4 research outputs found
Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading
Personal IoT data is a new economic asset that individuals can trade to
generate revenue on the emerging data marketplaces. Typically, marketplaces are
centralized systems that raise concerns of privacy, single point of failure,
little transparency and involve trusted intermediaries to be fair. Furthermore,
the battery-operated IoT devices limit the amount of IoT data to be traded in
real-time that affects buyer/seller satisfaction and hence, impacting the
sustainability and usability of such a marketplace. This work proposes to
utilize blockchain technology to realize a trusted and transparent
decentralized marketplace for contract compliance for trading IoT data streams
generated by battery-operated IoT devices in real-time. The contribution of
this paper is two-fold: (1) we propose an autonomous blockchain-based
marketplace equipped with essential functionalities such as agreement
framework, pricing model and rating mechanism to create an effective
marketplace framework without involving a mediator, (2) we propose a mechanism
for selection and allocation of buyers' demands on seller's devices under
quality and battery constraints. We present a proof-of-concept implementation
in Ethereum to demonstrate the feasibility of the framework. We investigated
the impact of buyer's demand on the battery drainage of the IoT devices under
different scenarios through extensive simulations. Our results show that this
approach is viable and benefits the seller and buyer for creating a sustainable
marketplace model for trading IoT data in real-time from battery-powered IoT
devices.Comment: Accepted in SmartComp 202
Internet Data Bandwidth Optimization and Prediction in Higher Learning Institutions Using Lagrangeās Interpolation: A Case of Lagos State University of Science and Technology
This research work studies the performance of the internet services of institution of higher learning in Nigeria. Data was collated from Lagos State University of Science and Technology (LASUSTECH) as case study of this research work. The problem of Internet Bandwidth optimization in the institution of higher learning in Nigeria was extensively addressed in this paper. The operation of the Link-Load balancer which provides an efficient cost-effective and easy-to-use solution to maximize utilization and availability of Internet access is discussed. In this research work, the Lagrangeās method of interpolation was used to predict effective internet data bandwidth for significantly increasing number of internet users. The linear Lagrangeās interpolation model (LILAGRINT model) was proposed for LASUSTECH.Ā The predictions allow us to view the effective internet data bandwidth with respect to the corresponding acceptable number of internet users as the number of userās increases. The integrity of the model was examined, verified and validated at the ICT department of the institution. The LILAGRINT model was integrated into the management of ICT and tested. The result showed that the proposed LILAGRINT model proved to be highly effective and innovative in the area of internet data bandwidth predictability. Keywords:Internet Data Bandwidth, Optimization, Link-load balancer, Lagrangeās interpolation, Predictions, Management of ICT DOI: 10.7176/CEIS/10-1-04 Publication date:September 30th 202