A Hybrid Approach for Data Analytics for Internet of Things

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.Comment: Accepted to be published in the Proceedings of the 7th ACM International Conference on the Internet of Things (IoT 2017

Exploring the effectiveness of service decomposition in fog computing architecture for the Internet of Things

The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to create linked-microservices (LMS). Linked-microservices are services that run on multiple nodes but closely linked to their linked-partners. Linked-microservices allow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10% - 70%. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service

Towards an efficient data analytics architecture for the Internet of Things

In the Internet of Things (IoT), the traditional architecture aims to process the data in the cloud. This creates several challenges such as high communication latency between the end devices and the cloud while making the network busy by sending all the raw data continuously. In this thesis, we propose an alternative architecture for the IoT which processes part of the data in the fog to avoid all raw data to be sent to the cloud. However, the cloud processes intensive data analytics. We conduct a trade-off analysis to show the advantages of applying data fusion closer to the data source and then processing the intensive data analytics algorithms in the cloud. We explore the effectiveness of the available architectures including centralised, decentralised, and distributed architecture to propose the most effective data analytics architecture for the IoT. The trade-off analysisshows the effectiveness of various service decomposition strategies leading to an understanding the various balances between Fog and IoT processing and their effectiveness in data communications reduction and result accuracy allowing achievements of 70% data communication reduction while still achieving approximately 90% accuracy. We propose a service distribution strategy called Most Efficient IoT Node (MEIN), which aims to distribute the services to either cloud nodes or fog nodes based on their capabilities while maintaining the usage of resource in IoT architecture. This strategy selects the best nodes and distributes the services on nodes based on the demands of services and capabilities of nodes.</div

Towards an Off-the-cloud IoT data processing Architecture via a Smart Car Parking Example

Nowadays, it is obvious that technology has revolutionised our lives by supporting us to do complicated jobs. The Internet of Things (IoT) is one of the emerging technologies. One of the most significant current research topics in the IoT is smart city. The smart city includes several applications such assmart home, smart industry and smart mobility. The smart car parking system is an aspect of smart mobility and an important application in smart city projects, because of the rapidly increasing number of cars in urban areas. However, most of the current proposals in smart car parking systems manage the data on the cloud side which is a problem since the system needs to send the raw data from sensor to cloud and receive instructions back: this is expensive in terms of energy and data transmission cost. To tackle this issue we present a proposal to save energy and to reduce the amount of data that is transmitted over the network to cloud by processing closer to source in this paper. The architecture is demonstrated through a case study

FABIoT: A Flexible Agent-Based Simulation Model for IoT Environments

The Internet of Things aims to digitize everyday physical objects by connecting them to the internet. As a result, cyber-physical environments of multiple sizes emerge, imposing new requirements on applications and software systems in regards support to heterogeneity and volatility. A challenging stage in the engineering of these systems is the validation. Although, there have been significant efforts to offer shared real-world testbeds, the simulations platforms are required to make the validation process cost and time effective. Existing simulation approaches only offer partial coverage to the key IoT environment characteristics, focus on communication or are specific for particular use cases and domains. In this paper, we propose a novel agent-based model that enables the simulation of the IoT systems with the key characteristics of an IoT environment. This model is designed to be flexible and adaptable to different experiments. Our approach introduces events in IoT environments as stochastic processes, enabling the evaluation of IoT systems under different conditions that otherwise would be time consuming and costly. We present the results of our experiments for evaluation of our model. These show that our proposal is a practical solution for the validation of IoT software systems, complementary to the real-world tests

Security strategy for autonomous vehicle cyber-physical systems using transfer learning

Abstract Cyber-physical systems (CPSs) are emergent systems that enable effective real-time communication and collaboration (C&C) of physical components such as control systems, sensors, actuators, and the surrounding environment through a cyber communication infrastructure. As such, autonomous vehicles (AVs) are one of the fields that have significantly adopted the CPS approach to improving people's lives in smart cities by reducing energy consumption and air pollution. Therefore, autonomous vehicle-cyber physical systems (AV-CPSs) have attracted enormous investments from major corporations and are projected to be widely used. However, AV-CPS is vulnerable to cyber and physical threat vectors due to the deep integration of information technology (IT), including cloud computing, with the communication process. Cloud computing is critical in providing the scalable infrastructure required for real-time data processing, storage, and analysis in AV-CPS, allowing these systems to work seamlessly in smart cities. CPS components such as sensors and control systems through network infrastructure are particularly vulnerable to cyber-attacks targeted by attackers using the communication system. This paper proposes an intelligent intrusion detection system (IIDS) for AV-CPS using transfer learning to identify cyberattacks launched against connected physical components of AVs through a network infrastructure. First, AV-CPS was developed by implementing the controller area network (CAN) and integrating it into the AV simulation model. Second, the dataset was generated from the AV-CPS. The collected dataset was then preprocessed to be trained and tested via pre-trained CNNs. Third, eight pre-trained networks were implemented, namely, InceptionV3, ResNet-50, ShuffleNet, MobileNetV2, GoogLeNet, ResNet-18, SqueezeNet, and AlexNet. The performance of the implemented models was evaluated. According to the experimental evaluation results, GoogLeNet outperformed all other pre-rained networks, scoring an F1- score of 99.47%