A new QoS-aware TDMA/FDD MAC protocol with multi-beam directional antennas

With the ever-increasing demand for wireless real-time services and continuing emergence of new multimedia applications especially for mobile users, it is necessary for the network to support various levels of quality of service (QoS) while maximizing the utilization of scarce and expensive wireless channel resources. Considering this fact, a new TDMA/FDD MAC protocol integrating a novel QoS management algorithm and multi-beam Directional Antennas (DAs) to efficiently exploit wireless resources has been developed and presented in this paper. It supports all ATM CBR, VBR, ABR and UBR service classes by adopting a well-managed dynamic guarantee-based QoS scheduling algorithm. The work mainly aims at increasing the wireless system throughput as well as improving the call-blocking ratios and end-to-end delays for real-time applications. This seamless communication enables both handling real-time multimedia traffics in a fair manner and granting call requests on the basis of the connection types. The system has been developed, modeled and simulated using OPNET Modeler. The simulation results show that the QoS-aware TDMA/FDD MAC with multi-beam DAs has substantially increased the system throughput and that the call-blocking ratio has been reduced from 86% to 18%, when the proposed MAC with 8-Beam antennas is employed instead of the regular MAC.     

Integration of a speech activated control system and a wireless interworking unit for a CAN-based distributed application

Ever increasing number of mobile devices in automation systems have revealed the requirement of utilizing wireless communication systems enabling interoperability between existing wired and wireless systems. Therefore, a wireless interworking unit (WIU) is usually employed to provide required interworking functionality. This work briefly exploits a controller area network (CAN)/IEEE 802.11b WIU and a speech activated control application to be used in a CAN-based industrial networking environment and presents its prototype. The WIU employed provides communication skills with the speech activated control system including a speech recognition process and CAN-based distributed control application over the wireless medium.     

A new MAC protocol for broadband wireless communications and its performance evaluation

This paper presents a new Time Division Multiple Access/Frequency Division Duplexing (TDMA/FDD) based Medium Access Control (MAC) protocol for broadband wireless networks, supporting Quality of Service (QoS) for real-time multimedia applications. It also gives the Call Blocking Probability (CBP), packet end-to-end delay and utilization analysis of different service classes, as they are most essential performance criterions in broadband wireless network assessment. The Connection Admission Control (CAC) mechanism in the proposed MAC efficiently organizes the bandwidth allocation for different service classes by means of a fairness based scheduling algorithm. In addition, the simulation model of the proposed MAC scheme is realized by using OPNET Modeler network simulator. The results of the analytical calculations for the CBPs are compared to those of the simulation of the proposed MAC, thus validity of the MAC protocol is proved.     

Decision Based Model for Real-Time IoT Analysis Using Big Data and Machine Learning

Use of internet of things (IoT) in different fields including smart cities, health care, manufacturing, and surveillance is growing rapidly, which results in massive amount of data generated by IoT devices. Real-time processing of large-scale data streams is one of the main challenges of IoT systems. Analyzing IoT data can help in providing better services, predicting trends and timely decision making for industries. The systematic structure of IoT data follows the pattern of big data. In this paper, a novel approach is proposed in which big data tools are used to perform real-time stream processing and analysis on IoT data. We have also applied Spark’s built-in support of the machine learning library in order to make real-time predictions. The efficiency of the proposed system is evaluated by conducting experiments and reporting results on the case scenario of IoT based weather station.