TRULLO - local trust bootstrapping for ubiquitous devices

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

Trust based collaborative filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy

Adaptive routing for intermittently connected mobile ad hoe networks

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 in 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.In this paper 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.</p

An experimental study on a motion sensing system for sports training

In sports science, motion data collected from athletes is used to derive key performance characteristics, such as stride length and stride frequency, that are vital coaching support information. The sensors for use must be more accurate, must capture more vigorous events, and have strict weight and size requirements, since they must not themselves affect performance. These requirements mean each wireless sensor device is necessarily resource poor and yet must be capable of communicating a considerable amount of data, contending for the bandwidth with other sensors on the body. This paper analyses the results of a set of network traffic experiments that were designed to investigate the suitability of conventional wireless motion sensing system design � which generally assumes in-network processing - as an efficient and scalable design for use in sports training

Evolving attackers against wireless sensor networks using genetic programming

Recent hardware developments have made it possible for the Internet of Things (IoT) to be built. A wide variety of industry sectors, including manufacturing, utilities, agriculture, transportation, and healthcare are actively seeking to incorporate IoT technologies in their operations. The increased connectivity and data sharing that give IoT systems their advantages also increase their vulnerability to attack. In this study, the authors explore the automated generation of attacks using genetic programming (GP), so that defences can be tested objectively in advance of deployment. In the authors' system, the GP-generated attackers targeted publish-subscribe communications within a wireless sensor networks that was protected by an artificial immune intrusion detection system (IDS) taken from the literature. The GP attackers successfully suppressed more legitimate messages than the hand-coded attack used originally to test the IDS, whilst reducing the likelihood of detection. Based on the results, it was possible to reconfigure the IDS to improve its performance. Whilst the experiments were focussed on establishing a proof-of-principle rather than a turnkey solution, they indicate that GP-generated attackers have the potential to improve the protection of systems with large attack surfaces, in a way that is complementary to traditional testing and certification

Compressing Inertial Motion Data in Wireless Sensing Systems – An Initial Experiment

The use of wireless inertial motion sensors, such as accelerometers, for supporting medical care and sport’s training, has been under investigation in recent years. As the number of sensors (or their sampling rates) increases, compressing data at source(s) (i.e. at the sensors), i.e. reducing the quantity of data that needs to be transmitted between the on-body sensors and the remote repository, would be essential especially in a bandwidth-limited wireless environment. This paper presents a set of compression experiment results on a set of inertial motion data collected during running exercises. As a starting point, we selected a set of common compression algorithms to experiment with. Our results show that, conventional lossy compression algorithms would achieve a desirable compression ratio with an acceptable time delay. The results also show that the quality of the decompressed data is within acceptable range

Rendezvous Planning for Multiple Autonomous Underwater Vehicles using a Markov Decision Process

Multiple Autonomous Underwater Vehicles (AUVs) are a potential alternative to conventional large manned vessels for mine countermeasure (MCM) operations. Online mission planning for cooperative multi-AUV network often relies or predefined contingency on reactive methods and do not deliver an optimal end-goal performance. Markov Decision Process (MDP) is a decision-making framework that allows an optimal solution, taking into account future decision estimates, rather than having a myopic view. However, most real-world problems are too complex to be represented by this framework. We deal with the complexity problem by abstracting the MCM scenario with a reduced state and action space, yet retaining the information that defines the goal and constraints coming from the application. Another critical part of the model is the ability of the vehicles to communicate and enable a cooperative mission. We use the Rendezvous Point (RP) method. The RP schedules meeting points for the vehicles throughput the mission. Our model provides an optimal action selection solution for the multi-AUV MCM problem. The computation of the mission plan is performed in the order of minutes. This quick execution demonstrates the model is feasible for real-time applications

Who Watches the Watchers: A Multi-Task Benchmark for Anomaly Detection

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