Data Transmission and Network Architecture in Long Range Low Power Sensor Networks for IoT

Kim, Dae-Young; Jung, Minwoo

doi:10.1007/s11277-016-3482-7

Cited by 29 publications

(17 citation statements)

References 13 publications

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“…Here, we take the maximum dimension value of 6. Further, calculate the growth threshold φ and disturbance factor ξ according to formulas (9) and (10). As to the initial neighborhood radius, it reflects the clustering range of the request commands, and also the command classification is often very vague at the beginning, so the value σ 0 is bigger, but the value has little influence on the clustering result because a different initial neighborhood can obtain the same result after a different number of iterations.…”

Section: Discussion Of Experiments Parametersmentioning

confidence: 99%

“…From formula (10), it can be seen that φ is comparatively small at this moment and can make a rough classification of the transmission command, which is able to have a holistic mastering of the group transmission command. As the number of iterations increase, the value of ξ will increase, and an accurate clustering result can be acquired and so can layered clustering.…”

Section: Analysis Of Msdgasmentioning

confidence: 99%

“…Descending f(X i ) and record the best solution X g (7) for j � 1 : m do (8) Calculate X g and X w , put X b into niche group DN (9) Choose X ib and X jb (10) Search X w using Algorithm 1 (11) repeat (12) Update X w using Algorithm 2 (13)…”

Section: Analysis Of Msdgasmentioning

confidence: 99%

“…erefore, a universal and integrated network is generated by the fusion of human, machine, and things. In the process of such fusion of the upper IoT application system, a variety of value type and nonvalue type of data are continually transmitted by different types of sensors [10]. In this process, different kinds of control requests will be delivered to the masses of sensing devices by a great quantity of end users.…”

Section: Introductionmentioning

confidence: 99%

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Multistage Dynamic Packet Access Mechanism of Internet of Things

Meng

Chen

Zhang

2018

Mobile Information Systems

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In the scenario of mass control commands requesting for network access, confined by the best-effort network service mode, it is easy to bring about resource competition and thus a phenomenon of access failure on major and urgent service request at the data access center for the Internet of ings. In this event, the dynamic diversification of control command is unable to access the necessary resources on a comparatively fair basis, causing low efficiency in heterogeneous resource utilization at the access center. is paper defines the problem of group request dynamic resource allocation and further converts it into the problem of 0-1 integer and linear programming and proposes a multistage dynamic packet access strategy. is strategy works first on dynamic group division on the users' mass control requests using the high ability of self-organizing feature maps and then searches for the optimized matching resources based on the frog-leaping algorithm which has a better capacity for global searching for the best resources. is paper analyzes the feasibility of this strategy and its astringency. e experimental results demonstrate that the strategy can effectively improve the success rate of access to the data center for the Internet of ings and reduce network blockage and response delay.

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Section: Discussion Of Experiments Parametersmentioning

confidence: 99%

Section: Analysis Of Msdgasmentioning

confidence: 99%

Section: Analysis Of Msdgasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multistage Dynamic Packet Access Mechanism of Internet of Things

Meng

Chen

Zhang

2018

Mobile Information Systems

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“…This study is an AHP-based network interface and channel-selection algorithm for multi-channel MAC protocols in the IoT ecosystem that takes into account a multitude of decision factors, such as expected channel condition, latency, and frame reception ratio. The paper by Kim and Jung [8] presents a data transmission and network architecture in long-range low-power sensor networks for the IoT. This study is on a network and data transmission architecture for long-range sensor networks.…”

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confidence: 99%

ICT-Based Wireless Personal Computing

Rhyu¹,

2017

Wireless Pers Commun

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Information and communications technologies (ICT)-based wireless personal computing has created increasing interest in convergence technology-based personal services, in which personal equipment is combined with wireless network-based services. Smart services, in which convergence technology services and hybrid peer-to-peer, personal environment network technology plus wireless network information technology are combined, are future-oriented. With the emergence of the smart mobile age, personal users can enjoy a lifestyle that uses content, such as information recommendation and personal services, based on various applications in smart IoT devices. This special issue introduces selected research papers by the authors who submitted them. This issue includes trends in ICTbased wireless personal computing, including digital convergence, hybrid networking systems, big data mining, interactive mobile applications, cloud services, secure data transmission, P2P network architectures, hybrid wireless protocols, index structures, multichannel protocols, crowdsourcing, future communications systems, network architectures, personal computing, short URL generation, and multicasting communications.The paper by Jeong and Shin [1] proposes an efficient access reduction scheme for big data based on total probability theory. Big data collected from a wide variety of areas shows a little variation in information search processing time, depending on the type and size of data. This study is on the reduction approach of divide-and-conquer; that is, it distinguishes all the attributes of data so as to minimize the data to improve the efficiency of data access based on the probability values within the big data group. The paper by Jung et al. [2] presents efficient malicious packet capture through an advanced & SangYeob Oh

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A combined network control approach for the edge cloud and LPWAN‐based IoT services

Kim

Park

2017

Concurrency and Computation

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Recently, low-power wide area network (LPWAN) has attracted attention as a wireless network for long-range Internet of Things (IoT) services. IoT devices in an LPWAN are managed by a network server in the cloud. Various data are delivered from the IoT devices to the network server, and the role of the centralized network server in the cloud has become important for efficient data transfer over an LPWAN. However, control by the network server concentrates the control load on the network server and is vulnerable in terms of response time and bandwidth utilization.Therefore, this paper takes the approach of moving the control of the LPWAN to the edge cloud, which provides computing and storage environments at base stations. An LPWAN gateway is then integrated with the edge cloud, and LPWAN data are cached at the edge cloud in the gateway for LPWAN control. LPWAN control such as report interval control for data, transmission power control, and data aggregation is applied to improve the efficiency of data transmission. Network control is performed by learning using cached data in the edge cloud. Compared with the existing LPWAN control approach, the proposed approach exhibits improved performance for IoT data transmission. The simulation results show the proposed approach's efficiency. KEYWORDSedge cloud, IoT, LPWAN, mobile edge computing, network control INTRODUCTIONComputer systems for intelligent services are becoming convergent in various areas. Tiny computer systems collect and exchange information using a network. They are applied to things or objects; that is, everything is connected to a network and the collected information in the network is provided as services in convergent areas. This is known as the Internet of Things (IoT). The IoT creates various intelligent services in combination with cloud computing. The information generated in the IoT area is delivered to a server in the cloud; the cloud server performs computing for services.Then, users can exploit the results of the computing as a service. In the cloud, users do not consider physical resources on the server side. Cloud computing provides computing resources regardless of physical considerations, and it has an important place in modern IT services. [1][2][3][4][5] Several wireless technologies are used to collect information about things in the IoT area, such as wireless sensor network (WSN) and low-power wide area network (LPWAN). Both WSN and LPWAN are wireless access technologies. Objects (i.e., IoT devices) named sensor nodes gather information around them using sensors in the networks. The sensor nodes are small computer systems and operate with small batteries; they have insufficient resources and use a low data rate. WSN is a wireless technology with a short transmission range; it considers personal operating space (POS) within 10 m. Thus, it is suitable for gathering information about personal space, and its applications are focused on intelligent personal area services. 6-10 In contrast, LPWAN is a wireless technology with a long transmis...

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Data Transmission and Network Architecture in Long Range Low Power Sensor Networks for IoT

Cited by 29 publications

References 13 publications

Multistage Dynamic Packet Access Mechanism of Internet of Things

Multistage Dynamic Packet Access Mechanism of Internet of Things

ICT-Based Wireless Personal Computing

A combined network control approach for the edge cloud and LPWAN‐based IoT services

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