•Propose a novel dynamic programming that uses RL techniques for resource allocations in IoT.•Combines QoE with RL to create pre-stored cost mapping tables for optimal resource allocations.•Implement ...content-centric network to enhance the fulfillment of the resource allocation.
The exponential growing rate of the networking technologies has led to a dramatical large scope of the connected computing environment. Internet-of-Things (IoT) is considered an alternative for obtaining high performance by the enhanced capabilities in system controls, resource allocations, data exchanges, and flexible adoptions. However, current IoT is encountering the bottleneck of the resource allocation due to the mismatching networking service quality and complicated service offering environments. This paper concentrates on the issue of resource allocations in IoT and utilizes the satisfactory level of Quality of Experience (QoE) to achieve intelligent content-centric services. A novel approach is proposed by this work, which utilizes the mechanism of Reinforcement Learning (RL) to obtain high accurate QoE in resource allocations. Two RL-based algorithms have been proposed for cost mapping tables creations and optimal resource allocations. Our experiment evaluations have assessed the efficiency of implementing the proposed approach.
Recent booming growth of networking-based solutions have brought numerous challenges to security and privacy from both perspectives of insider and outsider threats. The encrypted data are relatively ...considered a safe storage status; however, the process of encrypting data is still facing adversarial actions and data process generally is inapplicable over ciphertexts. As a type of the encryption approach allowing computations over ciphertexts, a fully homomorphic encryption (FHE) can concurrently deal with the adversarial hazards and support computations on ciphertexts. This paper focuses on the issue of blend arithmetic operations over real numbers and proposes a novel tensor-based FHE solution. The proposed approach is called a FHE for blend operations model that uses tensor laws to carry the computations of blend arithmetic operations over real numbers. In our paper, we provide both theoretical proof and experimental evaluations in order to evince the adoptability of the proposed approach.
In smart city, all kinds of users' data are stored in electronic devices to make everything intelligent. A smartphone is the most widely used electronic device and it is the pivot of all smart ...systems. However, current smartphones are not competent to manage users' sensitive data, and they are facing the privacy leakage caused by data over-collection. Data over-collection, which means smartphones apps collect users' data more than its original function while within the permission scope, is rapidly becoming one of the most serious potential security hazards in smart city. In this paper, we study the current state of data over-collection and study some most frequent data over-collected cases. We present a mobile-cloud framework, which is an active approach to eradicate the data over-collection. By putting all users' data into a cloud, the security of users' data can be greatly improved. We have done extensive experiments and the experimental results have demonstrated the effectiveness of our approach.
Employing mobile cloud computing (MCC) to enable mobile users to acquire benefits of cloud computing by an environmental friendly method is an efficient strategy for meeting current industrial ...demands. However, the restrictions of wireless bandwidth and device capacity have brought various obstacles, such as extra energy waste and latency delay, when deploying MCC. Addressing this issue, we propose a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model. In this paper, we examine our model by a simulation of practical scenario and provide solid results for the evaluations. The main contributions of this paper are twofold. First, this paper is the first exploration in solving energy waste problems within the dynamic networking environment. Second, the proposed model provides future research with a guideline and theoretical supports.
•The first attempt for extending the functionality of cloudlets.•Achieve energy-aware performances in the dynamic networking environment.•Provide future research in the field with the theoretical support and exploring directions.•The model may be migrated and applied in multiple industries.
Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic ...random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.
Contemporarily, two emerging techniques, blockchain and edge computing, are driving a dramatical rapid growth in the field of Internet-of-Things (IoT). Benefits of applying edge computing is an ...adoptable complementarity for cloud computing; blockchain is an alternative for constructing transparent secure environment for data storage/governance. Instead of using these two techniques independently, in this article, we propose a novel approach that integrates IoT with edge computing and blockchain, which is called blockchain-based Internet of Edge model. The proposed model, designed for a scalable and controllable IoT system, sufficiently exploits advantages of edge computing and blockchain to establish a privacy-preserving mechanism while considering other constraints, such as energy cost. We implement experiment evaluations running on Ethereum. According to our data collections, the proposed model improves privacy protections without lowering down the performance in an energy-efficient manner.
As an emerging fast-growing technology, smart grid networks (SGNs) have been dramatically accepted by the current power supply industry for achieving high performance power governance system. The ...wireless SGN (WSGNs) have enabled numerous flexible power management solutions without the restrictions of the wired infrastructure. The cognitive radio network (CRN) is one of the widely deployed wireless networking approaches. The communication security is a major concern while CRN is used in WSGNs. Currently, jamming and spoofing are two common attack approaches that are active in the deployment of WSGNs when using CRNs. This paper proposes an attack strategy, maximum attacking strategy using spoofing and jamming (MASS-SJ), which utilizes an optimal power distribution to maximize the adversarial effects. Spoofing and jamming attacks are launched in a dynamic manner in order to interfere with the maximum number of signal channels. Our proposed approach has been evaluated by our experiments and the results have shown the positive performance of using MAS-SJ.
The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only ...much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.
Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to ...utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D) caching, Small cell Base Station (SBS) caching and Macrocell Base Station (MBS) caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching). In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user's quality of experience (QoE) and the heterogeneity of mobile terminals in terms of caching and computing capabilities.
The trade-off between system performance and energy efficiency (service time) is critical for battery-based embedded systems. Most of the previous work focuses on saving energy in a deterministic way ...by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments (e.g., fluctuant network bandwidth and different user inputs). By adopting a probabilistic approach, this paper proposes a model and a set of algorithms to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with a guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task. Finally, to escape from local optima, a local search with restarts searches the optimal solution from candidate solutions by updating the objective function, until the stop criteria are reached or a time bound is elapsed. The experimental results demonstrate that for probability 1.0, our approach yields slightly better results than the well-known algorithms like ASAP/ALAP (As Soon As Possible/As Late As Possible) and ILP (Integer Linear Programming) with/without DVS (Dynamic Voltage Scaling). However, for probabilities 0.8 and 0.9, our approach significantly outperforms those algorithms (maximum improvement of 50.3 percent).
PDF
