Virtualization of multicast services in WiMAX networks

Multicast service is one of the methods used to efficiently manage bandwidth when sending multimedia content. To improve bandwidth utilisation, virtualization is often invoked because of its additional features such as bandwidth sharing and support of services that require high volumes of transactional data. Currently, network providers are concerned with the bandwidth amount for efficient use of the limited wireless network capabilities and the provision of a better quality of service. The virtualization design of a multicast service framework should satisfy several objectives. For example, it should enable the interchange of service delivery between multiple networks with one shareable network infrastructure. Also, it should ensure efficient use of network resources and guarantee users' demands of Quality of Service (QoS). Thus, the design of virtualization of multicast service framework is a complex research study. Due to the bandwidth-related arguments, a strong focus has been put on technical issues that facilitate virtualization in wireless networks. A well-designed virtualized network guarantees users with the required quality service. Similarly, virtualization of multicast service is invoked to improve efficient utilisation of bandwidth in wireless networks. As wireless links prove to be unstable, packet loss is unavoidable when multicast service-oriented virtual artefacts are incorporated in wireless networks. In this thesis, a virtualized multicast framework was modelled by using Generalized Assignment Problem (GAP) methodology. Mixed Integer Linear Programing (MILP) was implemented in MATLAB to solve the GAP model. This was to optimise the allocation of multicast traffic to the appropriate virtual networks. Thus, the developed model allows users to have interchangeable services offered by multiple networks. Furthermore, Network Simulator version 3 (NS-3) was used to evaluate the performance of the virtualized multicast framework. Three applications, namely, voice over IP (VoIP), video streaming, and file download have been used to evaluate the performance of a multicast service virtualization framework in Worldwide Interoperability for Microwave Access (WiMAX) networks using NS-3. The performance evaluation was based on whether MILP is used or not used. The results of experimentation have revealed that there is good performance of virtual networks when multicast traffic is sent over one single virtual network instead of sending it over multiple virtual networks. Similarly, the results show that the bandwidth is efficiently used because the multicast traffic is not delivered through multiple virtual networks. Overall, the concepts, the investigations and the model presented in this thesis can enable mobile network providers to achieve efficient use of bandwidth and provide the necessary means to support services for QoS differentiations and guarantees. Also, the multicast service virtualization framework provides an excellent tool that can enable network providers to interchange services. The developed model can serve as a basis for further extension. Specifically, the extension of the model can boost load balancing in the flow allocation problem and activate a virtual network to deliver traffic. This may rely on the QoS policy between network providers. Therefore, the model should consider the number of users in order to guarantee improved QoS

Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets

Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO, CO2 and O2 vis-à-vis the Estimated Fire Intensity Prediction (EFIP). Results show that, using fuzzy logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws, policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places

Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications

The application of the Internet of Things (IoT), vehicles to infrastructure (V2I) communication and intelligent roadside units (RSU) are promising paradigms to improve road traffic safety. However, for the RSUs to communicate with the vehicles and transmit the data to the remote location, RSUs require enough power and good network quality. Recent advances in technology have improved lithium-ion battery capabilities. However, other complementary methodologies including battery management systems (BMS) have to be developed to provide an early warning sign of the battery’s state of health. In this paper, we have evaluated the impact of the received signal strength indication (RSSI) and the current consumption at different transmission frequencies on a static battery-based RSU that depends on the global system for mobile communications (GSM)/general packet radio services (GPRS). Machine learning (ML) models, for instance, Random Forest (RF) and Support Vector Machine (SVM), were employed and tested on the collected data and later compared using the coefficient of determination (R2). The models were used to predict the battery current consumption based on the RSSI of the location where the RSUs were imposed and the frequency at which the RSU transmits the data to the remote database. The RF was preferable to SVM for predicting current consumption with an R2 of 98% and 94%, respectively. It is essential to accurately forecast the battery health of RSUs to assess their dependability and running time. The primary duty of the BMS is to estimate the status of the battery and its dynamic operating limits. However, achieving an accurate and robust battery state of charge remains a significant challenge. Referring to that can help road managers make alternative decisions, such as replacing the battery before the RSU power source gets drained. The proposed method can be deployed in other remote WSN and IoT-based applications