Browsing by Author "Ogundile, Olayinka"
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Item Comparison of Vertical Handover Decision-Based Techniques in Heterogeneous Networks(Int. J. Communications, Network and System Sciences, 2018) Edia, Adada; Osanaiye, Opeyemi; Aina, Folayo; Ogundile, OlayinkaIndustry leaders are currently setting out standards for 5G networks projected for 2020 or even sooner. Future generation networks will be heterogeneous in nature as no single network type will be capable of optimally meeting all the rapid changes in customer demands. With the advent of multi-homed devices and heterogeneous network (HetNet) solution, great concerns arise in the processes involved for successful handover. Active calls that get dropped or cases of poor quality of service experienced by mobile users can be attributed to the phenomenon of delayed handover (HO) or an outright case of an un-successful handover procedure. This work compares multiple criteria hand-over basis to its traditional single relative signal strength (RSS) base counter-part. It analyses the performance of a fuzzy-based VHO algorithm scheme in a Wi-Fi, WiMAX, UMTS and LTE integrated network using OMNeT++ event simulator. The loose coupling network architecture is adopted and simulation results analysed for the two major categories of handover; the multiple and single criteria. Results obtained show a better overall through-put, better call dropped rate and shorter handover time for the multiple crite-ria based decision method as compared to the single criteria based technique. This work also highlights current research trends, challenges of seamless handover and initiatives for Next Generation HetNet.Item FEATURE SELECTION FOR INTRUSION DETECTION SYSTEM IN A CLUSTER-BASED HETEROGENEOUS WIRELESS SENSOR NETWORK(2019) Osanaiye, Opeyemi; Ogundile, Olayinka; Aina, Folayo; Periola, AyodeleWireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment. Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period.