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The assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.

The internet-of-things (IoT) refers to the growing field of interconnected pervasive computing devices and the networking that supports smart, embedded applications. The IoT has multiple human-computer interaction challenges due to its many formats and interlinked components, and central to these is the need to provide sensory information and situational context pertaining to users in a more human-friendly, easily understandable format. This work addresses this by applying mixed reality toward expressing the underlying behaviors and states internal to IoT devices and IoT-enabled objects. It extends the authors’ previous research on IoT Avatars (mixed reality character representations of physical IoT devices), presenting a new head-mounted display framework and interconnection architecture. This contributes i) an exploration of mixed reality for smart spaces, ii) an approach toward expressive avatar behaviors using fuzzy inference, and iii) an early functional prototype of a hybrid physical and mixed reality IoT-enabled object. This approach is a step toward new information presentation, interaction, and engagement capabilities for smart devices and environments.

Ubiquitous computing has potentials to harness the flexibility of distributed computing systems including cloud, edge, and internet of things devices. Mobile edge computing (MEC) benefits time-critical applications by providing low latency connections. However, most of the resource-constrained edge devices are not computationally feasible to host deep learning (DL) solutions. Further, these edge devices if deployed under denser deployments may result in topological dependencies which if not taken into consideration may adversely affect the MEC performance. To bring more intelligence to the edge under topological dependencies, compared to optimization heuristics, this work proposes a novel collaborative distributed DL approach. The proposed approach exploits topological dependencies of the edge using a resource-optimized graph neural network (GNN) version with an accelerated inference. By exploiting edge collaborative learning using stochastic gradient (SGD), the proposed approach called CGNN-edge ensures fast convergence and high accuracy. Collaborative learning of the deployed CGNN-edge incurs extra communication overhead and latency. To cope, this work proposes compressed collaborative learning based on momentum correction called cCGNN-edge with better scalability while preserving accuracy. Performance evaluation under IEEE 802.11ax-high-density wireless local area networks deployment demonstrates that both the schemes outperform cloud-based GNN inference in response time, satisfaction of latency requirements, and communication overhead.

The ever-growing Artificial Intelligence (AI) applications have greatly reshaped our world in many areas, e.g., smart home, computer vision, natural language processing, etc. Behind these applications are usually machine learning (ML) models with extremely large size, which require huge datasets for accurate training to mine the value contained in the big data. Large machine learning models, however, can consume tremendous computing resources to achieve decent performance and thus it is difficult to train them in resource-constrained Internet of Things (IoT) environments, which would prevent further development and application of AI techniques in the future. To deal with such challenges, there are many efforts on accelerating the training process for large machine learning models in IoTs. In this paper, we provide a comprehensive review on the recent advances toward reducing the computing cost during the training stage while maintaining comparable model accuracy. Specifically, the optimization algorithms that aim to improve the convergence rate are emphasized over various distributed learning architectures that exploit ubiquitous computing resources. Then, the paper elaborates the computation hardware acceleration and communication optimization for collaborative training among multiple learning entities. Finally, the remaining challenges, future opportunities and possible directions are discussed.

City services are frequently supported by software services that are managed by service-oriented architectures. However, a large number of software services is likely to cause performance issues when discovering software services. The distributed organisation of services information improves discovery performance. Existing research proposes to organise services information according to service location, domains, or city context, keeping that organisation constant under an assumption that cities do not change. However, cities are dynamic environments where entities interact, causing events that in turn, effect changes in the city. The organisation of services information must evolve or it will become outdated, negatively impacting discovery performance. We propose a self-adaptive service model for smart cities to support service discovery. This model adapts the organisation of services information according to city events. We introduce a self-adaptive architecture that keeps track of the discovery metrics and moves information about services between registries to maintain the discovery efficiency. We evaluate the proposed model in simulated environments and a real IoT testbed. Results show that our model outperforms competitors when reactive adaptation is triggered by a specific event. However, proactive adaptation needs further research. Results from the real IoT testbed present the costs of the proposed model.

COVID-19 has caused many disruptions in conducting smart health research. Both in-lab sessions and in-home deployments had to be delayed or canceled because in-person meetings were no longer allowed. Our research project on “in-home monitoring with personalized recommendations to reduce the stress of caregivers of Alzheimer’s patients”was affected. To enable continued research without any person-to-person contact, we created an out-of-the-box deployment solution. The solution is multifaceted and deals with everything from technical adjustments, deployment documentation, EMA additions, additional monitoring software, use of videos, Zoom and TeamViewer, budget changes, new logistics, and changes to IRBs. This article briefly describes the purpose and design of the original system and then articulates the necessitated changes. We also provide lessons learned and an initial evaluation of the effectiveness of the solutions after the changes. The evaluation surveys the opinions of seven people that assembled, initialized, and deployed our system in home environments. We believe that the various solutions we developed can be applied to other similar projects, and will be helpful to new projects even when personal contact returns. IEEE

Internet-of-Things (IoT) integrates ubiquitous computing to extend the Internet connectivity to the various application environments such as e-Health, smart cities, cyber–physical systems, etc. It connects the real-time objects to experience high-speed data transfer in 5G network deployment. It may periodically collect and process the data to control the access mechanism that utilizes a dedicated network protocol to fetches the information from different network domains. However, most of the access mechanisms cannot be applicable to the industrial application as it is based on a centralized architecture. This architecture has a complexity of computation overhead to deteriorate the performance of device-to-device (D2D) communication. In the industrial sectors, security and privacy preservation are majorly concerned to perform expensive operations and to prevent malicious cloud servers. Moreover, the real-time objects should be coordinated to optimize communication efficiencies. Thus, this paper presents a robust blockchain-based lightweight distributed architecture (RB-LDA) for 5G-enabled networks. The proposed RB-LDA authenticates the device access that advertises genuine data possession to restrict the key exposure. Moreover, the qualitative analysis proves the identity management continuously monitors the activities of IoT devices to perform a better auditing process. Above all, the experimental analysis shows that the proposed RB-LDA can achieve better data privacy to preserve sensitive information against the trusted third parties.

The Internet-of-Things (IoT) (or Internet-of-Everything ) is a new name for a concept that has evolved over several decades. Mark Weiser of Xerox PARC coined the term ubiquitous computing to describe a range of smart and interconnected devices as well as the applications to which these devices could be put. Moore's Law has allowed us to improve those early devices in several important ways: modern IoT devices provide more computing and storage; they operate at lower energy levels; and they are cheaper. Advances in wireless communications have also allowed these devices to communicate much more effectively with each other and with traditional communication systems. The result of these advances is an explosion of devices, systems, and applications.

Smart spaces such as smart homes deliver digital services to optimize space use and enhance user experience. They are composed of an Internet of Things (IoT), people, and physical content. They differ from traditional computer systems in that their cyber-physical nature ties intimately with the users and the built environment. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash, risking potentially physical harm to the space and its users. Ensuring smart space safety is therefore critically important to successfully deliver intimate and convenient services surrounding our daily lives. By modeling smart space as a highly dynamic database, we present IoT Transactions, an analogy to database transactions, as an abstraction for programming and executing the services as the handling of the devices in smart space. Unlike traditional database management systems that take a “clear room approach,” smart spaces take a “dirty room approach” where imperfection and unattainability of full control and guarantees are the new normal. We identify Atomicity, Isolation, Integrity and Durability (AI 2 D) as the set of properties necessary to define the safe runtime behavior for IoT transactions for maintaining “permissible device settings” of execution and to avoid or detect and resolve “impermissible settings.” Furthermore, we introduce a lock protocol, utilizing variations of lock concepts, that enforces AI 2 D safety properties during transaction processing. We show a brief proof of the protocol correctness and a detailed analytical model to evaluate its performance.

PurposeIn cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.Design/methodology/approachIn this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.FindingsIn this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.Originality/valueThe implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

Wireless sensor network (WSN) provides a significant contribution in emerging fields such as ubiquitous computing, ambient intelligence, Internet of Things, etc. WSN is a highly distributed computing network of tiny sensors which are self-conscious and are deployed in different geographical area randomly for multiple applications. These tiny sensor motes are insecure about resources such as bandwidth, battery, and communication range, etc. To increase the life span of WSN, effective utilization of available network resources is very important. Consequently, WSN’s efficient and productive resource management is a challenging task involving the identification of resources, scheduling of resources, allocation of resources. In this paper, a type-2 fuzzy logic system (T2FLS) based resource scheduling technique using a software agent approach to resolve the scheduling issues by considering energy, number of neighboring nodes, and available bandwidth of WSN is proposed. Proposed software agent based resource scheduling scheme comprises of node agency (NA) and sink agency (SA). Both agencies comprised of a mobile agent and static agent. SA periodically gathers information related to a node such as available energy, bandwidth, and degree of connectivity collected by NA. Based on these parameters SA makes the suitable resource scheduling decision. Simulation results show that the performance of the proposed type-2 fuzzy with agent based approach is better as compared with the trusted real time scheduling model for wireless sensor networks (TRTSM) scheme in terms of scheduling accuracy, scheduling delay, and bandwidth utilized.

The ongoing Coronavirus Disease 2019 (COVID-19) pandemic has caused 3.8 million deaths since December 2019. At the current vaccination pace, this global pandemic could persist for several years. Throughout the world, contact tracing (CT) apps were developed, which play a significant role in mitigating the spread of COVID-19. This work examines the current state of security and privacy landscape of mobile CT apps. Our work is the first attempt, to our knowledge, which provides a comprehensive analysis of 70 CT apps used worldwide as of the year Q1 2021. Among other findings, we observed that 80%of them may have handled sensitive data without adequate protection, 70%of them used weak cryptographic algorithms, and 35%of them embedded data trackers. We also observed key developments in app privacy protection and security assurance initiatives. Our findings provide useful insights into the design and deployment of more secure and privacy-preserving CT apps moving forward. IEEE

The revolution of online shopping in recent years demands corresponding evolution in delivery services in urban areas. To cater to this trend, delivery by the crowd has become an alternative to the traditional delivery services thanks to the advances in ubiquitous computing. Notably, some studies use public transportation for crowdsourcing delivery, given its low-cost delivery network with millions of passengers as potential couriers. However, multiple practical impact factors are not considered in existing public-transport-based crowdsourcing delivery studies due to a lack of data and limited ubiquitous computing infrastructures in the past. In this work, we design a crowdsourcing delivery system based on public transport, considering the practical factors of time constraints, multi-hop delivery, and profits. To incorporate the impact factors, we build a reinforcement learning model to learn the optimal order dispatching strategies from massive passenger data and package data. The order dispatching problem is formulated as a sequential decision making problem for the packages routing, i.e., select the next station for the package. A delivery time estimation module is designed to accelerate the training process and provide statistical delivery time guarantee. Three months of real-world public transportation data and one month of package delivery data from an on-demand delivery platform in Shenzhen are used in the evaluation. Compared with existing crowdsourcing delivery algorithms and widely used baselines, we achieve a 40% increase in profit rates and a 29% increase in delivery rates. Comparison with other reinforcement learning algorithms shows that we can improve the profit rate and the delivery rate by 9% and 8% by using time estimation in action filtering. We share the data used in the project to the community for other researchers to validate our results and conduct further research.1 [1].

Pervasive collaborative computing within the Internet of Things (IoT) has progressed rapidly over the last decade. Nevertheless, emerging architectural models and their applications still suffer from limited capacity in areas like power, efficient computing, memory, connectivity, latency and bandwidth. Technological development is still in progress in the fields of hardware, software and wireless communications. Their communication is usually done via the Internet and wireless via base stations. However, these models are sometimes subject to connectivity failures and limited coverage. The models that incorporate devices with peer-to-peer (P2P) communication technologies are of great importance, especially in harsh environments. Nevertheless, their power-limited devices are randomly distributed on the periphery where their availability can be limited and arbitrary. Despite these limitations, their capabilities and efficiency are constantly increasing. Accelerating development in these areas can be achieved by improving architectures and technologies of pervasive collaborative computing, which refers to the collaboration of mobile and embedded computing devices. To enhance mobile collaborative computing, especially in the models acting at the network’s periphery, we are interested in modernizing and strengthening connectivity using wireless technologies and P2P communication. Therefore, the main goal of this paper is to enhance and maintain connectivity and improve the performance of these pervasive systems while performing the required and expected services in a challenging environment. This is especially important in catastrophic situations and harsh environments, where connectivity is used to facilitate and enhance rescue operations. Thus, we have established a resilient mobile collaborative architectural model comprising a peripheral autonomous network of pervasive devices that considers the constraints of these resources. By maintaining the connectivity of its devices, this model can operate independently of wireless base stations by taking advantage of emerging P2P connection technologies such as Wi-Fi Direct and those enabled by LoPy4 from Pycom such as LoRa, BLE, Sigfox, Wi-Fi, Radio Wi-Fi and Bluetooth. Likewise, we have designed four algorithms to construct a group of devices, calculate their scores, select a group manager, and exchange inter- and intra-group messages. The experimental study we conducted shows that this model continues to perform efficiently, even in circumstances like the breakdown of wireless connectivity due to an extreme event or congestion from connecting a huge number of devices.

This paper presents Delfos, a model for multi-temporal analysis based on historical contexts. The article describes the model architecture, implementation, and tests with two real datasets to evaluate the functionality and its potential to real applications. The first scenario used historical data from an electric power transmission substation, and the second with data from the performance analysis of futsal athletes. The results obtained through inference rules perform with 100% accuracy of the Profiles creation and that these are a better representation of the present, than only the current context data of the application. The results on the prediction of contexts were relevant in the case of the existence of behavior patterns in the contexts of the analyzed entities. For the application related to the electric power transmission substation, the assertiveness was between 86% and 92% in a 12-hour interval. In the application of performance analysis of futsal athletes, we made predictions about the athletes movement, with results varying between 72% and 92% for the time of up to 20 seconds. The results were encouraging and show potential for implementing Delfos in real-life situations.

The aerial platform based wide-area Internet of Things (WAIoT) can effectively settle wireless information interactions in emergency scenarios. However, it confronts lots of unprecedented service requirements and technical challenges. To solve these issues, this article establishes an unmanned aerial vehicle (UAV)-based WAIoT deployment framework by considering three typical scenarios including power outage, massive user connections, and information island relief. Serving as an aerial base station or mobile relay, the UAV provides simultaneous information and energy transfer for IoT users with the consideration of user distribution range, massive user connections, and quality of service (QoS) requirements. For each scenario, the joint UAV deployment and resource allocation scheme is designed to improve the system performance, and some numerical results are presented for validation. This article aims to provide continuous services for users in the emergency scenario through optimizing the aerial platform deployment.

The Ubiquitous IoT (UIoT) is a three-dimensional network that spans space-air-ground. It contains multiple network elements such as satellites, terrestrial cellular networks, terrestrial gateways, and data centers. Due to the need for openness and sharing among different operators, it is necessary to research wireless spectrum sharing. The new technologies of blockchain and the 6G hybrid cloud pose a potential solution. We propose to migrate the spectrum sharing system of 6G to the hybrid blockchain. Subdividing the granularity of the spectrum resources of UIoT devices in multiple dimensions can enable reliable and secure random access to a large amount of UIoT terminal data. Cloud computing and smart contract technology can effectively ensure the interconnection of massive data and the authenticity of externally perceived data.

The space-air-ground integrated network (SAGIN) is regarded as a promising approach for providing ubiquitous Internet access anytime and anywhere. With virtualization technologies and multi-access edge computing, data transmission and data processing in SAGINs are abstracted as services. Space-air-ground service computing flexibly integrates and manages these services in SAGINs based on service-oriented architecture. However, it is significant but very challenging to provide Internet of Things service with high QoS in space-air-ground service computing due to the distributed service management, and the mobility of both infrastructures and users. Therefore, in this article, we investigate service coordination to guarantee the QoS in space-air-ground service computing. In particular, we first introduce three service coordination scenarios: fine-grained, medium-grained, and coarse-grained service coordination. Then we design a service coordination framework that contains three tiers: edge node tier, service function routing tier, and global control tier. After that, we propose a service coordination approach to reduce the service delay at low cost, which considers the selection with foresight and updates based on threshold. Experimental results show the advantages of our service coordination approach in terms of service delay and cost.

Home-based sensor platforms offer novel methods to evaluate individuals with cognitive impairment. Having high-frequency information on changes in health status, activity, and functioning in real-life settings could improve our ability to assess and provide care to these individuals. The insidious onset, slow progression, and day-to-day variability seen in neurodegenerative disorders make them ideal candidates for continuous forms of assessment. There have been significant advances in technologies related to the monitoring of health and well-being through pervasive computing, wearable technologies, wireless and mobile technologies, and high dimensional data analytics. High-frequency assessment with digital technologies has been demonstrated to provide earlier and more sensitive detection of changes in health, cognition, and function, with the potential to improve person-specific diagnosis, prevention, and treatment enabled by health care professionals.

Space-aerial-assisted computation offloading has been recognized as a promising technique to provide ubiquitous computing services for remote Internet of Things (IoT) applications, such as forest fire monitoring and disaster rescue. This article considers a space-aerial-assisted mixed cloud-edge computing framework, where the flying unmanned aerial vehicles (UAVs) provide IoT devices with low-delay edge computing service and satellites provide ubiquitous access to cloud computing. We aim to minimize the maximum computation delay among IoT devices with the joint scheduling for association control, computation task allocation, transmission power and bandwidth allocation, UAV computation resource, and deployment position optimization. Through exploiting block coordinate descent and successive convex approximation, we develop an alternating optimization algorithm with guaranteed convergence, to solve the formulated problem. Extensive simulation results are provided to demonstrate the remarkable delay reduction of the proposed scheme than existing benchmark methods.

While the Internet of Things (IoT), coupled with integrated terrestrial-aerial-space networks, has revolutionized the domain of ubiquitous remote sensing for natural resource management, several research challenges emerge due to the explosion of the collected IoT data. For instance, the edge nodes in these hybrid, next-generation networks are anticipated to carry out edge computing on the collected data to provide localized computing to enable early warning systems, including forest fire occurrence and spread, earth- quake wave detection, tsunami forecasting, and so forth. While edge computing can significantly reduce the high communication time required in the traditional cloud-based remote sensing analytics, it is important to develop a lightweight training framework to obtain smart remote sensing analytics at the edge devices in the integrated network while preserving the privacy of the collected data. In this article, we propose an asynchronously updating federated learning model for the edge nodes to build local artificial intelligence models for smart remote sensing with a forest fire detection use case without the need for explicit data exchange with the cloud. This jointly preserves data privacy and also alleviates the network overhead. Extensive experimental results demonstrate the viability of our proposal in terms of significantly high remote sensing accuracy, low convergence time, and low bandwidth overhead compared to existing methods.

Purpose Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model. Design/methodology/approach In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model. Findings The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism. Research limitations/implications In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies. Practical implications The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks. Social implications The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission. Originality/value In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

Traditional design development processes have come a long way from the use of drawing boards. The accelerated use of ICT-based digital systems means that the industry has steadily moved towards a digitized future. A future where increasingly unstructured information is created, shared, manipulated, stored, and archived in various digital media support the four pillars of visualization, integration, communication, and intelligence on which typical construction projects currently stand. The ICT field's growth, combined with the unprecedented advances in communication and network media usage, has resulted in hyper-interconnectivity globally. This hyper-connectivity through developments such as the Internet of Things (IoT) creates global opportunities for collaboration, which was not previously possible. It links 'human' and 'social' networks with 'technical' systems. With the vast volumes of digitally connected systems and the systems' data, new opportunities for learning have surfaced in the construction domain. The special issue targeted the state-of-the-art developments of next generation ICTs in the global arena. As with any new developments, new opportunities emerge and new challenges surface. The cautious late majority and the laggards, adopt a skeptical approach, that which is reserved and strewn in doubt. The innovators and the early majority on the other hand pave the way to technology adoption and help drive change in the industry. This special issue recognises that the emergence of next-generation ICTs, combined with developments in ubiquitous computing, presentopportunities that challenge the current status quo of the construction sector. This special issue recognizes that for an industry to remain truly competitive, due consideration need to be given to the ongoing and emerging technological developments, and a deep understanding of which would lead to novel responsive approaches for their significant uptake. The collection of papers in this special issue gives a comprehensive overview of research and developments in the field of next-generation ICTs. It bridges the gap between the two domains of construction and computer science. Of the ten papers in this collection, two (by Akamu et al, and Karmarkar and Delhi) were invited papers and include a world view on thewider applicability of next-generation ICTs in the construction domain, the opportunities they present and the challenges that emerge. The remaining papers, amongst other aspects, cover theinnovative application of next-gen ICTs in specific industry sectors(e.g., in the water industry by Alani et al, 2021)or in meeting specific project goals such as to manage energy consumption (Watfa et al), improve information retrieval (Wang et al), integrate AR and BIM for specific building submission processes (Schranz et al), identify challenges to collaborative working within globally dispersed virtual project teams (Anderson and Ramalingam), and ontology for robot navigation and data fusion (Karimi et al).

Purpose Pervasive computing environment allows the users to access the services anywhere and anytime. Due to the dynamicity, mobility, security, heterogeneity, and openness have become a major challenging task in the Pervasive computing environment. To solve the security issues and to increase the communication reliability, an authentication-based access control approach is developed in this research to ensure the level of security in the Pervasive computing environment. Design/methodology/approach This paper aims to propose authentication-based access control approach performs the authentication mechanism using the hashing, encryption, and decryption function. The proposed approach effectively achieves the conditional traceability of user credentials to enhance security. Moreover, the performance of the proposed authentication-based access control approach is estimated using the experimental analysis, and performance improvement is proved using the evaluation metrics. It inherent the tradeoff between authentication and access control in the Pervasive computing environment. Here, the service provider requires authorization and authentication for the provision of service, whereas the end-users require unlinkability and untraceability for data transactions. Findings The proposed authentication-based access control obtained 0.76, 22.836 GB, and 3.35 sec for detection rate, memory, and time by considering password attack, and 22.772GB and 4.51 sec for memory and time by considering without attack scenario. Originality/value The communication between the user and the service provider is progressed using the user public key in such a way that the private key of the user can be generated through the encryption function.

In the Internet of Things (IoT), security and privacy issues of physical objects are crucial to the related applications. In order to clarify the complicated security and privacy issues, the life cycle of a physical object is divided into three stages of pre-working, in-working, and post-working. On this basis, a physical object-based security architecture for the IoT is put forward. According to the security architecture, security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail. Considering the development of IoT technologies, potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized. At the same time, possible directions for dealing with these challenges are also pointed out.

Prediction of human physical activities has become a necessity for some applications that come with the development of wearable and portable hardware such as smartwatches and smartphones. The task of Human Activity Recognition (HAR) is to recognize human physical activities, e.g., walking, sitting, and running, using the data collected from sensors, e.g., accelerometers and gyroscope. HAR is commonly applied on smart systems, such as smartphones, to serve the understanding of a user’s behaviors and provide assistance to the user because of the rapid development of ubiquitous computing technology in recent years. Thus, predicting activities, such as standing, walking, sitting, during the day have become a popular topic in machine and deep learning. The aim of this study is to predict the user’s activities based on context information gathered by sensors such as gyroscopes and accelerometers. The conducted classification algorithms extract features from training data and learn a classification model based on the features to predict activity. In this paper, various classical machine and deep learning techniques have been studied and compared for human activity recognition. A comparative analysis is performed between techniques in order to select the classifier with the best recognition performance. Experimental results show that established Deep Neural Network (DNN) model achieved an accuracy of up to 96.81% and mean absolute error of up to 0.03 on publicly available UCI-HAR dataset. This method has given the best performance between conducted classification methods in this study to predict human activity.

From in-body implantables to geotextiles and large-area spacecraft blankets, electronic fabric is now poised to operate across geometric scales that span many orders of magnitude, and thus across operational contexts with divergent material resiliency requirements, reaching far beyond the wearable device regime that is typically considered. This article reviews the key technical trends and lingering hurdles that are relevant to using functional fibers and e-textiles for operating at disparate scales—from microns to kilometers. We focus in particular on leveraging the unique material properties of a textile and the miniaturization of electronic devices in concert with the revolution in mass-manufacturing and digital fabrication technologies used to customize the device at the level of polymer, fiber, fabric, three-dimensional form, and system. We also offer a personal perspective on interdisciplinary collaboration between engineers, scientists, designers, and manufacturers for tackling some of the challenges in scaling and translation of electronic textiles.

This article focuses on thwarting reverse-engineering attacks and intellectual property (IP) theft by leveraging charge-recycling adiabatic circuits. The adiabatic circuit operation has recently received attention for the Internet-of-Things (IoT) applications due to high energy efficiency and enhanced security characteristics. Such applications typically consist of resource-constrained designs and are often deployed in the field, making them particularly vulnerable to malicious attacks. PhaseCamouflage is a circuit obfuscation technique that leverages the inherent phase differences (PDs) in power supply voltage of adiabatic logic gates and exhibits strong resistance against structural/removal attacks. The proposed method relies on inserting camouflaged PDs in the power supply voltage of subsequent logic gates while still producing a functional netlist. PhaseCamouflage is a unique logic obfuscation technique with low overhead, particularly applicable to pervasive computing applications where both efficiency and security are of primary concern.

We develop an innovative low-cost approach for characterizing fresh produce by repurposing inexpensive commercial-off-the-shelf green light sensors for quality estimation. Our approach has been designed to support all stages of the supply chain while being inexpensive and easy to deploy. We validate our approach through extensive empirical benchmarks, showing that it can correctly distinguish organic produce from nonorganic items, establish unique fingerprints for different produce, and estimate the quality or ripeness of produce. Specifically, we demonstrate that changes in the reflected green light values correlate with the so-called transpiration coefficients of the produce. We also discuss the practicability of our approach and present application use cases that can benefit from our solution.

Healthcare information systems (HIS) have become an important area of research due to ever-increasing healthcare costs to the national economy. Many recent technological developments such as mobile technology and cloud computing have profoundly affected the current state of HIS and further facilitated the developments of ubiquitous computing and ubiquitous learning systems (ULS). The authors propose a multi-agent system for ubiquitous learning (MASUL) to facilitate various learning tasks. They use JADE (Java agent development framework) for developing the multi-agent system. MASUL provides a series of functionalities that can be used by the patients and clinicians. The system simplifies the mechanisms to access learning information via mobile devices, and it also facilitates the learning-teaching process centered on the physical surroundings of the user.

Widespread research is being conducted on computational materials. The challenge for the realization of computational materials is how to weave computers into everyday objects consisting of various shapes and form factors. One idea to achieve this vision is to build physical computational particles that can cooperatively communicate among others and, thereby, change the whole shape like clay. Wireless communication and power transfer are keys to making such computing particles happen. Thus, we introduce an approach to embody these particles using multiple tiny IC chips, which wirelessly cooperate with each other. This article shows the current state-of-the-art wireless communication and power transfer technologies integrated into IC chips to achieve fine-grained shape deformation of computers. Moreover, this article also presents recent trends and future directions in shape-changing human–computer interfaces research.

Over the past two decades, many toolkits for prototyping interactive and ubiquitous electronic devices have been developed. Although their technical specifications are often easy to look up, they vary greatly in terms of design, features and target audience, resulting in very real strengths and weaknesses depending on the intended application. These less technical characteristics are often reported inconsistently, if at all. In this paper we provide a comprehensive survey of interactive and ubiquitous device prototyping toolkits, systematically analysing their characteristics within the framework of a new taxonomy that we present. In addition to the specific characteristics we cover, we introduce a way to evaluate toolkits more holistically, covering user needs such as 'ease of construction' and 'ease of moving from prototype to product' rather than features. We also present results from an online survey which offers new insights on how the surveyed users prioritize these characteristics during prototyping, and what techniques they use to move beyond prototyping. We hope our analysis will be valuable for others in the community who need to build and potentially scale out prototypes as part of their research. We end by identifying gaps that have not yet been addressed by existing offerings and discuss opportunities for future research into electronics prototyping toolkits.

Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users’ efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.

This article assesses the experience with i-Log at the European Big Data Hackathon 2019, a satellite event of the New Techniques and Technologies for Statistics (NTTS) conference, organised by Eurostat. i-Log is a system that enables capturing personal big data from smartphones’ internal sensors to be used for time use measurement. It allows the collection of heterogeneous types of data, enabling new possibilities for sociological urban field studies. Sensor data such as those related to the location or the movements of the user can be used to investigate and gain insights into the time diaries’ answers and assess their overall quality. The key idea is that the users’ answers are used to train machine-learning algorithms, allowing the system to learn from the user’s habits and to generate new time diaries’ answers. In turn, these new labels can be used to assess the quality of existing ones, or to fill the gaps when the user does not provide an answer. The aim of this paper is to introduce the pilot study, the i-Log system and the methodological evidence that emerged during the survey.

Radio frequency identification (RFID) is becoming one of the best optimistic technology in the area of ubiquitous computing that plays the most crucial role in object identification as a ubiquitous infrastructure. Indeed, an important role of RFID technology is to provide wireless communication between the RFID tags and readers without line-of-sight (LoS) readability. The tags store the sensitive information of an object or thing in RFID systems. The reader reads and writes data, while the backend server stores the information sent by the readers through radio frequency signals. Moreover, the reader collects data from the distinct tags and broadcasts them, and then sends it to the server for further processing. However, security and privacy is a key concern in RFID systems, where an adversary may perform several types of security attacks. To overcome such issues, we have proposed a novel vector-space-based lightweight RFID authentication protocol which integrates the concept of vector space, basis, and linear mapping in this paper that achieves secure authentication. The security analysis has been done that demonstrates the proposed protocol provides resistance to various known attacks. The performance analysis shows the less computation cost associated with the tags, less storage, and communication overhead as well. The proposed protocol provides higher security with lesser cost and shows suitability for low-cost RFID systems.

Untimely interaction in critical moments, content that does not capture a user’s attention, and missed opportunities for delivering relevant information are just some of the issues caused by ubiquitous computing devices’ inability to understand their users cognitive engagement. However, current means of inferring cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user, thus of limited applicability in ubiquitous computing. In this paper we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio radar that measures sub-millimeter movements related to a person’s breathing and heart beats, which, in turn allows us to infer the person’s cognitive load. We built and tested the system with 23 volunteers engaged in different tasks. Initial results show that while Wi-Mind manages to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging.

In Internet of Things (IoT) based systems, the multi-level user requirements are satisfied by the integration of communication technology with distributed homogeneous networks termed as the ubiquitous computing systems (UCS). The PCS demands openness in heterogeneity support, management levels and communication for distributed users. However, providing these features is still a major challenge. In wearable IoT (WIoT) based medical sensors based applications, the end users reliability of communication is enhanced using a scalable distributed computational framework introduced in this paper. The demand and sharing parameters forms the basis of analysis of resource allocation by means of recurrent learning in this framework. The rate of communication may be improved while reducing the time delay for the end users of WIoT based medical sensors with the help of UCS and estimated resource requirements. Other than data transfer, sharing and resource allocation, end-user mobility management may also be performed on the WIoT medical sensors using the proposed framework. Certain metrics are used for proving the consistency of the framework that are assessed with the help of experimental analysis and performance estimation. Parameters inclusive of storage utilization, bandwidth, request backlogs, requests handled, request failure and response time are estimated. Reduced response time, backlogs and request failure with improved storage utilization, bandwidth and requests handled are evident using the proposed framework when compared to the existing models.

The creation of context-aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

Temperature is an important data source for weather forecasting, agriculture irrigation, anomaly detection, etc. While temperature measurement can be achieved via low-cost yet standalone hardware with reasonable accuracy, integrating thermal sensing into ubiquitous computing devices is highly non-trivial due to the design requirement for specific heat isolation and proper device layout. In this paper, we present the first integrated thermometer using commercial-off-the-shelf acoustic-enabled devices. Our software sonic thermometer (SST) utilizes on-board dual microphones on commodity mobile devices to estimate sound speed, which has a known relation with temperature. To precisely measure temperature via sound speed, we propose a chirp mixing approach to circumvent low sampling rates on commodity hardware and design a pipeline of signal processing blocks to handle channel distortions. SST, for the first time, empowers ubiquitous computing devices with thermal sensing capability. It is portable and cost-effective, making it competitive with current thermometers using dedicated hardware. SST is potential to facilitate many interesting applications such as large-scale distributed thermal sensing, yielding high temporal/spatial resolutions with unimaginable low costs. We implement SST on a commodity platform and results show that SST achieves a median accuracy of ${0.5^\circ \mathrm{C}}$ 0 . 5 ∘ C even at varying humidity levels.

Context-aware systems are getting rapid popularity towards enabling a pervasive computing environment over hand-held mobile devices, wearable devices, and Tablets. These systems deliver services tailored to the specific needs and user’s context. The context-aware system services can utilize information about the user’s context to adapt services based on the user’s location. The prime aim of this research paper is to investigate and analyse the critical issues and challenges while delivering the job information to the job seekers when they migrate from one place to another in search of jobs. In addition, the research also tries to investigate the challenges faced by employers/ job providers when they search and recruit the right person for the right jobs. In this paper the issues and challenges investigated and analysed were timeliness of information dissemination, opportunity missing, fake/genuine information, anywhere, anytime accessibility over small handheld devices. After rigorous analysis of the investigated issues and challenges in the existing state of art employment/job information dissemination systems, a real-time context-aware employment information delivery system framework for the pervasive environment is proposed. The research parameters considered are Preference of Job seekers, Location of Jobs, Context, Qualification, CGPA, Experience, everywhere access at any time, and Identity. The paper used both the quantitative and qualitative approaches for proposing a research-based applied solution. Survey of job seekers, Interview of recruiters, and technical observation of the researcher are used for collecting the relevant facts to check the researchability of the issues and challenges. Protégé version 4.3 is used for modelling, Edraw-Max for designing the framework, Google Form for Survey, and Justin mind tool for Prototype design and testing. The framework and its Prototype was examined using a user acceptance test. The acceptance test indicated that 84.6% of target stakeholders accepted the proposed system framework as a new knowledge solution for the aforementioned issues and challenges in the employment sectors

Radio frequency identification (RFID) has been identified as one of the emerging technologies in supply chain management. With the integration of RFID with internet of things (IoT), called RFID-IoT has bring the advancement of support chain management (SCM) to the automated sensing, pervasive computing and ubiquitous data access by manufacturer, distributor, retailer and even consumer. This paper intended to introduce the concept of RFID-IoT technology in SCM, recent application in SCM such as identification, monitoring, process enhancement, logistic tracking, product quality and analyze the current challenges and future potential of RFID-IoT technology. In future, RFID-IoT is expected to be integrated with other technologies to produce a comprehensive solution in SCM.

Wireless sensor network is the significant element in pervasive computing for various distributed applications which divides the resources or data among the peers between the sensor nodes to managing the bandwidth of network, node participants of the network, processing powers of the nodes. During the process of the distribution of the data, the one to one environment of the sensor networks, computation complexity and decentralization is the important issues because it completely reduces the entire system performance. So, this paper implementing the system to distributing the datum among the nodes in one to one environment using the distributed data clustered through the help about the honey-bee optimization with genetic algorithm approach. The analyzes of approach of cluster and relationship of the neighboring managing on the nodes of peer of the sensors in order to assist to decentralization process on the dynamic environment. Further a clustering search space is identified and each node is evaluated with the help of position, velocity, distance values which is examined in terms of using fitness value. Then a performance at a system is analyzed using the results of experimental while discussions such as packet delivery ratio, energy consumption and total number of alive nodes.

Due to the rapid development of information and communication technologies with several emerging computing paradigms, such as ubiquitous computing, social computing, and mobile computing, modeling of information diffusion becomes an increasingly significant issue in the big data era. In this study, we focus on a periodic-aware intelligent prediction method based on a comprehensive modeling of user and contagion features, which can be applied to support information diffusion across social networks in accordance with users’ adoption behaviors. In particular, the Dynamically Socialized User Networking (DSUN) model and sentiment-Latent Dirichlet Allocation (LDA) topic model, which consider a series of social factors, including user interests and social roles, semantic topics and sentiment polarities, are constructed and integrated together to facilitate the information diffusion process. A periodic-aware preception mechanism usingreinforcement learning with a newly designed reward rule based on topic distribution is then designed to detect and classify different periods into the so-called routine period and emergency period. Finally, a deep learning scheme based on multi-factor analysis is developed for adoption behavior prediction within the identified different periods. Experiments using the real-world data demonstrate the effectiveness and usefulness of our proposed model and method in heterogenous social network environments.

A new vision of harnessing the ocean of video data captured by cameras in an efficient, secure, and manageable way. Following the hardware trends in reality, we advocate to treat cameras as the first-class computing and storage platform, and expose their services to third parties. To this end, the cameras should move from hard-coded to software-defined, from dumb data source to autonomous data processor, from isolated to orchestrated, and from one-shot to human-in-the-loop.

The Internet of Medical Things (IoMT) is an extension and specialization of that original Internet of Things (IoT) concept, and applies to the interconnectedness of devices, software applications, and data which are specific to the medical industry. IoMT can add smart technologies to medical devices to monitor the progression of a disease away from the doctor’s office and learn things that could impact future care guidelines and patients. It can also provide a better way to care for our elderly by tracking vitals and heart performance, glucose and other body systems, and activity and sleeping levels. During the outbreak of pandemic (e.g., COVID-19), IoMT can even be used to detect main symptoms ubiquitously using intelligent sensors and trace the origin of the outbreak based on aggregated IoT data (e.g., geographic mobile data and purchase history). Although most of the contemporary IoMT systems can measure risks, make decisions, and take actions automatically, the lack of emotion-aware abilities will be an obstacle to more harmonious human–machine interaction and more efficient medical process. Besides, mental disorders, such as depression, schizophrenia, and anxiety, have become a more noticeable cause of suffering. The integration of emotion-aware abilities into IoMT can also contribute to monitor emotional dysregulation continuously in subjects with mental disorders or undergoing serious pandemic such as COVID-19, and give these patients personalized therapy recommendations. Research on affective computing has defined a framework to recognize, interpret, and process human affects, but more research is needed to investigate its application to biomedical applications, especially “in the wild” and over extended periods of time, and how to integrate emotion-aware abilities into IoMT organically is still an open question. This special issue aims to create a platform for researchers, developers, and practitioners from both academia and industry to disseminate the state-of-the-art results and to advance the Emotion-Aware ubiquitous computing in IoMT.

Wireless computing has revolutionized our life with the technological advancement from the traditional networking into a new epoch for communication in ad hoc decorum. An energy efficient network of sensors based on wireless communication and networking principles can enhance the effectiveness of computing during unpredictable circumstances. If a computable required resource is readily available within the reachable region and it is identified to be idle and it is ready to share the corresponding information from its end without affecting the normal behavior of the device or the node, then there exists an opportunity to utilize those resources for computing. Opportunistic computing has a great potential of growth in the field of wireless ad hoc network computing. In traditional network computing technology, mobile computing, grid computing, distributed computing, ubiquitous computing and cloud computing, formerly ensues the communication with minimal resources currently available at the terminal point. This paper proposes a novel framework for an effective utilization of sharable resources, which are available within the reachable region, by creating an opportunity to frame an opportunistic computing while preserving the user’s privacy. The proposed framework adopts a bio-inspired technique for identifying and collecting resources information, and thereby recognizes which resource is ready to participate in the opportunistic computing. Experimental results of a system that implements the bio-inspired technique along with the natural behavior of the bee colony approach was analyzed and found that the proposed system shows comparatively high performance in terms of computation resource searching, identifying, emergency data transfer, and participative node privacy preserving.