Lua-based self-management framework for Internet of Things

2015 ◽  
Author(s):  
Nemanja Ignjatov ◽  
Milan Z. Bjelica ◽  
Mica Cetkovic ◽  
Sasa Radovanovic ◽  
Gordana Velikic
Keyword(s):  
Internet Of Things ◽  
Self Management ◽  
Management Framework

scholarly journals Big data analytics and the limits of privacy self-management

2015 ◽  
Vol 19 (4) ◽  
pp. 579-596 ◽  
Author(s):  
Lemi Baruh ◽  
Mihaela Popescu
Keyword(s):  
Big Data ◽  
Privacy Protection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Collective Good ◽  
Self Management ◽  
Management Framework ◽  
Individual Privacy ◽  
Algorithmic Analysis ◽  
Individual Strategies

This article looks at how the logic of big data analytics, which promotes an aura of unchallenged objectivity to the algorithmic analysis of quantitative data, preempts individuals’ ability to self-define and closes off any opportunity for those inferences to be challenged or resisted. We argue that the predominant privacy protection regimes based on the privacy self-management framework of “notice and choice” not only fail to protect individual privacy, but also underplay privacy as a collective good. To illustrate this claim, we discuss how two possible individual strategies—withdrawal from the market (avoidance) and complete reliance on market-provided privacy protections (assimilation)—may result in less privacy options available to the society at large. We conclude by discussing how acknowledging the collective dimension of privacy could provide more meaningful alternatives for privacy protection.

scholarly journals Autonomic Fog Computing: Towards Sustainable and Reliable Integration of Cloud & Internet of Things

2019 ◽  
Vol 8 (9S) ◽  
pp. 970-979
Keyword(s):  
Internet Of Things ◽  
System Performance ◽  
Fog Computing ◽  
Self Management ◽  
Self Awareness ◽  
Effective Solution ◽  
Challenging Issue ◽  
Iot Devices ◽  
Day By Day

With the growth of IoT based applications day by day huge volume of data is generated, which becomes a challenging issue for researchers. Fog computing is seem to be an effective solution for managing huge volume of data which is mainly security critical and time sensitive produced by IoT devices or sensors. In this paper we first present an integration of cloud and IoT as substantial number of application scenarios empowered by their Integration and discuss threats challenges & existing solutions related to it. Followed by this, we discussed fog computing which supports the integration of cloud and IoT, further the issues related to fog has been explored. We proposed a concept of self-awareness in Fog computing termed as Autonomic Fog Computing. Autonomic fog computing is introducing the features of Self-management and hence increase the efficiency and enhance the overall system performance.

Autonomic Trust Management in Cloud-Based and Highly Dynamic IOT Applications

2016 ◽  
Vol 1 (5) ◽  
pp. 26-32
Author(s):  
Suneth Namal ◽  
Hasindu Gamaarachchi ◽  
Gyu Myoung Lee ◽  
Tai-Won Um
Keyword(s):  
Cloud Computing ◽  
Feedback Control ◽  
Internet Of Things ◽  
Life Style ◽  
Trust Management ◽  
Control Loop ◽  
Management Framework ◽  
Iot Applications ◽  
Iot Devices ◽  
The Way

In this paper, we propose an autonomic trust management framework for cloud based and highly dynamic Internet of Things (IoT) applications and services. IoT is creating a world where physical objects are seamlessly integrated in order to provide advanced and intelligent services for humanbeings in their day-to-day life style. Therefore, trust on IoT devices plays an important role in IoT based services and applications. Cloud computing has been changing the way how provides are looking into these issues. Many studies have proposed different techniques to address trust management although non of them addresses autonomic trust management in cloud based highly dynamic IoT systems. To our understanding, IoT cloud ecosystems help to solve many of these issues while enhancing robustness and scalability. On this basis, we came up with an autonomic trust management framework based on MAPE-K feedback control loop to evaluate the level of trust. Finally, we presents the results that verify the effectiveness of this framework.

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