Telecommunication Engineering

Permanent URI for this collectionhttp://repository.futminna.edu.ng:4000/handle/123456789/750

Telecommunication Engineering

Browse

Author: search.filters.author.Adeiza J. Onumanyi

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Oil and Gas Monitoring Through Wireless Sensor Networks: A Survey
    (Ozean Journal of Applied Sciences, 2013) Achonu O. Adejo; Adeiza J. Onumanyi; Jane M. Anyanya; Oyewobi S. Stephen
    Effective measurement and monitoring of certain parameters (temperature, pressure, flow etc) is crucial for the safety and optimization of processes in the Oil and Gas Industry. Wired sensors have been extensively utilized for this purpose but are costly and difficult to deploy and maintain. Wireless S ensor Network (WS N) technology is an emerging alternative that introduces significant benefits in cost, ease of deployment, flexibility and convenience. The impact of WS N is expected to be tremendous in industrial automation owing to a report that projected the deployment of 24 million wireless-enabled sensors and actuators worldwide by 2016. With limited literature on this specific subject matter, this paper presents a critical survey into WS N applications as it directly impacts the Oil and Gas Industry. An overview of WS N is presented, case study applications from literature are highlighted and finally research challenges are discussed
  • No Thumbnail Available
    Item
    An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things
    (MDPI Sensors, 2019-03-21) Oyewobi S. Stephen; Gerhard Hancke; Adnan M. Abu-Mahfouz; Adeiza J. Onumanyi
    The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches