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Item type:Publication, Access status: without files , M2M Communications in 5G: State-of-the-Art Architecture, Recent Advances, and Research ChallengesM2M communication offers ubiquitous applications and is one of the leading facilitators of the Internet of Things paradigm. Unlike human-tohuman communication, distinct features of M2M traffic necessitates specialized and interoperable communication technologies. However, most existing solutions offering wired or wireless connectivity have limitations that hinder widespread horizons of M2M applications. To cope with the peculiar nature of M2M traffic, the evolving 5G system considers the integration of key enabling networking technologies for ubiquitous connectivity and guaranteed QoS. This study outlines the state-of-the-art architecture, recent advances, and open research challenges in communication technologies for M2M. Also, an overview of considerable architectural enhancements and novel techniques expected in 5G networks is presented, followed by the resultant services and benefits for M2M communications.Publicationtype: Journal Article219 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A radio resource sharing scheme for IoT/M2M communication in LTE-A downlink(2016-07-05); ; Machine-to-Machine (M2M) communication is an emerging technology which offers various ubiquitous services and is one of the main enablers of the Internet-of-Things (IoT) paradigm. Nevertheless, the notion of cellular-M2M communication is emerging due to the wide range, high reliability, increased capacity and decreased costs of future mobile networks. Consequently, M2M traffic is anticipated to pose severe challenges to mobile networks due to a myriad of devices sending and receiving small sized data. Moreover, mobile M2M traffic is expected to degrade the performance of traditional cellular traffic due to inefficient utilization of the scarce radio spectrum. This paper proposes a packet aggregation scheme to efficiently utilize radio spectrum for downlink M2M/IoT traffic. Therefore, the small sized data packets are aggregated at the Donor eNBs (DeNB) by considering the latest 3rd Generation Partnership Project (3GPP) standardized Long-Term-Evolution-Advanced (LTE-A) networks. Additionally, 3GPP standardized layer 3 inband Relay Nodes (RNs) are used for de-multiplexing of aggregated packets in downlink. 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 the number of M2M/IoT devices served per PRB (Physical Resource Block) is approximately doubled with the proposed packet aggregation scheme as compared to conventional relaying.Publicationtype: Conference Paper153 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , M2M Potentials in logistics and transportation industryAll 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 system-level simulations are performed using an LTE-Advanced-based 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.Publicationtype: Journal ArticleScopus© Citations 11 389 457 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Data aggregation of mobile M2M traffic in relay enhanced LTE-A networksMachine-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 r d 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.Publicationtype: Journal ArticleScopus© Citations 10 454 471 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Mobile M2M communication architectures, upcoming challenges, applications, and future directionsMachine-to-machine (M2M) communication is becoming an increasingly important part of mobile traffic and thus also a topic of major interest for mobile communication research and telecommunication standardization bodies. M2M communication offers various ubiquitous services and is one of the main enablers of the vision inspired by the Internet of Things (IoT). The concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability, and decreasing costs of future mobile networks. Nevertheless, M2M communications pose significant challenges to mobile networks, e.g., due to the expected large number of devices with simultaneous access for sending small-sized data, and a diverse application range. This paper provides a detailed survey of M2M communications in the context of mobile networks, and thus focuses on the latest Long-Term Evolution-Advanced (LTE-A) networks. Moreover, the end-to-end network architectures and reference models for M2M communication are presented. Furthermore, a comprehensive survey is given to M2M service requirements, major current standardization efforts, and upcoming M2M-related challenges. In addition, an overview of upcoming M2M services expected in 5G networks is presented. In the end, various mobile M2M applications are discussed followed by open research questions and directions.Publicationtype: Journal ArticleScopus© Citations 62 279 465