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Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme. [ABSTRACT FROM AUTHOR]

Log-structured-merge (LSM) trees are a highly write-optimized data structure for lightweight, high-performance key-value (KV) stores. Furthermore, solid-state drives (SSDs) are a crucial component for I/O acceleration. Conventional LSM-over-SSD designs involve multiple software layers, including the LSM tree, host file system, and flash translation layer (FTL), which introduce cascading write amplifications. To manage the write amplifications from different layers, we propose KVSSDs, which are a close integration of LSM trees and the FTL. KVSSDs exploit the FTL mapping mechanism to implement copy-free compaction of LSM trees, and they enables direct data allocation in flash memory for efficient garbage collection. Our design also uses a fine-grained, dynamic striping policy to fully exploit the rich internal parallelism of multichip SSDs. The experimental results indicated that our LSM-SSD integrated design reduced the write amplification by 86% and improved the throughput by 383% compared with a conventional LSM-over-SSD design. [ABSTRACT FROM AUTHOR]

The proliferation of the greedy heterogeneous wireless network demands the ability to handle the sharply increasing resource requirements of power and spectrum. Thus, the study of novel and effective methods to allocate power and spectrum resources in order to reduce energy consumption for heterogeneous devices has become an urgent issue. In this context the article proposes a DJRA in which data fusion, combined with NOMA, SIC and beamforming techniques, are utilized to allocate power and spectrum resources to heterogeneous devices with a reduced energy consumption, where different radio access technologies are integrated into a cellular network for communication between devices. The data correlations between the devices are computed to group the devices so as to reduce data transmission. In addition, NOMA, SIC and beamforming are used in combination to reduce network interference between BSs and the heterogeneous devices. The main contribution of this article is the design of a large-scale heterogeneous wireless network paradigm to improve the percentage of resource utilization also reducing the energy consumption. [ABSTRACT FROM AUTHOR]

Rapidly growing wireless networks are facing spectrum shortages, so how to improve spectrum utilization becomes critical. The rise of artificial intelligence (AI) technologies can provide a more intelligent and effective strategy for realizing cognitive wireless communication to improve spectrum utilization. Therefore, this article uses AI technology for data analytics, and combines cognitive technology to perform dynamic spectrum allocation. In terms of data analytics, AI technology is utilized in both feature extraction and data dimensionality, and the data correlation calculation between users. Then the data analytics results are applied to the spectrum allocation. Combined with deep learning, an AI-driven data-analytics-based spectrum allocation (ADASA) algorithm is proposed. ADASA enables the adaptive adjustment of the allocation parameters according to the network environmental status when allocating spectrum to users. Finally, the simulation results prove that the proposed ADASA algorithm can effectively improve the spectrum utilization in heterogeneous wireless networks. [ABSTRACT FROM AUTHOR]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]