Self-Organized Clustering technique based on sink mobility in heterogeneous M2M sensor networks

Al-Kaseem, Bilal R.; Nyanteh, Andrews O.; Al-Raweshidy, Hamed S.

doi:10.1109/icsae.2016.7810230

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…In (17), there is a tradeoff between cluster size and transmission power for MTDs. Thus, as the cluster size increases, the transmit power per cluster also increases, due to the increasing number of MTDs.…”

Section: A Proof Of Theoremmentioning

confidence: 99%

“…Thus, as the cluster size increases, the transmit power per cluster also increases, due to the increasing number of MTDs. The proposed utility function in (17) captures the average transmission power for all MTDs across the M2M and cellular links within the cluster, sending all the gathered data of the cluster. From (3), we can rewrite (17) as follows: (…”

Section: A Proof Of Theoremmentioning

confidence: 99%

“…Furthermore, the work in [15], investigated the problem of random access contention between cooperative groups of MTDs that coordinate their random access channel, while taking into account energy consumption and time varying queue length. On the other hand, clustering techniques based on the QoS requirements and locations of MTDs, are proposed in [9], [17]- [20] and [21], in order to maximize the number of supported MTDs. A cluster prioritization scheme for massive access management is studied in [9], where MTDs are clustered based on QoS requirements.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], a distributed resource (time) allocation scheme was proposed, to address the diverse QoS requirements in an IoT network, while taking into account data rate of CTDs and energy consumption of MTDs. The work in [17] proposes a self-organized cluster formation mechanism in which MTDs form clusters with neighboring MTDs. Additionally, the work in [18] considers cluster formation based on location to the designated cluster head and a QoS threshold, while minimizing interference in the M2M network.…”

mentioning

confidence: 99%

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Evolutionary Games for Correlation-Aware Clustering in Massive Machine-to-Machine Networks

Sawyer

Soorki

Saad

et al. 2019

IEEE Trans. Commun.

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In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-tomachine networks, MTDs are typically located within close proximity and will gather correlated data, and, thus, clustering MTDs based on data correlation will lead to a decrease in the number of redundant bits transmitted to the base station. To analyze this clustering problem, a novel utility function that captures the average MTD transmission power per cluster is derived, as a function of the MTD location, cluster size, and inter-cluster interference. Then, the clustering problem is formulated as an evolutionary game, which models the interactions among the massive number of MTDs, in order to decrease MTD transmission power. To solve this game, a distributed algorithm is proposed to allow the infinite number of MTDs to autonomously form clusters. It is shown that the proposed distributed algorithm converges to an evolutionary stable strategy (ESS), that is robust to a small portion of MTDs deviating from the stable cluster formation at convergence. The maximum fraction of MTDs that can deviate from the ESS, while still maintaining a stable cluster formation is derived. Simulation results show that the proposed approach can effectively cluster MTDs with highly correlated data, which, in turn, enables those MTDs to eliminate a large number of redundant bits. The results show that, on average, using the proposed approach yields reductions of up to 23.4% and 9.6% in terms of the transmit power per cluster, compared to forming clusters with the maximum possible size and uniformly selecting a cluster size, respectively. A preliminary version of this work appeared in IEEE GLOBECOM 2017 [1]. 2 I. INTRODUCTION Machine-to-machine (M2M) communications is an important component of the emerging Internet of Things (IoT) system, as it enables advanced networked applications such as smart home technologies, smart grid, healthcare, drone systems, manufacturing systems, and surveillance [2]-[7]. Within an M2M network, a massive number of machine-type devices (MTDs) will be densely deployed over wireless cellular networks [8]. An MTD can be a sensor, actuator, or smart meter whose typical role is to sense or measure an environment, and transmit the collected data to cellular base stations (BSs). MTDs enable real-time monitoring and control of any physical environment, without direct human involvement, thus making processes more efficient and improving human welfare [4], [9]. Since the number of MTDs is expected to be massive and much larger than the number of cellular-type devices (CTDs), it is expected that a massive-scale MTD deployment will lead to quality-of-service (QoS) degradation, increased traffic and signaling, increased latency, and reduced reliability [4]. Therefore, deploying MTDs over cellular networks faces many challenges ranging from network modeling to resource management, massive-scale, access, and MTD clustering as mention...

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Section: A Proof Of Theoremmentioning

confidence: 99%

Section: A Proof Of Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Evolutionary Games for Correlation-Aware Clustering in Massive Machine-to-Machine Networks

Sawyer

Soorki

Saad

et al. 2019

IEEE Trans. Commun.

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“…Most probably it chooses the best part of proactive routing or Reactive routing. b) SOC-M2M: M2M sensor nodes [26] act as building blocks for IoT. The main purpose of using this protocol to reduce the energy consumption and increase the lifetime of network.…”

Section: Hybrid Protocolmentioning

confidence: 99%

A Literature Review on Routing Strategy in the Internet of Things

Poluru¹,

Naseera²

2017

JESTR

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Recently Internet of Things (IoT) has gain popularity as the number of smart devices being used in day to day human life having network lifetime as a constraint. In providing connectivity between nodes, passing of routing information plays a prominent role. We identified that maximum energy of smart devices is utilised in routing the data (or) control packets. The objective of our research is to address the gaps in optimizing the network usage, which in turn maximize the network lifetime. In these directions, so far the literature review made on scalability, energy efficiency, Quality of Service (QoS), network lifetime, node deployment with Wireless Sensor Networks (WSN) perspective. In the present work we made a systematic review addressing the challenges and issues in routing with IoT perspective from the year 2014 to 2017. Additionally, we compare the performance of the routing protocols using measures like latency, bandwidth, jitter, delay.

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