WiMAX: Performance Analysis and Enhancement of Real-time Bandwidth Request

This paper carried out a study on the bandwidth request for real-time polling services. In our study, we discovered that although the base station granted the subscriber station an allocation to send the bandwidth request, the subscriber station may not be able to allocate the bandwidth request to the allocation. It is due to processing delay and multicast polling in the subscriber station, which results the bandwidth request being padded unintentionally. The loss of bandwidth requests will cause the degradation of the real-time polling service performance. Therefore, we propose a scheme to overcome this problem. The results of the experiment show that the proposed scheme improves the performance of real-time polling services

Enhanced dynamic bandwidth allocation proportional to queue length with threshold value for VBR traffic

In Asynchronous Transfer Mode (ATM) network, Variable Bit Rate (VBR) service category has been defined to support any application for which the end-system can benefit from statistical multiplexing, by sending information at a variable rate, and can tolerate or recover from a potentially small random loss ratio. Due to its burst characteristic, bandwidth allocation strategy is necessary in order to share the network resources with other traffics fairly. The implementation of proposed approaches; heuristic, Unused Buffer Reallocation (UnBR) and Higher-priority Queue Sharing (HQS), in bandwidth strategy perform better improvement if compare to the proposed strategy. In addition, we observed that a bandwidth strategy did not always perform well, hence, suitable strategies should be chosen depending on the different conditions in order to fulfill its network demand

ATM switch: impact of heuristic approach to static bandwidth allocation

In this paper, the performance evaluation of two Asynchorous Transfer Mode (ATM) scheduler strategies, static bandwidth allocation with and without heuristic approach are presented. Several experiments were carried out by using the ON-OFF distribution source model based on the ATM Various Bit Rate (VBR) service category. The strategy with the heuristic approach improves the buffer cell loss ratio (CLR) performance significantly. Several numerical results are presented to show the effects of the heuristic approach to bandwidth allocation

Bandwidth granting and scheduling schemes for homogeneous real-time traffic in IEEE 802.16 broadband wireless networks

A number of QoS related research have been conducted in order to enhance the performance of wireless networks. The studies only focused on cross-layer designs and medium access control (MAC) layer. Cross-layer designs investigate the co-relationship between layers (application, MAC and physical) and consider these layers as one of the factors in resource decision-making. However, the MAC focused research only concentrates on the components in MAC layer, but not others. There are many aspects that contribute to QoS provisioning in a network. Among these, uplink/downlink scheduling, bandwidth request/grant process, call admission control, data mapping and hand-over mechanism are the major research topics

A study of Bayesian scheduling for M2M traffic in wireless LTE network

In the event of phenomenal growth in Internet of Things (IoT), Machine-to-Machine (M2M) devices are projected to reach figure of multiple billions in foreseeable future. Operators around the world are aggressively refarming their spectrum from older network and moving quickly to Long Term Evolution (LTE) for guarantee of future-proof services. With every new M2M application being invented or deployed at this pace, unprecedented factors are unceasingly induced to existing LTE protocols which caused undesirable performance degradation. In this paper, a realistic LTE network environment are modelled and simulated with tractable M2M traffic modules to observe such impacts on conventional scheduling schemes and to identify the major causes. A prominent conventional Bayesian approach is hence adopted for revision to adapt the new M2M traffics. The results obtained shown that the proposed M2M-enabled True Bayesian Estimate (TBE-M) algorithm is capable of outperforming the conventional TBE on a great scal

Causal discovery and reasoning for intrusion detection using Bayesian network

Computer security is essential in information technology world today; confidentiality, availability and integrity of data are the aspects concerned. Firewall has been widely deployed as a protection but it is no longer adequate to against the intelligent intrusions and attacks which keep changing and transforming. A network intrusion detection and analysis system has been introduced in this paper to resolve the problems of data confidentiality, availability and integrity. The challenge of the study is; first, to model the network intrusion detection domain and second, to perform causal reasoning for intrusion detection and analysis based on the domain model constructed earlier. In this paper, a methodology has been proposed to resolve the two problems mentioned above. Both problems will be addressed under causal knowledge driven approach where intrusion detection is viewed as fault diagnosis and prognosis processes. We have proposed Bayesian network for the modeling of network intrusion domain. Also, powerful reasoning capabilities of Bayesian network have been applied to discover intrusion attacks. Since the capabilities of causal reasoning using Bayesian network have not been fully discovered in the domain of intrusion detection by most of the researchers before, this research work is to bridge the gap. From the results of the experiment, we have concluded that the capability of Bayesian learning is reasonably accurate and efficient

Intensive bandwidth request and handling design in PMP

This paper carried out a study on the uplink bandwidth request and handling for different service classes in WiMAX. Real time Polling Service (rtPS) acquires uplink bandwidth through contention based and polling mechanism. Each of the rtPS service is polled by base station (BS) on a periodic basis. Long waiting in polling mechanism may delays packets transmission. In this paper, we observed that polling mechanism used by rtPS could be further improved. On top of that, bandwidth wastage is observed when a BS is allocating bandwidth in uplink transmission. Conversion from bandwidth request in byte to physical slot (PS) causes extra unused bandwidth been allocated. A more precise conversion from byte to PS could ease this problem. Thereby, this paper proposed a scheme to overcome these two problems. Two sub modules of the proposed scheme were introduced at BS and customer premise equipment (CPE) respectively. Through extensive simulations, results show that the proposed scheme improves the performance for both real time and non real time polling services

A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map

People often want to know the root cause of things and events in certain application domains such as intrusion detection, medical diagnosis, and fault diagnosis. In many of these domains, a large amount of data is available. The problem is how to perform root cause analysis by leveraging the data asset at hand. Root cause analysis consists of two main functions, diagnosis of the root cause and prognosis of the effect. In this paper, a method for root cause analysis is proposed. In the first phase, a causal knowledge model is constructed by learning a Bayesian belief network (BBN) from data. BBN’s backward and forward inference mechanisms are used for the diagnosis and prognosis of the root cause. Despite its powerful reasoning capability, the representation of causal strength in BBN as a set of probability values in a conditional probability table (CPT) is not intuitive at all. It is at its worst when the number of probability values needed grows exponentially with the number of variables involved. Conversely, a fuzzy cognitive map (FCM) can provide an intuitive interface as the causal strength is simply represented by a single numerical value. Hence, in the second phase of the method, an intuitive interface using FCM is generated from the BBN-based causal knowledge model, applying the migration framework proposed and formulated in this paper

An evaluation of the role of fuzzy cognitive maps and Bayesian belief networks in the development of causal knowledge systems

Fuzzy cognitive maps (FCM) and Bayesian belief networks (BBN) are two of the most frequently used causal knowledge frameworks for modelling, representing and reasoning about causal knowledge. In this paper, an evaluation of their different roles in the engineering process of developing causal knowledge systems is conducted, based on their inherent features. The evaluation criteria adopted in this research are understandability, usability, modularity, scalability, expressiveness, inferential capability, rigour, formality and preciseness. All of these are commonly used to evaluate the strengths and weaknesses of traditional knowledge representation frameworks. These criteria are used to reveal the fundamental characteristics of FCM and BBN. The findings of this study show that FCM is more appropriate for use in modelling causal knowledge, whereas BBN is more superior in model representation and inference. This study deepens the understanding of the role of FCM and BBN in the development of causal knowledge systems

Reasoning with cause and effect in intrusion detection

Intrusion detection is an essential tool to protect hacking and unauthorized access in computer networks nowadays. Mechanisms used to attack keep evolving as the internet technology is improving. Hence, the task of differentiating authorized and unauthorized access has become more and more challenging. The modeling of network intrusion domain and causal reasoning for the intrusion detection has been proposed in this paper to address the security issues of a network. Bayesian network modeling with causal knowledge-driven approach has been selected for a network intrusion domain. Reasoning capabilities of Bayesian network have been adapted to perform detection and analysis in the domain.There are two main problems to be addressed in this paper: the first problem is to model the network intrusion domain and the second problem is to perform causal reasoning for intrusion detection and analysis. A methodology has been proposed to solve the two problems mentioned above. Intrusion detection is viewed as fault diagnosis in causal reasoning, and the analysis of the effect is viewed as fault prognosis. To address the first problem under causal knowledge-driven approach, we propose Bayesian network for the modeling of network intrusion domain. The second problem is addressed by applying the powerful reasoning capabilities of Bayesian network. The capabilities of causal reasoning using Bayesian network have not been fully discovered in the domain of intrusion detection. This research work is to bridge the gap