Resource Management in a Peer to Peer Cloud Network for IoT

Software-Defined Internet of Things (SDIoT) is defined as merging heterogeneous objects in a form of interaction among physical and virtual entities. Large scale of data centers, heterogeneity issues and their interconnections have made the resource management a hard problem specially when there are different actors in cloud system with different needs. Resource management is a vital requirement to achieve robust networks specially with facing continuously increasing amount of heterogeneous resources and devices to the network. The goal of this paper is reviews to address IoT resource management issues in cloud computing services. We discuss the bottlenecks of cloud networks for IoT services such as mobility. We review Fog computing in IoT services to solve some of these issues. It provides a comprehensive literature review of around one hundred studies on resource management in Peer to Peer Cloud Networks and IoT. It is very important to find a robust design to efficiently manage and provision requests and available resources. We also reviewed different search methodologies to help clients find proper resources to answer their needs

Nik Defense: An Artificial Intelligence Based Defense Mechanism against Selfish Mining in Bitcoin

The Bitcoin cryptocurrency has received much attention recently. In the network of Bitcoin, transactions are recorded in a ledger. In this network, the process of recording transactions depends on some nodes called miners that execute a protocol known as mining protocol. One of the significant aspects of mining protocol is incentive compatibility. However, literature has shown that Bitcoin mining's protocol is not incentive-compatible. Some nodes with high computational power can obtain more revenue than their fair share by adopting a type of attack called the selfish mining attack. In this paper, we propose an artificial intelligence-based defense against selfish mining attacks by applying the theory of learning automata. The proposed defense mechanism ignores private blocks by assigning weight based on block discovery time and changes current Bitcoin's fork resolving policy by evaluating branches' height difference in a self-adaptive manner utilizing learning automata. To the best of our knowledge, the proposed protocol is the literature's first learning-based defense mechanism. Simulation results have shown the superiority of the proposed mechanism against tie-breaking mechanism, which is a well-known defense. The simulation results have shown that the suggested defense mechanism increases the profit threshold up to 40\% and decreases the revenue of selfish attackers.Comment: Paper is submitted to Journal of IEEE Transactions on Dependable and Secure Computin

Enabling peer-to-peer remote experimentation in distributed online remote laboratories

Remote Access Laboratories (RALs) are online platforms that allow human user interaction with physical instruments over the Internet. Usually RALs follow a client-server paradigm. Dedicated providers create and maintain experiments and corresponding educational content. In contrast, this dissertation focuses on a Peer-to-Peer (P2P) service model for RALs where users are encouraged to host experiments at their location. This approach can be seen as an example of an Internet of Things (IoT) system. A set of smart devices work together providing a cyber-physical interface for users to run experiments remotely via the Internet. The majority of traditional RAL learning activities focus on undergraduate education where hands-on experience such as building experiments, is not a major focus. In contrast this work is motivated by the need to improve Science, Technology, Engineering and Mathematics (STEM) education for school-aged children. Here physically constructing experiments forms a substantial part of the learning experience. In the proposed approach, experiments can be designed with relatively simple components such as LEGO Mindstorms or Arduinos. The user interface can be programed using SNAP!, a graphical programming tool. While the motivation for the work is educational in nature, this thesis focuses on the technical details of experiment control in an opportunistic distributed environment. P2P RAL aims to enable any two random participants in the system - one in the role of maker creating and hosting an experiment and one in the role of learner using the experiment - to establish a communication session during which the learner runs the remote experiment through the Internet without requiring a centralized experiment or service provider. The makers need to have support to create the experiment according to a common web based programing interface. Thus, the P2P approach of RALs requires an architecture that provides a set of heterogeneous tools which can be used by makers to create a wide variety of experiments. The core contribution of this dissertation is an automaton-based model (twin finite state automata) of the controller units and the controller interface of an experiment. This enables the creation of experiments based on a common platform, both in terms of software and hardware. This architecture enables further development of algorithms for evaluating and supporting the performance of users which is demonstrated through a number of algorithms. It can also ensure the safety of instruments with intelligent tools. The proposed network architecture for P2P RALs is designed to minimise latency to improve user satisfaction and learning experience. As experiment availability is limited for this approach of RALs, novel scheduling strategies are proposed. Each of these contributions has been validated through either simulations, e.g. in case of network architecture and scheduling, or test-bed implementations, in case of the intelligent tools. Three example experiments are discussed along with users' feedback on their experience of creating an experiment and using others’ experimental setup. The focus of the thesis is mainly on the design and hosting of experiments and ensuring user accessibility to them. The main contributions of this thesis are in regards to machine learning and data mining techniques applied to IoT systems in order to realize the P2P RALs system. This research has shown that a P2P architecture of RALs can provide a wide variety of experimental setups in a modular environment with high scalability. It can potentially enhance the user-learning experience while aiding the makers of experiments. It presents new aspects of learning analytics mechanisms to monitor and support users while running experiments, thus lending itself to further research. The proposed mathematical models are also applicable to other Internet of Things applications

Hydrodynamics-Biology Coupling for Algae Culture and Biofuel Production

International audienceBiofuel production from microalgae represents an acute optimization problem for industry. There is a wide range of parameters that must be taken into account in the development of this technology. Here, mathematical modelling has a vital role to play. The potential of microalgae as a source of biofuel and as a technological solution for CO2 fixation is the subject of intense academic and industrial research. Large-scale production of microalgae has potential for biofuel applications owing to the high productivity that can be attained in high-rate raceway ponds. We show, through 3D numerical simulations, that our approach is capable of discriminating between situations where the paddle wheel is rapidly moving water or slowly agitating the process. Moreover, the simulated velocity fields can provide lagrangian trajectories of the algae. The resulting light pattern to which each cell is submitted when travelling from light (surface) to dark (bottom) can then be derived. It will then be reproduced in lab experiments to study photosynthesis under realistic light patterns

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa