242 research outputs found
Networks, Communication, and Computing Vol. 2
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
âThe 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 deviceâs 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-2â--Abstract, page iv
Learning from accidents : machine learning for safety at railway stations
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
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-userâs 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
âproportionatelyâ share resources among distributed participants. Although CEEIâs
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
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(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 controllerâs 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
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
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 dâĂȘtre une expĂ©rience trĂšs stressante pour les conducteurs, la congestion a Ă©galement un impact nĂ©gatif sur lâenvironnement
et lâĂ©conomie. Dans ce contexte, des pressions sont exercĂ©es sur les autoritĂ©s afin quâelles 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. Dâune 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 dâinfrastructure surveillent le trafic sur le rĂ©seau mais sont contraints Ă utiliser le moins de ressources possibles. Cette contrainte implique que lâĂ©tat du trafic ne peut pas ĂȘtre mesurĂ© partout car il nâest 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. Dâun autre cĂŽtĂ©, de nombreuses Ă©tudes sur la congestion routiĂšre ont Ă©tĂ© consacrĂ©es aux autoroutes plutĂŽt quâaux rĂ©gions urbaines, qui sont pourtant beaucoup plus susceptibles dâĂȘtre surveillĂ©es par les autoritĂ©s de la circulation. De plus, les systĂšmes actuels de collecte de donnĂ©es de trafic nâincluent pas la possibilitĂ© dâenregistrer 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 nâest pas suffisant, nous devrions ĂȘtre en mesure de mieux caractĂ©riser lâĂ©vĂ©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 itâs 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
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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