A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

Motorized cart

Motorized cart is known as an effective tool and timeless that help people carry heavy loads. For farmers, it has an especially vital tool for moving goods. Oil palm farmers typically uses the wheelbarrow to move the oil palm fruit (Figure 10.1). However, there is a lack of equipment that should be further enhanced in capabilities. Motorized carts that seek to add automation to wheelbarrow as it is to help people save manpower while using it. At present, oil palm plantation industry is among the largest in Malaysia. However, in an effort to increase the prestige of the industry to a higher level there are challenges to be faced. Shortage of workers willing to work the farm for harvesting oil palm has given pain to manage oil palm plantations. Many have complained about the difficulty of hiring foreign workers and a high cost. Although there are tools that can be used to collect or transfer the proceeds of oil palm fruits such as carts available. However, these tools still have the disadvantage that requires high manpower to operate. Moreover, it is not suitable for all land surfaces and limited cargo space. Workload and manpower dependence has an impact on farmers' income

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

Artificial Intelligence for Resilience in Smart Grid Operations

Today, the electric power grid is transforming into a highly interconnected network of advanced technologies, equipment, and controls to enable a smarter grid. The growing complexity of smart grid requires resilient operation and control. Power system resilience is defined as the ability to harden the system against and quickly recover from high-impact, low-frequency events. The introduction of two-way flows of information and electricity in the smart grid raises concerns of cyber-physical attacks. Proliferated penetration of renewable energy sources such as solar photovoltaic (PV) and wind power introduce challenges due to the high variability and uncertainty in generation. Unintentional disruptions and power system component outages have become a threat to real-time power system operations. Recent extreme weather events and natural disasters such as hurricanes, storms, and wildfires demonstrate the importance of resilience in the power system. It is essential to find solutions to overcome these challenges in maintaining resilience in smart grid. In this dissertation, artificial intelligence (AI) based approaches have been developed to enhance resilience in smart grid. Methods for optimal automatic generation control (AGC) have been developed for multi-area multi-machine power systems. Reliable AI models have been developed for predicting solar irradiance, PV power generation, and power system frequencies. The proposed short-horizon AI prediction models ranging from few seconds to a minute plus, outperform the state-of-art persistence models. The AI prediction models have been applied to provide situational intelligence for power system operations. An enhanced tie-line bias control in a multi-area power system for variable and uncertain environments has been developed with predicted PV power and bus frequencies. A distributed and parallel security-constrained optimal power flow (SCOPF) algorithm has been developed to overcome the challenges in solving SCOPF problem for large power networks. The methods have been developed and tested on an experimental laboratory platform consisting of real-time digital simulators, hardware/software phasor measurement units, and a real-time weather station

Adaptive Network Based Fuzzy Inference System Model For Minimizing Handover Failure in Mobile Networks

For seamless connection between mobile users on the same and different mobile technologies there is need for the deployment of a more complex algorithm for a successful switching of mobile users. Signal to interference ratio, speed of the mobile users and traffic distance are the three input used in the Adaptive network based Fuzzy inference system (ANFIS) which is an hybrid of two techniques of artificial intelligence which make it suitable to handle complexities such as ping-pong effect and interference which impair on the quality of service (QoS) during call handover process as the mobile users move from one coverage area (cell) to anothe

Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Deep knowledge of how radio waves behave in a practical wireless channel is required for effective planning and deployment of radio access networks in urban environments. Empirical propagation models are popular for their simplicity, but they are prone to introduce high prediction errors. Different heuristic methods and geospatial approaches have been developed to further reduce path loss prediction error. However, the efficacy of these new techniques in built-up areas should be experimentally verified. In this paper, the efficiencies of empirical, heuristic, and geospatial methods for signal fading predictions in the very high frequency (VHF) and ultra-high frequency (UHF) bands in typical urban environments are evaluated and analyzed. Electromagnetic field strength measurements are performed at different test locations within four selected cities in Nigeria. The data collected are used to develop path loss models based on artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and Kriging techniques. The prediction results of the developed models are compared with those of selected empirical models and field measured data. Apart from Egli and ECC-33, the root mean squared error (RMSE) produced by all other models under investigation are considered acceptable. Specifically, the ANN and ANFIS models yielded the lowest prediction errors. However, the empirical models have the lowest standard deviation errors across all the bands. The findings of this study will help radio network engineers to achieve efficient radio coverage estimation; determine the optimal base station location; make a proper frequency allocation; select the most suitable antenna; and perform interference feasibility studies.This work was supported jointly by the funding received from IoT-Enabled Smart and Connected Communities (SmartCU) Research Cluster and the Center for Research, Innovation and Discovery (CUCRID) of Covenant University, Ota, Nigeria.Faruk, N.; Popoola, SI.; Surajudeen-Bakinde, NT.; Oloyede, AA.; Abdulkarim, A.; Olawoyin, LA.; Ali, M.... (2019). Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models. IEEE Access. 7:77293-77307. https://doi.org/10.1109/ACCESS.2019.2921411S7729377307