In industrial environments, most production-related activities performed by human operators are often complex. Accurate detections of these activities are pivotal as it can greatly help to assess productivity that can lead to improvement in worker training, as well as in other scenarios ensure a safe work environment and reducing injuries. Existing datasets on wearable Internet of Things (IoT) for human activity recognition primarily focuses on general activities, such as walking, running, etc., and therefore, related machine learning models and datasets are not suitable for application to industrial environments. In this paper, we present a novel dataset for classifying human operator activities in a meat processing plant where production line operators use knives to cut, process and produce meat products. Our dataset contains human operator activity data captured using wearable IoT sensors collected from a meat processing production facility. Through extensive experiments using machine and deep learning, we demonstrate that our dataset is effective and useful for detecting different activities of a human operator working in an industrial environment. To the best of our knowledge, this is the only real-world IoT dataset that will be made publicly available to support further research into industrial activities recognition. Our dataset and related experiments are available at https://digitalinnovationlab.github.io/mppdataset.

Internet of Things (IoT) sensor data, which capture time series physical measurements such as temperature and humidity, often lack proper classification. This limits their effective understanding, integration, and reuse. While sensor metadata—textual descriptions of the measurements—is sometimes available, it is frequently incomplete or ambiguous. As a result, classification often depends solely on the time series data. Leveraging both time series sensor readings and textual metadata for automated and accurate classification remains a challenge due to the heterogeneity and inconsistency of these data sources. In this paper, we propose DeepMetaIoT, a multimodal deep learning framework that integrates time series and textual data for classification. DeepMetaIoT employs a cross-residual architecture comprising a time series encoder and a text encoder based on a pre-trained large language model, enabling effective fusion of both modalities. Experimental results on real-world IoT sensor datasets show that DeepMetaIoT consistently outperforms state-of-the-art machine learning and deep learning baselines.

Producing high-quality product consistently is crucial in manufacturing, as discarding or reprocessing low-quality products increases waste and energy consumption and reduces overall production efficiency. Ensuring high-quality manufactured products is challenging due to relying on human activities for product quality and related consistency assessment, which is often performed postproduction instead of assessing these during each production run and making real-time production adjustments that can mitigate product quality and related consistency issues. In this article, we proposes a novel machine-state learner algorithm that captures the dependencies between product quality and related consistency and machine data (specifically the machine settings and corresponding sensor data). In addition, this article shows how this novel machine-state learner algorithm can be used to predict product quality during the production runs and how such prediction are used to make machine setting recommendations that mitigate product quality and related consistency issues before or during the production runs. These advances in machine state-based data modeling, predictive data analysis and recommendation are incorporated into an inline prediction and decision support system that achieves significant improvement in producing high-quality products consistently by guiding decision-making via recommendations during production in a digital manufacturing environment. In this article, we present an evaluation of the above contributions in a real-world manufacturing plant and yield double digit first pass and nearly perfect second pass product improvements in terms of product quality and related consistency and production efficiency.

Abstract The transformation of cities into smarter and more efficient environments relies on proactive and timely detection and maintenance of city-wide infrastructure, including roadside infrastructure such as road signs and the cleaning of illegally dumped rubbish. Currently, these maintenance tasks rely predominantly on citizen reports or on-site checks by council staff. However, this approach has been shown to be time-consuming and highly costly, resulting in significant delays that negatively impact communities. This paper presents AIoT-CitySense, an AI and IoT-driven city-scale sensing framework, developed and piloted in collaboration with a local government in Australia. AIoT-CitySense has been designed to address the unique requirements of roadside infrastructure maintenance within the local government municipality. A tailored solution of AIoT-CitySense has been deployed on existing waste service trucks that cover a road network of approximately 100 kms in the municipality. Our analysis shows that proactive detection for roadside infrastructure maintenance using our solution reached an impressive 85%, surpassing the timeframes associated with manual reporting processes. AIoT-CitySense can potentially transform various domains, such as efficient detection of potholes and precise line marking for pedestrians. This paper exemplifies the power of leveraging city-wide data using AI and IoT technologies to drive tangible changes and improve the quality of city life.

The Sensor Sharing Marketplace (SenShaMart) enables IoT applications to find IoT sensors, which are owned and managed by other parties, integrate them, and pay for using their data. To provide corresponding services that implement that FAIR (Findable, Accessible, Interoperable, Reusable) principles of IoT, SenShaMart incorporates a specialized blockchain that manages all the information its services need to allow different parties in IoT to describe, query, integrate, pay for, and use IoT sensors and their data. The paper presents the SenShaMart's architecture, implementation, evaluation, and demonstration.

With increased deployment of IoT application on cloud platforms, assessing the performance of such application is an open problem. Currently, approaches are limited to legacy database-based applications and does not cater for the needs of IoT applications. This paper proposes, implements and validates a framework namely, IoTQGen, that can generate situation-based queries to conduct performance evaluation of IoT application hosted by cloud IoT middleware platform. The framework comprises: (i) a model to capture the query requirements of IoT applications; (ii) a data generator to generate IoT data based on specified configuration; and (iii) a set of queries designed to represent data analytic IoT applications. The framework supports different query types that can be typically used to represent and address IoT application scenarios. An important functionality of the framework is its ability to issue queries based on dynamic changes in the state of IoT entities (situations). The framework is evaluated based on two smart city use cases to highlight how the framework can be used to generate complex and dynamic queries tailored for IoT application scenarios.

Public Internet of Things (IoT) platforms, such as Thingspeak, significantly increased the availability of open IoT data and enabled faster and cheaper development of novel IoT applications by reducing or even eliminating the need for deploying their own IoT sensors and platforms. However, open IoT data is often heterogeneous, sparse, fuzzy, and lacks accurate description (which we refer to as IoT metadata). These limitations make open IoT data challenging to integrate and use, and prevent the efficient development of IoT applications. In fact, while several sensor data description models have been proposed and standardized, open IoT data currently lack or include only partial metadata description. Therefore, novel techniques for automatically annotating open IoT data are needed to fully unleash the power of open IoT. This article proposes a novel metadata-assisted cascading ensemble classification framework (MACE) for the automatic annotation of IoT data. MACE is capable of sequentially combining standalone classifiers, enabling it to cope with heterogeneous IoT data and different domains of information (e.g., numerical and textual), which have not been considered previously. MACE incorporates a novel ensemble approach for automatically selecting, sorting, filtering, and assembling classifiers in a way that improves annotation performance. This article presents extensive experimental evaluations of MACE using public IoT data sets. Results demonstrate that the MACE framework significantly outperforms existing solutions for open IoT data by as much as 10% in classification accuracy.

The unprecedented growth of Internet of Things (IoT) underpinned by machine to machine communication, analytics and actuation is spearheading the development of autonomic IoT applications in areas such as Smart Cities. Such autonomic IoT applications have minimal human involvement in the decision making and actuation process. A key challenge in developing such autonomic IoT applications is uncertainty in the data produced by the IoT devices with data freshness being a critical aspect. In this paper, we address this challenge by introducing Age of Data (AoD), a metric to quantify the freshness of the data produced by IoT devices. We analyse the impact of AoD on IoT applications and propose a model for computing AoD that can be used by IoT applications in the decision making process. We validate the proposed model via experimental evaluations using real-world data obtained from parking sensors. Our analysis found that in real-world scenarios, 21.4% of sensors provide data that is outdated by several hours. We show that incorporating AoD in the application logic leads to improved application decision making.

Timely detection of roadside assets that require maintenance is essential for improving citizen satisfaction. Currently, the process of identifying such maintenance issues is typically performed manually, which is time consuming, expensive, and slow to respond. In this paper, we present Mobile IoT-RoadBot, a mobile 5G-based Internet of Things (IoT) solution, powered by Artificial Intelligence (AI) techniques to enable opportunistic real-time identification and detection of maintenance issues with roadside assets. The Mobile IoT-RoadBot solution has been deployed on 11 bin service (waste collection) trucks in the western suburbs of Melbourne, Australia, performing real-time assessments of road-side assets as they service areas within the local government. We present the architecture of Mobile IoT-RoadBot and demonstrate its capability via an online 'points of maintenance' (PoMs) map.

The significant advances in the Internet of Things (IoT) have led to IoT applications being widely used in various scenarios ranging from smart city, smart farming, to Industrial IoT (IIoT) solutions. With the explosion of IoT application development, IoT middleware platforms are increasingly being used for hosting such IoT applications. This has given rise to the need for developing benchmarking solutions to analyze and test the performance of different middleware platforms that host these IoT applications. To develop such benchmarks, there are a number of key components that are needed. One of these components is an IoT dataset. To generate such datasets, representing IoT application requirements in a general and formal way is important. In this paper, we propose a framework to model the IoT Applications Requirements and enable Data Generation(ARDG-IoT). The framework supports a formal way to capture IoT application requirements and use these requirements to generate IoT data that can be used to create benchmarks for different IoT middleware platforms. ARDG-IoT consists of our proposed model, IoTSySML, which captures the application requirements, and an IoT data simulator tool, which is used to generate IoT data. We present an evaluation of the framework using a real world Industrial IoT application case study.

Abstract The integration and crosscoordination of big data processing and software‐defined networking (SDN) are vital for improving the performance of big data applications. Various approaches for combining big data and SDN have been investigated by both industry and academia. However, empirical evaluations of solutions that combine big data processing and SDN are extremely costly and complicated. To address the problem of effective evaluation of solutions that combine big data processing with SDN, we present a new, self‐contained simulation tool named BigDataSDNSim that enables the modeling and simulation of the big data management system YARN, its related programming models MapReduce, and SDN‐enabled networks in a cloud computing environment. BigDataSDNSim supports cost‐effective and easy to conduct experimentation in a controllable, repeatable, and configurable manner. The article illustrates the simulation accuracy and correctness of BigDataSDNSim by comparing the behavior and results of a real environment that combines big data processing and SDN with an equivalent simulated environment. Finally, the article presents two uses cases of BigDataSDNSim, which exhibit its practicality and features, illustrate the impact of data replication mechanisms of MapReduce in Hadoop YARN, and show the superiority of SDN over traditional networks to improve the performance of MapReduce applications.

Smart buildings equipped with various building management systems and digital control systems produce enormous amounts of sensor data that can be used to investigate and diagnose operational issues such as unsatisfactory thermal comfort outcomes, excessive energy consumption and/or predicting failures before they occur. However, current building management systems often face the issues with incomplete or unstructured metadata associated with sensor data which prevent such pro-active, predictive and prescriptive analysis. Currently, building service engineers manually map the sensor data streams to aid their diagnostic process. This process is expensive, ineffective and is also prone to human errors. This paper proposes a novel semi-automated approach that annotates incoming sensor data streams. We also propose extensions to Project Haystack, a well-known ontology used for naming conventions and taxonomies for building equipment and operational data. We have developed a tool that is currently used by our industry partner and incorporates the proposed automatic annotation approach and maps the data streams to our Haystack-extended ontology. The tool includes an easy to use interface for engineers to easily diagnose issues in mechanical building services. The proposed approach has been validated via both usability and technical evaluation.

Industry 4.0 involves enhancing industrial processes with high-fidelity and high-value information from machines, workers, and products. Industry 4.0 applications improve production efficiency, product quality, etc., by using Internet of Things (IoT) and Artificial Intelligence (AI). Existing industry 4.0 application development approaches are centered on commercial IoT platforms that provide siloed development and runtime environments (leading to vendor lockdown) and only support individual sensors and actuators instead of entire machines. Therefore, Industry 4.0 applications need to construct representations of complex machines from such basic elements, which is a costly, error-prone, inefficient hindering portability across machines and plants. This paper proposes Cyber Twins, a comprehensive solution for efficient Industry 4.0 application development, testing, and portability. The Cyber Twins solution includes a model for machine representation and services that facilitate Industry 4.0 application development. Finally, a prototype Cyber Twin implementation is presented, with its functionality described using a sample Industry 4.0 application.

The Internet of Things (IoT) is growing fast and is gaining significant adoption in areas such as smart cities and manufacturing. The variety and low-cost of IoT devices with excellent audio/visual sensors is fueling the growth of multimedia IoT applications, many of which are bandwidth-hungry and time-sensitive (i.e., must produce their results within an application specific time-sensitive requirement). While a large body of related work has studied distribution of such Time Sensitive Multimedia IoT (TS-MIoT) applications in simulated environments, there is lack of a platform that can be used to experiment with techniques for meeting their time-sensitive and computing resource requirements on real-world IoT infrastructure, i.e., a combination of IoT devices, close by computers and a cloud data centre connected by a variety of networks. This paper proposes APOLLO, a platform for experimental analysis of TS-MIoT applications. APOLLO provides mechanisms to load TS-MIoT application execution plans and execute the plans on available IoT infrastructure. We describe a proof-of-concept implementation using Orleans and present experimental evaluations to validate APOLLO's ability to support the experimental analysis of TS-MIoT applications.

The Internet of Things (IoT) is the latest Internet evolution that incorporates billions of sensors, actuators, and related software services that collectively distill high value information, perform actions that affect the physical world, and support a variety of applications controlled by different organizations and individuals. IoT's ability to observe and affect the physical world presents a unprecedented opportunity for creating IoT-based smart services and products that address grant challenges in emerging opportunities in areas such as climate change, precision agriculture, smart health, advanced manufacturing, and smart cities. This special issue identifies and addresses some of the key issues that hinder the development of IoT-based solutions. It includes articles that present the latest innovations in IoT security and privacy, IoT data quality and analysis, IoT resources and task management, as well as examples of IoT-based application services and domains.

The Industrial Internet of Things (IIoT) is a key pillar of the Fourth Industrial Evolution or Industry 4.0. It aims to achieve direct information exchange between industrial machines, people, and processes. By tapping and analysing such data, IIoT can more importantly provide for significant improvements in productivity, product quality, and safety via proactive detection of problems in the performance and reliability of production machines, workers, and industrial processes. While the majority of existing IIoT research is currently focusing on the predictive maintenance of industrial machines (unplanned production stoppages lead to significant increases in costs and lost plant productivity), this paper focuses on monitoring and assessing worker productivity. This IIoT research is particularly important for large manufacturing plants where most production activities are performed by workers using tools and operating machines. With this aim, this paper introduces a novel industrial IoT solution for monitoring, evaluating, and improving worker and related plant productivity based on workers activity recognition using a distributed platform and wearable sensors. More specifically, this IIoT solution captures acceleration and gyroscopic data from wearable sensors in edge computers and analyses them in powerful processing servers in the cloud to provide a timely evaluation of the performance and productivity of each individual worker in the production line. These are achieved by classifying worker production activities and computing Key Performance Indicators (KPIs) from the captured sensor data. We present a real-world case study that utilises our IIoT solution in a large meat processing plant (MPP). We illustrate the design of the IIoT solution, describe the in-plant data collection during normal operation, and present the sensor data analysis and related KPI computation, as well as the outcomes and lessons learnt.

Graphs are often drawn into straight-line node-link diagrams for better understanding of the underlying data. However, curves have also been used in graph visualization for various purposes. Orthogonal drawing is one type of graph drawings in which each edge is made up with an alternating sequence of vertical and horizontal line segments. In the past, many algorithms have been developed for orthogonal drawing so that the resulting drawings meet some pre-defined aesthetic criteria and constraints. Experiments have also been conducted to evaluate performance of algorithms and effectiveness of orthogonal drawings. In this paper, we briefly summarize the research that has been done in relation to orthogonal drawing and provide directions for future research.

Open multidimensional data from existing sources and social media often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, users can more easily interact with and pick out meaningful information from a large dataset. In this paper, we present VisCrime, a system that uses visual analytics to maps out crimes that have occurred in a region/neighbourhood. VisCrime is underpinned by a novel trajectory algorithm that is used to create trajectories from open data sources that reports incidents of crime and data gathered from social media. Our system can be accessed at http://viscrime.ml/deckmap

The Internet of Things (IoT) represents a technology revolution transforming the current environment into a ubiquitous world, whereby everything that benefits from being connected will be connected. Despite the benefits, the privacy of these things becomes a great concern and therefore it is imperative to apply privacy preservation techniques to IoT data collection. One such technique is called data obfuscation in which data is deliberately modified to blur the sensitive information, while preserving the data utility. The current obfuscation techniques, however, focus on the privacy of published datasets shared with untrusted parties. The high connectivity and distributed nature of IoT, opens up the possibility of privacy compromise before obfuscation can take effect, and therefore privacy enforcement should be deployed at earlier stages. Additionally, classical privacy treatments are too restrictive for IoT, where coarser/finer data details should be revealed for different applications. Motivated by these challenges, we propose a framework for privacy preservation in IoT environments that is capable of multi-granular obfuscation by enforcing context-driven disclosure policies. Then, we customize our framework for a smart vehicle system and make use of data stream watermarking techniques to protect privacy at different stages of the data lifecycle. To address possible concerns about additional performance overhead, we show the burden to be very lightweight, thus validating the suitability of ubiquitous use of our framework for IoT settings.

Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS-Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

This special issue asked for high quality original research papers (including smart city experience papers) that made significant contributions to the state-of-the-art in "method and techniques to build sustainable smart city solutions" research area. Rapid urbanization is a global megatrend with 66 percent of the world’s population expected to live in urban areas by 2050. The staggering exponential increase in urbanization is leading to more people migrating to major cities in the search of better opportunities and quality of life. Cities need to increase the efficiency in which they operate and use their resources sustainability in order to meet the demands imposed by rapid urbanisation. The challenge is to continue providing basic resources such as sufficient fresh water; cleaner energy; transportation alternatives to commute efficiently from one place to another; adaption to changing climatic conditions; safety and security; while also ensuring economical, social, and environment sustainability.

The Internet of Things (IoT) is a new internet evolution that involves connecting billions of internet-connected devices that we refer to as IoT things. These devices can communicate directly and intelligently over the Internet, and generate a massive amount of data that needs to be consumed by a variety of IoT applications. This paper focuses on the automatic contextualisation of IoT data, which also involves distilling information and knowledge from the IoT aiming to simplify answering the following fundamental questions that often arises in IoT applications: Which data collected by IoT are relevant to myself and the IoT Things I care for? Related work around context management and contextualisation ranges from database techniques that involve query re-writing, to semantic web and rule-based context management approaches, to machine learning and data science-based solutions in mobile and ambient computing. All such existing approaches have two main aspects in common: They are highly incompatible and horribly inefficient from a scalability and performance perspective. In this paper, we discuss a new RISC Contextualisation Framework (RCF) we have developed, implemented key aspects of, and assess its scalability. RCF provides fundamental contextualisation concepts that can be mapped to all existing contextualisation approaches for IoT data (and in this sense, it provides a common denominator that unifies the contextualisation space). RCF can be easily implemented as a cloud-based service, and provides better scalability and performance that any of the existing content management and contextualisation approaches in the IoT space.

The Internet of Things (IoT) is the latest Internet evolution that interconnects billions of devices, such as cameras, sensors, RFIDs, smart phones, wearable devices, ODBII dongles, etc. Federations of such IoT devices (or things) provides the information needed to solve many important problems that have been too difficult to harness before. Despite these great benefits, privacy in IoT remains a great concern, in particular when the number of things increases. This presses the need for the development of highly scalable and computationally efficient mechanisms to prevent unauthorised access and disclosure of sensitive information generated by things. In this paper, we address this need by proposing a lightweight, yet highly scalable, data obfuscation technique. For this purpose, a digital watermarking technique is used to control perturbation of sensitive data that enables legitimate users to de-obfuscate perturbed data. To enhance the scalability of our solution, we also introduce a contextualisation service that achieve real-time aggregation and filtering of IoT data for large number of designated users. We, then, assess the effectiveness of the proposed technique by considering a health-care scenario that involves data streamed from various wearable and stationary sensors capturing health data, such as heart-rate and blood pressure. An analysis of the experimental results that illustrate the unconstrained scalability of our technique concludes the paper.

Summary A large number of cloud middleware platforms and tools are deployed to support a variety of internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration and cooperation and can also lead to innovative use of the data. Multi‐cloud, privacy‐aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics‐as‐a‐service providers. There is a lack of both architectural blueprints that can support such diverse, multi‐cloud environments and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data‐processing architecture that utilises semantics at all the levels of IoT stack in multi‐cloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads. Copyright © 2016 John Wiley & Sons, Ltd.

Special issue on Big Data and Cloud of Things (CoT)Cloud computing and Internet of Things (IoT) are two technologies that are already becoming part of our daily lives and are attracting significant interest from both industry and academia.The Cloud of Things (CoT) is a vision inspired from the IoT paradigm where everyday devices, namely, 'smart objects', are fully connected to the internet and are integrated with the cloud.It is expected the IoT will grow to 35 billion units by 2020, making it one of the main sources of 'Big Data' with characteristics such as volume, heterogeneity, complexity, velocity, and value.In recent years, IoT has given rise to a number of new CoT paradigms (but not limited to) including: Sensing-as-a-Service, Sensing-and Actuation-as-a-Service, Video-Surveillance-as-a-Service, Big Data Analytics-asa-Service, Data-as-a-Service, Sensor-as-a-Service, and Sensor-Event-as-a-Service. Cloud computing is a more mature technology compared to IoT.It can offer virtually unrestricted capabilities (e.g., storage and computation) to support IoT services and application that can exploit the data produced from IoT devices.The cloud essentially acts as a transparent layer between the IoT and applications providing flexibility, scalability, and hiding the complexities between the two layers (IoT and applications).However, the integration of cloud and IoT into Cloud of Things is not straightforward and imposes several challenges.These challenges include IoT device and service discovery, IoT device integration, big data management and analytics, cloud monitoring and orchestration for distributed IoT applications, mobility issues in cloud access, privacy and security, and SLA management for both cloud and IoT.Specific attention must be paid to address a range of issues from IoT data collection, storage, processing, analytics on demand to automatic provision and management of cloud resources to support the growing population of things.Hence, this special issue solicits paper related to topics including CoT architectures and models for smart provision of CoT applications, data management challenges facing CoT applications, software and tools to monitor, manage, deploy and deliver CoT applications, quality of service and related SLA management and policies for CoT applications, and security and privacy challenges facing CoT applications.The call for special issues received a number of submissions.After a two-phase peer review process, we have accepted 10 high-quality papers related to the aforementioned areas of interest.The first paper titled Using adaptive resource allocation to implement an elastic MapReduce framework by Jiaqi Zhao, Changlong Xue, Xinlin Tao, Shugong Zhang, and Jie Tao addresses the runtime resource demand challenge faced by application running on MapReduce frameworks.The proposed approach is capable of making the map reduce application, aware of overloading or under-loading situations with the resources allocated.They have extended the existing Hadoop MapReduce resource manager to implement the proposed strategy and validated the concept on an high-performance computing cluster with standard benchmark applications.Experimental results show a significant performance gain, for example, an up to 45% improvement in execution time for running multiple applications.The second paper titled A traffic hotline discovery method over cloud of things using big taxi GPS data by Xiaolong Xu, Wanchun Dou, Xuyun Zhang, Chunhua Hu, and Jinjun Chen addresses the challenge of discovering traffic hotline in CoT environments.Traffic hotlines are identified as the traffic lines with intensive traffic flows among traffic spots.They propose a hotline discovery method over CoT by establishing a hotline discovery principle.They have implemented their approach on SAP HANA cloud and tested it using big taxi global positioning system data under two application scenarios.

Rapid urbanization is a global megatrend with 66 percent of the world’s population expected to live in urban areas by 2050. The staggering exponential increase in urbanization is leading to more people migrating to major cities in the search of better opportunities and quality of life. Cities need to increase the efficiency in which they operate and use their resources sustainability in order to meet the demands imposed by rapid urbanization. The challenge is to continue providing basic resources such as sufficient fresh water; cleaner energy; transportation alternatives to commute efficiently from one place to another; adaption to changing climatic conditions; safety and security; while also ensuring economical, social, and environment sustainability. These challenges represent a huge opportunity for a paradigm shift that will require the need for data processing, analysis, and security close to the connected "things" i.e., towards the edge of the network in-order to support the growing smart city ecosystem. This paradigm shift will lead to an explosive growth of independent, owned and operated things and services including gateways, repeaters, smart infrastructure, and systems. Such a paradigm needs to be architected in a way that is easy to operate and dramatically simplifies the management of service offerings through scalable orchestration and proper automation. It must allow management, integration, and deployment of different tenants (such as services and things independently owned) within the smart city ecosystem in a uniform way. It should also have a suitable policy framework, letting specific stakeholders have access to data produced by other tenants, and analyze and extract values from the data. In order to address these challenges, this special issue solicits high quality original research papers (including smart city experience papers) that made significant contributions to the state-of-the-art in "method and techniques to build sustainable smart city solutions" research area. The call for papers received a number of submissions. After a two-phase peer review process, we have accepted five high-quality papers related to the aforementioned areas of interest which will be published in the October-December 2017 as Part 1. The papers in this issue are briefly summarized.

In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients' privacy.

The Internet of Things (IoT) is the latest Internet evolution that incorporates a diverse range of things such as sensors, actuators, and services deployed by different organizations and individuals to support a variety of applications. The information captured by IoT present an unprecedented opportunity to solve large-scale problems in those application domains to deliver services; example applications include precision agriculture, environment monitoring, smart health, smart manufacturing, and smart cities. Like all other Internet based services in the past, IoT-based services are also being developed and deployed without security consideration. By nature, IoT devices and services are vulnerable to malicious cyber threats as they cannot be given the same protection that is received by enterprise services within an enterprise perimeter. While IoT services will play an important role in our daily life resulting in improved productivity and quality of life, the trend has also “encouraged” cyber-exploitation and evolution and diversification of malicious cyber threats. Hence, there is a need for coordinated efforts from the research community to address resulting concerns, such as those presented in this special section. Several potential research topics are also identified in this special section.

This paper describes an advanced seminar presented at the 16th IEEE International Conference on Mobile Data Management. The advanced seminar presents the state-of-the-art in the Internet of Things, which is fast emerging as the disruptive technology for years to come. The seminar focusses on the Internet-scale sensor information management, related mobile analytics and open source IoT technologies and emerging standards.

A note on resource orchestration for cloud computingWelcome to the special issue of Concurrency and Computation: Practice and Experience (CCPE) journal.This special issue compiles a number of excellent technical contributions that significantly advance the state-of-the-art in the areas of orchestrating cloud resources, composing new cloud services from existing ones, increasing energy efficiency via cloud resource orchestration, and developing cloud-based image processing solutions.Over the past few years, cloud computing [1-4] has emerged as the latest and most dominant utility computing solution offering both hardware and software resources as virtualization-enabled services.Cloud computing providers such as Amazon Web Services and Microsoft Azure currently provide application owners the option of deploying their applications over a network of a virtually infinite resource pool with practically no up-front capital investment and with operating cost proportional to the actual use (i.e., implementing a pay-as-you-go model).An increasing number of cloud vendors offer information and communication technology (ICT) resources such as hardware (CPUs, GPUs, storage, and networks), software infrastructure (e.g., databases, webservers, stream-processing systems, and data-mining packages), and collaboration/communication applications (e.g., email, video on demand, and social networks) as infrastructure as a service (IAAS), platform as a service (PAAS), and software as a service (SAAS), respectively.This approach allows enterprises to easily, cost effectively, and reliably offer business services that are supported by computing and software resources that are provided and maintained by IAAS, PAAS, and SAAS providers.This makes cloud computing attractive to especially small and medium size enterprises (SMEs), as it allows them to focus more on their core business and less on ICT infrastructure.One of the fundamental issues in exploiting cloud computing in this fashion is developing better Resource Orchestration (RO) [1-5] techniques and programming frameworks.More specifically, Resource Orchestration (RO) is 'the set of operations that cloud providers (e.g., AWS) and application owners (e.g., Netflix) undertake (either manually or automatically via computer programs) for selecting, deploying, monitoring, and dynamically controlling configuration of hardware and software resources as a system of QoS assured components that can be seamlessly delivered to end-users' [1].Since RO operations span across all layers of cloud computing stack [1], an overall goal of RO is to ensure successful hosting and delivery of applications (SAAS) by managing the fulfillment of the QoS objectives of both the application owners (e.g., maximize availability, maximize throughput, minimize latency, and avoid overloading) and the Cloud resource providers (e.g., maximize utilization, maximize energy efficiency, and maximize profit).One of the main complexities in Cloud resource management is that Cloud resources are typically identified by unique functional specifications, and then evaluated via their Quality of Service (QoS) properties.However, in practice, each resource may have multiple unique functional specifications that enable serving diverse user needs.For example, a song retrieval application is a Cloud resource that can be identified and enacted by the name of a song or via its lyrics, as users do may not know or remember the names of all song they want to find.The paper titled 'A Service Evaluation Method for Cross-cloud Service Choreography' [6] addresses this challenge by proposing a multifunctional specification solution for cross-cloud service choreography.This solution is based on a mixed integer programming model for multifunctional specification/identification that decomposes global resource and application constraints into local constraints.This allows constraint evaluations to be performed for cross-cloud service choreography.Experimental verification of this approach is also provided.Provisioning cloud resources requires providers and consumers to reach an agreement on the service usage terms and conditions.Such agreements are captured as Service Level Agreements (SLAs).The paper titled 'AutoSLAM -A Policy-based Framework for Automated SLA Establishment in Cloud

Cloud computing assembles large networks of virtualised ICT services such as hardware resources (such as CPU, storage, and network), software resources (such as databases, application servers, and web servers) and applications. Big Data applications have become a common phenomenon in domain of science, engineering, and commerce. Large-scale, heterogeneous, and uncertain Big Data applications are becoming increasingly common, yet current cloud resource provisioning methods do not scale well and nor do they perform well under highly unpredictable conditions (data volume, data variety, data arrival rate, etc.). Much research effort have been paid in the fundamental understanding, technologies, and concepts related to autonomic provisioning of cloud resources for Big Data applications, to make cloud-hosted Big Data applications operate more efficiently, with reduced financial and environmental costs, reduced under-utilisation of resources, and better performance at times of unpredictable workload. Targeting the aforementioned research challenges, this special issue compiles recent advances in Autonomic Provisioning of Big Data Applications on Clouds. The special issue articles are briefly summarized.

Mobile devices are rapidly becoming the primary computing device in people’s lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud-based infrastructure services (e.g. storage, network, CPU). The outcomes of this will be decision support tools and techniques, which will automate and map users' specified application requirements to Cloud service configurations.

The Cloud infrastructure services landscape advances steadily leaving users in the agony of choice. As a result, Cloud service dentification and discovery remains a hard problem due to different service descriptions, nonstandardised naming conventions and heterogeneous types and features of Cloud

Mobile phones play increasingly bigger role in our everyday lives. Today, most smart phones comprise a wide variety of sensors which can sense the physical environment. The Internet of Things vision encompasses participatory sensing which is enabled using mobile phones based sensing and reasoning. In this research, we propose and demonstrate our DAM4GSN architecture to capture sensor data using sensors built into the mobile phones. Specifically, we combine an open source sensor data stream processing engine called ‘Global Sensor Network (GSN)’ with the Android platform to capture sensor data. To achieve this goal, we proposed and developed a prototype application that can be installed on Android devices as well as a AndroidWrapper as a GSN middleware component. The process and the difficulty of manually connecting sensor devices to sensor data processing middleware systems are examined. We evaluated the performance of the system based on power consumption of the mobile client.

Data collected from sensor networks are often analysed by cross-domain scientists who produce results that are requested by a variety of clients. In such a collaborative environment, scientific experiments include data collection form sensors, and data analysis performed by scientists. To meet the c

The Video Event Awareness System (VEAS) analyzes surveillance video streams from thousands of video cameras and automatically detects complex events in near real-time - at pace with their input video streams. For events of interest to security personnel, VEAS generates and routes alerts and related video evidence to subscribing security personnel.

Awareness-enabled coordination (AEC) is a platform designed to address the problem of scaling collaboration to large multi-organizational teams. Such collaboration is inhibited by the complexity in multi-organizational environments and lack of efficiency in achieving team objectives. AEC provides a contextualization mechanism that deals with such complex, real world environments where teams involve humans, tools, software services, and agents that come from different organizations, are subject to multiple jurisdictions, and provide diverse expertise. To provide efficiency in achieving team objectives, AEC provides situation- and project-related awareness, as well as process-based coordination and automation. We describe the AEC architecture and discuss AEC models and mechanisms for computing awareness and coordinating action. We use examples from the homeland security domain to illustrate these AEC technical capabilities and their benefits

Operation support systems (OSSs) integrate software components and network elements to automate the provisioning and monitoring of telecommunications services. This paper illustrates Telcordia's Cable OSS and shows how customers may use this OSS to provision IP and telephone services over the cable infrastructure. Telcordia's Cable OSS is a process-based application, i.e. a collection of flows, specialized components (e.g. a billing system, a call agent soft switch, network services and elements, cable modems, etc.) and corresponding adaptors that are integrated, coordinated and monitored using CMI (Collaboration Management Infrastructure), Telcordia's advanced process-based integration technology. Customers interact with the Cable OSS by using Web or IVR (interactive voice response) interfaces.

Collaboration management involves capturing the collaboration process, coordinating the activities of the participating applications and humans, and/or providing awareness, i.e. information that is highly relevant to a specific role and situation of a process participant. In this paper, we propose an awareness provisioning solution that allows focusing, customizing, and temporally constraining the awareness delivered to each process participant. Unlike existing collaboration management technologies (such as workflow and groupware) that provide only a few built-in awareness choices, the proposed awareness solution allows the specification of what information is to be given to what users and at what time. To support this advanced level of awareness, we require the definition of awareness roles and the specification of corresponding awareness descriptions. Awareness roles can be dynamically created and associated with any process scope. Awareness descriptions define what information is to be given to users in an awareness role. Since awareness roles are created or become visible when they are needed, the existence of an awareness role also determines the appropriate time interval during which the information specified in the awareness description can be delivered. This awareness provisioning approach minimizes information overloading and allows the combination of process-relevant information with external information as needed by the process participants. The proposed awareness provisioning solution is employed by the Collaboration Management Infrastructure (CMI), a federated system for collaboration process management. In this paper, we introduce an Awareness Model (AM) for creating awareness specifications and defining related execution semantics. Awareness specifications in AM are specialized composite event specifications that define patterns of process-related events and external events, as well as how information should be digested from them. We also describe the implementation of CMI's awareness provisioning engine and related tools.

Processes for crisis mitigation must permit coordination flexibility and dynamic change to empower crisis mitigation coordinators and experts to deal with unexpected situations. However, such mitigation processes must also provide enough structure to prevent chaotic response and increase mitigation effectiveness. Such combination of structure and flexibility cannot be effectively supported by existing workflow or groupware technologies. In this paper, we introduce the Collaboration Management Infrastructure (CMI) and describe its capabilities for supporting crisis mitigation processes. CMI provides a comprehensive Collaboration Management Model (CMM) and a corresponding federated system. CMM supports process templates that provide the initial activities, control and data flow structure, and resources needed to start mitigating a variety of crisis situations. In the event of a crisis, the appropriate process template is selected and instantiated. Crisis mitigation is achieved by escalating the instantiated process template. Escalation involves selecting and adding new process templates, creating new activities, roles, and task forces as needed to deal with the current demands in the crisis, and delegating responsibilities to process participants and task forces. CMM provides advanced composable primitives that empower crisis mitigation coordinators and experts to escalate the process. We provide an overview of the implementation of a federated CMI system and discuss our initial experience with various applications in the area of crisis management.

The Collaboration Management lnfrastructure (CMI) has been developed at MCC to manage collaboration processes in both traditional and virtual enterprises, and to provide combined process and situation awareness. CMI technology development is driven by the requirements of many advanced applications provided by the companies that are members of the consortial CMI project. Such advanced applications include crisis mitigation, command and control, logistics, and service provisioning in virtual enterprises. These applications are not effectively supported by existing workflow and groupware technologies. To address the requirements imposed by these applications CMI provides a sophisticated Collaboration Management Model (CMM) and a corresponding component-oriented system that implements the CMM. CMM draws existing primitives from workflow and groupware models and introduces new primitives that address previously unsupported requirements of the CMI driver applications. In this paper, a crisis mitigation application is presented that involves several process templates which are extended dynamically as details about the crisis become known.

Collaboration management involves capturing the collaboration process, coordinating the activities of the participating applications and humans, and/or providing awareness, i.e., information that is highly relevant to a specific role and situation of a process participant. We propose an awareness provisioning solution that allows customization of the awareness delivered to each process participant. Unlike existing collaboration management technologies (such as workflow and groupware) that provide only a few built-in awareness choices, the proposed awareness solution allows the specification of what information is to be given to what users and at what time. To support this advanced level of awareness, we require the definition of awareness roles and the specification of corresponding awareness descriptions. Awareness roles can be dynamically created and associated with any process scope. Awareness descriptions define what information is to be given to users in an awareness role. Since awareness roles are created or become visible when they are needed, the existence of an awareness role also determines the appropriate time interval during which the information specified in the awareness description can be delivered. This customized awareness provisioning approach minimizes information overloading and allows the combination of process-relevant information with external information as needed by the process participants. The proposed awareness provisioning solution is employed by the Collaboration Management Infrastructure (CMI), a federated system for collaboration process management. Examples from the crisis management domain are presented.

A Transaction Specification and Management Environment (TSME) is a programmable system that supports implementation-independent specification of application-specific extended transaction models (ETMs) and configuration of transaction management mechanisms (TMMs) to enforce specified ETMs. The TSME can ensure correctness and reliability while allowing the functionality required by workflows and other advanced applications that require access to multiple heterogeneous, autonomous, and/or distributed (HAD) systems. To support ETM specification, the TSME provides a transaction specification language that describes dependencies between transactions. Unlike other ETM specification languages, TSME's dependency descriptors use a common set of primitives, and are enforceable, i.e., can be evaluated at any time during transaction execution to determine whether operations issued violate ETM specifications. To determine whether an ETM can be enforced in a specific HAD system environment, the TSME supports specification of the transactional capabilities of HAD systems, and comparison of these with ETM specifications to determine mismatches. To enforce ETMs that are more restrictive than those supported by the union of the transactional capabilities of HAD systems, the TSME provides a collection of transactional services. These services are programmable and configurable, i.e., they accept instructions that change their behavior as required by an ETM and can be combined in specific ways to create a run-time TMM capable of enforcing the ETM. We discuss the TSME in the context of a distributed object management system. We give ETM specification examples and describe corresponding TMM configurations for a telecommunications application.

Focuses on transactional workflows, i.e., the advanced transaction technology required to (i) ensure the reliability of tasks in a workflow, and the correctness and reliability of concurrent workflows, and (ii) support the specification and management of extended transactions models. In addition, the author discusses research and development at GTE Laboratories to produce a Transaction Specification and Management Environment (TSME) that can satisfy such requirements. He also discusses the integration of DOM and TSME technologies.>

A Transaction Specification and Management Environment (TSME) is a transaction processing system toolkit that supports the definition and construction of application-specific extended transaction models (ETMs). The TSME provides a transaction specification language that allows a transaction model designer to create implementation-independent specifications of extended transactions. In addition, the TSME provides a programmable transaction management mechanism that assembles and configures a run-time environment to support specified ETMs. The authors discuss the TSME in the context of a distributed object management system (DOMS), and describe specifications of extended transactions and corresponding configurations of transaction management mechanisms.>

A variety of extensions to the traditional (ACID) transaction model have resulted in a plethora of extended transaction models (ETMs). Many of these ETMs are application-specific, i.e. they are designed to provide correctness guarantees adequate for a particular application, but not others. Similarly, an application-specific ETM may impose restrictions that are unacceptable in one application, yet required in another. To define new ETMs, to determine whether an ETM is appropriate for an application, and to integrate ETMs to produce new ETMs, we need a framework for ETM specification and reasoning. In this paper, we describe such a framework. Our framework supports implementation-independent specification of ETMs described in terms of dependencies between transactions. Dependencies are specified using dependency descriptors. Unlike other transaction specification frameworks, dependency descriptors use a common set of primitives, and are enforceable, i.e. can be evaluated at any time during transaction execution to determine whether issued operations violate ETM specifications. We discuss specifications of (i) structure dependencies between transaction states, and (ii) correctness dependencies for serializability, various cooperative and temporal correctness criteria, and recoverability. We give ETM specification examples for a telecommunications application illustrating the definition of a new application-specific ETM using our framework.

To enforce global serializability in a multidatabase environment the multidatabase transaction manager must take into account the indirect (transitive) conflicts between multidatabase transactions caused by local transactions. Such conflicts are difficult to resolve because the behavior or even the existence of local transactions is not known to the multidatabase system. To overcome these difficulties, we propose to incorporate additional data manipulation operations in the subtransactions of each multidatabase transaction. We show that if these operations create direct conflicts between subtransactions at each participating local database system, indirect conflicts can be resolved even if the multidatabase system is not aware of their existence. Based on this approach, we introduce optimistic and conservative multidatabase transaction management methods that require the local database systems to ensure only local serializability. The proposed methods do not violate the autonomy of the local database systems and guarantee global serializability by preventing multidatabase transactions from being serialized in different ways at the participating database systems. Refinements of these methods are also proposed for multidatabase environments where the participating database systems allow schedules that are cascadeless or transactions have analogous execution and serialization orders. In particular, we show that forced local conflicts can be eliminated in rigorous local systems, local cascadelessness simplifies the design of a global scheduler, and that local strictness offers no significant advantages over cascadelessness.>

A global concurrency control mechanism for multidatabase systems that preserves the autonomy of local databases and is free from global deadlocks is presented. The mechanism extends the notion of timestamps to a multidatabase environment to enforce the global serialization order through additional data operations on a data item stored in local systems. The main advantage of the mechanism is that it allows a fully distributed architecture, in which concurrency control decisions can be made on the basis of locally available information. Since no centralized information is maintained by the mechanism, it provides a higher degree of fault tolerance and allows incremental growth.>

Future information processing environments will consist of a vast network of heterogeneous, autonomous, and distributed computing resources, including computers (from mainframe to personal), information-intensive applications, and data (files and databases). A key challenge in this environment is providing capabilities for combining this varied collection of resources into an integrated distributed system, allowing resources to be flexibly combined, and their activities coordinated, to address challenging new information processing requirements. In this paper, we describe the concept of distributed object management, and identify its role in the development of these open, interoperable systems. We identify the key aspects of system architectures supporting distributed object management, and describe specific elements of a distributed object management system being developed at GTE Laboratories.

A multidatabase transaction management mechanism called the optimistic ticket method (OTM) is introduced for enforcing global serializability. It permits the commitment of multidatabase transactions only if their relative serialization order is the same in all participating local database systems (LDBSs). OTM requires the LDBSs to guarantee only local serializability. The basic idea in OTM is to create direct conflicts between multidatabase transactions at each LDBS in order to determine the relative serialization order of their subtransactions. A refinement of OTM, called the implicit ticket method (ITM), is also introduced that uses implicit tickets and eliminates ticket conflicts but works only when the participating LDBSs use rigorous transaction scheduling mechanisms. ITM uses the local commitment order of each subtransaction to determine its implicit ticket value. It achieves global serializability by controlling the commitment (execution order) and thus the serialization order of multidatabase transactions. Both OTM and ITM do not violate the autonomy of the LDBSs and can be combined in a single comprehensive mechanism.>

The class of transaction scheduling mechanisms in which the transaction serialization order can be determined by controlling their commitment order, is defined. This class of transaction management mechanisms is important, because it simplifies transaction management in a multidatabase system environment. The notion of analogous execution and serialization orders of transactions is defined and the concept of strongly recoverable and rigorous execution schedules is introduced. It is then proven that rigorous schedulers always produce analogous execution and serialization orders. It is shown that the systems using the rigorous scheduling can be naturally incorporated in hierarchical transaction management mechanisms. It is proven that several previously proposed multidatabase transaction management mechanisms guarantee global serializability only if all participating databases systems produce rigorous schedules.>

The design and implementation of an experimental fault-tolerant distributed database management system is described. The system provides a logically integrated view of data with distribution transparency and a controlled data replication. A commitment protocol used to guarantee atomicity of update operations is discussed. Efficient algorithms used to recover a site from a failure and restore data consistency are described. Recovery can be interleaved with the processing of regular database transactions and does not seriously limit the availability of data. The proposed solutions to the problems of fault recovery are designed to take advantage of the properties of a high-bandwidth local area network.>