The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introduces bias into the otherwise purely data-driven network representations. Here, we eliminate the need for handcrafted features by introducing an attention mechanism and utilizing message-iteration profiles, in addition to an effective algorithmic approach to generate a structurally diverse training set of small synthetic networks. Thereby, we build an expressive message-passing framework and use it to efficiently solve the NP-hard problem of Network Dismantling, virtually equivalent to vital node identification, with significant real-world applications. Trained solely on diversified synthetic networks, our proposed model—MIND: Message Iteration Network Dismantler—generalizes to large, unseen real networks with millions of nodes, outperforming state-of-the-art network dismantling methods. Increased efficiency and generalizability of the proposed model can be leveraged beyond dismantling in a range of complex network problems.

We describe an architecture for a decentralised data market for applications in which agents are incentivised to collaborate to crowd-source their data. The architecture is designed to reward data that furthers the market's collective goal, and distributes reward fairly to all those that contribute with their data. We show that the architecture is resilient to Sybil , wormhole , and data poisoning attacks. In order to evaluate the resilience of the architecture, we characterise its breakdown points for various adversarial threat models in an automotive use case.

A growing issue across multiple fields involves verifying that an individual or object is truly in the location it claims to be and, despite the significance of this problem, the scientific community has not extensively explored how to provide proof for such claims. Accordingly, this article presents a novel class of proof-of-position algorithms: tree-proof-of-position (T-PoP). These algorithms are decentralized, collaborative and can be computed in a privacy preserving manner, such that agents do not need to reveal their position publicly. We make no assumptions of honest behavior in the system, and consider varying ways in which agents may misbehave. T-PoP is therefore resilient to adversarial scenarios, which makes it suitable for a wide class of applications, namely those where trust in a centralized infrastructure may not be assumed, or high security risk scenarios. Our algorithm has a worst case quadratic runtime, making it suitable for hardware constrained IoT applications. We also provide a mathematical model that summarizes T-PoP’s performance for varying operating conditions. Using a large number of agent-based simulations, we verify the agreement between TPoP’s performance and our mathematical predictions. T-PoP can achieve high levels of reliability and security by tuning its operating conditions, both in high and low density environments. Finally, we also present a mathematical model to probabilistically detect platooning attacks.

An optimal randomized strategy for design of balanced, normalized mass transport plans is developed. It replaces—but specializes to—the deterministic , regularized optimal transport (OT) strategy, which yields only a certainty-equivalent plan. The incompletely specified—and therefore uncertain—transport plan is acknowledged to be a random process. Therefore, hierarchical fully probabilistic design (HFPD) is adopted, yielding an optimal hyperprior supported on the set of possible transport plans, and consistent with prior mean constraints on the marginals of the uncertain plan. This Bayesian resetting of the design problem for transport plans—which we call HFPD-OT—confers new opportunities. These include (i) a strategy for the generation of a random sample of joint transport plans; (ii) randomized marginal contracts for individual source-target pairs; and (iii) consistent measures of uncertainty in the plan and its contracts. An application in fair market matching is outlined, in which HFPD-OT enables the recruitment of a more diverse subset of contracts—than is possible in classical OT—into the delivery of an expected plan.

Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Regularization (RL-AR), an algorithm that enables safe RL exploration by combining the RL policy with a policy regularizer that hard-codes the safety constraints. RL-AR performs policy combination via a "focus module," which determines the appropriate combination depending on the state—relying more on the safe policy regularizer for less-exploited states while allowing unbiased convergence for well-exploited states. In a series of critical control applications, we demonstrate that RL-AR not only ensures safety during training but also achieves a return competitive with the standards of model-free RL that disregards safety.

This article describes the use of acrlong DLTs as a means to create personalized social nudges and to influence the behavior of agents in a smart city environment. Specifically, we present a scheme to price personalized risk in sharing economy applications. We provide proofs for the convergence of the proposed stochastic system and we validate our approach through the use of extensive Monte Carlo simulations.

Usability of distributed ledgers is crucial to their mainstream adoption, especially for enterprise applications in which most users do not wish to operate full-node infrastructure. Some attempts have been made to solve the problem of user-node interaction for blockchains in which leaders assemble users’ transactions into blocks, but in the case of leaderless DAG-based ledgers such as IOTA, many of these solutions cannot be applied due to the absence of a shared mempool and the ability of nodes to issue blocks in parallel. In this work, we propose a user-node interaction mechanism for ledgers of this kind that is designed to balance user traffic across nodes and ensure the risk of a user experiencing a poor quality of service is low. Our mechanism involves users selecting nodes to issue their transactions to the ledger based on quality of service indicators advertised by the nodes. Simulation results are presented to illustrate the efficacy of the proposed policies.

In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. This behaviour can often be modelled as observations of an unknown dynamical system with an unobserved state. When the training data for the subgroups are not controlled carefully, however, under-representation bias arises. To counter under-representation bias, we introduce two natural notions of fairness in timeseries forecasting problems: subgroup fairness and instantaneous fairness. These notion extend predictive parity to the learning of dynamical systems. We also show globally convergent methods for the fairness-constrained learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate the efficacy of our methods.

In the Internet of Things (IoT) domain, devices need a platform to transact seamlessly without a trusted intermediary. Although distributed ledger technologies (DLTs) could provide such a platform, blockchains, such as Bitcoin, were not designed with IoT networks in mind, hence are often unsuitable for such applications: they offer poor transaction throughput and confirmation times, put stress on constrained computing and storage resources, and require high transaction fees. In this article, we consider a class of IoT-friendly DLTs based on directed acyclic graphs, rather than a blockchain, and with a reputation system in the place of Proof of Work (PoW). However, without PoW, the implementation of these DLTs requires an access control algorithm to manage the rate at which nodes can add new transactions to the ledger. We model the access control problem and present an algorithm that is fair, efficient, and secure. Our algorithm represents a new design paradigm for DLTs in which concepts from networking are applied to the DLT setting for the first time. For example, our algorithm uses distributed rate setting, which is similar in nature to transmission control used in the Internet. However, our solution features novel adaptations to cope with the adversarial environment of DLTs in which no individual agent can be trusted. Our algorithm guarantees utilization of resources, consistency, fairness, and resilience against attackers. All of these are achieved efficiently and with regard for the limitations of IoT devices. We perform extensive simulations to validate these claims.

We consider the design of distributed algorithms that govern the manner in which agents contribute to a social sensing platform. Specifically, we are interested in situations, where fairness among the agents contributing to the platform is needed. A notable example is the platforms operated by public bodies, where fairness is a legal requirement. The design of such distributed systems is challenging due to the fact that we wish to simultaneously realize an efficient social sensing platform but also deliver a predefined quality of service to the agents (for example, a fair opportunity to contribute to the platform). In this article, we introduce iterated function systems (IFSs) as a tool for the design and analysis of systems of this kind. We show how the IFS framework can be used to realize systems that deliver a predictable quality of service to agents, can be used to underpin contracts governing the interaction of agents with the social sensing platform, and which are efficient. To illustrate our design via a use case, we consider a large, high-density network of participating parked vehicles. When awoken by an administrative center, this network proceeds to search for moving missing entities of interest using RFID-based techniques. We regulate which vehicles are actively searching for the moving entity of interest at any point in time. In doing so, we seek to equalize vehicular energy consumption across the network. This is illustrated through simulations of a search for a missing Alzheimer’s patient in Melbourne, Australia. The experimental results are presented to illustrate the efficacy of our system and the predictability of access of agents to the platform independent of initial conditions.

Access control is a fundamental component of the design of distributed ledgers, influencing many aspects of their functionality, such as fairness, efficiency, traditional notions of network security, and adversarial attacks such as Denial-of-Service (DoS) attacks.1In this work, we consider the security of a recently proposed access control protocol for directed acyclic graph-based distributed ledgers. We present a number of attack scenarios and potential vulnerabilities of the protocol and introduce a number of additional features which enhance its resilience. Specifically, a blacklisting algorithm, which is based on a reputation-weighted threshold, is introduced to handle both spamming and multirate malicious attackers. A solidification request component is also introduced to ensure the fairness and consistency of the network in the presence of attacks. Finally, a timestamp component is also introduced to maintain the consistency of the network in the presence of multirate attackers. Simulations to illustrate the efficacy and robustness of the revised protocol are also presented.

We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications. This architecture is based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both Proof-of-Work and Proof-of-Position mechanisms to provide protection against spam attacks and malevolent actors. In addition to enabling individuals to retain ownership of their data and to monetize it, the architecture is also suitable for distributed privacy-preserving machine learning algorithms, is lightweight, and can be implemented in simple internet-of-things (IoT) devices. To demonstrate its efficacy, we apply this framework to reinforcement learning settings where a third party is interested in acquiring information from agents. In particular, one may be interested in sampling an unknown vehicular traffic flow in a city, using a DLT-type architecture and without perturbing the density, with the idea of realizing a set of virtual tokens as surrogates of real vehicles to explore geographical areas of interest. These tokens, whose authenticated position determines write access to the ledger, are thus used to emulate the probing actions of commanded (real) vehicles on a given planned route by “jumping” from a passing-by vehicle to another to complete the planned trajectory. Consequently, the environment stays unaffected (i.e., the autonomy of participating vehicles is not influenced by the algorithm), regardless of the number of emitted tokens. The design of such a DLT architecture is presented, and numerical results from large-scale simulations are provided to validate the proposed approach.

In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. When the amounts of training data for the subgroups are not controlled carefully, under-representation bias arises. We introduce two natural notions of subgroup fairness and instantaneous fairness to address such under-representation bias in time-series forecasting problems. In particular, we consider the subgroup-fair and instant-fair learning of a linear dynamical system (LDS) from multiple trajectories of varying lengths and the associated forecasting problems. We provide globally convergent methods for the learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate both the beneficial impact of fairness considerations on statistical performance and the encouraging effects of exploiting sparsity on run time.

In this article, we propose a stochastic decentralized algorithm to recommend the most convenient charging station (CS) to plug-in electric vehicles (PEVs) that need charging. In particular, we use different cost functions to describe the possibly different priorities of PEV drivers, such as the preference to minimize charging costs, charging times, or the distance between them and the CS. For this purpose, we leverage on an Internet of Things architecture based on a permissioned distributed ledger technology (DLT) to enforce compliance of drivers and reduces the occurrence of detrimental misbehaviors of drivers. Extensive simulations performed with the mobility simulator SUMO in realistic city-wide networks have been provided to illustrate how the proposed PEV assignment procedure works in practice, and to validate its performance.

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show-as proof of concept grounded on rigorous mathematical evidence-that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn-while not eliminating the virus-this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP_Covid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system.

Distributed ledgers have been proposed for a number of applications in the Internet-of-Things domain where it is essential to have an immutable and irreversible record of transactions. Directed acyclic graph (DAG)-based architectures, in particular, seem to provide a vast array of advantages over the more traditional Blockchain; however, it can be challenging to conduct a thorough analysis of DAG-based ledgers and derive reliable performance guarantees. In this article, we analyze one commonly discussed attack scenario known as the parasite chain attack, which aims at disrupting the immutability and irreversibility of the ledger, in the context of the IOTA Foundation's DAG-based system. Using a Markov chain model, we study the vulnerabilities of IOTA's core tip selection method against this attack and we present an extension of the algorithm to improve the resilience of the ledger in this scenario.

A stochastic algorithm is presented for a class of optimisation problems that arise when a group of agents compete to share a single constrained resource in an optimal manner. The approach uses intermittent single-bit feedback, which indicates a constraint violation and does not require inter-agent communication. The algorithm is based on a positive matrix model of AIMD, which is extended to the nonhomogeneous Markovian case. The key feature is the assignment of back-off probabilities to the individual agents as a function of the past average access to the resource. This leads to a nonhomogeneous Markov chain in an extended state space, and we show almost sure convergence of the average access to the social optimum.

This paper proposes a congestion balancing parking guidance system that suggests to a driver a sequence of streets to follow around the desired destination with the aim to reduce the total distance that is travelled while searching for a free parking spot. The system requires only limited infrastructure information, and neither requires parking spaces to be instrumented, nor vehicles to communicate with each other. Specifically, the system utilizes parking vacancy information on each street. The system also accounts for the added cost of not finding a free space, which is typically expressed as the additional distance that needs to be travelled to find an available parking spot. To avoid local congestion, different drivers respond to different suggestions based on a probability distribution that considers the total distance that needs to be travelled. A mobility simulator is used to model the searching behaviors of vehicles for parking spaces with and without the smart parking algorithm and experimental results are provided using the road network of the city of Dublin, Ireland.

The effect of transport-related pollution on human health is fast becoming recognized as a major issue in cities worldwide. Cyclists, in particular, face great health risks, as they typically are most exposed to tail-pipe emissions. Four avenues are being explored to combat urban air pollution: 1) policy interventions such as outright bans on polluting vehicles, 2) embracing zero tailpipe emission vehicles, 3) measuring air-quality as a means to better informing citizens of zones of higher pollution, and 4) developing smart mobility devices that seek to minimize the effect of polluting devices on citizens as they transport goods and individuals in our cities. Following this latter direction, in this paper, we present a new way to protect cyclists from the effect of urban pollution. Namely, by exploiting the actuation possibilities afforded by pedelecs or e-bikes (electric bikes), we design a cyber-physical system that mitigates the effect of urban pollution by indirectly controlling the ventilation rates (volume of air inhaled per minute) of cyclists in polluted areas. Results from a real device are presented to illustrate the efficacy of our system.

In this paper, we demonstrate a system for locating missing entities using radio-frequency identification (RFID)based techniques. A key feature of our system is that we utilize the large, high-density networks of parked vehicles incident to urban areas for the detection and reporting process. RFID readers and antennas are placed within the vehicles, while RFID passive tags are carried on the entity of interest via some means, e.g., a wrist band. If an entity is reported as missing, then the application on board each of the parked vehicles is awoken by an administrative center. The technology on board the vehicles enables those participating in the service to attempt to locate the missing entity, sending useful information back to the administration center, which could be tied to an organization like the police. We demonstrate our system via a use case of a missing Alzheimer's patient in inner-city Dublin, Ireland. One of the key challenges in validating our system is being able to replicate a large-scale, realworld setting. Our technique for obtaining an early evaluation of our system thus employs the use of the microscopic traffic simulation package Simulation of Urban MObility (SUMO). SUMO permits multiple emulations of hundreds or thousands of parked vehicles participating in the service to be carried out, while simulated pedestrians walk random routes. Our results show that a simulated wandering person in need can be detected within a 30-min time frame, in the heart of Dublin city center, during a typical weekday, up to approximately 98% of the time, depending on how various parameters of the system are set.

Two new distributed speed advisory systems (SASs) are introduced in this paper. The systems implement consensus algorithms that guide a set of vehicles toward a common driving speed. A major innovation is that consensus is achieved over a multi-layer network, in which parallel network topologies of connected vehicles are superimposed. The reason for the use of these parallel networks is that, in this way, the state obfuscation is possible, with the benefit that common driving speed is attained with no vehicle knowing the exact state of other vehicles. Convergence of the SASs is formally proved and two new results for the consensus of multi-layer networks modeled via stochastic differential equations are introduced. The SASs are also validated via simulation and via a hardware-in-the-loop setup, in which a real vehicle interacts with simulated entities.

Electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. While hybrid vehicles are usually designed with the objective of minimizing fuel consumption, in this paper we propose a engine management strategies that also consider environmental effects of the vehicles to pedestrians outside of the vehicles. Specifically, we present the optimisation-based engine energy management strategies for PHEVs that attempt to minimize the environmental impact of pedestrians along the route of the vehicle, while taking account of route-dependent uncertainties. We implement the proposed approach in a real PHEV and evaluate the performance in a hardware-in-the-loop platform. A variety of simulation results are given to illustrate the efficacy of our proposed approach.

Pollution in urban centers is becoming a major societal problem. While pollution is a concern for all urban dwellers, cyclists are one of the most exposed groups due to their proximity to vehicle tailpipes. Consequently, new solutions are required to help protect citizens, especially cyclists, from the harmful effects of exhaust-gas emissions. In this context, hybrid vehicles (HVs) offer new actuation possibilities that can be exploited in this direction. More specifically, such vehicles, when working together as a group, have the ability to dynamically lower the emissions in a given area, thus benefiting citizens, whilst still giving the vehicle owner the flexibility of using an internal combustion engine. This paper aims to develop an algorithm that can be deployed in such vehicles, whereby geofences (virtual geographic boundaries) are used to specify areas of low pollution around cyclists. The emissions level inside the geofence is controlled via a coin-tossing algorithm to switch the HV motor into, and out of, electric mode, in a manner that is in some sense optimal. The optimality criterion is based on how polluting vehicles inside the geofence are, and the expected density of cyclists near each vehicle. The algorithm is triggered once a vehicle detects a cyclist. Implementations are presented both in simulation and in a real vehicle, and the system is tested using a hardware-in-the-loop platform (video provided).

We discuss a recently introduced ECO-driving concept known as smart procurement of naturally generated energy (SPONGE) in the context of plug-in hybrid electric buses. Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realize SPONGE are discussed, paying attention to the privacy implications of the underlying optimization problems.

Parking spaces are resources that can be pooled together and shared, particularly when there exist complementary daytime and nighttime users. We provide solutions to two design questions. First, given a quality of service requirement, how many spaces should be set aside as contingency during the day for nighttime users? Next, how can we replace the first-come-first-served access method by one that aims for optimal efficiency while keeping user preferences private?

One of the key ideas to make intelligent transportation systems work effectively is to deploy advanced communication and cooperative control technologies among vehicles and road infrastructures. In this spirit, we propose a consensus-based distributed speed advisory system that optimally determines a recommended common speed for a given area in order that the group emissions, or group battery consumptions, are minimized. Our algorithms achieve this in a privacy-aware manner; that is, individual vehicles do not reveal in-vehicle information to other vehicles or to infrastructure. A mobility simulator is used to illustrate the efficacy of the algorithm, and hardware-in-the-loop tests involving a real vehicle are given to illustrate user acceptability and ease of deployment.

A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time. The platform provides an opportunity for real drivers to gain a feel of being in a large-scale connected vehicle scenario. Various applications of the platform are presented.

We study the effect of customer choices in bicycle-sharing systems based on bicycle availability predictions. We show that such systems may lead to flapping behavior between bicycle stations. The consequences of flapping instability include poor user experience and suboptimal usage of the available bicycle stock. We propose a simple assignment strategy aimed at eliminating flapping and balancing demand at each station based on actual availability.

Motivated by the need to provide services to alleviate range anxiety of electric vehicles, we consider the problem of balancing charging demand across a network of charging stations. Our objective is to reduce the potential for excessively long queues to build up at some charging stations, although other charging stations are underutilized. A stochastic balancing algorithm is presented to achieve these goals. A further feature of this algorithm is that it is fully decentralized and facilitates a plug-and-play type of behavior. Using our system, the charging stations can join and leave the network without any changes to, or communication with, a centralized infrastructure. Analysis and simulations are presented to illustrate the efficacy of our algorithm.

This paper introduces and illustrates some novel stochastic policies that assign parking spaces to cars looking for an available parking space. We analyze in detail both the main features of a single park, i.e., how a car could conveniently decide whether to try its luck at that parking lot or try elsewhere, and the case when more parking lots are available, and how to choose the best one. We discuss the practical requirements of the proposed strategies in terms of infrastructure technology and vehicles' equipment and the mathematical properties of the proposed algorithms in terms of robustness against delays, stability, and reliability. Preliminary results obtained from simulations are also provided to illustrate the feasibility and the potential of our stochastic assignment policies.

Ever increasing expectations regarding the penetration level of electric vehicles (EVs) are driving several areas of research related to EV charging. One topic of interest treats EVs not only as controllable loads but also as storage systems, which can be used to mitigate the load on the grid during peak times by offering power. This is known as vehicle to grid (V2G). Since returning energy to the grid affects mobility patterns, V2G has an associated environmental cost. In this paper, to investigate this issue, we formulate the problem of returning electrical load to the grid as an optimization whose goal is to return the desired energy in a fashion that minimizes the cost on the environment. We show that this optimization is highly complex, and in some circumstances, the cost of V2G can be prohibitive.

Large public cloud infrastructure can utilise power which is generated by a multiplicity of power plants. The cost of electricity will vary among the power plants and each will emit different amounts of carbon for a given amount of energy generated. This infrastructure services traffic that can come from anywhere on the planet. It is desirable, for latency purposes, to route the traffic to the data centre that is closest in terms of geographical distance, costs the least to power and emits the smallest amount of carbon for a given request. It is not always possible to achieve all of these goals so we model both the networking and computational components of the infrastructure as a graph and propose the Stratus system which utilises Voronoi partitions to determine which data centre requests should be routed to based on the relative priorities of the cloud operator.

We present a new approach to regulate traffic-related pollution in urban environments by utilizing hybrid vehicles. To do this, we orchestrate the way that each vehicle in a large fleet combines its two engines based on simple communication signals from a central infrastructure. Our approach can be viewed both as a control algorithm and as an optimization algorithm. The primary goal is to regulate emissions, and we discuss a number of control strategies to achieve this goal. Second, we want to allocate the available pollution budget in a fair way among the participating vehicles; again, we explore several different notions of fairness that can be achieved. The efficacy of our approach is exemplified both by the construction of a proof-of-concept vehicle and by extensive simulations, and is verified by mathematical analysis.

This paper presents and develops a novel delay-based additive increase, multiplicative decrease (AIMD) congestion control algorithm. The main features of the proposed solution include: 1) low standing queues and delay in homogeneous environments (with delay-based flows only); 2) fair coexistence of delay- and loss-based flows in heterogeneous environments; 3) delay-based flows behave as loss-based flows when loss-based flows are present in the network; otherwise they revert to delay-based operation. It is also shown that these properties can be achieved without any appreciable increase in network loss rate over that which would be present in a comparable network of standard TCP flows (loss-based AIMD). To demonstrate the potential of the presented algorithm, both analytical and simulation results are provided in a range of different network scenarios. These include stability and convergence results in general multiple-bottleneck networks and a number of simulation scenarios to demonstrate the utility of the proposed scheme. In particular, we show that networks employing our algorithm have the features of networks in which RED AQM's are deployed. Furthermore, in a wide range of situations (including high-speed scenarios), we show that low delay is achieved irrespective of the queueing algorithm employed in the network, with only sender-side modification to the basic AIMD algorithm.

Pricing and load distribution in a cloud are difficult problems. In order to recover the construction costs of the data center, the owner must endeavour to have high utilization while maintaining performance levels for the users. Distributed Rate Limiting (DRL) is a mechanism that can be used to attack this problem. A mathematical framework and the convergence and stability properties of two DRL algorithms were examined in previous work. In this paper we build upon that work by evaluating the performance of these algorithms on a testbed against the results of a simulation carried out in previous work. We also evaluate the algorithms resilience to failure and propose the good-neighbours enhancement to improve its performance.

In recent years we have witnessed a great interest in large distributed computing platforms, also known as clouds. While these systems offer enormous computing power, they are major energy consumers. In existing data centers CPUs are responsible for approximately half of the energy consumed by the servers. A promising technique for saving CPU energy consumption is dynamic speed scaling, in which the speed at which the processor is run is adjusted based on demand and performance constraints. In this paper we look at the problem of allocating the demand in the network of processors (each being capable to perform dynamic speed scaling) to minimize the global energy consumption/cost subject to a performance constraint. The nonlinear dependence between the energy consumption and the performance as well as the high variability in the energy prices result in a nontrivial resource allocation. The problem can be abstracted as a fully distributed convex optimization with a linear constraint. On the theoretical side, we propose two low-overhead fully decentralized algorithms for solving the problem of interest and provide closed-form conditions that ensure stability of the algorithms. Then we evaluate the efficacy of the optimal solution using simulations driven by the real-world energy prices. Our findings indicate a possible cost reduction of 10-40% compared to power-oblivious 1/N load balancing, for a wide range of load factors.

With the expansion of cloud-based services, the question as to how to control usage of such large distributed systems has become increasingly important. Load balancing (LB), and recently proposed distributed rate limiting (DRL) have been used independently to reduce costs and to fairly allocate distributed resources. In this paper we propose a new mechanism for cloud control that unifies the use of LB and DRL: LB is used to minimize the associated costs and DRL makes sure that the resource allocation is fair. From an analytical standpoint, modelling the dynamics of DRL in dynamic workloads (resulting from LB cost-minimization scheme) is a challenging problem. Our theoretical analysis yields a condition that ensures convergence to the desired working regime. Analytical results are then validated empirically through several illustrative simulations. The closed- form nature of our result also allows simple design rules which, together with extremely low computational and communication overhead, makes the presented algorithm practical and easy to deploy.

Delay-based TCP variants continue to attract a large amount of attention in the networking community. Potentially, they offer the possibility to efficiently use network resources while at the same time achieving low queueing delay and virtually zero packet loss. One major impediment to the deployment of delay-based TCP variants is their inability to coexist fairly with standard loss-based TCP. In this paper we propose a simple strategy to make the fair coexistence possible and to ensure that delay-based flows will revert back to the delay-based operation when loss-based flows are no longer present. Analytical and ns-2 simulation results are presented to validate the proposed algorithm.

The distributed rate limiting (DRL) paradigm is a recently proposed mechanism for decentralized control of cloud-based services. DRL is a simple and efficient approach to resolve the issues of pricing and resource control/engineering of cloud based services. The existing DRL schemes focus on very specific performance metrics (such as loss rate and fair-share) and their design heavily depends on the assumption that the traffic is generated by elastic TCP sources. In this paper we tackle the DRL problem for general workloads and performance metrics and propose an analytic framework for the design of stable DRL algorithms. The closed-form nature of our results allows simple design rules which, together with extremely low communication overhead, makes the presented algorithms practical and easy to deploy with guaranteed convergence properties under a wide range of possible scenarios.

Control of large distributed cloud-based services is a challenging problem. The Distributed Rate Limiting (DRL) paradigm was recently proposed as a mechanism for tackling this problem. The heuristic nature of existing DRL solutions makes their behavior unpredictable and analytically untractable. In this paper we treat the DRL problem in a mathematical framework and propose two novel DRL algorithms that exhibit good and predictable performance. The first algorithm Cloud Control with Constant Probabilities (C3P) solves the DRL problem in best effort environments, emulating the behavior of a single best-effort queue in a fully distributed manner. The second problem we approach is the DRL in processor sharing environments. Our algorithm, Distributed Deficit Round Robin (D2R2), parameterized by parameter α, converges to a state that is, at most, O(1/α) away from the exact emulation of centralized processor sharing queue. The convergence and stability properties are fully analyzed for both C3P and D2R2. Analytical results are validated empirically through a number of representative packet level simulations. The closed-form nature of our results allows simple design rules which, together with extremely low communication overhead, makes the presented algorithms practical and easy to deploy.

Small Flow Completion Time (FCT) of short-lived flows, and fair bandwidth allocation of long-lived flows have been two major, usually concurrent, goals in the design of resource allocation algorithms. In this paper we present a framework that naturally unifies these two objectives under a single umbrella; namely by proposing resource allocation algorithm Markov Active Yield (MAY). Based on a probabilistic strategy: "dropproportionaltotheamountofpastdrops", MAY achieves very small FCT among short-lived flows as well as max-min fair bandwidth allocation among long-lived flows, using only the information of short history of already dropped packets. It turns out that extremely small amount of on-chip SRAM (roughly 1 bit per flow in Pareto-like flow size distributions) is enough for storing this drop history. Analytical models are presented and analyzed and accuracy of results is verified experimentally using packet level ns2 simulations.

In this paper we present a methodology based on multiple models and switching for realtime estimation of center of gravity (CG) position in automotive vehicles. The method utilizes simple linear vehicle models and assumes availability of standard stock automotive sensors. We demonstrate the efficacy of our technique with numerical simulations. We also give a simple application example for implementing the idea in automotive vehicles as a switch for rollover controller activation

Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. It is shown that there exists a close relationship between dynamic Takagi-Sugeno fuzzy models and dynamic linearization when using affine local model structures, which suggests that a solution to the multiobjective identification problem exists. However, it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes. Due to the multiobjective nature of the identification problem studied here, special considerations must be made during model structure selection, experiment design, and identification in order to meet both objectives. Some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied. These include constrained and regularized identification and locally weighted identification. Their usefulness in the present context is illustrated by examples.

A nonlinear adaptive filter for use in steel mill applications is described. This filter takes the form of a generalised regression network and is used to remove eccentricities from a steel mill force signal. Online adaptation is achieved by means of standard recursive parameter update algorithms suitable for linear regression type models. It is demonstrated that second order methods can lead to severe parameter biasing effects and a more serious effect known as bursting, whereby the parameter estimation becomes numerically unstable. This paper discusses the above effects form a theoretical and practical viewpoint, and considers the suitability of several learning algorithms for the eccentricity filtering application. This analysis leads to a numerically robust filtering structure, the efficacy of which is demonstrated by means of results from a real steel rolling mill.

Normalisation of the basis function activations in a Radial Basis Function (RBF) network is a common way of achieving the partition of unity often desired for modelling applications. It results in the basis functions covering the whole of the input space to the same degree. However, normalisation of the basis functions can lead to other effects which are sometimes less desirable for modelling applications. This paper describes some side effects of normalisation which fundamentally alter properties of the basis functions, e.g. the shape is no longer uniform, maxima of basis functions can be shifted from their centres, and the basis functions are no longer guaranteed to decrease monotonically as distance from their centre increases--in many cases basis functions can 'reactivate', i.e. re-appear far from the basis function centre. This paper examines how these phenomena occur, discusses their relevance for non-linear function approximation and examines the effect of normalisation on the network condition number and weights.

The recursive least squares (RLS) algorithm with exponential forgetting (/spl lambda/RLS) is perhaps the best known and most widely used algorithm for tracking the time varying parameters of a linear regression model. The implicit assumption in using the /spl lambda/RLS algorithm is that the information is uniformly distributed over the time horizon. Frequently this assumption does not hold and serious difficulties can be encountered when using many model structures. These include convergence of the parameters to local system or noise characteristics and output bursting, i.e. a large error when the operating point changes. In this paper several simple alternatives to the standard /spl lambda/RLS algorithm are proposed. The proposed algorithms extend the idea of a sliding window by quantising the whole input space. These algorithms considerably reduce the risk of forgetting useful information and eliminate the possibility of output bursting by relating the adaptation capabilities of the algorithm to the amount of input stimulation. Simulation results confirm the efficacy of our approach.

Quantisation noise prevalent in transform encoded images becomes increasingly objectionable as the required bit rate is reduced. The perceptual effect of this coding noise is highly dependent on the focal behaviour of the signal upon which it is superimposed. In this paper, a computationally-efficient edge classifier, employing a histogram treatment of image sub-blocks, is proposed. The classifier forms the basis of an adaptive, non-linear postprocessing algorithm incorporating adaptive /spl alpha/-trimmed mean filtering (where the /spl alpha/-value and window size are determined by the output of the edge classifier) and a transform domain dithering technique. Subjective test results confirm the efficacy of the approach.>