Konstantinos Katzis

Challenges Implementing Internet of Things (IoT) Using Cognitive Radio Capabilities in 5G Mobile Networks

This chapter aims at identifying the main design and operation constraints, that smart environments are expected to experience within a 5G wireless/mobile network and how these constraints can be addressed using cognitive radio networks. This chapter first provides a general description of 5G wireless/mobile networks and stresses their role in the future wireless communications with emphasis given on smart environments. Then, the smart environments are presented based on their architecture characteristic and the applications associated with their operation. In addition, an overview of various current standards related to IoT applications is presented followed by the concept of cognitive radio networks and the available experimental platforms stressing the benefits of employing this technology in the future 5G wireless/mobile networks. Finally, the research challenges associated with integrating 5G wireless/mobile networks and IoT are outlined.

Spectrum sensing infrastructure support for IEEE 1900.6b sensing-assisted spectrum databases

Spectrum databases are increasingly being used, particularly in spectrum sharing mechanisms, but also in realms such as network optimization, novel licensing regimes, and regulatory monitoring, among others. Spectrum databases are often far more effective, reactive, or sometimes even are required to operate, in conjunction with spectrum sensing - especially if their operation requires automation. Given such observations, this paper presents an update on the IEEE 1900.6b standards work on spectrum sensing to support such databases. Specifically, this paper provides an overview of IEEE 1900.6 and the current work towards 1900.6b, pinpointing the latest updates and thoughts on aspects of the system model incorporating spectrum databases, as well as use cases for the standard. It particularly concentrates on some of the more recent developments and challenges that IEEE 1900.6 is addressing for 1900.6b, such as sharing of sensing infrastructures with different client constraints (noting that spectrum databases - the clients - may have very different requirements in terms of measures such as accuracy and reliability), flexibility in the definition of sensing infrastructures, and security requirements, among many others. This paper finishes by presenting some detail on an experimental set-up for an upcoming trial of the IEEE 1900.6 standard as amended with IEEE 1900.6b capabilities, supporting the operation of a spectrum database as a key European showcase.

IEEE 1900.6b: Sensing support for spectrum databases

A number of key examples of spectrum databases in wireless communications either persist or are in the process of being instantiated. Perhaps one of the most notable recent developments in this area is the spectrum databases that enable secondary usage of TV White Space (TVWS), authorized by regulators such as the FCC in the US, Ofcom in the UK, and various others internationally. Such developments have moved away from spectrum sensing for detection and secondary usage of TV band spectrum opportunities. However, it is clear that spectrum sensing might still viably assist opportunistic spectrum usage, even from a regulatory point of view, both in TVWS and in other forms of spectrum sharing. This also might be the case in wireless communications in general (e.g., in the context of self-organizing networks), particularly when spectrum sensing methods are employed to enhance or verify the operation of spectrum databases. To this end, the IEEE 1900.6 working group is undertaking an amendment standard project, IEEE 1900.6b, on spectrum sensing support for spectrum databases. This paper addresses the IEEE 1900.6 background, and reasoning for the 1900.6b amendment standard, as well as the use cases for the amendment standard and the deployment scenarios and benefits for such standardized spectrum sensing support for spectrum databases. It also provides qualitative arguments of the benefits of the approach using real information from an operational TVWS spectrum database compared with measurements at the same location. It is shown that spectrum sensing to support such a database might viably increase the amount of TV band spectrum available at that location for opportunistic usage, with 4 Watts EIRP, from around 24 MHz to around 240 MHz.

Reinforcement learning in hierarchical cognitive radio wireless networks

Opportunistic networks are an emerging networking paradigm aiming to exploit the spectrum availability in a distributed ad-hoc manner. In such types of networks communication between source and destination is established on-the-fly and depends on a number of parameters related to the channel. In this paper, we present an algorithm for routing optimization in hierarchical cognitive radio enabled networks, using the access base stations. We describe how spatial and temporal system parameters between nodes can be employed to design optimum routes between the nodes thus becoming invaluable for deriving optimum opportunistic algorithms. Initial results of this work indicate that traffic history can improve the performance of the routing algorithm by identifying the nodes that are most likely to be available for routing thus minimizing retransmissions and reducing blocking probability. One of the challenges in dealing with these records is the memory and processing requirements needed by the power hungry algorithms.

Forest Fires: Proactive and Reactive Surveillance employing in-situ, aerial and space technology

Employing technological advances in communications along with the available aerial and space platforms can enable us, to setup a safety monitoring and management system for delivering proactive and reactive surveillance of our forests. This work stresses the importance of protecting our forests considering the socioeconomic impact they have on our world. It also outlines the available platform technologies and proposes a tool for setting up a monitoring system that employs current technologies. This tool simulates forest fires and uses wireless sensors to monitor the fire. Results show that the number and the location of the sensors in a forest are critical in increasing the chances of sensing the fire on time. Since positioning sensors in an organized manner within a forest is tedious if not impossible to implement, it is recommended that areas with higher flammability are equipped with greater number of sensors.

Comparison of the effectiveness of Logisim software tool and remote experiments based on Nexys 2 FPGA platform in learning digital circuits design

This paper describes the findings from our study on the parallel usage of Logisim software tool and remote experiment based on Nexys 2 FPGA hardware platform for learning digital circuits design and computer architecture by students of the first and the second-year of Electrical, Computer and Mechatronics Engineering undergraduate study programs at the Faculty of Technical Science Čačak, University of Kragujevac. For the purpose of comparison of the effectiveness of these two learning tools, a survey was conducted between students who used these tools in learning digital circuits design within the course Foundations in Computer Technics 2 during the winter semester of the academic year 2016–2107. Based on analysis of given results some conclusions were drawn regarding to parallel usage of Logisim software tool and remote experiments based on FPGA hardware platform in learning digital circuits design.

Resource Management Supporting Big Data for Real-Time Applications in the 5G Era

The main storage schemes used in mobile cloud computing require appropriate resources and infrastructure to operate. It is forecasted that with the deployment of IoT devices, large volumes of mobile data will be generated constantly at a very high rate. Current and future 5G wireless mobile networks are called to support the operation of such databases. This chapter presents the most widely used database models in the context of mobile cloud, followed by the state of the art technologies in streaming data access and processing methods. It then demonstrates the importance of Cognitive Radios forming the basis for the operation of 5G wireless networks. Finally, a number of IEEE 802 standards are presented as possible candidates for delivering 5G wireless services.