M2M Potentials in logistics and transportation industry

Mehmood, Yasir et al. Article M2M Potentials in logistics and transportation industry Logistics Research Provided in Cooperation with: Bundesvereinigung Logistik (BVL) e.V., Bremen Suggested Citation: Mehmood, Yasir et al. (2016) : M2M Potentials in logistics and transportation industry, Logistics Research, ISSN 1865-0368, Springer, Heidelberg, Vol. 9, Iss. 1, pp. 1-11, https://doi.org/10.1007/s12159-016-0142-y This Version is available at: https://hdl.handle.net/10419/157740 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ ORIGINAL PAPER M2M Potentials in logistics and transportation industry Yasir Mehmood 1 •Safdar Nawaz Khan Marwat 2 •Koojana Kuladinithi 1 • Anna Fo ¨rster 1 •Yasir Zaki 3 •Carmelita Go ¨rg 1 •Andreas Timm-Giel 4 Received: 15 December 2015 / Accepted: 16 July 2016 / Published online: 28 July 2016 The Author(s) 2016. This article is published with open access at Springerlink.com Abstract 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-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. Keywords Road congestion Intelligent transportation systems Information and communication technologies  Machine to machine Relay nodes LTE-Advanced 1 Introduction The dramatic use of communication technologies (wired and wireless), embedded systems as well as increasing penetration of the Internet has not only revolutionalized human lives, but also reshaped almost all types of business This article is part of a focus collection on ‘‘Dynamics in Logistics: Digital Technologies and Related Management Methods.’’ &Yasir Mehmood [email protected] Safdar Nawaz Khan Marwat [email protected] Koojana Kuladinithi [email protected] Anna Fo ¨rster [email protected] Yasir Zaki [email protected] Carmelita Go ¨rg [email protected] Andreas Timm-Giel [email protected] 1 Communication Networks, University of Bremen, Bremen, Germany 2 Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Peshawar, Pakistan 3 Computer Science Department, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE 4 Institute of Communication Networks, University of Technology, Hamburg, Germany 123 Logist. Res. (2016) 9:15 DOI 10.1007/s12159-016-0142-y models and processes [1]. M2M communication is one of the emerging technologies which offers ubiquitous connectivity among intelligent devices, hence is one of the major enablers of the Internet-of-Things (IoTs) vision [2]. IoT is an innovative concept which offers to connect smart devices often called things endowed with several sensing, automation as well as computing capabilities, with the Internet [3]. Resultantly, the connected devices are revolutionalizing the future cyber physical systems, yielding several applications. Moreover, the mobile network operators are partnering with industrial organizations in order to bring forth innovative IoT services to facilitate end consumers. For instance, M2M applications include intelligent transportations, logistics and supply chain management, e-health, smart metering, surveillance and security, smart cities, and home automation [4–6]. Thus, M2M communication is foreseen to reshape the business of operators, service providers, M2M enterprises, and M2M enablers [7]. Vodafone revealed that M2M communication is becoming one of the driving forces for businesses which inspires to bring forth innovative solutions almost in every sector such as logistics, automotive industry, cities, homes, schools, and workplaces [8]. Approximately 90 % of the companies worldwide have adopted M2M technology and imparted it as one of the most favorable technologies for achieving noticeable outcomes. Automotive industry is one of the top sectors for adopting M2M technology. Approximately 32 and 17 % increasing growth rates for adopting M2M technology have been noticed in automotive and logistic sectors, respectively, as shown in Fig. 1. Thus, ITS and logistics are considered as one of the potential M2M users worldwide [9]. In addition, NOKIA forecasted that the use of M2M technology in automotive industry and logistical processes will dominate other applications in the future [10]. One of the major motivations is to deliver a fully managed infrastructure which primarily guarantees, e.g., safe and secure driving, in time delivery, smart monitoring, and tracking of assets. Resultantly, this can revolutionize the existing methods of transportation and freight movements. In addition, optimum system performance can be achieved by reducing factor of costs, pollution and emission of harmful gases. Mobile M2M communication greatly differs from traditional human-to-human (H2H) communication in terms of traffic density, data packet size, and quality of service (QoS) requirements [11]. For instance, an experimental study done in [12] shows that M2M traffic exhibits a significantly different behavior than the traditional smartphone traffic in various aspects. For example, unlike traditional mobile traffic, M2M is an uplink 1 dominant traffic which particularly generates bursty traffic volumes. Besides, it also exhibits unique characteristics such as an increasing device volume, sending small payloads, the demand for various mobility profiles, time-controlled, and mainly delay tolerant. Since the spectrum for mobile networks will remain a scarce resource, efficient utilization of radio spectrum is one of its major requirements. Therefore, the objective of this paper is to exploit LTE-Advanced RNs to multiplex small-sized M2M data packets in order to ensure efficient LTE-Advanced radio resource utilization and thus to support a large number of devices. The rest of the paper is structured as follows. We firstly present an overview of the leading communication technologies used in ITS and logistics, followed by a generic overview of the two latest technologies, i.e., the Institute of Electrical and Electronics Engineers (IEEE) 802.11p and 3GPP LTE-Advanced in Sect. 2. An overview of major M2M services in ITS and logistics are discussed in Sect. 2. Then, we discuss problem definitions by highlighting how LTE-Advanced resources can be used inefficiently by smart M2M devices in Sect. 3. Section 4presents the proposed uplink M2M data multiplexing scheme [13]. The simulation environment and parameter settings are presented in Sect. 5. We discuss our simulation results in Sect. 6. In the end, conclusions are drawn in Sect. 7.In addition, a list of most frequently used acronyms is presented in Table 1. 2 Mobile M2M communications This section presents an overview of ETSI (European Telecommunications Standards Institute) M2M architecture followed by the major M2M use cases and services in transportation and logistics. 2.1 ETSI M2M architectural overview The high-level ETSI M2M network architecture is shown in Fig. 2[14]. The major components of mobile M2M communication architecture include the device, communication as well as server domains. The primary functionality of the device domain is to collect and send sensor data such Energy and Ulies Automove Retail Consumer electronics e-healthcare Logiscs Manufacturing 37% 32% 32% 29% 28% 19% 17% Adopon of M2M technology in industry – 2015 Fig. 1 An illustration of leading industries adoption M2M technology worldwide, based on the Vodafone report 2015 [8] 1 Sending information from M2M devices to base station. 15 Page 2 of 11 Logist. Res. (2016) 9:15 123 as the internal temperature and humidity level of a container, position and speed of a vehicle, and fuel consumption. The role of the communication network is to create a communication path between the devices and servers through either wired or wireless networks such as Digital Subscriber Line (DSL) and cellular networks (e.g., LTE-Advanced), respectively. Finally, the server domain consists of a middleware layer where the collected packets go through several application services and later are used by related agencies. Thus, M2M technology employs wired, wireless, and hybrid communication opportunities among devices to ensure a fully automatic acquisition, processing, and transmission of data. Thus, M2M represents a broad next-generation technology that is primarily incorporated in modern automobile industries to improve the ease, safety, and quality of human life. The wired access technologies provide less delay, high throughput and are more reliable. Despite the aforementioned capabilities, wired systems cannot be used in transport and logistical systems due to several limitations such as lack of scalability, cost efficiency, and mobility. However, abovementioned limitations can be overcome by incorporating wireless (cellular) technologies to achieve maximum system efficiency and reliability. Recently, the key emerging wireless technologies used in modern transport and logistical systems include IEEE 802.11p [15] (standardized to support vehicular communication) and 3GPP 2 LTE-Advanced mobile networks [16]. IEEE 802.11p is achieved by making few advancements in well-known 802.11 Wireless Local Area Network (WLAN) technology. It is also considered as the most feasible technology for Vehicular Ad hoc Networks (VANET). IEEE 802.11p wireless access introduces minimum delay in ITS. However, its employment is limited due to its decentralized nature. The recent research has revealed that maximum operating efficiency of IEEE 802.11p can be achieved by supporting it with the LTE-Advanced mobile networks. The main features of LTE-Advanced technology include wide availability of modules/devices, system existence, decreased costs as well as easy deployment. Since the cellular modules and sensors are easily available, e.g., in vehicles, the applications of LTE-Advanced mobile networks are dramatically increasing in automotive sector. The authors in [16–18] compared the performance of two different technologies under varying channel and traffic load conditions. Moreover, several issues due to decentralized nature of IEEE 802.11p are highlighted in [16]. For instance, the main shortcomings are less reliability due to uncoordinated Medium Access Control (MAC) procedures, risk of network congestion, higher vehicle mobility, and low scalability. Moreover, the number of network-connected vehicles adversely affect the performance of the standard system. On the other hand, LTE-Advanced networks are more reliable, scalable, and capable of supporting higher density of vehicles. In addition, the authors analyzed the performance of the above technologies in terms of delay, reliability, scalability, and mobility support. The authors concluded that 3GPP LTE- Advanced systems are more efficient in terms of high scalability and mobility support than IEEE 802.11p standard. However, it is further concluded that the performance of IEEE 802.11p is more sensitive during increased density, traffic load, and higher mobility. 2.2 M2M use cases 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. To avoid further incidents, communication between the infrastructure and the vehicles must be very fast to detect emergency messages and deliver warning messages immediately. Similarly, traffic and infrastructure management play a prominent role in handling the problem of road congestion. It tackles the problem by providing two-way communication opportunities between vehicles and infrastructure. Vehicles can send status updates about the position, speed, fuel consumption, and delivery status reports to the infrastructure and can also receive relevant instructions about road accidents and emergency braking system [20]. Moreover, M2M communications support Table 1 List of used abbreviations Abbreviation Acronym MAC Medium Access Control MME Mobility Management Entity PHY Physical Layer PRB Physical Resource Block QCI QoS Class Identifiers RLC Radio Link Control RRC Radio Resource Control SAE System Architecture Evolution S-GWs Serving Gateways SGSN Serving GPRS Support Node TTI Transmission Time Interval WLAN Wireless Local Area Network WPAN Wireless Personal Area Network WSN Wireless Sensor Network 2 3rd Generation Partnership Project—3GPP—is the leading standardization organization for mobile networks. Logist. Res. (2016) 9:15 Page 3 of 11 15 123 several operations such as tracking of a stolen vehicle, traffic reports, and route planning as well as infotainment services [6]. For instance, to recover a stolen vehicle, SVT (Stolen Vehicle Tracking) service providers request data about the location from Telematic Control Unit (TCU) located inside the vehicle. In addition, drivers are also updated by sending reports regarding traffic in a particular region so that they can change or plan new routes in case of traffic jam or an emergency. Furthermore, infotainment services aim to provide news/information to drivers and passengers through mobile TV, web-browsing, etc. Fleet management is also one of the major M2M applications in logistics [9]. The movements of vehicles, containers, buses, and cars are being tracked regularly through devices which collect data of the location, vehicle speed, temperature, distribution progress, fuel consumption and send this information to monitoring servers. Through regular monitoring, several activities of the system can be performed in an efficient way. For instance, the goods which are transported from one place to another are monitored regularly in order to accomplish in time delivery and to handle any undesirable situation during shipment processes. The cargo moves across several regions; therefore, it must be monitored in order to stay updated. Moreover, reporting gives the exact location of the freight, and thus the conditions of the objects can be easily monitored. In applications such as warehouse management, fleet management, robotics, and control systems, alarms are also used to detect critical or emergency situations. In addition to the above applications, M2M communication provides additional services in logistics such as decreased operational cost, high inventory flexibility, increased supply chain visibility, and reduced loss of vehicles and containers [21]. In supply chains, M2M technology enables tracking the status of goods in real-time via M2M devices. This increasing visibility allows for significant reduction in uncertainties in supply chain [22]. Similarly, in a warehouse, M2M devices can be deployed to track the inventory so that stockholders and enterprises can respond to the market dynamics and to decide when to refill and when to go on sale. Moreover, cross talk among vehicles can also be effective to get immediate assistance. Additionally, direct delivery of inventory from one vehicle to another without storing it in a warehouse can also be accomplished through mutual information sharing. Consequently, it can significantly reduce the required space of warehouse, customer’s waiting time as well as the operational costs for business entities. 3 Problem descriptions Mobile systems such as LTE-Advanced are mainly designed to support the increasing traditional mobile traffic by enabling improved broadband services [23]. For instance, Ericsson in its mobility report [24] anticipates approximately 5.6 billion cellular-based active smartphones by 2019. Therefore, in order to support such a Mobile communicaon domain (e.g., LTE- Advanced) Applicaon domain (e.g., ITS and logiscs) Backend server (e.g., transportaon control center) Inventory Accidental/emergency uplink informaon Downlink informaon unicast/broadcast to all devices (vehicles) Vehicles cross talk for on-board security, status update, fleet management, etc. Warehouse management (monitoring all sensors deployed for various metering operaons) Gateway DeNBDeNB DeNB DeNB DeNB: Donor eNB Downlink: Uplink: Smart sensors deployed to monitor temperature, humidity, pressure, power, water, etc. Fig. 2 ETSI mobile M2M communication architecture along with an overview of major M2M applications in intelligent transportation and logistics, based on [14] 15 Page 4 of 11 Logist. Res. (2016) 9:15 123 massive mobile traffic, several enhancements in existing LTE systems [25] such as Relay Nodes (RNs) [26], massive Multiple Input–Multiple Output (MIMO) systems [27], and femto cells [28] were introduced to fulfill the IMT-Advanced (International Mobile Telecommunications Advanced) demands of maximum throughput of upto 1 Gbps in downlink and 500 Mbps in uplink [29]. On the other hand, mobile standards are foreseen as an attractive option to support future M2M applications such as in transportation, logistics, smart city, and living [2]. Several forecasts have also reported a considerable market growth for M2M device volume, making quality of service provisioning a huge challenge [10]. Consequently, mobile networks will not be able to support the increasing number of M2M devices worldwide [30]. According to 3GPP, the smallest unit of the radio spectrum allocatable to a single device is 1 PRB 3 which is capable of transmitting several hundred bits under favorable channel conditions. However, allocating 1 PRB to a single M2M device used in transportation/logistics could significantly degrade radio spectrum utilization [13,19,31]. This is due to the fact that the capacity of a PRB can be much higher than the actual size of the device payload under favorable channel conditions. Figure 3 illustrates how inefficiently a PRB can be utilized when a device sends small-sized packets when experiencing good channel conditions. An exemplary device payload of 4 bytes is considered in order to evaluate the utilization of a PRB against all the possible values of the Modulation and Coding Scheme (MCS). A PRB utilization efficiency is evaluated in terms of percentage of the actual payload and the padded zeros with respect to MCS index. It can be seen from Fig. 3that with the lower MCS values depicting unfavorable channel conditions, the PRB capacity is smaller, and therefore, it is fully utilized to transmit the given payload. On the other hand, with the higher MCS values, e.g., 20–26 representing favorable channel conditions, the capacity of the PRB increases. However, small packets with increasing percentage of padded zeros are transmitted. This shows that more packets can be accommodated per PRB under favorable channel conditions in the given exemplary scenarios. Since mobile radio resources are valuable assets and scarcely available, it is required to ensure efficient utilization of radio resources for M2M communications. 4 Proposed M2M data multiplexing In the proposed data multiplexing scheme, the small-sized M2M data packets are multiplexed at the Packet Data Convergence Protocol (PDCP) layer of the RN. A highlevel illustration of E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) architecture with the functionalities of LTE-Advanced relaying is depicted in Fig. 4. The RN communicates with devices using access link (Uu), whereas it communicates with DeNB using backhaul link (Un). Thus, it behaves like an eNB toward the M2M devices, whereas it acts as a UE toward the DeNB. The RN PDCP is selected to aggregate M2M packets in order to maximize the multiplexing gain. This increasing multiplexing gain is achieved without aggregating the additional overheads such as those from the PDCP, Radio Link Control (RLC), and Medium Access Control (MAC). A high-level illustration of the proposed scheme is given in Fig. 5. Generally, the PDCP layer is a part of LTE air interface control and user plane protocols. It exists in the UE, Donor eNB (DeNB), and in the RN. The major functionalities of PDCP layer for the user plane include header compression and decompression, user data transfer, delivery of upper layer Packet Data Units (PDUs) in sequence, and retransmission of PDCP Service Data Units (SDUs). Moreover, the control plane services include ciphering and integrity protection and transfer of control plane data. The proposed multiplexing scheme can also be applied at the lower layers of the RN such as RLC and MAC on the Uu interface or in the PDCP of Un interface. However, in these cases, the multiplexing gain will be reduced due to the multiplexing of additional headers along with the original small-sized data packets [13]. A multiplexing buffer is created at the RN PDCP layer which multiplexes the incoming packets according to the available service rate which is equal to Transport Block Size (TBS)—RN Un protocol overheads in terms of bits per TTI, as shown in Fig. 5. The Un protocol overhead includes 12, 28, and 4 bytes for GTP (GPRS Tunneling Protocol), UDP (User Datagram Protocol)/IP, and layer 2, respectively. The multiplexed packet is sent to the RN GTP over the Un link. The additional overheads such as from 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 0 2 4 6 8 101214161820222426 MCS Index Ulizaon/PRB in % Device payload (%) Zero padding (%) Fig. 3 An example illustration of inefficient PRB utilization for M2M communication 3 Physical Resource Block. Logist. Res. (2016) 9:15 Page 5 of 11 15 123 the GTP, UDP/IP, PDCP, and RLC are added. The block diagram of the data differentiating algorithm at the DeNB is given in Fig. 6. From the physical layer of the RN, the multiplexed packets are sent to the DeNB over the Un interface, as depicted in Fig. 5. The multiplexing approach significantly improves PRB utilization. However, there are certain constraints regarding latency requirements of high priority M2M traffic such as accidental information, emergency alerting, and e-health. This is due to the fact that each packet waits until the size of the buffer B is equal to the available TBS—RN Un protocol overheads. In a highly loaded scenario, the waiting time is not long due to a high arrival rate. However, in a low loaded scenario, there is a comparatively longer delay due to the low arrival rate. Consequently, the performance of delay sensitive M2M applications can be degraded. To tackle this issue, an expiry timer, Tmax is introduced. The timer is set with a fixed value such as 10 ms in the current implementation. This means that the buffer serves the multiplexed packet after 10 ms at the latest. The value of the timer could also be adaptive, i.e., it can change its value according to priorities of the incoming packets. For this purpose, the algorithm must be fully aware of various priorities of M2M applications. In this work, a fixed RN is implemented and used to multiplex the uplink traffic from M2M devices deployed Hotspot coverage for ITS and logiscal operaons Overcoming shadowing problems (DeNB) (RN) (M2M) Serving gateway (S-GW) Applicaon server Uu Un Uu Uu Un Un Coverage extensions Fig. 4 An illustration of LTE-Advanced relaying technology. Re-drawn based on [26]) M2M device RN DeNB aGW Applicaon TCP/UDP IP PDCP RLC MAC PHY RLC MAC PHY GTP UDP IP PDCP RLC MAC PHY GTP UDP IP PDCP RLC MAC PHY GTP UDP IP PDCP RLC MAC PHY GTP UDP IP Layer 2 PHY IP MUX λ1λ2λN λ3 Fig. 5 A high-level illustration of the proposed multiplexing scheme along with protocols stack of the M2M device, RN, and DeNB 15 Page 6 of 11 Logist. Res. (2016) 9:15 123 in ITS and logistics (i.e., for onboard security and metering purposes). The position of the RN corresponds to an MCS of 16, whereas 5 PRBs are allocated to RN to serve M2M traffic. The given values of MCS and PRBs correspond to TB size of 1608 bits per TTI. Since a TTI is of 1-ms duration, the TB size can also be given as 1608000 bits/s. According to the 3GPP standardizations, the capacity of a single PRB varies according to the MCS. Therefore, under the given values of MCS and PRBs, 321 bits per TTI or 321000 bits/s can be sent within a single PRB. In general, the system utilization, q can be determined in terms of the arrival rate, kand the service rate, l. Additionally, the maximum number of served devices, Nmax can be determined according to the following procedure, •The size of the each transmitted M2M packet per second is Psize ¼656 bits. •The arrival rate kdue to Nnumber of devices is ðN656Þb=s. •The service rate per PRB is lPRB ¼321000 b/s. •The overall service rate is l¼lPRB 5ðPRBsÞb/s. •So, the system utilization qcan be given as Utilization;q¼Arrival rate Service rate ¼k lð1Þ Utilization;q¼N656 321600 ð2Þ •Moreover, the maximum number of devices served can be given as follows, Nmax ¼q321600 656 ð3Þ 5 Simulation model and parameters The Optimized Network Engineering Tool (OPNET) Modeler which is newly named as RIVERBED Modeler is used as a primary modeling, simulation, and analysis tool for this research. It provides a simulation environment for the performance measurements of communication networks [32]. The project editor of the LTE-Advanced-based model developed in the simulator with several nodes along with LTE-Advanced functionalities and protocols is shown in Fig. 7. Additionally, an RN is implemented and placed within the coverage of DeNB to relay and multiplex M2M data traffic. The DeNB is responsible to connect UEs/M2M devices with the transport network, thus includes both radio interface and transport protocols to communicate with UEs/M2M devices and the core network, respectively. The radio protocols traffic coming from aGW toward the RN. The remote server and the aGW (access gateway) are interconnected with an Ethernet link with an average delay of 20 ms. Moreover, aGW acts as the tunneling point for the downlink and uplink traffic coming from PDN-GW and DeNB, respectively. The aGW node protocols include the Packet arrival at the PDCP layer of the DeNB Identify the destination IP of the received packet If (incoming packet IP == DeNB IP) The received packet belongs to the RN True The received packet belongs to the standalone user Send the packet to GTP-U False Send to GTP-U via IP and UDP De-tunnel to extract the original transmitted standalone packets Tunnel each packet again and route towards aGW end Start Fig. 6 Block diagram of the data differentiating algorithm at the DeNB n Access link Backhaul link X2 M2M devices Remote server Access gateway Router DeNB RN Fig. 7 Project editor of the LTE-Advanced-based model developed in the RIVERBED Modeler simulator Logist. Res. (2016) 9:15 Page 7 of 11 15 123 Internet Protocol (IP) and Ethernet. The aGW and DeNB nodes (names as eNB1..) communicate through IP routers (R1..). QoS parameters at the Transport Network (TN) ensure QoS parameterization and traffic differentiation. The user movement in a cell is emulated by the mobility model by periodically updating the location of the user. The user mobility information is stored in the global user database (Global-UE-List). The channel model parameters for the air interface include path loss, slow fading, and fast fading models. In this paper, the simulation modeling mainly focuses on the user plane to perform E2E performance evaluations. Furthermore, Table 2presents a detailed description of simulation parameters and settings. 6 Results and discussion This section investigates the performance of the proposed multiplexing scheme in the low and high loaded scenarios. We firstly describe all scenarios which are simulated in this work. Later, we discuss our simulation results in detail with varying load conditions. In this work, we consider M2M devices used in ITS and logistics in order to generate uplink traffic. 6.1 Simulation scenarios description The scenarios are simulated according to three major categories, i.e., no multiplexing,multiplexing without timer, and multiplexing with timer. In the first category, M2M data packets are relayed in uplink without multiplexing. In the second category, the data packets from all the active devices are multiplexed at the RN before being sent to the DeNB. However, in this category, no expiry timer is considered to control the multiplexing process, and the multiplexed packet is served when its size is large enough to utilize full capacity of the available spectrum. In the third category, an expiry timer is introduced in order to limit the multiplexing delay especially in the low loaded scenarios. In this case, the multiplexed packet is served after Tmax at the latest. Each category is further divided into several scenarios. The scenarios are simulated for both load conditions (i.e., low and high). In the low loads, the number of devices, N, is kept 200 in the first scenario. The value of Nis incremented by 200 in the subsequent scenarios till the limiting case, i.e., when all 5 PRBs are fully utilized. In high loads, 2800 4 devices are placed in the first scenario, and the number is also incremented by 200 in the subsequent scenarios. 6.2 Simulation results The simulation results for the mean number of used PRBs with 95 % confidence interval (CI) are illustrated in Figs. 8 and 9for the low and high load conditions, respectively. The values of the upper and lower bound of CI are very small in most of the scenarios as shown in Figs. 8and 9. These PRBs are used by RN to transmit the multiplexed data toward the DeNB. The simulation results clearly show the efficient utilization of PRBs in uplink with the proposed multiplexing scheme. For instance, in the no multiplexing scenario with 400 devices, the arrival rate at the PHY layer of the RN is 262.4 bit/TTI which almost utilize 1 PRB. However, in the case of multiplexing, only half of the PRBs are used to serve the 400 devices, see Fig. 8. Similarly, without multiplexing, the RN serves nearly 2400 devices with 5 PRBs in uplink, which is actually the limiting case as shown in Table 2 Simulation parameters Parameters Values Simulation length 1000 s eNB coverage radius 350 m Min. eNB UE distance 35 m Max terminal power 23 dBm Terminal speed 120 km/h Mobility model Random way point (RWP) Frequency reuse factor 1 Transmission bandwidth 5 MHz No. of PRBs 25 MCS QPSK, 16QAM, 64QAM, Channel models Pathloss, slow fading, fast fading Path loss 128:1þ37:6 log10ðRÞ, R in km Slow fading Log-normal shadowing, correlation 1, deviation 8 dB Fast fading Jakes-like method [33] RN parameters PRBs for RN 5 Corresponding MCS 16 TBS 1608 bits Simulated scenarios No multiplexing, multiplexing without timer, and multiplexing with timer Timer expiry values 10 ms (Sect. 6.2) Timer expiry values 5, 10 and 20 ms (Sect. 6.3) Type of RN Fixed M2M Traffic model [34] Message size 38 bytes (constant) at the device PDCP Inter-send time 1 s (constant) 4 5 PRBs are allocated to RN in order to serve M2M devices. These PRBs are fully utilized when the number of devices is raised to 2600 without multiplexing. So the high load scenarios start with 2800 devices. 15 Page 8 of 11 Logist. Res. (2016) 9:15 123