Narrowband Internet of Things: Implementations and Applications

Chen, Jiming; Kang, Ho-Min; Wang, Qi; Sun, Yuyi; Shi, Zhiguo; He, Shibo

doi:10.1109/jiot.2017.2764475

Cited by 264 publications

(112 citation statements)

References 17 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…Most of the existing works [17][18][19][20][21][22][23] propose effective methods to estimate the cost of a given workload over candidate physical configurations. However, only a few of them consider the situations where data can be replicated [20,21].…”

Section: Related Workmentioning

confidence: 99%

Diverse Mobile System for Location‐Based Mobile Data

Liao

Tan

Luo

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The value of large amount of location-based mobile data has received wide attention in many research fields including human behavior analysis, urban transportation planning, and various location-based services. Nowadays, both scientific and industrial communities are encouraged to collect as much location-based mobile data as possible, which brings two challenges: (1) how to efficiently process the queries of big location-based mobile data and (2) how to reduce the cost of storage services, because it is too expensive to store several exact data replicas for fault-tolerance. So far, several dedicated storage systems have been proposed to address these issues. However, they do not work well when the ranges of queries vary widely. In this work, we design a storage system based on diverse replica scheme which not only can improve the query processing efficiency but also can reduce the cost of storage space. To the best of our knowledge, this is the first work to investigate the data storage and processing in the context of big location-based mobile data. Specifically, we conduct in-depth theoretical and empirical analysis of the trade-offs between different spatial-temporal partitioning and data encoding schemes. Moreover, we propose an effective approach to select an appropriate set of diverse replicas, which is optimized for the expected query loads while conforming to the given storage space budget. The experiment results show that using diverse replicas can significantly improve the overall query performance and the proposed algorithms for the replica selection problem are both effective and efficient.

show abstract

Section: Related Workmentioning

confidence: 99%

Diverse Mobile System for Location‐Based Mobile Data

Liao

Tan

Luo

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…As the IoT devices normally have very limited computation and storage capabilities, MEC enables the latency-sensitive IoT applications to offload the huge amount of sensed data to the MEC servers, which are deployed near the base stations (BSs) and offer large storage and computation facilities [1]- [4]. To upload the sensed data from the IoT devices to the MEC server, NB-IoT cellular transmission technology is an attractive option, which is recently introduced in Third Generation Partnership Project (3GPP) Release 13, and is a longterm evolution (LTE) variant designed specifically for IoT [5], [6]. It enables mobile operators to efficiently support a massive number of IoT devices with low data rate transmissions and improved coverage using a small portion of their existing available licensed spectrum.…”

Section: Introductionmentioning

confidence: 99%

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Lei

Xiong

et al. 2019

IEEE Internet Things J.

View full text Add to dashboard Cite

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both the communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this paper, we propose a joint computation offloading and multi-user scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multi-user resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi-distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

show abstract

“…There exist several typical coordination methods, such as time division multiple access (TDMA), 16 frequency division multiple access (FDMA), 17 orthogonal frequency division multiple access (OFDMA), 18 and non-orthogonal multiple access (NOMA), 19 etc. There exist several typical coordination methods, such as time division multiple access (TDMA), 16 frequency division multiple access (FDMA), 17 orthogonal frequency division multiple access (OFDMA), 18 and non-orthogonal multiple access (NOMA), 19 etc.…”

Section: Introductionmentioning

confidence: 99%

“…To transmit data, IoT devices need to coordinate the access to radio channels. There exist several typical coordination methods, such as time division multiple access (TDMA), 16 frequency division multiple access (FDMA), 17 orthogonal frequency division multiple access (OFDMA), 18 and non-orthogonal multiple access (NOMA), 19 etc. Particularly, considering that NOMA applies the successive interference cancelation (SIC) technology and controls proper transmission power, 20 we consider employing the NOMA technique to coordinate IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

Luo

et al. 2019

Int J Communication

View full text Add to dashboard Cite

Energy harvesting (EH) has been considered as one of the promising technologies to power Internet of Things (IoT) devices in self-powered IoT networks. By adopting a typical harvest-then-transmit mode, IoT devices with the EH technology first harvest energy by using wireless power transfer (WPT) and then carry out wireless information transmission (WIT), which leads to the coordination between WPT and WIT. In this paper, we consider optimizing energy consumption of periodical data collection in a self-powered IoT network with non-orthogonal multiple access (NOMA). Particularly, we take into account time allocation for the WPT and WIT stages, node deployment, and constraints for data transmission. Moreover, to thoroughly explore the impact of different multiple access methods, we theoretically analyse and compare the performance achieved by employing NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) in the considered IoT network. To validate the performance of the proposed method, we conduct extensive simulations and show that the NOMA outperforms the FDMA and TDMA in terms of energy consumption and transmission power. KEYWORDSInternet of Things (IoT), non-orthogonal multiple access, wireless information transmission, wireless power transfer Int J Commun Syst. 2020;33:e4174.wileyonlinelibrary.com/journal/dac

show abstract

Narrowband Internet of Things: Implementations and Applications

Cited by 264 publications

References 17 publications

Diverse Mobile System for Location‐Based Mobile Data

Diverse Mobile System for Location‐Based Mobile Data

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

Contact Info

Product

Resources

About