A Novel Congestion Reduction Scheme for Massive Machine-to-Machine Communication

Liu, Jianlong; Zhou, Wei; Song, Lijun

doi:10.1109/access.2017.2753797

Cited by 26 publications

(25 citation statements)

References 16 publications

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“…where the event A i is as defined in the for k = 0,1,2,... ,s − 2. 16 proof of Theorem IV.1. Recall that l n r = ⌈(log 2 n r )⌉.…”

Section: Appendixmentioning

confidence: 94%

“…The size of the current backoff window is, in turn, used to estimate the current number of active nodes. In [16], a novel scheme for congestion reduction and active node cardinality estimation that uses a 6-dimensional Markov chain is proposed. In [14], a MAC protocol, which incorporates an active node cardinality estimation scheme, for an M2M network that uses multiple channels is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Several wireless technologies such as Bluetooth, Wi-Fi, ZigBee and cellular networks including LTE-Advanced and 802. 16 are potential candidates for enabling M2M communications; however, these technologies have some shortcomings 2 [6]. Cognitive Radio technology is a promising alternative to the above wireless technologies for enabling M2M communications [3].…”

Section: Introductionmentioning

confidence: 99%

“…In a heterogeneous M2M network, i.e., one in which different types of nodes are present (e.g., those that send emergency, periodic and normal type data), we need to obtain separate estimates of the number of active nodes of each traffic type. In prior work, several protocols have been designed [12]- [16] to estimate the number of active nodes in a homogeneous M2M network (see Section II). However, to the best of our knowledge, so far no estimation protocol has been designed for obtaining separate estimates of the number of active nodes of each traffic type in a heterogeneous M2M network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast node cardinality estimation and cognitive MAC protocol design for heterogeneous machine-to-machine networks

Kadam

Raut²,

Meena³

et al. 2020

Wireless Netw

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Machine-to-Machine (M2M) networks are an emerging technology with applications in numerous areas including smart grids, smart cities, vehicular telematics, and healthcare. In this paper, we design two estimation protocols for rapidly obtaining separate estimates of the number of active nodes of each traffic type in a heterogeneous M2M network with T types of M2M nodes (e.g., those that send emergency, periodic, normal type data etc), where T ≥ 2 is an arbitrary integer. One of these protocols, Method I, is a simple scheme, and the other, Method II, is more sophisticated and performs better than Method I. Also, we design a medium access control (MAC) protocol that supports multi-channel operation for a heterogeneous M2M network with an arbitrary number of types of M2M nodes, operating as a secondary network using Cognitive Radio technology. Our Cognitive MAC protocol uses the proposed node cardinality estimation protocols to rapidly estimate the number of active nodes of each type in every time frame; these estimates are used to find the optimal contention probabilities to be used in the MAC protocol. We compute a closed form expression for the expected number of time slots required by Method I to execute as well as a simple upper bound on it. Also, we mathematically analyze the performance of the Cognitive MAC protocol and obtain expressions for the expected number of successful contentions per frame and the expected amount of energy consumed. Finally, we evaluate the performances of our proposed estimation protocols and Cognitive MAC protocol using simulations.

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“…where the event A i is as defined in the for k = 0,1,2,... ,s − 2. 16 proof of Theorem IV.1. Recall that l n r = ⌈(log 2 n r )⌉.…”

Section: Appendixmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast node cardinality estimation and cognitive MAC protocol design for heterogeneous machine-to-machine networks

Kadam

Raut²,

Meena³

et al. 2020

Wireless Netw

View full text Add to dashboard Cite

show abstract

“…In the past 30 years, evolutionary multiobjective optimization (EMO) has been popular in research and application [1][2][3][4][5][6][7][8][9][10], and a lot of multiobjective evolutionary algorithms (MOEAs) have been presented [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The nondominated sorting genetic algorithm (NSGA) being one of the first MOEA was introduced in [12].…”

Section: Introductionmentioning

confidence: 99%

An Improved Multiobjective Algorithm: DNSGA2-PSA

Ding

Wang

2018

Journal of Robotics

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In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases.

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Mitigating the Massive Access Problem in the Internet of Things

Gelenbe

Nakıp

Marek

et al. 2022

Communications in Computer and Information Science

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The traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP), that results in packet losses, delays and missed deadlines for real-time data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines, but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized, that describe the benefits of these techniques.

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A Novel Congestion Reduction Scheme for Massive Machine-to-Machine Communication

Cited by 26 publications

References 16 publications

Fast node cardinality estimation and cognitive MAC protocol design for heterogeneous machine-to-machine networks

Fast node cardinality estimation and cognitive MAC protocol design for heterogeneous machine-to-machine networks

An Improved Multiobjective Algorithm: DNSGA2-PSA

Mitigating the Massive Access Problem in the Internet of Things

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