Public Key Authenticated Encryption with Keyword Search (PAEKS) allows keyword searches over encrypted data in the cloud without revealing actual data and the receiver can verify the sender’s authenticity or detect tampering. However, the existing PAEKS schemes are based on classical hard problems that are vulnerable to quantum attacks. To overcome these issues, lattice-based PAEKS schemes have been proposed, which provide post quantum security but incur high computational overhead and suffer from inherent issues such as the Certificate Management Problem (CMP) or Key Escrow Problem (KEP). To address the above problems, in this paper, we introduce a Module Lattice-based Certificateless Signcryption with Keyword Search (ML-CLSCKS), which relies on Module Learning with Errors (MLWE) and Module Short Integer Solution (MSIS). The security analysis proves that ML-CLSCKS achieves both confidentiality and unforgeability against Type I and Type II adversaries in the Random Oracle Model (ROM). The performance analysis shows that ML-CLSCKS outperforms than existing lattice-based PAEKS schemes and makes the practical quantum-resistant scheme suitable for searchable encryption in cloud environments.

The rise of Internet of Things (IoT) applications has led to massive data generation. However, dealing with such massive data is challenging. Nowadays, data pipelines are popular mechanisms used to properly deal with data operations at scale in the IoT continuum. Serverless Data Pipelines (SDP) is one such approach to performing event-driven data analysis on data streams. Data pipelines are composed of many components, and scaling the entire pipeline without leaving any bottlenecks is challenging. This study aims to assess the performance of scaling mechanisms in handling stochastic workloads efficiently and understanding critical resource utilization in fog environments. We applied workload-based techniques (Request per Second, Queue Length, Message Rate) and resource-based scaling (CPU) on SDP components of two IoT applications: Aeneas (long-running functions) and PuhatuMonitoring (short-running functions). Using Azure serverless workload patterns, we compared scaling approaches in real-time fog environments, evaluating QoS metrics like processing time and CPU utilization. Our analysis of suitability, using the weighted average scoring method on two QoS metrics, revealed that for compute-intensive tasks, the resource-based scaling approach works effectively for jump, steady, spike, and fluctuation workloads. For short execution time tasks, workload-based scaling suits all four workloads.

Public key encryption with keyword search (PEKS) is a method to search over encrypted data in the cloud. However, traditional PEKS schemes are based on classical mathematical problems, which are vulnerable to quantum attacks. To address the issues, several authors proposed lattice-based PEKS. Although lattice-based PEKS resist quantum attacks, they are inefficient and also suffer from certificate management problem (CMP) or key escrow problem (KEP). To address these issues, in this paper, we propose module lattice-based certificateless encryption with keyword search (ML-CLEKS) without trapdoor algorithms. The security of ML-CLEKS is proved against TYPE-I and TYPE-II adversaries based on Module Learning with Error (Module LWE) in random oracle model. Theoretical analysis and experimental results demonstrate that ML-CLEKS is efficient.

ABSTRACT Recently, the usage of Internet of Things (IoT) devices has been increasing drastically and thus producing huge amounts of data. To handle this big data, researchers have proposed hybrid model SDP (Serverless data pipeline) approaches, supporting intermediate data processing across the fog topology. However, these predetermined path SDPs never consider the task expectation requirements and current node capabilities, and data flows through fixed paths. But in shared network environments like fog and cloud, we cannot expect that the resources will always be reserved for the pipeline. This predetermined SDP's behavior ultimately produces a low Quality of Experience (QoE) for the user regarding pipeline performance. This paper proposes a QoE‐based dynamic path SDP, which can dynamically reroute its data path on the nodes and offer better QoE. For the node selection, we used the hierarchical fuzzy‐based placement strategy and demonstrated the approach with an SDP image processing application. The designed QoE‐based SDP outperforms the Predetermined SDP in handling real‐time data without any pipeline interrupts. The experiment results showed that the proposed method is almost free from the bottleneck effect with a loss of 2% packet drops. In Predetermined SDP, we found 74% of packet loss when the load on the SDP is significant.

Microservice architecture has transformed traditional monolithic applications into lightweight components. Scaling these lightweight microservices is more efficient than scaling servers. However, scaling microservices still faces the challenges resulting from the unexpected spikes or bursts of requests, which are difficult to detect and can degrade performance instantaneously. To address this challenge and ensure the performance of microservice-based applications, we propose a status-aware and elastic scaling framework called StatuScale , which is based on load status detector that can select appropriate elastic scaling strategies for differentiated resource scheduling in vertical scaling. Additionally, StatuScale employs a horizontal scaling controller that utilizes comprehensive evaluation and resource reduction to manage the number of replicas for each microservice. We also present a novel metric named correlation factor to evaluate the resource usage efficiency. Finally, we use Kubernetes, an open source container orchestration and management platform, and realistic traces from Alibaba to validate our approach. The experimental results have demonstrated that the proposed framework can reduce the average response time in the Sock-Shop application by 8.59% to 12.34% and in the Hotel-Reservation application by 7.30% to 11.97%, decrease service level objective violations, and offer better performance in resource usage compared to baselines.

Sharing Internet of Things (IoT) data across different sectors, such as in smart cities, becomes complex due to heterogeneity. This poses challenges related to a lack of interoperability, data quality issues and lack of context information, and a lack of data veracity (or accuracy). In addition, there are privacy concerns as IoT data may contain personally identifiable information. To address the above challenges, this paper presents a novel semantic technology-based framework that enables data sharing in a GDPR-compliant manner while ensuring that the data shared is interoperable, contains required context information, is of acceptable quality, and is accurate and trustworthy. The proposed framework also accounts for the edge/fog, an upcoming computing paradigm for the IoT to support real-time decisions. We evaluate the performance of the proposed framework with two different edge and fog-edge scenarios using resource-constrained IoT devices, such as the Raspberry Pi. In addition, we also evaluate shared data quality, interoperability and veracity. Our key finding is that the proposed framework can be employed on IoT devices with limited resources due to its low CPU and memory utilization for analytics operations and data transformation and migration operations. The low overhead of the framework supports real-time decision making. In addition, the 100% accuracy of our evaluation of the data quality and veracity based on 180 different observations demonstrates that the proposed framework can guarantee both data quality and veracity.

Abstract Technological advancement in the digital era has continued to produce voluminous amounts of data through various devices. Even though data is produced distributively, it needs to be accumulated centrally for processing, analysis, and knowledge extraction that faces several challenges such as bandwidth, latency, congestion, privacy, and security. Fog computing paradigm addresses some of these issues, and can be used as a distributed data processing unit. Federated learning trains a shared model over distributed nodes. However, a fog node can not process continuously growing data due to computational limitations. In this paper, we propose FIDEL: a fog integrated federated learning framework for neural network training using resource‐constrained devices. The federation of resource‐constrained Internet of Things (IoT) devices creates a shared global model trained on local data, which is generalized on the unseen dataset for prediction/inferences. We have also designed an online training scheme to process continuous data with limited compute resources. The FIDEL supports both synchronous and asynchronous federate learning that empowers resource‐constrained devices to train machine learning models. To test the learning capabilities of the FIDEL, we have trained three neural networks (i) Shallow network; (ii) Deep Network; (iii) Convolutional Neural Network (CNN) models for human position detection in industrial IoT setup on rapidly changing datasets. The experimental results show that the framework can learn input–output relationships with significantly high accuracy. The overall system efficiency of the framework is reasonable in terms of latency and memory usage for resource‐constrained devices.

Abstract Recent developments in the Internet of Things (IoT) and real‐time applications, have led to the unprecedented growth in the connected devices and their generated data. Traditionally, this sensor data is transferred and processed at the cloud, and the control signals are sent back to the relevant actuators, as part of the IoT applications. This cloud‐centric IoT model, resulted in increased latencies and network load, and compromised privacy. To address these problems, Fog Computing was coined by Cisco in 2012, a decade ago, which utilizes proximal computational resources for processing the sensor data. Ever since its proposal, fog computing has attracted significant attention and the research fraternity focused at addressing different challenges such as fog frameworks, simulators, resource management, placement strategies, quality of service aspects, fog economics and so forth. However, after a decade of research, we still do not see large‐scale deployments of public/private fog networks, which can be utilized in realizing interesting IoT applications. In the literature, we only see pilot case studies and small‐scale testbeds, and utilization of simulators for demonstrating scale of the specified models addressing the respective technical challenges. There are several reasons for this, and most importantly, fog computing did not present a clear business case for the companies and participating individuals yet. This article summarizes the technical, non‐functional, and economic challenges, which have been posing hurdles in adopting fog computing, by consolidating them across different clusters. The article also summarizes the relevant academic and industrial contributions in addressing these challenges and provides future research directions in realizing real‐time fog computing applications, also considering the emerging trends such as federated learning and quantum computing.

Abstract The traditional cloud‐centric approach in IoT applications lack the speed and efficiency required for time‐critical tasks, resulting in network inefficiencies. To address this, the notions of Edge and Fog computing have emerged as alternatives. Fog computing facilitates the deployment of services and applications closer to the network's edge, lowering latency and allowing real‐time capabilities. It enhances reliability, fault tolerance, and connectivity in areas with spotty network coverage. Despite the fact that fog computing overcomes the limitations of cloud‐centric IoT processing, its adoption faces challenges like platform independence, interoperability, and portability. To tackle these challenges, the FogDEFT (Fog computing out of the box: Dynamic dEployment of Fog service containers with TOSCA) framework was developed. It complies to OASIS‐TOSCA standards and guarantees dynamic deployment of fog services on resource‐constrained devices while leveraging Docker containerization technology to ensure platform independence and interoperability. Due to its tight coupling with Docker Swarm, which is designed for medium‐sized deployments, the fogDEFT framework is constrained by Docker Swarm's limitations, hindering its ability to effectively manage large‐scale, automated, and resource‐efficient microservice deployments. To address these limitations, we propose FogDEFTKube, an extension of the FogDEFT architecture that incorporates Kubernetes for orchestration, Jenkins for continuous integration and deployment, and a comprehensive redefinition of the core capabilities of the FogDEFT architecture. This offers a promising solution that supports Kubernetes for handling scalable and highly available fog applications with ease while offering CI/CD. FogDEFTKube simplifies the modeling and deployment of fog services while abstracting the complexities of underlying fog networks.

With the evolutionary development of the latency-sensitive industrial Internet-of-Things (IIoT) applications, delay restriction becomes a critical challenge, which can be resolved by distributing IIoT applications on nearby fog devices. Besides that, efficient service provisioning and energy optimization are confronting serious challenges with the ongoing expansion of large-scale IIoT applications. However, due to insufficient resource availability, a single fog device cannot execute large-scale applications completely. In such a scenario, a partial service provisioning strategy provides a promising outcome to enable the services on multiple fog devices or collaboration with cloud servers. By motivating this scenario, in this article, we introduce a new deep reinforcement learning (DRL)-enabled partial service provisioning strategy in the green industrial fog networks. With this strategy, multiple fog devices share the excessive workload of an application among themselves. To reflect this, a task partitioning policy is introduced to partition the requested applications into a set of independent or interdependent tasks. Furthermore, we develop an intelligent partial service provisioning strategy to utilize maximum fog resources in the network. The experimental results express the significance of the proposed strategy over the traditional baseline algorithms in terms of energy consumption and latency up to 25% and 16%, respectively.

The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for financial technology (FinTech). The emergence of the next-generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyze the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this article, we develop a ranked-based service deployment strategy and an artificial intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users’ satisfaction ratio. Besides that, an efficient service deployment strategy is developed using$Hall's$theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVMs) algorithm is used at edge networks for analyzing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.

Abstract Fog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in‐time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE‐aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic. The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.44%, 37.83% improvement in RG, and 28.46%, 37.5%, 23.07% improvement in PTRR, when compared with QoE‐aware, randomized, FIFO algorithms, respectively.

With the exponential growth of the Internet of Things (IoT) devices in Maritime Transportation Systems (MTS), the centralized cloud-centric framework can hardly meet the requirements of the applications in terms of low latency and power consumption. By inventing the distributed edge-centric framework, real-time IoT applications can meet the requirements of the MTS by analyzing the tasks at the edge of the networks. However, one of the critical challenges of the edge-centric MTS is to provide security and privacy between local IoT devices and distributed edge nodes. Motivated by that, in this paper, we design a blockchain-enabled edge-centric framework for analyzing the real-time data at the edge of the networks with minimum latency and power consumption while meeting the security and privacy issue of MTS. The introduction of blockchain and smart contract in the edge-centric MTS frameworks help to validate the transactions of each block at edge nodes by estimating the lifetime, belief, and trustfulness, and mitigate various types of security threats. Further, we introduce different classification models to predict the malicious vessels over the real-time maritime dataset at a secured edge-centric MTS framework. Extensive simulation results demonstrate that the superiority of the proposed strategy with baseline approaches under various performance metrics.

Fog computing is the key technology to overcome the limitation of cloud computing in the domain of IoT applications. The service placement in the nearest fog devices drastically reduces the network delay, connectivity, and reliability issues and delivers real-time capabilities as an extension, reduces energy consumption and network overhead in the case of large sensor networks. However, the adoption rate of fog computing is not in proportion with the performance it proposes because, resource constraints, heterogeneity, and lack of standardization, require application-specific proprietary solutions. Therefore, we propose a framework that extends OASIS - Topology and Orchestration Specification for Cloud Applications (TOSCA) standard for modeling IoT applications and uses containerization technology to handle platform independence and interoperability, creating seamless coordination and cooperation across fog devices. The framework abstracts all the heterogeneity and complexities and offers a user-friendly paradigm to model and dynamically deploy fog services, on-demand, on the fly, from a remote system. The framework is demonstrated with a case study of the dynamic deployment of climate control service on the fog prototype.

Abstract Driven by the vision of real‐time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve. These challenges are dealing with solving complex optimization problems including scheduling and processing real‐time applications. Nature‐inspired meta‐heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP‐complete problems, from computationally tractable approximate solutions to real‐time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.

Cybertwin leverages the capabilities of networks and serves in multiple functionalities, by identifying digital records of activities of humans and things, from the Internet of Everything (IoE) applications. Cybertwin emerges as a promising solution along with next-generation communication networks, i.e., 6G technology; however, it increases additional challenges at the edge networks. Motivated by the aforementioned perspectives, in this article, we introduce a new cybertwin-driven edge framework using 6G-enabled technology with an intelligent service provisioning strategy for supporting a massive scale of IoE applications. The proposed strategy distributes the incoming tasks from IoE applications using the deep reinforcement learning technique based on their dynamic service requirements. Besides that, an artificial-intelligence-driven technique, i.e., the support vector machine (SVM) classifier model, is applied at the edge network to analyze the data and achieve high accuracy. The simulation results over the real-time financial datasets demonstrate the effectiveness of the proposed service provisioning strategy and the SVM model over the baseline algorithms in terms of various performance metrics. The proposed strategy reduces the energy consumption by 15% over the baseline algorithms, while increasing the prediction accuracy by 12% over the classification models.

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.

Fog computing has emerged as a promising paradigm that borrows the user-oriented cloud services to the proximity of the Internet-of-Things (IoT) users in sixth-generation (6G) networks. Currently, service providers establish a proprietary fog architecture to prolong a specific group of IoT users by offering resources and services to the edge level. However, this sort of activity creates a service barrier and limits the development of fog services to the IoT-users. Keeping this in mind, we develop a 6G-aware fog federation model for utilizing maximum fog resources and providing demand specific services across the network while maximizing the revenue of fog service providers and guaranteeing the minimum service delay and price for IoT-users. To achieve this goal, we formulate our objective function into a mixed-integer nonlinear problem. By jointly optimizing the dynamic services cost and user demands, a noncooperative Stackelberg game interaction algorithm is formulated to schedule the fog and cloud resources distributively. Further maximizing the profit for the service providers and the seamless resource provisioning, a resource controller is initiated to manage the available fog resources. Extensive simulation analysis over 6G-aware Quality-of-Service parameters demonstrates the superiority of the proposed fog federation model and it reduces up to 15%-20% service delay and 20%-25% of service cost over the standalone fog and cloud frameworks.

With the growth in the Internet of things (IoT) paradigm, there has been a tremendous makeshift in how the distributed devices work to achieve a common goal. However, it remains essential that all these devices work in a coherent manner to perform a collective action. This makes the task of resource provisioning extremely important in such a paradigm. The end-user level in IoT mostly comprises of low computation and communication powered devices. Improper utilization of the available resources in such a scenario burdens the complete system and degrades the quality of service. In such a scenario, the use of cloud computing techniques can help to manage the resources effectively. More so, with the emergence of relatively newer cloud-based technologies such as edge and fog computing, resource management in the IoT has become far more effective. These technologies bring the computation and communication capabilities closer to the IoT devices where some of the services can be offloaded to the edge devices. These devices are called IoT edge devices and they provide a unique opportunity to tackle some of the existing and pertinent issues for resource management in IoT paradigms; yet at the same time, they face their own set of challenges. However, the use of IoT edge devices in a traditional IoT paradigm results in better utilization of the available resources as well as improving the overall quality of service. Keeping this in mind, this special issue addressed some of the aspects related to resource management in IoT edge devices with the focus on various challenges faced, and potential techniques and solutions to address such challenges by leveraging IoT edge devices. We received numerous submissions in the issue, and we accepted 13 high-quality submissions for publication as a result after following a rigorous review process. Each of the accepted papers is summarized as follows. In the first paper, Khan et al.1 presented "A cache-based approach toward improved scheduling in fog computing" for efficient resource allocation in the fog computing environment, while maintaining the quality of service. The authors use first-in first-out scheme to place the jobs in queue and cache the job type, fog server, arrival time, time to leave, and internal processing time. The jobs are then moved from the queue by the fog broker which selects fog server having sufficient required power and resources to execute the job. The authors' proposed cache-based scheme showed promising results in terms of reducing the execution time, latency, processing delays and power consumption as compared to the conventional first-come-first-serve and shortest job first policies. The second paper on "Extensive review of cloud resource management techniques in industry 4.0: Issue and challenges" by Dewangan et al.2 sheds light on various types of resource provisioning schemes and classified those into different categories (to help understand them better) on the basis of the underlying technique and their overall objective. This survey helps to understand the optimal schemes for catering to different performance metrics such as time, cost, energy, service level of agreement rate, power consumption, resource utilization, etc. Moreover, the authors also highlighted some of the current research challenges in the domain of resource management. The next paper, "An energy efficient and low overhead fault mitigation technique for internet of thing edge devices reliable on-chip communication" by Ibrahim et al.3 presents a coding scheme to make the network-on-chip fault-tolerant. The network-on-chip provides communication backbone in the underlying network for which the proposed scheme handled both single and multibit adjacent bit errors. The next paper in this issue is on "Design and data analytics of electronic human resource management activities through Internet of Things in an organization" by Nasar et al.4 The authors focus on designing a data analytical human resource management system for IoT devices in an organization for ensuring the policies, strategies, and practices within the organization. The activities covered under this improved system include e-recruitment, e-Selection, e-performance management, e-learning, and e-compensation and the performance of the system was validated on four Kaggle databases. In the fifth paper on "A Mobile Data Offloading Framework based on a Combination of Blockchain and Virtual Voting", Hassija et al.5 enable mobile users to offload computation tasks to resource-rich mobile-devices in order to reduce energy consumption and enhance performance. The authors used directed acyclic graphs (DAGs) for mobile offloading algorithm where the users can securely submit a transaction (powered by blockchain) request for task offloading a DAG, while a game-theoretic scheme was employed in order to model the interactions between various mobile devices for bargaining cost and time. The sixth paper by Lu is on "Security of Internet of Things edge devices".6 The paper focuses on securing the edge nodes and edge gateways in IoT to meet its future security needs to eliminate the data leakage risk. The edge nodes were optimized by using a cache replacement algorithm, namely Max-PSN and the results illustrate that the proposed mechanism performed superiorly to the lead frequently used and least recently used algorithms with respect to the hit rate and average response speed of centralized and distributed systems. In the seventh paper, Balasubramanian and Jolfaei present "A scalable framework for healthcare monitoring application using the Internet of Medical Things".7 The authors made use of IoT for providing real-time alarm and assistance in order to ease the activities of pregnant women by merging the advantages of event-driven and assistive care loop framework architecture. In the next paper, Bodkhe and Tanwar shed some light on "Secure data dissemination techniques for IoT applications: Research challenges and opportunities".8 As the name suggests, the authors presented a comprehensive summary of secure data dissemination schemes present in the existing literature for IoT applications along with their potential research issues and possible countermeasures. The majority of the researched literature in this survey covers the Internet of Vehicles, Internet of Drones, and Internet of Battlefield things with respective open issues and challenges of each of these. As countermeasures, the authors researched opportunities in the directions of the requirement of secure dissemination protocols, efficient data aggregation methods, and cluster-based data dissemination. The ninth paper on "Comparative study of support vector machines and random forests machine learning algorithms on credit operation" by Teles et al.9 compares the support vector machine (SVM) and random forest (RF) scheme for their application to predict financial risks on credit operation. The outcomes of this paper suggest that while both can be effectively used for the specified task, RF has an advantage of the speed and operational simplicity over SVM; while SVM has the benefit of higher classification accuracy. The tenth paper presented by Zhao et al. titled "Message-Sensing Classified Transmission Scheme Based on Mobile Edge Computing in the Internet of Vehicles".10 The authors make use of mobile edge computing for secure message transmission by prioritizing secure messages using the analytic hierarchy process to guarantee a higher transmission level for urgent messages. Moreover, using the Lagrangian relaxation method, an optimal task offloading model was devised for delay and energy loss by assigning different weight factors to these parameters. The next paper is "FPFTS: A Joint Fuzzy PSO Mobility-aware Approach to Fog Task Scheduling Algorithm for IoT Devices" by Javanmardi et al.11 The authors build a fog task scheduler leveraging the particle swarm optimization along with fuzzy theory to assign tasks of the users to fog devices. The proposed task schedular was tested on iFogSim simulator and results show that it outperformed first-come-first-serve and delay-priority algorithms with respect to delay and network utilization. Zhang et al.,12 in their paper "Service offloading oriented edge server placement in smart farming" made use of the edge resources to support the real-time intelligent controls in smart farming. The authors presented a service offloading oriented architecture for reducing delay in data transmission from sensors to the edge servers while balancing the load on the servers and optimizing the energy consumption. The final accepted paper in this special issue is on "A metaheuristic optimization approach for energy efficiency in the IoT networks" by Iwendi et al.13 The authors proposed a hybrid metaheuristic algorithm, namely, WOA-SA, for optimizing the energy consumption of the sensors in IoT-based wireless sensor networks. The two metaheuristic approaches, namely, whale optimization algorithm and simulated annealing for choosing the cluster heads in order to optimize the energy consumption in the network. The proposed approach was found to be more effective than its counterparts in terms of load, temperature, residual energy, and cost function. We sincerely hope that after reading the accepted contributions in this special issue would help the readers of the journal and a wider research community to gain knowledge on the presented research challenges, techniques and solutions, and encourage them to further work on different aspects of resource management in IoT devices. We thank the editor-in-chief and editorial board members for providing us with the opportunity to conduct a special issue in Software: Practice and Experience. We also like to thank the administrative staff, reviewers and most importantly, the authors, for their help and contributions in successful organization of this issue.

Abstract Due to using less secured and movable devices in Internet of Things (IoT) platform, cyber‐attacks have been a major issue nowadays. Different researches have been conducted to detect the probable security attacks, but faced constraints like storage, computation cost, system failure and high latency. Existing systems require continuous monitoring, controlling, and collecting the data in entire network for delivering services with the maximum security and defense mechanism against cyber‐attacks. In this context, a decentralized mechanism of security has been presented in this article using a software‐defined network (SDN) integrated with blockchain for IoT in mobile edge and fog computing. The SDN continuously monitors and analyzes the system traffic for providing an attack identification model. The blockchain has been used to overcome the failure issues addressed in the existing models by delivering decentralized attack identification scheme which detects attacks in fog and reduces it in the edge node.

Thousands of high-end physical servers are used to fulfill the huge resource demand of diverse applications or services, ranging from healthcare data analytic services to gaming services. The network latency, as one of the major limitations of cloud computing, becomes the primary reason for introducing fog computing by pushing the computing environment towards the edge of the network. The ability to offer computing environments in close proximity to the user's device improves the delivery of high-quality services. The majority of the research is devoted to providing the high quality of services using either fog or cloud environment. In this paper, a novel deep reinforcement learning-based service dispersal approach for fog and cloud computing (DRLSD-FC) is adopted for offering the service using both environments simultaneously. The request to avail services is sliced and dispersed between the nearby fog and cloud environments. By taking advantage of cloud resources, the proposed approach minimizes the workload on the fog environment without compromising the service quality. The proposed approach is implemented using the Keras framework. Implementation results show that DRLSD-FC can outperform over other related approaches.

Due to high demand, many cloud data centers have been developed across the world, consuming a large amount of energy. Making cloud datacenters energy efficient has become essential. Energy consumption of data centers can be minimized by designing energy-efficient hardware, software, and infrastructure. In this paper, we aim at giving an overview of software techniques, affected stakeholders, performance features, datasets, and tools used to make cloud data centers energy-efficient. To achieve this goal, we conducted a systematic mapping study using five online databases. After applying inclusion/exclusion and quality criteria, we selected 58 publications for further analysis. Our results indicate that all publications are solution and validation type of publications. We did not find publications containing evaluations in industry. We found that workload scheduling is the most frequently proposed technique used to improve cloud datacenters' energy efficiency. We found that not considering violations of service level agreements mostly affects end-users of cloud data centers. When analyzing how suggested solutions are validated, we identified the need to develop a standardized set of performance measures to benchmark software techniques proposed to make cloud data centers greener.

Modern scientific instruments generate enormous amount of data. Typically, the data collected from the instruments are stored in one or more files that are then moved to a distant supercomputer for processing. The final results are sent back to the user. In order to make effective use of the time on expensive instruments, experimenters want to process the data as they are generated. They want to stream the data from instruments’ memory directly to a supercomputer’s memory for analysis. Since the compute nodes in a supercomputer are not connected directly to the wide area network, the data streams need to be passed through intermediate gateway nodes. As opposed to the best effort file transfers, data streaming applications require resources at a specific time for a specific period. In this paper, we present a system model for enabling data streaming through gateway nodes and an algorithm to efficiently allocate gateway node resources along with compute nodes. We evaluate the algorithm using real-world traces on the Chameleon Cloud. The results show that our system can schedule compute and gateway resources efficiently for streaming analysis.

This paper presents the empirical results of the coverage analysis of two LPWAN technologies i.e. NB-IoT and Sigfox, conducted on university campuses in the two main cities of Estonia, i.e. Tartu and Tallinn, using the two commercially available NB-IoT operators and the single Sigfox operator in Estonia. Most of the existing literature on NB-IoT coverage is replete with RSSI-based coverage analyses. However, RSSI is most of the time not sufficient for evaluating LTE-based technologies including NB-IoT. Thus, our investigation of NB-IoT coverage considers three parameters: RSSI, RSRP, and RSRQ such that in situations where RSSI values are unavailable, the coverage analysis is based on RSRP and RSRQ. For Sigfox coverage, we base our analysis only on the RSSI factor, as Sigfox being a Non-LTE technology. Both technologies are evaluated in indoor, outdoor and deep-indoor/underground environments to provide an understanding of their coverage in various propagation and penetration conditions. Our results indicate that in outdoor scenarios, both Sigfox and NB-IoT achieve good to excellent coverage with almost 0% packet losses. However, in indoor scenarios, few packet losses were observed in Sigfox while no packet losses were observed in NB-IoT, even with a weaker coverage, and possibly due to re-transmissions that is a salient feature of NB-IoT, making it more reliable than its competitive LPWAN technologies. However, in deep-indoor or underground scenarios, coverage outages were recorded for NB-IoT, especially in Tartu area, indicating its weaker coverage in that city.

The amount of data generated by millions of connected IoT sensors and devices is growing exponentially. The need to extract relevant information from this data in modern and future generation computing system, necessitates efficient data handling and processing platforms that can migrate such big data from one location to other locations seamlessly and securely, and can provide a way to preprocess and analyze that data before migrating to the final destination. Various data pipeline architectures have been proposed allowing the data administrator/user to handle the data migration operation efficiently. However, the modern data pipeline architectures do not offer built-in functionalities for ensuring data veracity, which includes data accuracy, trustworthiness and security. Furthermore, allowing the intermediate data to be processed, especially in the serverless computing environment, is becoming a cumbersome task. In order to fill this research gap, this paper introduces an efficient and novel data pipeline architecture, named as CCoDaMiC (Coherent Coordination of Data Migration and Computation), which brings both the data migration operation and its computation together into one place. This also ensures that the data delivered to the next destination/pipeline block is accurate and secure. The proposed framework is implemented in private OpenStack environment and Apache Nifi.

By providing the flexible and shared computing and communication resources along with the cloud services, the fog computing became an attractive paradigm to support delay-sensitive tasks in the Internet of Things (IoT). The existing researches for offloading delay-sensitive tasks in a hierarchical fog-cloud environment mostly focused on minimizing the overall communication delay. However, a fair offloading strategy selects a suitable computing device in terms of fog node or cloud server based on the resource requirements of the task while meeting the deadline. In this article, we design a new delay-dependent priority-aware task offloading (DPTO) strategy for scheduling and processing the tasks, generated from the IoT devices to suitable computing devices. The proposed strategy assigns a priority on each task based on its deadline and assigns it to a suitable multilevel-feedback queue. This schema reduces the waiting time of the delay-sensitive tasks on the queue and minimizes the starvation problem of the low priority tasks. Moreover, the DPTO strategy selects an optimal computing device for each task based on its resource availability and transmission time from the IoT device. This strategy minimizes the overall offloading time of the tasks while meeting the deadlines. Finally, the extensive simulation results with various performance parameters show the effectiveness of the proposed strategy over the existing baseline algorithms.

Nowadays, billions of Internet-of-Things devices generate various types of delay-sensitive tasks to process within a limited time frame. By processing the tasks at the network edge using distributed fog devices can efficiently overcome the deficiency of the centralized cloud data center (CDC), i.e., long latency and network congestion. Moreover, to overcome the inefficiency of the local fog devices, i.e., limited processing and storage capabilities, we investigate the collaboration between distributed fog devices and centralized CDC, where the delay-sensitive tasks can preferably be offloaded on the local fog devices, whereas the resource-intensive tasks are offloaded on the resource-rich CDC. However, one of the challenging tasks in the fog-cloud environment is to find a suitable computing device for each real-time task by considering tradeoff between the latency and cost. To meet the above-mentioned challenge, in this article, we introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique. The proposed APSO-based strategy finds an optimal computing device (i.e., fog device or cloud server) for each real-time task using multiple quality-of-service parameters, namely, cost and resource utilization (RU). The performance of the proposed algorithm is evaluated using four different real-time data sets with various performance matrices. The experimental results indicate that the proposed strategy outperforms the existing schemes in terms of average delay, computation time, RU, and average cost by 18%, 21%, 27%, and 23%, respectively.

Energy consumption of mobile apps is receiving a lot of attention from researchers. Recent studies indicate that energy consumption of mobile devices could be lowered by improving the quality of mobile apps. Frequent refactoring is one way of achieving this goal. We explore the performance and energy impact of several common code refactorings in Android apps. Experimental results indicate that some code smell refactorings positively impact the energy consumption of Android apps. Refactoring of the code smells 'Duplicated code' and 'Type checking' reduce energy consumption by up to 10.8%. Significant reduction in energy consumption, however, does not seem to be directly related to the increase or decrease of execution time. In addition, the energy impact over permutations of code smell refactorings in the selected Android apps was small. When analyzing the order in which refactorings were made across code smell types, it turned out that some permutations resulted in a reduction and some in an increase of energy consumption for the analyzed apps.

The ability to fulfil the resource demand in runtime is encouraging the businesses to migrate to cloud. Recently, to provide real-time cloud services and to save network resources, fog computing is introduced. To further improve the quality of service in delivery process, Artificial Intelligence is being applied extensively. However, the state-of-the-art in this regard is still immature as it mainly focuses at either fog or cloud. To address this issue, a novel reinforcement learning-based personalized service delivery (RLPSD) mechanism is proposed in this paper, which allows the service provider to combine the fog and cloud environments, while providing the service. RLPSD distributes the user's service requests between fog and cloud, considering the users' constraints (e.g. the distance from fog), thus resulting in personalized service delivery. The proposed RLPSD algorithm is implemented and evaluated in terms of its success rate, percentage of service requests' distribution, learning rate, discount factor, etc.

Cloud computing, Internet of Things (IoT), and big data are three important technology trends affecting all the major enterprises across the world. All these three areas are successors of classical areas of data centers, sensor networks, and data processing and prediction solutions. However, the way industries, governments, and individuals are changing across the globe, cloud, IoT, and big data analytics are going to contribute a lot in future technology transformations. We see that a number of past forecasts and anticipations hold true for the growing cloud computing adoption. A report such as Gartner1 forecasts a heavy growth of around 17% in the overall revenues from public cloud computing infrastructure and related services in year 2019. Various cloud services contributing to this revenue include services such as cloud business process services (BPaaS), cloud application infrastructure services (PaaS), cloud application Services (SaaS), cloud management and security services, and cloud system infrastructure services (IaaS). Out of all these services, the major stakeholder with the highest revenue share is cloud application services, which are mostly SaaS services. These services provide variety of solutions to a number of domains including user computing, content management, hosting, analytics, and many more. The domain of cloud computing and its services are also seeing notable changes due to a sizable adoption of IoT environments and their increasing applications. A report in the work of Puranik2 envisaged that IoT will play a major stake in extensions of cloud applications. This report also forecasts that the recent time will see a heavy usage of data analytics driven IoT services running is the cloud. The integration of cloud, IoT, and data analytics is quickly becoming a center of the technology support for a variety of applications starting from improved customer experience, accurate predictions to better supply chain management. Coming to emerging IoT adoption, a major survey revealed that IoT will be a center of the upcoming technologies and will have the most impactful “machine-aided commerce” applications in coming five years from now.3 The impact of IoT is envisaged in this report ahead of cloud computing and artificial intelligence. Gartner in a report4 forecasted a number of trends related to IoT technologies and their role in shaping the current and upcoming businesses. They expect that there will be more than 20 billion IoT devices within two years from now. The report also anticipates a lack of trained data science specialists who can fully utilize the potential of these IoT devices. In particular, this report anticipates a 4:1 ratio between the devices and human beings to highlight the role of IoT devices. We are seeing a growing role of data analytics technologies in all technology sectors including advertising, finance, market research, and many other areas. A detailed report by Gartner in the work of Laney and Jain5 shows multiple faces of data analytics with a focus on its effects on various technical and nontechnical stakeholders of the industry. This report shows a number of important statistics and predictions including a major role of data analytics-based decisions and its dependency of IoT and cloud computing as enabler technologies. There is a growing interest among the research communities and academic groups to pursue and solve various related research problems at the intersection point of three different areas of cloud, IoT, and data analytics. Buyya et al6 showcase a detailed analysis of future directions and research problems of cloud computing. The authors list a number of challenges yet to be addressed, which include challenges related to scalability, security, heterogeneity, and economics. The authors also envisage a growing role of IoT and data analytics applications running in the cloud. A discussion in the work of CACM Staff7 highlights the role of IoT, data security, machine learning, and cloud computing. Eugster et al8 showcase that the growing computational requirements by big data analytics may even replace a single cloud with a cloud of clouds. A number of recent contributions address the growing security issues in amalgamation of cloud, IoT, and Big data. Kumarage et al9 show applications of homomorphic encryption scheme to provide secure cloud-based data analytics for IoT applications. Newer intermediate node-based paradigms such as fog computing have also evolved to provide quick and scalable solutions to support IoT applications.10 Siow et al11 provide a detailed treatment to the data analytics methods for IoT applications in the areas of health, transport, living, environment, and other industry related problems. In addition, authors provide a detailed taxonomy of predictive data analytics solutions with their objectives. On the other hand, Botta et al12 provide a detailed perspective on integration of cloud and IoT technologies and the new form of “CloudIoT” applications. In addition, the authors detail various complementary aspects such as displacement, reachability, and role of big data while seeing this integration. In the coming times, it is inevitable to see the success of any one of the three technology paradigms to deliver without the help of the other two. The role of these three paradigms is also very well suited where the cloud provides infrastructure, IoT devices work as real-time data and knowledge generators, and big data analytics to provide meaningful predictions. This Special Issue on “Integration of Cloud, IoT and Big Data Analytics” has five research contributions. These contributions focus on various important aspects of the intersection of these three paradigms. The first article of this special issue is titled Cloud-based video analytics using convolutional neural networks.13 The authors in this contribution provide a video analytics approach using convolutions neural networks, which uses an “in-memory” distributed computing scheme on cloud infrastructure. The contribution highlights an object classification approach that performs a threshold-based comparison among the stored objects and the input objects in videos streams. The authors provide a detailed mathematical analysis of video analytics process with a focus on their “in-memory” distributed computing approach. The authors also provide a detailed description of experimentation performed on a spark-based private cloud platform. The authors also provide role of data size and computing node in the overall processing of the video data. The authors in this paper show a matching accuracy of 97%. A number of IoT applications are based on video and this contribution demonstrates the role of cloud infrastructure scalability in video data analytics. The second article in this special issue is titled A middleware solution for integrating and exploring IoT and HPC capabilities.14 The authors in this paper provide a new middleware solution, “JCL”, for collaboration of IoT applications and high performance computing (HPC) facilities. The authors argue that there is a strong need of having middleware solutions for the emerging IoT devices and the computations on HPC resources. To address these issues, the authors showcase JCL middleware API that supports one API to program different device categories, supporting various programming models, interoperability among various IoT services, and security issues. The authors consider IoT tasks as HPC tasks and perform the processing in JCL. The authors state that the heterogeneity issues of IoT devices are addressed in JCL using Java-enabled Android and Arduino devices. To show the simplicity, authors demonstrate a small prototype IoT-HPC application in JCL with a focus on battery consumption studies. The middleware and related APIs are need of the hour for the IoT applications. The third article in this special issue is on A multi-time steps ahead prediction approach for scheduling live migration in cloud data centers.15 This contribution focuses on a prediction approach to anticipate the live virtual machine (VM) migration in cloud computing infrastructure. The authors in this contribution state that the short duration prediction decisions in the cloud infrastructure lack accuracy due to the dynamic nature of cloud resource management. The authors focus primarily on live VM migration problem as a prediction problem and assess linear and nonlinear methods for this purpose. The authors propose a multitime step-based recurrent neural network-based prediction approach to forecast CPU utilization and bandwidth data during a live migration. Authors evaluate single time step ahead and multitime step-ahead prediction algorithms for prediction of bandwidth and CPU data using recurrent neural network. The authors show simulation results for cloud infrastructure and reveal that the recurrent neural network-based approach outperforms various traditional prediction approaches. The fourth article in this special issue is titled Evolutionary mutation testing for IoT with recorded and generated events.16 The authors in this contribution testify the event processing language (EPL) in the context of IoT events using evolutionary mutation testing (EMT) approach. The authors argue that EPL is a suitable programming language of event-based IoT applications and its testing using EMT. The authors in this contribution focus on two research questions. The first question aims at the possibility of reducing the complexity of EMT beyond the random selections. The second question aims to test the suitability of IoT-TEG (test event generator). The authors address the first question by performing experiments and concluded that guided EMT helps in finding more strong mutants. On the other hand, the second question helps in evaluating IoT-TEG and the authors show that IoT-TEG can serve as a suitable automated alternative of handwritten generation in mutation testing and EMT. Finally, the fifth article in this special issue is titled Reducing the network overhead of user mobility-induced virtual machine migration in mobile edge computing.17 The authors in this work focus on mobile edge computing where the cloud resources are placed at the network edge. This not only helps the smartphones to extend their computation and storage capacity but also helps in achieving an improved latency. The authors in this work consider the cases of Cloud VM migrations from one edge cloud to another edge cloud owing to the user mobility and suggest improved VM migration algorithms to address the network overhead issues. The paper first details the network overheads involved in the VM migrations forced by mobility of smartphone users. The authors present a classification of user movement trajectory in the form of certain and uncertain moving trajectories. Based on these movement guidelines, the authors proposed two migration algorithms. M-Weight algorithm shows reduction in the network overhead for the VMs by assigning weights to different cloud data centers based on the latency requirements of the user. For uncertain trajectories, the authors propose an M-Predict algorithm to predict mobility. We see that the set of articles compiled in this special issue focus on various facets of intersection of cloud computing, IoT, and data analytics. We are thankful to the authors for presenting their latest contributions in the form of these high-quality articles. We also show our gratitude toward the reviewers contributing to this special issue in the form of their time to ascertain quality peer reviews. At the end, we hope that the articles presented in this special issue will add a great value to the current research directions in the target area, open up more research problems, and benefit the readers.

The papers in this special section focus on mobile cloud computing. The papers address variety of interesting topics covering different aspects of the Mobile Cloud, such as process offloading, work sharing, performance enhancement of Mobile Clouds, security issues in Mobile Clouds, and applications of Mobile Clouds.

Due to the limited computational capabilities of the mobile device they usually need to delegate resource intensive tasks to the cloud. Code offloading is one of the techniques used for such purposes. In Code offloading, the mobile application is partitioned to identify resource intensive tasks which are then transferred to the server for remote processing. Various techniques have been in use for performing code offloading but none of them is economically viable due to which this model is not frequently used in the industry. In this paper, we tried to address the issues which make code offloading expensive and came up with code offloading model that can make the process economically viable. We developed a game theoretic model that provides incentive to mobile users to open their devices for offloading. Simulations have been done to validate the mathematical model and a prototype has also been developed to see how the framework behaves in the real world scenario.

Mobile Mesh Social Network (MMSN) represents an environment where the mobile device users are capable of performing various virtual social network activities such as sharing information, forming social groups, text messaging when they encounter each other in the physical vicinity within the wireless network range. Moreover, the characteristics of MMSN such as the Internetless activities and Wireless Mesh Network (WMN)-based connectivity provides various potentials including but not limited to business opportunities, scalable crowdsourcing or crowdsensing deployment, edge computing and so on. Although there exist a fair number of software platforms that help developers to implement MMSN, they still cannot fully overcome the limitation derived from the hardware resource constraint nature of the participative mobile devices. In order to enhance the MMSN in terms of cost efficiency, we introduce Fog Social Network (FSN) model, which utilises the computing and networking resources in users' close vicinity to improve the overall efficiency of MMSN. Further, the proposed FSN framework consists of an adaptive resource-aware cost-performance index (CPI) scheme, which performs dynamic approach selection autonomously at runtime to choose the most efficient route for the delivery of the messages for MMSN activities. With this intention, we have implemented and validated a proof-of-concept prototype.

Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices' proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.

Over the past years, organizations have been moving their enterprise applications to the cloud with the aim of reducing infrastructure ownership and maintenance costs, and to take advantage of the elasticity and heterogeneity of the cloud. This paper joined the approaches of multi-cloud deployment using CloudML and identifying the ideal resource provisioning and deployment configuration using an optimization model, in order to dynamically scale an enterprise application across multiple clouds, without any user intervention. The approaches are discussed in detail along with the introduced extensions. Benchmark experiments were conducted on Amazon cloud infrastructure, based on one system with a single scalable component and two other systems with the basic workflow control structures, parallel and exclusive. The results of the experiments suggest that the approach is plausible for dynamic deployment and auto-scaling any web/services based enterprise workflow/application on the cloud.

Emerging modern smartphones are powerful and capable of exchanging larger files in a peer-to-peer manner. Such a connection can be used in opportunistic networks where the permanent end-to-end path does not exist. In this study, we present an energy-aware forwarding protocol for large data messages. We implemented a mobile peer-to-peer network application to forward messages over the Wi-Fi link. The preliminary experimental result shows that the proposed protocol can optimise the energy consumption across the network.

The distant data centre-centric Internet of Things systems face the latency issue especially in the real-time-based applications. Recently, Fog Computing models have been introduced to overcome the latency issue by utilising the proximitybased computational resources. However, the increasing users of Fog Computing servers will cause bottleneck issues and consequently the latency issue arises again. This paper introduces the utilisation of Mist Computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of IoT networks. The proposed service-oriented mobile-embedded Platform as a Service framework enables the edge IoT devices to provide a platform that allows requesters to deploy and execute their own program models. The framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. Additionally, the framework also supports task distribution among a group of Mist nodes. The prototype has been tested and performance evaluated on the real world devices.

Mobile cloud computing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.

Emerging Mobile Phone Sensing (M-Sense) systems enable a flexible large scale wireless sensing capability and also reduce the need of establishing the infrastructure of Wireless Sensor Network for collecting sensory information in the Internet of Things applications. M-Sense has been applied in numerous scenarios including mobile-health systems, environmental monitoring, vehicle ad hoc network, mobile social network, and so on. The drawback of existing M-Sense systems in terms of privacy, trust, less efficiency of participating in multiple sensing networks, has motivated the next generation sensing service provisioning approach. This paper introduces a generic service-oriented Mobile Host Sensing as a Service provisioning framework that allows a mobile device to provide sensing data to multiple parties based on mobile Web services. The proposed framework consists of the hybrid workflow-based control system, the dynamic Utility Cloud service, and the service provisioning scheduling model to enhance the quality of service provisioning. The prototype has been tested on real mobile devices and the details of the performance evaluation are presented.

Internet of Things (IoT) represents a cyber-physical world where physical things are interconnected on the Web. This paper presents an architecture designed for Energy-efficient Inter-organizational wireless sensor data collection Framework (EnIF). Environmental monitoring and urban sensing are two major application scenarios in IoT. Different from the traditional sensor environments, environmental sensing in IoT may require battery-powered nodes to perform the sensing tasks. Such a requirement raises a critical challenge to ensure that sensor data gathering can be collected in a timely and energy-efficient manner. Although numerous energy-efficient approaches for IoT scenarios have been proposed, previous works assumed the entire network was managed by a single organization in which the network establishment and communication have been pre-configured. This assumption is inconsistent with the fact that IoT is established in a federated network with heterogeneous devices controlled by different organizations. The aim of the framework is to enable a dynamic inter-organizational collaborative topology towards saving energy from data transmissions using a service-oriented architecture.

Summary Platforms with automatic memory management, such as the JVM, are usually considered free of memory leaks. However, memory leaks can happen in such environments, as the garbage collector cannot free objects, which are not used by the application anymore, but are still referenced. Such unused objects can eventually fill up the heap and crash the application. Although this problem has been studied extensively, nevertheless, there are still many rooms for improvement in this area. This paper describes the statistical approach for memory leak detection, as an alternative, along with a commercial tool, Plumbr, which is based on the method. The tool is later analyzed with three case studies of real applications and in the process also analyzes strengths and weaknesses of the statistical approach for memory leak detection. Copyright © 2014 John Wiley & Sons, Ltd.

Over the past years organizations have been moving their enterprise applications to the cloud to take advantage of cloud's utility computing and elasticity. However, in enterprise applications or workflows, generally, different components/tasks will have different scaling requirements and finding an ideal deployment configuration and having the application to scale up and down based on the incoming requests is a difficult task. This paper presents a novel resource provisioning policy that can find the most cost optimal setup of variety of instances of cloud that can fulfill incoming workload. All major factors involved in resource amount estimation such as processing power, periodic cost and configuration cost of each instance type and capacity of clouds are considered in the model. Additionally, the model takes lifetime of each running instance into account while trying to find the optimal setup. Benchmark experiments were conducted on Amazon cloud, using a real load trace and through two main control flow components of enterprise applications, AND and XOR. In these experiments, our model could find the most cost-optimal setup for each component/task of the application within reasonable time, making it plausible for auto-scaling any web/services based enterprise workflow/application on the cloud.

Mobile devices are capable of acting as smart assistances not only to serve their users but also to collaborate with each other remotely via wireless Internet to accomplish common goals. The latter is achieved by establishing a Social Private Cloud (SPC). SPC is a cluster formed by a scalable group of social network participants using their mobile devices that are capable of providing their resources to accomplish computational tasks. In this paper, we propose a workflow-based SPiCa framework that enables task delegation in SPC. In order to support adaptive task scheduling based on resource availabilities, a resource-aware task scheduling scheme has been proposed and implemented as a proof of concept. The evaluation demonstrates that the framework is capable of dynamically reacting to runtime changes in order to adjust the task delegation process.

Device-to-Device (D2D) communication is a potential strategy to release the mobile network from unnecessary data transfer, accelerate the responsiveness of end-to-end apps, and decentralize the provisioning of traditional services. D2D coordination is a critical challenge, which cannot be overcome without the explicit intervention of the user as D2D communication represents a threat for user's privacy. However, social attributes can be leveraged to equip the devices with trusted mechanisms that can automate D2D communication. In this paper, we build and design a mobile cloud system that relies on audio data obtained from user's environment to determine whether a set of devices are located in proximity. Audio analysis is performed on the cloud using classical machine learning principles, and the cloud instance (server) also informs the devices about the coordination plan to establish D2D communication. The framework is evaluated using a smartphone app for sharing files and the evaluation shows that the approach is feasible in practice.

Scientific computing is a field that applies computer science to solve scientific problems from domains like genetics, biology, material science, chemistry etc. It is strongly associated with high performance computing (HPC) and parallel programming fields as scientific computing typically utilizes large scale computer modeling and simulation and thus requires large amounts of computer resources. Public clouds seem to be very suitable for solving scientific computing problems, but they are often built on commodity hardware and it's not simple to design applications that can efficiently utilize large amounts of computing resources. This paper gives an overview of a study that researches the use of distributed computing frameworks like MapReduce to greatly simplify solving scientific computing problems in the cloud and compares how well the results measure up to the current de facto standard practices of the distributed computing field.

Memory leaks are major problems in all kinds of applications, depleting their performance, even if they run on platforms with automatic memory management, such as Java Virtual Machine. In addition, memory leaks contribute to software aging, increasing the complexity of software maintenance. So far memory leak detection was considered to be a part of development process, rather than part of software maintenance. To detect slow memory leaks as a part of quality assurance process or in production environments statistical approach for memory leak detection was implemented and deployed in a commercial tool called Plumbr. It showed promising results in terms of leak detection precision and recall, however, even better detection quality was desired. To achieve this improvement goal, classification algorithms were applied to the statistical data, which was gathered from customer environments where Plumbr was deployed. This paper presents the challenges which had to be solved, method that was used to generate features for supervised learning and the results of the corresponding experiments.

No abstract available.

The increasing demand of the mobile applications for processing power, storage space and energy saving have led them in adapting the cloud. To ease the offloading of these resource-intensive activities to the cloud, we have developed the Mobile Cloud Middleware (MCM), which also helps in combining services from multiple clouds. While MCM is shown to be horizontally scalable, the dynamic loads of telecommunication networks demand for identifying ideal topology and hot re-configuration of the deployment. The paper proposes the characteristics to be considered for identifying the topology and using concurrent languages like Erlang for adapting the topology dynamically.

The cloud services invocation from the handset enables the next generation of mobile applications that are not limited by storage space and processing power. Bakabs is one such application for Android and iOS devices, which makes use of Google Analytics cloud services to track the traffic of websites, replicated on multiple instances in different locations. Bakabs also suggests the number and type of instances that are required to handle the loads, based on the linear programming model. The prediction is performed at the mobile cloud middleware, which facilitates invocation of multiple cloud services from mobiles. Based on the suggestions, the user can decide to turn on/off instances, thus saving costs taking advantage of the pay-as-you-go model and the elasticity of the cloud. The performance analysis of the application shows that Bakabs can utilize cloud services with significant ease and reasonable performance latencies on the devices, with the considered technological choices.

Mobile technologies are drawing their attention to the cloud computing due to the increasing demand of the applications, for processing power, storage space and energy. However, developing mobile cloud applications involves working with services and APIs from different cloud vendors. Most often these APIs are not interoperable and the information processed and stored into the cloud is non-transferable across clouds. To counter these problems, a generic middleware framework, Mobile Cloud Middleware (MCM) is designed, which handles the interoperability issues, and eases the use of process-intensive services from mobile phones. A prototype of MCM is developed and several applications are demonstrated in different domains. Moreover, to verify the scalability of MCM, load tests are performed on the hybrid cloud resources. The detailed performance analysis of the middleware framework shows that MCM improves the quality of service for mobiles and helps in maintaining soft-real time responses for mobile cloud applications.

SciCloud is a project studying the scope of establishing private clouds at universities. With these clouds, researchers can efficiently use the already existing resources in solving computationally intensive scientific, mathematical, and academic problems. The project established a Eucalyptus based private cloud and developed several customized images that can be used in solving problems from mobile web services, distributed computing and bio-informatics domains. The poster demonstrates the SciCloud and reveals two applications that are benefiting from the setup along with our research scope and results in scientific computing.

Multimedia information systems have been developed into service-ware. With the paradigms of web services, service oriented architectures (SOA), and Web 2.0 widgets, multimedia has become truly ubiquitous. However, interoperability, scalability, reliability and security are arising challenges at mobile multimedia service development. This paper focuses on the analysis, design, development and evaluation of a middleware that allows access from mobile devices to a bundle of multimedia services. The services are based on the international multimedia metadata description standard MPEG-7. The implementation is based on new generation of service-oriented application servers called Lightweight Application Server (LAS). Mobile web services refer to the fact that mobile servers host web services. A prototype was developed as a proof of concept, showing how to access MPEG-7 based multimedia services from a Mobile Host and the analysis results of providing MPEG-7 based multimedia services in the form of web services from the Mobile Host to other mobile devices. An alternative solution is to apply enterprise service bus technology as the middleware. The performance evaluation results of both approaches show the reliable accessibility of MPEG-7 based multimedia services via the enterprise service bus solution.

Due to the astonishing development in memory and processing capabilities of hand held devices such as smart phones, it is not a dream anymore to enable mobile devices not only as conventional web service requesters but even as providers. The willingness and enthusiasm of service providers place abundant services at the disposal. But this abundance makes the efficiency of service discovery a critical issue. Centralized registries have severe drawbacks in such a scenario due to the dynamic and spontaneous nature of mobile peers. In the quest for a more appropriate approach for mobile web service discovery, we observed P2P to share very similar characteristics with behaviors of peers in mobile network. Hence we tried to find alternate mobile web service discovery mechanisms by using the features of the P2P networks like JXTA modules. The scalability analysis of the approach proves that the discovery can scale to the needs of large cellular networks.