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

The papers in this special section focus on blockchain-based computing services. Blockchain has become a hot research area in academia and industry. A blockchain is a continuously growing list of records of value-transferred transactions maintained by a peer-to-peer network through a distributed consensus mechanism. Blockchain technology has promising characteristics regarding decentralization, persistency

Recently, the emergence of blockchain has stirred great interests in the field of Internet of Things (IoT). However, numerous non-trivial problems in the current blockchain system prevent it from being used as a generic platform for large-scale services and applications in IoT. One notable drawback is the scalability problem. Lots of projects and researches have been done to solve this problem. Nevertheless

We propose Chronos $^{{\mathbf +}}$+ , an accurate blockchain-based time-stamping scheme for outsourced data, where both the storage and time-stamping services are provided by cloud service providers. Specifically, Chronos $^{{\mathbf +}}$+ integrates a file into a transaction on a blockchain once the file is created, which guarantees the file's latest creation time to be the time when the block containing

Blockchains offer opportunities for developing advanced digital services. While current research on this topic is still growing, various security concerns have been raised and the security of blockchains has been becoming the most important issue which must be well addressed. Blockchain transactions are based on digital signatures, where the public key is associated with the ownership of the digital

Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain

Powered by a number of smart devices distributed throughout the whole network, the Internet of Things (IoT) is supposed to provide services computing for massive data from devices. Fog computing, an extension of cloud-based IoT-oriented solutions, has emerged with requirements for distribution and decentralization. In this respect, the conjunction with Blockchain provides a natural solution for decentralization

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many

This paper introduces a novel blockchain-based decentralized federation model that embodies quality verification for cloud providers who lease computing resources from each other. The blockchain structure removes the barriers of a traditional centralized federation and offers a fully distributed and transparent administration by enforcing the involved agents to maintain consensus on the data. For a

The maturity of network storage technology drives users to outsource local data to remote servers. Since these servers are not reliable enough for keeping users’ data, remote data auditing mechanisms are studied for mitigating the threat to data integrity. However, many traditional schemes achieve verifiable data integrity for users only without resolutions to data possession disputes, while others

Known as a distributed ledger, blockchain is becoming prevalent due to its decentralization, traceability and tamper resistance. Particularly, permissioned blockchain such as Hyperledger Fabric shows great application prospects as the infrastructure of IoT security, credit management, etc. Many cloud platforms like AWS, Azure, Oracle and IBM cloud currently provide blockchain as a service, in which

Ethereum smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. Smart contracts handle and transfer assets of values, offering strong incentives for malicious attacks. Intrusion attacks are a popular type of malicious attacks. In this article, we propose ContractGuard , the first intrusion detection system (IDS) to defend Ethereum smart contracts against

Today, the Internet of Things (IoT) technology is applied everywhere, providing tremendous amounts of IoT service. The GS1, a non-profit international standards organization, has established standards for IoT technology. Especially, the GS1 standardized an Object Name Service (ONS) leveraging DNS's distributed and federated infrastructure, enables users to manage and discover IoT services such as the

With the explosion of “big data” in the past decade, exploring and mining the value hidden in the data has already generated a lot of innovative applications, especially the recent advances of AI applications. The data governance, including activities of data creation, sharing, exchange, management, analytics, tracing, and accounting, has drawn a lot of attentions. Services computing establishes the

The mining process in public blockchains with the Nakamoto consensus protocol requires solving a computational puzzle, i.e., proof-of-work, which is resource expensive to implement in lightweight devices with limited computing resources and energy. Thus, renting mining service from cloud providers becomes a reasonable solution, which is called cloud mining. This enables users who want to mine, i.e

Process descriptions are used to create products and deliver services. To lead better processes and services, the first step is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs. Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful but complex, or

Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction

Presents the introductory editorial for this issue of the publication.

In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation

In this paper, we propose a novel localization service to monitor the position of residents in assisted living facilities. The service supports a configurable balancing between precision and privacy, in such a way that the right of the residents to move freely in the environment in which they live without being tracked is preserved. However, in case of need, they can always be quickly localized. To

Traditional assurance solutions for software-based systems rely on static verification techniques and assume continuous availability of trusted third parties. With the advent of cloud computing, these solutions become ineffective since services/applications are flexible, dynamic, and change at runtime, at high rates. Although several assurance approaches have been defined, cloud requires a step-change

Likely system invariants model properties that hold in operating conditions of a computing system. Invariants may be mined offline from training datasets, or inferred during execution. Scientific work has shown that invariants’ mining techniques support several activities, including capacity planning and detection of failures, anomalies and violations of Service Level Agreements. However their practical

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability

With the growing popularity of web services, more and more developers are composing multiple services into mashups. Developers show an increasing interest in non-popular services (i.e., long-tail ones), however, there are very scarce studies trying to address the long-tail web service recommendation problem. The major challenges for recommending long-tail services accurately include severe sparsity

We consider several Software as a Service (SaaS) providers that offer services using the Cloud resources provided by an Infrastructure as a Service (IaaS) provider which adopts a pay-per-use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. For this scenario, we study the virtual machine provisioning and spot pricing strategies. We consider a

The rapid growth in the number and diversity of Web APIs, coupled with the myriad of functionally similar Web APIs, makes it difficult to find most suitable Web APIs for users to accelerate and accomplish Mashup development. Even if the existing methods show improvements in Web APIs recommendation, it is still challenging to recommend Web APIs with high accuracy and good diversity. In this paper, we

Distributed Denial of Service (DDoS) constitutes a major threat against cloud systems owing to the large financial losses it incurs. This motivated the security research community to investigate numerous detection techniques to limit such attack's effects. Yet, the existing solutions are still not mature enough to satisfy a cloud-dedicated detection system's requirements since they overlook the attacker's

Cloud computing is a new resource provisioning mechanism, which represents a convenient way for users to access different computing resources. Periodical workflow applications commonly exist in scientific and business analysis, among many other fields. One of the most challenging problems is to determine the right amount of resources for multiple periodical workflow applications. In this paper, the

With the increasing popularity and great economic benefit from vulnerability exploitation, it is important to study mobile vulnerability in the mobile ecosystem. Beyond the traditional technical solutions such as developing technologies to identify potential vulnerabilities, discover the widely available exploitations and protect consumers from attacks, constructing the vulnerability market, a marketplace

The new paradigm of outsourcing data to the cloud is a double-edged sword. On the one hand, it frees data owners from the technical management, and is easier for data owners to share their data with intended users. On the other hand, it poses new challenges on privacy and security protection. To protect data confidentiality against the honest-but-curious cloud service provider, numerous works have

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges in terms of adaptation to diverse network appliances and reduction of the capital and operational expenses of the service providers. In particular, to deploy service chains, providers must consider different objectives, such as minimizing the network latency or

Presents the 2019 subject/author index for this publication.

Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same

Internet of Things (IoT) is a disruptive technology in many aspects of our society, ranging from communications to financial transactions to national security (e.g., Internet of Battlefield / Military Things), and so on. There are long-standing challenges in IoT, such as security, comparability, energy consumption, and heterogeneity of devices. Security and energy aspects play important roles in data

Existing works adopt the Edge-Oriented Resource Allocation (EORA) scheme, in which edge nodes cache services and schedule user requests to distribute workloads over cloud and edge nodes, so as to achieve high-quality services and low latency. Unfortunately, EORA does not fully take into account the fact that service providers are sometimes independent from the edge operators with their own objectives

Frequently occurred oil leaking accidents can induce significant damage to the ocean ecosystem and environment. Integrated buoy sensing, which functions as a tool for periodically monitoring oil existence, plays an essential role in oil leakage detection in an offshore petroleum Internet of Things (IoT) and Cyber Physical System (CPS). Buoy sensor cyberattack can severely affect the ability to detect

With the development of GPS technology, a new Mobile Internet of Things (M-IoT) is emerging, which perceives the city's rhythm and pulse day and night to collect a large scale of city data. It is urgent to innovate M-IoT service system for these large-scale and heterogeneous data. To cope with the problem, this article proposes a Mobile-IoT based multi-modal reinforcement learning service framework

Recommendation technology is an important part of the Internet of Things (IoT) services, which can provide better service for users and help users get information anytime, anywhere. However, the traditional recommendation algorithms cannot meet user's fast and accurate recommended requirements in the IoT environment. In the face of a large-volume data, the method of finding neighborhood by comparing

Internet of Things (IoT) has become a new paradigm of communication to reform traditional industries, in which distributed data automatically collected via IoT in a low cost enables many new IT services that were even impossible decades ago. This research reports on an IoT-enabled production management service for crude-oil industry. In practice, even if an optimal management decision is achieved,

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users’ legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a

Internet of Things (IoT) devices, such as intelligent road side units and video-based detectors, are being deployed in emerging applications like sustainable and intelligent transportation systems. The primary obstacles against the development of these IoT devices are various security threats and huge energy consumption. In this article, we study the problem of scheduling tasks onto a heterogeneous

Smart grid (SG) is an emerging technology which provides many services to the end users and utilities, such as load management, frequency regulation, and grid stability. Although many solutions exist to provide these services in a secure manner, but these solutions are not adequate keeping in view of the heavy cryptographic primitives execution on these devices. Hence, in this article, GUARDIAN , a

Online electroencephalograph (EEG) classification is a core service of recently booming brain e-health, but its performance often becomes unstable because (1) conventional end-to-end models (e.g., deep neural network, $DNN$ ) largely remain static , while brain states of diseases are highly dynamic and exhibits significant individuality; and (2) EEG analytics are too complicated and have to be sustained

With the increasing number of Internet of Things (IoT) devices, the volume and variety of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its high latency and scalability. In order to process this data in less time, fog computing has evolved as an extension to Cloud computing. In a fog computing environment, a resource monitoring service

Oblivious RAM (ORAM) is important for applications that require hiding of access patterns. However, most of existing implementations of ORAM are very expensive, which are infeasible to be deployed in lightweight devices, like the terminal equipment for Internet of Things (IoT). In this article, we focus on how to apply the expensive ORAM to protect access pattern in IoT devices and propose an ORAM

Transferring and processing huge amounts of data in the cloud can violate the low latency requirements of modern IoT applications, considering underlying network infrastructure limitations. Edge data analytics is a promising solution. However, edge resources have usually less computational capabilities than cloud nodes, resulting in a higher failure rate of IoT systems. Consequently, near-real-time

There is a growing trend for service providers to migrate their services from local clusters to the cloud data center. When there is no single service can satisfy the functionality requirement of the end user, existing services are combined together to fulfill the requirements. The data communication between component service hosting servers imposes a heavy burden on the data center network. In this

Data-intensive applications need to address the problem of properly placing the set of data items in geo-distributed storage nodes. Traditional techniques use the hashing method to achieve the load balance among nodes such as those used in Hadoop and Cassandra, but are not efficient for the requests reading multiple data items in one transaction, especially when the source locations of requests are

Searchable encryption is an important technique for public cloud storage service to provide user data confidentiality protection and at the same time allow users performing keyword search over their encrypted data. Previous schemes only deal with exact or fuzzy keyword search to correct some spelling errors. In this paper, we propose a new wildcard searchable encryption system to support wildcard keyword

Because of severe network congestion experienced during peak hours in the urban area, dynamic time-dependent pricing has been proposed by some mobile operators to shift users’ data usage from peak hours to off-peak time slots. We look at the performance of time-dependent pricing on a large scale cellular network comprising ten thousand base stations. Our investigation reveals two important observations

The challenge of maintaining confidentiality of stored data in cloud is of utmost importance to realize the potential of cloud computing as an emerging storage solution service. Storing data in encrypted form may solve the problem, but exposes data to an adversary for each required computation. This repeated encryption decryption also diminishes the essence of cloud for storing encrypted database and

Attribute based encryption (ABE) is a popular cryptographic technology to protect the security of users’ data. However, the decryption cost and ciphertext size restrict the application of ABE in practice. For most existing ABE schemes, the decryption cost and ciphertext size grow linearly with the complexity of access structure. This is undesirable to the devices with limited computing capability and

Cloud computing encourages users to outsource their data to cloud storage. Data outsourcing means that users lose physical autonomy on their own data, which makes remote data integrity verification become a critical challenge for potential cloud users. To free user from the burden incurred by frequent integrity verifications, Third Party Auditor (TPA) is introduced to perform verifications on behalf

Efficient service composition in real time, while satisfying desirable Quality of Service (QoS) guarantees for the composite solution has been one of the topmost research challenges in the domain of services computing. On one hand, optimal QoS aware service composition algorithms, that come with the promise of solution optimality, are inherently compute intensive, and therefore, often fail to generate

Smartphones and a growing number of wearable devices are equipped with powerful sensors. This has led to increased interest in developing applications using sensor feeds from such devices to offer services across a wide variety of domains including healthcare, entertainment, environmental monitoring and transportation. However, most of these applications require continuous sensing, which places a heavy

Many production datacenters nowadays service multiple applications with dynamically allocated network bandwidth, and some applications are time-critical so their data transfers or flows are constrained by deadlines. To meet deadlines, several flow scheduling schemes for datacenter networks are proposed, but most of them are unaware of bandwidth variations, leading to suboptimal throughputs. In this

With the prosperity of mobile application markets, mobile advertising is becoming an increasingly important economic force. In order to maximize revenue, ads are recommended to be delivered to potentially interested users, which requires user targeting, i.e., analyzing users’ profiles and exploring users’ interests. However, collecting user personal information for targeted mobile advertising services

The overwhelming amount of information continuously flowing through the Twitter environment makes topic derivation essential. It indeed plays a valuable role in a variety of Twitter-based applications, including content recommendations, news summarization, market analysis, etc. Topic derivation methods are typically based on semantic features of tweet contents. Because tweets are short by nature, such

As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure

With the rapid development of service-oriented computing, a large number of software applications have been developed based on the services computing framework. It is well known that software engineering is a knowledge-intensive activity, and thus the effective management of service-related knowledge facilitates service-oriented software development. Although many methodologies have been proposed for