Mobile M2M communication architectures, upcoming challenges, applications, and future directions

Mehmood, Yasir; Görg, Carmelita; Muehleisen, Maciej; Timm‐Giel, Andreas

doi:10.1186/s13638-015-0479-y

Cited by 60 publications

(29 citation statements)

References 52 publications

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“…For instance, 712 bits are sent in a TTI (transmission time interval) with an MCS (modulation and coding scheme) of 26. However, allocating 1 PRB to a single device can significantly degrade PRB utilization [11,[17][18][19].…”

Section: Problem Statementsmentioning

confidence: 99%

Data aggregation of mobile M2M traffic in relay enhanced LTE-A networks

Marwat

Mehmood

Görg

et al. 2016

J Wireless Com Network

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Machine-to-machine (M2M) communication is becoming an increasingly essential part of mobile traffic and thus also a major focus of the latest 4G and upcoming 5G mobile networks. M2M communication offers various ubiquitous services and is one of the main enablers of the Internet-of-things (IoTs) vision. Nevertheless, the concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability as well as decreasing costs of future mobile networks. Resultantly, M2M traffic poses drastic challenges to mobile networks, particularly due to the expected large number of devices sending small-sized data. Moreover, mobile M2M traffic is anticipated to degrade the performance of traditional cellular traffic due to inefficient utilization of the scarce radio spectrum. This paper presents a novel data aggregation and multiplexing scheme for mobile M2M traffic and thus focuses on the latest 3GPP (3 rd Generation Partnership Project) tong-term-evolution-advanced (LTE-A) networks. 3GPP standardized layer 3 inband Relay Nodes (RNs) are used to aggregate uplink M2M traffic by sharing the Physical Resource Blocks (PRBs) among several devices. The proposed scheme is validated through extensive system level simulations in an LTE-A based implementation for the Riverbed Modeler simulator. Our simulation results show that besides coverage extensions, RNs serve approximately 40 % more M2M devices with the proposed data multiplexing scheme compared to the conventional without multiplexing approach. Moreover, in this paper an analytical model is developed to compute the multiplexing transition probabilities. In the end, the simulation and analytical results of multiplexing transition probabilities are compared in order to analyze the multiplexing scheme.

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Section: Problem Statementsmentioning

confidence: 99%

Data aggregation of mobile M2M traffic in relay enhanced LTE-A networks

Marwat

Mehmood

Görg

et al. 2016

J Wireless Com Network

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“…Mobile M2M communication offers manifold applications and services in modern transport and logistical processes such as onboard security, traffic and infrastructure management, fleet management, and route planning [19]. In case of an emergency, the collected data are sent to other vehicles as well as infrastructure to gain immediate attention.…”

Section: M2m Use Casesmentioning

confidence: 99%

M2M Potentials in logistics and transportation industry

et al. 2016

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All over the world, road congestion is among the most prevalent transport challenges usually in urban environments which not only increases fuel consumption and emission of harmful gases, but also causes stress for the drivers. Intelligent Transportation System (ITS) enables a better use of the infrastructure by connecting vehicles to other vehicles as well as infrastructure and thus delivers a faster communication opportunity to ensure safe and secure driving. Machine-to-machine (M2M) communication is one of the latest information and communication technologies which offers ubiquitous connectivity among several smart devices. The use of mobile (cellular) M2M communications has emerged due to the wide range, high reliability, increased data rates, decreased costs as well as easy and short-term deployment opportunities. Since the radio spectrum is a scarce resource, M2M traffic can potentially degrade the performance of mobile networks due to the large number of devices sending small-sized data. This paper presents an efficient data multiplexing scheme by using Long-Term Evolution Advanced (LTE-Advanced) Relay Nodes, which aggregates M2M traffic to maximize radio resource utilization. Extensive systemlevel simulations are performed using an LTE-Advancedbased model developed in the RIVERBED modeler to evaluate the performance of the proposed data multiplexing scheme. Simulation results show that approximately 40 % more smart M2M devices used in ITS and logistics are served per LTE-Advanced cell under the present system settings. Keywords Road congestion Á Intelligent transportation systems Á Information and communication technologies Á Machine to machine Á Relay nodes Á LTE-Advanced This article is part of a focus collection on ''Dynamics in Logistics: Digital Technologies and Related Management Methods.''

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“…The architecture of IoT mainly consists of five key elements: IoTDs, IoT area networks, IoT gateways (IoTGs), communication networks, and IoT application domains. The generic network architecture of IoT communication is shown in Figure .…”

Section: Introductionmentioning

confidence: 99%

“…3,4 There are many unique characteristics related to IoT devices (IoTDs) where IoTDs create traffic with various quality of service (QoS) requirements, there is a huge number of IoTDs per cell, and each IoTD creates small data volumes in intervals. 5 The architecture of IoT mainly consists of five key elements 6 : IoTDs, IoT area networks, IoT gateways (IoTGs), communication networks, and IoT application domains. The generic network architecture of IoT communication is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

AlQahtani

2019

Int J Communication

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Summary Recently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required. In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.

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Mobile M2M communication architectures, upcoming challenges, applications, and future directions

Cited by 60 publications

References 52 publications

Data aggregation of mobile M2M traffic in relay enhanced LTE-A networks

Data aggregation of mobile M2M traffic in relay enhanced LTE-A networks

M2M Potentials in logistics and transportation industry

Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

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