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

Approximate Reasoning in the Knowledge-based Dynamic Fuzzy Sets

Intan and Mukaidono discussed that knowledge plays an important role in determining the membership function of a given fuzzy set by introducing a concept, called Knowledge-based Fuzzy Sets (KFS) in 2002. Here, the membership degree of an element given a fuzzy set is subjectively determined by the knowledge. Every knowledge may have each different membership degree of the element given the fuzzy set. In 1988, Wang et al. extended the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS) by considering that the membership degree of an element given a fuzzy set might be dynamically changeable over the time. Both generalized concepts, KFS and DFS, were hybridized by Intan et al. to be a Knowledge-based Dynamic Fuzzy Set (KDFS). As usually happened in the real-world application, the KDFS showed that a membership function of a given fuzzy set subjectively determined by a certain knowledge may be dynamically changeable over time. Moreover, the concept of fuzzy granularity was discussed dealing with the KDFS. Related to the concept of fuzzy granularity in KDFS, this paper discusses the concept of approximate reasoning of KDFS in representing fuzzy production rules as generally applied in the fuzzy expert system. Ke

Rough Set Applied to Air Pollution: A New Approach to Manage Pollutions in High Risk Rate Industrial Areas

This study presents a rough set application, using together the ideas of classical rough set approach, based on the indiscernibility relation and the dominance-based rough set approach (DRSA), to air micro-pollution management in an industrial site with a high environmental risk rate, such as the industrial area of Syracuse, located in the South of Italy (Sicily). This new data analysis tool has been applied to different decision problems in various fields with considerable success, since it is able to deal both with quantitative and with qualitative data and the results are expressed in terms of decision rules understandable by the decision-maker. In this chapter, some issue related to multi-attribute sorting (i.e. preference-ordered classification) of air pollution risk is presented, considering some meteorological variables, both qualitative and quantitative as attributes, and criteria describing the different objects (pollution occurrences) to be classified, that is, different levels of sulfur oxides (SOx), nitrogen oxides (NOx), and methane (CH4) as pollution indicators. The most significant results obtained from this particular application are presented and discussed: examples of ‘if, … then’ decision rules, attribute relevance as output of the data analysis also in terms of exchangeable or indispensable attributes/criteria, of qualitative substitution effect and interaction between them

Approximate Reasoning in the Knowledge-Based Dynamic Fuzzy Sets

Intan and Mukaidono discussed that knowledge plays an important role in determining the membership function of a given fuzzy set by introducing a concept, called Knowledge-based Fuzzy Sets (KFS) in 2002. Here, the membership degree of an element given a fuzzy set is subjectively determined by the knowledge. Every knowledge may have each different membership degree of the element given the fuzzy set. In 1988, Wang et al. extended the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS) by considering that the membership degree of an element given a fuzzy set might be dynamically changeable over the time. Both generalized concepts, KFS and DFS, were hybridized by Intan et al. to be a Knowledge-based Dynamic Fuzzy Set (KDFS). As usually happened in the real-world application, the KDFS showed that a membership function of a given fuzzy set subjectively determined by a certain knowledge may be dynamically changeable over time. Moreover, the concept of fuzzy granularity was discussed dealing with the KDFS. Related to the concept of fuzzy granularity in KDFS, this paper discusses the concept of approximate reasoning of KDFS in representing fuzzy production rules as generally applied in the fuzzy expert system

Modelling traffic flow on interchange

The rapid urbanization alters the life style in high scale and metropolitan cities adapt traffic diverting structures like interchanges and bridges to handle the ever increasing traffic growth. It is high time to effectively utilize these traffic systems, hence a traffic model explaining the effective travel pattern is obligatory. Fuzzy logic is an effective concept in interpreting and reciprocating performance similar to human reasoning and can describe complex systems in linguistic terms instead of numerical values. In this thesis, a system was established based on Fuzzy Inference System (FIS) with output data as Vehicular Speed (S) and input data as various highway geometric elements. The study was conducted on two steps as for up - ramp condition and down – ramp condition. Two Traffic models (TFup & TFdn) were developed with radius of curvature, super elevation, frictional coefficient and slope as governing factors. The inferences show that these models can be used to predict and understand the traffic flow along the interchanges with the effect of gravity and friction on the travelling vehicle. A correlation was established between the geometric elements and speed of the vehicle. A simulation study and real life data analysis were performed to demonstrate model fitting the performances of the proposed model