# Energy-Efficient MAC for Cellular IoT: State-of-the-Art, Challenges, and
Standardization

Syed Waqas Haider Shah, Student Member, IEEE, Adnan Noor Mian, Member, IEEE,
Adnan Aijaz, Senior Member, IEEE, Junaid Qadir, Senior Member, IEEE, and Jon
Crowcroft, Fellow, IEEE

###### Abstract

In the modern world, the connectivity-as-we-go model is gaining popularity.
Internet-of-Things (IoT) envisions a future in which human beings communicate
with each other and with devices that have identities and virtual
personalities, as well as sensing, processing, and networking capabilities,
which will allow the developing of smart environments that operate with little
or no human intervention. In such IoT environments, that will have battery-
operated sensors and devices, energy efficiency becomes a fundamental concern.
Thus, energy-efficient (EE) connectivity is gaining significant attention from
the industrial and academic communities. This work aims to provide a
comprehensive state-of-the-art survey on the energy efficiency of medium
access control (MAC) protocols for cellular IoT. we provide a detailed
discussion on the sources of energy dissipation at the MAC layer and then
propose solutions. In addition to reviewing the proposed MAC designs, we also
provide insights and suggestions that can guide practitioners and researchers
in designing EE MAC protocols that extend the battery life of IoT devices.
Finally, we identify a range of challenging open problems that should be
solved for providing EE MAC services for IoT devices, along with corresponding
opportunities and future research ideas to address these challenges.

###### Index Terms:

MAC protocols, energy efficiency, green IoT, cellular networks, machine-type
communication.

000
Syed Waqas Haider Shah and Jon Crowcroft are with the Computer Lab, University
of Cambridge, 15 JJ Thomson Avenue, Cambridge, UK CB3 0FD ({sw920,
jon.crowcroft}@cl.cam.ac.uk). Syed Waqas Haider Shah is also with the
Electrical Engineering Department, Information Technology University, Lahore
54000, Pakistan (waqas.haider@itu.edu.pk). Adnan Noor Mian is with the
Computer Science Department, Information Technology University, Lahore 54000,
Pakistan (adnan.noor@itu.edu.pk). Adnan Aijaz is with the Telecommunications
Research Laboratory, Toshiba Research Europe Ltd., Bristol, UK
(adnan.aijaz@toshiba-trel.com). Junaid Qadir is with the Electrical
Engineering Department, Information Technology University, Lahore 54000,
Pakistan (junaid.qadir@itu.edu.pk).

##  I Introduction

Internet-of-Things (IoT) enables a paradigm shift from a manual approach to an
automated system. It manages resources in an efficient way that not only
improves productivity but also cost and time efficiency. The Internet, which
is the backbone of the IoT, has grown exponentially since the last decade. The
“connectivity-as-we-go” model becomes a reality with the help of wireless
Internet connectivity. As the popularity of IoT grows, it attracts more
sectors of society to become automated. IoT applications are generally
categorized into massive IoT (m-IoT) and critical IoT (c-IoT). The m-IoT
applications are generally delay-tolerant, such as smart home, personal IoT,
and smart metering. Whereas, c-IoT applications are delay-sensitive, like
smart healthcare, industrial automation, and asset tracking [1]. Although IoT
integration in multiple sectors brings many benefits, it also creates
connectivity and energy challenges. There are various wireless networks
available, which are broadly divided into licensed and unlicensed. Licensed
networks, such as cellular networks, render numerous benefits over unlicensed
networks, as shown in IoT wireless connectivity ecosystem in Fig. 1. The
cellular network attributes, such as quality-of-service (QoS) and quality-of-
experience (QoE) assurance, mobility and service-level-agreement (SLA)
support, high data rates, and global coverage, make them a better candidate
not only for m-IoT applications but also for c-IoT applications. However, an
IoT device consumes more energy when it is connected to a cellular network. It
is mainly due to the complex and power-hungry network infrastructure of
cellular networks. Furthermore, IoT devices are mostly battery-operated. Thus,
to make a device last longer, it is necessary to save as much power as
possible in its operation.

Energy-efficient (EE) IoT paradigm (also known as green IoT) aims at reducing
the power consumption of an IoT device with the help of efficient and
sustainable technologies. The maximum energy consumption of an IoT device
associates with its physical and medium access control (MAC) layer operations
[2]. MAC designs for Cellular networks were primarily designed for
conventional cellular communication. These MAC protocols are unable to address
large-scale concurrent channel access in an IoT network. Recently, the 3rd
generation partnership project (3GPP), which develop protocols for mobile
telecommunications, propose various modifications in cellular MAC in its
Release 12-16. These modifications, which ensure the EE operation of IoT
devices in cellular networks, help realize the green cellular IoT. To this
end, we survey various EE MAC protocols and cellular standards specifically
designed for green IoT.

Figure 1: IoT wireless connectivity ecosystem: covers licensed and unlicensed
spectrum operating networks.

Due to its paramount importance, the energy efficiency of MAC protocols has
been widely studied in the literature. The authors in [3] survey the technical
challenges associated with the MAC layer for machine-type communication (MTC).
They discuss some of the key elements of the MAC layer designs, including
throughput, latency, scalability, and energy efficiency. Although the EE
aspect of the MAC layer protocols for IoT is discussed, the authors do not
discuss cellular IoT. Another article surveys the MAC protocols for IoT [4].
The authors classify MAC protocols according to the IoT connectivity scenario.
They discuss MAC protocols for IoT in both licensed and unlicensed networks.
This survey also lacks a discussion on the EE aspects of the protocols.
Similarly, another study surveys the suitability of the current random access
procedure of cellular networks for IoT [5]. The authors present the case for
the unsuitability of the conventional cellular networks for the EE IoT
connectivity. Various other surveys exist as well, which study the EE MAC
protocols [6, 7, 8, 9]. However, their scope is limited to one network
connectivity, and none of them discusses the EE MAC protocols for cellular
IoT. For example, the authors in [7] study MAC protocols for terahertz
communication only. The authors in [8] only discuss EE MAC protocols for
wireless multimedia sensor networks. Another survey [6] provides EE MAC
protocols for cognitive radio sensor networks. Similarly, the authors in [9]
present a survey on the EE narrowband IoT (NB-IoT). Nevertheless, there is a
gap in the literature regarding the energy efficiency aspects of MAC layer
protocols for cellular IoT, as shown in Table I.

TABLE I: Literature Review Summary (Yes = Included, No = Not Included,
Partially = Partially Included)

Survey Paper | Year | Network | EE-MAC |  | Energy
---
Consumption
Brief Summary
Sources |  | Saving
---
Techniques
| Rajandekar
---
et al.[3]
2015 | Non-cellular | No | No | No |  | The authors perform a survey of the technical challenges and the
---
requirements of the MAC layer for M2M communication. The s-
urvey outlines key requirements of MAC layer design for M2M,
such as throughput, latency, scalability, energy efficiency, and co-
existence, followed by a taxonomy of M2M MAC protocols.
| Luitel
---
et al. [6]
2018 |  | Cognitive
---
radio sensor
network
Partially | No | No |  | The survey provides a qualitative comparison of the EE MAC pr-
---
otocols for CRSN followed by a discussion on the design issues
for the MAC layer protocols. Later, the authors also present sev-
eral research challenges related to MAC designs for CRSN.
| Popli
---
et al. [9]
2018 | NB-IoT | Partially | No | No |  | The survey highlights various design challenges, which are acting
---
as limiting factors for the success of the NB-IoT. It also t presents
two EE techniques named EE adaptive health monitoring system
and zonal thermal pattern analysis, for establishing green IoT appl-
ications.
| Oliveira
---
et al. [4]
2019 |  | Cellular +
---
Non-cellular
No | No | No |  | This survey discusses MAC protocols for both licensed and unlice-
---
nsed wireless networks, including LPWA technologies and cellular
standards for IoT. It also provides a comparative study for each gr-
oup of protocols to provide insights and a reference study for IoT
applications, considering their characteristics and limitations.
| Shu Li
---
et al. [8]
2019 |  | Wireless
---
multi-media
sensor
network
Partially | Partially | No |  | The survey provides unique features and the requirements for desi-
---
gning the WMSN, followed by a summary of the possible solutions.
It also studies several MAC protocols for WMSN in terms of their
prioritization and service differentiation mechanisms. It also provid-
es EE and QoS-enabled routing protocols for the WMSN.
| Ghafoor
---
et al. [7]
2020 | Terahertz | No | No | No |  | The survey investigates the key features of the Terahertz band that
---
needs to be regarded for designing efficient MAC protocols. It also
provides design challenges and their possible remedies for MAC pr-
otocols in applications at macro and nano scales. It also compares
MAC protocols based on channel access, network topologies, and
link establishment procedures followed by a discussion on the open
research challenges for Terahertz MAC protocols.
| D. Jie
---
et al. [10]
2020 |  | Cellular +
---
Non-cellular
No | Partially | No |  | The survey highlights fundamental challenges and bottlenecks of s-
---
everal emerging connectivity technologies, like compressive sensing,
nonorthogonal multiple access, and machine learning-based random
access. It also provides a detailed discussion on the possible solut-
ions to address these challenges. Lastly, it classifies IoT applications
for different technical domains and a discussion on the suitable IoT
connectivity technologies.
This Paper | 2021 | Cellular | Yes | Yes | Yes |  | This survey identifies various sources of energy dissipation at the
---
MAC layer and their possible solutions. It also raises several ques-
tions on the suitability of various MAC protocols for EE IoT conn-
ectivity in future cellular networks. Further, it provides a quantitative
comparison between proprietary LPWA technologies and cellular
standards for IoT in the context of their energy consumption. Later, it
identifies various challenges for possible future research directions.

The main objective of this paper is to fill this gap by providing a
comprehensive survey on the EE MAC designs for green cellular IoT. We identify
various sources of energy dissipation at the MAC layer and their possible
solutions. We raise several questions on the suitability of various MAC
protocols for EE IoT connectivity in future cellular networks. Moreover, a
quantitative comparison between different proprietary low power wide area
(LPWA) technologies and cellular standards for IoT is provided in the context
of their energy consumption. Lastly, we identify various challenges for
possible future research direction.

The rest of the paper is organized as follows. Section II explains the energy
consumption at the MAC layer; Section II-A provides sources of energy
dissipation, and Section II-B presents techniques for minimizing energy
consumption. Section III presents the EE MAC protocols for cellular IoT.
Section IV provides the EE cellular standards especially designed for IoT
connectivity. Section V presents cellular community efforts in providing EE
access to massively deployed IoT devices. Section VI introduces open
challenges for future research, and Section VII concludes the paper.

##  II Energy Consumption at the MAC Layer

In this section, we identify sources of energy dissipation and the possible
techniques for minimizing energy consumption at the MAC layer for IoT
connectivity, as shown in Fig. 2.

Figure 2: Taxonomy of energy consumption sources at the MAC layer and
approaches to minimize the energy consumption.

###  II-A Sources of Energy Dissipation at the MAC Layer

The majority of IoT devices are battery-powered, and the energy consumption
occurs in two basic operational modes; active mode and sleep mode. In an
active mode, the radio of a device remains on, and the power consumption of
the device depends on the coverage scenario of the network in which the device
is connected. If the network coverage is good, a device requires less power to
establish a connection and data transfer. However, in a poor network coverage
scenario, the power consumption of a device is high due to the re-transmission
of the lost packets and because of the low order modulation schemes for a
better signal-to-noise ratio. In a sleep mode, the power consumption of a
device is comparatively low because the radio of the device remains off for a
longer period of time. Now, we will discuss some common sources of energy
dissipation at the MAC layer, as shown in Fig. 2.

####  II-A1 Idle Listening

Idle listening leads to the undesirable energy consumption of an IoT device.
It happens when a device expects to receive a packet and keeps its radio on,
but the packet was not sent from the transmitting device. Similarly, when a
device transmits a packet to a device that is in sleep mode, the transmitting
device keeps its radio on for an acknowledgment. It can happen due to
unsynchronized sleep-wake scheduling between transmitting and receiving
devices. It can be avoided using the power-saving mode (PSM), which will be
discussed later in the section.

####  II-A2 Packet and Control Signaling Overhead

In cellular networks, devices and the BS share preliminary information to keep
the network updated; this is called control information. Different protocols
use control signaling to avoid collisions, such as ready to send /clear to
send packets. These packets do not contain any data but consume power, which
is undesirable in some cases. Moreover, the data payload in MTC communication
is small, and the control signaling is overwhelmingly large.

####  II-A3 Packet Collision

Packet collision occurs when multiple devices transmit a packet on a shared
channel simultaneously. When a collision occurs, the transmitted packet is
lost, and devices need to retransmit the packet. This way, devices consume
more energy, first in collision avoidance and then in retransmitting the data.
In cellular IoT networks where connected devices are in large number, packet
collision becomes more critical.

####  II-A4 Traffic and Channel Fluctuation

In cellular networks, channel conditions change rapidly, and channel
estimation is required to propose an efficient system model. The channel state
information (CSI) provides the overall behavior of the channel on a
time/frequency scale, and it needs to be updated periodically. Resource
scheduling for the devices in a cellular network is also based on channel
fluctuation [11]. When the channel or traffic conditions change rapidly,
devices need to keep their radio on for expected packets arrival; thus, they
consume an extra amount of energy.

####  II-A5 Overhearing

Overhearing is a phenomenon in which a device receives a packet that was not
sent for it. Due to this, devices end up consuming extra energy. One of the
main reasons for overhearing is continuous listening of the channel to find an
idle space for transmission. It becomes more critical in cellular IoT networks
due to a large number of connected devices.

###  II-B Techniques for Minimizing Energy Consumption

####  II-B1 Duty Cycling

Generally, an IoT device generates a small amount of sensed information or
control signaling information. In most applications, an IoT device generates a
packet every few seconds and remains idle otherwise. Power consumption when an
IoT device is in idle mode is of no use and should be reduced. To do so, the
duty cycling mechanism can be used that allows an IoT device to follow a
sleep-awake schedule. Energy consumption of an IoT device can be characterized
in four operational modes; sleep, idle, transmit, and listen. In transmit,
listen, and idle modes, maximum energy consumption is due to the radio being
on. Whereas, when the radio is off (in sleep mode), a small amount of energy
is consumed. Turning the radio off of an IoT device when there is no active
communication can save up to 50% energy [12]. However, there is a tradeoff
between energy consumption and transmission latency in duty cycling. Although
a small duty cycle can save energy, it will increase the transmission latency.
Multiple MAC protocols for duty cycling are available in the literature [13,
14, 15]. A survey on duty cycle protocols for wireless sensor networks is also
available in [16]. The authors in this survey characterize various duty-cycled
MAC protocols in synchronous and asynchronous approaches.

####  II-B2 Low Power Listening (LPL)

In LPL, an IoT device wakes up without any synchronization procedure for a
short time to check the channel status [17]. It uses a channel polling
mechanism, in which if a device senses a busy channel, it remains in an active
mode for data reception, and the rest of the devices in the network go to
sleep mode. When a device is transmitting, it uses a long preamble with every
packet to ensure that the receiver is awake when the packet arrives. The
preamble size is adjustable according to the traffic situation in the network.
LPL is a suitable technique for MTC communication in IoT networks, as it
requires low implementation complexity and can afford small duty cycling.
However, its performance can be compromised due to the false wake-up of a
device. In noisy environments, channel-induced noise may be detected as a
channel activity, which causes a device to remain in an active mode. The
energy level detection is a critical factor in LPL performance, and the
authors in [18] proposed an adaptive energy detection protocol. In this
protocol, an IoT adaptively adjusts its energy threshold level to avoid false
wake-up. Hwang et al. The authors in [19] provide a detailed survey on
taxonomy and evaluation of LPL protocols for the suitability of IoT networks.

####  II-B3 On-Demand Request Originated Transmissions

In an IoT network, devices are generally deployed on a large scale, including
high-rise buildings and hard to reach areas. These devices, which are mostly
battery-operated, generate sporadic traffic, and need not follow any periodic
schedule. Accordingly, on-demand request originated transmissions can allow
these devices to stay in sleep mode for a very long period and only wake-up
when requested for a response. It can help in reducing the energy consumption
of an IoT device.

####  II-B4 Clustering

Clustering is a mechanism that allows devices to communicate with a BS through
a cluster head (CH). A CH collects data from cluster members (CM) and forwards
the aggregated data to the BS. The CH can be a fixed or a randomly chosen
device, to distribute the energy load on all the devices [20]. This process
restricts CMs to forward their data only to the CH, thus saving the
transmission power and channel resources. Clustering can be done for various
purposes, including scalability, load balancing, reducing routing delay,
collision avoidance, and EE routing. Clustering algorithms are generally
designed based on probabilistic (random or hybrid) and deterministic (fuzzy-
based, weight-based, or compound-based) approaches [21]. Multiple algorithms
have been proposed for clustering, including hybrid, EE, and distributed
clustering [22], power-efficient and adaptive clustering [23], and link-aware
clustering mechanism [24]. These algorithms provide 25%-50% reduction in
energy consumption of an IoT device.

####  II-B5 Power Saving Mode (PSM)

In this mode, a device remains connected to the network even during the sleep
mode. Therefore, the energy required to establish a new connection for data
transmission can be saved. When a device is in the PSM, it can only be reached
for device termination services after a periodic tracking area update.
Reduction in energy consumption using the PSM comes with a cost of
transmission latency, which makes it less attractive for most of the c-IoT
applications. There are some advancements in the PSM mode that provide desired
delay performance keeping the energy consumption to a minimum, such as a smart
PSM (SPSM) [25].

####  II-B6 Energy-Efficient Scheduling and Scheduled Rendezvous

An efficient network scheduling mechanism that can adapt to the network
conditions is necessary for massive IoT networks. It can not only increase the
number of connections in the network but also reduce the energy consumption of
the connected devices. Network scheduling is done in either a centralized or a
distributed way. The authors in [26] performs a survey on the EE scheduling
mechanism for sensor networks. The authors have concluded that choosing an
efficient scheduling mechanism depends on many factors, including connected
devices’ capabilities, network structure, and deployment strategy. One of the
examples of scheduling an IoT network is scheduled rendezvous [27]. In this
type, devices adjust their wake/sleep schedules according to the rendezvous
time. The advantage of this type of MAC protocol is that the network knows
when a device wakes up, and it assigns the network resources accordingly. This
type of scheduling can reduce the energy consumption of a device because it
schedules the device’s wake up only when the network expects to receive or
send data to that device.

The above-discussed techniques have been used in various IoT applications for
energy conservation. However, most of these techniques cause communication
delays and poor QoS [28]. There is always a trade-off between energy
conservation and QoS; thus, necessary optimization is required to achieve a
specific QoS target for any IoT application. Improving energy efficiency and
providing sufficient QoS are the basic requirements for designing a MAC
protocol for future IoT applications. Moreover, with the introduction of new
computing paradigms (cloud and fog), IoT devices can spend most of their life
in sleep/idle mode by employing duty cycling, and on-demand request originated
transmissions. We have also identified that in low traffic load conditions,
the power consumed during sleep mode becomes a determining factor for the IoT
device’s battery lifetime. Furthermore, we raise the following questions that
might help researchers to find potential research problems.

  * •

Is adaptive clustering more energy-efficient when compared to static
clustering? What kind of adaptive clustering is more suitable for a long
battery lifetime (location-based, content-based, grid-based, or device’ type-
based)?

  * •

Can prior estimation of traffic and channel fluctuations reduce the power
consumption of an IoT device? If yes, then where (device or AP) and how to do
it?

Next, we will discuss MAC designs for cellular IoT connectivity in terms of
their energy consumption.

TABLE II: Different Features of MTC & H2H Communication

Features | Machine-Type Communication (MTC) | Human-to-Human Communication (H2H)
---|---|---
Message Size |  | Small amount of data (for sensing purpose)
---
| Large amount of data (Multimedia streaming, etc.)
---
Traffic Generation | Huge UL data, Small DL data | Small UL data, Huge DL data
| Transmission Periodicity
---
| Rapidly change according to the requirements (Sporadic traffic)
---
| Asynchronous & random in nature
---
| Connection & Access Delay
---
| Moderate (Application-oriented)
---
| Demanding (Connection delays are not tolerated)
---
Mobility |  | Generally devices are fixed (Not a big concern)
---
Always required
Control Signaling |  | Very small (Signaling required for connection establishment only)
---
| Very high (For reliable and long-time connection)
---
| Lifetime & Energy Efficiency
---
| Very critical (Operate in remote areas for years)
---
| Energy may not be a critical issue (Humans can charge devices)
---
| No. of devices connected
---
| Very large (Ericsson predicted 50 billion [29])
---
| Large (The Independent predicted 7 billion )
---
Device Duty Cycle |  | Generally 1% (Mostly in sleep mode)
---
100%

##  III Energy-Efficient MAC Design for Cellular IoT Networks

As the need for a global IoT network increasing rapidly, the unlicensed band
operating techniques cannot fulfill the requirements of a diverse IoT network
alone. To address this issue, 3GPP has standardized MTC communication in
Release 11 of the LTE standard. The advantage of using cellular networks is
that it can provide ubiquitous coverage, mobility, roaming, security, and
lower deployment cost. It makes cellular IoT a favorable infrastructure-based
solution for global IoT networks. Moreover, the cellular community has a
mature ecosystem that enables industry partnerships for future developments.
Cellular networks were primarily designed for H2H communication, and
integration of MTC might raise new challenges. QoS and traffic requirements
for H2H communication and MTC are different from each other. H2H communication
requires high throughput, delay sensitivity, and efficient mobility
management, whereas MTC is generally delay-tolerant and needs low data rate
for sporadic data transmission. Table II provides a detailed comparison of MTC
and H2H communication types.

Major energy consumers for MTC communication in conventional cellular networks
are large control signaling overhead and large duty cycling [30]. In a
cellular network, MTC devices contend to access the channel using a physical
random access channel (PRACH), as shown in Fig. 3(a). The normal RACH process,
which was initially designed for H2H communication, requires a large control
signaling, making it power-hungry and inefficient for MTC traffic [5]. To
reduce the duty cycling and for optimizing the power consumption, the PSM and
various discontinuous reception (DRx) mechanisms were introduced. Moreover,
there are various other challenges associated with massive MTC in cellular
networks. The readers are referred to [31]. It is important to update the
conventional cellular networks to address these challenges. The 3GPP also
highlights the importance of improving the conventional cellular networks
according to the MTC requirements, as current cellular networks are not
feasible to accommodate IoT traffic [32].

There are improvements proposed in the RACH and the DRx mechanism for MTC
traffic. We will discuss these improvements in the context of their energy
efficiency below.

Figure 3: (a) Physical random access channel of LTE (b) Sending data with
preamble message (c) Sending data with connection request message.

###  III-A Optimized MAC

A conventional RACH process contains four control messages for establishing a
radio connection, as shown in Fig. 3(a). This mechanism is power-hungry for
MTC traffic due to its large control signaling. To reduce the control
signaling overhead, the authors of [33] propose the idea of an optimized MAC.
In this protocol, data transmission is possible without establishing a radio
resource control (RRC) connection. This protocol embeds data in either
preamble or the connection request message, as shown in Figs. 3(b) and 3(c).
Therefore, devices do not need to support the complex RRC protocol and thus,
save the network resources and enhance energy efficiency.

###  III-B MAC Design Using Partial Clustering

The authors of [34] propose the idea of partial clustering. In this mechanism,
intra-cluster communication uses a contention-based protocol, and
communication between CHs and the BS uses a reservation-based protocol. As
clustering is a tool to decrease the number of access requests to the BS, this
mechanism makes the overall system scalable. However, when the BS has the same
distance to CMs and CH, energy consumption due to two-hop communication
becomes critical. To address this, the authors propose to use clustering only
when MTC devices are far from the BS. The frame structure for transmission is
divided into two subframes, as shown in Fig. 4. The use of two subframes makes
it compatible with existing cellular network architectures. The inter-cluster
subframe is further divided into a reservation phase and a working phase. This
idea of two-hop transmission is designed for energy efficiency in situations
where multiple short-lived sessions are available.

Figure 4: Frame structure for cluster-based MAC protocol [34].

###  III-C Discontinuous Reception (DRx) Mechanism

MTC devices generate sporadic data traffic and the time of active data
transmission is very small. Some devices (e.g., temperature sensing devices)
generate only a few bytes of data in a whole day; thus, keeping them active
all the time is inefficient. For this purpose, the DRx mechanism was
introduced for MTC communication in cellular networks [35]. In this mechanism,
MTC devices turn their radio on periodically to listen to the physical
downlink control channel (PDCCH) for incoming traffic, as shown in Fig. 5.
There are two states in the DRx mode; RRC-idle state and RRC-connected state.
In the RRC-idle, the BS releases the RRC connection of the MTC device and
requests the mobility management entity (MME) to remove the MTC device s1
connection(the connection between eNodeB and extended packet core (EPC)).
Although the device kept registered with the MME, it does not have an active
session with MME. Therefore, the device itself is responsible for mobility
management, and the network is unaware of its movements during that period.
For initiating UL traffic, the MTC device needs to request its serving BS for
an RRC connection. For the DL traffic, the BS either page the device or wait
for the device to wake-up. In the RRC-connected state, the MTC device does not
terminate its connection with the network. When the MTC device is idle, it may
request the initiation of the DRx mode. When a packet arrival is detected, the
MTC device terminates the DRx mode and returns to the active mode for packet
reception. In this state, the network keeps track of the device, and mobility
management is not the device’s responsibility. By employing the DRx mechanism
with a suitable configuration, the power consumption of a device can be
significantly reduced without affecting the QoS requirement [36].

The conventional DRx mechanism enhances the energy efficiency of an MTC device
at the cost of increased packet delay. Because if the receiving device is in
the DRx mode, then the packet destined for it is stored at the BS until that
device listens to PDCCH for its DL traffic. There are multiple schemes [37,
38, 39] proposed for DRx parameters configuration based on traffic activity in
the network to ensure power efficiency along with reduced packet delay. There
are also some further improvements in the DRx mechanism, which can enhance
energy efficiency even further. Table III provides a brief summary of these
improvements.

Figure 5: Discontinuous reception (DRx) mechanism in LTE [40].  TABLE III:
Discontinuous Reception (DRx) Mechanism for Power Saving and Latency Reduction

References | Model | Task | Brief Summary
---|---|---|---
Hsieh _et al._ [41] | Semi-Markov | Power Saving |  | Semi-Markov process is used to analytically model DRx mechanism and fixed
---
sleep duration is replaced with variable sleep duration. High power saving is
a-
hieved at the expense of large buffering delay.
Liu _et al._ [42] |  | Counter Driven Adaptive DRx
---
(CDA-DRx)
Power Saving |  | Reduction in signaling overhead of DRx mechanism using counters at both en-
---
ds(UE and eNodeB). A considerable decrease in power consumption is achiev-
ed keeping the average latency same.
Jha _et al._ [43] | Semi-Markov |  | Power Saving +
---
Latency Reduction
| Tuning of DRx parameters is done to optimize balanced power consumption
---
and average latency. Authors also stated that with the knowledge of mean
inter-arrival time, benefits can be extracted from DRx operation in terms of
balance between power consumption and latency.
Sergio _et al._ [44] | Adaptive DRx | Power Saving |  | It allows an adjustable DRx cycle according to the network requirements.
---
It allows devices to remain in the PSM for longer period of time; thus,
help reducing their energy consumption.
Balasubramanya _et al._ [45] | DRx with Quick Sleep | Power Saving |  | It uses quick sleep indication technique, in which bits are allocated to each
---
group of devices that indicated the upcoming paging request from the BS. It
provides 66% better energy efficiency as compated to conventional DRx.
Yu _et al._ [38] |  | Partially Observable Markov
---
Decision Process (POMDP)
Power Saving |  | Traffic-based DRx cycle adjustment (TDCA) scheme has been proposed to
---
adaptively control DRx cycles for decreasing signaling overhead. It enhances
energy consumption efficiency keeping QoS intact.
Fowler _et al._ [46] | Extended DRx (eDRx) | Power Saving |  | It allows a device to extend its sleep cycle upto 60 frames each with 10.24 s
---
before it responds to a paging request from the BS. The device may or
may not be allowed to adopt eDRx based on network configuration.
Karthik _et al._ [37] | Discrete Probability Model | Power Saving |  | A pragmatic algorithm is proposed for selecting suitable DRx configuration ino
---
rder to gain high power efficiency with tolerable delay constraints.
Wang _et al._ [47] |  | Truncated Pareto Distributed
---
Model
| Power Saving +
---
Latency Reduction
| Online power saving scheme (OPSS) is proposed for DRx mechanism using
---
estimation & optimization phases. Effect of various traffic conditions is
presented.
It outperforms legacy DRx schemes both in packet delay and power consumption.

Next, we discuss the EE cellular standards designed specifically for IoT
applications.

##  IV Energy-Efficient Cellular Standards for IoT

IoT applications are diverse, and almost every other application requires a
distinct technological demand. Therefore, the “one solution fits all” approach
does not necessarily fulfill the requirements of diverse IoT applications. To
help address this issue, 3GPP proposed three standards, especially for IoT
applications, in its Release 13, which are extended-coverage GSM (EC-GSM),
narrow-band IoT (NB-IoT), and long term evolution for machines (LTE-M). Their
design is based on the power consumption, QoS, and latency requirements of IoT
applications. We discuss these standards in terms of their power consumption
in this section. We also provide a technical comparison of these standards
with other proprietary LPWA technologies in Table IV.

###  IV-A LTE-M

It is a low-cost and low-power standard proposed by 3GPP in its Release 12 for
IoT applications [48]. LTE-M is a solution for IoT applications that require
low-power consumption, reliability, low-latency, and a large coverage area. It
can be deployed in the conventional LTE system with only a software upgrade,
which makes it a very promising solution in terms of fast and low-cost
deployments [49]. LTE-M utilizes lesser bandwidth (1.4 MHz) as compared to the
conventional LTE (20 MHz). Data rates for its UL and DL channels are also
reduced to less than 1 Mbps, which makes it suitable for m-IoT applications
only. It uses two different MAC layer protocols for channel access. For m-IoT
application, it utilizes contention-based channel access in which frame-
slotted ALOHA (FSA) is used. FSA is used for initial channel access because
thousands of devices try to access the channel, and allocating separate
resources is not feasible. On the other hand, for c-IoT applications,
contention-free channel access is used, to provide mobility management and
seamless handover. For contention-free channel access, frequency division
multiple access (FDMA) and time slot reservation mechanism is used [5]. To
ensure less power consumption, LTE-M employs different mechanisms such as PSM,
eDRx, and C-DRx. These mechanisms allow IoT devices to go to sleep mode by
turning their radio off while keeping their connection intact with the core
network for allowing paging [50]. The current consumption in a sleep mode can
be as low as 4 $\mu A$ and reaches up to 320 $mA$ in an active mode.

###  IV-B EC-GSM

EC-GSM, also known as EC-eGPRS, is an attempt to utilize legacy second-
generation (2G) cellular networks for low-power, low data rate connectivity.
2G cellular networks (GPRS/ eGPRS), which operate in multiple frequency bands,
such as 850, 900, 1800, and 1900 MHz. EC-GSM based on 2G cellular networks can
become a global infrastructure-based solution for IoT connectivity, as they
are already deployed in most parts of the world. This advantage over other
cellular networks-based solutions can make it a feasible, easy, and cheaper
solution for global IoT integration. Moreover, with the introduction of added
features of the EE operation, and improved coverage by almost 20 dB over
eGPRS, it is also suitable for the energy-constrained IoT applications. These
newly added features can be integrated into existing GSM networks using only
software upgrades to the core and radio access networks. 3GPP has also issued
new technical features for EC-GSM in its Release 13. After the integration of
these features, EC-GSM can achieve a maximum of 240 kbps data rate on its UL
channel with a total bandwidth of 600 kHz, compared to GSM bandwidth of 2.4
MHz. Each channel of the EC-GSM is allocated a bandwidth of 200 kHz, and both
the UL and the DL channels support TDMA and FDMA-based transmissions. EC-GSM
uses PSM and eDRX mechanisms for the EE operation, which makes it less power-
hungry compared to the conventional GSM. Some changes at the MAC layer are
required to implement these new features, such as the introduction of new MAC
layer messages (for the extended coverage-packet data channel), changes in the
RAN (to support paging in eDRX), and reduction in stringent requirement of
neighbor cell monitoring (for power-efficient operation).

TABLE IV: Proprietary Solutions vs. Cellular Standards for IoT

Technology | LoRaWAN[51] |  SigFox [52] |  RPMA [53] | NB-IoT | EC-GSM | LTE-M
---|---|---|---|---|---|---
Spectrum |  | Un-licensed
---
<1 GHz
| Un-licensed
---
<1GHz
| Un-licensed
---
2.4 GHz
| Licensed
---
(1) Stand alone in GSM band
(2) LTE Guard band
(3) In-band LTE
| Licensed
---
In GSM band
| Licensed
---
In-band LTE
| Peak data rate
---
(UL/DL)
50 kbps/290 bps | 100 bps/600 bps | 624Kbps/156Kbps |  | 204.8 kbps (Multi-tone)
---
20 kbps (single-tone)
/234.7 kbps
240 kbps/240 kbps | <1 Mbps/<1 Mbps
Peak current | 32 $m$A | 11 $m$A | 30 $m$A | 120 $m$A – 300 $m$A | 120 $m$A – 320 $m$A | 320 $m$A
Sleep current |  $1\mu$A |  $1\mu$A |  $1\mu$A |  $5\mu$A |  $4\mu$A |  $4\mu$A
Transmit power | 2 dBm-20 dBm |  | 14dBm (Compliant
---
with local constraints)
22 dBm | 23 dBm | 23 dBm – 33 dBm |  | 23 dBm:Class 3 UE
---
20 dBm:Class 5 UE
Power saving | LoRaWAN |  | Deep sleep
---
mechanism
| Deep sleep
---
mechanism
PSM + eDRx | PSM + eDRx |  | PSM + eDRx
---
C-DRx
Power efficiency | High | Very High | High | Moderate | Moderate | Moderate
MAC protocols |  | Un-slotted
---
ALOHA
| Un-slotted
---
ALOHA
CDMA-like |  | SC-FDMA
---
CDMA
| TDMA
---
FDMA
| SC-FDMA
---
TDMA, FSA
Range (km) |  | Rural:15
---
Urban:2-5
| Rural:30-50
---
Urban:3-10
| Rural:30-50
---
Urban:1-5
| Rural:10-15
---
Urban:1-2
<25 | 7-10
Deployment status |  | Commercially
---
deployed
| Early deployment
---
in the US & the EU
| Commercially
---
deployed
| Commercially
---
deployed
| Commercially
---
deployed
| Commercially
---
deployed
Latency |  | Insensitive to
---
latency
upto 6 s | 2 s – 4 s | 1.6 s – 10 s | 700 ms – 2 s | 10 ms – 15 ms
| Battery
---
lifetime
10 years |  | 20 years (2.5 Ah)
---
3 messages per day
| 21.8 years (2.5 Ah)
---
200 kB data per day
| 19 years (2.5 Ah)
---
1 message per day
10 years (5Ah) |  | 19 years (2.5 Ah)
---
1 message per day
| Link Budget/
---
Coverage
157 dB | 149 dB | 177 dB | 164 dB | 164 dB | 155 dB

###  IV-C NB-IoT

NB-IoT is another 3GPP standard for IoT, which ensures minimized signaling
overhead and improved battery life [54]. It was released by 3GPP in its
Release 13. It can be deployed in three different scenarios; stand-alone in
GSM band with 200 kHz channel, in-band LTE with 180 kHz channel, and guard-
band of LTE [55]. In early versions of NB-IoT, it uses the same channel access
mechanism as LTE to establish an RRC connection [56]. However, in later
versions, a new single-tone frequency hopping random access (NPRACH) procedure
has been introduced [57]. In NPRACH, both single-tone and multi-tone frame
structures can be used for UL transmission. In a single-tone frame structure,
only one subcarrier with a frequency spacing of 15 kHz and 3.75 kHz is
available that allows 12 and 48 different preambles, respectively. On the
other hand, in a multi-tone frame structure, the number of subcarriers can be
3, 6, or 12 with 15 kHz frequency spacing [58]. The single-tone UL data rate
can reach up to 20 kbps, whereas the multi-tone UL data rate can be as high as
204.8 kbps [59]. For reduced power consumption, NB-IoT utilizes PSM and eDRx
mechanism. The eDRX mechanism is supported in RRC-idle and RRC-connected
modes. To keep a device registered with the network, it supports a paging
cycle of 10.24 s and 3 h in RRC-connected and RRC-idle modes, respectively
[35]. It supports the highest eDRx cycle length as compared to EC-GSM and
LTE-M. The authors in [60] propose a prediction based EE channel access
mechanism for NB-IoT. In this mechanism, prediction-based resource allocation
is done. This mechanism predicts the processing delay and UL occurrences by
examining the transmitted packet. Moreover, the BS pre-assigns the channel
resources for the UL transmission without a scheduling request process. The
authors claimed a 34% reduction in energy consumption when compared to the
conventional NB-IoT channel access method. There are some limitations of NB-
IoT as well. It does not support QoS, as its traffic is the best effort, and
only half of its messages are acknowledged. It also does not ensure ultra-
reliability and low latency (it can reach up to 10 s), which makes it less
suitable for c-IoT applications, such as traffic safety and control, smart
grid, and other industrial applications [61].

##  V Energy-Efficient Massive MTC (mMTC) in 5G and Beyond

In conventional cellular networks, the focus of the network providers is to
provide reliable and high data rate connectivity. The need to ensure this type
of communication has led to sophisticated and complex MAC layer designs that
require large control signaling. Control signaling overhead is only admissible
when it is small compared to the payload data [62]. Because most of the IoT
applications generate a small amount of data, conventional MAC layer protocols
are inefficient for MTC in cellular networks. This phenomenon creates new
challenges for designing MAC layer protocols for 5G and beyond cellular
network. 5G introduces mMTC to accommodate a large number of small data-
generating devices [63]. There are some underline requirements for 5G and
beyond that need to be addressed while designing MAC layer protocols. Some of
these requirements are: a BS should support a large number of devices (up to
300,000); small packet size; large UL traffic as compared to DL traffic;
support sporadic traffic generation and EE transmission [64].

Several research groups are actively working on the EE MAC designs for mMTC in
5G and beyond cellular networks [65, 66, 67, 68, 69]. One of the solutions is
the one-shot random access, in which a device sends data right after it wakes
up without any synchronization and coordination procedure [70]. Although this
procedure is EE, it poses multiple challenges, including a high collision rate
due to its asynchronous nature, which limits the channel throughput. This
issue of limited throughput is addressed in [71] using compressive coded
random access mechanism. The authors propose that using a common overloaded
control channel makes the one-shot random access procedure possible. The
authors in [72] use the UL sparse code multiple access enabled contention-
based mechanisms to overcome the problems of scalability and network overload.
Their simulation results show that their proposed solution can accommodate
three times more MTC devices as compared to the contention-based orthogonal
frequency division multiple access. The same authors presented another
solution using coded random access (CRA) coupled with compressed sensing-based
multiuser detection. The idea of CRA is similar to the frameless ALOHA, in
which devices independently contend for the channel on the slot basis, with a
fixed prior channel access probability, without any coordination between
devices. This solution can significantly enhance scalability, energy
efficiency, and channel resource allocation.

The evolution of 5G New Radio (NR) also paves the way for the EE Industrial
IoT and URLLC. The 3GPP has finalized its release 16 and release 17, which
includes a mechanism for the wake-up signal for the IoT devices [73]. By using
this specific wake-up signal, an IoT device can further reduce its power
consumption [74]. It also offers enhancements to the legacy scheduling and
control signaling mechanisms of the cellular networks to efficiently
accommodate mMTC communication. Some other enabling technologies of 5G and
beyond cellular networks can also help establish EE mMTC communication.
Device-to-Device (D2D) communication is one of those technologies. In D2D
communication, devices present nearby communicate directly without routing
their data through the BS [75]. D2D communication is a suitable candidate for
reducing the energy consumption of IoT devices due to its reduced transmission
power levels. It also enables reuse of the cellular spectrum; thus helps to
reduce the spectrum scarcity issue. The authors of [76] propose an EE DRX
scheduling for D2D users. Their scheme optimizes the sleep operation of the
D2D users using the DRx mechanism while maintaining the required QoS
guarantees. This scheme also maintains the transmission and interference
relationship graph to mitigate the effects of D2D interference, which leads to
better channel quality. Moreover, 5G and beyond cellular networks envision to
allow licensed and unlicensed interoperable communication. IEEE 1932.1 working
group has proposed a standard for licensed and unlicensed spectrum
interoperability. This standard includes an interoperation mechanism among MAC
protocols designed for licensed and unlicensed spectrum operations and a
controller for devices’ coordination. The authors of [77] propose licensed and
unlicensed interoperable D2D communication. The proposed scheme not only
enhance the spectral efficiency of the D2D devices, but it also enables
massive connectivity using an unlicensed network while keeping the advantages
of the cellular networks. To this end, the authors of [78] propose a scalable
MAC protocol that offloads the D2D users from the cellular network to the
unlicensed network. This protocol uses the point coordination function access
mechanism to manage D2D users in a dense environment.

Similar to D2D communication, Unmanned aerial vehicle (UAV)-assisted MTC is
also gaining popularity in 5G-NR. In this type of communication, UAVs/drones
are used for data collection, which is one of the most critical tasks of MTC.
UAVs (data collectors) can complement cellular BSs and process MTC traffic to
reduce network congestion. Although UAV-assisted MTC communication brings many
benefits [79], there are challenges associated with spectrum sharing in
cellular networks. The authors in [80] studies the spectrum sharing between
MTC devices and cellular users in the presence of UAVs, which also use the
cellular spectrum for communication. The authors propose a time-division
duplexing protocol and provide a comparison with state-of-the-art orthogonal
and nonorthogonal spectrum sharing protocols. Further, the paper also
investigates the optimized transmit powers for EE operation of devices
connected in the network. Similarly, the authors in [81] aim to reduce the
energy consumption of MTC devices by jointly optimizing devices’ transmission
schedule over time and the UAV trajectory. In short, UAV-assisted MTC can play
an important role in m-MTC in future air-ground integrated IoT networks. To
conclude, EE and massive connectivity is still an open research challenge in
5G and beyond cellular networks. The readers are referred to [63] and the
references therein for further details.

##  VI Open Research Challenges

In this section, we present some of the key research challenges for the EE MAC
protocols for cellular IoT.

###  VI-A Energy-Efficient Ambient Computing

Nowadays, IoT is no longer a collection of simple sensors, rather an
integration of intelligent devices. These devices can sense and gather
information from their environment and respond to our needs intelligently.
This yields the emergence of ambient computing. This type of computing is
becoming popular recently in various domains of the IoT landscape. Although
billions of sensors are deployed in various IoT applications, a small number
is connected to an access network, and an even smaller number have Internet
access. It is expected that billions more will be deployed soon, to provide
Internet connectivity, and to manage the huge amount of data generated by
these devices is a crucial task. An intelligent device must have storage and
processing capabilities, high QoS, and it also needs to perform online data
analytics for local decision making. To perform these tasks on an energy-
constrained device is challenging and requires new research efforts to ensure
reduced power consumption while supporting ambient computing. One solution is
ambient backscatter communication in which an IoT device utilizes available
radio frequency signals to transmit data without a battery [82]. Although it
provides benefits in terms of energy consumption, it has many limitations,
including short-range and low data rate transmission.

###  VI-B Energy-Efficient Mobility Management

Cellular network densification is one of the most promising solutions to
overcome the capacity issue in future networks. The macro-cell based solutions
are falling apart in providing access to a large number of IoT devices. This
problem will even get worse with the exponential growth of connected IoT
devices and their demand for high data rates and enhanced QoS. Ultra-dense
multi-tier cellular networks (UDMTCNs) can provide an efficient solution to
address the issue of high data rate connectivity [83]. Although UDMTCNs bring
in many advantages like increased network resources, enhanced throughput,
decreased network load associated with a cell, and reduced transmission power
of a device, it also raises several new challenges. One of the major
challenges is mobility management, as the number of handovers will increase
overwhelmingly in UDMTCNs. These frequent handovers can increase the signaling
overhead and power consumption of a device. Several IoT applications require
mobility, such as asset tracking, vehicle-to-vehicle communication, smart
cities, and mobile healthcare facilities. Devices connected in these
applications are battery-operated and undergo frequent handovers, which
require extra power consumption. A proactive approach is required to overcome
this power consumption issue caused due to high mobility. It can open doors
for new research on EE mobility management solutions in future cellular
networks.

###  VI-C Handling Bursty MTC Traffic in Green Cellular IoT Networks

Unlike conventional cellular network traffic, MTC traffic is bursty in nature,
and therefore different mechanisms are required to accommodate such traffic.
One of them is access class barring (ACB), in which traffic is categorized
into different classes based on their access priorities. Several ACB
mechanisms are available in the literature [84, 85], which adjusts ACB factors
based on different network parameters. Nonetheless, congestion is still one of
the major issues for bursty MTC traffic in cellular IoT networks. It is also
important to note that traffic fluctuation and congestion at the network level
also lead to more power consumption of IoT devices. To ensure a long battery
life of an IoT device, new EE congestion control mechanisms for cellular IoT
networks are required.

###  VI-D Network-Level Energy Efficiency

With the emergence of IoT, conventional cellular networks are lagging in
providing access to the massive number of IoT devices. The issue of massive
access leads to the deployment of small cells. Although a large number of
small cells provides resource and EE access to a huge number of devices, it
also increases the network level energy consumption. Channel access type for
devices connected in a small cell plays an important role in defining the
energy consumption of the cell. The channel access types can be characterized
as closed, open, and hybrid. In closed access, only the authorized users can
access the network, whereas open access allows any user to access the network
without an authorization requirement. In the hybrid access mode, both
techniques can be combined. Although it looks like open access techniques will
consume more energy, there is no research available that compares the energy
consumption of these access techniques and the trade-off they offer.

###  VI-E Energy Harvesting

IoT devices can be deployed in areas where replacing the batteries is
economically inefficient. Many research groups are working on energy
harvesting for these unattended devices to increase their operational
lifetime. In [86], the authors propose an idea in which mobile energy sources
radiates electromagnetic energy in a pre-decided pattern, sufficient for
charging an IoT device. Similarly, other research groups have also shown the
advantages of energy harvesting for IoT devices [87, 88]. It is an open
research area with a huge potential since efficient wireless energy transfer
and practical implementation of energy harvesting for IoT devices are both
important open questions.

##  VII Conclusions

The influx of billions of IoT devices in recent times is a huge challenge for
wireless networks. It leads to finding new wireless network solutions for
providing access to these devices. Although new solutions can provide cost and
resource-efficient access, energy efficiency is still a major challenge to be
resolved. With the emergence of intelligent IoT devices, which need to perform
storage and processing locally for data analytics, the battery lifetime of a
device will be a bottleneck. To this end, we provided a comprehensive survey
of the EE MAC techniques for green cellular IoT. We started with the
motivation for energy efficiency in IoT networks by briefly explaining why
power consumption at the MAC layer is one of the major determinants for the
battery life of an IoT device. To address this question, various energy
dissipation sources at the MAC layer and their solutions were presented. Some
questions were also raised in the context of low power consumption of an IoT
device that can help researchers find potential research problems. Following
this, various EE MAC designs for cellular IoT were presented. These designs
can provide EE IoT connectivity in different use cases and applications. One
of the main focuses of these designs is on the enhancement of the battery life
of the IoT devices. Then, we provided a detailed discussion on the EE of the
cellular standards for IoT, followed by a brief discussion on the EE massive
MTC in 5G and beyond cellular networks. By looking at the recent advances in
industry and possible new demanding use cases for IoT, we encourage further
research on the EE massive access in 5G and beyond cellular standards for IoT
that not only provides reliability but also QoS and EE connectivity.

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