Scientific Workshop 4: Intelligent Objects for the Internet of Things: Internet of Things – Application of Sensor Networks in Logistics

Author(s):  
Christian Flügel ◽  
Volker Gehrmann
Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2417
Author(s):  
Andrzej Michalski ◽  
Zbigniew Watral

This article presents the problems of powering wireless sensor networks operating in the structures of the Internet of Things (IoT). This issue was discussed on the example of a universal end node in IoT technology containing RFID (Radio Frequency Identification) tags. The basic methods of signal transmission in these types of networks are discussed and their impact on the basic requirements such as range, transmission speed, low energy consumption, and the maximum number of devices that can simultaneously operate in the network. The issue of low power consumption of devices used in IoT solutions is one of the main research objects. The analysis of possible communication protocols has shown that there is a possibility of effective optimization in this area. The wide range of power sources available on the market, used in nodes of wireless sensor networks, was compared. The alternative possibilities of powering the network nodes from Energy Harvesting (EH) generators are presented.


2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jun Huang ◽  
Liqian Xu ◽  
Cong-cong Xing ◽  
Qiang Duan

The design of wireless sensor networks (WSNs) in the Internet of Things (IoT) faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS). Unlike the ant colony optimization (ACO) algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant) to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.


Author(s):  
Matt Zwolenski ◽  
Lee Weatherill

The Digital Universe, which consists of all the data created by PC, Sensor Networks, GPS/WiFi Location, Web Metadata, Web-Sourced Biographical Data, Mobile, Smart-Connected Devices and Next-Generation Applications (to name but a few) is altering the way we consume and measure IT and disrupting proven business models. Unprecedented and exponential data growth is presenting businesses with new and unique opportunities and challenges. As the ‘Internet of Things’ (IoT) and Third Platform continue to grow, the analysis of structured and unstructured data will drive insights that change the way businesses operate, create distinctive value, and deliver services and applications to the consumer and to each other. As enterprises and IT grapple to take advantage of these trends in order to gain share and drive revenue, they must be mindful of the Information Security and Data Protection pitfalls that lay in wait ─ hurdles that have already tripped up market leaders and minnows alike.


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