# Enhancing Wireless Networks with Attention Mechanisms: Insights from Mobile
Crowdsensing

Yaoqi Yang, Hongyang Du, Zehui Xiong, , Dusit Niyato, , Abbas Jamalipour, ,
and Zhu Han Manuscript received xxx. Yaoqi Yang is with the College of
Communications Engineering, Army Engineering University of PLA, Nanjing
210000, China. (e-mail: yaoqi$\\_$yang@yeah.net)Hongyang Du and Dusit Niyato
are with the College of Computing and Data Science, NTU, Singapore. (e-mail:
<EMAIL_ADDRESS>dniyato@ntu.edu.sg)Zehui Xiong is with the
Information Systems Technology and Design Pillar, Singapore University of
Technology and Design, Singapore. (e-mail: zehui$\\_$xiong@sutd.edu.sg)Abbas
Jamalipour is with the School of Electrical and Computer Engineering,
University of Sydney, Sydney, NSW 2006, Australia (e-mail:
a.jamalipour@ieee.org).Zhu Han is with the Department of Electrical and
Computer Engineering at the University of Houston, Houston, TX 77004, USA.
(e-mail<EMAIL_ADDRESS>
###### Abstract

The increasing demand for sensing, collecting, transmitting, and processing
vast amounts of data poses significant challenges for resource-constrained
mobile users, thereby impacting the performance of wireless networks. In this
regard, from a case of mobile crowdsensing (MCS), we aim at leveraging
attention mechanisms in machine learning approaches to provide solutions for
building an effective, timely, and secure MCS. Specifically, we first evaluate
potential combinations of attention mechanisms and MCS by introducing their
preliminaries. Then, we present several emerging scenarios about how to
integrate attention into MCS, including task allocation, incentive design,
terminal recruitment, privacy preservation, data collection, and data
transmission. Subsequently, we propose an attention-based framework to solve
network optimization problems with multiple performance indicators in large-
scale MCS. The designed case study have evaluated the effectiveness of the
proposed framework. Finally, we outline important research directions for
advancing attention-enabled MCS.

###### Index Terms:

Attention mechanisms, wireless networks, mobile crowdsensing, multiple
performance indicators optimization.

##  I Introduction

Attention mechanisms 111In the paper we use “attention” and “attention
mechanism” interchangeably. can enable machine learning (ML) models to focus
on specific parts of input data that are more relevant to the task at hand.
Such an ability leads to advantages on model performance improvement, long
sequence handling, and better interpretability. Such benefits have been
evidently evaluated in various Artificial Intelligence (AI) applications, for
example, 1) In the natural language processing (NLP) domain, attention has
revolutionally enhanced performances in machine translation, text
summarization, and sentiment analysis; 2) In the computer vision (CV) area,
attention can enable image captioning and visual question answering, where the
model needs to focus on specific parts to provide answers; 3) In the speech
processing field, attention helps the ML model focus on relevant temporal
parts of an audio signal, improving the accuracy of transcriptions. In
summary, attention can offer significant benefits:

  * •

_Handling long-distance dependencies of the input data_. Attention enables AI
models to focus on different parts of the input data regardless of their
position. This is particularly beneficial for complex structured inputs where
relationships span across different segments of data, enhancing the retention
of contextual information across longer sequences.

  * •

_Increasing the accuracy and fluency of generative outputs_. By focusing on
important parts of the input when generating data, attention can improve the
accuracy and quality of the output result. This is vital in tasks such as text
generation, where maintaining grammatical correctness and context relevance is
crucial for high-quality outputs.

  * •

_Enhancing the model flexibility and generalization performances_. Attention
can help models to better generalize by learning which features to focus on,
rather than just memorizing patterns specific to the training data. This
ability enables AI models to perform better on varied and unseen data, thus
enhancing their flexibility and utility across different scenarios and tasks.

  * •

_Improving the interpretability of the generative model_. By providing a
mechanism to trace back to the parts of the input that were most influential
in generating a specific output, attention can make AI model’s decision-making
process more transparent, aiding in understanding, debugging, and explaining
the model’s behavior, which is critical in deploying AI solutions responsibly.

Simultaneously, wireless networks serve as the foundation for connecting
mobile users to a vast array of services, facilitating communication, data
exchange, and information access. However, due to various service
requirements, complex data interaction relationships, and open transmission
environments, wireless networks need to efficiently and carefully deal with a
large amount of data to make decisions on provided services, mobile users, and
wireless data aspects. In this regard, it can be difficult to achieve high
efficiency, satisfied quality, and strong security simultaneously within
wireless networks. Specifically, wireless networks face challenges on fairness
concerns of provided services, effectiveness management of mobile users, and
timeliness guarantee of wireless data.

The widespread adoption of wireless networks in diverse mobile scenarios
necessitates addressing above various challenges. While AI and machine
learning have been widely adopted to address the challenges, they face certain
limitations due to, for example, complex traffic data, complicated protocol
optimizations, and limited generative network management solutions. Therefore,
researchers have explored attention in wireless networks. To this end, we
focus on mobile crowdsensing (MCS) as a compelling case to provide a
comprehensive investigation and specific solutions, enhanced by attention. MCS
facilitates wireless data collection through crowdsourcing, where a service
requester (SR) delegates sensing tasks to a service provider (SP). The SP
recruits mobile workers (MWs), such as intelligent sensing terminals, to
collect required data [1]. This approach effectively addresses concerns
related to requirements for specialized sensing equipment, deployment of fixed
sensing networks, and limitations of single sensing capability (i.e., data
type or modality can be collected by terminals). However, MCS may face various
challenges attributed to sensing tasks, MWs, and sensing data aspects.
Inspired by aforementioned benefits, we investigate the use of attention to
address concerns in MCS. Specifically, given the ability to focus on most
important parts of input data, attention can potentially improve efficiency
and accuracy of various tasks in MCS. Moreover, attention can offer insights
to built fair, efficient, secure, and timely MCS in the following ways:

  * •

_Sensing task optimization_. Attention can dynamically allocate tasks by
selectively processing historical and current environment information,
ensuring better efficiency in task distribution. Additionally, by analyzing
specific patterns in task completion and worker recruitment costs, attention
can help design rational, fair, and cost-effective incentives, encouraging
broader participation.

  * •

_MWs management_. Attention can help to better recruit MWs based on the MWs’
current capabilities and historical performance, improving task completion
rates and MWs’ reward satisfaction. Furthermore, by monitoring sensing data
traffic to detecting malicious attacks, attention can enhance privacy
protections and create a more secure environment for MWs.

  * •

_Sensing data enhancement_. Attention can effectively deal with complicated
and dynamic sensing environments, facilitating better data collection policy
learning and reducing interaction complexity among MWs and environments.
Moreover, attention can help MCS to optimize wireless resource utilization for
data transmission, ensuring timeliness and usefulness of the sensing data.

In summary, attention excels at handling extensive input information by
focusing on the most beneficial parts for further processing. Hence,
integrating attention into MCS can improve task allocation, incentive design,
MW recruitment, privacy protection, data collection and transmission within
MCS. Specifically, the major contributions of this paper are summarized as
follows:

  * •

We first introduce preliminaries of MCS and attention. Next, we provide a
comprehensive review to demonstrate necessities and benefits of integrating
attention into MCS.

  * •

From the sensing task, MW, and sensing data perspectives, we investigate how
to adopt attention in MCS, aiming at address fairness, effectiveness, and
quality concerns.

  * •

We propose an attention-enabled network performance optimization framework for
the large-scale MCS, and the case study has evaluated the effectiveness of the
proposal.

##  II Preliminaries for Mobile Crowdsensing and attention mechanism

###  II-A Preliminaries for Mobile Crowdsensing

MCS represents a paradigm for leveraging the ubiquity of MWs (e.g., smart
phones, watches, and vehicles) to gather and analyze sensing data. Generally,
MCS is composed of an SR, an SP, and MWs. The SR first generates sensing data
demand. Then, due to its limited sensing capacity, the SR publishes the demand
to the SP. By recruiting MWs, the SP can obtain the required sensing data and
send it to the SR. Following the above process, the data interactions among
SR, SP, and MWs mainly include [1]:

  * •

_MW recruitment_. The SR recruits MWs capable of fulfilling SR’s sensing
demands.

  * •

_Incentive design_. The SP designs incentive mechanisms to attract MWs to
contribute sensing capabilities, in which the incentive can be monetary
rewards, reputation credit, or other benefits.

  * •

_Task allocation_. After the MWs are recruited, they are allocated to specific
sensing tasks based on factors such as proximity, availability, and
capability.

  * •

_Data collection_. The MWs equipped with sensing modules collect required
sensing data to finish the allocated tasks.

  * •

_Data transmission_. After collecting the sensing data, the MWs send it to the
SP over a wireless channel.

  * •

_Privacy protection_. To safeguard personal information and sensitive data
collected from MWs, privacy protection measures should be implemented.

By orchestrating these interactions effectively, MCS is able to utilize MWs to
obtain required sensing data. However, due to the complex data inputs and
interactions, heterogeneous sensing abilities and task demands, and open
sensing environments and channel accesses, MCS faces challenges related to
fairness, effectiveness, and data quality. In this context, it is essential to
explore attention mechanisms to efficiently leverage extensive input
information for constructing a sustainable, secure, and timely MCS.

###  II-B Preliminaries for Attention Mechanism

TABLE I: Summarization and comparison of different categories attention
mechanisms.

---

An attention mechanism is typically adopted in neural networks, targeting at
enhancing performance of machine learning models by allowing them to focus on
relevant parts of the input data. As shown in Fig. 1(m), within the attention
mechanism, context vectors indicate intermediate states or features extracted
from the input sequence at each position (i.e., the index of each element in
the input sequence data). Besides, a query refers to the element computing for
the attention, key means the element compared against the query, and value
indicates the element that are weighted and summed to produce the final
output. Specifically, to implement attention mechanism, three steps should be
executed [2]:

  1. 1.

_Score calculation_. For a given query, the attention mechanism computes a
score with each key. This score represents the relevance or importance of each
key to the query. A common method for calculating the score is the dot product
between the query and the key.

  2. 2.

_Softmax normalization_. The scores are then normalized using the softmax
function to produce attention weights. These weights sum to one and represent
the distribution of focus across all keys.

  3. 3.

_Weighted sum_. The final step involves multiplying attention weights by
corresponding values and summing them together. This weighted sum is the
output of the attention mechanism for the given query.

Generally, the major categories of attention mechanisms include soft and hard
attentions, local and global attentions, layered attentions, multi-dimension
self-attentions, and structured self-attention (Table I and Fig. 1).

  * •

_Soft and hard attention mechanisms._ Soft attention assigns weights to all
parts of the input, representing a weighted average of the input. These
weights sum to one, indicating the relative importance of each part. In
contrast, hard attention selects a subset of the input, focusing exclusively
on these chosen parts. This involves making a discrete decision about the
parts that are most relevant.

  * •

_Global and local attention mechanisms._ Global attention considers all input
elements when generating each output element. It recalculates context vectors
for every output step, capturing global dependencies. Conversely, local
Attention focuses on a smaller, dynamic subset of the input elements for each
output time step, allowing for more efficient processing for long sequential
data.

  * •

_Layered attention mechanisms._ Layered attention is structured as layers,
allowing the model to first attend to small local parts and progressively
integrate information to focus on higher-level features. It mainly includes
the hierarchical attention, top-down neural attention, multi-step attention,
and multi-head attention.

  * •

_Multi-dimension self-attention mechanisms._ Multi-dimension self-attention is
an extension of self-attentions, which allows the model to attend not just to
one dimension (such as time in sequences) but to multiple dimensions
simultaneously. In this regard, rather than compute a scalar score for each
component of the word embedding vector222A word embedding vector represents
each word as a fixed-length vector, which captures the semantic information of
the word. Each word embedding vector consists of multiple dimensions, each
dimension representing a different feature or attribute of the word., multi-
dimension self-attention computes a vector score for each component of the
token embedding vector. Multi-dimension self-attention mainly includes the
directional self-attention, bidirectional block self-attention, and
reinforcement self-attention.

  * •

_Structured self-attention mechanisms._ Structured self-attention considers
structural dependencies of deep neural networks, transforming typical
attention from a common soft choice, where each input is assigned a
probability of being selected, to a new mechanism that models information
about internal structures without disrupting end-to-end training. As a result,
the structured self-attention models continuous, subsequence multiple-input
selection instead of modelling single-input selection.

Recently, attention-enabled neural networks have been widely applied in the
wireless networking systems mainly on three aspects: 1) Attention-based deep
reinforcement learning (DRL) framework for strategies making, for example,
_Graph attention-based DRL for resource allocation[3]_. In vehicular
communication networks, [3] aimed at maximizing the communication throughput
by jointly optimizing spectrum and power resources. Subsequently, the study
adopted a multi-agent DRL to solve above formulated problem. However, since
the environments are complex and full of diverse dynamic information, it is
difficult for vehicle agents to realize effective state information sensing to
allocate resources. In this regard, by focusing on more relevant information
(e.g., network status and channel conditions), attention can enhance
efficiency of interaction and sensing of collaboration among vehicle agents.
This can further significantly reduce signal overheads and improve
communication performances. Experiment results showed that under the 1,060
Bytes payload, the proposed scheme can realize 54 Mb/s sum rate, while the
meta-DRL scheme is only with 50 Mb/s sum rate.

2) Combing attention with discriminative AI for results prediction,
classification and regression, for example, _Sequence-to-sequence (seq2seq)
attention-based convolutional neural networks (CNN)-long short-term memory
(LSTM) for long time series forecasting[4]_. To effectively predict long time
series with rich information, CNN is first employed to extract multiscale
features. This involves utilizing different convolution kernels to capture
short-term features at various scales. Subsequently, the study integrated the
seq2seq attention mechanism into the hidden state of the LSTM, enabling the
generation of the output sequence by leveraging all previous input
information. To this end, the proposed CNN-attention-LSTM scheme can
accurately forecast long time series. Simulation results using real-world
datasets demonstrate effectiveness of the proposed approach, achieving a Root
Mean Square Error (RMSE) of 0.97, compared with the RMSE of 1achieved by the
baseline LSTM without attention.

3) Integrating attention into generative AI for data generation and
augmentation, for example, _Slot attention-based Variational Autoencoder (VAE)
for object-centric scene generation[5]_. When applying Variational
Autoencoders (VAEs) to generate object-centric scene images, challenges arise
in modeling complex object relationships within highly structured scenes and
achieving high-quality sample generation. Slot attention offers a solution to
these challenges by enabling object-centric representation inference. By
integrating slot attention into a hierarchical VAE framework, the resulting
model can simultaneously infer a global scene representation and object-
centric slot representations. This allows for the capture of high-level scene
structure and the embedding of individual object components, respectively.
Numerical evaluations on the Arrow Room dataset demonstrate that the proposed
approach achieves an S-Acc score of 0.94, significantly surpassing the
baseline SIR scheme¡¯s score of 0.18. Such an attention application can
provide image data to train specific wireless ML models, e.g., slot attention-
based VAE can generate high-quality spectral maps of radiation sources for
identification and
localization333https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cmu2.12560?af=R.

We summarize above attention mechanisms in Table I, where we present their
categories, specific type, traits, advantages, disadvantages, and application
scenarios. Moreover, we show their structures and typical networks in Fig. 1,
where we highlight the implementation details of various attention mechanisms.

Figure 1: Structure of different attention mechanisms and applications in
wireless networking systems. (a) Soft attention. (b) Hard attention. (c)
Global attention. (d) Local attention. (e) Top-down neural attention. (f)
Structured self-attention. (g) Hierarchical attention. (h) Multi-step
attention. (i) Multi-head attention. (j) Bidirectional block self-attention.
(k) Directional self-attention. (l) Reinforcement self-attention. (m) Graph
attention-based DRL for resource allocation [3]. (n) Seq2seq attention-based
CNN-LSTM for long time series forecasting [4]. (o) Slot attention-based VAE
for object-centric scene generation [5].

##  III Integrating attention mechanisms into mobile crowdsensing

###  III-A Research Focuses and Solutions

Figure 2: The role of attention mechanism from the sensing task, MWs, and
sensing data aspects for MCS development and deployment, including task
allocation, incentive design, MW recruitment, privacy preservation, data
collection and transmission aspects.

Fig. 2 presents a summary of how attention can enhance MCS. Attention can
address fairness, effectiveness, security, and timeliness concerns from the
sensing task, MWs, and sensing data aspects within MCS. Specifically, we
summarize the primary research focuses and solutions as follows.

####  III-A1 Attention for MCS on sensing task aspect

_Attention-enabled task allocation_. For the task assignment scenario in a
ground-air confrontation system, the general agent is SR, and narrow agents
are MWs [6]. When allocating tasks to the narrow agents, the decision making
can be based on a number of of global features supported by the general agent.
However, dynamic and complex battlefields necessitate decision networks that
consider both current and historical information, posing a challenge due to
the volume of input data. Attention mechanisms can address this issue by
selectively retaining or ignoring input information, thus improving task
assignment efficiency. [6] utilizes a Proximal Policy Optimization (PPO)
framework for the task assignment, incorporating a multi-head attention
mechanism (shown in Fig. 1(i)) within the general agent to process global
situations. Simulation results show the proposed approach achieving a mean win
ratio444The win ratio is proportion of wins achieved by a particular player or
team out of the total number of games played. A high win ratio indicates
efficient task allocation. of 0.8, exceeding the 0.66 ratio of the baseline
Alpha-C2 scheme.

_Attention-enabled incentive design_. Typically, with a Stackelberg game
framework, SP and MWs act as the leader and followers, respectively [7].
During competitive interactions, the SP first determines its own payoff. Then,
the MWs employ a multi-agent DRL to adjust their strategies. However, the
agents for MWs may suffer from partial observability of the environment, only
exploring a single view. Creating multiple agents allows for observing
multiple views of the environment. While multiple views can benefit learning
performance, accurately calculating their importance levels is challenging.
Attention can precisely determine the importance of each view. For example,
[8] uses attention (shown in Fig. 1(i)) to generate a single feature
representation from multiple views based on their importance during decision-
making. Numerical results demonstrate that the proposed approach achieves the
data quality-oriented reward of 4,100 compared with 2,000 and 3,000 for the
baseline Asynchronous Advantage Actor-Critic (A3C) and Deep Deterministic
Policy Gradient (DDPG) schemes, respectively.

####  III-A2 Attention for MCS on MWs aspect

_Attention-enabled MW recruitment_. Within a terminal recruitment scheme, both
SP and MWs can make an agreement to successfully finish sensing tasks.
Generally, the MW recruitment problem is NP-hard, and heuristic solutions
could suffer from unstable performance during dynamics in different
environments. In this regard, attention can help the SP and MWs to derive the
solutions with better flexibility and self-adaptability. For example, [9]
first models the NP-hard terminal recruitment problem as a graph-based
optimization problem. Then, through integrating a graph attention network into
DRL, the MWs-oriented agents can more efficiently explore the solution space
of the targeted problem, realizing better solution performance. Numerical
results have demonstrated that when the number of tasks is 60, the proposed
attention-based approach can achieve the profit of 8,000 units, while the
baselines Greedy and deep Q-learning (DQN) schemes are only with around 2,500
and 4,300 units.

_Attention-enabled privacy preservation_. MCS raises privacy concerns as
collected data may contain sensitive information about MWs [1]. Protecting MW
privacy is crucial, but existing traffic detection approaches for privacy
preservation are computationally expensive and struggle to capture the
structure of large amounts of sensing data. Attention can enhance the input
traffic data processing efficiency and improve model detection performance.
For example, [10] introduces an attention to the fuzzy neural networks-based
abnormal traffic detection scheme. Specifically, fuzzy rules can simulate
factors relationships in the input data. By combining incremental and batch
fuzzy neural network training methods, the detection network is trained
comprehensively. Moreover, attention (shown in Fig. 1(i)) can boost feature
extraction, increase the model interpretability, and improve detection
accuracy. Numerical results have shown that the proposed scheme can realize
the detection accuracy of 0.9995, while decision tree and support vector
machine (SVM) approaches achieve only 0.7884 and 0.8575, respectively.

####  III-A3 Attention for MCS on sensing data aspect

_Attention-enabled data collection_. Unmanned Aerial Vehicles (UAVs) can serve
as MWs to collect sensing data within MCS. However, due to limited energy
supply of UAVs, it is important to minimize UAV’s and sensor’s energy
consumption as well as maintaining data acquisition efficiency by optimizing
the UAV trajectory. However, when optimizing the trajectory with DRL approach
in the solution space, the computational cost and solution performance can be
excessive owing to the high-dimensional data and complex network
architectures. In this regard, attention can address such concerns by
selecting subsets of inputs and better capture the local dependencies among
extracted features. For example, [11] transforms the input data as the graph
structural data, and then integrated the graph attention into the evaluation
and target networks of DRL model. After training the attention-enabled DRL
model, an optimal UAV trajectory can be derived from the outputs. Simulation
results demonstrate that, under identical packet reception rate settings, the
proposed approach achieves 27.1% reduction in total energy consumption
compared with the Successive Convex Approximation (SCA)-based scheme for both
UAVs and sensors.

_Attention-enabled data transmission_. In UAV-enabled MCS, optimizing resource
allocation for data transmission from ground sensors is crucial. Traditional
approaches using PPO-based DRL may struggle with dynamic changes in the number
of active sensors. Since the input size of DRL model’s general network is
fixed, it is difficult to effectively respond to the changing sensor
information. In this regard, attention can alleviate the issue of varying
number of sensors in the input of the PPO-based DRL [12]. Specifically, by
adding multi-head attention in front of the input layer of the DRL model, a
learning network is proposed to handle a varying number of active sensors
[12]. On this basis, attention (shown in Fig. 1(i)) can encode a diverse
number of observations within the DRL framework and provide a well-designed
reward to the UAV agent. Simulation experiments have evaluated that the
proposed scheme can achieve 3.53% and 19.74% achievable rate improvement
compared with fair and random resource allocation baseline schemes.

###  III-B Lessons Learned

In summary, attention can significantly enhance input data processing
efficiency, improve feature extraction within neural network models, and
improve model interpretability. These advantages contribute significantly to
MCS by improving fairness, effectiveness, sustainability, security, and
timeliness. However, despite its benefits, attention-enabled MCS faces certain
challenges due to limitations inherent to attention mechanisms.

  1. 1.

_Overfitting risk_. Attention may focus too heavily on specific data features
that can lead to overfitting, where the model learns noise or irrelevant
patterns, hindering the ability to generalize to new data.

  2. 2.

_Hyperparameters tuning_. Attention introduces additional hyperparameters that
require careful tuning for optimal performance, often requiring significant
experience and experimentation.

  3. 3.

_Limited sample efficiency_. Attention may not be efficient with limited data,
struggling to extract meaningful patterns and relationships when data is
scarce or expensive to obtain.

##  IV Attention-enabled multiple performance indicators optimization for
large-scale mobile crowdsensing

###  IV-A Research Motivations

To meet the requirements of MCS, multiple performance indicators need to be
jointly optimized. While multi-objective evolutionary algorithms can
effectively solve such problems [2], complex sensing tasks requiring large
datasets, and numerous MWs introduce scalability and complexity challenges.
This leads to exponential growth in decision variables and search spaces,
resulting in the curse of dimensionality for traditional multi-objective
evolutionary algorithms. To address this, balancing exploration and
exploitation of decision variables is crucial, as they contribute differently
to the optimization process [2]. Attention mechanisms, having the ability to
focus on specific areas, can enhance processing efficiency and improve
performance. Therefore, we integrate attention into multi-objective
evolutionary algorithms for optimizing multiple performance indicators in
large-scale MCS. Attention can assign a unique weight to each decision
variable, enabling a balanced exploration and exploitation at the variable
level. This effectively addresses the curse of dimensionality and improves
optimization results [2].

###  IV-B Proposed Framework

Figure 3: The framework of attention mechanism-based optimization with
multiple performance indicators in large-scale MCS. In the multi-object
optimization algorithm implementation step, three stages are included.
Specifically, stage A determines the key matrix. Stage B calculates the query
population, and Stage C implements the large-scale multi-objective
optimization algorithm with attention mechanism. $n$, $d$, $k$, and $g$
represent the population’s dimension, the individuals’ number, the query’s
dimension, and random selected individuals’ number, respectively.

With the help of an attention mechanism, multiple performances can be jointly
optimized in large-scale MCS. Accordingly, as shown in Fig. 3, following a
similar analysis in [2], we propose a framework for realizing attention-based
multi-performance-indicator optimization in large-scale MCS, which is
implemented with following five steps.

_Step 1: Performance indicator determination_. After receiving a specific
sensing task, a comprehensive analysis should be performed to identify key
performance indicators (KPIs) for task-oriented, large-scale MCS. For example,
in a real-time monitoring-oriented MCS scenario involving ground sensors, KPIs
such as Age of Information (AoI), sensor sensing energy, and transmission
energy consumption should be considered for joint optimization to ensure
sustainability and timeliness.

_Step 2: Decision variables determination_. Based on the identified
performance indicators, the decision variables influencing those indicators
should be determined. In essence, controllable parameters within the large-
scale MCS that can be adjusted to optimize target KPIs should be identified
and selected. Then, decision variables influencing the KIPs of energy
consumption and transmission delay could include sensing rate, transmission
power, and allocated bandwidth.

_Step 3: Attention mechanism adoption_. A large scale of MCS can lead to an
exponential increase in the dimension of decision variables and solution
spaces. To address this dimensionality challenge, attention mechanisms are
employed. Specifically, the key matrix within the attention mechanism is
derived from the variance of the current solution space across different
decision variables, enabling decision variable classification and extraction.
A query is then calculated by multiplying the decision vector with the key
matrix. The resulting attention vector is obtained by multiplying the query
with the transpose of the key. This attention vector guides the evolution of
the solution, effectively addressing the dimensionality problem.

_Step 4: Multi-objective evolutionary algorithm implementation_. As explained
in Step 4 of Fig. 3, when implementing a multi-objective optimization
algorithm, an individual represents a candidate solution to the problem,
consisting of a set of decision variables. A population is a collection of
individuals representing the current search space in each generation. Fitness
measures an individual¡¯s performance based on the objectives, and selection
involves choosing better-performing individuals to create the next generation.
Crossover combines characteristics from multiple individuals to generate new
solutions, while mutation introduces random changes. The new individuals are
integrated into the current population through an update process, forming a
new population. Diversity maintenance aims to prevent premature convergence
and ensure adequate exploration of the search space by preserving diversity in
the population. Within the considered framework, the optimization performance
indicators can be regarded as fitness, decision variables constitute the
individuals and population, and attention is utilized to assign weights to
decision variables for enhanced solution derivation. Moreover, crossover and
mutation are the evolutionary operations, while diversity maintenance guides
the derivation of optimal solutions. To be detailed, stage A determines the
key matrix. A1 can determine all the decision variables from population. A2
can calculate column variance from all decision variables to obtain the
variance vector. A3 can construct the key matrix based on variance vector,
where $k$ is a hyperparameter indicating dimension of query. Besides, stage B
calculates the query population. B1 aims at randomly selecting $g$ solutions
from population. B2 can determine the value individual based on largest
crowding distance on the first front. B3 refers to multiplying each decision
vector and value individual by key matrix. Then, by calculating the ratio of
each dimension of above two obtained matrices, B4 can determine the query’s
elements. Moreover, stage C implements the proposed attention-based algorithm.
C1 optimizes the query results through the evolution algorithm. C2 conducts
the transpose operation for the key matrix. C3 can obtain the attention vector
by multiplying optimized query and key’s transpose matrix. By multiplying
attention vector and value individual, then adding the results to population,
C4 can realize the individual evolution.

_Step 5: Results derivation and guidance for MCS_. After solving the multi-
objective optimization problems for large-scale MCS, the derived solutions can
guide the behavior of MWs, SP, and SR. For instance, based on the optimization
results, the sensing rate and transmission power strategies of MWs can be
adjusted to achieve better data freshness and energy efficiency.

###  IV-C Case Study and Results Analysis

Figure 4: Performance evaluation for the proposed framework. (a) Target
performances with the proposed approach. (b) HV performance of the proposed
approach. (c) IGD performance of the proposed approach. (d) Target
performances with the LMOCSO approach. (e) HV performance of the LMOCSO
approach. (f) IGD performance of the LMOCSO approach.

We consider a large-scale MCS scenario involving UAV-aided sensing data
collection, which can be applied in environmental monitoring, target
reconnaissance, and low-altitude warning tasks [13]. In this scenario, the SR
publishes sensing tasks to the SP, which is a UAV (tethered balloon in this
context). The UAV recruits ground sensors to collect the required data. After
the data collection, the sensors transmit the data to the UAV for further
processing. To ensure efficient and timely data transmission, the target
performance indicators to be optimized are the transmission energy consumption
of ground sensors and transmission delay [14]. The decision variables are the
individual transmission powers of different ground sensors.

To evaluate the effectiveness of the proposed framework, we conduct simulation
experiments for the above considered scenario with parameter settings in [2]
and [14]. All the experiments are conducted on PlatEMO
platform555https://github.com/BIMK/PlatEMO, running with Matlab R2023b on a
laptop with Intel(R) Core(TM) i7-11700 CPU @ 1.60GHz, NVIDIA GeForce RTX 3060
GPU, 32.0 GB memory and Windows 11. The code of this case study is available
on github666https://github.com/Yaoqi-Yang97/Attention4MCS. When the number of
MWs is set as 300, and the maximum number of functions evaluation is 50,000,
Figs. 4(a) and (d) show the evolution results of the proposed approach and
baseline, large-scale multi-objective optimization based on a competitive
swarm optimizer (LMOCSO) scheme [15]. To demonstrate the effectiveness of the
proposal, we adopt two indicators, Hypervolume (HV) and Inverted Generational
Distance (IGD), to evaluate the performance of optimized multiple indicators.
On the one hand, since HV refers to the volume of the multi-dimensional space
from the reference point to all solutions, the larger the HV, the closer the
solution set is to the Pareto front, and the better the diversity of the
solutions. In this regard, from Figs. 4 (b) and (e), the proposed approach can
achieve a larger HV value, evaluating its superiority. On the other hand, IGD
is the average minimum distance from each point in the reference Pareto front
to the solution set. A smaller IGD value indicates that the solution set is
closer to the true Pareto front and has better coverage. On this basis, as
shown in Figs. 4 (c) and (f), the proposed approach can achieve a smaller IGD
value, demonstrating its advantages.

##  V Future research directions

###  V-A Designing Transfer Learning-oriented Attention

Designing transfer learning-oriented attention mechanisms can enable models to
leverage pre-trained knowledge more effectively across different MCS tasks.
For example, few-shot learning can be adopted to enhance attention mechanisms
to work effectively with limited data, facilitating rapid adaptation to new
tasks with minimal training.

###  V-B Lightweighting the Attention Model to Extend Applications

In MCS, the implementation environment of attention is resource-constrained
due to limited resource supply of mobile devices. Therefore, lightweighting
attention models is crucial. Advanced compression techniques, such as pruning,
quantization, and knowledge distillation, can be used to lightweight the
attention model without compromising performances.

###  V-C Investigating Context-aware Attention Mechanisms

Context-aware attention mechanisms can significantly enhance the relevance and
accuracy of data processing in MCS. Such a research can develop attention
mechanisms that dynamically adjust focus based on contextual information,
e.g., location, time, and user activity, leading to more precise and timely
data analysis and improving MCS overall performances.

##  VI Conclusion

In this paper, we investigated the combination of attention with wireless
networks, where MCS was set as a specific case. To be detailed, we first
comprehensively introduced the preliminaries of MCS and attention, disclosing
the motivations and benefits of integrating attention into MCS. Then, we
presented several promising attention applications to MCS, mainly including
task allocation, incentive design, terminal recruitment, privacy preservation,
data collection, and data transmission aspects. In addition, their possible
challenges of such integrations were also discussed. Moreover, we proposed a
framework of attention mechanism-based multiple performance indicators
optimization in large-scale MCS. The case study and simulation results have
fully evaluated the effectiveness of the proposal framework. Finally, several
potential research directions were highlighted for future work.

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