242 research outputs found

    Networks, Communication, and Computing Vol. 2

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    Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields

    Man-in-the-Middle Attacks on MQTT based IoT networks

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    “The use of Internet-of-Things (IoT) devices has increased a considerable amount in recent years due to decreasing cost and increasing availability of transistors, semiconductor, and other components. Examples can be found in daily life through smart cities, consumer security cameras, agriculture sensors, and more. However, Cyber Security in these IoT devices are often an afterthought making these devices susceptible to easy attacks. This can be due to multiple factors. An IoT device is often in a smaller form factor and must be affordable to buy in large quantities; as a result, IoT devices have less resources than a typical computer. This includes less processing power, battery power, and random access memory (RAM). This limits the possibilities of traditional security in IoT devices. To help evaluate the state of IoT devices and further enforce them, we present an easy to use program that requires little to no prior knowledge of the target infrastructure. The process is a Man-in-the-Middle (MITM) attack that hijacks packets sent between IoT devices using the popular MQTT protocol. We do this by using a WiFi Pineapple from Hak5, in the device’s raw form, is a WiFi access point with specific offensive capabilities installed as software. We then pass these packets into a custom General Adversarial Network (GAN) that utilizes a Natural Language Processing (NLP) model to generate a malicious message. Once malicious messages are generated, the messages are passed back to the WiFI Pineapple and sent as a legitimate packet among the network. We then look at the efficiency of these malicious messages through different NLP algorithms. In this particular work, we analyze an array of BERT variants and GPT-2”--Abstract, page iv

    Learning from accidents : machine learning for safety at railway stations

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    In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

    A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing

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    With the rapid growth of Internet of Things (IoT), cloud-centric application management raises questions related to quality of service for real-time applications. Fog and edge computing (FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource management on multiple resources from distributed and administrative FEC nodes is a key challenge to ensure the quality of end-user’s experience. To improve resource utilisation and system performance, researchers have been proposed many fair allocation mechanisms for resource management. Dominant Resource Fairness (DRF), a resource allocation policy for multiple resource types, meets most of the required fair allocation characteristics. However, DRF is suitable for centralised resource allocation without considering the effects (or feedbacks) of large-scale distributed environments like multi-controller software defined networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to ‘proportionately’ share resources among distributed participants. Although CEEI’s decentralised policy guarantees load balancing for performance isolation, they are not faultproof for computation offloading. The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of decentralised SDN controller deployment. We apply multi-agent reinforcement learning (MARL) with robustness against adversarial controllers to enable efficient priority scheduling for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by applying the principles of feedback (positive or/and negative network effects) in reverse game theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask offloading/forwarding in FEC environments. In the first piece of study, monotonic scheduling for joint offloading at the federated edge is addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL approach applying partition form game (PFG) to guarantee second-best Pareto optimality viii | P a g e (SBPO) in allocation of multi-resources from deterministic policy in both population and resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical networks by applying constrained coalition formation (CCF) games to implement MARL. The multi-objective optimisation problem for fog throughput maximisation is solved via a constraint dimensionality reduction methodology using fairness constraints for efficient gateway and low-level controller’s placement. For evaluation, we develop an agent-based framework to implement fair allocation policies in distributed data centre environments. In empirical results, the deterministic policy of IP-DRF scheme provides SBPO and reduces the average execution and turnaround time by 19% and 11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets in population non-monotonic settings. The processing cost of tasks shows significant improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair (MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria

    Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

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    Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

    Road Traffic Congestion Analysis Via Connected Vehicles

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    La congestion routiĂšre est un Ă©tat particulier de mobilitĂ© oĂč les temps de dĂ©placement augmentent et de plus en plus de temps est passĂ© dans le vĂ©hicule. En plus d’ĂȘtre une expĂ©rience trĂšs stressante pour les conducteurs, la congestion a Ă©galement un impact nĂ©gatif sur l’environnement et l’économie. Dans ce contexte, des pressions sont exercĂ©es sur les autoritĂ©s afin qu’elles prennent des mesures dĂ©cisives pour amĂ©liorer le flot du trafic sur le rĂ©seau routier. En amĂ©liorant le flot, la congestion est rĂ©duite et la durĂ©e totale de dĂ©placement des vĂ©hicules est rĂ©duite. D’une part, la congestion routiĂšre peut ĂȘtre rĂ©currente, faisant rĂ©fĂ©rence Ă  la congestion qui se produit rĂ©guliĂšrement. La congestion non rĂ©currente (NRC), quant Ă  elle, dans un rĂ©seau urbain, est principalement causĂ©e par des incidents, des zones de construction, des Ă©vĂ©nements spĂ©ciaux ou des conditions mĂ©tĂ©orologiques dĂ©favorables. Les opĂ©rateurs d’infrastructure surveillent le trafic sur le rĂ©seau mais sont contraints Ă  utiliser le moins de ressources possibles. Cette contrainte implique que l’état du trafic ne peut pas ĂȘtre mesurĂ© partout car il n’est pas rĂ©aliste de dĂ©ployer des Ă©quipements sophistiquĂ©s pour assurer la collecte prĂ©cise des donnĂ©es de trafic et la dĂ©tection en temps rĂ©el des Ă©vĂ©nements partout sur le rĂ©seau routier. Alors certains emplacements oĂč le flot de trafic doit ĂȘtre amĂ©liorĂ© ne sont pas surveillĂ©s car ces emplacements varient beaucoup. D’un autre cĂŽtĂ©, de nombreuses Ă©tudes sur la congestion routiĂšre ont Ă©tĂ© consacrĂ©es aux autoroutes plutĂŽt qu’aux rĂ©gions urbaines, qui sont pourtant beaucoup plus susceptibles d’ĂȘtre surveillĂ©es par les autoritĂ©s de la circulation. De plus, les systĂšmes actuels de collecte de donnĂ©es de trafic n’incluent pas la possibilitĂ© d’enregistrer des informations dĂ©taillĂ©es sur les Ă©vĂ©nements qui surviennent sur la route, tels que les collisions, les conditions mĂ©tĂ©orologiques dĂ©favorables, etc. Aussi, les Ă©tudes proposĂ©es dans la littĂ©rature ne font que dĂ©tecter la congestion ; mais ce n’est pas suffisant, nous devrions ĂȘtre en mesure de mieux caractĂ©riser l’évĂ©nement qui en est la cause. Les agences doivent comprendre quelle est la cause qui affecte la variabilitĂ© de flot sur leurs installations et dans quelle mesure elles peuvent prendre les actions appropriĂ©es pour attĂ©nuer la congestion.----------ABSTRACT: Road traffic congestion is a particular state of mobility where travel times increase and more and more time is spent in vehicles. Apart from being a quite-stressful experience for drivers, congestion also has a negative impact on the environment and the economy. In this context, there is pressure on the authorities to take decisive actions to improve the network traffic flow. By improving network flow, congestion is reduced and the total travel time of vehicles is decreased. In fact, congestion can be classified as recurrent and non-recurrent (NRC). Recurrent congestion refers to congestion that happens on a regular basis. Non-recurrent congestion in an urban network is mainly caused by incidents, workzones, special events and adverse weather. Infrastructure operators monitor traffic on the network while using the least possible resources. Thus, traffic state cannot be directly measured everywhere on the traffic road network. But the location where traffic flow needs to be improved varies highly and certainly, deploying highly sophisticated equipment to ensure the accurate estimation of traffic flows and timely detection of events everywhere on the road network is not feasible. Also, many studies have been devoted to highways rather than highly congested urban regions which are intricate, complex networks and far more likely to be monitored by the traffic authorities. Moreover, current traffic data collection systems do not incorporate the ability of registring detailed information on the altering events happening on the road, such as vehicle crashes, adverse weather, etc. Operators require external data sources to retireve this information in real time. Current methods only detect congestion but it’s not enough, we should be able to better characterize the event causing it. Agencies need to understand what is the cause affecting variability on their facilities and to what degree so that they can take the appropriate action to mitigate congestion

    Context-aware management of multi-device services in the home

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    MPhilMore and more functionally complex digital consumer devices are becoming embedded or scattered throughout the home, networked in a piecemeal fashion and supporting more ubiquitous device services. For example, activities such as watching a home video may require video to be streamed throughout the home and for multiple devices to be orchestrated and coordinated, involving multiple user interactions via multiple remote controls. The main aim of this project is to research and develop a service-oriented multidevice framework to support user activities in the home, easing the operation and management of multi-device services though reducing explicit user interaction. To do this, user contexts i.e., when and where a user activity takes place, and device orchestration using pre-defined rules, are being utilised. A service-oriented device framework has been designed in four phases. First, a simple framework is designed to utilise OSGi and UPnP functionality in order to orchestrate simple device operation involving device discovery and device interoperability. Second, the framework is enhanced by adding a dynamic user interface portal to access virtual orchestrated services generated through combining multiple devices. Third the framework supports context-based device interaction and context-based task initiation. Context-aware functionality combines information received from several sources such as from sensors that can sense the physical and user environment, from user-device interaction and from user contexts derived from calendars. Finally, the framework supports a smart home SOA lifecycle using pre-defined rules, a rule engine and workflows
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