A review of smart home applications based on Internet of Things

The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms– branches of Artificial Intelligence (AI)– can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

/pdf/security-threats-and-artificial-intelligence-based-2qjruosllv.pdf

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

pdf/an-exploration-of-iot-platform-development-1j5f57520s.pdf

An exploration of IoT platform development

Quick And Dirty

Although there are many attempts to engineer a domain specific language for the Internet of Things, most of them forget the fact that with the evolving of the Internet of Things, the end user will probably be a common person without an engineering or software development background. The designers of the UML had the same problem: how to make a language powerful enough for the professionals, but at the same time simple enough to be understood by a non-technical end user that gives the requirements. Inspired by this idea a Visual Domain Specific Modeling Language was developed for the IoT and proved that it is powerful enough for the professional and at the same time simple enough to be used by non-technical users.

An Internet of Things visual domain specific modeling language based on UML

With the goal of relieving the Internet of things (IoT)application designers of the complexities and heterogeneity of wireless sensory networks (WSNs), devices, communication media, protocols and operating systems, the development of higher level domain specific language has been undertaken The result is DSL-4-IoT Editor-Designer, which is based on high level visual programming language, established on the class of visual domain specific modeling languages (VDSMLs) DSL-4-IoT is using formal presentations and abstract syntax in a metamodel The visual front-end of the Editor has been developed in JavaScript language The runtime execution of generated IoT application configuration files is done by the open source project “OpenHAB” runtime engine In order to demonstrate the viability and usability of the developed DSL-4-IoT visual model based language, an experimental IoT testbed including 15 heterogeneous wireless sensory devices spanning two application domain (smart home and remote patient monitoring), has been designed and deployed

Design of a domain specific language and IDE for Internet of things applications

Most research on network traffic prediction has been done on small datasets based on statistical methodologies. This research analyzes an internet traffic dataset spanning multiple months using the data mining process. Each data mining phase was carefully fitted to the network analysis domain and systematized in context of data mining. The second part of the paper evaluates various seasonal time series prediction models (univariate), including ANN, ARIMA, Holt Winters etc., as a data mining phase on the given dataset. The experiments have shown that in most cases ANNs are superior to other algorithms for this purpose.

/pdf/data-mining-meets-network-analysis-traffic-prediction-models-4kuxm58tvx.pdf

Data mining meets network analysis: Traffic prediction models

In this paper we present a Recommendation system that uses the Social Network Services like Facebook Connect, OpenSocial etc. to get useful interest and friend interest data as an input for a Trust Clustering - Friends interest clustering applied on the results of a Collaborative filtering algorithm. We also discuss the advantages and disadvantages of Collaborative filtering and Trust based Clustering and how our solution uses the advantages and solves the disadvantages by combining them.

Using social network services as an input for a trust clustered: collaborative filtering recommendation system

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Design of a domain specific language and IDE for Internet of things applications

An Internet of Things visual domain specific modeling language based on UML

Data mining meets network analysis: Traffic prediction models

Using social network services as an input for a trust clustered: collaborative filtering recommendation system