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When machine-type communication (MTC) devices perform the long-term evolution (LTE) attach procedure without bit rate limitations, they may produce congestion in the core network. To prevent this congestion, the LTE standard suggests using group-based policing to regulate the maximum bit rate of all traffic generated by a group of MTC devices. However, previous studies on the access point name-aggregate maximum bit rate based on group-based policing are relatively limited. This study proposes a multistage control (MSC) mechanism to process the operations of maximum bit rate allocation based on resource-use information. For performance evaluation, this study uses a Markov chain with to analyze MTC application in a 3GPP network. Traffic flow simulations in an LTE system indicate that the MSC mechanism is an effective bandwidth allocation method in an LTE system with MTC devices. Experimental results show that the MSC mechanism achieves a throughput 22.5% higher than that of the LTE standard model using the group-based policing, and it achieves a lower delay time and greater long-term fairness as well.

The ability to accurately and consistently estimate software development efforts is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural network. [ABSTRACT FROM AUTHOR]

It is difficult to measure the maintainability of a software system early in the development life cycle from its requirement descriptions written in a natural language because informal specifications cannot be analyzed. With the uses of Formal Description Techniques (FDTs) in the communication protocol area since the mid-1980s, avenues have been opened up for a system to be analyzed early in the specification phase. Quantitative measures on its maintainability can then be extracted from such a formal specification, so that we can develop easily maintainable communication software systems and further reduce the increasingly high cost of software maintenance. To date, there is hardly any work done on measuring the maintainability of a system early in its specification phase. This paper presents a method for measuring the maintainability of a communication by using maintainability metrics derived from its formal specification written in Estelle. The methodology for building the Estelle maintainability metrics hierarchy is presented. We have also developed an automated tool, called PSAMS, to automate the calculation of the maintainability indices. We also found that there is a significant correlation between the specification metrics proposed and the widely adopted implementation metrics, thus demonstrating that our proposed metrics are a reliable means of measuring the maintainability of a communication protocol early in the specification phase. [ABSTRACT FROM AUTHOR]

Good project management is key when developing a software system successfully. To manage a project well, it is important to have the optimal resource allocation which is affected by the size of an implementation. Early software size estimation is essential for good project management. Existing software size models estimate the size of an implementation usually in terms of the number of lines of code. The main drawback of these models is that there is a wide margin of uncertainty as the actual size depends on the type of application and the software development method adopted. To address this drawback, we focus our work on communication protocol, and propose that the size of a formal specification needs to be estimated from an informal specification. This paper presents a two-stage size model for estimating the sizes of a formal communication protocol specification and its implementation, with the model validated using a test data set. The main benefit of this work is that it can give an indication of the likely sizes of both a formal specification and its implementation early at the development stage, giving developers a technique for managing communication software project better. [ABSTRACT FROM AUTHOR]