The explosive popularity of small-cell and Internet of Everything devices has tremendously increased traffic loads. This increase has revolutionised the current network into 5G technology, which demands increased capacity, high data rate and ultra-low latency. Two of the research focus areas for meeting these demands are exploring the spectrum resource and maximising the utilisation of its bands. However, the scarcity of the spectrum resource creates a serious challenge in achieving an efficient management scheme. This work aims to conduct an in-depth survey on recent spectrum sharing (SS) technologies towards 5G development and recent 5G-enabling technologies. SS techniques are classified, and SS surveys and related studies on SS techniques relevant to 5G networks are reviewed. The surveys and studies are categorised into one of the main SS techniques on the basis of network architecture, spectrum allocation behaviour and spectrum access method. Moreover, a detailed survey on cognitive radio (CR) technology in SS related to 5G implementation is performed. For a complete survey, discussions are conducted on the issues and challenges in the current implementation of SS and CR, and the means to support efficient 5G advancement are provided.

Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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One of the best options to support fiber to the home is Ethernet passive optical network (EPON) as it provides high bandwidth as well as it is cost effective. However, the issues arise in the EPON system nowadays is the bandwidth allocation. There are numerous research works that are able to allocate the upstream bandwidth dynamically to the optical network units (ONUs). In this paper, we compile and classify these research works in order to provide the state-of-art of the dynamic bandwidth allocation algorithm (DBA). The classifications provide perceptive presentations of the previous research works on EPONs. The survey allows the researchers to better understand the DBA research quickly.

Landsat 8 was launched in 2013 by the National Aeronautics and Space Administration (NASA). On board of the Landsat 8 is the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Data for visible, panchromatic band, short-wave infrared spectral bands are collected by the OLI while TIRS collect images in the thermal region. As data for Landsat 8 is available to be used for public, researchers have utilized the data for numerous applications. However, to the best of our knowledge, there is yet a review paper on the various applications of Landsat 8 data. Hence, this paper presented an innovative survey on Landsat 8 data in the application of agriculture and forestry, land use and mapping, geology, hydrology, coastal resources and environmental monitoring. The potential of utilizing Landsat 8 data for power utility companies is also discussed in this paper. As Landsat 8 data is predicted to be available for more years to come, this paper provides insight for researchers to utilize the data better for their research.

Sick Building Syndrome (SBS) is a health condition whereby a patient is presented with either vague temporary symptoms such as fatigue, aches and sensitivity to odour or more significant temporary symptoms such as itchy eyes, skin rashes and nasal allergy when they are in a building. Numerous factors have been associated with SBS, but the lack of an accurate diagnosis for these symptoms make treatment more difficult, as risk of treating the patient with wrong diagnosis is relative when the cause root is not known. Thus, taking a preventive approach is a more viable solution to the problem. In this paper, a simple, mobile and cost efficient Internet of Things (IoT) based SBS system is proposed. The system is built using Raspberry Pi minicomputer that would then be integrated with an IoT middleware. The middleware would enable the user to monitor parameters that are to be tested; which are temperature, humidity, light, sound and dust. Three IoT middleware are used to evaluate which one works best for the SBS system proposed. The combination of recorded sensor data would then be used to determine whether or not the building is causing SBS to the occupant. The studies show that FavorIoT platform is the most suitable IoT platform to be used with the SBS system and the system has successfully identified whether or not a building is causing SBS.

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