Machine-type Devices Research Articles

Aggregation and Resource Scheduling in Machine-Type Communication Networks: A Stochastic Geometry Approach

Data aggregation is a promising approach to enable massive machine-type communication. This paper focuses on the aggregation phase where a massive number of machine-type devices (MTDs) transmit to aggregators. By using non-orthogonal multiple access (NOMA) principles, we allow several MTDs to share the same orthogonal channel in our proposed hybrid access scheme. We develop an analytical framework based on stochastic geometry to investigate the system performance in terms of average success probability and average number of simultaneously served MTDs, under imperfect successive interference cancellation (SIC) at the aggregators, for two scheduling schemes: random resource scheduling and channel-aware resource scheduling (CRS). We identify the power constraints on the MTDs sharing the same channel to attain a fair coexistence with purely orthogonal multiple access (OMA) setups. Then, power control coefficients are found, so that these MTDs perform with similar reliability. We show that under high access demand, the hybrid scheme with CRS outperforms the OMA setup by simultaneously serving more MTDs with reduced power consumption.

Group Communications in Narrowband-IoT: Architecture, Procedures, and Evaluation

Narrowband-Internet of Things (NB-IoT) has been released by 3GPP to provide extended coverage and low energy consumption for low-cost machine-type devices. Requiring only a reasonably low-cost hardware update to the already deployed long term evolution base stations and being compatible with current core network and enhanced core solutions that aim to reduce the battery consumption and minimize the signaling, NB-IoT deployments are quickly increasing, making NB-IoT a dominating technology for low-power wide area networks. To this aim, in this paper, we focus on group communications (i.e., multicast) in NB-IoT to efficiently support the transmission of firmware, software, task updates, or commands toward a large set of devices. We discuss the architectural and procedural enhancements needed to support the unique features of group communications in machine-type environments, such as customer-driven group formation. We also extend the NB-IoT frame to include a channel for multicast transmissions. Finally, we propose two transmission strategies for multicast content delivery and evaluate their performance considering the impact on the downlink background traffic and the channel occupancy.

Packet size adjustment for minimizing the average delay in buffer-aided cognitive machine-to-machine networks

In this paper, a cognitive machine-to-machine (CM2M) network is considered in which underlaid on a macro cell, there exist some co-existing small cell based M2M networks. In each small cell, M2M network is comprised of some machine type devices (MTDs) and an MTD gateway. The MTD gateway collects data of MTDs within each small cell and transmits it to the small cell base station reusing the spectrum of the macro cell. To avoid data loss, MTD gateway is equipped with a buffer. In the considered CM2M network, MTDs with delay sensitive application are prioritized over MTDs having delay tolerant packets. In order to minimize the average delay of the MTDs, a novel packet adjustment scheme is proposed. The average delay and the average throughput of the CM2M network are computed. Simulation results show that the proposed scheme outperforms conventional policy without packet adjustment in terms of average delay of MTDs.

Massive Random Access of Machine-to-Machine Communications in LTE Networks: Modeling and Throughput Optimization

A key challenge for enabling machine-to-machine (M2M) communications in long-term evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of the M2M communications in LTE networks. Specifically, a novel double-queue model is established, which can both incorporate the queueing behavior of each machine-type device (MTD) and be scalable in the massive access scenarios. To evaluate the access efficiency, the network throughput is further characterized, and optimized by properly choosing the backoff parameters including the access class barring (ACB) factor and the uniform backoff (UB) window size. The analysis reveals that the maximum network throughput is solely determined by the number of preambles, and can be achieved by either tuning the ACB factor or the UB window size based on statistical information such as the traffic input rate of each MTD. Simulation results corroborate that with the optimal tuning of backoff parameters, the network throughput can remain at the highest level regardless of how many MTDs in the network, and is robust against feedback errors of the traffic input rate and burstiness of data arrivals.

Coded Tandem Spreading Multiple Access for Massive Machine-Type Communications

Future IoT applications provoke the connectivity and reliability requirements of machine-type communications, which yield mMTC and URLLC, respectively. In mMTC, grant-free NOMA is adopted to serve massive machine-type devices with low signaling overhead. The challenge of grantfree NOMA for mMTC is the collision induced by non-orthogonal resource allocation. Various grantfree NOMA techniques have been proposed to address this challenge, aiming at high connectivity. In this article, we introduce a novel grant-free NOMA approach, namely coded tandem spreading multiple access (CTSMA). The basic idea is that the data packet of each user is divided into segments, and redundancy segments are generated by coding through the data segments, and multiple orthogonal spreading codes are utilized to tandemly spread the data symbols of different segments from one user. Thereby, collision only occurs on the segments using identical spreading codes, which can be resolved by the redundancy segments to achieve reliable access. In addition to the transmitter and receiver designs, a closedform expression of the collision resolution capability is derived in this article to reveal the trade-off among the connectivity, the reliability, and the user data rate when using CTSMA. Theoretical analysis shows that CTSMA achieves both high connectivity and ultra-reliability at the expense of user data rate.

IoT Traffic Management and Integration in the QoS Supported Network

This paper proposes: 1) a traffic flow management policy, which allocates and organizes machine type communication (MTC) traffic flows network resources sharing within evolved packet system (EPS); 2) an access element as a wireless sensor network gateway for providing an overlaying access channel between the machine type devices and EPS; and 3) it addresses the effect and interaction in the heterogeneity of applications, services and terminal devices, and the related quality of service (QoS) issues among them. This paper overcomes the problems of network resource starvation by preventing deterioration of network performance. The scheme is validated through simulation, which indicates the proposed traffic flow management policy outperforms the current traffic management policy. Specifically, simulation results show that the proposed model achieves an enhancement in QoS performance for the MTC traffic flows, including a decrease of 99.45% in packet loss rate (PLR), a decrease of 99.89% in packet end to end (E2E) delay, a decrease of 99.21% in packet delay variation (PDV). Furthermore, it retains the perceived quality of experience of the real time application users within high satisfaction levels, such as the voice over long term evolution service possessing a mean opinion score (MOS) of 4.349 and enhancing the QoS of a video conference service within the standardized values of a 3GPP body, with a decrease of 85.28% in PLR, a decrease of 85% in packet E2E delay and a decrease of 88.5% in PDV.

Cognitive Hierarchy Theory for Distributed Resource Allocation in the Internet of Things

In this paper, the problem of distributed resource allocation is studied for an Internet of Things (IoT) system, composed of a heterogeneous group of nodes compromising both machine-type devices (MTDs) and human-type devices (HTDs). The problem is formulated as a noncooperative game between the heterogeneous IoT devices which seek to find the optimal time allocation so as to meet their quality-of-service (QoS) requirements in terms of energy, rate, and latency. Since the strategy space of each device is dependent on the actions of the other devices, the generalized Nash equilibrium (GNE) solution is first characterized, and the conditions for uniqueness of the GNE are derived. Then, to explicitly capture the heterogeneity of the devices, in terms of resource constraints and QoS needs, a novel and more realistic game-theoretic approach, based on the behavioral framework of cognitive hierarchy (CH) theory, is proposed. This approach is then shown to enable the IoT devices to reach a CH equilibrium (CHE), a concept that takes into account the various levels of rationality corresponding to the heterogeneous computational capabilities and the information accessible for each one of the MTDs and HTDs. Simulation results show that the CHE solution maintains a stable performance. In particular, the proposed CHE solution keeps the percentage of devices with satisfied QoS constraints above 96% for IoT networks containing up to 10000 devices without considerably degrading the overall system performance in terms of the total utility. Simulation results also show that the proposed CHE solution brings a two-fold increase in the total rate of HTDs and deceases the total energy consumed by MTDs by 78% compared with the equal time policy.

Massive Machine-Type Communications

The articles in this special section address the techology and applications supported by machine-type communications (MTC). Here, MTC is characterized by fully automatic data generation, exchange, processing, and actuation among intelligent machines, without or with low intervention of humans. With the rapid penetration of embedded devices, MTC is becoming the dominant communication paradigm for a wide range of emerging smart services including healthcare, manufacturing, utilities, consumer goods, and transportation. Industry analysts predict that 50 billion devices will be connected to mobile networks worldwide by 2020. While mobile phone devices communicating among humans will still exist, machine-type devices sending bits of information to other machines, servers, clouds, or humans will account for a much larger proportion and thus create what is referred to as massive MTC (MMTC).

Stochastic Coalitional Games for Cooperative Random Access in M2M Communications

In this paper, the problem of random access contention between machine type devices (MTDs) in the uplink of a wireless cellular network is studied. In particular, the possibility of forming cooperative groups to coordinate the MTDs’ requests for the random access channel (RACH) is analyzed. The problem is formulated as a stochastic coalition formation game in which the MTDs are the players that seek to form cooperative coalitions to optimize a utility function that captures each MTD’s energy consumption and time-varying queue length. Within each coalition, an MTD acts as a coalition head that sends the access requests of the coalition members over the RACH. One key feature of this game is its ability to cope with stochastic environments in which the arrival requests of MTDs and the packet success rate over RACH are dynamically time-varying. The proposed stochastic coalitional game is composed of multiple stages, each of which corresponds to a coalitional game in stochastic characteristic form that is played by the MTDs at each time step. To solve this game, a novel distributed coalition formation algorithm is proposed and shown to converge to a stable MTD partition. Simulation results show that, on the average, the proposed stochastic coalition formation algorithm can reduce the average fail ratio and energy consumption of up to 36% and 31% for a cluster-based distribution of MTDs, respectively, compared with a noncooperative case. Moreover, when the MTDs are more sensitive to the energy consumption (queue length), the coalitions’ size will increase (decrease).

Massive Machine Type Communication With Data Aggregation and Resource Scheduling

To enable massive machine type communication (mMTC), data aggregation is a promising approach to reduce the congestion caused by a massive number of machine type devices (MTDs). In this work, we consider a two-phase cellular-based mMTC network where MTDs transmit to aggregators (i.e., aggregation phase) and the aggregated data is then relayed to base stations (i.e., relaying phase). Due to the limited resources, the aggregators not only aggregate data, but also schedule resources among MTDs. We consider two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). By leveraging the stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference ratio (SIR) for each phase, thereby computing the MTD success probability, the average number of successful MTDs and probability of successful channel utilization, which are the key metrics characterizing the overall mMTC performance. Our numerical results show that, although the CRS outperforms the RRS in terms of SIR at the aggregation phase, the simpler RRS has almost the same performance as the CRS for most cases with regards to the overall mMTC performance. Furthermore, the provision of more resources at the aggregation phase is not always beneficial to the mMTC performance.

APRA: Affinity Propagation-Based Resource Allocation Scheme in M2M for System Capacity Maximization

In this paper, we propose an enhanced affinity propagation (AP)-based resource allocation scheme (APRA) to overcome major issues in machine-to-machine (M2M), such as delay, complexity, throughput, and system capacity. There would be rapid increase of added devices, such as cellular and machine-type devices. It would be difficult for Evolved Node B (eNB) to control all of them. Considering this problem, we propose an AP-based group formation method in which machines make groups with other similar type of machines. After making groups, group members in each group can communicate directly with each other by getting a channel from eNB via their group head. A resource allocation method is proposed for different groups that can use the same channel at the same time. Considering energy constraints, we also propose different methods to rotate the role of a group head among group members, through the modification of AP or the application of Markov chain model. As expected, the group head will drain energy at a higher rate than the group members. Thus, the rotation of the group head will increase the overall performance. Simulation results show that the proposed method can minimize both data delivery delay and operation complexity while increasing the throughput, system capacity, and energy efficiency through the rotation of the group head.

Dynamic Resource Allocation and Access Class Barring Scheme for Delay-Sensitive Devices in Machine to Machine (M2M) Communications.

Supporting simultaneous access of machine-type devices is a critical challenge in machine-to-machine (M2M) communications. In this paper, we propose an optimal scheme to dynamically adjust the Access Class Barring (ACB) factor and the number of random access channel (RACH) resources for clustered machine-to-machine (M2M) communications, in which Delay-Sensitive (DS) devices coexist with Delay-Tolerant (DT) ones. In M2M communications, since delay-sensitive devices share random access resources with delay-tolerant devices, reducing the resources consumed by delay-sensitive devices means that there will be more resources available to delay-tolerant ones. Our goal is to optimize the random access scheme, which can not only satisfy the requirements of delay-sensitive devices, but also take the communication quality of delay-tolerant ones into consideration. We discuss this problem from the perspective of delay-sensitive services by adjusting the resource allocation and ACB scheme for these devices dynamically. Simulation results show that our proposed scheme realizes good performance in satisfying the delay-sensitive services as well as increasing the utilization rate of the random access resources allocated to them.

Extending the coverage of the internet of things with low-cost nanosatellite networks

Recent technology advances have made CubeSats not only an affordable means of access to space, but also promising platforms to develop a new variety of space applications. In this paper, we explore the idea of using nanosatellites as access points to provide extended coverage to the Internet of Things (IoT) and Machine-to-Machine (M2M) communications. This study is mainly motivated by two facts: on the one hand, it is already obvious that the number of machine-type devices deployed globally will experiment an exponential growth over the forthcoming years. This trend is pushed by the available terrestrial cellular infrastructure, which allows adding support for M2M connectivity at marginal costs. On the other hand, the same growth is not observed in remote areas that must rely on space-based connectivity. In such environments, the demand for M2M communications is potentially large, yet it is challenged by the lack of cost-effective service providers. The traffic characteristics of typical M2M applications translate into the requirement for an extremely low cost per transmitted message. Under these strong economical constraints, we expect that nanosatellites in the low Earth orbit will play a fundamental role in overcoming what we may call the IoT digital divide. The objective of this paper is therefore to provide a general analysis of a nanosatellite-based, global IoT/M2M network. We put emphasis in the engineering challenges faced in designing the Earth-to-Space communication link, where the adoption of an efficient multiple-access scheme is paramount for ensuring connectivity to a large number of terminal nodes. In particular, the trade-offs energy efficiency–access delay and energy efficiency–throughput are discussed, and a novel access approach suitable for delay-tolerant applications is proposed. Thus, by keeping a system-level standpoint, we identify key issues and discuss perspectives towards energy efficient and cost-effective solutions.

An Optimal Service Strategy for Grouped Machine-Type Communications in Cellular Networks

Machine-type communication devices (MTCDs) served as groups are an efficient way to solve device management and resource allocation problems in cellular networks. We propose an MTCD group model based on the traffic of devices in the network. A group state transition matrix and revenue and constraint vectors are introduced and the optimal service solution is derived that the best service strategy is to serve devices in only two groups. Results can be used as a guide for cellular base stations to efficiently manage resources for MTCD applications.

Dynamic Group-based Scheduling of Machine-to-Machine Communication for Uplink Traffic in LTE Networks

In wireless machine-to-machine communication, the signalling load and radio resource allocation are critical to successful service because the technology features a large number of connected devices that introduce significant connection- and resource-related signalling overheads to eNodeB. This paper proposes an uplink scheduling scheme to minimise signalling overhead and maximise system throughput in machine-type communication devices (MTCDs) in long-term evolution networks. The proposed scheme dynamically adjusts group members by considering channel conditions and quality of service and applies the allocation-before-request (AbR) concept to allocate the residual bandwidth and realise the above objectives. Exhaustive simulations were conducted to examine the performance of the proposed scheme. The simulation results showed that the proposed scheme achieves not only better system throughput but also decreased buffer status report (BSR) signalling when compared with the static group-based scheme.

A novel hybrid medium access control protocol for inter-M2M communications

A scalable medium access control (MAC) protocol is crucial for machine-type devices to access the channel simultaneously in a machine-to-machine (M2M) network. There can be two types of scenarios in an M2M network. First, where the M2M devices communicate with the base station one-by-one, and second, where the M2M devices directly communicate with each other within a group or cluster without any human intervention (Inter-M2M communication). Considering the second type of scenario, the devices can use basic contention or reservation-based MAC protocols. But with such a large number of M2M devices, adaptability, and scalability become bottlenecks. Therefore, in this paper, we propose a novel scalable hybrid-MAC protocol, which combines the benefits of both contention-based and reservation-based medium access schemes. We assume that the contention and reservation portion of M2M devices is a frame structure, which mainly has two parts: contention interval (CI) and data transmission interval (DTI). The devices contend for the channel access during CI. After contention, the successful devices win time-slots and transmit data packets during DTI. In our proposed hybrid-MAC scheme, each M2M device is IEEE 802.11 DCF enabled, and within each time slot during DTI, the devices share data with each other. The analytical and simulation results show that the proposed hybrid-MAC protocol performs better than slotted-ALOHA, p-persistent CSMA, and TDMA in terms of aggregate throughput, average transmission delay, channel utility, and energy consumption.

Energy-efficient scheduling and grouping for machine-type communications over cellular networks

In this paper, energy-efficient scheduling and grouping for battery-driven machine-type devices in cellular networks is investigated. We introduce a scheduling-based cooperation incentive scheme which enables machine nodes to organize themselves locally, create machine groups, and communicate through group representatives to the base station. This scheme benefits from a novel scheduler design which takes into account the cooperation level of each node, reimburses the extra energy consumptions of group representatives, and maximizes the network lifetime. As reusing cellular uplink resources for communications inside the groups degrades the Quality of Service (QoS) of the primary users, analytical results are provided which present a tradeoff between maximum allowable number of simultaneously active machine groups in a given cell and QoS of the primary users. Furthermore, we extend our derived solutions for the existing cellular networks, propose a cooperation-incentive LTE scheduler, and present our simulation results in the context of LTE. The simulation results show that the proposed solutions significantly prolong the network lifetime. Also, it is shown that under certain circumstances, reusing uplink resource by machine devices can degrade the outage performance of the primary users significantly, and hence, coexistence management of machine devices and cellular users is of paramount importance for next generations of cellular networks in order to enable group-based machine-type communications while guaranteeing QoS for the primary users.

Connectionless access for mobile cellular networks

The traffic characteristics of Internet of Things and machine type devices differ significantly from that of smart phone applications. The connection oriented approach in Long Term Evolution (LTE) that involves establishing a bearer prior to data transmission is inefficient for small burst traffic because of the signaling involved. We propose a connectionless access method for efficient small burst transmission for future cellular networks. With the proposed protocol for connectionless access, over-the-air signaling can potentially be reduced substantially while meeting the security requirements of mobile networks.

Zero-Outage Cellular Downlink With Fixed-Rate D2D Underlay

Two of the emerging trends in wireless cellular systems are device-to-device (D2D) and machine-to-machine (M2M) communications. D2D enables efficient reuse of the licensed spectrum to support localized transmissions, while M2M connections are often characterized by fixed and low transmission rates. D2D connections can be instrumental in localized aggregation of uplink M2M traffic to a more capable cellular device, before being finally delivered to the base station (BS). In this paper we show that a fixed M2M rate is an enabler of efficient machine-type D2D underlay operation taking place simultaneously with another downlink cellular transmission. In the considered scenario, aBS B transmits to a user U, while there are N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> machine-type devices (MTDs) attached to U, all sending simultaneously to U and each using the same rate R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> . While assuming that B knows the channel B - U, but not the interfering channels from the MTDs to U, we prove that there is a positive downlink rate that can always be decoded by U, leading to zero-outage of the downlink signal. This is a rather surprising consequence of the features of the multiple access channel and the fixed rate RM. We also consider the case of a simpler, single-user decoder at U with successive interference cancellation. However, with single-user decoder, a positive zero-outage rate exists only when N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> = 1 and is zero when N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> > 1. This implies that joint decoding is instrumental in enabling fixed-rate underlay operation.

An M2M cognitive MAC protocol for overlaid OFDMA environments

AbstractMachine to machine (M2M) communications have gained in the last years an increasing interest due to ever growing number of machine‐type devices that are used in different application fields by allowing low cost and efficient communications among devices mainly in an autonomous manner. Even if M2M protocols need of dedicated resources, a new paradigm called cognitive M2M (CM2M) has been recently introduced in order to exploit cognitive/opportunistic radio communications. The aim of this paper is to outline a CM2M mechanism, where the primary network is based on the orthogonal frequency division multiple access technique, while the M2M communication‐based secondary network uses a novel medium access control technique, named data aided cognitive technique (DACT). The performance of the proposed DACT protocol is derived by means of suitable analytical methods under different operational conditions. Analytical predictions are also validated by comparisons with numerical results obtained through computer simulations, in order to show the effectiveness of the proposed solution in terms of throughput, delay, resource wastage and CM2M devices queue length; to this aim, the proposed DACT protocol has been implemented with different alternatives. Among them, an adaptive approach allows to optimise the system performance by increasing the overall throughput while keeping under control the system delay and the resource wastage. Copyright © 2015 John Wiley &amp; Sons, Ltd.