Data integrity and authenticity are among the key challenges faced by the interacting devices of Internet of Things (IoT). The resource-constrained nature of sensor-embedded devices makes it even more difficult to design lightweight security schemes for these networks. In view of limited resources of the IoT devices, this article proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Initially, a trusted authority generates and assigns identities (IDs) and mask them to servers and clients, also known as member devices, in an offline phase. These IDs are utilized to prevent possible infiltration of the adversary device(s). Next, every device ensures the authenticity of requesting devices using a sophisticated challenge, which is encrypted using a 128-b secret key,$\lambda _{i}$. Each device expects a reply from the intended destination device for resolving the encrypted challenge within the defined timeframe,$i.e., \bigtriangleup T$. Moreover, authenticity of the requesting device is verified through the stored IDs, which are shared in the offline phase. Simulation results have verified the exceptional performance of the proposed authentication scheme against field proven approaches in terms of computational and communication costs.

Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.

At present, in the face of the huge and complex data in cloud computing, the parallel computing ability of quantum computing is particularly important. Quantum principal component analysis algorithm is used as a method of quantum state tomography. We perform feature extraction on the eigenvalue matrix of the density matrix after feature decomposition to achieve dimensionality reduction, proposed quantum principal component extraction algorithm (QPCE). Compared with the classic algorithm, this algorithm achieves an exponential speedup under certain conditions. The specific realization of the quantum circuit is given. And considering the limited computing power of the client, we propose a quantum homomorphic ciphertext dimension reduction scheme (QHEDR), the client can encrypt the quantum data and upload it to the cloud for computing. And through the quantum homomorphic encryption scheme to ensure security. After the calculation is completed, the client updates the key locally and decrypts the ciphertext result. We have implemented a quantum ciphertext dimensionality reduction scheme implemented in the quantum cloud, which does not require interaction and ensures safety. In addition, we have carried out experimental verification on the QPCE algorithm on IBM's real computing platform. Experimental results show that the algorithm can perform ciphertext dimension reduction safely and effectively.

Abstract The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long-battery life, and heavy-computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this article, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC.

Nowadays, trust and reputation models are used to build a wide range of trust-based security mechanisms and trust-based service management applications on the Internet of Things (IoT). Considering trust as a single unit can result in missing important and significant factors. We split trust into its building-blocks, then we sort and assign weight to these building-blocks (trust metrics) on the basis of its priorities for the transaction context of a particular goal. To perform these processes, we consider trust as a multi-criteria decision-making problem, where a set of trust worthiness metrics represent the decision criteria. We introduce Entropy-based fuzzy analytic hierarchy process (EFAHP) as a trust model for selecting a trustworthy service provider, since the sense of decision making regarding multi-metrics trust is structural. EFAHP gives 1) fuzziness, which fits the vagueness, uncertainty, and subjectivity of trust attributes; 2) AHP, which is a systematic way for making decisions in complex multi-criteria decision making; and 3) entropy concept, which is utilized to calculate the aggregate weights for each service provider. We present a numerical illustration in trust-based Service Oriented Architecture in the IoT (SOA-IoT) to demonstrate the service provider selection using the EFAHP Model in assessing and aggregating the trust scores.

Cloudlets are small self maintained clouds, with hotspot like deployment, to enhance the computational capabilities of the mobile devices. The limited resources of cloudlets can become heavily loaded during peak utilization. Consequently, per user available computational capacity decreases and at times mobile devices find no execution time benefit for using the cloudlet. Researchers have proposed augmenting the cloudlet resources using mobile devices; however, the proposed approaches do not consider the offered service to load ratio while using mobile device resources. In this paper, we propose easy to implement Mobile Device based Cloudlet Resource Enhancement (MobiCoRE) while ensuring that: (i) mobile device always have time benefit for its tasks submitted to the cloudlet and (ii) cloudlet induced mobile device load is a fraction of its own service requirement from the cloudlet. We map MobiCoRE on M/M/c/K queue and model the system using birth death markov chain. Given the arrival rate of λ, c cpu cores in cloudlet, maximum tasks in the cloudlet to be K and P0= f(λ, c, K, μ) be probability of having no user in cloudlet, we derive the condition 1/(P0) = (λK)/(cK-cdμK) 1000 for optimal average service time 1/μ of cloudlet such that the mobile applications have maximum benefit for using cloudlet services. We show that the optimal average service time is independent of the applications service requirement. Evaluation shows that MobiCoRE can accommodate up to 50 percent extra users when operating at optimal service time and sharing mobile resources for remaining task, compared to completing the entire user applications in cloudlet. Similarly, up to 47 percent time benefit can be achieved for mobile devices by sharing only 16 percent computational resources with the cloudlet.

Mobile Cloud Computing (MCC) extends cloud services to the resource-constrained mobile devices. Compute-intensive mobile applications can be augmented using cloud either in client/server model or through cyber foraging. However, long or permanent network disconnections due to user mobility increase the execution time and in certain cases refrain the mobile devices from getting response back for the remotely performed execution. In this paper, we propose use of process state synchronization (PSS) as a mechanism to mitigate the impact of network disconnections on the service continuity of cloud-based interactive mobile applications. To validate the PSS-based execution, we develop a mathematical model that incorporates the disconnection and synchronization intervals, and mobile device capabilities along with that of cloud. The comparison with existing mechanisms shows that PSS reduces the execution time by upto 47% for intermittent network connectivity compared to COMET and by upto 35% for optimized VM-based offloading.

The Vehicular Ad-hoc Networks (VANET) raises as an emerging technology for smart transport as observed in the recent decade. However, there are some hurdles affect the applications of VANET, the advancement of VANET needed to cope up with the requirement of smart transport in smart cities. The routing is the important factor for having effective communication between smart vehicles, which need to be addressed smartly. Many factors affect the communication between the vehicles such as topology fragmentation which results in the phenomenon of Local-Maximum-Problem that lead to packet delivery failure. The traditional routing schemes failed to address the problem of the trade-off between Packet-Delivery-Ratio and the high cost, in term of End-to-End-Delay, of packet recovery from the failures that occurs frequently because of Local-Maximum-Problem. Therefore, this article works out to analyses the performance of existing position-based routing protocols for Inter-vehicle ad-hoc network and introduced Intersection-based Distance and Traffic-Aware Routing (IDTAR) protocol. IDTAR aims to provide optimal performance in environments of smart transport in smart cities. The experiments evaluate IDTAR against famous position-based routing protocols such as GyTAR, A-STAR-SR and GSR considering several densities of vehicles. Simulation result shows that IDTAR protocol has a fewer End-to-End-Delay and high packet delivery ratio in smart cities circumstances. This concludes that IDTAR can be adaptive for smart transport in smart cities communication with some consideration in terms of its security, compatibility and reliability.

Software-defined networking (SDN) is new network architecture that emerges as to implement network virtualization (NV) with vast features, especially when it is applied it in multi-tenant scenarios. The rapid growth of wireless network applications and services, let to adopt NVs into software-defined wireless network (SDWN). This is because wireless networks require specific features that can be hindered of implementing NVs such as updated location information, dynamic channel configuration, and rapid client re-association. This paper presents state-of-the-art NV methods for SDWN with the aim of highlighting issues and challenges of applying NVs techniques of SDN into SDWN. We discuss three SDN techniques that facilitate NV in the cloud, namely proxy-based virtualization, layer two prefixes-based virtualizations and programing language-based virtualization. Moreover, the paper points out the possibility of providing effective VNs in the SDWN architecture. We also taxonomies the SDWN proposed virtualization methods based on hypervisor controller in the different networks. Finally, the potential requirements and challenges and open issues of SDWN NVs are also identified and presented as the future directions in SDWN research.

There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ‘big data’. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer’s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.

Abstract The unabated flurry of research activities to augment various mobile devices in terms of compute‐intensive task execution by leveraging heterogeneous resources of available devices in the local vicinity has created a new research domain called mobile ad hoc cloud (MAC) or mobile cloud. It is a new type of mobile cloud computing (MCC). MAC is deemed to be a candidate blueprint for future compute‐intensive applications with the aim of delivering high functionalities and rich impressive experience to mobile users. However, MAC is yet in its infancy, and a comprehensive survey of the domain is still lacking. In this paper, we survey the state‐of‐the‐art research efforts carried out in the MAC domain. We analyze several problems inhibiting the adoption of MAC and review corresponding solutions by devising a taxonomy. Moreover, MAC roots are analyzed and taxonomized as architectural components, applications, objectives, characteristics, execution model, scheduling type, formation technologies, and node types. The similarities and differences among existing proposed solutions by highlighting the advantages and disadvantages are also investigated. We also compare the literature based on objectives. Furthermore, our study advocates that the problems stem from the intrinsic characteristics of MAC by identifying several new principles. Lastly, several open research challenges such as incentives, heterogeneity‐ware task allocation, mobility, minimal data exchange, and security and privacy are presented as future research directions. Copyright © 2016 John Wiley & Sons, Ltd.

Mobile devices, especially smartphones are increasingly gaining ground in several domains, particularly healthcare, tele-monitoring, and education to perform Resource-intensive Mobile Applications (RiMA). However, constrained resources, especially CPU and battery hinder their successful adoption. Mobile Cloud Computing (MCC) aims to augment computational capabilities of resource-constraint mobile devices and conserve their native resources by remotely performing intensive tasks. In typical MCC solutions, intensive tasks are offloaded to distant VM-based cloud data centers or cloudlets whose exploitation originates long WAN latency and/or virtualization overhead degrading RiMA execution efficiency. In this paper, a lightweight Resource-oriented MCC (RMCC) architecture is proposed that exploits resources of plethora of Adjacent Service-based Mobile Cloudlets (ASMobiC) as fine-grained mobile service providers. In RMCC, ASMobiCs host prefabricated Restful services to be asynchronously called by mobile service consumers at runtime. RMCC is a Restful cross-platform architecture functional on major mobile OSs (e.g., Android and iOS) and realizes utilization of the computing resources of off-the-shelve outdated or damaged-yet-functioning mobile devices towards green MCC. Results of benchmarking advocate significant mean time- and energy-saving of 87% and 71.45%, respectively when intensive tasks are executed in ASMobiCs.

The data center network (DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.