy through harvesting and reaps data through sensing. The node has a wireless transmitter that sends out a data packet whenever there is at least one " energy packet " and one " data packet " , where an energy packet represents the amount of accumulated energy at the node that can allow the transmission of a data packet. We show that such a system is unstable when both the energy storage space and the data backlog buffer approach infinity, and we obtain the stable stationary solution when both buffers are finite. We then show that if a single energy packet is not sufficient to transmit a data packet, there are conditions under which the system is stable, and we provide the explicit expression for the joint probability distribution of the number of energy and data packets in the system. Since the two flows of energy and data can be viewed as flows that are instantaneously synchronised, this paper also provides a mathematical analysis of a fundamental problem in computer science related to the stability of the " join " synchronisation primitive. Keywords: energy packets; data packets; wireless sensor nodes; instability of synchronisation primitives; stable operation of synchronised flows

—Energy harvesting has recently attracted much interest due to the emergence of the Internet of Things, and the increasing need to operate wireless sensing devices in challenging environments without much human intervention and maintenance. This paper presents a novel approach for modeling the performance of an energy harvesting wireless sensor node, which takes into account fluctuations in the amount of energy extracted from the environment, energy loss due to battery leakage, as well as the energy cost of sensing, data processing and communication. The proposed approach departs from the common queueing-theoretic framework used in the literature, and instead uses Brownian motion to represent more accurately the time evolution of the distribution of the node's battery level. The paper derives some performance measures of interest along with the stationary solution of the system, and discusses possible directions for reducing the number of parameters and states of the model without compromising accuracy.

We study an energy harvesting wireless sensor node which harvests energy and senses and transmits data. Both data and energy are represented as discrete quantities using the previously introduced in " Energy Packet Network " paradigm. For each data packet, the sensor requires and consumes Ke energy packets for sensing and storage and Kt energy packets for transmission. Assuming random processes for sensing and energy harvesting, we obtain a two-dimensional random walk model and reduce its complexity using companion matrices and matrix algebra techniques. The resulting solution allows us to obtain, in steady-state, all the metrics of interest such as the backlog of energy and data in the sensor. We also consider the case when M sensors operate in proximity and create some interference for each other.

Performance Modelling to predict the quality of service (QoS) of computer systems and networks is widely applied. Similarly there is an abundant literature on the analysis and optimisation of supply chains and inventory control. This paper addresses some problems at the interface between these two areas, since computer systems and networks are widely used to control supply chains and E-Commerce systems. We develop an analytical modelling approach using queueing theory to study the impact of cyber-attacks on the economic performance of warehouse that sells perishable goods such as food that has a limited shelf life. We derive expressions to predict the loss of income that results from cyber-attacks on a web site used to order goods, and illustrate them with numerical examples.

Mobile communications are a powerful contributor to social and economic development worldwide, including in less developed or remote parts of the world. However they are large users of electricity through their base stations, backhaul networks and Cloud servers, so that they have a large environmental impact when they use the electric grid. On the other hand, they could operate with renewable energy sources and thus reduce their CO2 impact and be accessible even in areas where the electric grid is unavailable or unreliable. The counterpart is that intermittent sources of energy, such as photovoltaic and wind, can affect the quality of service (QoS) that is experienced by mobile users. Thus in this paper we model the performance of mobile telecommunications that use intermittent and renewable energy sources. In such cases to analyse the performance of such systems, both the energy supply and the network traffic, can be modeled as random processes, and we develop mathematical models using the Energy Packet Network paradigm, where both data and energy flows are discretised. QoS metrics for the users are computed based on the traffic intensity and the availability of energy.

These Proceedings [15] start with an overview [2] of the contents of this volume, providing insight into how some of these contributions are interconnected and linking them to prior ideas and work. It then follows with a series of research papers on Cyber- security research in Europe that covers five projects funded by the European Commis- sion:
– KONFIDO on the security of communications and data transfers for interconnected European national or regional health services,
– GHOST regarding the security of IoT systems for the home, and the design of secure IoT home gateways,
– SerIoT on the Cybersecurity of IoT systems in general with a range of applications in supply chains, smart cities, and other areas,
– NEMESYS, a now completed research project on the security of mobile networks, and SDK4ED, a new project that addresses security only incidentally but that focuses on broader issues of computation time, energy consumption and reliability of software.

We present the European research project GHOST, (Safeguarding home IoT environments with personalised real-time risk control), which challenges the traditional cyber security solutions for the IoT by proposing a novel reference architecture that is embedded in an adequately adapted smart home network gateway, and designed to be vendor-independent. GHOST proposes to lead a paradigm shift in consumer cyber security by coupling usable security with transparency and behavioural engineering.

—Mobile communications are a powerful contributor to social and economic development worldwide, and in particular in less developed parts of the world. However the extension and penetration of mobile communications are often hampered by the state of the electrical grid, which may not cover all areas and which may also be intermittent and unreliable. Thus we review some of the economic and technological challenges for mobile telecommunications that integrate and exploit potentially plentiful renewable energy sources, such as photovoltaic, in developing areas of the world. Such sources of energy for communication networks are also useful to mitigate the environmental impact of energy consumption of Information and Communication Technologies (ICT) worldwide. However renewable energy sources are themselves intermittent, and this raises new challenges concerning network design. Hence this paper also develops an analytical approach that can be used to evaluate the quality of service of networks that operate with intermittent sources of energy.

This article is a summary description of the cognitive packet network (CPN) which is an early example of a completely software-defined network (SDN) and of a fully implemented self-aware computer network (SAN). CPN has been completely implemented and is used in numerous experiments. CPN is able to observe its own internal performance as well as the interfaces of the external systems that it interacts with, in order to modify its behaviour so as to adaptively achieve objectives, such as discovering services for its users, improving their quality of service (QoS), reducing its own energy consumption, compensating for components that fail or malfunction, detecting and reacting to intrusions, and defending itself against attacks. Abstract This article is a summary description of the cognitive packet network

This article is a summary description of the cognitive packet network (CPN) which is an early example of a completely software-defined network (SDN) and of a fully implemented self-aware computer network (SAN). CPN has been completely implemented and is used in numerous experiments. CPN is able to observe its own internal performance as well as the interfaces of the external systems that it interacts with, in order to modify its behaviour so as to adaptively achieve objectives, such as discovering services for its users, improving their quality of service (QoS), reducing its own energy consumption, compensating for components that fail or malfunction, detecting and reacting to intrusions, and defending itself against attacks. Abstract This article is a summary description of the cognitive packet network

We consider auction mechanism design and performance analysis for data transactions in mobile social networks. Existing mobile network plans can result in some users ending a monthly plan with excess data, while others may have to pay a costly fee to buy more data. Thus we suggest data auctions with a single seller, or a multiple-seller networked data auction, that operate in mobile social networks, to deal with the asymmetry between extra unused data resources and urgent data demands. Based on earlier work on the analysis of auctions, we design the data transaction mechanism, and summarise the analysis on state transmission, stationary probabilities of the system, and the expected income for data sellers. To improve the efficiency and performance of the system, a socially-aware mobility model is also proposed. The proposed data auction mechanisms and friendship-based mobility model are then simulated as operating on Flickr, a real-world online social network database. Results show that the number of data bidders in different auctions can be balanced through the proposed mobility model, and also increase the income per unit time of sellers in the networked data auction.