Due to dynamic characteristic of demand response and stochastic nature of power generation, it brings great challenge to smart energy management. In this paper, a demand response model is created with two-level dynamic pricing transaction among grid operator, service provider and customers, which also involves customers’ active participation with load shifting issue. To effectively control system load on the demand side, an improved deep reinforcement learning approach is proposed with a recursive least square (RLS) technique to deal with the dynamic pricing demand response problem, which accelerates the on-line training and optimization efficiency. On the power generation side, a probabilistic penalty-based boundary intersection (PBI) based multi-objective optimization algorithm is improved to optimize the economic cost, emission rate and statistic voltage stability index (SVSI) simultaneously with generated stochastic scenarios, which can ensure energy conservation and environmental protection, as well as system security. The case results reveal that the proposed two-level optimization strategy successfully deals with energy management with dynamic pricing demand response.Note to Practitioners—This paper is motivated by solving stochastic energy management issue of isolated power system with dynamic pricing demand response. Those existing methods merely focus on the load demand or power generation side, and the methods for demand response issue lacks efficient on-line learning ability, while this work proposes a recursive least square based deep reinforcement learning approach to tackle with the two-level dynamic pricing demand response issue, scenario based PBI multi-objective optimization is proposed to solve the power dispatch issue on power generation side, and the numerical analysis results suggest that the proposed optimization strategy can deal with the whole energy management issue well. The future work will focus on the dynamic power-load coordination in the energy management issue.

Uncertainty aggregation characterization of multi spatial–temporal distributed energy resources (DERs) is crucial for effective decision-making and control in power systems. In this article, we propose a cloud-edge-end collaboration approach to quantify the aggregated uncertainty of power generation from multi spatial–temporal DERs. First, considering the temporal dynamic and electrical topology correlation of DERs, a local uncertainty aggregation model based on a spatial-temporal graph neural network (STGNN) is developed. This model can effectively extract the spatial-temporal characteristics of data. Second, addressing the data silo problem caused by the unwillingness of various stakeholders managing the DERs to share data due to privacy concerns, an uncertainty aggregation model training mechanism based on an adaptive secure federated learning is proposed. This mechanism enables collaborative modeling of uncertainty aggregation models across stakeholders while preserving user privacy. In addition, it improves the quality of local model training by adaptively extracting parameter information from the global model for local model initialization. Moreover, since the probability distribution of the aggregated uncertainty is unknown, this article combines STGNN with the weighted quantile regression model to characterize the aggregated uncertainty without prior assumptions about the distribution, and by assigning differentiated weights to aggregation results under different confidence levels based on their importance, the proposed method can better meet the diverse needs of power grid. Finally, simulations conducted on the IEEE 33-bus system and IEEE 69-bus system validate the effectiveness of the proposed method.

The intermittency of solar irradiance is the main cause of rapid fluctuations in the power output of photovoltaic (PV) systems. These fluctuations hinder the large-scale integration of solar power generation equipment into the grid, which in turn hinders the process of utilizing solar energy resources to reduce carbon emissions. The main way to solve this dilemma is to achieve high-precision forecasting of solar irradiance. Although various methods exist to forecast the variations of solar irradiance, few focus on fully utilizing multimodal data information and fuzzy method to improve the forecasting performance. Therefore, a forecasting method combining multimodal data fusion and fuzzification is proposed to forecast ultra short-term global horizontal irradiance (GHI). First, a modal conversion method is designed to convert temporal modal data to spatial modal data. Then, the fused data are formed by fusing the converted data with normal and under exposure all-sky images. Subsequently, the fuzzy method is used to generate fuzzy GHI data with low nonlinear features. Last, we utilize deep neural networks to learn potential patterns between fused data and fuzzy GHI data in an end-to-end manner. Our method has been comprehensively validated on data provided by the National Renewable Energy Laboratory, demonstrating its effectiveness, and achieving the highest forecasting accuracy compared to state-of-the-art methods.

Integrated energy system (IES) facilitates efficient energy conversion and utilization. However, the joint optimization of energy use and carbon emissions (CEs) remains a significant and widely recognized challenge in this field. In this article, to solve the problem, a novel decision-making framework is proposed with leveraging a large language model (LLM) to assist deep reinforcement learning (DRL). First, a dynamic priority trading strategy is designed based on real-time supply and demand, which is adjusted dynamically through a trading matrix. Furthermore, a bidirectional equilibrium pricing mechanism is designed to determine reasonable prices that balance the interests of trading parties. Finally, the powerful inference and analysis capabilities of the LLM are leveraged to optimize DRL algorithms through interactive iterations and feedback loops, thereby enhancing decision-making performance. The experimental results demonstrate that the improved algorithm outperforms the baseline algorithm in terms of cost control, CE limitation.

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.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

False data injection attack (FDIA) can pose a severe threat to the distribution networks (DN), and the accurate detection of FDIA plays a key role in the safe and reliable operation of the DN. In this article, an end-edge-cloud collaboration-based detection framework is proposed to detect FDIA in the DN. First, in order to effectively preserve the privacy of different stakeholders in the DN and solve the problem of data island, a federated-learning-based edge-cloud collaboration mechanism is designed according to the proposed end-edge-cloud collaboration framework to jointly train the local FDIA detection models and eventually build a comprehensive FDIA detection model. Then, considering the temporal–spatial correlation of measurement data, a local data-driven FDIA detection model is proposed based on a novel temporal–spatial graph convolutional network, which can extract temporal–spatial features of the measurement data and improve the FDIA detection performance. In general, compared with the traditional centralized FDIA detection methods, the proposed method can make full use of the computational capacity of distributed edge devices and reduce the pressure of computation on the control center. Finally, simulation results based on the modified IEEE 14-bus and IEEE 118-bus distribution systems indicate that the proposed method can effectively improve the accuracy of FDIA detection compared with other methods.

The integration of large-scale distributed generators into active distribution networks (ADNs) will aggravate voltage fluctuations, which can affect the secure operation of power grids seriously. In this article, we investigate a cooperated voltage regulation problem of ADNs. Specifically, we first formulate a two-timescale voltage regulation problem considering the coordination of various hybrid devices while reducing the power loss of the whole ADNs. Given that the aforementioned problem is challenging to solve directly, we reformulate it as bilevel Markov games. Then, we propose a hierarchical multi-agent attention-based deep reinforcement learning algorithm to solve them. To be specific, the upper level Markov game is solved by a discrete multi-actor-attention-critic (MAAC) algorithm, and the lower level Markov game is solved by a continuous MAAC algorithm. In addition, the two-timescale coordination between upper level and lower level agents is implemented through the information exchange of rewards during the training process. Simulation results show that the proposed algorithm has good effectiveness, robustness, and scalability in voltage regulation.

The ever-increasing false data injection (FDI) attack on the demand side brings great challenges to the energy management of interconnected microgrids. To address those aspects, this article proposes a resilient optimal defensive strategy with the distributed deep reinforcement learning (DRL) approach. To evaluate the FDI attack on demand response (DR), an online evaluation approach with the recursive least-square (RLS) method is proposed to evaluate the extent of supply security or voltage stability of the microgrids system is affected by the FDI attack. On the basis of evaluated security confidence, a distributed actor network learning approach is proposed to deduce optimal network weight, which can generate an optimal defensive scheme to ensure the economic and security issue of the microgrids system. From the methodology's view, it can also enhance the autonomy of each microgrid as well as accelerate DRL efficiency. According to those simulation results, it can reveal that the proposed method can evaluate FDI attack impact well and an improved distributed DRL approach can be a viable and promising way for the optimal defense of microgrids against the FDI attack on the demand side.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

To address the false data injection (FDI) and denial of service (DoS) attack, this article proposes an event-trigger-based resilient distributed energy management approach for cyber–physical system of smart grid. Here, an event-trigger-based resilient consensus algorithm (ERCA) is proposed with the attack identification and compensation mechanism. The event-triggered mechanism is improved within distributed optimization combined with reliable acknowledgment (ACK) signals technique to mitigate the impact of data loss or transmission delay, and trust nodes-based compensation approach is proposed during resilient coordinated optimization for state correction to ensure the stability and security of power grid system. The optimality and convergence of the proposed method are proved theoretically that the proposed method can approximate to optimal solution well and achieve consensus by ensuring the proactive involvement of all participants under coordinated cyber attack. According to those obtained simulation results, it reveals that the proposed algorithm can effectively solve the energy management issue under coordinated DoS and FDI attack.

Due to the existence of different stakeholders, it makes competitive game characteristic in hybrid energy systems (HESs). Combined with the high-dimensional complexity and output uncertainty of distributed energy resources, the optimal operation of HESs can be a more challenging problem. Here, this article proposes a potential game-based two-layered hierarchical optimization strategy to deal with this problem. With consideration of its high-dimensional complexity, a two-layered hierarchical HES model is created, consisting of an upper-level and a lower-level model. For properly solving competitive relationships among different stakeholders in the upper-level model, a multiagent system for stakeholders is created and a potential game is employed with a distributed primal-dual perturbed algorithm, and its convergence and optimality have been both proved. Moreover, an uncertainty and robustness analysis is done with coordination between lower and upper models, which deduces a feasible robust uncertainty interval in the lower-level model. For better dealing with the lower-level model, a gradient descent-based multiobjective differential evolution (GD-MODE) algorithm is utilized to optimize the economic cost and emission issue simultaneously, producing a set of Pareto-optimal schemes. Combined with simulation results, it is proven that the proposed method can reduce computational complexity as well as properly deal with uncertainty problems for the optimal operation of HESs.

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.

Due to the uncertain output of intermittent energy resources and dynamic communication topology, it brings a great challenge for the optimal security control of isolated power system. To address this problem, this article proposes a two-level optimal control strategy with event-triggered switching mechanisms. For ensuring the security of the isolated power system, event-triggered switching mechanisms are proposed in the upper-level model to decrease potential risk of supply security and voltage stability, which can ensure system security as well as a low switching cost. In the lower-level model, a distributed optimization with switching topology is developed to minimize power generation cost under the above switching mechanisms, and the convergence ability of the proposed distributed optimization method is well proved with a uniformly globally exponentially stable condition. The obtained simulation results reveal that the proposed optimization approach can properly deal with the security issue of an isolated power system as well as dynamically minimize the economic cost.

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.

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.

Every year, over 4700 traffic fatalities and 75000 crash injuries involve pedestrians in the United States. Effective solutions are urgently needed to prevent vehicle–pedestrian collision accidents. Many driving assistance systems are proposed to address this problem; however, they require additional infrastructures that may result in higher costs and be difficult to deploy on a large scale. In this article, we propose SafePath, which uses the ubiquitous smartphones to avoid vehicle–pedestrian collision. Specifically, SafePath utilizes the smartphones to broadcast the redesigned service set identifier (SSID) messages containing users’ information (e.g., location, direction, etc.) and scan the surroundings via wireless communications. Considering the limited communication range and the possible interference, and obstruction of obstacles, we propose a collaborative mechanism to enhance the transmission capability, hence predicting the collisions in advance effectively. We also design a risk evaluation scheme to calculate the probability of accidents and inform users to take actions against accidents at different levels. We implement SafePath on the Android platform and conduct extensive real-road experiments to evaluate the system performance. The experimental results demonstrate that SafePath can provide twice the transmission range compared with other collision-avoiding systems. Moreover, it also can significantly reduce the probability of vehicle–pedestrian collisions by up to 81.4%, with respect to other compared collision-avoiding systems in our real-road test.

Due to the high resistance/reactance (R/X) ratio of a low-voltage microgrid (LVMG), virtual complex impedance-based P-· V/Q-ω droop control is adopted in this article as the primary control (PC) technique for stabilizing the system. A distributed event-triggered restoration mechanism (ETSM) is proposed as the secondary control (SC) technique to restore the output-voltage frequency and improve power sharing accuracy. The proposed ETSM ensures that neighboring communication happens only at some discrete instants when a predefined event-triggering condition (ETC) is fulfilled. In general, the design of the ETC is the crucial challenge of an event-triggered mechanism (ETM). Thus, in this article, a static ETM (SETM) is proposed as the ETC at first, where two static parameters are utilized to reduce the triggering frequency. Bounded stability is ensured under the SETM, which means that the output-voltage frequency is restored to the vicinity of its nominal value, and close to fair utilization of the distributed generators (DGs) is achieved. To further improve the power sharing accuracy and accelerate the regulation process, a dynamic ETM (DETM) is then introduced. In the DETM, two dynamic parameters that converge to zero in the steady state are designed, which promises asymptotic stability of the system. Besides, Zeno behavior is excluded in both mechanisms. An LVMG consisting of four DGs is constructed in MATLAB/Simulink to illustrate the effectiveness of the proposed methods, and the simulations correspond with our theoretical analysis.

The Industrial Internet of Things (IIoT) promises numerous benefits for smart cities and intelligent transport is not an exception. This work demonstrates one such benefit by designing and implementing an IoT bicycle sharing service for smart city transportation. The proposed system is also based on renewable energy further pushing the drive towards zero carbon emission. The proposed system is composed of two subsystems namely a smart docking rack and a bicycle subsystem. This is coupled with server communication based on a Low Power Wide Area Network (LPWAN) protocol and a mobile application for efficient bicycle tracking, obstacle monitoring and billing management. The proposed sharing service was deployed on a university campus with a vision to scale up to smart city applications.

Multi-functional grid-tied inverters (MFGTIs) have been investigated recently for improving the power quality (PQ) of microgrids (MGs) by exploiting the residual capacity (RC) of distributed generators. Several centralized and decentralized methods have been proposed to coordinate the MFGTIs. However, with the increasing number of the MFGTIs, it demands a method with improved reliability and flexibility, which are characteristics of distributed framework that has not been introduced into the PQ improvement (PQI) field before. In this paper, we propose a distributed consensus method to undertake the PQI task. The task is proportionally shared among the MFGTIs according to their instant RCs. Besides, most of the existing methods assume that the RCs of the MFGTIs are sufficient for tackling the PQ problem (PQP), which is not always true. In the case of insufficient RC, the active power output of each MFGTI is scaled down by the same factor determined by a proposed leader-follower protocol to make room for the task. In summary, the PQP is dealt with in both cases of sufficient and insufficient RC under the distributed control framework. Finally, simulations and hardware-in-the-loop experiments of an MG consisting of three 10kVA MFGTIs are presented to verify the effectiveness of the proposed methods.

Biometric authentication has gained popularity in recent years as knowledge-based authentication methods overburden users. This is because users are required to remember distinct and secure passwords for each system where they are registered. As speech recognition systems are embraced the smart technologies of the future, speaker recognition is going to be a natural authentication mechanism. This paper presents a biometric authentication system for industrial applications using speaker recognition. The system uses an autocorrelation voice activity detector and gaussian mixture models to identify users of the system. The equal error rate of the overall designed system was found to be 12.1% and the average verification time was found to be 4.67 seconds.

Due to the uncertainty of intermittent energy and system load, it is a big challenge to optimally operate an isolated power system. This article proposes a two-stage optimal operation strategy with a Takagi–Sugeno–Kang (TSK) fuzzy system to address the supply security under uncertainty circumstance. For proper analysis of the uncertainty characteristics, adjustable uncertainty parameters of intermittent energy resource and system load are taken as fuzzy sets; with the consideration of the robustness of these uncertainty parameters on isolated power system, it creates a supply-security identification model with the TSK fuzzy approach under radial basis function (RBF) neural network, and deduces optimal weight values with a recursive least square method. For properly avoiding potential risks, security index is classified into several degrees, each degree of risk can switch a different operation model, which can ensure the supply security of an isolated power system. For properly solving the optimization model, gradient descent-based multiobjective cultural differential evolution is employed to minimize economic cost and emission rate simultaneously. With simulations on isolated regional network, the obtained results reveal that the proposed method can be a viable alternative for optimal operation in isolated power systems.

Electrical power theft is one of the main causes of Non-Technical Losses (NTLs) in an electrical power system. NTLs in electrical power grids refers to the transmission and distribution loses originating from electrical theft and various other illegal uses of electricity. According to Eskom, South Africa loses at least R20 billion a year due to electricity theft, three-quarters of which is reported to be losses suffered by municipalities. Thus, to address this issue, this paper presents the design and implementation of a device capable of detecting electricity theft by identifying the small changes between the real-time measurements and that of a trained model. The energy metering system consists of a voltage and current sensing circuits while the theft detection algorithm consists of a Support Vector Machine (SVM) model trained and used to classify users as either a clean or fraudulent user. The data that was used to train the SVM model was obtained from a test bench. The test bench consisted of 100 appliances that were emulated using purely resistive loads. Each appliance has its own probability function which consists of a PRNG that generates pseudorandom numbers according to a chosen distribution type. The probability function also considers the probability of a specific appliance being used for that day and for how long the appliances are active. The detection device was implemented using a Raspberry Pi 3 and several evaluations were carried out. Evaluation result showed that the device can accurately detect electricity theft.

The need to keep track of the activity a miner is engaged in, is germane to the productivity and safety of the worker. Apart from this, it also serves as a possible early warning system if any irregular movements are detected. Existing tracking methods suffer from high energy consumption and the inability to identify a person based on their gait as they only identify movements such as walking or sitting. To solve this challenge, this paper presents a low power inertial measuring unit (IMU) based system capable of identifying various activities, specifically those that a worker would engage in while working in a mine. The system is extended to perform a gait analysis to identify the person performing the activity as well. Three sensor nodes were designed and etched onto a printed circuit board (PCB). Housings for the nodes were designed and 3D-printed. Firmware for the sensor node microcontrollers was developed in C to incorporate I2C data sampling with XBee API mode packet construction. A back-end program was then developed in C# to handle all the incoming data with the use of 2 neural networks. The test of the proposed system revealed a high degree of activity identification accuracy while the results obtained for the gait analysis revealed that the system can distinguish between different users with reasonable accuracy. The energy consumption test also revealed a satisfactory performance.

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.

The system proposed in this paper is smartcard terminal that interfaces with a smartphone device, rather than a computer. In attempt to make point of sale (POS) systems more accessible, the customer needs to have a pre-paid value loaded on their card before they can make a purchase. To authenticate a purchase the user will need to present their card and enter personal PIN and the amount will be deducted from the total they have loaded on their card when a payment is made. The data in the system is secured using a software implementation of 3DES which had an 8-bit variable limitation to control memory usage.

This paper describes the design and implementation of an embedded wireless vibration condition monitoring device. The goal was to design a device that uses machine learning techniques for fault diagnosis. A MEMS accelerometer attached to a microcontroller measures vibration and transmits the data wirelessly to a gateway. It is shown that the device is able to diagnose faults.

The papers in this special section examine the use of fog computing applications in industrial electronics. Due to the increased number of connected things in industrial applications, the growing volume and velocity of Internet of Things (IoTs) data exchange urge for more and more communication resources, leading to the bottleneck in terms of data processing, data latency, and traffic overhead. Fog computing emerges as an alternative for traditional cloud computing to support geographically distributed, latency-sensitive, and QoS-aware IoT applications while reducing the burden of data centers in traditional cloud computing. In particular, fog computing with the features (e.g., low latency, location awareness, and capacity of processing large number of nodes with wireless access) to support heterogeneity and real-time applications is an attractive solution to delay- and resource-constraint large-scale industrial applications. However, with the benefits of fog computing, the research challenges arise regarding fog computing for industrial applications.

Hosting multiple applications in a shared infrastructure of wireless sensor networks is a trend nowadays, and sharing sensory data for answering concurrent applications is a promising and energy-efficient strategy. To address this challenge, this paper proposes an energy-efficient query optimization mechanism for supporting multiple concurrent applications leveraging our two-tier cooperative caching mechanism. Specifically, query requests for concurrent applications are represented as binary strings, which are reduced to a single one for avoiding the reprocessing of shared subquery requests. This reduced query request is answered through our cooperative caching mechanism, where sensory data, which are highly possible to be reused for answering forthcoming query requests, are cached at the sink node (SN). Besides, the gray model GM(1, 1) is adopted for forecasting sensory data units which may be interested mostly by forthcoming query requests. These units of sensory data may be prefetched from the network and cached at the SN. Experimental evaluation shows that this approach can reduce the energy consumption significantly, and improve the network capacity to an extent, especially when the number of concurrent query requests is relatively large.

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.

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.

South Africa could potentially face a shortage of water in the near future. Efficient and low cost methods to determine water usage are vital to help prevent this issue. Flow and usage meters are necessary to ensure accurate usage readings. An electromagnetic flow meter and a water usage meter were developed. The designed module can be implemented on a standard water pipe at residential areas. The developed system harvests energy using a solar panel.

Conventional fire detection systems have a tendency of being triggered by false positives. In this paper, we discuss the design and implementation of a smart fire detection system using a Wireless Sensor Network (WSN) and Global System for Mobile (GSM) communication to detect fires effectively and reduce false positives. The proposed system uses smoke and temperature sensors. SMS capability via GSM was implemented so that occupants can interact with the fire detection system and aid in the detection of false positives. The aim of this work was to design and implement a fire detection system that detects fires effectively and reduces false positives. The results show that the system meets the specifications.

This paper describes the work that was carried out in designing a public addressing system based on Wireless Sensor Networks (WSN). The system uses a VS1053 codec to perform all the required encoding and decoding, and an STM32W108CC as a controller, with the capability of performing wireless communication. Satisfactory results were obtained from the public address system. The playback had an average Mean Opinion Score (MOS) of 4.0. The average data transfer rate was maintained over a distance of 20 m.

Many vehicle accidents are caused by driver fatigue. Systems to monitor driver fatigue can contribute to a decrease in fatalities. This paper describes the development of a multi-sensor system for driver fatigue detection. The system achieves this by monitoring eye closure and wheel steering movements, and is able to alert the driver of any detected anomalies. Various techniques are suggested for registering the location coordinates. If it is detected that the driver is falling asleep, the system alerts the driver through audible and visual cues and sends an SMS to a third party reporting the occurrence.

In this paper, an indoor office locator system is developed. The product developed uses body sensors to detect incoming objects using indoor positioning tracking algorithm which run on a server application to guide the user in an office environment were designed and developed. It is shown that overall the final system allowed a partially blind user to navigate the office with relative safety and accuracy.

Biometric systems are important for access control and authentication in various application scenarios. Single biometrics can be selected for authentication, but a multimodal approach is typically more reliable. In this paper, a multimodal biometric authentication system is developed. It consists of wireless sensor nodes equipped with accelerometers and infrared cameras, which enable analysis of gait and vein scanning for authentication. Observed error equal rates for the gait and vein authentication are 13% and 11% respectively, whilst the equal error rate for the combined metrics is 8%.

Stress in the workplace is known to be detrimental to one's physiological and psychological health. This paper proposes a wearable stress monitoring system using a body sensor network (BSN). BSNs are made up of a number of wearable/implantable biosensors that work together to perform a common task The system was accurately able to monitor the stress of an individual and display the results in an intelligible (and useful) way to the user.

Electric shocks due to high voltage lines can pose serious health risks to workers. Reliable and efficient systems to alert workers of nearby live power lines are necessary. In this paper, a personal high voltage safety system with wear monitoring is designed and implemented. A wrist module which consists of an electric field detector and optical heart rate monitor is developed from first principles. The complete system allows the heart rate of a subject to be monitored and reported wirelessly to a GSM/GPS enabled module placed on a vehicle. The system can operate reliably in an area with high electromagnetic fields.

Maintenance of the water quality in swimming pools is a challenge for home owners with limited budgets. Existing pool automation systems are expensive and entry-level models can only control pH and chlorine levels. This paper presents a web-based swimming pool information system that is capable of taking various sensor measurements and interface with actuators to automate numerous elements of pool maintenance. Apart from measuring pH and chlorine levels, the system is also able to maintain a set water level and can activate/deactivate the pool pump remotely according to a user-defined schedule. The system can be remotely monitored and configured via WiFi.

Hazardous environments at the workplace are a significant contributor to injuries due to accidents as well as chronic diseases. There are many occupational health and safety (OHS) systems, but they are costly or not flexible. This paper presents a low-cost OHS. It consists of various sensors that can be used to monitor whether a safety helmet is being worn, the worker is mobile and safety boots are being worn. The system interfaces with a wireless sensor network and is suitable for operation in environments such as underground mines and sawmills.

Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energy-efficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geographical size. These grid cells are assigned to clusters through an algorithm inspired by the${{k}}$-dimensional tree algorithm, such that the energy consumption of each cluster is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.

Recently, there has been significant research focus on smart cities and how to use resources efficiently. Parking space, in particular, is scarce in most metropolitan areas and intelligent systems are required to coordinate parking. This paper presents a wireless system for locating parking spots remotely via a smartphone, and a wireless sensor node which determines if parking spots are vacant or not. It is found that the system is highly efficient and has high accuracy, even at long ranges.

This paper proposes a novel method for detecting the Parkinson's diseases via applying the empirical mode decomposition to filtered electromyograms. First, the electromyograms are processed by different linear phase finite impulse response bandpass filters with different pairs of cutoff frequencies. Second, each filtered electromyogram is decomposed into several intrinsic mode functions. Third, both the entropies and the total numbers of the extrema of the intrinsic mode functions of each filtered electromyogram are computed and they are used as the features for detecting the Parkinson's diseases. Computer numerical simulation results show that the features are linearly separable. Hence, a simple perceptron can be employed for the detection of the Parkinson's diseases. Finally, the algorithm is implemented via a mobile application. Compared to conventional empirical mode decomposition approaches in which a predefined number of features is employed for detecting the Parkinson's diseases, our proposed method allows to use a flexible number of features for detecting the Parkinson's diseases. This is because the total number of filters to be employed is very flexible. As a result, our proposed method is more flexible than the existing methods.

Eye tracking has a number of useful applications ranging from monitoring a vehicle driver for possible signs of fatigue, providing an interface to enable severely disabled people to communicate with others, to a number of medical applications. Most eye tracking applications require a non‐intrusive way of tracking the eyes, making a camera‐based approach a natural choice. However, although significant progress has been made in recent years, modern eye tracking systems still have not overcome a number of challenges including eye occlusions, variable ambient lighting conditions and inter‐subject variability. This study describes the development of a robust real‐time camera‐based eye tracker, which is mainly suitable for indoor applications. The developed eye tracker relies on the so‐called bright/dark pupil effect for both the eye detection and eye tracking phases. Furthermore, this study also aims to determine how strong the bright/dark pupil effect is among people from an African ethnical background, a feature that is very relevant to a country such as South Africa.

A multi-interface ZigBee building area network (MIZBAN) for a high-traffic advanced metering infrastructure (AMI) for high-rise buildings was developed. This supports meter management functions such as Demand Response for smart grid applications. To cater for the high-traffic communication in these building area networks (BANs), a multi-interface management framework was defined and designed to coordinate the operation between multiple interfaces based on a newly defined tree-based mesh (T-Mesh) ZigBee topology, which supports both mesh and tree routing in a single network. To evaluate MIZBAN, an experiment was set up in a five-floor building. Based on the measured data, simulations were performed to extend the analysis to a 23-floor building. These revealed that MIZBAN yields an improvement in application-layer latency of the backbone and the floor network by 75% and 67%, respectively. This paper provides the design engineer with seven recommendations for a generic MIZBAN design, which will fulfill the requirement for demand response by the U.S. government, i.e. a latency of less than 0.25 s.

The convergence of smart energy system, telemedicine system and smart home/building solution will be the future development focus of next generation building and home automation system. This paper discusses the impact of the convergence on WiMAX development in terms of QoS management and network design. A performance evaluation has been also conduct to visualize the impact and promising result has been found. This confirms that the current WiMAX standard service class definitions and QoS management framework can fulfill the future development of WiMAX convergent automation network after slight modification.

Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.

Topology control plays an important role in the design of wireless ad hoc and sensor networks; it is capable of constructing networks that have desirable characteristics such as sparser connectivity, lower transmission power, and a smaller node degree. In this research, a new distributed topology control technique is presented that enhances energy efficiency and reduces radio interference in wireless sensor networks. Each node in the network makes local decisions about its transmission power and the culmination of these local decisions produces a network topology that preserves global connectivity. Central to this topology control technique is the novel Smart Boundary Yao Gabriel Graph (SBYaoGG) and optimizations to ensure that all links in the network are symmetric and energy efficient. Simulation results are presented demonstrating the effectiveness of this new technique as compared to other approaches to topology control.

With the vast amount of vehicles on roads worldwide on any given time of day, the severity of fatigue related accidents have become a major concern. The obvious solution to prevent or at least decrease fatigue related accidents, is to ensure that the driver rests frequently. However, the simple fact of the matter is that frequent resting periods cannot be effectively enforced, and as a result there is a need for a system to monitor the level of driver fatigue in real-time. The ultimate goal of this research is to develop a camera-based driver fatigue monitoring system, centered around the tracking of driver's eyes, since the eyes provide the most information with regards to fatigue. The most critical aspect of eye tracking is to first accurately detect the eyes, and although a number of eye trackers have already been illustrated in the literature, the process of eye detection has seldom been described in much detail. Given a number of possible eye candidate sub-images, eye detection is in essence the classification of these sub-images as either eyes or non-eyes. To the knowledge of the authors, different classification techniques have not been directly compared for the purpose of eye detection, and therefore the aim of this paper is to evaluate different classification techniques to determine which technique will be the most suitable for a driver fatigue monitoring system. The classification techniques that have been considered are artificial neural networks (ANN), support vector machines (SVM) and adaptive boosting (AdaBoost). Results have shown that AdaBoost will be the most suitable eye classification technique for a real-world driver fatigue monitoring system.

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

In underground mine and road tunnels, multipath fading is much more severe than in the terrestrial wireless channels. To overcome the multipath fading in underground tunnels, MIMO (Multiple Input Multiple Output) and Cooperative Communication system can be utilized. Since the underground channel characteristics are significantly different from those in terrestrial environments, the channel capacity and the outage behavior of such systems need to be investigated based on underground tunnel channel models, which had not been addressed by the research community yet. In this paper, the capacity distribution and outage probability of MIMO and cooperative communication systems are investigated in underground tunnel environments. Explicit formulas of the capacity distribution and outage probability are developed as functions of environmental conditions and system configurations. Based on the capacity and outage analysis in underground tunnels, the optimal MIMO antenna geometry design scheme is proposed for MIMO systems; and the cooperative relay assignment protocol is developed for cooperative communication systems. Simulations are conducted to validate the theoretical results.

In wireless underground sensor networks (WUSNs), due to the dynamic underground channel characteristics and the heterogeneous network architecture, the connectivity analysis is much more complicated than in the terrestrial wireless sensor networks and ad hoc networks, which was not addressed before, to our knowledge. In this paper, a mathematical model is developed to analyze the dynamic connectivity in WUSNs, which captures the effects of the environmental parameters such as the soil composition and the soil moisture, and the system parameters such as the operating frequency, the sensor burial depth, the sink antenna height, the density of the sensor and sink devices, the tolerable latency of the networks, and the number and the mobility of the above-ground sinks. The lower and upper bounds of the connectivity probability are derived to analytically provide principles and guidelines for the design and deployment of WUSNs in various environmental conditions.

Current standardised offline vending systems play a critical role in supporting electricity prepayment-metering infrastructure by enabling convenient access to point of sales for customers to purchase prepaid electricity tokens. Electricity utilities are now opting for online vending systems over offline vending systems. Online vending represents a step change in prepayment vending systems, which promises several benefits to utilities. However, the lack of an industry specification for online vending systems was a cause of major concern and risk for utilities, as they faced the problem of being locked into proprietary online systems. Further, the unchecked proliferation of proprietary online vending systems would have a detrimental impact on already successful standardisation efforts in the electricity prepayment industry. Thus, the South African prepayment industry, initiated a project to develop an open industry specification for online vending systems. This paper analyses the industries migration to online vending systems, the specification development process, the design issues that emerged and implementation of the specification as a Web service.

Existing communication utilities, such as the ISO/OSI model and the associated automation pyramid, have limitations regarding the increased complexity of modern automation systems. The introduction of profiles for fieldbus systems, or field-area networks (FANs), was an important innovation. However, in the foreseeable future the number of FAN nodes in building automation systems is expected to increase drastically. And here the authors see an opportunity to revolutionize the operation of intelligent, autonomous systems based on FANs. The paper introduces a system based on bionic principles to process the information obtained from a large number of diverse sensors. By means of multilevel symbolization, the amount of information to be processed is substantially reduced. A symbolic processing model is introduced that enables the processing of real world information, creates a world representation, and evaluates scenarios that occur in this representation. Two applications involving human actions in a building automation environment are briefly discussed. It is argued that the use of internal symbolization leads to greater flexibility in the case of a large number of sensors, providing the ability to adapt to changing sensor inputs in an intelligent way.

In this paper, the authors discuss their recent contribution to the methodology of active product modeling and propose integrated model objects for engineering activities in mechanical systems. The purpose of the proposed model objects is to react to changes in the inside and the related modeled world by analysis of behaviors and behavior driven generation of adaptivity features for modification of model entities inside and outside the object. They are composed of elementary, structural, relationship, behavior, knowledge and adaptivity features. The proposed model objects are inherently highly integrated. An overview of product modeling introduces the authors' approach to modeling by using the proposed model objects. Following this, the architecture of integrated, environment adaptive model objects is detailed. Then activities of integrated objects are placed on four levels of the model. Finally, integration and implementation issues are discussed.