SINR-based Network Selection for Optimization in Heterogeneous Wireless Networks (HWNs)

To guarantee the phenomenon of "Always Best Connection" in heterogeneous wireless networks, a vertical handover optimization is necessary to realize seamless mobility. Received signal strength (RSS) from the user equipment (UE) contains interference from surrounding base stations, which happens to be a function of the network load of the nearby cells. An expression is derived for the received SINR (signal to interference and noise ratio) as a function of traffic load in interfering cells of data networks. A better estimate of the UE SINR is achieved by taking into account the contribution of inter-cell interference. The proposed scheme affords UE to receive high throughput with less data rate, and hence benefits users who are located far from the base station. The proposed scheme demonstrates an improved throughput between the serving base station and the cell boundary

Regressive Prediction Approach to Vertical Handover in Fourth Generation Wireless Networks

The over increasing demand for deployment of wireless access networks has made wireless mobile devices to face so many challenges in choosing the best suitable network from a set of available access networks. Some of the weighty issues in 4G wireless networks are fastness and seamlessness in handover process. This paper therefore, proposes a handover technique based on movement prediction in wireless mobile (WiMAX and LTE-A) environment. The technique enables the system to predict signal quality between the UE and Radio Base Stations (RBS)/Access Points (APs) in two different networks. Prediction is achieved by employing the Markov Decision Process Model (MDPM) where the movement of the UE is dynamically estimated and averaged to keep track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The performances of various handover approaches influenced by different metrics (mobility velocities) were evaluated. The results presented demonstrate good accuracy the proposed method was able to achieve in predicting the next signal level by reducing the total handover latency

Predicting breastfeeding practice of Nigerian child using machine learning and deep learning algorithms

The accessibility and rapid growth of data with the conspicuous rise in hardware technologies have led to the development of new studies in distributed and deep learning. This work reports the prediction of breastfeeding practice of a child using machine and deep learning algorithms. Despite that exclusive breastfeeding reduces the child mortality rate caused by pneumonia and diarrhea and other benefits attached to it the percentage of mothers who practice it in Nigeria still fall short to 29% in 2018 according to Nigeria Demographic Health Survey (NDHS) compare to the global target of 70% by 2030. The aim of the study is to develop a model for the prediction of breastfeeding practice in Nigeria. This study adopted Sample, Explore, Modify, Model and Access (SEMMA) data mining methodology using 2018 NDHS dataset. Experiments were performed with ML algorithms (RF, J48, JRIP, and SVM) built with WEKA software as well as with DNN algorithm using python. Some control parameters were applied to configure the DNN model while considering the number of layers, neurons within each layer, activation function for each layer, ADAM algorithm, epochs (iterations), learning rate and percentage split of dataset between training and testing subsets. The performance of the DNN model in predicting breastfeeding practice was evaluated and compared with previous study that used ML models only. It was found that DL has a better child prediction of breastfeeding practice than ML models with accuracy of 97.9%. This could serve as a supporting tool for healthcare practitioners to support breastfeeding practice in Nigeria