Music categorization based on acoustic features extracted from music clips and user-defined tags forms the basis of recent music recommendation applications, because relevant tags can be automatically assigned based on the feature values and their relation to tags. In practice, especially for handheld lightweight mobile devices, there is a certain limitation on the computational capacity, owing to consumers’ usage behavior or battery consumption. This also limits the maximum number of acoustic features to be extracted, and results in the necessity of identifying a compact feature subset that is used for the music categorization process. In this study, we propose an approach to compact feature subset-based multi-label music categorization for mobile music recommendation services. Experimental results using various multi-labeled music datasets reveal that the proposed approach yields better performance when compared to conventional approach.
相似文献In a wireless networked control system (W-NCS), energy is required to transmit a sensor reading to the controller. It should be noted that the packet success rate (PSR) is an essential factor in the control performance, and PSR is directly proportional to the energy per symbol. Hence, it requires a significant amount of energy to have perfect control performance. However, in most cases in wireless sensor network scenarios, each node is attached to a limited power battery. Therefore, an energy optimization scheme that can harvest energy while maintaining the control performance is essentially required. The combination of Kalman filter and Linear Quadratic Regulator (LQR) that is known as Linear Quadratic Gaussian (LQG) is used as the backbone of the scheme to estimate the state and synthesize the optimal control. In addition, the optimal power scheduler (PS) is introduced to minimize energy usage while maintaining control performance. The finite block length approach is applied to achieve the upper bound of packet error rate. The results of energy consumption optimization showed that the scheme worked perfectly, wherein the energy per symbol usage is low, and the stability of the dynamic system is well maintained.
相似文献With the popularity of mobile devices, the next generation of mobile networks has faced several challenges. Different applications have been emerged, with different requirements. Offering an infrastructure that meets different types of applications with specific requirements is one of these issues. In addition, due to user mobility, the traffic generated by the mobile devices in a specific location is not constant, making it difficult to reach the optimal resource allocation. In this context, network function virtualization (NFV) can be used to deploy the telecommunication stacks as virtual functions running on commodity hardware to meet users’ requirements such as performance and availability. However, the deployment of virtual functions can be a complex task. To select the best placement strategy that reduces the resource usage, at the same time keeps the performance and availability of network functions is a complex task, already proven to be an NP-hard problem. Therefore, in this paper, we formulate the NFV placement as a multi-objective problem, where the risk associated with the placement and energy consumption are taken into consideration. We propose the usage of two optimization algorithms, NSGA-II and GDE3, to solve this problem. These algorithms were taken into consideration because both work with multi-objective problems and present good performance. We consider a triathlon circuit scenario based on real data from the Ironman route as an use case to evaluate and compare the algorithms. The results show that GDE3 is able to attend both objectives (minimize failure and minimize energy consumption), while the NSGA-II prioritizes energy consumption.
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下载免费PDF全文 The adoption and popularization of mobile devices, such as smartphones and tablets, accentuated after the second decade of this century, has been motivated by the growing number of mobile applications, which can solve problems in different areas of contemporary societies. Conversely, the software development industry is motivated by the increasing number and quality of resources that mobile devices possess nowadays (e.g., memory, sensors, processing power or battery). While powerful mobile devices do exist, one of the main driving factors behind the increase of resources is the usage of Cloud technology, which strongly complement mobile computing. As expected, the adoption of measures to mitigate security issues has not accompanied the growth and speed of development for Cloud and Mobile software, to ensure that these are resilient to attacks by design. Aiming to contribute to decrease the gap between software and security engineering, this paper presents a deep approach to attack taxonomy, security mechanisms, and security test specification for the Cloud and Mobile ecosystem of applications. This is also the first time an encompassing and conjoined approach is provided for attack taxonomy and specification of security tests automation tools for this ecosystem.
相似文献Mobile cloud computing is a form of cloud computing that incorporates mobile devices such as smartphones and tablet PCs into the cloud infrastructure. As mobile devices are resource-constrained in nature, new scheduling strategies are required when using them as resource providers. Based on our previous group-based scheduling algorithm, we present fault-tolerant scheduling algorithms considering checkpoint and replication mechanisms to actively cope with faults. We carried out the performance evaluation with simulation to demonstrate that our algorithm is more efficient than the existing one lacking fault tolerance in terms of accuracy rate, resource consumption, and average execution time. In particular, the average execution time was reduced by about 60%, resulting in the reduction of resource consumption.
相似文献Organic light-emitting diode (OLED) displays have a high power efficiency; however, the frequent use of user interaction-based applications such as instant messengers, video players, and games contributes strongly to the total power consumption. The power consumption varies significantly depending on the display contents, and thus, color transformation, which is a representative low-power technique, is used for OLED displays. Previously developed low-power color transformation methods have not been thoroughly researched for satisfying the human visual system and have not considered optimal visual satisfaction and power consumption simultaneously. In this paper, a novel low-power color transformation approach is proposed, which is aimed at simultaneously optimizing both visual satisfaction and power consumption. In addition, it is implemented on an active-matrix OLED (AMOLED) display-based Android smartphone at runtime. Experimental results show that the proposed technique achieves better human visual satisfaction and shows up to 22.32% power saving on average on the AMOLED display and offers 6.23% more extended battery life over that of an existing leading technique.
相似文献In recent years, Fog Computing (FC) is known as a good infrastructure for the Internet of Things (IoT). Using this architecture for the mobile applications in the IoT is named the Mobile Fog Computing (MFC). If we assume that an application includes some modules, thus, these modules can be sent to the Fog or Cloud layer because of the resource limitation or increased runtime at the mobile. This increases the efficiency of the whole system. As data is entered sequentially, and the input is given to the modules, the number of executable modules increases. So, this research is conducted to find the best place in order to run the modules that can be on the mobile, Fog, or Cloud. According to the proposed method, when the modules arrive at gateway, then, a Hidden Markov model Auto-scaling Offloading (HMAO) finds the best destination to execute the module to create a compromise between the energy consumption and execution time of the modules. The evaluation results obtained regarding the parameters of the energy consumption, execution cost, delay, and network resource usage shows that the proposed method on average is better than the local execution, First-Fit (FF), and Q-learning based method.
相似文献Todays, XML as a de facto standard is used to broadcast data over mobile wireless networks. In these networks, mobile clients send their XML queries over a wireless broadcast channel and recieve their desired XML data from the channel. However, downloading the whole XML data by a mobile device is a challenge since the mobile devices used by clients are small battery powered devices with limited resources. To meet this challenge, the XML data should be indexed in such a way that the desired XML data can be found easily and only such data can be downloaded instead of the whole XML data by the mobile clients. Several indexing methods are proposed to selectively access the XML data over an XML stream. However, the existing indexing methods cause an increase in the size of XML stream by including some extra information over the XML stream. In this paper, a new XML stream structure is proposed to disseminate the XML data over a broadcast channel by grouping and summarizing the structural information of XML nodes. By summarizing such information, the size of XML stream can be reduced and therefore, the latency of retrieving the desired XML data over a wirless broadcast channel can be reduced. The proposed XML stream structure also contains indexes in order to skip from the irrelevant parts over the XML stream. It therefore can reduce the energy consumption of mobile devices in downloading the results of XML queries. In addition, our proposed XML stream structure can process different types of XML queries and experimental results showed that it improves the performace of XML query processing over the XML data stream compared to the existing research works in terms of access and tuning times.
相似文献针对移动传感器网络中的目标跟踪问题, 以及现有控制策略在保持网络拓扑结构连通性和降低能量消耗方面存在的不足, 提出一种基于蜂拥控制的移动传感器网络目标跟踪算法. 首先, 利用网络中部分节点检测目标, 并使用卡尔曼一致性滤波算法估计目标的状态, 在获得比较精确的估计状态的同时降低能量消耗; 然后, 在蜂拥控制下传感器网络始终保持拓扑结构连通性和目标对网络可见, 同时避免节点之间发生碰撞. 仿真结果验证了所提出算法的有效性.
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