Wu et al.

J Wireless Com Network (2023) 2023:77 EURASIP Journal on Wireless

https://doi.org/10.1186/s13638-023-02288-7
Communications and Networking

RESEARCH Open Access

A Q‑learning‑based distributed queuing Mac

protocol for Internet‑of‑Things networks
Chien‑Min Wu1* , Yu‑Hsiu Chang1, Cheng‑Tai Tsai1 and Cheng‑Chun Hou1

*Correspondence:
cmwu@nhu.edu.tw Abstract
1
Department of Computer Many previous studies showed that some nodes should be in a sleeping state
Science and Information while the traffic is high. These nodes can wake up periodically to transmit data
Engineering, Nanhua University, while they have data to transmit. The system throughput will be increased while each
Chiayi, Taiwan
node can change between wake-up and sleeping states periodically. If a node can
estimate the active rate and wake up at the optimal time, the collision probability
decreases. This study proposes a Q-learning-based distributed queuing medium access
control (QL-based DQMAC) protocol for Internet-of-Things (IoT) networks. In the pro‑
posed QL-based DQMAC, we derive the optimal number of contention IoT nodes. Each
node calculates the active rate by itself through the Q-learning algorithm. Then, each
node determines whether it will be active or in sleeping mode in the next contention
period according to the active rate. Finding the optimal IoT nodes in each contention
period decreases the probability of collision. The energy consumption due to the con‑
tention and delay for MAC contention is reduced owing to the lower number of con‑
tentions. Protocol comparison with other DQMAC protocols shows that the proposed
QL-based DQMAC protocol achieves higher performance in IoT networks.
Keywords: Internet of Things, Medium access control, Q-learning, Distributed
queuing, Active rate

1 Introduction
Each device in an Internet of Things (IoT) network has a unique address and connects to
other devices. Any device in an IoT network can communicate with other devices, and
the communication is not affected by the location, network, or internet service provider.
The IoT is a hot topic in the field of communication. For machine-to-machine (M2M)
communication of portable devices, communication with a fixed location may not meet
the requirements of mobile users. Therefore, communication in mobile IoT is a general
application [1, 2]. The communication of applications in IoT can be attained through
wireless technology, thereby achieving ubiquitous and seamless communication access
[3, 4].
However, the schedule and channel access of data transmission remain a critical prob-
lem for efficient system performance in IoT networks. Therefore, the implementation of
an efficient MAC protocol is important to achieve high channel utilization.

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 2 of 26

In addition, the TDMA MAC protocol still presents the collision problem, especially
when the traffic increases. To solve the collision problem, some authors proposed a dis-
tributed queuing random access protocol (DQRAP) MAC protocol [5, 6]. In DQRAP,
the channel can share all contention nodes, thereby detaching the performance from the
number of nodes. In DQRAP, the transmission delay does not increase and the system
throughput does not decrease. Moreover, DQRAP presents two queues. One is the data
transmission queue (DTQ) and the other is the collision resolution queue (CRQ).
In DQRAP, all nodes randomly select the time slot during the contention period.
Therefore, the collision problem becomes significant when the traffic load is heavy.
In addition, the length of the collision resolution queue increases as the traffic load
increases.
In [7, 8], the authors proposed an schedule mechanism of wake-up and sleep to over-
come the hidden terminal problem and reduce energy consumption.
In [7], the authors proposed a synchronized sensor-MAC (S-MAC) that is suitable for
wireless sensor networks (WSNs). S-MAC assumes that the sensor nodes will be in the
sleeping state most of the time. When a sensor node detects the signal, it returns to the
wake-up mode. Therefore, the MAC protocol design in WSNs is different from that of
traditional wireless networks such as IEEE 802.11. If the propagation delay and fairness
are no longer important, energy consumption and self-configuration will be the main
goals of the MAC protocol in sensor networks. Therefore, the sensor nodes in the pro-
posed S-MAC will go into the sleeping mode periodically. The nodes in a sensor network
are divided into clusters. All these nodes will be self-configured by adaptive listening to
obtain the status of the neighbors.
In [9], the authors proposed a non-synchronized B-MAC using an extended preamble.
The operation of the sensor nodes in B-MAC is low-power. In addition to operating at
lower power, B-MAC achieves collision efficiency, and increases the channel utilization
by using the adaptive preamble mechanism. In [8], the authors proposed a low-power
MAC (X-MAC) protocol based on a shorter preamble for WSNs. X-MAC also features
a low energy consumption and decoupling mechanism of the sleep schedule for both the
transmitter and receiver.
In previous MAC protocols, the number of contention nodes for dynamic traffic load
is different and unknown. If the number of contention nodes is larger than the network
load, the collision probability will increase. If we can control the number of contention
nodes within each contention period, then the collision probability will be reduced.
Therefore, predicting a suitable number of contention nodes in each contention period
in IoT networks is required for dynamic traffic load. In this study, we let some conten-
tion nodes go into sleep during the contention period. Only a reduced set of conten-
tion nodes is permitted to transmit data. The motivation of this study was to propose
an adaptive protocol based on the Q-learning (QL) algorithm to improve the channel
utilization under dynamic traffic load in IoT networks.
There are three types of machine-learning (ML) algorithms, namely supervised
learning, unsupervised learning, and reinforcement learning. Inputs and outputs are
required for supervised learning to map an optimal model. In general, supervised learn-
ing involves classification and regression. There are only inputs but no outputs in unsu-
pervised learning. Unsupervised learning determines the rules from the training data

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during the learning process. Reinforcement learning is an interactive learning process

that determines actions from observation of the environment [10].
ML can enable IoT nodes to perform intelligent actions in IoT networks. IoT nodes
can access the channel on the basis of an ML algorithm under dynamic traffic load in
IoT networks. An reinforcement-learning(RL) algorithm is a possible scheme for ML.
Q-learning(QL) is an RL scheme. ML, RL, and QL were used in previous studies to eval-
uate the system performance in wireless networks [10].
In [11], the authors proposed a novel Markov decision process (MDP) model. The
transmission power and transmission probabilities were adaptively adjusted in com-
munication pairs in wireless networks. Consequently, high system performance was
achieved.
RL in previous studies was used to address the channel access problem in wireless net-
works. The IEEE 802.11 backoff scheme was formulated and optimized using MDP [12].
In [12], the authors proposed an RL algorithm to solve the IEEE 802.11 backoff problem.
In [13], the authors proposed an energy-efficient channel utilization mechanism based
on an RL algorithm for wireless networks. In [14], the authors proposed a unicast and
delay-aware MAC protocol based on a QL algorithm in vehicle ad hoc networks.
RL can also decrease the end-to-end delay, energy consumption, and increase the
system throughput when applied to wireless networks. Furthermore, in other envi-
ronments, ML mechanisms have been widely used to solve problems without prior
knowledge.
In this study, a QL-based MAC protocol for IoT networks is proposed to improve the
system performance. In this QL-based MAC protocol, the frame length of the conten-
tion period is adaptive and regulated by the QL algorithm to ensure the quality of service
of the system in an IoT network.
The main contributions of the proposed QL-based DQMAC protocol are as follows:

1. A Q-learning-based distributed queuing MAC protocol is proposed for energy-effi-

cient delay-aware IoT networks.
2. The proposed protocol is applied to reduce the collision probability of distributed
queuing MAC protocols in IoT networks.
3. The proposed protocol is applied to decrease end-to-end delay, reduce energy con-
sumption, and increase the system throughput for MAC contention.

The remainder of this paper is organized as follows. Methods/Experimental is intro-

duced in Sect. 2. Related work is introduced in Sect. 3. The proposed Q-learning algo-
rithm is introduced in Sect. 4. The proposed QL-based DQMAC protocol is introduced
in Sect. 5, and detailed principles and steps of QL-based DQMAC are introduced in
Sect. 6. The results and discussion are discussed in Sect. 7, and Sect. 8 presents our
conclusions.

2 Methods/experimental
This section presents the simulation results for the QL-based DQMAC protocol for an
IoT network. The programming language C was used to complete the simulation. We
ran our simulation programs on a computer with a single CPU (Intel Core i7-9700K) and

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 4 of 26

GPU (NVIDIA Quadro RTX 4000). We used Keras as the learning platform to train the
neural networks [15].
The data transmission time and propagation delay for wireless transmission, recep-
tion, and listening for each node were measured. The energy consumption for each node
was calculated multiple times under the wireless operation. The energy consumptions
of transmission, reception, and sleeping mode according to the radio-transceiver data-
sheet are 13.5 mW, 24.75 mW and 15 µW, respectively. Here, the energy consumption of
reception and listening in the radio-transceiver model is the same [7].
The difference between QL-based DQMAC and traditional DQMAC protocols is
the design of the contention period. For the QL-based DQMAC protocol, the optimal
number of contentions in each contention period is calculated based on Q-learning. In
addition, part of the contention nodes before entering the beacon interval go into the
sleeping state. All the contention nodes in a beacon interval for traditional DQMAC go
to the contention period and then to DTQ or CRQ.
In traditional DQ MAC, the slot selection of the SAR control frame in the contention
period is random. Each node randomly selects one slot from the SAR contention field.
No sleeping mechanism and no optimal number of contention nodes concept in the tra-
ditional DQMAC protocol are executed. Therefore, the collision probability of the tradi-
tional DQ MAC is higher than that of the QL-based DQMAC.
Thus, the end-to-end delay of the traditional DQMAC protocol will increase for a
high collision probability resulting from heavy traffic load. In addition, the system per-
formance will be decreased owing to the high collision probability. In this study, the
proposed model incorporates a control channel. The data transmission for each node is
achieved by the exchange of control frames in the control channel.
In addition, owing to the time-varying characteristics of traffic loads, traditional
DQMAC result in low-resource transmission efficiency.
The main difference between QL-based DQMAC and traditional DQMAC is the use
of QL learning and a sleeping mechanism. Therefore, we compared the performance of
QL-based DQMAC with that of the traditional DQMAC scheme. The transmission rate
of the channel was 2 Mbps. The frame length of each contention period was 3, and the
system throughput was maximized in the DQ mechanism [5]. The simulation param-
eters of the proposed QL-based DQMAC protocol are listed in Table 1.
In this simulation, 25 different topologies were created using 25 different seeds. The
presented results are the average of the simulation results for 25 seeds. System through-
put, end-to-end delay, and energy consumption constituted the metrics of performance
evaluation.
A simple energy model based on basic tasks or activities performed by nodes in an
IoT network is proposed. All energy components that contributed to the overall energy
consumption in the active mode are considered in this model. First, the node is turned
on at the activation time (tON ). A switching time (tSW ) is then required to change the
state before sending the packets to the medium. Here, the node initially uses the time
of (tCSMA ) to execute the carrier sense multiple access (CSMA) mechanism. Next, the
node spends the transmission time (tTX)sending messages. After the transmission of the
packet is completed, the node requires a switching time (tSW ) to enter the inactive state
(tinActive) and changes the task again. Furthermore, the node requires switching time

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 5 of 26

Table 1 Parameters for Our Proposed QL-based DQMAC Scheme

Simulation time 10,000 s

Number of IoT nodes 100
Bounded region 200 m × 200 m
Channel data rate 2 Mbps
Number of contention nodes 3, 4, 5, . . ., 49 and 50
Number of SAR slots for one contention period 3
Variation in the number of contention nodes (d) 1
Power consumption for transmission 24.75 mW
Power consumption for reception 13.5 mW
Power consumption for listening 13.5 mW
Power consumption for sleeping 15 µW
Discount factor (γ) 0.9
Learning rate (α) 0.1

(tSW ) to start receiving messages with reception time (tRX ). The number of packet trans-
missions and receptions were equal. Finally, the node shuts down with a shutdown time
(tOFF). This process measured the energy consumption of each active node in the IoT
network. Based on the tasks and the time required for nodes to perform them, which
corresponds to a given voltage and current, the total energy consumed by each activity
in the IoT network can be obtained according to the previous model [16].

3 Related work
S-MAC can reduce the energy consumption and support network scalability by over-
coming collision problems in sensor networks. Sources of energy consumption in sensor
networks may be monitoring, collision among nodes, overhearing, and control overhead
for successful transmission. S-MAC uses periodic wake-up and sleep to reduce energy
consumption owing to the above problems.
In Fig. 1, each node sleeps for a period of time and then wakes up to monitor whether
there is a neighboring node to communicate with it. After the monitor operation, the
sensor nodes return to the sleep mode. All nodes in the sensor network will go over
wake-up and sleep cycles. The node turns off the power during the sleep cycle and sets a
start time point to return to the awake state. To reduce the control overhead of the com-
munication among nodes, the duration of the wake-up and sleeping periods is fixed. The
sensor nodes can thus be synchronized. However, all the nodes in a sensor network will
be switched off during the sleeping period because of the fixed length of the wake-up
and sleeping periods. This is not suitable for IoT networks [7].
In Fig. 2, an asynchronous extended preamble of B-MAC is proposed. The preamble
is longer than the length of the sleeping period for other nodes. When a sender wants
to transmit data, it must first send a leading preamble to the receiver. This leading

Fig. 1 The S-MAC protocol

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Fig. 2 The B-MAC protocol

Fig. 3 The X-MAC protocol

preamble increases the wake-up period of the receiver. Thus, the receiver node can
receive the leading preamble and communicate with the sender. The one-hop neigh-
bors will receive the preamble and judge whether the destination node belongs to
itself. If the destination is not a node, the node will return to the sleeping mode [9].
In [8], the authors proposed a short preamble MAC (X-MAC) protocol to improve
the extended preamble of B-MAC. X-MAC can reduce unnecessary energy consump-
tion by reducing the monitoring time to one-hop neighboring nodes. In Fig. 3, if
the sender wants to transmit data to the receiver, the sender will send a short but
inconsistent preamble until the sender receives an ACK from the receiver. Although
X-MAC reduces unnecessary energy waste during node sleep, it also increases the
control consumption because the leading symbols are used more often. The advan-
tage of X-MAC is that the sensor nodes can determine the wake-up time by itself.
Distributed queuing features stable performance, and nearly optimal channel access
rate, propagation delay, and energy consumption. There are two queues in distributed
queuing. One is the contention resolution queue (CRQ), and the other is the data trans-
mission queue (DTQ). When a collision occurs, any collision node enters the CRQ,
whereas the successful nodes enter the DTQ. In Fig. 4a–c, six nodes perform the so-
called tree-splitting algorithm. Each node sends an access request sequence (ARS) con-
trol frame in the selected slot. The successful nodes are assigned to the DTQ and then
transmit data. The nodes in which collisions occur will go into the CRQ sequentially. In
Fig. 4, nodes d1, d2 and d3 send the ARS control frame at slot 1 of frame 1 and nodes
d4 and d6 send the ARS control frame at slot 3 of frame 1. Therefore, d1, d2, d3, d4 and
d6 will enter the CRQ. Only node d5 sends the ARS control frame at slot 2 of frame 1.
Thus, d5 successfully enters the DTQ of frame 2. The collision nodes will go into the
CRQ sequentially, and then into the queue step by step. The successful nodes sequen-
tially enter the DTQ after queuing resolution. All the nodes transmit data successfully
owing to the distributed queuing mechanism [17].

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Fig. 4 The distributed queuing MAC protocol

In [18], the authors proposed a QL-based cooperative algorithm to achieve high chan-
nel utilization and low energy consumption in wireless networks. In [19], the authors
proposed an ML-based algorithm to improve the channel utilization efficiency for
opportunistic spectrum access in wireless networks. The ML-based algorithm does not
require prior characteristics of wireless networks. The ML-based scheme can achieve
high system performance by learning from observation of wireless networks.
In [20], the authors proposed an improved Sarsa mechanism named as expected Sarsa.
The updated target was used by the average value, and eligibility traces were introduced
for expected Sarsa in wireless communication networks. All possible actions were aver-
aged as the updated target. The historical access recording of each state-action pair
was applied by the eligibility trace. The authors demonstrated that expected Sarsa can
improve the convergence rate and learning efficiency.
In [21], the authors proposed a deep-reinforcement learning multiple access (DLMA)
scheme. This DLMA considers the time-slot sharing problem in multiple wireless net-
works and learns the experience from observations of state-action-reward without pre-
knowledge of the co-existing networks.
System lifetime is a key metric for obtaining high network performance in WSNs. In
[22], the authors proposed a QL-based MAC protocol (QL-MAC) that adaptively sched-
ules the sleep/wake-up time of sensor nodes according to the traffic load. An efficient
sleep/wake-up mechanism will reduce the energy consumption by using idle listening
and overhearing.

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Table 2 Comparing the advantages and disadvantages of the proposed QL-based DQMAC and
related protocols
Advantage Disadvantage

Our QL-based MAC Optimal nodes in each contention achieved Slow convergence in large environments
DQRAP [5, 6] Transmission delay does not increase. Unsuitable for large networks
S-MAC [7] Preserving energy Time synchronization is required.
X-MAC [8] Low power listening The receiver node needs to remain awake.
B-MAC [9] Can be scaled to a large network Unable to provide multi-packet mechanisms
ML-based MAC [19] Does not need prior characteristics of the Unsuitable for large networks
node
Sarsa [20] Model-free algorithm Slow convergence in large environments
DLMA [21] Faster convergence and more robust High computational cost
QL-MAC [22] Adaptively schedule according to the traffic Increase energy consumption
load
RL-MAC [23] Individual traffic load for each node The trade-off for latency and energy
efficiency

In [23], the authors proposed an RL-based MAC (RL-MAC) protocol for WSNs. For
an efficient schedule, the sleep/wake-up period minimizes the energy consumption and
optimizes the energy utilization. Energy utilization was optimized through adaptive
duty cycles. Previous protocols determine the sleep/wake-up duty cycles according to
the traffic node itself. RL-MAC schedules the sleep/wake-up period based on RL. The
comparing the advantages and disadvantages of the proposed QL-based DQMAC and
related protocols is listed in Table 2.
This study proposes novel concepts using a QL-based DQMAC protocol for IoT net-
works. First, the proposed model can achieve a higher throughput, lower delay, and
lower energy consumption in IoT networks. Improved system performance is achieved
by the QL-based distributed queuing MAC. Second, a decrease in transmission delay is
achieved by the QL-based DQMAC protocol through control of the number of conten-
tion nodes. An increase in the system throughput is also achieved.

4 Q‑learning
4.1 Q‑learning in Markovian environments
If the estimation of the number of contention nodes can be achieved under a fixed frame
length of the contention period, then the collision probability will decrease. When the
number of contention nodes is large under a fixed frame length, the collision probability
increases, and the packet propagation delay increases as well. Thus, the channel utiliza-
tion of IoT networks is low. Therefore, the number of contention nodes will be limited
and suitable for a fixed frame length.
However, the traffic load is dynamic in IoT networks, and the number of contention
nodes is also dynamic. The QL algorithm does not require a prior knowledge of the
environment’s characteristics. Therefore, the QL algorithm is suitable for estimating the
number of contention nodes. QL can use the ongoing interaction between IoT nodes
and the environment, and then estimates the optimal number of contention nodes to
increase the system performance.

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 9 of 26

Fig. 5 The agent-environment interaction in a Markov decision process

The QL algorithm does not require complex computational capability from MAC
controllers and has a low communication overhead for IoT network nodes. In wireless
networks, the acknowledgment of reception is applied to verify the reliability of unicast
communication.
Most contention-based MAC protocols have this function to confirm whether the
packet is received by the receiver. Therefore, contention-based MAC protocols have a
high overhead owing to the acknowledgement scheme [24].
In Fig. 5, the MDP defines a problem under a simple and direct outline. Interaction
learning is used to achieve this goal. The number of elements for states (S), actions (A),
and rewards (R) in the MDP is finite.
Rt and St are random discrete probability distribution random variables. Rt and St are
also dependent on the previous state and action. In fact, all previous states and actions
have a probability under the specified values for all s′ , s ∈ S , r ∈ R and a ∈ A(s) [25]:
.
p(s′ , r|s, a) = Pr(St = s′ , Rt = r|St−1 = s, At−1 = a), (1)

with

p(s′ , r|s, a) = 1, s ∈ S, a ∈ A(s),

(2)
s′ ∈S r∈R

where p is the dynamic function and is defined in the MDP and p : S × R × S×

A −→ [0, 1] is an ordinary deterministic function with four arguments.
The policy is a possible action that is selected by one state in the IoT environment.
If the agent follows policy π at time step t, then π(a|s) is the probability of At = a
under St = s.
π(a|s) is the probability that At = A under St = s if the agent follows policy π at time
t. The MDP is solved using an RL mechanism without requiring complete information.
The agent, environment, policy, reward, and Q-value function constitute the elements of
the QL system.

4.2 Q‑learning function
A policy is an executed action in specified states within an IoT environment. A policy
is generally a function or lookup table. In fact, a policy is the core of an RL agent. The
policy determines the behavior, which is sufficient. In addition, the policy for each action
may also be determined by the stochastic or specified probabilities.
The reward defines the purpose of the RL mechanism. The reward is a single number
that is sent to the agent in the RL IoT environment. The only purpose of the agent is to
maximize the reward. If the reward under the policy selection of action results is low,

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 10 of 26

then the other action will be selected and the policy will be changed in the future for that
situation.
In contrast, the reward under the policy selection of action results is high, and the
Q-value creates a good signal for that situation. Therefore, the Q-value is the cumulative
reward of a state. The action will be decided by this Q-value and will be performed by
the agent in the future [25]. The number of successful transmissions of time slots will be
reduced, and the number of failed transmissions will be increased under high collision
probability in IoT networks.
In an IoT environment, a greater channel collision probability takes place when the
number of time slots with successful transmission is reduced, and the number of time
slots with failed transmission is increased. Here, we consider two items to calculate the
reward. One is the number of contention periods for each beacon interval. Another
item is the number of contentions for each node until the slot is successfully accessed. A
higher channel collision probability indicates that the number of contention periods will
be higher than that of lower channel collision probability. Thus, the reward is lower and
may be negative. In contrast, the reward is higher under a lower channel collision prob-
ability. The additional reward is obtained by the Q-value estimation. The agent selects
the optimal action based on the Q-value. The reward is zero, while the Q-value is absent.
The agent decides the optimal action in accordance with the information provided by
the Q-value function. Then, the optimal action maximizes the Q-value function. In the
long term, the greatest reward is obtained through a series of actions. In addition, maxi-
mizing an individual reward is not necessary for an agent [10].

4.3 Q‑learning algorithm
A Q(St , At ) table is maintained by the agent in QL. The state St of the MDP is observed
by the agent in an IoT network for t = 1, 2, 3, ..... The agent will selects an action At
from the set of actions (A). The agent receives a reward R(t) and then observes the next
state St+1 after the action At . The sequence of events creates the learning experience
( St , At , R( t), St+1) of the agent. The sequence of events under the ( St , At ) will be updated
in the Q-table according to the QL function (3).

Q(St , At ) ←− Q(St , At ) + α[Rt+1 + γ max Q(St+1 , a) − Q(St , At )] (3)

a

An action based on the state St will be selected by the agent. The maximum Q-value can
be determined for the next state St+1 according to the action At . Then the maximum
Q-value updates the current Q-value.
The preferred value range of the discount factor γ is 0 < γ < 1. The learning speed is
defined as the learning rate α, with 0 < α < 1.
The discount factor models the importance of rewards in the future. The agent ignores
the future reward and only considers the current reward when the discount factor γ is
set to 0. By contrast, the agent attempts to obtain a high long-term reward when the
discount factor γ is set to 1. In general, the discount factor γ is set between 0.6 and 0.99
[24], and is considered as a part of the problem.
The extent to which the new message overrides the old message is determined by
the value of the learning rate. When the estimated value is modified, the estimated
speed is also controlled by the learning rate. In general, an increase in the learning

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 11 of 26

rate increases the estimated speed. A decrease in the learning rate will decrease the
estimated speed. There is no new learning message when the learning rate α is set at
0. By contrast, only the most recent message must be considered when the learning
rate α is set to 1.

5 QL‑based DQMAC protocol

The proposed IoT network architecture is shown in Fig. 6. There are two types of nodes
in IoT networks, namely the IoT cluster head and IoT sensor nodes. The cluster head
is powerful and provides a reliable source of energy. An IoT network can have many
clusters, and each cluster has its own cluster head. Large IoT networks may have many
clusters. These clusters create a multihop environment among nodes. However, in this
study, we focused only on one cluster and a single-hop environment by applying the QL
mechanism to an IoT network.
In the proposed QL-based DQMAC protocol, there are one control channel for node
contention and data transmission.
The control channel of an IoT network is shown in Fig. 7. The time is divided into
beacon intervals. Each beacon interval includes the sensing period, contention periods,
and data transmission period. Each contention period has slots for slot access request
(SAR). First, each IoT node wants to send data using a selected one time slot in the con-
tention period. The cluster head receives all SAR control frames in the single-hop IoT
network. After an SAR, the cluster head broadcasts a slot access confirm (SAC) con-
trol frame to all cluster members, and then the successful sensor nodes perform data
transmission.

Fig. 6 The architecture of IoT networks

Fig. 7 The control channel of QL-based distributed queuing MAC protocol for IoT networks

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 12 of 26

In this study, the optimal number of contention nodes within the contention period in
IoT networks according to the dynamic traffic rate is achieved by the QL mechanism. In
Fig. 5, the agent is a learner and a decision IoT node. Each IoT node must interact with
other IoT nodes. The agent selects the optimal action and creates a new situation. Then,
the environment will make it suitable for the new situation and create a reward. The
agent selects the optimal action to maximize the reward. The procedure is repeated until
the maximized reward is achieved.
The agent receives the state information of the environment at each time step t, St ∈ S .
Then, the agent selects an action, such that At ∈ A. After one t under At , the agent
receives a reward, Rt ∈ R. Then, the agent changes to the new state, St+1 [25].
In the proposed model of QL-DQ MAC protocol, the IoT node is the agent. The state,
St , is the optimal contention node of the contention period at time t. The action, At , is
the step size for adjusting the number of the contention nodes in the contention period.
The step size may be plus d contention nodes or minus d contention nodes under the
current number of contention nodes. In addition, the step size may also be zero and then
the next number of the contention nodes is the same as the number of contention nodes.
Note that d is an integer. The reward, Rt , depends on the contention results of the con-
tention period at time t.
Therefore, the interactions between the IoT node (agent) and the environment at time
step t are as follows [20]:

1. The IoT node observes the environment in the IoT network and obtains the current
number of contention nodes, St , after one beacon interval.
2. The IoT node obtains the active rate in the next contention period by itself and then
determines the next action At.
3. The IoT node applies the selected action At in the MAC protocol. After one time
step, the IoT node obtains the feedback reward Rt+1 of the IoT network.
4. The IoT node moves from the state St to the new state St+1.

5.1 Control channel descriptions

For the traditional DQ mechanism [5], all nodes enter the contention period by ran-
domly selecting a time slot. The collision increases as the traffic rate increases. Con-
sequently, the propagation delay is also increased. In fact, the maximum number of
successful nodes equals the number of contention slots. Therefore, the number of con-
tention nodes will not be larger than the number of slots in each contention period. To
achieve the above concept, the specified contention nodes will enter the sleeping state,
with a greater number of contention nodes than contention slots. The rest of contention
nodes will go into the sleeping state. The optimal number of contention nodes will enter
the contention period.
However, what is the optimal number of contention nodes in each contention
period? How many contention nodes should go into the sleeping state? For the pro-
posed QL-based DQMAC, the optimal number of contention nodes is calculated
using the QL algorithm. Then, the active rate for all the contention nodes is calcu-
lated. The QL algorithm for the proposed QL-based DQMAC is used in DQMAC

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 13 of 26

to determine the optimal number of contention nodes. When the optimal number
of contention nodes is determined, the probability of collision among contention
nodes is decreased, the system throughput will increase, and the propagation delay
will decrease. The energy consumption is also decreased by the proposed QL-based
DQMAC.
Next, we describe the scheme of QL-based DQMAC, which determines the opti-
mal number of contention nodes in each contention period by using the QL algo-
rithm to improve the distributed queuing MAC protocol.
When one node wants to send data, it sends the SAR control frame during the
contention period. The cluster head will broadcast the SAC control frame when the
contention period ends. The SAC contains detailed contention information during
the contention period. Thus, the successful nodes will send the data and the collision
nodes will continue to perform the QL-based DQMAC sequentially. Figure 7 shows
a QL-based DQMAC protocol control channel for IoT networks. The two phases of
QL-based DQMAC are as follows:

• Sensing period: The decision about whether the licensed channel can be used or
not is made according to the sensing success probability. The IoT node senses the
licensed channel; its sensing success probability is larger than the sensing thresh-
old. The previous three-channel status will be used to calculate the sensing suc-
cess probability. Hello contains the channel-sensing status and the selected con-
trol channel for cluster head. Announce is sent by a new node when it wants to
JOIN or to LEAVE a cluster.
• Contention period: This period involves the exchange of SAR. In addition, there
is a SAC for the cluster head to announce the IoT nodes that win the contention
and assigned slots in the contention period.

5.2 Contention period descriptions

Each beacon interval includes the sensing period, contention period, and data. Each
contention period contains the following control frames:

• SAR contains the following fields: DCCid , Headid , IoTsnd , IoTrcv , NAVdata , and
SLOTid . DCCid denotes the ID for the control channel, Headid denotes the ID of
the cluster head, IoTsnd denotes the IoT node that sends an SAR, IoTrcv denotes
the IoT node that receives an SAR, NAVdata denotes the duration of transmitting
data for the IoT node sender, and SLOTid denotes the selected time slot in the
contention period for the IoT node sender.
• SAC contains the following fields: DCCid , Headid , m, Navg , Nsuc
i , Slot 1
nodeID ,
· · ·,
n
SlotnodeID . DCCid denotes the ID for the control channel, Headid denotes the ID
of the cluster head, m denotes the frame length of the contention period, Navg
denotes the average number of contention nodes in one beacon interval, Nsuc i

denotes the number of successful contention nodes in the contention period

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 14 of 26

i
i, and SlotnodeID denotes the time slot i selected successfully in the contention
period for the IoT node sender nodeID.

5.3 Active and sleep

In [7], the authors proposed an S-MAC for WSNs. In S-MAC, the sensor node
switches between the sleeping and active states. Therefore, the sensor node will
remain in a sleeping state for a long time until an active message is detected. Further-
more, the sensor nodes in S-MAC will enter the sleeping state periodically, thereby
reducing the energy consumption.
In the distributed queuing MAC protocol, each node sends a request control frame
in the selected time slot. The successful node enters the DTQ and then transmits
data. The collision node enters the CRQ. Finally, all the nodes successfully transmit
their data according to the distributed queuing mechanism.
For a distributed queuing MAC protocol, the propagation delay will be longer
when the traffic load is heavy. Moreover, collisions will be frequent under heavy traf-
fic loads. Consequently, the energy consumption is increased when the traffic load is
increased. Therefore, decreasing the collision is an important issue when using a dis-
tributed queuing MAC protocol for IoT networks.
In QL-based DQMAC, the specified number of IoT nodes will be in a sleeping state
in each beacon interval while the distributed queuing mechanism is executed. This
specified number of IoT nodes will go into sleep mode according to the Q-learning
mechanism.
Then the number of contention IoT nodes in contention period i is calculated as
follows:
 i−1 j
i Navg − j=1 Nsuc 2 ≤ i ≤ n;
Ncon = (4)
Navg i = 1.

The active rate of the number of contention nodes in contention period i is defined as
follows:

Nsleep i −X
Ncon
Rateactive = 1 − i
= 1 − i (5)
Ncon Ncon

Each contention node determines itself whether to be active or in sleeping state in the
next contention period according to the active rate.
Nsleep denotes the number of sleeping nodes during the contention period, and X
denotes the intended optimal number of nodes in the contention period.
For a distributed queuing MAC protocol, the IoT nodes will be successful and then
transmit data sequentially according to the resolution mechanism. Therefore, Navg
and Nsleep are variable numbers with time according to the resolution mechanism.
In the proposed QL-based DQMAC protocol, the number of contention IoT nodes
in the next contention period is determined. If such number is large, then the distrib-
uted queuing mechanism is preferred. If the number is small, then all the contention

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 15 of 26

nodes will enter the same contention period. In general, the optimal contention nodes
cannot increase the number of time slots in the contention period, that is, X ≤ m.
All the sleeping nodes that want to transmit data will wake up in the next conten-
tion period. The new sleeping nodes are set again during the next contention period.
All contention nodes in the next contention period include the fail contention node
and the sleeping nodes in the previous contention period. The active rate is calculated
by the IoT node itself according to Eq. (5) after one contention period. Accordingly,
the IoT node will know whether it will be in active or sleep mode in the next conten-
tion period.
The executed condition of the distributed queuing mechanism is used to determine
which condition is suitable to use the distributed queuing mechanism. Here, the exe-
cuted condition of the distributed queuing mechanism is defined as follows:

DQcondition = X (6)

If the number of contention nodes is greater than DQcondition, then the above distributed
queuing mechanism is executed. Otherwise, all the contention nodes will enter the next
contention period.

6 Detailed principles of, and steps in, QL‑based DQMAC protocol

6.1 Action selection
The optimal number of contention nodes is adapted prior to each beacon interval by
performing one of the following actions:

Xt+1 ←− Xt , a ∈ (Xt − d, Xt , Xt + d), (7)

where Xt is the optimal number of contention nodes at time step t, and d denotes the
variation in the number of contention nodes.
RL is different from supervised and unsupervised learning, which are strategies cur-
rently being broadly investigated in the field of machine learning. One challenge of RL
is the trade-off between exploration and exploitation. To obtain the reward, an RL agent
must take one preferred action. This preferred action results from the fact it was effec-
tive in generating rewards in past situations.
However, to discover such preferred actions, the agent must not attempt previous
actions that did not generate rewards, and must exploit experiences that can be used
to obtain rewards. However, the agent must also explore potential outcomes to identify
better actions for future situations. As such, neither exploration nor exploitation can be
carried out exclusively, which results in a dilemma [25].
The simplest action selection rule is used to select the action with the greatest esti-
mated value. If there is more than one “greedy” action (an action that presents the maxi-
mum estimated value), then the action is randomly selected. The greedy-action selection
method can be expressed as [25]
.
π(s) = argmax Q(s, a), (8)
a

where argmaxa denotes the action a for which the expression is maximized.

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 16 of 26

6.2 Convergence requirements
The convergence of the QL algorithm is based on all actions that are repeatedly sam-
pled in all states. Furthermore, the action values are indicated discretely, with an optimal
action-value probability of 1 when the QL algorithm converges [26].
The current state of knowledge is used to maximize the immediate reward through
greedy-action selection. However, greedy action does not require additional sampling
time to determine whether better options can be selected. Thus, greedy-action selection
behaves in most cases with a small probability ε.
The ε-greedy method is defined such that the greedy action is not randomly selected
from all actions with the same probability. Instead, it is independently selected according
to the action-value estimates. As the number of steps increases, the number of samplings
for each action approaches infinity. Qt (a) is guaranteed to converge to q ∗ (a), which implies
that the converged probability of selecting an optimal action will be greater than 1 − ε.
The convergence speed of QL algorithms depends on the application and its associ-
ated environmental complexities [27]. When QL is applied in a new environment, the
agent must explore and exploit the reward to gradually discover the optimal action At
that maximizes the Q-value. Note that ε is defined as follows:

− TTrun (9)
ε=e simu ,

where Trun is the running time and Tsimu is the system simulation time. Convergence to
an optimal policy is guaranteed by the decay function in our proposed QL-based MAC
for an IoT-enabled MANET.

6.3 A priori approximate controller

From the first transmission of the SAR control packet, the application of the strategy
defined in Eq. (9) can result in instantaneous performance benefits.
An initial condition is required for an iterative algorithm such as QL. After the first
update, the initial condition will be changed. For QL, the initial condition is always zero. The
initial untrained Q-table is set as shown in Table 3: the rows represent the possible states,
and the columns represent the action space. Here, the rows represent the optimal num-
ber of contention nodes, and Xt denotes the current number of contention nodes at time
step t. Xt − d denotes the number of contention nodes decreased by d, whereas Xt + d
denotes that the number of contention nodes increased by d. The initial value for the initial
untrained Q-table with size [m, 3] is zero, except for Q[0, 0] and Q[m, 2], which are set to
extreme negative values (−100) to ensure that they are never visited by the IoT node.

Table 3 Initial Q-value table for our proposed QL-based MAC scheme
Xt − d Xt Xt + d

1 − 100 0 0
2 0 0 0
: : : :
: : : :
m−1 0 0 0
m 0 0 − 100

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 17 of 26

Each node uses a controller that depends on the traffic rate and the number of conten-
tion nodes for each contention period. In this study, all the nodes are in a one-hop envi-
ronment, and the traffic destination is either the cluster head or other IoT nodes in the
IoT network. The controller is trained a priori for γ = 0.9.

6.4 Implementation details
An SAR control packet must be sent in the SAR sub-period for each node that wants to
transmit data in the IoT networks. Once a beacon interval period ends, the cluster head
can know the number of all contention nodes. The cluster head collects the number of
contention periods in the beacon interval. The SAC control frame contains the conten-
tion information in each contention period and broadcasts by the cluster head when
each contention period ends. There are three possible statuses for each slot: successful,
collision, and idle. Each node calculates the results of Eq. (10) by assigning suitable val-
ues for the parameters µ and ν.

Navg Navg
Rt 7µ +ν
CPnum × m Slottol
Navg Navg (10)
=µ + ν N
CPnum × m avg
Slotcon (i)
i=1

where CPnum denotes the total number of contention periods in the beacon interval.
Slotcon (i) denotes the total number of contentions in the beacon interval for IoT node i
until achieving successful contention.
Algorithm 1 shows the contention mechanism selection for the proposed QL-based
DQMAC protocol in IoT networks.

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 18 of 26

In the Algorithm 2, the action selection is based on Pε. When Pε < ε, the optimal num-
ber of contention nodes in the next period is randomly selected from (Xt − d, Xt , Xt + d).
Otherwise, the action is determined by the controller (Eq. (8)). The reward is defined by
Eq. (10). The value of Pε is randomly selected from the interval (0, 1).

Algorithm 3 shows the Q value and reward calculation for the proposed QL-based
DQMAC protocol in IoT networks.

7 Results and discussion

7.1 Throughput
In [28], the author derived a performance analysis of IEEE 802.11. The saturation
throughput of the licensed channels was analyzed in [29].
The throughput per contention period size for IoT networks owing to the simulation
ending, ζ , is defined as follows:

RCH Tsuc
ζ = . (11)
Tsimu

where RCH is the data transmission rate for the licensed channel, Tsuc is the total success-
ful data transmission time, and the Tsimu is the system simulation time.
Figure 8 shows the system throughput in traditional DQMAC and QL-based DQMAC
in IoT networks. The system throughput in QL-based DQMAC ranges from 0.455 to

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 19 of 26

Fig. 8 Comparison of system throughput of IoT node in traditional DQMAC, QL-based DQMAC in a IoT
network

0.485 Mbps for µ = 0.7 and ν = 0.3 while the number of IoT nodes is greater than 11.
Under the same conditions, the number of contention for MAC contention in tradi-
tional DQMAC ranges from 0.445 to 0.450 Mbps. The largest improvement for system
throughput in QL-based DQMAC compared with traditional DQMAC is 6.59%.

7.2 Number of contention for MAC

Each IoT node sends SAR in the contention period for the QL-based DQMAC protocol
until it succeeds. Each node contends only once for each contention period until the suc-
cessful transmission. Therefore, the number of contend periods for each IoT node equals
the transmission number of the SAR.
The average number of contentions for one IoT node to achieve a successful transmis-
sion is defined as follows[30]:
 
1 γ 1
nCP ≃ logm (Ncon − 1) + + + (12)
2 log m 2Ncon log m

Here, γ ≈ 0.5772 is the Euler’s constant. The value of nCP is finite, whereas m is very
low. Ncon is the number of contention nodes and is defined in Eq. (4); m is the number
of contention slots for the contention period, and is the executed condition of Eq. (6). In
addition, when Ncon is low or high, the value of nCP is similar regardless of the number of
contention slots, m.
In the proposed QL-based DQMAC protocol, the IoT node has two states: sleeping
and active. The number of MAC contentions is the sum of the number of contention
failure and one successful contention. Therefore, the number of MAC contention in the
proposed QL-based DQMAC for a node is nCP:

nCP = NCRQ (13)

where NCRQ is the average number of collision resolution in MAC contention periods.
Figure 9 shows the level of MAC contention in traditional DQMAC and QL-based
DQMAC in IoT networks. The level of MAC contention before successful transmission

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 20 of 26

Fig. 9 Comparison of average contentions before successful transmission (nCP) of IoT node for traditional
DQMAC, QL-based DQMAC in a IoT network

in QL-based DQMAC ranges from 1.74 to 2.43 for µ = 0.7 and ν = 0.3. Under the same
conditions, the level for MAC contention before successful transmission in traditional
DQMAC ranges from 1.87 to 4.57. The largest improvement in the level for MAC con-
tention before successful transmission in QL-based DQMAC compared with traditional
DQMAC is 46.83%.
MAC collisions occur in traditional DQMAC with a greater number of active IoT nodes
and are more relevant than in QL-based DQMAC because of the lack of a sleeping-mode
mechanism in the contention period.

7.3 Delay for MAC contention

The arrival process of IoT nodes in IoT networks presents a Poisson distribution with
arrival rate . In the proposed QL-based DQMAC, the arrival process is memoryless, and
the MAC protocol uses a tree-splitting mechanism to perform the collision resolution. The
probability of Nconi for active IoT nodes contending in contention period i is denoted by

P(k = Nconi ) and expressed as follows [31]:

i
Ncon
P(k = i
Ncon ) = i e− (14)
Ncon !

Then, the probability of an active IoT node randomly and successfully selecting a free
mini-slot when there are Ncon
i active IoT nodes in a given frame is given by the following

expression:.
 i
1 Ncon −1
 
1
P(succ|k = i
Ncon ) =m 1− (15)
m m

Let P() be the probability of an IoT node successfully selecting one idle mini-slot in
contention period i. P() is defined as follows [32]:

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 21 of 26

∞

i i
P() = P(succ|k = Ncon )P(k = Ncon )
i =0
Ncon
∞

i i
= e− + P(succ|k = Ncon )P(k = Ncon )
i =1
Ncon
(16)
∞ i  i
Ncon − 1 Ncon −1
 
−
 1
=e + i !
e m 1 −
i
Ncon m m
Ncon =1

−m
me − e−
=
m−1

The service time of the collision resolution queue is a Poisson-distributed random vari-
able with mean [31]
  −1
1
tCRQ = ln (17)
1 − P()

Therefore, if the model for the collision-resolution queue is M/M/1, the service rate is
defined as follows:
 
1
µCRQ = (tCRQ )−1 = ln (18)
1 − P()

Here, we define the average delay for CRQ in the proposed QL-based DQMAC as
follows:
   −1
1 1
T CRQ = = ln − (19)
µCRQ −  1 − P()

Fig. 10 Comparison of delay of MAC contention before success transmission of IoT node for traditional
DQMAC, QL-based DQMAC in a IoT network

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 22 of 26

Figure 10 shows the delay of MAC contention before successful transmission in DQMAC
and QL-based DQMAC in IoT networks. The delay of MAC contention before successful
transmission in QL-based DQMAC ranges from 3.16 to 6.30 slots for µ = 0.7 and ν = 0.3.
Under the same conditions, the delay of MAC contention in traditional DQMAC ranges from
4.62 to 12.72 slots. The largest improvement in the delay of MAC contention before successful
transmission in QL-based DQMAC compared with traditional DQMAC is 50.49%.

7.4 Energy consumption for MAC contention

The average energy consumption for an IoT node for MAC contention in a QL-based
DQMAC can be expressed as follows:

E = E CRQ + ǫsleep T sleep (20)

where E CRQ is the average consumption during the collision resolution in the contention
period, ǫsleep is the power consumption in the sleeping mode, and T sleep is the average
time in the sleeping mode.
E CRQ can be expressed as follows:

E CRQ = nCP E SAR + nlisten ǫlisten T listen (21)

where E SAR is the average energy consumption when the IoT node sends one SAR con-
trol frame and contends with other IoT nodes in the contention period until it succeeds;
T listen is the average time in listening mode.
Each node sends the SAR control frame in a selected mini-slot in the contention
period; this node in other mini-slots will be in listening mode. After the SAR period,
each node receives one SAC control frame, which is broadcast by the cluster head.
Then, the ESAR can be expressed as follows:

E SAR = (ǫtx + (m − 1)ǫlisten )TSAR + ǫrx TSAC (22)

where ǫtx , ǫlisten , and ǫrx are the power consumption in the transmission, listening and
reception modes, respectively; TSAR is the average time required to send one SAR con-
trol frame to a contention slot; and TSAC is the average time required to send one SAC
control frame by a cluster head in a contention slot.
Therefore,

E = E CRQ + ǫsleep T sleep

= nCP ESAR + nlisten ǫlisten T slot + ǫsleep T sleep
(23)
= nCP ((ǫtx + (m − 1)ǫlisten )TSAR + ǫrx TSAC )
+ nlisten ǫlisten T slot + ǫsleep T sleep

Figure 11 shows the energy consumption for MAC contention in traditional DQMAC
and QL-based DQMAC in IoT networks. The energy consumption for MAC contention
in QL-based DQMAC ranges from 34.35 to 46.44 mW for µ = 0.7 and ν = 0.3. Under the
same conditions, the energy consumption for MAC contention in traditional DQMAC
ranges from 60.29 to 113.19 mW. The largest improvement in energy consumption for
MAC contention in QL-based DQMAC compared with traditional DQMAC is 46.73%.

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 23 of 26

Fig. 11 Comparison of energy consumption for MAC contention of IoT node in traditional DQMAC, QL-based
DQMAC in a IoT network

An IoT node will enter the sleeping mode after successful transmission for QL-
based DQMAC. The IoT node, after successful transmission, will also enter the sleep-
ing mode for traditional DQMAC. Figure 12 shows the total energy consumption per
beacon interval in traditional DQMAC and QL-based DQMAC in an IoT network.
The total energy consumption per beacon interval in QL-based DQMAC ranges from
90.49 to 158.76 mW for µ = 0.7 and ν = 0.3. Under the same conditions, the total
energy consumption per beacon interval in traditional DQMAC ranges from 122.46
to 300.22 mW. The largest improvement in the total energy consumption per beacon
interval in QL-based DQMAC compared with traditional DQMAC is 47.12%.

Fig. 12 Comparison of total energy consumption of IoT node in traditional DQMAC, QL-based DQMAC in a
IoT network

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 24 of 26

To demonstrate the effectiveness of reinforcement learning sufficient training data is

required. Therefore, when the amount of data is insufficient, Q-learning cannot be per-
formed optimally. However, when the amount of data becomes excessively large and
exceeds the saturation point of the system, Q-learning may diverge and degrade system
performance [33].
In [5], the authors showed that the target system throughput was achieved when three
contention slots were used in the DQ mechanism. Therefore, the number of SAR slots
for one contention period was set to 3 in Table 1. In Figs. 8, 9, 10, 11 and 12, there is a
degrades suddenly for system performance under 8 and 45 IoT nodes for µ = 0.7 and
ν = 0.3. The system performance becomes unstable when the number of contention
nodes is either excessively low (i.e. under 8) or excessively high (i.e. beyond 45) under 3
SAR slots for one contention period in our proposed QL-based DQMAC in an IoT net-
work for µ = 0.7 and ν = 0.3. The simulation results in Figs. 8, 9, 10, 11 and 12 show that
the total number of IoT contention nodes between 8 and 44 for our proposed QL-based
DQMAC in an IoT network will have superior and more stable system performance. The
average delay for CRQ, denoted by T CRQ and defined in Eq. (19). In Fig. 10, and the line
curve of T CRQ versus the number of IoT nodes are consistent with the average delay in
MAC contention before successful transmission for the proposed QL-based DQMAC
protocol, whereas the number of contention nodes is between 8 and 44. Moreover, the
proposed QL-based DQMAC protocol had a lower average delay in MAC contention
before successful transmission than the DQMAC protocol.

8 Conclusion
This study proposes a QL-based DQMAC protocol to achieve an energy-efficient
MAC contention with a low number of contentions and high system throughput.
The contention reduction in QL-based DQMAC was found to be greater than in tra-
ditional DQMAC because of the sleeping mode mechanism and optimal number of
contention IoT nodes in QL-based DQMAC. In addition, the QL-based DQMAC
protocol reduces the number of MAC contention collisions with respect to tradi-
tional DQMAC. The proposed QL-based DQMAC scheme effectively achieves not
only a lower number of contentions, but also lower energy consumption and lower
end-to-end delay than traditional DQMAC. Simulation results show that the largest
improvement in the number of contentions for MAC contention before successful
transmission in QL-based DQMAC compared with traditional DQMAC was 46.83%
(for α = 0.7 and β = 0.3). The largest improvement in the delay of MAC contention
before successful transmission in QL-based DQMAC compared with traditional
DQMAC was 50.49% (for α = 0.7 and β = 0.3). The largest improvement in the total
energy consumption per beacon interval in QL-based DQMAC compared with tra-
ditional DQMAC was 47.12%. The largest improvements in energy consumption for
MAC contention in QL-based DQMAC compared with traditional DQMAC was
46.73% (for α = 0.7 and β = 0.3). Finally, the largest improvement in system through-
put in QL-based DQMAC compared with traditional DQMAC was 6.59% when the
number of IoT nodes was larger 11.

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Wu et al. J Wireless Com Network (2023) 2023:77 Page 25 of 26

Abbreviations
MAC Medium access control
DQMAC Distributed queuing medium access control protocol
QL-based DQMAC Q-learning-based distributed queuing medium access control protocol
IoT Internet-of-Things
M2M Machine-to-machine
TDMA Time division multiple access
DQRAP Distributed queuing random access protocol
DTQ Data transmission queue
CRQ Collision resolution queue
S-MAC Synchronized sensor-MAC
WSN Wireless sensor networks
X-MAC Low-power MAC
MDP Markov decision process
ML Machine-learning
QL Q-learning
RL Reinforcement-learning
ARS Access request sequence
DLMA Deep-reinforcement learning multiple access
RL-MAC RL-based MAC
SAC Slot access confirm
SAR Slot access request
CSMA Carrier sense multiple access

Acknowledgements
The authors would like to thank the editor and reviewers for their valuable comments and suggestions.

Author contributions
CMWU author wrote all the sections. The other authors did the simulation programming. All authors read and approved
the final manuscript.

Funding
This work is supported by Ministry of Science and Technology, Taiwan, R.O.C. under Grant MOST 108-2221-E-343-002 -.

Availability of data and materials

No availability of data and materials.

Declarations
Competing interests
The authors declare that they have no competing interests.

Received: 14 April 2022 Accepted: 7 August 2023

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