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Jinhuan Zhang

Bio: Jinhuan Zhang is an academic researcher from Central South University. The author has contributed to research in topics: Network packet & Wireless sensor network. The author has an hindex of 7, co-authored 16 publications receiving 123 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper proposes a solution to exploit the load of IoT devices offloaded to VEC server only through vehicles by three steps and develops a low complexity algorithm to jointly optimize Selection decision, Computation resource allocation and Return result.

39 citations

Journal ArticleDOI
TL;DR: A data relay mule–based collection scheme (DRMCS) is proposed to improve the quality of service (QoS) and the sensing task completion rate of DRMCS has been improved by 78.6%.
Abstract: The fast development of Internet of Things (IoT) has greatly driven the development of mobile crowdsensing vehicular sensor network (CVSN). A lot of fascinating big data–based applications have been developed such as traffic management, health monitoring, and smart city. How to effectively collect enough data while not increasing too much redundancy is still a challenging problem in the big data application for CVSN. In this paper, a data relay mule–based collection scheme (DRMCS) is proposed to improve the quality of service (QoS). Comparing with the previous researches, the innovation of DRMCS is as follows: First, a data collection framework which considers the sensing task completion rate, redundancy rate and delay is proposed. Second, the micro mobile data center (MMDC) is proposed to solve the problem of connecting the huge number of intelligent sensing devices with data centre. Third, a MMDC selection strategy based on simulated annealing algorithm is proposed by DRMCS to improve the data collection performance. Compared with traditional vehicular network opportunistic communication without data relay mule (OCDRM), the sensing task completion rate of DRMCS has been improved by 78.6%.

36 citations

Journal ArticleDOI
Jinhuan Zhang1, Peng Hu2, Xie Fang1, Jun Long1, An He1 
TL;DR: A novel ring-based in-network data aggregation scheme that adaptively unicasts variable number of aggregated packets copies continuously in a window according to the request transmission reliability and the imbalance of nodes energy cost.
Abstract: Data aggregation can reduce the data transmission between the nodes, and thus save the energy and extend the life of the network. Many related researches on in-network data aggregation take the generalized maximum functions. For the cases that the original packets of $N$ nodes aggregated into $M$ ( $1 ) packets, it is a challenge to improve the energy efficiency and reduce the transmission delay under the transmission reliability guarantee. In this paper, a novel ring-based in-network data aggregation scheme is proposed to this problem. The network is partitioned into rings and the data aggregation is executed ring by ring from outside to inside. To ensure transmission reliability, the source or intermediate aggregating node unicasts multiple aggregated packet copies to its next hop node in the inner ring with the maximum residual energy. The reliability is higher with the more unicasting packet copies. However, more sending packets copies will lead to more additional energy cost. Besides, nodes close to the sink tend to relay more size of data packets and the energy is depleted more quickly than nodes far to the sink. Meanwhile, the nodes close to the sink need to relay the aggregated packets, which contain more information. If the number of packet copies is too small, the packets loss will greatly worse the transmission reliability. Based on this, the number of unicasting packet copies is adaptively adjusted through fuzzy logic. The proposed scheme adaptively unicasts variable number of aggregated packets copies continuously in a window according to the request transmission reliability and the imbalance of nodes energy cost. Our analysis and simulation results show the effectiveness of the proposed scheme.

36 citations

Journal ArticleDOI
TL;DR: An Active and Verifiable Trust Evaluation (AVTE) approach is proposed to identify the credibility of IoT devices, so to ensure reliable data collection for Edge Computing with low cost and theoretical analysis shows that AVTE approach can improve the data collection rate by 0.5 ~ 23.16% while ensuring long network lifetime compared with the existing scheme.
Abstract: Billions of Internet of Thing (IoT) devices are deployed in edge network. They are used to monitor specific event, process and to collect huge data to control center with smart decision based on the collected data. However, some malicious IoT devices may interrupt and interfere with normal nodes in data collection, causing damage to edge network. Due to the open character of the edge network, how to identify the credibility of these nodes, thereby identifying malicious IoT devices, and ensure reliable data collection in the edge network is a great challenge. In this paper, an Active and Verifiable Trust Evaluation (AVTE) approach is proposed to identify the credibility of IoT devices, so to ensure reliable data collection for Edge Computing with low cost. The main innovations of the AVTE approach compared with the existing work are as follows: (1) In AVTE approach, the trust of the device is obtained by an actively initiated trusted detection routing method. It is fast, accurate and targeted. (2) The acquisition of trust in the AVTE approach is based on a verifiable method and it ensures that the trust degree has higher reliability. (3) The trust acquisition method proposed in this paper is low-cost. An encoding returned verification method is applied to obtain verification messages at a very low cost. This paper proposes an encoding returned verification method, which can obtain verification messages at a very low cost. In addition, the strategy of this paper adopts initiation and verification of adaptive active trust detection according to the different energy consumption of IoT devices, so as to reliably obtain the trust of device under the premise of ensuring network lifetime. Theoretical analysis shows that AVTE approach can improve the data collection rate by 0.5 ~ 23.16% while ensuring long network lifetime compared with the existing scheme.

30 citations

Journal ArticleDOI
17 Feb 2017-Sensors
TL;DR: The analysis and simulations show that TSR-DARDA leads to lower delay with reliability satisfaction, and the optimization problem is transformed to minimize the delay under reliability constraints by controlling the system parameters.
Abstract: Physical information sensed by various sensors in a cyber-physical system should be collected for further operation. In many applications, data aggregation should take reliability and delay into consideration. To address these problems, a novel Tiered Structure Routing-based Delay-Aware and Reliable Data Aggregation scheme named TSR-DARDA for spherical physical objects is proposed. By dividing the spherical network constructed by dispersed sensor nodes into circular tiers with specifically designed widths and cells, TSTR-DARDA tries to enable as many nodes as possible to transmit data simultaneously. In order to ensure transmission reliability, lost packets are retransmitted. Moreover, to minimize the latency while maintaining reliability for data collection, in-network aggregation and broadcast techniques are adopted to deal with the transmission between data collecting nodes in the outer layer and their parent data collecting nodes in the inner layer. Thus, the optimization problem is transformed to minimize the delay under reliability constraints by controlling the system parameters. To demonstrate the effectiveness of the proposed scheme, we have conducted extensive theoretical analysis and comparisons to evaluate the performance of TSR-DARDA. The analysis and simulations show that TSR-DARDA leads to lower delay with reliability satisfaction.

13 citations


Cited by
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Journal ArticleDOI
TL;DR: A multiagent deep reinforcement learning (DRL)-based computation offloading scheme is proposed, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward.
Abstract: The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users’ daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

112 citations

Journal ArticleDOI
TL;DR: This paper investigates the total computation bits maximization problem for IRS-enhanced wireless powered MEC networks, by jointly optimizing the downlink/uplink phase beamforming of IRS, transmission power and time slot assignment used for WET and task offloading, and local computing frequencies of IoT devices.
Abstract: The combination of wireless energy transfer (WET) and mobile edge computing (MEC) has been proposed to satisfy the energy supply and computation requirements of resource-constrained Internet of Things (IoT) devices. However, the energy transfer efficiency and task offloading rate cannot be guaranteed when wireless links between the hybrid access point (HAP) and IoT devices are hostile. To address this problem, this paper aims at utilizing the intelligent reflecting surfaces (IRS) technique to improve the efficiency of WET and task offloading. In particular, we investigate the total computation bits maximization problem for IRS-enhanced wireless powered MEC networks, by jointly optimizing the downlink/uplink phase beamforming of IRS, transmission power and time slot assignment used for WET and task offloading, and local computing frequencies of IoT devices. Furthermore, an iterative algorithm is presented to solve the non-convex non-linear optimization problem, while the optimal transmission power and time allocation, uplink phase beamforming matrixes and local computing frequencies are derived in closed-form expressions. Finally, extensive simulation results validate that our proposed IRS-enhanced wireless powered MEC strategy can achieve higher total computation rate as compared to existing baseline schemes.

80 citations

Journal ArticleDOI
01 Apr 2019
TL;DR: A time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties, to reduce the delivery latency caused by unreasonable task allocation and optimize the energy consumption, which makes the sensor-cloud sustainable.
Abstract: The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable.

72 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a UAV Speed Control based Fairness Data Collection (USCFDC) scheme to improve the fairness of data collection by controlling the flight speed of the UAV in areas with a large number of nodes.
Abstract: The rapid and convenient travel of people and the timely transportation of goods depend on the correct decision of the Intelligent Transportation Systems (ITS). Due to the decision-making of ITS requires a large amount of data to support, UAV-enabled periodic data collection is an effective method. However, due to the limited resources of UAV, UAV cannot directly collect data from all storage devices, resulting in unfair data collection. Therefore, we propose a UAV Speed Control based Fairness Data Collection (USCFDC) scheme. First, since the fairness of data collection will affect the decision-making of ITS, a framework for controlling the flight speed of the UAV is proposed to improve the fairness of data collection. The flight speed of UAV will slow down in areas with a large number of nodes, thereby improving the fairness of data collection. Second, a novel method is proposed to maximize the amount of data collected by UAV from each node. With this method, the value of the amount of data will be used as the dichotomous value in the dichotomy algorithm, and the UAV must collect a certain amount of data from each node. The upper and lower limits of the dichotomy algorithm are adjusted according to the time duration for UAV to collect data. Compared with previous schemes, the fairness of data collection can be improved by a maximum of 15.89% under the same flight time of UAV. Besides, the energy consumption is reduced by 49.31%–52.55% and the flight time of the UAV is reduced by 48%–62.38% when the amount of collected data is the same.

71 citations

Posted Content
TL;DR: In this paper, the authors provide a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks.
Abstract: The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.

69 citations