The 5G mobile network aims to enhance wireless communication by providing faster and reliable connectivity. Open radio access network (RAN) architecture, which offers flexibility and innovation in Radio Resource Allocation (RRA), is central for optimal network performance. However, traditional RRA methods fall short of meeting the complex demands of 5G due to scalability issues. Incorporating machine learning (ML) techniques into open RAN can enhance adaptability and intelligence, ensuring that 5G networks meet high performance and service quality standards. This paper presents a comprehensive review of ML-based and traditional RRA methods in meeting the evolving demands of wireless networks. Literature from relevant articles were selected and analysed to highlight the techniques used, trends, strengths, and limitations. The findings reveal the potential and transformative impact of ML on the future of wireless communications, particularly in achieving the key performance indicators and quality of service expected from 5G and beyond networks. It also shows that research in ML-based RRA methods is at its infancy stage and more research is needed to advance the technology.

This paper proposes a framework for creating educational software systems that effectively meet student engagement and pedagogical goals. While different design methodologies have been used in developing educational software, most fail to satisfy the demands of users, stakeholders, and students, making it difficult to incorporate them into daily activities and support optimal learning outcomes. The proposed framework combines important techniques in Scrum, dynamic system development methods, and instructional design models. It comprises seven key phases: initial, instructional orientation, analysis, design, production, integration and implementation, and evaluation. The framework aims to guide the creation of educational software that successfully satisfies teachers' and students' demands and can be easily incorporated into teaching and learning procedures. We present the proposed framework components and compare them with other existing related models. Implementing the framework is expected to improve teaching/ learning, reduce development costs and time.

Transparency remains a new and emerging concept in software engineering that the stakeholders of a software system, especially developers, must deal with during the software engineering process. One of the transparency concerns is the transparency of software products and processes. This paper proposes a definition of transparency and stakeholders’ transparency requirements relating to software artefacts and the processes that birth them. The requirements were elicited based on a well-defined systematic literature review strategy. The proposed requirements would enable the evaluation of the transparency of SDLC products and processes and their improvement towards achieving effective communication, software maintainability, and stakeholders’ productivity.

Long Range Wide Area Networking (LoRaWAN) has established itself as one of the leading Media Access Control (MAC) layer protocols in the realm of Low Power Wide Area Networks (LPWAN). Although the technology itself is quite mature, the resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm it uses is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. As such, studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors chose four proposed algorithms that focused on improving the ADR in terms of data extraction rate (DER) and evaluated them to study and critically analyze their performances. LoRaSim was used and the algorithms were employed in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the implemented algorithms outperformed the ADR algorithm. However, as network size increases, these superior performances are not adequate for a reliable and energy-efficient LoRaWAN network. Though attempts have been made to improve the ADR algorithm, arriving at its ideal implementation is still an open research area and therefore, we recommend more improvement should be proposed.

Wireless Sensor Networks (WSNs) are network paradigm that are constrained by several challenges such as management of the network, energy consumption, data processing, quality of services (QoS) provisioning, and security. Software-Defined Networking (SDN) emerged as a viable solution to mitigate these inherent challenges yielding SDWSN. SDWSN is gaining momentum and has brought innovation, ease of network management and configuration through network programmability. However, SDWSN is not immune to challenges due to several issues inherited from both the WSN and SDN. Although several research works have been carried out aimed at proffering solutions, there is still more to be done to ensure SDWSN is secure, dependable, and scalable. Therefore, this paper brings together some of the notable issues that needs to be addressed and some of the solutions already proposed or developed. The objective is to get insights into these challenges and provide some solutions. We presented and discussed specifically, the security issues with respect to SDWSN model, threats, attacks, and some of the existing countermeasures.

Vehicle security is an emergent issue in the technology sector that has benefited from the continuous advancement in technology through the creation of more complex and advanced security systems. This serves to address the pandemic of vehicle theft that is prevalent in numerous countries due to inadequate security in vehicles. Consequently, security devices in vehicles are susceptible to attacks such as man-in-the-middle, replay attacks, deciphering attacks and signal disruption, all of which caused the devices to function below the expected parameters. Current technology has loopholes in its security implementation creating attack vectors from benign devices such as the infotainment system to more severe systems like the CAN bus network. Therefore, this paper presents analysis of the numerous works and approaches that exists in the literature to tackle this menace. Moreover, we performed an in-depth comparative analysis of the type of technology implemented, the strengths and the weaknesses of the proposed systems. Based on the analysis, we found that there is a need for more holistic approaches to tackling the pre-existing security vulnerabilities in an effective manner that reduces chances of compromise to the most minimal degree.

Internet of Things (IoT) has attracted widespread applicability not only limited to smart cities and communities but also in water, waste management and so on. It strength lies in the high impacts it created in the daily life and the potential user's behavior. However, for it to be more effective and increase its adoption, it is require to be energy efficient, able to communicate and share information across extended coverage. Existing technology such as Low Power Wide Area Network (LPWAN) with Long Range (LoRa) has been promising. In the perspective of waste management, several different IoT-enable solutions have been proffered with each having its own strengths and weaknesses that requires improvements. Therefore, this paper performs a review of existing IoT-enabled solutions in smart cites' waste management to bring together the state-of-the-art. The objective is to gain insights into the strengths and weaknesses in order to bring improvements and innovations to manage waste effectively and efficiently as well as maintain a healthy environment in our cities. We performed reviews on 15 research articles in the literature and the results obtained shows that existing solutions were similar in the technologies used but have some drawbacks such as sensing accuracy hindered by various weather conditions, users prone to unauthorized access and short range capabilities. This thus, calls for further improvement and innovation.

One of the challenges faced by Wireless sensor networks (WSNs) is the issue of uncontrollable interference as the spectrum becomes congested due to the current proliferation of wireless devices. Cognitive radio (CR) emerged as one of the promising solutions to overcome the challenges while having the sensor nodes to access the licensed spectrum band. However, these sensors nodes consume huge amount of energy to accommodate the CR functionalities of sensing and switching between the spectrum bands. Consequently, an efficient mechanism is needed to enhance energy efficiency in the resulting cognitive radio sensor networks (CRSNs). Therefore, this paper surveys and analyses energy inefficiency challenges in the realm of WSN and CRSNs as well as some of the proposed approaches. The objective is to comprehend what has been done and how to improve the impeding challenges. We conducted the analysis on 11 related papers in the literature to analyze and identify the existing energy inefficiency challenges and the mechanisms to overcome them. The findings shows that energy inefficiency challenges is due to WSN performing CR capabilities thus, consuming a considerate amount of energy which causes the wireless sensor nodes energy to deplete incessantly. Moreover, several mechanisms have been proposed but more research need to be performed to find efficient solutions that are dynamic with technological advancements.

Software defined network (SDN) is a paradigm developed achieve great flexibility and cope with the limitations of traditional networks architecture such as the wireless sensor networks (WSNs). Introducing SDN in WSN leads to SDWSN. However, due to the challenges that are inherent in SDN and WSN, SDWSN is faced with number of challenges such network and Internet traffic classification (TC). Several solutions have been offered such as machine learning (ML) technique but there are several challenges that still exist which need attention. Therefore, this paper present a review on the approaches of TC in SDWSN using ML and their challenges. The objective is to identify existing approaches and the challenges in order to provide ways to enhance them. We performed review of the existing works on TC in the literature based on the aspect of enterprises network, SDN and WSN has been done as well as findings reported. Our findings shows that the approaches to TC using ML were based on supervised or unsupervised learning. Moreover, TC is faced with challenges which include energy efficiency, shareable test data and design. Thus, ML technique to TC in SDWSN is still at its early stage and need to improve in order to accurately classify traffics that normal or abnormal.

Software defined networking (SDN) is a paradigm developed to cope with inherent limitations posed by the lack of flexibility in the traditional networking architecture like the Wireless Sensor Network (WSN). The application of SDN in WSN has been advantageous with respect to network management and configuration leading to a new network paradigm called SDWSN. Despite the benefits, SDWSN is faced with several challenges dominated by network management, security, and scalability. These have prompted several research activities among industrial and academic researchers worldwide. Though several solutions have been proposed or developed, most of the challenges still exist and more research works are needed to address them. Therefore, this paper presents a review of the challenges of SDWSN in the aspects of network management, security and its application on Internet of Things (IoT). The essence is to comprehend the existing challenges in an effort to find effective and efficient solutions to ensure more secure, dependable and energy efficient SDWSN. We reviewed several literature on WSN, SDN and SDWSN and presented the findings in the form of challenges and solutions. The analysis shows that SDWSN challenges originates from SDN, WSN and the technology is still at its early stage, though is developing.

In the OpenFlow-based software defined networking (SDN), a single controller controls the entire network resources. However, it poses a single point of failure and has restricted processing capacity. Multiple controllers emerged as a solution to ensure network reliability, scalability and high availability for large scale networks. Despite the benefits, multiple controllers also brings about increased complexity with several new challenges affecting network management and schedule. Albeit the centralized controller is suitable for small and medium sized networks, the challenge is how to ensure its reliability and resiliency. This means faults have to be detected and failure recover as quickly as possible. Therefore, this paper proposes a fault tolerance framework (FTF) consisting of three controllers and a FT manager (FTM). The FTM has several components that contribute to FT by monitoring and detecting faults using heartbeat messages and recover from failure using checkpointing. The approach is passive replication where only one controller manages the networks and in the event of failure, another controller is elected using a novel voting technique. Additionally, the issue of network state consistency are handled adequately. We theoretically assessed the FTF using several FT design requirements. The evaluation shows our FTF has an acceptable performance operations in ensuring strict consistency and fault tolerant system.

Internet of Things (IoT) has revolutionized the traditional Internet where only human-centric services were offered. It has enabled objects to have the ability to connect and communicate through the Internet. IoT has several applications such as smart water management systems. However, they require high energy-efficient sensor nodes that are able to communicate across long distance. This motivates the development of many Low-Power Wide Area Networks (LPWAN) technologies, such as LoRa, to fulfill these requirements. Therefore, in this paper, we survey IoT devices and different applications based on LoRa and LoRaWAN in order understand the current stream of devices used. The objective is to contribute toward the realization of LoRa as a viable communication technology for applications that needs long-range links and deployed in a distributed manner. We highlighted the device parameter settings and the output of each experiment surveyed.

Software Defined Networks (SDNs) is a new network paradigm and is gaining significant attention in recent years. However, security remains a great challenge, though several improvements have been proposed. A key security challenge is the lack of trust between the SDN controller and the applications running atop the control plane. SDN controller can easily be attacked if these applications are malicious or compromised by an attacker to control the entire network or even result in network failure since it represents a single point of failure in the SDN. Though trust mechanisms to verify network devices exist, mechanisms to verify management applications are still not well developed. Therefore, this paper proposes a unique direct trust establishment framework between an OpenFlow-based SDN controller and the applications. The objective is to ensure that SDN controller is protected and multitude of applications that regularly consume network resources are always trusted throughout their lifetime. Additionally, the paper introduced the concept of trust access matrix and application identity to ensure efficient control of network resources. Based on its operation, if this proposed trust model is adopted in the OpenFlow architecture, it could go a long way to improve the security of the SDN and protect the controller.

Object-oriented software (OOS) is dominating the software development world today and thus, has to be of high quality and maintainable. However, their recent size and complexity affects the delivering of software products with high quality as well as their maintenance. In the perspective of software maintenance, software change impact analysis (SCIA) is used to avoid performing change in the “dark”. Unfortunately, OOS classes are not without faults and the existing SCIA techniques only predict impact set. The intuition is that, if a class is faulty and change is implemented on it, it will increase the risk of software failure. To balance these, maintenance should incorporate both impact and fault-proneness (FP) predictions. Therefore, this paper propose an extended approach of SCIA that incorporates both activities. The goal is to provide important information that can be used to focus verification and validation efforts on the high risk classes that would probably cause severe failures when changes are made. This will in turn increase maintenance, testing efficiency and preserve software quality. This study constructed a prediction model using software metrics and faults data from NASA data set in the public domain. The results obtained were analyzed and presented. Additionally, a tool called Class Change Recommender (CCRecommender) was developed to assist software engineers compute the risks associated with making change to any OOS class in the impact set.

Software changes are inevitable during maintenance, Object-oriented software (OOS) in particular. For change not to be performed in the “dark”, software change impact analysis (SCIA) is used. However, due to the exponential growth in the size and complexity of OOS, classes are not without faults and the existing SCIA techniques only predict change impact set. This means that a change implemented on a faulty class could increase the likelihood for software failure. To avoid this issue, maintenance has to incorporate both change impact and fault-proneness (FP) prediction. Therefore, this paper proposes an extended approach for SCIA that integrates both activities. The goal is to assist software engineers with the necessary information of focusing verification and validation activities on the high risk components that would probably cause severe failures which in turn can boost maintenance and testing efficiency. This study built a model for predicting FP using software metrics and faults data from NASA data set in the public domain. The results obtained were analyzed and presented. Additionally, a class change recommender (CCRecommender) tool was developed to assist in computing the risks associated with making change to any component in the impact set.

Object-oriented (OO) approaches of software development promised better maintainable and reusable systems, but the complexity resulting from its features usually introduce some faults that are difficult to detect or anticipate during software change process. Thus, the earlier they are detected, found and fixed, the lesser the maintenance costs. Several OO metrics have been proposed for assessing the quality of OO design and code and several empirical studies have been undertaken to validate the impact of OO metrics on fault proneness (FP). The question now is which metrics are useful in measuring the FP of OO classes? Consequently, we investigate the existing empirical validation of CK + SLOC metrics based on their state of significance, validation and usefulness. We used systematic literature review (SLR) methodology over a number of relevant article sources, and our results show the existence of 29 relevant empirical studies. Further analysis indicates that coupling, complexity and size measures have strong impact on FP of OO classes. Based on the results, we therefore conclude that these metrics can be used as good predictors for building quality fault models when that could assist in focusing resources on high risk components that are liable to cause system failures, when only CK + SLOC metrics are used.