Correlation-aware machine selection for M2M data gathering in cellular networks

Song, Homin; Hsieh, Hung‐Yun; Tsai, Yun-Da; Choi, Wan

doi:10.1109/pimrc.2015.7343478

Cited by 2 publications

(7 citation statements)

References 11 publications

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“…Since x * is mixed strategy Nash equilibrium, i.e., u K i,m (x * , t) ≤ j ′ ∈S u K i,mj ′ (t)x * j ′ , and based on (13)ū K i,m (x * , t) = j ′ ∈S u K i,mj ′ (t)x * j ′ , and thus, ∂ẋ * j (t) ∂x * m (t) < 0. Consequently, the convergence state of the proposed algorithm, and specifically the steady state of the replicator dynamic in (12) is asymptotically stable. Since steady state of the replicator dynamic is an asymptotically stable state in our problem, then the proposed Algorithm 1 will converge to ESS [39].…”

Section: B Proof Of Theoremmentioning

confidence: 92%

“…The replicator dynamics in (12) capture the dynamics of distributed MTD clustering in our proposed algorithm. Algorithm 1 converges to percentages of various cluster sizes within the MTD population, and if so to which percentages [39].…”

Section: B Evolutionary Game Stability Analysismentioning

confidence: 99%

“…Recently, the idea of clustering MTDs into smaller groups has emerged as a promising technique to reduce the traffic load on the cellular BS and improve spatial reuse and energy efficiency, while reducing interference in the network, as studied in [3], [9], [11], and [12].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the dense deployment of MTDs in M2M networks, MTDs within close proximity will gather correlated data, thus often sending the same information (redundant bits) to the BS [12], [23], [24]. Hence, a data-centric clustering approach can be used to improve the data quality sent to the BS.…”

Section: Introductionmentioning

confidence: 99%

“…Existing work on clustering MTDs with respect to data correlation remains limited. Primarily, the works in [11] and [12] have studied the possibility of MTD clustering based on location and correlation, however, these works rely on centralized approaches that are not practical for large-scale M2M networks. Such centralized clustering approaches can lead to significant signaling overhead as they require gathering of global information, such as location and data correlation factors, for a large number of MTDs.…”

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: B Proof Of Theoremmentioning

confidence: 92%

Section: B Evolutionary Game Stability Analysismentioning

confidence: 99%