Infrared-Visible Image Fusion (IVIF) aims to generate a single, information-rich image for downstream tasks. However, prevailing methods exhibit two key limitations. First, many approaches lack explicit hierarchical semantic decoupling, failing to effectively integrate semantic features across different levels, which restricts their ability to capture complex scene structures. Second, task-driven fusion frameworks typically adopt a cascaded design, with unidirectional supervision provided by geometry-centric downstream tasks like detection. This architecture not only limits mutual reinforcement between the fusion and task networks, but also creates a ”supervision bottleneck” by lacking interaction with the linguistic modality that captures richer scene relationships. To tackle these challenges, we propose CCSFusion, the first framework that leverages Chain-of-Thought captioning as supervision, redirecting IVIF optimization from narrow geometric accuracy to multimodal scene comprehension. It establishes a mutually reinforcing coupling between the fusion network and the captioning task. Specifically, we introduce a Segmentation Mask Calibration Unit (SMCU) to refine coarse semantic priors, providing precise pixel-level guidance. Subsequently, the calibrated features are fed into Chained Semantic Fusion Module (CSFM) which explicitly decomposes the semantic priors into three hierarchical levels, and then feeds them into the Hierarchical Semantic Attention module. Finally, a bidirectional knowledge distillation mechanism transfers the reasoning ability of the teacher network to the student. Experiments show that CCSFusion achieves superior fusion performance and generates more semantically coherent images for high-level cognitive tasks. The code is available at: https://github.com/Snaillms/CCSFusion.

LoRaWAN is an overwhelmingly popular wide area networking protocol that is capable of deploying city-wide Internet of Things (IoT) networks. Increase on the deployment of LoRaWAN in the last few years has led to exponential increase on the number of LoRa sensory and actuating end devices and results in more congestion in LoRaWAN network traffic. Moreover, the evolving AI-IoT (AIoT) applications (e.g., AI-powered environment monitoring system) create needs for more computing power with low latency requirements. To mitigate the high latency issue and the shortage of computing power due to continuously demanding quality of service (QoS) requirements of IoT applications, the Edge-Cloud server structure has been explored in a thorough manner in research area in recent years. An IEEE 2668 standardized IoT infrastructure system is presented in this work focusing on analyzing the performance of applying Edge-Cloud network server structure to LoRaWAN networks and provide standardized edge computing LoRaWAN infrastructure. This work uses queuing network to quantitively analyze the performance of an edge computing infrastructure applied to LoRaWAN and uses IEEE 2668 to quantitively present the performance and applicability of the Edge-Cloud LoRaWAN infrastructure.

Digital twin (DT) has emerged as a promising technology for improving resource allocation decisions in Internet of Vehicles (IoV) networks. In this paper, we consider an IoV network where mobile edge computing (MEC) servers are deployed at the roadside units (RSUs). The IoV network provides ubiquitous connections even in areas uncovered by RSUs with the assistance of unmanned aerial vehicles (UAVs) which can act as a relay between RSUs and task vehicles. A virtual representation of the IoV network is established in the aerial network as DT which captures the dynamics of the entities of the physical network in real-time in order to perform efficient resource allocation for delay-intolerant tasks. We investigate an intelligent delay-sensitive task offloading scheme for the dynamic vehicular environment which provides computation resources via local execution, vehicle-to-vehicle (V2V), and vehicle-to-roadside-unit (V2I) offloading modes based on the energy consumption of the system. Moreover, we also propose a multi-network deep reinforcement learning (DRL)-based resource allocation algorithm (RADiT) in the DT-assisted network for maximizing the utility of the IoV network while optimizing the task offloading strategy. Further, we compare the performance of the proposed algorithm with and without the presence of V2V computation mode. RADiT is further evaluated by comparing it with another benchmark DRL algorithm called soft actor-critic (SAC) and a non-DRL approach called greedy. Finally, simulations are performed to demonstrate that the utility of the proposed RADiT algorithm is higher under every condition compared to its respective conditions in SAC and greedy approach. Consequently, the proposed framework jointly improves energy efficiency and reduces the overall delay of the network. The proposed algorithm with UAV relay further increases the efficiency of the network by increasing the task completion rate.

Network automation has originated in the early 21st century by the International Business Machines Corporation (IBM), which was initialized conceptually, including automated configuration, optimization, healing, and protection of network deployment. In the framework of 5G and upcoming 6G, softwarization and virtualization, as well as the conceived pervasive artificial intelligence (AI), have been activating and further proliferating network automation, supporting ubiquitous applications with diverse network demands, which have recently attracted plenty of research efforts.

Network automation has been bred by the deployment of 5G based Industrial Internet-of-Things (IIoT) in Industry 4.0, and further approaching pervasive AI and human-robot-interaction/-collaboration toward 6G based Industry 5.0. Hitherto, to the best of the authors knowledge, research efforts are still required to provide a comprehensive review of the state-of-the-art network automation technologies for IIoT in 5G based Industry 4.0 and summary of challenges for next-generation network automation regarding the stricter network requirements of 6G based Industry 5.0. Therefore, in this article, we conduct a comprehensive overview of the state-of-the-art network automation technologies, standardizations, and corresponding impact on IIoT of Industry 4.0. We also forecast the next-generation network automation development toward 6G based Industry 5.0. This article provides blueprint of the next-generation network automation, meanwhile conducting comprehensive overview of the SoA network automation technologies in Industry 4.0, which gains high referable value for the researchers in the relative domain.

Industrial Internet-of-Things (IIoT) technology has been rigorously developed in recent years, moving towards the ambitious goal of industry 4.0, Network Automation. However, there are a few critical challenges regarding the implementation of a reliable IIoT ecosystem for different applications; security, battery life, and bandwidth are controversial challenges. All the challenges regarding IIoT are mainly struggling within the edge computing smaller box of the big picture in which a cloud is an upstream object while sensors and actuators act as downstream devices. Therefore, having a reliable IIoT ecosystem necessitates focusing on the whole IIoT’s challenges in the edge computing smaller box; first realizing the vital, practical demands of a specific IIoT application, and then defining the compatible protocols to form the effective edge computing configuration. This paper reviews four IIoT case study applications with their specific requirements and their counterpart sensor/actuator properties to find the appropriate edge computing protocols satisfying their demands.

Small cell (SC) resource allocation for the next-generation cellular networks embraces ultra-low latency, energy efficiency, and reliable challenges. Conventional optimization algorithms may not be capable of supporting the abovementioned scenarios, with aggregated and centralized traffic burden causing excessive latency, especially for the conceived large-scale eHealth networks in Healthcare 4.0. In this paper, we propose a new Decentralized Integer-based Non-Dominated Sorting Genetic Algorithm (DI-NSGA), on top of the authors’ previous work. Integer-based resource allocation process are formulated, and decentralized to mobile edge computing embedded SCs for releasing centralized traffic burden. Overall latency and achieved data rate are considered as the optimization objectives. Simulation analysis shows that the proposed DI-NSGA achieves low computation cost while maintaining high optimality by searching for the Pareto Front, compared with the selected benchmarks.

Internet of Things (IoT) consists of devices that generate, process, and exchange vast amounts of images. Unfortunately, these images always contain some kinds of noise, which significantly degrades data utility after the processing center receives the images. Image denoising plays a crucial role to recover the original image approximately from its noisy image according to the features of noise distribution and the structure information of the original image. However, the existing denoising algorithms are invalid when the original images are highly corrupted due to the high computational complexity. Therefore, this paper proposes a novel denoising algorithm based on the multi-low-rank model (MLR), which successively enforces similar blocks, dictionaries, and coefficient matrices approximating to low rank, thereby gradually removing noise. Extensive experimental simulations demonstrate that the MLR algorithm has the optimal denoising performance in terms of denoising quality and efficiency, especially in the case of strong salt noise.

LoRa Wide Area Network (LoRaWAN) is one the of the most popular Internet of Things (IoT) technologies for long-range and low-cost communication. At present, LoRaWAN has been applied in a variety of applications, including localization, smart metering, etc. However, the increasing number of LoRaWAN devices would degrade their quality of service (QoS). There are two main reasons. The first reason is the competition of bandwidth and channel resources between large number of connected end devices. Another one is the redundant channel resources allocation configurations of most end devices to achieve better transmission reliability. To address these challenges, this work proposes an enhanced resource allocation scheme based on both k-means and k-prototype classification algorithms to mitigate the affection of ALOHA scheme and the multi-gateway interference problem. In this proposed scheme, intense network resources under dense end device scenario and the redundant claim of network resources configuration in end devices are considered. An outlier improved spreading factor distribution method is also proposed to reduce the negative effect of the problems. By evaluating and comparing packet loss rate and the relative distribution of spreading factors, an average of 22% increment in transmission performance of LoRaWAN networks is achieved.

The aim of this Special Issue is to share the state-of-the-art research and developments on the emerging Industry 5.0 concepts, technologies, use cases and future applications. The outstanding benefits of Industry 5.0 in terms of cost and efficiency facilitate a reality sooner than expected. However, the benefits of Industry 5.0 must not come at a price-any negative social or economic impact must be prevented. To this end, it is beneficial and important that businesses can identify the ethical issues associated with these technologies and find solutions ahead of implementation. Ethically aligned designs and standards must be the backbone of the next Industrial Revolution. Hence, there is a desperate need for the further exploration of the role of Industry 5.0 in all verticals and for the exploitation of computational intelligence. Following a series of rigorous reviews, twelve papers, presenting original research, have been selected for publication in this Section.

LoRa wide area network (LoRaWAN), an emerging IoT protocol, has been popularized in large-scale applications, given its long-range and low-power properties. Hitherto, there is no appropriate traffic model for LoRaWAN to estimate the heterogeneous arriving traffic at the network server cluster (NSC). Inefficient computation power planning or even processing failure might be further caused. Radio replication, commonly existed in the arriving traffic at NSC in LoRaWAN, also causes difficulty estimating the makespan (i.e., mean processing time in NSC). To overcome the abovementioned limitations, a heterogeneous radio-replication-aware traffic aggregation model is proposed to estimate the arriving traffic for LoRaWAN. In addition, a radio-replication-combined supermarket model (RRC-SM), on top of HTAM, is proposed to achieve load balancing among servers in LoRaWAN. Furthermore, a nondominated sorting genetic algorithm based on multiobjective optimization is developed to simultaneously minimize cost and latency on NSC. Experiments reveal that the proposed HTAM and RRC-SM agree well with the simulation outcome. Under the arriving traffic estimated as 6.16 erlangs with four radio replications of each arriving packet on average, the proposed RRC-SM provides more than 50% reduction on the total processing latency and 75% reduction on the number of servers in NSC than other existing models.

Network controllability robustness (CR) reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the CR is determined by attack simulations, which is computationally time-consuming or even infeasible. In this article, an improved method for predicting the network CR is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.

This paper introduces the time synchronization approaches to the Institute of Electrical and Electronics Engineers (IEEE) P1451.0 standard-based sensor networks for Internet of Things (IoT) applications. A time synchronization architecture of IEEE P1451.0 standard-based sensor networks is described including two-level time synchronization systems in IEEE P1451.0 and P1451.1.X standards-based wide-area network (WAN) and IEEE P1451.0 and P1451.5.X standards-based local area networks (LANs). However, this paper mainly focuses on the time synchronization approach of IEEE P1451.0 and P1451.1.6 standards-based WANs and provides two implementations of time synchronization of IEEE P1451.0 and P1451.1.6 using wireline and wireless networks with their preliminary results to verify that the time synchronization approach of IEEE P1451.1.6 functions properly. In addition, the time synchronization transducer electronic data sheets (TEDS) of P1451.1.6 is described.

Internet of Things (IoT) has become one of the most popular technologies in recent years, covering from citywide services to industrial applications, which enlarges the smart life for human beings. Through IoT, billions of IoT end devices can be interconnected to support various applications. The emergence of low-power wide-area network (LPWAN) technologies provides a great opportunity to support such an enormous network with their kilometer-level coverage and uA-level power consumption. To improve the efficiency of network resources of LPWANs, the cooperated IoT is proposed by researchers. However, the current LPWAN consists of diverse protocols, equipment, and design standards, rendering the increasing development effort on designing a compliance network by developers. To address this issue, the IEEE 1451, developed by Instrumentation and Measurement Society, is proposed. The IEEE 1451 standardized the wireless IoT systems with wireless transducer interface module (WTIM), network capable application processor server (NCAP Server) and NCAP Client. Besides, the application programming interfaces (APIs) and transducer electronic data sheet (TEDS) are also standardized. Based on the IEEE 1451, a standardized structure for LPWANs, namely IEEE1451-LPWAN is introduced. In addition, an M/M/1/N based queueing model is built to analyze the queueing performance of IEEE1451-LPWAN, which provides guidance for adopters in the future.

The papers in this special section focus on cognitive analytics of social media for industrial manufacturing. Business innovation and industrial intelligence pave the way to a future in which smart factories, intelligent machines, networked processes, and big data are brought together to foster industrial growth and shift the modalities. Industry 4.0 or the Industrial Internet of Things (IIoT) is the latest catchphrase of technological innovation in manufacturing with the goal of increasing productivity in a flexible and efficient manner. Concurrently, the new collaborative Web (called Web 2.0) resiliently defines the notion of the techno-social system of computer-mediated, web/internet-based technologies and channels that have the primary objective of creating and enabling a collaborative and interactive virtual community of participants who can share or communicate information. These social technologies are essentially transforming the way we communicate, collaborate, consume, and create data and characterize one of the insurgent impacts of information technology on any industry, both within and outside industrial boundaries. Social media augments as a nontrivial element to this industrial value chain with the intent of making it more efficient. Collaborative sensing or crowd sensing can be used to help producers, suppliers, and customers understand and use insights learned from large amounts of sensing data in order to obtain competitive advantages

The received signal strength (RSS) finger-print-based approaches are widely used for indoor location-based services (LBSs). The emerging long range wide area network (LoRaWAN) is a cost-effective solution for indoor latency-tolerant LBSs attributed to its long-range property. In general, there are serious RSS fluctuations due to fadings along the communication path, thus significantly jeopardizing the localization accuracy. To overcome the challenge, in this article we propose the extreme RSS (ERSS) to stabilize the fingerprint database and formulate boundary autocorrelation to downsize tremendously the searching complexity and thus proliferating localization accuracy. In essence, the RSS fluctuations are modeled as a Bernoulli random process so that the RSS stability can be estimated by a newly defined fluctuation analytic function. To mitigate the impact of the perturbative fluctuation, the ERSS is further defined to cultivate a highly stable and robust fingerprint database which withstands environmental dynamics. In addition, boundary autocorrelation is developed to measure and compare the similarity between the measured RSS values versus the prestored fingerprint database. RSS values with low autocorrelation coefficients are eradicated from the typically lengthy searching. The downsized complexity significantly improves the localization accuracy. Experiments were carried out and the results revealed that the proposed method achieved sub-10-m localization accuracy in indoor environments. Such accuracy is encouraging and superior in contemporary LoRaWAN measurements.

With the widespread of consumer electronics, household appliances and electric vehicle (EV), the household energy consumption is gradually increasing. To reduce the burden of distribution grid and meet the growing energy demand, photovoltaic (PV) panels and energy storage could be introduced and deployed at home. Thus, the home energy system is gradually becoming an integrated multiple energy system including the distribution grid, PV panels, battery energy storage, EV and home loads. An efficient energy management is important for the integrated energy system to save cost and comprehensively utilize their distinct characteristics. In this paper, the energy management problem is formulated to minimize the daily electricity purchase cost. The dual attributes of EV, i.e. energy storage and mobility, are both considered in the problem. The numerical result demonstrates that the energy management solution can well meet the demand requirement and significantly reduced the electricity purchase cost. In addition, comparison of results shows that benefits can be acquired from the usage of vehicle to grid and battery energy storage.

In recent years, electric vehicles (EVs) have been widely applied to improve environment. The EV could provide environmentally friendly transportation but have the demerit of low battery capacity. Rapid charging by charging stations (CS) is critically needed especially for those drivers in long distance trip. Thus, a routing optimization problem for EVs charging should be addressed. Furthermore, this problem becomes more practical when the EV density is high at peak. In this scenario EVs are only allowed to obtain energy that render them able to arrive at the destination. In this paper, we formulate an emergent EV charging optimization problem in EV high density area which has not been discussed in related work and a novel genetic algorithm based emergent charging scheduling (GECS) scheme is proposed. The genetic algorithm (GA) is presented to simplify the multi-objectives optimization process in this case. Furthermore, incorporation of the Earliest Deadline First (EDF) which indicates the minimum recharging deadline time as the subject and Nearest Job First (NJF) which indicates the minimum recharging path as the subject into genetic optimization process can relieve the charging emergent condition and improve optimized results. The simulation results show that the proposed scheme can provide an optimal solution to minimize the average distance and waiting time for emergent charging in EV high density region.

The papers in this special section provides a forum to present recent advances on 5G mobile communications f(5G) tactile Internet. The Internet, which was created to provide resilient and interoperable communication across the globe, evolved to transport a vast amount of content with which to enrich our real-life experience. Pervasive ultra-broadband, programmable networks, and cost reduction of IT systems are paving the way to new services and commoditization of telecommunications infrastructure while lowering entry barriers for new players and giving rise to new value chains. Today, it provides a depth of information and social sophistication that rivals the real world. The Tactile Internet, the next evolutionary step, will enable remote, real-time physical interactionwith real and virtual objects, creating a two-way interactive experience in which boundaries between the real world and virtual world will blur.

With the rapid exhaustion of fossil energy, electric vehicles (EVs) become one of the key candidates for the next generation of transportation. Increasingly perfect technology developed makes EVs grow significantly. Therefore, Charging Stations (CSs), as accessories and necessities of EVs, should form a network with optimal planning. Inappropriate CS network design could cause series of negative effects to the popularization of EVs, the layout of the city traffic network and the financial cost of CS network construction, etc. Besides, the charging infrastructure planning for cities with large population and high EV density becomes even difficult. In this paper, an Effective Planning of CSs Network (CSN) is proposed. Comprehensive environmental elements (e.g. cities' and CSs' information, EV charging status, etc.) are considered in CSN. The CSN deals with complicated city planning in giant cities (i.e. large population, high EV density, etc.). Hong Kong is selected as the case study because it can be regarded as a typical giant city. Results show that the proposed CSN can ensure the EVs can find a CS before it is out of power. Besides, the CSN saves ~18% financial cost for the charging infrastructure planning in giant cities.

Due to lack of heart monitoring device, heart problems have been causing more than 17 million people death every year. Recently, researches on IoT (Internet of things) system provide feasible solutions to solve the problems. Such as LoRa wireless communication protocol, it can cover more than 1km2area and reliable communication connection. In heart monitoring system, packet transmission frequency should be considered carefully because of the reliability. Therefore, in this paper, the packet loss of heart monitoring system has been analyzed through packet length, transmission frequency and communication distance. Results shows that the packet loss can be less than 1% through specified communication property. The result in the paper can provide reference for reliable heart monitoring system design.

In the last decade, the significant development of smart building technologies has led to the formation of various energy sensing and monitoring applications. Energy monitoring of appliances relies on techniques such as Intrusive Load Monitoring (ILM) and Non-Intrusive Load Monitoring (NILM). ILM is referred to a technique that a sensor installed for each load. In NILM method, disaggregation of measured energy of all appliances at utility service entry is the main goal to provide a simple and cost-effective method of monitoring the appliances like sequence time domain reflectometry (STDR). This manuscript provides an overview of developments in energy consumption sensing and monitoring in three key areas of Internet of things (IoT), WSN and STDR for the advancement of smart building technology. This paper also provides some research directions for smart home of the future.

In a metropolitan city, the power consumption of buildings is a great concern for not only economic but also environmental concerns. Researchers is proposing some new low power wide area (LPWA) IoT communication protocols to monitor and control power consumption in buildings. Three common LPWA communication protocols include: NB-IoT, LoRa and Sigfox, which can provide different functions or performance under different conditions. For building management system, these protocols can create platform to monitor and control the “things”, such as energy. Light Poles are used to be built to continuously lighten up the city which are served as passive devices to be controlled by the Highway Department. With the LWPA communication protocols and sensors or actuators, they can provide a low power low cost network nodes to serve local community; and uplink the data to Cloud via mobile communication network or Ethernet, as Smart Poles. In this paper, some fundamental scheme and based on LPWA protocols to design building management system are introduced; and prototype of Smart Poles have been networked and applied in a traditional industrial area.

Vocational safety problems have been causing millions of workers dead even with related safety policies launched. Sleep apnea is one of the main causes that renders insufficient sleep and thus becomes a high potential risk of working accidents. To assess sleep apnea, sleep stage monitoring and classification is the main and accurate way. Therefore, in this paper, a new classifier is designed for the sleep stage classification among awake, light sleep and deep sleep. A new kernel is designed for the sleep apnea classification. Results show that the proposed method can achieve an accuracy up to 97% and~18% higher than the previous related works. Such a high accuracy ensures the efficient diagnosis of sleep apnea.

In Day-ahead Power Market (DAM), Load Serving Entities (LSEs) needs to submit their load schedule to market operator beforehand. For reduction of the total cost, the disparity of the price of DAM and the price of RDM (Real Day Market) should be considered by the LSEs. Therefore, the problem is that a more accurate load-forecasting model sometimes provide a price that has an interspace will lead to a lower cost. Facing this issue, this paper initiates a load forecasting model considering the Costing Correlated Factor (CCF) with deep Long Short-term Memory (LSTM). The target of the forecast model contains both accuracy section and power cost section. At the same time, the construct of LSTM can of fset the sacrificed accuracy. Also, this paper uses an Adaptive Moment Estimation algorithm for network training and the type of neuron is Rectified Linear Unit (ReLU). A numerical study based on practical data is presented and the result shows that LSTM with CCF can reduce energy cost with acceptable accuracy level.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, a benefit offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances including Industrial Internet of Things (IIoT). As the IIoT integrates many heterogeneous networks, such as Wireless Sensor Networks (WSNs), Wireless Local Area Networks (WLANs), Mobile Communication Networks (3G/4G/LTE/5G), Wireless Mesh Networks (WMNs) and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million healthcare workers by 2035. Hence, it is important to develop wearable healthcare systems to perform self-health monitoring. In general, wearable healthcare systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches and intelligent signal processing techniques play important roles in the developments of the wearable healthcare systems. Therefore, this special issue provides a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IIoT.

Over the past couple of decades, transportation systems have begun to receive widespread attention from the scientific community and emerged toward Intelligent Transportation Systems (ITS). Effective vehicular connectivity techniques can significantly enhance efficiency of travel, reduce traffic incidents and improve safety, and alleviate the impact of congestion; devising the ITS experience. Furthermore, during the past decades, the volume and density of vehicles increased significantly, especially the road traffic; this lead to a dramatic increase in the number of accidents and congestion, with negative impacts on the economy, environment, and quality of people’s lives. In particular, according to the World Health Organization (WHO), road traffic injuries are estimated to be the leading cause of death for young people aged 15–29 and the ninth cause of death worldwide in 2015. The enabling communication technologies are intended to realize the frameworks that will spur an array of applications and use cases in the domain of road safety, traffic efficiency, and driver’s assistance. Although these applications will allow the dissemination and gathering of useful information among vehicles and between transportation infrastructure and vehicles in pursuance of assisting drivers to travel safely and comfortably, much effort is required to implement these practices for the success of these applications.

Recent trends in the applications of batteries in the built environment are improving the efficiency of batteries and lowering costs. This paper introduces various types of battery technologies such as sodium sulfur, lithium ion, flow and lead acid batteries and discusses their models. Various applications of batteries such as adaptive battery systems, Battery Electrical Vehicles (BEVs), Battery Energy Storage Systems (BESS), the Internet of Things (IoT), and Smart Grid and Smart Environment applications are also discussed. In their selection and use of batteries, scholars are ultimately looking to maximize occupant comfort whilst keeping costs low and optimizing the energy efficiency of buildings. This paper will provide a review of current trends in this field.

The Standards Group of the IEEE Industrial Electronics Society (IES) has been active in standards for industrial electronics in recent years, focusing on sensors and sensors networks, real-time industrial communications and industrial agents in the automation fields. It has also participated and collaborated with other IEEE societies such as the Instrumentation and Measurement Society (IMS) in the IEEE 1451 sensor networks standards family, and with government entities such as the US National Institute of Standards and Technology (NIST). This paper gives a brief synopsis of IES standards activities and the trends it sees in industrial electronics standardization in the coming emerging technologies such as Internet of Things (IoT)/Industrial IoT (IIoT), 5G communications, industrial wireless and possibly transportation electrification. All these technologies are expected to be disruptive to the industry in the coming years and standards must be generated to be effective and beneficial to industry and society. The IES Standards Group anticipates more contributions to the IEEE 1451 standards family, industrial agents, industrial wireless applications with NIST, and possibly with standards activities within Industry 4.0 in the coming years.

Intelligent sensing and actuation in building applications has been considered by many Heating, ventilation and air conditioning (HVAC) experts. Wireless Sensor Network (WSN) performs as an efficient communication tool for human comfort (e.g. indoor thermal control). In this paper, an analysis of Packet Error Rate (PER) in WSN for HVAC system was implemented. Based on the collected PER information, a WSN network was designed and built for achieving low PER. The network provided guidance for large-scale WSN design in HVAC systems.

There are increasing preferences to employ wireless communication technologies for high mobility, high scalability and low-cost applications in smart city development. This paper gives a brief synopsis of typical wireless technologies in smart city applications and the comparison analysis between them. The trend for smart city wireless technology is also presented. Examples, for several key applications within smart city development (healthcare, smart grid, localization) are studied and current advanced solutions supporting these applications are summarized with futuristic trends and demands are presented.

Water management is an important issue in economics and environment. Recently, amount of water control system has been proposed and developed. For the type of intelligent water control, the related parameters will be the input of the control system. Hence, there is a need of developing a scalable, flexible and reliable sensor network for related parameters monitoring. To install and replace water sensors in building networks, wireless connection will be the first priority. However, improper time synchronization in the network will cause packet loss and long latency which degrades the network performance. In this paper, time-synchronized ZigBee building network (TS-ZBN) is proposed for water management. The node-to-node time synchronization is proposed. The concept is to calculate the clock difference by studying the propagation delay model. The simulation result shows that the mean synchronization error and variance are low.

In this paper, an optimal design of a multibit interpolative sigma delta modulator (SDM) based on the absolute stability criterion is proposed. There are two types of midtread quantizers, namely, the uniform midtread quantizer and the nonuniform midtread quantizer. It is shown in this paper that the uniform midtread quantizer is the optimal one between these two types of the midtread quantizers in the sense of minimizing the maximum output input ratio of the midtread quantizer. A loop filter of the multibit interpolative SDM is designed based on the minimization of the energy of the noise transfer function in signal band subject to the strictly stable or the marginally stable condition of the loop filter, the absolute stability criterion, and the specifications on both the noise transfer function in the signal band and the magnitude response of the loop filter outside the signal band. Computer numerical simulation results show that our proposed multibit interpolative SDM achieves a broader stability margin and a higher signal-to-noise ratio compared to the state-of-art designs.

The papers in this special section introduce various new works in the area of semantic technologies in industrial informatics. They address common problems in the state of the art and illustrate the benefit of semantic technologies to automation in many practical scenarios.

A new network applies on high traffic ZigBee based wireless network. The new network incorporates multi-radio multi-channel technology and improves the efficiency of data transmission. The latency performance of the proposed network is analyzed by OPNET.

Healthcare problems is a popular topic nowadays in both the market and the research area. The transmission of the healthcare data is one of the most urgent topics that requires to be solved. Hence, in this paper, a new ZigBee based wireless sensor network is designed under the hospital environment. The network is under beacon mode in the MAC layer to ensure the security issue. The network delay and the battery consumption of the devices are considered as the key objectives that need to be achieved. Therefore, Evidential reasoning method is adopted as the method to give trade off solutions for the network. The simulation shows that the network can perform better on the delay and battery consumption when count of backoff=2 and backoff index=1. Therefore, the multi-criteria making method is very suitable for this model.

Spatial Modulation is a recently developed low-complexity MIMO scheme that jointly uses antenna indices and a conventional constellation set to convey information. Different types of developed SM systems have been proposed to mitigate the limitations of basic SM systems. We compared the performance of different types of spatial modulation system in different channel environment and give the key factors that could affect the bit error rate.

Due to the upsurge of context-aware and proximity aware applications, device-to-device (D2D) enabled mobile cloud (MC) emerges as next step towards future 5G system. There are many applications for such MC based architecture but mobile data offloading is one of the most prominent one especially for ultra dense wireless networks. The proposed system exploits the short range links to establish a cluster based network between the nearby devices, adapts according to environment and uses various cooperation strategies to obtain efficient utilization of resources. We proposed a novel architecture of MC in which the total coverage area of a eNB is divided into several logical regions (clusters). Furthermore, UEs in the cluster are classified into Primary Cluster Head (PCH), Secondary Cluster Head (SCH) and Standard UEs (UEs). Each cluster is managed by selected PCH and SCH. An algorithm is proposed for the selection of PCH and SCH which is based on signal-to-interference-plus-noise (SINR) and residual energy of UEs. Finally each PCH and SCH distributes data in their respective regions by efficiently utilizing D2D links. Simulation results demonstrate that the proposed D2D-enabled MC based approach yields significantly better gains in terms of data rate and energy efficiency as compared to the classical cellular approach.

Indoor Air quality (IAQ) is an urgent topic that worsens because of several of pollutants in the product indoor. World Health Organization (WHO) has standardized the harmful level indicating the potential health risk caused by hazardous substance. The conventional schemes consider single substance only which have no significant contribution to IAQ practically as IAQ depends on lots of parameters. As a result, a new comprehensive control scheme which deal with the IAQ and indoor environmental controlling, is designed.

The Internet of Things (IOT) continues to transform the world, and in many countries is now an integral part of our everyday lives-influencing everything from the way that we intercommunicate to how we conduct business. Innovators continue to find ways to integrate IOT into uses as far flung as fashion to medicine. This survey looks at how IOT is currently being integrated into the built environment for the purpose of saving energy and improving occupants' livelihoods. In particular, it reviews three technologies that have received a lot of attention in the literature as the future of an IOT mediated built environment. Based on this literature, predictions are made of the likely trends in regards to the future scholarship on these technologies.

The application of localization in healthcare system is a crucial topic which helps to locate the position of patent or the elderly in case urgency happens. From this aspect, a wireless technology is adopted to provide an efficient localization monitoring system for patients or the elderly in indoor area. The location of patients can be obtained through the developed algorithm. Fuzzy C-Means clustering (FCM) is one of the applicable techniques to locate the position of patients. However, low accuracy of FCM is the main problem. For this reason, the revised FCM localization algorithm, Calibrated Fuzzy C-Means Clustering Algorithm (C-FCM) is proposed in this investigation based on received signal strength (RSS) in wearable device. The proposed algorithm is evaluated through experiment and it has a percentage improvement of 14% compared with FCM.

A fully integrated single-band 2.4 GHz low noise amplifier (LNA) is designed by using 0.18μm CMOS technology for ZigBee applications. For healthcare applications, high power consumption is not preferred. Increasing the sensitivity of receiver, therefore, could be a solution resulting in the use of LNA. The impedance expression is mathematically reconstructed into a quadratic equation and leads to the solutions by adding the LC tank in the matching networks. Besides, by using voltage controlled MOS varactor, the LC tanks at the input and output can be tuned. Such topology is convenient for calibrating the frequency drift due to the process variation and unexpected parasitics. The amplifier works at the supply voltage 1.2 V with current dissipation 10 mA. The gains achieved are over 15 dB at 2.4 GHz and the corresponding noise figure is about 2.1 dB.

A smart environment system should automatically control the devices according to the sensing information and users' requirements so as to keep the environmental elements (e.g., temperature, light) within the desired range. System control with minimum power is one key issue in such a system. In this paper, we propose a multi-dimension model for system control. In this model, each environmental element is abstracted into a dimension, such that a service with conditions and targets can be formulated as a multi-dimensional service space, and a smart environment with many services may map to a comprehensive multi-dimensional service space through space computation. Based on this model, we propose a minimum power adjustment algorithm for energy-efficient scheduling in smart environment, which transforms the optimal control problem into the problem of the shortest weighted distance of point-to-polygonal in multi-dimensional space. Theoretical analysis and experimental results show that the proposed model is effective and efficient in energy-efficient system control. It is important to point out that the proposed algorithms are scalable when the number of dimensions or services increases.

This paper introduces a high frequency Point-to-Point (PtP) Microwave Transmitter Module for Telemedicine for hospital(s). Such a telemedicine system links up remote communities from one building to another in hospital environment. The network configurations of the high frequency microwave system are illustrated in details. Key advantages such as high capacity, long propagation distance, and wide range of frequency bands are described. The high frequency PtP serves as a repeater so that paramedical staff or nurse(s) may receive data at appropriate time. Hence important tasks such as first aid, doctor's diagnosis and instructions to patients may be performed through the telemedicine system. This paper describes the design of a telemedicine system based on a high linearity Ku-band transmitter module that can improve the capacity (data rate) as well as increased the propagation distance. The module operates between 13 ~ 15.5 GHz and achieves 25 dB of gain and 33 dBm of output power at 1dB gain compression. The third order intermodulation (IM3) is -41 dBc at 22 dBm Pout/tone, when the two-tone space is 600KMz at the high centre frequency of 15GHz.

World Health Organization informed that traffic accidents potentially become the 5thleading cause of death if there is no effective way to restrict drunk driving. It is reported that 51 million people are injured or dead because of the traffic accidents every year. These traffic accidents lead to the expenditures of $500 billion dollars. Among these traffic accidents, drunk driving is one of the leading cause that drunk drivers can be found in 40 % of total traffic accidents. To protect the public from drunk driving, drunk driving detection (DDD) is considered as one of the effective ways. Among various types of DDD, electrocardiogram-based (ECG-based) detection can provide real-time monitor and response. In this paper, ECG-based drunk driving detection scheme was proposed. Among various types of DDD, the proposed work is able to provide early detection and fully automated detection with satisfied accuracy.

Logistics network optimization plays a critical role in contemporary logistics planning and supply chain network designs, and the vehicle route planning is essential for logistics network optimizations. In this paper, we present an efficient and effective approach for vehicle route planning. The new approach has utilized multiple spanning trees as a criterion to categorize the customers into several sub areas. In addition, by iterative choose some customers in the boundary of sub areas and resign them to different spanning trees, we can get a more compact ones with smaller distances. As a result, since the spanning tree represent the lower bound of vehicle route in all sub areas, we can also conduct the vehicular dispatching in each sub areas. Extensive simulation has verified the effectiveness of the proposed methods.

The papers in this special section focus on advancements in healthcare technologies and services. THE demand and market of healthcare services are increasing exponentially. This is due to the problem of aging and the reformation of healthcare services. The aging problem is a serious global issue that after 30 years, the portion of the elderly who are aged at least 60 years old will be 20% of the world’s population. The human immune system becomes weaker with aging and the elderly are more likely to suffer various injuries and diseases such as cancer, cardiovascular diseases, diabetes, and respiratory infection. Medical services such as hospitals and clinics are facing critical challenges to the quality of services and the capability of handling patients. To reform modern healthcare services, one solution is to realize smart healthcare in the concept of the smart city. There are four key components in futuristic smart healthcare, which are smart sensors, healthcare network, anomaly detection algorithm, and robot-assisted medical services.

An interference-mitigated ZigBee-based advanced metering infrastructure (AMI) solution, namely IMM2ZM, has been developed for high-traffics smart metering (SM). The IMM2ZM incorporates multiradios multichannels network architecture and features an interference mitigation design by using multiobjective optimization. To evaluate the performance of the network due to interference, the channel-swapping time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are significant improvements in the performance of the application-layer transmission rate (σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70% in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ > ~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM results in typically less than 0.43 s delay for a 30-floor building under interference. This performance fulfills the latency requirement of less than 0.5 s for SMs in the USA (Magazine of Department of Energy Communications, USA, 2010). The IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel Kxcorr,ijand the convolution kernel Kconv,ij. Kxcorr,ij, and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic algorithm. This renders an optimal KDDC that confidently serves as the full descriptor of the drowsiness. The performance of KDDC is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of KDDC is better than those of both Kxcorr,ijand Kconv,ijby more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17-63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, KDDC improves the accuracy by 48.4-87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

The papers in this special section focus on industrial wireless networks. With the rapid advance of wireless technologies, numerous emerging solutions and applications of industrial wireless systems have been developed. The present development of communication in industrial environments drives the need for ubiquitous access to distributed resources and services that are connected to things, devices, and systems. Service completions are typically perfected through smart APPS on wireless data delivery, such as WiFi, Bluetooth, ZigBee, and 5G. The occurrence of Internet of Things (IoT) further catalyzes the advent of the wireless era. These papers cover the comprehensive solutions of wireless network developments, industrial applications, and wireless prototype designs.

Focusing on the leak-points in petrochemical industries, this paper discusses the key factors (i.e., equipment temperature, gas pressure, and diffusion rate) in petrochemical industries. Data from sensors of petrochemical industries need to be timely operated because of time sensitivity and it is hard to achieve associated information from sensors located in production sites. To this end, we propose a three-level framework based on improved back propagation (TLBP). The real-time data streams are processed according to the arriving time in input layer. At the same time, a neuron-optimizing solution is introduced in learning process to deal with redundant and invalid neurons, thereby accelerating the response speed of learning and reducing the prediction time. Finally, we propose an improved mechanism of the multidimensional learning factor to lower the learning error and higher convergence rate. Meanwhile, to fulfill the distributed prediction on leak-points, we see one three-level data-processing unit as a logic machine with multiple operators. Using the assignment scheduling, the general scheduling problem is split into the common subproblem of every operator and the system overhead is reduced. With the processed data we can obtain the relative location or diffusion radius of leak-points, as well as the area of leak-points. Simulation results show that the TLBP performs better than related algorithms in different metrics. Besides, the adaptability of TLBP is verified in leak-points prediction of petrochemical equipment from the processed data.

To meet the requirements on data collection from wireless sensor network for healthcare application especially in hospital, the performance of wireless communication is an important issue. In particular, simultaneous transmissions from numerous sensors will cause serious collision which leads to transmission packets loss and delay. In this paper, a management scheme, multi-criteria decision making method using TOPSIS, for IEEE 802.15.4 is proposed for wireless sensor network. The performance of the network is determined by beacon order, superframe order, contention window, number of backoffs and backoff exponent. By analyzing slotted CSMA-CA mechanism, which is in the beacon-enabled mode, through OPNET, the proposed scheme can estimate the best combination of the parameters. The results show that the proposed scheme achieves the best combination of low end-to-end delay, high throughput and high successful probability.

This paper proposes a mobile based big data design patent image retrieval system via a deep learning approach. The images are represented via sparse vectors by a dictionary. The joint representation and dictionary design problem is formulated as a mixed L2 and Lp optimization problem. An iterative algorithm is employed for finding a locally optimal solution. Experimental results show that the retrieval accuracy is high.

Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

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.

This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram. Then, these discrete cosine transform coefficients are scaled down according to their frequency indices. These scaled discrete cosine transform coefficients of each electrocardiogram are employed as features for performing the discrimination. Second, the high order statistic moments of each feature of the training electrocardiograms corresponding to the same type of tachycardias are evaluated. These high order statistic moments of each feature corresponding to same type of tachycardias form a vector. Third, the high dimensional linear discriminant function is employed to minimize the intraclass separation and maximize the interclass separation of these statistic moment vectors. In particular, new vectors are formed by projecting these statistic moment vectors to the high dimensional linear discriminant function. Fourth, the principal component analysis is employed to reduce the dimension of the projected vectors. Finally, a bank of perceptrons with multi-piece domain activation functions having multi-level functional values is employed for performing the discrimination. By using this bank of perceptrons, the condition for general two class pattern recognition problems achieving the error free pattern recognition performance is guaranteed. Computer numerical simulation results show that our proposed method is robust and effective.

This paper proposes a novel method of killing birds and applying vaccines for suppressing the spread of avian influenza via a nonlinear switching control approach. The switching strategy is based on the population of the susceptible birds and the population of the susceptible humans. There are four switching cases. For the first three switching cases, the elimination control force and the quarantine control force are equal to either zero or one. For the last switching case, they are equal to one minus a scalar divided by the population of the susceptible birds and one minus another scalar divided by the population of the susceptible humans, respectively. The system state vectors of the avian influenza model are guaranteed to reach the desirable equilibrium point. Also, the positivity requirements on the system states as well as the constraints on both the lower bounds and the upper bounds of both the elimination control force and the quarantine control force are guaranteed to be satisfied. Computer numerical simulation results show that the proposed control strategy is very effective and efficient.

Wrist pulse signal is believed to contain critical information of the patients' health condition. This project aims to analyze the time series wrist pulse signals in order to distinguish patients suffering from various symptoms with healthy people. In this paper, the four inflammation symptoms tackled in this project are Appendicitis (A), Acute Appendicitis (AA), Pancreatitis (P) and Duodenal Bulb Ulcer (DBU). Moreover, studying the characteristic of blood flow in arteries and cardiac cycle is crucial for the sake of selecting features from the wrist pulse signals. The defined Doppler parameters in the wrist pulse signal are defined as the disease sensitive features. Furthermore, the features extracted are considered as the parameters for training the Support Vector Machine (SVM) classifier. The classification accuracy can reach over 88% in distinguishing patients with healthy persons from Acute Appendicitis and up to 98% from Pancreatitis. These results indicate the methodology proposed in this project can provide an advanced idea for enhancing the research of wrist pulse signal analysis.

Log-distance path loss model have been using as a simple positioning method because of its simplicity which is mainly depends on Received Signal Strength (RSS) along with path loss exponent and random Gaussian noise variable with zero-mean. This model can be extensively used in urban and remote area because it is related to energy representation. On the other hand, fingerprint positioning is also an alternative solution in positioning due to it reliable performance. It is noticed that antenna gain and background thermal noise should be included into the model such that the accuracy could be improved. In this investigation, a sub-urban route was selected as testing area and carried the Particle Swarm Optimization (PSO) for an optimal coordinate. Experimental results show that the new scheme was implemented successfully in RSS positioning resulting in about averaged 100 meters and 84 meters in daytime and evening time respectively in same experiment scene. This new scheme provides a more reliable way in calculating a sensors position.

With the rapidly development of smart grid, communication network will play more and more fundamental role in many smart grid applications and services. The interaction between power system and communication network will appear almost everywhere in the new services of smart grid, the investigation of mixture system combined power system and communication network reveals the mutual influence of each other, and will give accuracy and quantitative data for the planning of the future smart grid. This paper presents a novel cosimulation platform combined power system and communication network to meet the decision-making need in smart grid environment. The platform connects power system simulator and communication system simulator together via a middleware with interfaces, a synchronization method is proposed for the correct time and sequence of data exchange, a time step adjustment algorithm is proposed as well to balance the requirement of accuracy and efficiency.

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.

Sustainability, energy conservation and demand response have become an inevitable concern around the world. In the light of electricity companies' demand about what electric appliances that the end-users are switching on, appliance signature is suggested to increase the performance of demand response. The main idea behind appliance signature is that it utilizes the characteristic that same types of electric appliances should have similar features like current, power and harmonic distortion. Utility can get not only the energy profile of households with current metering system but also acquires evidence in energy management according to the energy usage pattern. In this paper, K-means clustering is used for the classification of eight types of common household electric appliances which is an appliance signature identification solution for appliance signature. A digital Butterworth filter has been firstly introduced to remove noisy data before data analyzing by K-means clustering. The performance is evaluated by 10-fold cross validation. Three indexes, CH index, DB index and SH index have been calculated to determine the optimal number of clusters used in K-means clustering. These indexes achieve accuracy of 55.5%, 42.1% and 67.7% respectively.

A Multipurpose Patient Monitoring system embedded with a new ZigBee mobile application profile is proposed and developed. This system enables the mobility of ZigBee devices. The usage, the architecture and the mobility framework are discussed in details. It saves people life by providing panic button and location tracking service. A case study based on the Pamela Youde Nethersole Eastern Hospital is also presented and findings are given. This investigation reveals that the developed mobile application profile offers promising value-added services for many potential ZigBee applications.

A novel ZigBee Reminder System is proposed as a remedy to the handicaps of current broadcasting systems in airports. The usage, the architecture and the implantation are discussed in details. Network design for the implantation is formulated in this paper. A case study based on the Hong Kong International Airport is presented and findings are given.

By employing Bayesian neural network, a new QoS prediction algorithm for high speed packet access network has been developed. Network performances are evaluated based on a well developed call admission control scheme, namely the tri-threshold bandwidth reservation scheme. The algorithm helps to improve the resource management, thus saving the cost and bandwidth.

A total QoS solution including dynamic call admission control scheme and bandwidth allocation algorithm is proposed for IEEE 802.16e mobile WiMAX. In this paper, the relationship between the channel utilization, the dropping and blocking probability versus traffic loads are investigated. The proposed WiMAX scheme supports voice, data and multimedia services with differentiated QoS.

A frequency synthesizer using the direct memory access (DMA) technique is designed for frequency hopping spread spectrum (FH-SS) communication systems. The frequency synthesizer provides fast channel acquisition by using simple memory table look-up technique. The technique simplify the frequency control process and reduces the channel switching time. As a result, the channel efficiency can be improved.

The design of oscillators using linear 2-port networks are well documented and are usually based on circuits using one active device. Recently there has been renewed interest in the cascode circuit because of its injection locking properties. This gives it tremendous possibilities, in providing very stable high frequency oscillators by locking it to a more stable lower frequency source. The design of oscillators using the cascode circuit has never been completely approached analytically before so the purpose of this paper is to present a method that will enable the analytic approach to oscillator design using the cascode circuit. This method is verified with a design of a low cost, low phase noise power oscillator at 850 MHz.>

An analytic approach to the design of high efficiency tuned power oscillator is presented. By employing large-signal S-parameters, theoretical conditions for optimum operation of the oscillator have been formulated and discussed. Loss due to non-zero switching time, saturation resistance etc. of the transistor employed is accounted for. Various oscillators at 900 MHz were designed using the derived theory. Experimental results showed that the measured data are in good agreement with the predicted data.>