With the increasing adoption of 5G networks in smart cities and Industry 4.0 applications, energy efficiency (EE) has become a critical concern, particularly for industrial Internet of Things (IIoT) devices that operate continuously in latency-sensitive environments. This paper presents a novel Paging Signal-Aware Ordered Statistical Decoding (PS-Aware OSD) algorithm to optimize energy use during paging operations. By intelligently predicting paging occasions (PO) based on historical patterns, the algorithm reduces unnecessary wake-ups in extended discontinuous reception (eDRX) cycles. Simulations demonstrate that this approach improves energy efficiency by up to 40%, reduces block error rate (BLER), and improves throughput and latency compared to standard third-generation partnership project (3GPP) paging techniques. The paper discusses practical implementation aspects, acknowledges system limitations, and proposes future work, including the integration of machine learning for dynamic paging optimization and the development of security measures against spoofing or unnecessary battery drain.

In an interference-limited 5G Narrowband internet of things (NB-IoT) heterogeneous networks (HetNets), device-to-device (D2D) relaying technology can provide coverage expansion and increase network throughput for cell-edge NB-IoT users (NUE). However, as D2D relaying improves the network’s spectral efficiency, it makes interference management and resource allocation more difficult. To improve cell-edge user quality of service (QoS), we propose an interference-aware and coverage analysis scheme for 5G NB-IoT D2D relaying. We divide the optimization problem into three sub-problems to reduce algorithm complexity. First, we use the max-max signal-to-noise plus interference ratio (Max-SINR) to select an optimal D2D relay with the highest channel-to-interference plus noise ratio (CINR) to relay the source NUE information to the NB-IoT base station (NBS). Second, we optimize the transmit power (TP) of the cell-edge NUE to the relay under the peak interference power constraints using a Lagrange dual approach to ensure the user’s service life. We fixed the TP between the D2D relay and the NBS and then transformed the D2D relay’s coverage problem that maximizes the network uplink data rate into a 0-1 integer programming problem. Then, we propose a heuristic algorithm to obtain the system performance. Due to the high channel gain between the two communicating devices, the simulation results show that the Max-SINR selection scheme outperforms the other relay selection schemes except for the D2D communication scheme in efficiency, data rate and SINR.

Electric vehicles (EVs) are crucial to the reduction of carbon emissions. However, their charging poses a threat to power system networks. Hence, EV charging control strategies are developed to curb this challenge, using charging prices to incentivize EV drivers to choose EV charging stations (EVCS) favourable to the grid's stability. The challenge of this strategy is the likelihood of EV drivers accepting EVCS suggestions. To increase the probability of accepting EVCS suggestions, we introduce a two-tailed incentive pricing (TTIP) scheme in an EV charging coordination model, where incentives are offered as charging prices and parking time. We formalized the EV charging problem as a multiobjective Markov decision process and proposed a deep deterministic policy gradient (DDPG) to solve it. To tackle the challenge of continuous action space that leads to the dimensionality curse, the proposed DDPG models the action space using a metaheuristic-based technique. The proposed scheme implements a multiple reward system to generate Pareto optimal solutions and a decision-making technique to choose the compromise reward. Using real-world electricity prices and the IEEE 33-bus distribution network, numerical simulations show that our proposed TTIP scheme yields an average of 18% improvement in grid stability than the sustainable policy following, random, and price-greedy algorithms. It also improves the EV charging profit margins by an average of 28%.

Small cell (SC) and device-to-device (D2D) communications can fulfill high-speed wireless communication in indoor industrial Internet-of-Things (IIoT) services and cell-edge devices. However, controlling interference is crucial for optimizing resource sharing (RS). To address this, we present an adaptive interference avoidance and mode selection (MS) framework that incorporates MS, channel gain factor (CGF), and power-allocation (PA) techniques to reduce reuse interference and increase the data rate of IIoT applications for 5G D2D-enabled SC networks. Our proposed approach employs a two-phase RS algorithm that minimizes the system's computational complexity while maximizing the network sum rate. First, we adaptively determine the D2D user mode for each cell based on the D2D pair channel gain ratios of the cellular and reuse mode. We compute the CGF for each cell with a D2D pair in reuse mode (RM) to select the reuse partner. Then we determine the optimal distributed power for the D2D users and IoT-user equipment using the Lagrangian dual decomposition method to maximize the network sum rate while limiting the interference power. The simulation results indicate that our proposed approach can maximize system throughput and signal-to-interference plus noise ratio, reducing signaling overhead compared to other algorithms.

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.

Narrowband Internet of Things (NB-IoT) provides low cost, low complexity, long battery life, increased coverage area, and increased density of connections per cell making it suitable for various use cases such as smart metering and smart cities. Billions of Internet of Things (IoT) devices were connected as of 2020 and the ever-growing need to urgently deploy NB-IoT solutions on 5G networks has led to the improvement of security aspects of the NB-IoT deployments on 5G receiving close attention. Network access security of NB-IoT on the Fifth Generation (5G) network was investigated by analysing the methods, strengths, and weaknesses of existing Internet of Things (IoT) security solutions. In addition, NB-IoT and 5G functional architectures were presented in this paper, as well as attacks faced by IoT devices at different layers. It was found that the current security solutions do not entirely offer robust access rights management of IoT users and devices. Thus, a holistic Access Control System (ACS) needs to be developed.

Peer-to-peer (P2P) energy trading built on a smart information system in networked microgrids (MGs) is an emerging economic approach to facilitate energy sharing among networked MGs to achieve mutual cost-effective operation and improve the reliability and stability of energy supply service. Such a distributed market urges the need for an efficient energy trading strategy that incentivizes self-interested MGs to participate in energy trading. In this paper, we propose a distributed real-time P2P energy trading strategy that integrates energy trading into energy management and enables MGs with renewable energy sources (RESs) and energy storage systems (ESSs) to manage their storage scheduling, energy supply and energy trading in a dynamic manner. Jointly considering the randomness of renewable energy generation, the time-dependent load demand, the operational constraints of ESSs and the distance-dependent energy transmission losses associated with energy exchange, the proposed energy control and trading mechanism minimizes the time average operational costs of individual MGs while reducing energy transmission losses within the system.

Compared to well-established underwater acoustic communication techniques, underwater wireless optical communication (UWOC) offers significantly more transmission bandwidth and can be used in multiple short-range marine applications. Color-shift keying (CSK) is a modulation techniques advocated for deploying high data rate transmission in red, green and blue (RGB) based laser technology. It utilizes light sources that produce Lambertian light patterns. Consequently, the broadcast message arrives at the receiver via line-of-sight (LoS) pathways, with the Rician distribution being used to model the appropriate channel. This paper analyzes UWOC visible light communication (VLC) links using light emitting diodes (LDs) as transmitter with small angular divergence. The channel direct current (DC) gains is considered in the analysis to show the channel gain, signal-to-noise ratio (SNR), channel capacitance, and outage probability. This study also looks at the outage probability or the locally observed SNR of the CSK link below the threshold set for a particular quality of services. The results are depicted for a random white light in an UWOC system clean ocean. They shows that the SNR of the RGB Based Laser UWOC is proportional to $P_{t}/N_{0}$. The results also shows that the channel capacity increases with $P_{t}/N_{0}$ depending on the attenuation intensity $\beta$. Finally, as the data rate increases with a fixed $P_{t}/N_{0}$, the outage probability increases. The data rate increases with $P_{t}/N_{0}$ and the transmission power with a fixed value outage probability.

In dense, interference-prone 5G narrowband Internet of Things (NB-IoT) networks, device-to-device (D2D) communication can reduce the network bottleneck. We propose an interference-avoidance resource allocation for D2D-enabled 5G NB-IoT systems that consider the less favorable cell edge narrowband user equipment (NUEs). To reduce interference power and boost data rate, we divided the optimization problem into three subproblems to lower the algorithm’s computational complexity. First, we leverage the channel gain factor to choose the probable reuse channel with better Quality of Service (QoS) control in an orthogonal deployment method with channel state information (CSI). Second, we used a bisection search approach to determine an optimal power control that maximizes the network sum rate, and third, we used the Hungarian algorithm to construct a maximum bipartite matching strategy to select the optimal pairing pattern between the sets of NUEs and the D2D pairs. According to numerical data, the proposed approach increases the 5G NB-IoT system’s performance in terms of D2D sum rate and overall network signal-to-interference plus noise ratio (SINR). The D2D pair’s maximum power constraint, as well as the D2D pair’s location, pico-base station (PBS) cell radius, number of potential reuse channels, and D2D pair cluster distance, all influence the D2D pair’s performance. The simulation results demonstrate the efficacy of our proposed scheme.

The papers in this special section focus on artificial intelligence-enabled software defined industrial networks. With the development of intelligent manufacturing, new manufacturing modes such as personalized customization and networked collaboration have been widely developed. These new manufacturing modes require frequent data exchanges between manufacturing machines and industrial information systems through the networks, and dynamically change according to the variations of orders, business, and environments, which cannot be supported in traditional manufacturing modes that focus on local and fixed processes. The current industrial network architecture cannot meet the needs of the aforementioned upcoming manufacturing mode. For example, there are many industrial network protocols, forming a complex industrial heterogeneous network, which seriously affects the interconnections between the underlying devices and the upper layer application systems. In addition, the layering information technology (IT) networks and the operation technology (OT) networks in the factory have hindered the developments of the industrial networks and intelligent manufacturing. There is an urgent need to build a flat, efficient, and flexible industrial network to support the new manufacturing modes.

Industrial Internet of Things (IIoT) systems are increasingly found in settings such as factories, smart cities/nations, and healthcare institutions. These systems facilitate the interconnection of automation and data analytics across different industrial technologies, such as cyber-physical systems, Internet of Things (IoT), and cloud and edge computing devices and systems. However, IIoT systems also generate significant volume of data, which can incur significant overheads in processing such data at cloud centers [A1]. Existing IIoT systems may be developed as monolithic architecture, where such a system is deployed as a single solution. In this architectural design, few programming languages can be used to create a single application or process composed of several classes, methods, and packages, in which the entire application is executed in one server irrespective of the application requirements.

Municipal solid waste management remains a major problem in urban areas, leading to serious health and environmental issues.Consequently, trash bins are placed in many places to handle the municipal solid waste, but these bins can overflow, spreading around the area, polluting the environment, and causing inconvenience to the public. Therefore, there is a need for a real-time remote monitoring system that alerts the level of garbage in the trash bins to the municipality or a waste management company. To manage the municipal solid waste efficiently, this article presents the development and validation of a self-powered, LoRaWAN Internet-of-Things (IoT)-enabled trash bin level monitoring system. The end nodes of the proposed IoT system are called trash bin level measurement unit (TBLMU) and are installed in each trash bin where the status needs to be monitored. The TBLMU measures the unfilled level and geographical location of a trash bin, processes the data, and transmits it to a LoRaWAN gateway at a frequency of 915 MHz. A LoRaWAN gateway serves as a concentrator for the TBLMUs and relays data between a TBLMU and an IoT trash bin level monitoring server. The users can view and analyze the status of every bin and its geolocation by using a smart graphical user interface. The accuracy of the developed system, wireless range between a TBLMU and a LoRaWAN gateway, average current consumption and life expectancy of the TBLMU, battery charging time, and the cost were studied and are reported here.

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.

The confidentiality, integrity and availability of critical infrastructure is crucial for any economy to operate efficiently. Water distribution critical infrastructure is a target of many attackers who aim to penetrate the system for malicious reasons. The use of cyber-physical systems (CPSs) in Water Distribution Systems unveils many vulnerabilities that attackers can use. Although preventative security mechanisms are put into place they too can be defeated, and in this case, a second layer of security is essential. Intrusion detection mechanisms are important reactive security mechanisms to limit the damage done by a successful attack in the system. In this paper machine learning (ML) techniques for anomaly detection (AD) are reviewed.

Smart homes are an integral component in developing smart cities. This paper studies the problem of real-time energy management of controllable loads for Internet of Things (IoT) connected homes with renewable energy generators and energy storage devices. By exploiting the delay tolerance of elastic loads, we develop a joint real-time energy storage control and load management system, aiming to minimize the long term time-averaged energy consumption cost without reducing energy consumption. This residential energy management problem is formulated as a constrained stochastic programming problem. A Lyapunov-based control algorithm is designed to decompose the long-term optimization problem into per-slot sub-problems and provide an asymptotically online optimal solution that is able to quickly adapt to the system dynamics without requiring any statistics of time-varying load demand and stochastic renewable generation. The proposed online control algorithm jointly optimizes energy consumption, load scheduling, and energy charging/discharging actions while satisfying the time-varying energy consumption preference of the user in each time slot. It is demonstrated through numerical simulations that the low-complexity online control algorithm ensures the load demand of the user served with a lower delay at a relatively low cost.

Video encoding is challenging in the energy-constrained environments Wireless Multimedia Sensor Networks (WMSN) operate. Among the many design considerations when developing a video encoding scheme, the first is the sparsity transform, however, the question of which transform is most suitable has not been conclusively answered. Three of the most popular transforms in video encoding literature, discrete cosine (DCT), discrete wavelet transform (DWT) and discrete Tchebichef transform (DTT) were tested against each other under low sampling rates using compressed sensing techniques. The transforms were evaluated using image quality and energy consumption. The image quality was measured using both peak signal to noise ratio (PSNR) and structural similarity (SSIM). The energy consumption was evaluated using the TelosB mote as a reference. The DCT transform had the best image quality at all the sampling rates while the DTT had the worst performance and failed to recover the image at very low sampling rates. Contrary to conventional wisdom, the DTT had higher energy consumption than the DCT. Another remarkable finding was that at high distortion, PSNR was a better predictor of image quality than SSIM. Overall, the DCT was shown to be best image transform in terms of both image quality and energy consumption.

The papers in this special section focus on sustainable and intelligent precision agriculture. Human society has experienced three industrial revolutions from mechanization, and electricity to information automation. Every industrial revolution significantly alters the form of agricultural industry from labor-intensive farming, mechanized production, precision agriculture to large-scale fine grained industrial agriculture. However, the agricultural industry at current stage still faces many challenges, such as global food security, food safety, poverty reduction, and sustainable natural resource management. Now the fourth industrial revolution is ongoing, that is characterized by a fusion of emerging technologies such as Industry 4.0, Internet of Things, Cloud/Edge Computing, Big Data, Artificial Intelligence, and Blockchain.

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

The normal and stable operation of the modern power systems rely on accurate situational awareness and visibility as recent researches and experiences have shown that the cyber-physical infrastructures are highly vulnerable to cyberattacks and intrusions. Attackers can design various intrusive injections to disrupt the operation thereby triggering failures, loss of synchronism, economic losses and sometimes injuries to employees. Hence, there have continuously been crucial need for timely, accurate identification and detection of these intrusions. Several traditional intrusion detection systems proposed in the literature have proven inefficient as they are computationally incompetent for the complex nature of the modern power systems. An alternative has been identified in form of machine learning techniques. This paper presents an empirical comparison of five prominent machine learning algorithms: K-nearest neighbors, Decision Tree, Naive Bayes, Random Forest and AdaBoost for predicting intrusion attacks into power systems network. The idea is to present the best possible classifier for the analyzed test systems and also to show that each of the developed algorithms can perform exceptionally well within some context. The developed algorithms were evaluated using a simulated voltage dataset generated from a load flow analysis of a 24-bus power systems case study. Experimental analysis and results obtained showed the feasibility of applying machine learning techniques in successfully predicting and detecting intrusions into power systems network.

Some recent survey statistics suggest that low power wide area networks (LPWANs) are fast becoming the most prevalent communication platform used in many applications of the Internet of Things (IoT). However, because most LPWANs are generally deployed in the presently congested industrial, scientific, and medical bands, they are invariably plagued by problems associated with spectral congestion, such as increased interference, reduced data rates, and spectra inefficiency. These problems are solvable by integrating cognitive radio (CR) technologies in LPWAN (termed CR-LPWAN), for which some pioneering solutions now exist in the literature. Consequently, this article takes an early look at some of these pioneering efforts pertaining to the development of CR-LPWAN systems. We discuss a general network architecture and a physical layer front-end model suitable for CR-LPWAN systems. Then, some notable state-of-the-art approaches for CR-LPWAN systems are discussed. Potential advantages of CR-LPWAN systems for IoT-based applications are also presented, and this article closes with a few research challenges and future research directions in this regard. This article aims to serve as a starting point for most budding researchers who may be interested in the development of effective and efficient CR-LPWAN systems for the enhancement of different IoT-based applications.

This paper draws attention to techniques available for the minimization of control overhead in software-defined wireless sensor networks (SDWSNs). Software-defined networking (SDN) promises improved management flexibility and control for inherently resource-constrained and heterogeneous wireless sensor network (WSN) implementations. However, due to the in-band traffic channel available for data and control traffic in SDN-based WSNs, overhead control traffic has been viewed as a bottleneck affecting network performance and controller responsiveness. A discussion on the need for control message quenching (CMQ) and the various categories of CMQ implementations in SDWSN is made in this paper. Furthermore, a CMQ algorithm based on reducing duplicate flow request packets is discussed and demonstrated for implementation in an SDN-based WSN framework. Results show a significant reduction in control overhead traffic and noticeable improvement in energy efficiency. However, trade-offs in terms of packet delivery rate and packet delay are also observed as a result of the CMQ algorithm. A discussion on future work necessary to optimize CMQ algorithms in order to minimize the associated trade-offs is also made.

In this paper, we have discussed the integration of Cognitive Radio (CR) in Low Power Wide Area Network (LPWAN) based on a generic network architecture and a PHY layer front-end model. Essentially, since most existing LPWAN technologies are proprietary in nature, it is necessary to present insights that may spur newer developments to enhance many Internet of Things (IoT)-based applications, including Industrial IoT (IIoT) applications such as smart factories, smart metering, and smart city architectures. Generally, this paper will benefit researchers who may be seeking to develop CR-LPWAN systems towards enhancing IoT-based applications.

Software-defined wireless sensor networks (WSNs) are a new and emerging network paradigm that seeks to address the impending issues in WSNs. It is formed by applying software-defined networking to WSNs whose basic tenet is the centralization of control intelligence of the network. The centralization of the controller rouses many challenges such as security, reliability, scalability, and performance. A distributed control system is proposed in this paper to address issues arising from and pertaining to the centralized controller. Fragmentation is proposed as a method of distribution, which entails a two-level control structure consisting of local controllers closer to the infrastructure elements and a global controller, which has a global view of the entire network. A distributed controller system brings several advantages and the experiments carried out show that it performs better than a central controller. Furthermore, the results also show that fragmentation improves the performance and thus have a potential to have major impact in the Internet of things.

Wireless Sensor Networks (WSN) and the Internet of Things play a critical role in many applications ranging from monitoring, tracking and surveillance, social enhancement and many more. Although WSN is used for applications above, still there are some challenges it faces. A few of the challenges faced by WSN include the inability to withstand a large number of sensor nodes deployed in a heterogeneous system leaving the WSN system unmanaged. So recently, there is a huge interest to utilize Software-Defined Networking (SDN) in WSN with more focus on the architecture, routing protocols, topology discovery, SDN controllers, etc. However, without an SDN-enabled sensor node, these systems/models will not be able to operate efficiently and reduce the complication of network configurations and management. Thus, this paper caters the design and development of SDN- enabled sensor node that is applicable to different applications and allowing functions of different processes within the WSN to run efficiently and reduce the costs, improving energy efficiency, scalability and render a system with multiple of functional sensors.

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.

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 Networking (SDN) is a developing networking paradigm that advocates a complete overhaul of the conventional networking. SDN decouples the control logic from the data forwarding functionality; which traditionally are coupled on the network device. The coupling stifles innovation and evolution because the network often becomes rigid. Software Defined Wireless Sensor Networks (SDWSN) is also an emerging network paradigm that infuses the SDN model into Wireless Sensor Networks (WSNs). WSNs have inherent constraints such as energy, memory etc. which have been a major hindrance of their progress. The application of SDN model in WSN is set to cultivate the potential of WSNs in modern communication and to bring about the efficiency that the WSNs have not yet achieved due to their inherent constraints. SDN based networks are anchored on the central controller for functionality. As the network scale up, issues of scalability, reliability and congestion arises and for that a distributed controllers are proposed. This paper investigates the viability of a distributed control system for SDWSN. The test results conducted show that it is viable to deploy a distributed control system for SDWSN; however an improvement is needed on the efficiency.

Software Defined Wireless Sensor Network (SDWSN) is realised by infusing Software Defined Network (SDN) model in Wireless Sensor Network (WSN), Reason for that is to overcome the challenges of WSN. Artificial Intelligence (AI) and machine learning play an important role in our society, give rise to systems that can manage themselves. WSNs have been used in various industrial applications, where reliability and network performance are critical success factors. Many advanced AI techniques can be utilised to improve the performance and reliability of these applications. Investigating the AI algorithms applied to SDN may bring improved network management, security or routing in SDWSN which may result in a more reliable network. We look at machine learning algorithms applied in SDN and discuss the possibility of using these AI in SDWSN to address the WSN challenges and improve its performance and reliability.

Software-Defined Wireless Sensor Networking (SDWSN) is an emerging paradigm that seeks to alleviate the inherent resource constraint issues present in Wireless Sensor Networks (WSN) by adopting a Software-Defined Networking (SDN) approach to the management of WSN. This SDWSN paradigm is said to play a crucial role in both the developing Internet of Things (IoT) paradigm and the development of smart city grids. The northbound and southbound SDWSN interfaces are important for realizing efficient network understanding and programmability, however there has been a lack of attention towards the northbound interface as most work done has been surrounding the southbound interface. Therefore some work is needed to improve the northbound interface so that it may allow for a better degree of network programmability. In order to achieve network programmability and automation, there is a need for a metadata based Application Programing Interface (API). The work done in this paper seeks to improve the northbound interface communications by addressing the issue of a metadata in REST as well as identifying potential platforms for the development of a metadata framework.

The analysis of water piping system has been presented by several authors in the past and in recent years proposing several solution algorithms. Among the notable methods are the Hardy cross method, linear approximation method, Newton Raphson method and the hybrid method to mention but a few, to solve a system of partly linear, and partly non-linear hydraulic equations. In this paper, the authors demonstrate the use of Excel solver to verify the Hardy Cross method for the analysis of flow in water piping networks. A single-loop water network derived from real situation was used as numerical example and case study. Detailed numerical data are presented to explain the results of the studied network.

The application of simulation optimization in water distribution network analysis and design is a promising method for generating solutions to existing challenges. The absence of a standard interface for coupling the open source EPANET software package to optimization algorithms increases the implementation effort and limits the comparison of results. This work presents a methodology for implementing an interface for coupling optimization algorithms with EPANET. The proposed technique uses the internal simulation clock events in a discrete event simulation platform to co-ordinate optimization loops and data exchange. The utilization of intermediate input/output files is avoided in order to increase the simulation speed. A water distribution network implemented in the EPANET solver is considered as a discrete event to be interfaced with optimization algorithms. The interface module is implemented as a C/C++ mex-file for EPANET in the MATLAB/Simulink platform. The methodology enables the user to evaluate the fitness of the design parameters with easy access to data logging and visualization tools at run-time. The proposed technique is used to implement the particle swarm optimization algorithm (PSO) and applied to design a benchmark water distribution network.

A Software-Defined Wireless Sensor Network (SD-WSN) is a recently developed model which is expected to play a large role not only in the development of the Internet of Things (IoT) paradigm but also as a platform for other applications such as smart water management. This model makes use of a Software-Defined Networking (SDN) approach to manage a Wireless Sensor Network (WSN) in order to solve most of the inherent issues surrounding WSNs. One of the most important aspects of any network, is security. This is an area that has received little attention within the development of SDWSNs, as most research addresses security concerns within SDN and WSNs independently. There is a need for research into the security of SDWSN. Some concepts from both SDN and WSN security can be adjusted to suit the SDWSN model while others cannot. Further research is needed into consolidating SDN and WSN security measures to consider security in SDWSN. Threats, challenges and potential solutions to securing SDWSN are presented by considering both the WSN and SDN paradigms.

Monitoring of a Water Distribution Network (WDN) requires information about the present state of the network. Since all variables are usually not measurable directly, State estimation is employed. State estimation is a process of determining the unknown variables of a system based on the measurements and mathematical network model. Measurements are often noisy, but state estimation procedure makes use of a set of redundant measurements in order to filter out such errors and find an optimal estimate. This paper presents a literature review of static state estimation problem pertaining to Water distribution Networks.

The concept of data distribution within cluster of sensor nodes to the source sink has resulted to intense research in Wireless Sensor Networks (WSNs). In this paper, in order to determine the scheduling length of packet distribution, a tree-based network topology is constructed indicating the distribution of various sensor nodes within a specific coverage area (CAi). To evaluate the performance of various channel assignment methods; Receiver-Based Channel Assignment (RBCA), Tree-Based Multichannel Protocol (TMCP), and Capacitated Minimal Spanning Tress (CMSTs), time slot assignment scheme is used in developing the various channel assignments. The performance of packet distribution based on the assignment schemes are evaluated using appropriate simulation tool for the tree-based network topology.

Wireless sensor networks (WSNs) have become increasingly popular in many applications across a broad range of fields. Securing WSNs poses unique challenges mainly due to their resource constraints. Traditional public key cryptography (PKC) for instance is considered to be too computationally expensive for direct implementation in WSNs. Elliptic curve cryptography (ECC) allows one to reach the same level of security as traditional PKC using smaller key sizes. In this paper, a key distribution protocol was designed to securely provide authenticated motes with secret system keys using ECC based cryptographic functions. The designed scheme met the minimum requirements for a key distribution scheme to be considered secure and efficient in WSNs.

Water scarcity and water stress issues pose a serious threat to the global population. The traditional way of manual meter reading is furthermore inconvenient and time consuming, and it wastes resources. This method is also unable to manage the sustainable water resources effectively since it requires efficient, accurate and reliable monitoring techniques that enable the utilities sector and consumers to know the level of water consumption in real-time. Real-time smart water meters that can be monitored by the user are essential and constitute a key component of the water management system. A smart water-monitoring system will make users mindful of their water consumption and help them to reduce their water usage. At the same time, users will be alerted to abnormal water usage to reduce water loss. This paper introduces the water management system based on wireless sensor networks (WSN). The system uses the IEEE 802.15.4 standard embedded in ContikiOS LibCoAP as an open-source application to create a robust and intelligent system. Visualisation and monitoring of the system is achieved following the development of a web-based system and through Pandora FMS.

Approximately 70% of South African mines are classified as fiery, where methane gas potentially could cause explosions. The number of flammable gas reports and accidents are increasing steadily for both gold and platinum mines. However, there is less awareness of the hazards of methane in hard rock mines (gold and platinum) than in coal mines. Currently, there is no wireless real-time gas sensing system used in South African hard rock mines. The main objective of this work is to investigate the possibility of using a wireless gas detector called GS01 in underground mines. Several experiments have been conducted to evaluate the GS01 performance, accuracy and the ability to communicate in underground mines. The results demonstrate the suitability of using GS01 in such harsh environments. A second motivation for the work was to evaluate the performance of wireless communication using different frequencies.

The need for implementing adequate security services in industrial applications is increasing. Verifying the physical proximity or location of a device has become an important security service in ad-hoc wireless environments. Distance-bounding is a prominent secure neighbor detection method that cryptographically determines an upper bound for the physical distance between two communicating parties based on the round-trip time of cryptographic challenge-response pairs. This paper gives a brief overview of distance-bounding protocols and discusses the possibility of implementing such protocols within industrial RFID and real-time location applications, which requires an emphasis on aspects such as reliability and real-time communication. The practical resource requirements and performance tradeoffs involved are illustrated using a sample of distance-bounding proposals, and some remaining research challenges with regards to practical implementation are discussed.