In this paper, we introduce Partial Information Decomposition of Features (PIDF), a new paradigm for simultaneous data interpretability and feature selection. Contrary to traditional methods that assign a single importance value, our approach is based on three metrics per feature: the mutual information shared with the target variable, the feature’s contribution to synergistic information, and the amount of this information that is redundant. In particular, we develop a novel procedure based on these three metrics, which reveals not only how features are correlated with the target but also the additional and overlapping information provided by considering them in combination with other features. We extensively evaluate PIDF using both synthetic and real-world data, demonstrating its potential applications and effectiveness, by considering case studies from genetics and neuroscience.

Decision-making agents based on pre-trained Large Language Models (LLMs) are increasingly being deployed across various domains of human activity. While their applications are currently rather specialized, several research efforts are underway to develop more generalist agents. As LLM-based systems become more agentic, their influence on human activity will grow and their transparency will decrease. Consequently, developing effective methods for aligning them to human values is vital. The prevailing practice in alignment often relies on human preference data (e.g., in RLHF or DPO), in which values are implicit, opaque and are essentially deduced from relative preferences over different model outputs. In this work, instead of relying on human feedback, we introduce the design of reward functions that explicitly and transparently encode core human values for Reinforcement Learning-based fine-tuning of foundation agent models. Specifically, we use intrinsic rewards for the moral alignment of LLM agents. We evaluate our approach using the traditional philosophical frameworks of Deontological Ethics and Utilitarianism, quantifying moral rewards for agents in terms of actions and consequences on the Iterated Prisoner's Dilemma (IPD) environment. We also show how moral fine-tuning can be deployed to enable an agent to unlearn a previously developed selfish strategy. Finally, we find that certain moral strategies learned on the IPD game generalize to several other matrix game environments. In summary, we demonstrate that fine-tuning with intrinsic rewards is a promising general solution for aligning LLM agents to human values, and it might represent a more transparent and cost-effective alternative to currently predominant alignment techniques.

Network Intrusion Detection (NID) remains a key area of research within the information security community, while also being relevant to Machine Learning (ML) practitioners. The latter generally aim to detect attacks using network features, which have been extracted from raw network data typically using dimensionality reduction methods, such as principal component analysis (PCA). However, PCA is not able to assess the relevance of features for the task at hand. Consequently, the features available are of varying quality, with some being entirely non-informative. From this, two major drawbacks arise. Firstly, trained and deployed models have to process large amounts of unnecessary data, therefore draining potentially costly resources. Secondly, the noise caused by the presence of irrelevant features can, in some cases, impede a model's ability to detect an attack. In order to deal with these challenges, we present Feature Selection for Network Intrusion Detection (FSNID) a novel information-theoretic method that facilitates the exclusion of non-informative features when detecting network intrusions. The proposed method is based on function approximation using a neural network, which enables a version of our approach that incorporates a recurrent layer. Consequently, this version uniquely enables the integration of temporal dependencies. Through an extensive set of experiments, we demonstrate that the proposed method selects a significantly reduced feature set, while maintaining NID performance. Code available at https://github.com/c-s-westphal/FSNID.

Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social dilemma environments to study the interactions of independent learning agents; however, they tend to ignore the moral heterogeneity that is likely to be present in societies of agents in practice. For example, at different points in time a single learning agent may face opponents who are consequentialist (i.e., focused on maximizing outcomes over time), norm-based (i.e., conforming to specific norms), or virtue-based (i.e., considering a combination of different virtues). The extent to which agents' co-development may be impacted by such moral heterogeneity in populations is not well understood. In this paper, we present a study of the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting. Using an Iterated Prisoner's Dilemma environment with a partner selection mechanism, we investigate the extent to which the prevalence of diverse moral agents in populations affects individual agents' learning behaviors and emergent population-level outcomes. We observe several types of non-trivial interactions between pro-social and anti-social agents, and find that certain types of moral agents are able to steer selfish agents towards more cooperative behavior.

In this paper, we propose a novel methodology to assist in identifying vulnerabilities in real-world complex heterogeneous industrial control systems (ICS) using two Evolutionary Multiobjective Optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well-known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these to generate combinatorial attacks. These attacks were aimed at compromising the safety of the system and inflicting economic loss. Results were compared against random attacks, and the performance of the EMO algorithms was evaluated using hypervolume, spread, and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using machine learning algorithms. The designed approach was further tested against the developed detection methods. The obtained results demonstrate that the developed EMO approach is a promising tool in the identification of the vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can serve as a proactive defense tool for control and security engineers to identify and prioritise vulnerabilities in the system. The approach can be employed to design resilient control strategies and test the effectiveness of security mechanisms, both in the design stage and during the operational phase of the system.

Practical uses of Artificial Intelligence (AI) in the real world have demonstrated the importance of embedding moral choices into intelligent agents. They have also highlighted that defining top-down ethical constraints on AI according to any one type of morality is extremely challenging and can pose risks. A bottom-up learning approach may be more appropriate for studying and developing ethical behavior in AI agents. In particular, we believe that an interesting and insightful starting point is the analysis of emergent behavior of Reinforcement Learning (RL) agents that act according to a predefined set of moral rewards in social dilemmas. In this work, we present a systematic analysis of the choices made by intrinsically-motivated RL agents whose rewards are based on moral theories. We aim to design reward structures that are simplified yet representative of a set of key ethical systems. Therefore, we first define moral reward functions that distinguish between consequence- and norm-based agents, between morality based on societal norms or internal virtues, and between single- and mixed-virtue (e.g., multi-objective) methodologies. Then, we evaluate our approach by modeling repeated dyadic interactions between learning moral agents in three iterated social dilemma games (Prisoner's Dilemma, Volunteer's Dilemma and Stag Hunt). We analyze the impact of different types of morality on the emergence of cooperation, defection or exploitation, and the corresponding social outcomes. Finally, we discuss the implications of these findings for the development of moral agents in artificial and mixed human-AI societies.

A driver in the rise of IoT systems has been the relative ease with which it is possible to create specialized-but- adaptable deployments from cost-effective components. Such components tend to be relatively unreliable and resource poor, but are increasingly widely connected. As a result, IoT systems are subject both to component failures and to the attacks that are an inevitable consequence of wide-area connectivity. Anomaly detection systems are therefore a cornerstone of effective operation; however, in the literature, there is no established common basis for the evaluation of anomaly detection systems for these environments. No common set of benchmarks or metrics exists and authors typically provide results for just one scenario. This is profoundly unhelpful to designers of IoT systems, who need to make a choice about anomaly detection that takes into account both ease of deployment and likely detection performance in their context. To address this problem, we introduce Aftershoc k, a multi-task benchmark. We adapt and standardize an array of datasets from the public literature into anomaly detection-specific benchmarks. We then proceed to apply a diverse set of existing anomaly detection algorithms to our datasets, producing a set of performance baselines for future comparisons. Results are reported via a dedicated online platform located at https://aftershock. dev, allowing system designers to evaluate the general applicability and practical utility of various anomaly detection models. This approach of public evaluation against common criteria is inspired by the immensely useful community resources found in areas such as natural language processing, recommender systems, and reinforcement learning. We collect, adapt, and make available 10 anomaly detection tasks which we use to evaluate 6 state-of-the-art solutions as well as common baselines. We offer researchers a submission system to evaluate future solutions in a transparent manner and we are actively engaging with academic and industry partners to expand the set of available tasks. Moreover, we are exploring options to add hardware-in-the-loop. As a community contribution, we invite researchers to train their own models (or those reported by others) on the public development datasets available on the online platform, submitting them for independent evaluation and reporting results against others.

Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society. In this work, we adopt the perspective of a central planner with a global view of a network of self-interested agents and the goal of maximizing some desired property in the context of a best-shot public goods game. Existing algorithms for this known NP-complete problem find solutions that are sub-optimal and cannot optimize for criteria other than social welfare.In order to efficiently solve public goods games, our proposed method directly exploits the correspondence between equilibria and the Maximal Independent Set (mIS) structural property of graphs. In particular, we define a Markov Decision Process which incrementally generates an mIS, and adopt a planning method to search for equilibria, outperforming existing methods. Furthermore, we devise a graph imitation learning technique that uses demonstrations of the search to obtain a graph neural network parametrized policy which quickly generalizes to unseen game instances. Our evaluation results show that this policy is able to reach 99.5\% of the performance of the planning method while being three orders of magnitude faster to evaluate on the largest graphs tested. The methods presented in this work can be applied to a large class of public goods games of potentially high societal impact and more broadly to other graph combinatorial optimization problems.

Social dilemmas have been widely studied to explain how humans are able to cooperate in society. Considerable effort has been invested in designing artificial agents for social dilemmas that incorporate explicit agent motivations that are chosen to favor coordinated or cooperative responses. The prevalence of this general approach points towards the importance of achieving an understanding of both an agent's internal design and external environment dynamics that facilitate cooperative behavior. In this paper, we investigate how partner selection can promote cooperative behavior between agents who are trained to maximize a purely selfish objective function. Our experiments reveal that agents trained with this dynamic learn a strategy that retaliates against defectors while promoting cooperation with other agents resulting in a prosocial society.

In a robot mounted camera arrangement, hand-eye calibration estimates the rigid relationship between the robot and camera coordinate frames. Most hand-eye calibration techniques use a calibration object to estimate the relative transformation of the camera in several views of the calibration object and link these to the forward kinematics of the robot to compute the hand-eye transformation. Such approaches achieve good accuracy for general use but for applications such as robotic assisted minimally invasive surgery, acquiring a calibration sequence multiple times during a procedure is not practical. In this paper, we present a new approach to tackle the problem by using the robotic surgical instruments as the calibration object with well known geometry from CAD models used for manufacturing. Our approach removes the requirement of a custom sterile calibration object to be used in the operating room and it simplifies the process of acquiring calibration data when the laparoscope is constrained to move around a remote centre of motion. This is the first demonstration of the feasibility to perform hand-eye calibration using components of the robotic system itself and we show promising validation results on synthetic data as well as data acquired with the da Vinci Research Kit.

Ensuring security in pervasive computing systems is an essential pre-requisite for their deployment. Typically, such systems are reliant on wireless networks for communication; however, whilst a considerable amount of attention has been given to cryptographic mechanisms for securing that wireless link, almost none has been devoted to the creation of covert channels capable of circumventing perimeter security. In systems that embody an element of control, covert channels offer the potential both to leak information that might be considered private and to alter the operation of the system in ways that are undesirable or unsafe. In this paper, we present two novel forms of covert channel designed to leak information from a compromised node within a secured network in ways that are statistically undetectable by other parts of that system. These two attacks rely on: modulation of transmission power, which impacts the RSSI/LQI of a message; and modulation of sensor data in a way that can be seen in the encrypted form of that data. We report the results of an extensive set of practical experiments designed to assess the channel capacity of these covert channels. Overall, this paper demonstrates that the creation of undetectable covert channels is a practical proposition in pervasive computing systems. This, in turn, has implications for key distribution: the use of individual, rather than group, keys is necessary to limit the exposure caused by a successful covert channel attack.

Motivated by the risks and costs associated with outdoor experiments, this paper presents a new multi-platform quadrotor simulator. The simulator implements a novel second-order dynamic model for a quadrotor, produced through evolutionary programming, and explained by domain knowledge. The model captures the effects of mechanics, aerodynamics, wind and rotational stabilization control on the flight platform. In addition, the simulator implements military-grade models for wind and turbulence, as well as noise models for satellite navigation, barometric altitude and orientation. The usefulness of the simulator is shown qualitatively by a comparing how coloured and white position noise affect the performance of offline, range-only SLAM. The simulator is intended to be used for planning experiments, or for stress-testing application performance over a wide range of operating conditions.

Carbon Monoxide (CO) induced by traffic pollution is highly dynamic and non-linear. In a pilot research, we collected some fine-grained 1Hz CO pollution data from a residential road and a busy motorway in Hyderabad, India, in preparation of the deployment of a larger scale, longer term wireless sensor monitoring system. Power conservation is an important issue as the sensor nodes are battery operated. We studied the characteristics of the collected data and designed an adaptive sampling algorithm, Bi-Scale temporal sampler, which adapts the sampling frequency to the statistics collected in real time. This design has incorporated practical engineering considerations including minimising electronic noise, sensor warm-up time and data characteristics. Results show that Bi-Scale sampler achieves better energy saving and statistical deviation ratio for our requirements than burst sampling and eSENSE sampling strategies, which are techniques popularly used in environmental monitoring applications.

Cost effectiveness is critical to the success of immunisation programmes in developing countries. Being able to continuously, flexibly and accurately monitor the vaccine supply chain can help reduce the cost of operating it, and savings can be applied to deliver further resources to those that need

Air pollution data exhibit characteristics like long range correlations and multi-fractal scaling that can be exploited to implement an energy efficient, adaptive spatial sampling technique for pollution sensor nodes. In this work, we present a) results from de-trended fluctuation analysis to prove the presence of non-linear dynamics in real pollution datasets gathered from trials carried out in Cyprus, b) a novel Multi-scale Nearest Neighbors based Adaptive Spatial Sampling (MNNASS) technique that determines the predictability and in turn the directional influences between data from different sensor nodes, and c) performance analysis of the algorithm in terms of energy savings and measurement accuracy.

A pollution data collection exercise was conducted in Hyderabad, India in February, 2012. Fifteen bespoke Carbon Monoxide (CO) monitors were deployed across a small section of a busy highway to collect data for an urban environmental engineering study targeting traffic-generated pollution. The monitors were used to record CO concentrations and temperature; however, in spite of the fact that the monitors were calibrated in advance of deployment, interpretation of the data collected has proved to be challenging. This paper reports the findings of the experiment and proposes a bias-adjustment separation technique that provides a consensual baseline for all the monitors. The result is that spatial variation in the distribution of CO can be studied at snapshots of time. Moreover, the cross-correlations between sensors can be reliability extracted after the bias adjustment.

The papers in this special issue focus on research and field trials of unmanned autonomous vehicles on land, in the air and underwater. The suite of selected papers covers key challenges that impact on the communications dynamics and behaviour of unmanned autonomous vehicles of varying size and resource capability and address UAV bridging, topology maintenance,path planning and link performance optimisation in highly changeable deployments.

Small-size, light-weight, flexible and programmable, yet low-cost sensor node is one of the keys to enable Wireless Sensor Network (WSN) research and a wide range of user applications. In this paper, the Bracelet computer is presented. The system is designed to be flexible and wearable that it supports a range of daughter boards of user's choice, and provides different ways for the boards to be connected to the main processor board. Despite a range of features, the monetary cost of the system is kept low. An application of the system - which uses the same wireless board as the Bracelet computer for localising a running athlete - is presented in this paper to demonstrate the applicability of the system. The localisation results show that the system achieves an average positional error of 28.945cm.

In this paper, we present an adaptive, bandwidth-based group scheduling mechanism that supports reconfiguration of components in compositional software architectures. The middleware-level scheduler allocates resources accurately for real-time threads while non-real-time threads are dealt with in a manner that makes the most of the remaining bandwidth within a thread group. We have designed and implemented the thread-group scheduler in the context of a middleware for embedded real-time control systems, where mixed-criticality tasks may co-exist on the same platform and temporal firewall is of immense value. Yet, it is often the case that better resource-management, flexibility and efficiency are rewarded a great deal due to the high cost of development and the bespoke nature of control systems. With the increasing complexity of large software systems, it is often difficult to reason about how architectural or compositional artefacts are to be linked with non-functional attributes, especially when it comes to scheduling of threads in reconfigurable component-based systems. In other words, there is no clean-cut binding between components and tasks or threads in terms of resource allocation and quality of service provision. For instance, when a component with a varying number of threads inside is to be replaced, it becomes challenging to allocate resources fairly and accurately due to the changing requirements (or number of threads) in the new component in terms of the required CPU time before and after the reconfiguration. The recent temporal firewall approach in the literature works well on a per-thread basis resource allocation, but when the knowledge of threads and the timing requirements are unknown at pre-runtime due to the possible replacement of components with a varying number of subordinate threads, it is impossible to allocate resources effectively. This work builds on the temporal firewall approach to cater for compositional software systems.

We present the design of a novel adaptive sampling technique called Exponential Double Smoothing-based Adaptive Sampling (EDSAS), in which the temporal data correlations provide an indication of the prevailing environmental conditions and are used to adapt the sensing rate of a sensor node. EDSAS uses irregular data series prediction to reduce sampling rate in combination with change detection to maintain data fidelity. The prediction method employs Wright's extension to Holt's method of Exponential Double Sampling (EDS) coupled with a change detection mechanism based on exponentially weighted moving averages (EWMA). The main advantages of EDSAS are that it does not require heavy computation, incurs low memory and communication overhead and the prediction model can be implemented with ease on resource constrained sensor nodes. EDSAS has been evaluated by using real urban road traffic Carbon Monoxide (CO) pollution datasets and has been compared and shown to give better results for performance metrics like sampling fraction and miss ratio. We have also undertaken analysis of the pollution data based on the information received and shown that EDSAS scores over other published technique called e-Sense in capturing the underlying characteristics of the real data.

Many existing Wireless Sensor Network (WSN) synchronisation protocols have demonstrated microsecond-level accuracy is achievable. Furthermore, sub-microsecond-level accuracy has recently been reported, although rather sophisticated, relatively bulky and custom-designed hardware were needed. This paper addresses a fundamental problem in WSN synchronisation: is there a more elegant way to achieve precise synchronisation in WSN? What are the obstacles to pushing the limit in WSN synchronisation? This paper identified the drawbacks caused by the assumptions made in existing WSN synchronisation protocols, and presented and discussed a range of novel solutions to improve the accuracy, reliability and scalability of WSN synchronisation through fusing various types of information and a sensible selection of hardware.

Collaborative applications for co-located mobile users can be severely disrupted by a sybil attack to the point of being unusable. Existing decentralized defences have largely been designed for peer-to-peer networks but not for mobile networks. That is why we propose a new decentralized defence for portable devices and call it MobID. The idea is that a device manages two small networks in which it stores information about the devices it meets: its network of friends contains honest devices, and its network of foes contains suspicious devices. By reasoning on these two networks, the device is then able to determine whether an unknown individual is carrying out a sybil attack or not. We evaluate the extent to which MobID reduces the number of interactions with sybil attackers and consequently enables collaborative applications. We do so using real mobility and social network data. We also assess computational and communication costs of MobID on mobile phones.

Accurate measurements of athletes' stride parameters, such as stride/step length, stride/step frequency, stance times and foot contact times are important to coaching support and biomechanics research in sprinting. Existing stride parameter monitoring approaches are either expensive, or insufficiently accurate, or not suitable for supporting daily training sessions. This paper investigates the use of cost-effective, low quality track-side and on-body sensors in a novel way to enable practical capture of accurate stride information of sprinters. It was investigated how information fusion could play a big part in practical sensing systems; and how novel system design could avoid the need of sophisticated synchronisation between intra-and inter-homogeneous and heterogeneous subsystems. The work also explored the importance of linking heterogeneous data streams to support easy information fusion. Experiment results show that the presented system has an accuracy of 1.661 ± 1.002cm (mean ± SD) for stride length measurements, with 50% errors fall within 3ms and 80% errors fall within 5ms for foot contact time.

Stride-related parameters of sprinters, such as split times (i.e. which is speed-related), foot contact times, stance times, stride/step length, and stride/step frequency, etc. are important factors which affect athletes' performances. Traditionally, this information is captured by biomechanics researchers and coaches using optical-based systems. However, these systems are expensive, time consuming to setup, and have limited viewing angles. Thus, existing biomechanics research work on sprinting is limited to small scale and short studies. This paper presents a practical, cost-effective, user-friendly stride-parameter sensing system - known as the SEnsing for Sports And Managed Exercise (SESAME) Integrated System (IS) - which is the first system for supporting practical and long-term biomechanics research studies in sprinting. The system includes a light-sensor-based split time monitoring system, a radio-based localisation athlete tracking system, a stride length monitoring system, and a centralised data repository. Part of the system has been commissioned at the National Indoor Athletic Centre (NIAC) at Cardiff, UK, since May 2009.

Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the system's top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.

Challenges face the Internet Architecture in order to scale to a greater number of users while providing a suite of increasingly essential functionality, such as multi-homing, traffic engineering, mobility, localised addressing and end-to-end packet-level security. Such functions have been designed and implemented mainly in isolation and retrofitted to the original Internet architecture. The resulting engineering complexity has caused some to think of 'clean slate' designs for the long-term future. Meanwhile, we take the position that an evolutionary approach is possible for a practical and scaleable interim solution, giving much of the functionality required, being backwards compatible with the currently deployed architecture, with incremental deployment capability, and which can reduce the current routing state overhead for the core network. By enhancing the way we use naming in the Internet Architecture, it is possible to provide a harmonised approach to multi-homing, traffic engineering, mobility, localised addressing and end-to-end packet-level security, including specific improvement to the scalability of inter-domain routing, and have these functions co-exist harmoniously with reduced engineering complexity. A set of proposed enhancements to the current Internet Architecture, based on naming, are described and analysed, both in terms of architectural changes and engineering practicalities.

Collaborative Filtering aims to predict user tastes, by minimising the mean error produced when predicting hidden user ratings. The aim of a deployed recommender system is to iteratively predict users' preferences over a dynamic, growing dataset, and system administrators are confronted with the problem of having to continuously tune the parameters calibrating their CF algorithm. In this work, we formalise CF as a time-dependent, iterative prediction problem. We then perform a temporal analysis of the Netflix dataset, and evaluate the temporal performance of two CF algorithms. We show that, due to the dynamic nature of the data, certain prediction methods that improve prediction accuracy on the Netflix probe set do not show similar improvements over a set of iterative train-test experiments with growing data. We then address the problem of parameter selection and update, and propose a method to automatically assign and update per-user neighbourhood sizes that (on the temporal scale) outperforms setting global parameters.

In wireless ad hoc networks, nodes are both routers and terminals, and they have to cooperate to communicate. Cooperation at the network layer means routing (finding a path for a packet), and forwarding (relaying packets for others). However, because wireless nodes are usually constrained by limited power and computational resources, a selfish node may be unwilling to spend its resources in forwarding packets that are not of its direct interest, even though it expects other nodes to forward its packets to the destination. In this paper, we propose a game-theoretic model to facilitate the study of the non-cooperative behaviors in wireless ad hoc networks and analyze incentive schemes to motivate cooperation among wireless ad hoc network nodes to achieve a mutually beneficial networking result.

The use of inertial sensors for distinguishing different types of users' everyday activities was demonstrated in existing work. The sensing for sports and managed exercise (SESAME) project investigates a different aspect of on-body wireless inertial sensing: one of the major aims of the project is to evaluate whether inertial sensors are useful for detecting the smallest details of a rapidly moving (foot) motion of a sprinter, which would be useful for coaching support. In this paper, we present our on-body wireless inertial sensing system, and analyse our system in three aspects: a) a foot motion analysis using the collected inertial data of sprinters; b) the system's physical characteristics (i.e. weight and operational behaviour); and c) the system's wireless performances.

To share services, portable devices may need to locate reputable in-range providers and, to do so, they may exchange ratings with each other. However, providers may well tweak ratings to their own advantage. That is why we have designed a new decentralized mechanism (dubbed MobiRate) with which portable devices store ratings in (local) tamperevident tables and check the integrity of those tables through a gossiping protocol. We evaluate the extent to which MobiRate reduces the impact of tampered ratings and consequently locates reputable service providers. We do so using real mobility and social network data. We also assess computational and communication costs of MobiRate on mobile phones.

In this paper, we investigate the problem of resource constraints in mobile peer-to-peer wireless sensor networks (MP2P WSNs). We propose a scheme to load share tasks among peer sensor nodes, taking into account both their computational capabilities and networking conditions. Experiments to evaluate the performance results were conducted over real Tmote Sky sensor testbeds. We demonstrate that significant performance improvements in terms of latency can be achieved for a MP2P WSN by considering both load and network constraints, and we argue that, when ignoring the latter, the performance of MP2P computing in the application overlay could be severely impacted.

In recent years, the use of inertial sensing for body motion recognition has been demonstrated. However, existing work generally focuses on upper-body movements, which involve smaller scale movements and are less rapid. In this paper, we present two distinctive types of demonstration that show how on-body wireless inertial sensing can be used to capture detail inertial information of the more rapidly moving lower-body segments (e.g. the foot). The first demonstration shows how useful coaching support information for a sprinting exercise are captured; the second demonstration shows how inertial information of the lower segments are used to support football computer game applications, through which the users may trigger the appropriate on-screen actions by their foot motion, instead of using the current hand-held inertial sensing controllers.

Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of user-rating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to this problem; however, the k-nearest neighbour (kNN) algorithm has become the dominant method used in this context. Much research to date has focused on improving the performance of this algorithm, without considering the properties that emerge from manipulating the user data in this way. In order to understand the effect of kNN on a user-rating dataset, the algorithm can be viewed as a process that generates a graph, where nodes are users and edges connect similar users: the algorithm generates an implicit social network amongst the system subscribers. Temporal updates of the recommender system will impose changes on the graph. In this work we analyse user-user kNN graphs from a temporal perspective, retrieving characteristics such as dataset growth, the evolution of similarity between pairs of users, the volatility of user neighbourhoods over time, and emergent properties of the entire graph as the algorithm parameters change. These insights explain why certain kNN parameters and similarity measures outperform others, and show that there is a surprising degree of structural similarity between these graphs and explicit user social networks.

Using mobile devices, such as smart phones, people may create and distribute different types of digital content (e.g., photos, videos). One of the problems is that digital content, being easy to create and replicate, may likely swamp users rather than informing them. To avoid that, users may organize content producers that they know and trust in a web of trust. Users may then reason about this web of trust to form opinions about content producers with whom they have never interacted before. These opinions will then determine whether content is accepted. The process of forming opinions is called trust propagation. We design a mechanism for mobile devices that effectively propagates trust and that is lightweight and distributed (as opposed to previous work that focuses on centralized propagation). This mechanism uses a graph-based learning technique. We evaluate the effectiveness (predictive accuracy) of this mechanism against a large real-world data set. We also evaluate the computational cost of a J2ME implementation on a mobile phone.

Handheld devices have become sufficiently powerful that it is easy to create, disseminate, and access digital content (e.g., photos, videos) using them. The volume of such content is growing rapidly and, from the perspective of each user, selecting relevant content is key. To this end, each user may run a trust model - a software agent that keeps track of who disseminates content that its user finds relevant. This agent does so by assigning an initial trust value to each producer for a specific category (context); then, whenever it receives new content, the agent rates the content and accordingly updates its trust value for the producer in the content category. However, a problem with such an approach is that, as the number of content categories increases, so does the number of trust values to be initially set. This paper focuses on how to effectively set initial trust values. The most sophisticated of the current solutions employ predefined context ontologies, using which initial trust in a given context is set based on that already held in similar contexts. However, universally accepted (and time invariant) ontologies are rarely found in practice. For this reason, we propose a mechanism called TRULLO (trust bootstrapping by latently lifting context) that assigns initial trust values based only on local information (on the ratings of its user's past experiences) and that, as such, does not rely on third-party recommendations. We evaluate the effectiveness of TRULLO by simulating its use in an informal antique market setting. We also evaluate the computational cost of a J2ME implementation of TRULLO on a mobile phone.

Collaborative filtering has become an established method to measure users' similarity and to make predictions about their interests. However, prediction accuracy comes at the cost of user's privacy: in order to derive accurate similarity measures, users are required to share their rating history with each other. In this work we propose a new measure of similarity, which achieves comparable prediction accuracy to the Pearson correlation coefficient, and that can successfully be estimated without breaking users' privacy. This novel method works by estimating the number of concordant, discordant and tied pairs of ratings between two users with respect to a shared random set of ratings. In doing so, neither the items rated nor the ratings themselves are disclosed, thus achieving strictly-private collaborative filtering. The technique has been evaluated using the recently released Netflix prize dataset.

There are many situations in which information from a Wireless Sensor Network (WSN) must be processed to provide a meaningful summary to an external agency in the minimum amount of time, all within the constraints of the processing power and bandwidth available within the network. Our interest is in supporting emergency response for indoors incidents. At present, there are only two choices about where computation might occur within a sensor network: (i) on individual sensor nodes, with the advantage of achieving substantial data reduction, decreasing the cost of transmission, and avoiding congestion; (ii) outside the sensor network, with the sensors simply supplying sinks or more powerful nodes with the data needed for the calculation. The latter approach does not require powerful nodes but necessitates a higher bandwidth network. If applications reach a level of sophistication at which they cannot be executed on a single node, then it would seem that the only option is to have processing performed centrally. The distributed systems community has proposed another solution to limited computing power on a single node: the distribution of complex applications within grids formed by high-end processors. However, since these devices are usually linked through high-speed connections, they do not experience bandwidth restrictions or congestion that are inevitable in any WSN due to the broadcast radio medium. So far, the existing approaches in this direction are hybrid, because they use clusters of nodes that rely on more powerful clusterheads to execute their computation. They tend to focus exclusively on the load availability of nodes during the distribution process, ignoring real communication issues because of the simulated environments in which they are mainly tested. The key contribution of our work is the introduction of a novel approach, which relies on distributing computation among a homogeneous grid of nodes, in an effort to port the Grid Computing paradigm within the WSNs. Moreover, we demonstrate by practical experimentation that there are significant benefits to be gained by considering local network conditions in addition to load information during distribution. We present results from our implementation of two different algorithms on real Tmote Sky sensor testbeds running the Contiki OS [3]. The first is a novel algorithm while the second is an adaptation of an already existing distribution algorithm [2], both modified to take into account real bandwidth requirements.

Mobile devices available today provide users the ability to communicate using a number of different wireless network interfaces. However, these devices do not yet fully exploit the potential for multi-homed and multi-path communication allowing them to better utilise all the connectivity that is available to them. We present here the coalition peering domain (CPD), an architecture that supports collaborative networking relationships between mobile devices. This improves the speed and robustness of communication through more flexible use of all available connectivity

In this paper we examine the complex behavior that emerges from the implementation of an energy-aware system using a simple transmission power control algorithm that exploits overheard MAC-level information to reduce a device's energy consumption. We evaluate this simple algorithm using two radio systems and show that, in spite of the complexity, energy savings can be obtained using a scheme that takes advantage of overheard information

There has been considerable work within the field of digital cash protocols that aims to provide security guarantees - non-repudiation, authentication, overspending checking and off-line checking - whilst protecting anonymity. However, considerably less attention has been given to the question of electronic ticketing, and what exists has been rather abstract or limited. Although eTickets aim at providing the same security guarantees and privacy preservation properties as digital cash, they are significantly different. Digital cash derives much of its anonymity from the fact that the denominations of electronic coins and notes are sufficiently universal that it is not possible for the bank to know in advance how they might be spent. In an eTicketing system, however, this is not the case: at the point the ticket is purchased, the ticket vendor knows for what it will be used and, if a non-anonymous payment system is used, can associate this with the customer. We present a novel protocol that enables users to purchase and spend electronic tickets (eTickets) of a range of two different types: those that can only be used a certain number of times, and those that expire after a certain date.

Distributed trust and reputation management mechanisms are often proposed as a means of providing assurance in dynamic and open environments by enabling principals to building up knowledge of the entities with which they interact. However, there is a tension between the preservation of privacy (which would suggest a refusal to release information) and the controlled release of information that is necessary both in order to accomplish tasks and to provide a foundation for the assessment of trustworthiness. However, if reputation-based systems are to be used in assessing the risks of privacy violation, it is necessary both to discover when sensitive information has been released, and then to be able to evaluate the likelihood that each of the set of principals that knew that information was involved in its release. We argue that statistical traceability can act as a basis for reaching a proper balance between privacy and trust. To enable this, we assume that interacting principals negotiate service level agreements that are intended to constrain the ways in which personal information may be used, and then monitor violations, ascribing likelihoods of involvement in release using an approach based on statistical disclosure control. Even though our approach cannot guarantee perfect privacy protection for personal information, it provides a framework using which detected privacy violation can be mapped onto a measure of accountability, which is useful in deterring such violation.

The vast majority of mobile ad hoc networking research makes a very large assumption - that communication can only take place between nodes that are simultaneously accessible within the same connected cloud (i.e., that communication is synchronous). In reality, this assumption is likely to be a poor one, particularly for sparsely or irregularly populated environments. We present the context-aware routing (CAR) algorithm. CAR is a novel approach to the provision of asynchronous communication in partially-connected mobile ad hoc networks, based on the intelligent placement of messages. We discuss the details of the algorithm, and then present simulation results demonstrating that it is possible for nodes to exploit context information in making local decisions that lead to good delivery ratios and latencies with small overheads.

The conceptual architecture of the access control system described here is based on automatic distributed acquisition and processing of knowledge about users and devices in computer networks. It uses autonomous agents for distributed knowledge management. Agents grouped into distributed communities act as mediators between users/devices and network resources. Communicating with each other, they make decisions about whether a certain user or device can be given access to a requested resource. In other words, agents in our system perform user/device authentication, authorisation, and maintenance of user credentials.

We introduce a new compressed representation for the geometry of dynamic polygonal 3-D meshes, suitable for streaming rate-controlled 3-D animations with fine-grained adaptation to the available network transmission rates. The new representation is based on both mesh simplification and dynamic geometry compression methods. This is in contrast to all other simplification algorithms reported to date, which are tailored to static meshes, whose vertex positions do not vary with time. Furthermore, recently reported methods for compressing dynamic meshes do not encode geometry in a progressive manner. In this work, we lay the foundations of progressive encoding of dynamic 3-D meshes, by formally expressing such models and their associated sequences of topological operations. We also propose a rate adaptation scheme for animated meshes, based on vertex pair candidate contractions and vertex splits, and show its efficiency compared to a typical quantisation-based approach.

Almost all work on mobile ad hoc networks relies on simulations, which, in turn, rely on realistic movement models for their credibility. Since there is a total absence of realistic data in the public domain, synthetic models for movement pattern generation must be used and the most widely used models are currently very simplistic, the focus being ease of implementation rather than soundness of foundation. Whilst it would be preferable to have models that better reflect the movement of real users, it is currently impossible to validate any movement model against real data. However, it is lazy to conclude from this that all models are equally likely to be invalid so any will do.We note that movement is strongly affected by the needs of humans to socialise in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modelled, and that are likely to bias their movement patterns. Thus, we propose a new mobility model that is founded on social network theory, because this has empirically been shown to be useful as a means of describing human relationships. In particular, the model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This grouping is only then mapped to a topographical space, with topography biased by the strength of social tie.We discuss the implementation of this mobility model and we evaluate emergent properties of the generated networks. In particular, we show that grouping mechanism strongly influences the probability distribution of the average degree (i.e., the average number of neighbours of a host) in the simulated network.

This paper establishes quantitatively, the case for proactive IP mobility versus its reactive mobile IP counterpart by means of simulations. Results attained, exhibit marked improvements on transmission delay over proactive mobile IP and demonstrate the case for context-transferred intelligence, assisting both the MN and its accommodating wireless network in effecting intelligent seamless IP handoffs.

The aim of this paper is to provide better support for the development of secure systems. We argue that current development practice suffers from two key problems:1. Security requirements tend to be kept separate from other system requirements, and not integrated into any overall strategy.2. The impact of security measures on users and the operational cost of these measures on a day-to-day basis are usually not considered.Our new paradigm is the full integration of security and usability concerns into the software development process, thus enabling developers to build secure systems that work in the real world. We present AEGIS, a secure software engineering method which integrates asset identification, risk and threat analysis and context of use, bound together through the use of UML, and report its application to case studies on Grid projects. An additional benefit of the method is that the involvement of stakeholders in the high-level security analysis improves their understanding of security, and increases their motivation to comply with policies.

The lack of synchronisation between a sender clock and a receiver audio clock in an audio application results in an undesirable effect known as "audio skew". This paper proposes and implements a new approach to detecting and correcting audio skew, focusing on the accuracy of measurements and on the algorithm's effect on the audio experience of the listener. The algorithms presented are shown to remove audio skew successfully, thus reducing delay and loss and hence improving audio quality.

To date, almost all work within the field of 3D animation has focused on raising perceptual appeal to an adequate level. However, it is a non-trivial task to match the requirements of real-time streams to the reality of the Internet. One of the key problems that must be addressed is that of how best to conceal errors in the event of packet loss. Thus we present the results of experiments designed to evaluate the effect of different possible schemes under different conditions of loss. We conclude that frame insertion methods show good performance at low loss rates, but that frame interpolation methods exhibit better performance at both low and high burst loss rates, though at the cost of added delay and complexity. Visualised traces subjectively validate these results, by exhibiting smoother animation and reduced artifacts on the animated wireframe models.

The IEEE802.11 group has ratified high rate (HR) extensions to enable high speed wireless communications over WLANs. The HR extensions specified in revision IEEE802.11b, encompass mainly new RF modulation schemes. This paper attempts an experimental evaluation of the performance characteristics of 802.11b in terms of throughput and loss over high speed transmission rates with respect to connection-less network traffic. We present a simple analysis of the protocol's throughput capacity over high speed rates while we reveal fundamental design considerations that prevent 802.11b from reaching its true throughput potentials in the light of rate adaptivity. We further recommend some extensions to the medium access control (MAC) protocol sub-layer that reconsider the multi-rate compatibility requirement while maintaining fairness in throughput between nodes at short or long distances within range from a base station (BS). The recommendations subject to simulations as work in-progress, expect to effect improvements in throughput over a proportionally-fair rate fallback scheme, in the order of 15% for transmission rates of 11 Mbps. We also provide some key observations to enable efficient protocol design for adaptive mobile environments.

Article A distributed trust model Share on Authors: Alfarez Abdul-Rahman Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United KingdomView Profile , Stephen Hailes Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United KingdomView Profile Authors Info & Claims NSPW '97: Proceedings of the 1997 workshop on New security paradigmsJanuary 1998 Pages 48–60https://doi.org/10.1145/283699.283739Online:01 January 1998Publication History 377citation5,976DownloadsMetricsTotal Citations377Total Downloads5,976Last 12 Months250Last 6 weeks42 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access