Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Anjum, Muhammad Junaid; Anees, Tayyaba; Tariq, Fatima; Shaheen, Momina; Amjad, Sabeen; Iftikhar, Fareeha; Ahmad, Faizan

doi:https://doi.org/10.1155/2023/6037882

Applied Computational Intelligence and Soft Computing

On this page

Abstract Introduction Background Analysis Related Work Conclusions Data Availability Conflicts of Interest References Copyright Related Articles

Review Article | Open Access

Volume 2023 | Article ID 6037882 | https://doi.org/10.1155/2023/6037882

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Muhammad Junaid Anjum,^1,2Tayyaba Anees,¹Fatima Tariq,³Momina Shaheen,^1,4Sabeen Amjad,²Fareeha Iftikhar,²and Faizan Ahmad⁵

Academic Editor: Said El Kafhali

Received22 Jul 2022

Revised21 Jan 2023

Accepted24 Jan 2023

Published14 Feb 2023

Abstract

The occurrence of any kind of natural disaster will eventually lead to the loss of life and property. Countries where such disasters occur make every effort to monitor such disasters and aid as quickly as possible. However, in some cases, a rescue cannot be sent because no information is available to initiate any type of rescue operation. This is usually because common disaster management systems (DMS) use on board or ground networks to route information from the disaster scene to rescue headquarters (HQ), which in most cases cannot provide the information efficiently. One effective approach is to use satellites in conjunction with existing air-to-ground systems. This study provides a comprehensive and systematic overview of the complexities of the space-air-ground integrated network (SAGIN) in disaster management applications, including different architectures and protocols. The main rationale behind this review is to provide an extensive analysis of existing disaster management systems that are making use of SAGIN. This paper also presents the taxonomy for disaster management systems and challenges. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues.

1. Introduction

The mitigation of loss of human lives in any kind of disaster situation is one of the key aspects of any disaster management system. Whether a disaster occurs in the form of a traffic accident or in the form of a natural disaster, the major objective for rescuers is to lessen the loss of lives. To prevent lives in such situations, an effective disaster management system (DMS) is significantly important. A DMS can be made highly effective with the provision of high-quality imagery and data to conduct a successful rescue operation. This data can be fetched through the use of technological applications and devices. As per [1], monitoring technologies such as sensor networks and unmanned aerial vehicles (UAV) can be employed for the reduction of disaster scenarios.

Disasters and hazards can occur at any time, whether they be natural or human-made and can result in the loss of lives. However, using information technology (IT) infused preventive techniques, researchers believe that such loss of lives can be entirely negated. Several recent research covers different aspects of IT applications in disaster management systems [2–4]. Despite these advancements, there are still several challenges related to the assimilation of data pertaining to a disaster-struck area. One of the major issues in data accumulation during a disaster scenario is the timely communication of data to the DMS rescue team. Without such data communication the detection of disaster and prevention of loss is difficult. Therefore, the use of air-based networks is a substantial research area for any type of disaster scenario.

Using UAV [5], it is possible to take aerial photos and then communicate this data to the disaster management (DM) headquarters for further processing. Additionally, vehicle networks can be used to obtain data from disasters [6–8]. Though such systems are effective to a certain extent, however, the effectiveness can be increased with the use of satellites along with air and ground support.

1.1. Motivation

The use of space, air, and ground networks has led to the specific field of the space-air-ground integrated network (SAGIN). SAGIN can attain more data relevant to the rescue operation of a disaster scenario effectively and efficiently. Thus, the utmost importance is to accumulate, review, and classify all of the research that has been performed in the area of SAGIN, especially in the case of disaster management.

Research studies for the advancement of disaster management using state-of-the-art techniques are available. However, the number of research papers related to SAGIN was extremely limited and the literature review of SAGIN for disaster management systems has not been conducted yet. Coupled with the use of such technology in the application of disaster managementa major problem is the lack of existing literature. This is evident from Table1 where a majority of the high-scoring research is survey-based research, but there are only 4-5 survey research studies that have focused on making use of SAGIN in general for emergency situations or disaster relief.

The rationale behind this study is to motivate and help the researchers to know about SAGIN and its effectiveness in the disaster management system. Moreover, this study analyses the progress of SAGIN for disaster management systems.

1.2. Contribution

This research provides a comprehensive systematic review of the complexities of SAGIN in the application of disaster management including different architectures and protocols. The main contribution of this review is that it provides insight into existing disaster management systems that are making use of SAGIN. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues. Their solutions need to be further investigated for disaster management systems.

1.3. Organization of Article

The objective of this paper is to present a systematic literature review for the sake of providing a comprehensive review of all the research that has been conducted. The contribution in this paper is as follows:(i)A comprehensive background on the different terminologies is provided in Section 2.(ii)Section 3 describes the research methodology through the definition of research objectives, questions, inclusion/exclusion criteria, and search strings.(iii)Search results are then provided in the form of tables in Section 4 so that significant information can be extracted.(iv)In the next section, the existing survey-based studies are summarized and compared to our study.(v)Finally, Section 6 presents the conclusion to the paper along with lessons learned and future outcomes.

2. Background

2.1. Disasters

Disasters can be categorized into two main types, that being natural and human-made disasters. Natural disasters are those that occur naturally and are considered one of the most destructive forces of nature. For instance, tsunamis are normally caused by underwater earthquakes and can cause a large flow of water toward the land which in turn can cause massive damage. The increased destructiveness of natural disasters is usually because of the unpredictability of their occurrence. Establishing an effective system for the verification of a disaster’s severity and potential damage is the way to prevent or mitigate the loss of lives and assets [133].

The second type of disaster that can occur is human-made, or more specifically any accident such as the collision of vehicles. The severity of this disaster in comparison to natural disasters is similar, however, the former can be avoided by following proper rules and regulations. Therefore, there is also a need for an effective management system that can ensure reducing the risk of accidents efficiently.

2.2. Space-Air-Ground Integrated Network

SAGIN was introduced to resolve the problems faced due to limited network capacity and coverage present in a ground communication system. It is expected that networks will provide more resources than the current situation to cope with the increasing demands in traffic and various other services. Though to compensate for such issues, new technologies such as cloud computing, big data, and the Internet of Things (IoT) are being employed. However, they also have certain limitations. To overcome all the limitations of existing technologies, the interconnection of space, air, and ground network segments with modern information network technologies are being investigated these days. This has resulted in the creation of SAGIN.

In recent years, SAGIN has been used by multiple organizations including SpaceX [134] and Global Information Grid [135]. SAGIN provides large coverage, strong resilience, and high throughput. Thus, different parts of the earth, such as rural or mountainous regions can be monitored for earth mapping through SAGIN. Moreover, it can be applied for the service of Intelligent Transportation System (ITS) [136], military reconnaissance, and disaster relief.

The SAGIN architecture comprises three main segments: space, air, and ground. These segments can either work interoperationally or independently (as presented in Figure 1). In the space segment, the network is composed of constellations and satellites as well as their corresponding terrestrial infrastructures (ground stations). These constellations and satellites are present in different orbits and have different characteristics. They are divided into three classifications, Medium Earth Orbit (MEO), Geostationary Earth Orbit (GEO), and Low Earth Orbit (LEO) satellites [137].

The air network segment can be considered an aerial mobile system that can use either aircraft for information transmission, procurement, and processing or UAVs or hot air balloons. And the ground network segment consists of terrestrial networks such as cellular networks, wireless local area networks (WLANS), or vehicular ad-hoc networks (VANETs).

3. Research Methodology

A systematic literature review (SLR) has been conducted for the purpose of examining the advancement in the disaster management area. The approach of SAGIN and ITS (Intelligent Transportation System) is closely examined and discussed in this research for their applicability in this area. The main rationale for this study is to scrutinize and review different architectures and solutions for disaster management systems. To provide an effective SLR, this paper employs the methodology given in [138] for the provision of impartial research in the selection of data and the representation of its results. The research methodology is presented in Figure 2.

In the initial step of this SLR, the research objectives to determine the purpose of this survey were defined (as demonstrated in Figure 2). Afterward, these research objectives were transformed into the research questions. In the next step, all the existing research was gathered using a search string. After the collection process, screening was performed to filter out the research articles based on the keywords, abstract, and then the contents of the research article. In the final step, the data was extracted to answer the research questions.

3.1. Research Objectives

This research provides attention to the following research objectives: RO1: To produce a thorough assessment of the latest research conducted in relation to disaster management systems RO2: To present different existing ITS-based disaster management systems for the case of human-made disasters RO3: To analyze the SAGIN-based architectures and solutions for disaster scenarios RO4: To identify different research gaps in terms of challenges and issues

3.2. Research Questions

The identification of research questions is required to provide the selection of research papers related to that specific question. The formulated research questions are presented in Table 2.

3.3. Search String

The next step in this systematic literature review is the selection of different publication channels used to search for relevant articles pertaining to the defined research questions (RQs). After this, a search string is defined to search the previously defined publication channels. Using the defined search string, multiple research articles were collected, and then their results were aggregated to provide a solution to the RQs. The selected publication channels have been specifically chosen that overly provide a close relationship between their scientific accuracy and the objects of this research. The selected publication database in which the search string has been applied ranges from IEEE to Science Direct. Complete detail of all the publication sources along with the search string are provided in Table 3.

3.4. Screening for Relevant Results

Not all the collected research articles were considered in this SLR as some were less related to the defined RQs. Therefore, some results were filtered and excluded using the study provided by [139]. The provided study defines the screening of research articles while considering two phases. In the first phase, the collected research articles are filtered according to their titles. In the next phase, the filtered articles were further excluded/included based on their abstracts. The criteria which define the exclusion of research articles are as follows:(i)Those research articles that are not published in any journal, conference, or technical report.(ii)There is no innovation or use of emerging ideas and technologies in the research articles.(iii)Those articles that have been published before 2010.(iv)Research articles that had no relationship with the defined search string.(v)Research papers that were not in the English language.

3.5. Quality Assessment

After the relevant research papers have been selected the next step in this SLR is to provide quality assessment (QA). This QA is conventionally performed to verify and evaluate the quality of the research articles collected and is performed using a questionnaire. However, for this SLR, the selected papers are assigned some quantitative values as per the mapping study presented by [140]. The values that are to be assigned are provided below as well as in Table 4.(i)The research article provides an effective solution to the management of disasters. The possible answer to this question is either Yes (+1) or No (0). This marking is denoted by using a.(ii)The research paper provides a concise answer to the use of SAGIN in favor of disaster management. The possible answer for this question ranges from Yes (+1), No (0), or 0.5 for partially answering the question. This scoring is performed while using the notation b.(iii)The research paper in the question has been cited by other research articles along with answers to the research questions. For the research works that have not been cited since their publication is assigned, No (0). For those research articles that have 1–6 citations since their publication are assigned, 0.5. And for those research works that have more than 6 citations are assigned a score of +1. For this scoring, the notation used is c.(iv)The research work in question is from a reputable and recognized journal or conference publication. To evaluate this, the journal citation reports (JCR) and CORE ranking for computer science are considered for evaluation. The assigned score for either JCR or CORE is provided in Table 4. The scoring of this evaluation is denoted by d.

4. Analysis

In this section, a complete analysis of each research question, shown in Table 2, has been performed against different selected research articles. For all the selected primary research articles, their obtained QA score along with their publication name has been listed in Table 1.

A comprehensive analysis of a disaster management system that employs the use of SAGIN is a difficult challenge as it consists of different architectures and different other factors such as machine learning, artificial intelligence, and different types of network functions. Using the previously defined research questions, this research has made use of 50 research works from the last 10 years and was used to analyze the different research questions. Due to the limitation of fewer available works on the topic, we have short-listed research articles which were most relevant to find the research gap.

4.1. RQ1 Assessment: Key Publication Channels Targeted for DMS

For the selection of different primary research articles, various publication channels were made use of. A list of the selected research papers along with their publication channel has been provided in Table 5. Also provided in the Table is the number of occurrences of research articles published in a particular journal (J) or conference (Conf.).

4.2. RQ2 Assessment: Frequency of Change in Research with Respect to Time

The quantity of the various selected research articles that have been published over the last decade is represented in Figure 3. It can be seen from the figure, that the majority of the research was conducted from 2018 to 2021 with the later years seeing more publications in this specific research area which ultimately points toward the growing interest in this research topic.

4.3. RQ3 Assessment: Approaches Used for Design of SAGIN Based DMS

From the analysis of different research articles, it is found that different approaches in research have been made to design an effective DMS as shown in Figure 4. Inspired by the research work conducted in [141–143], the findings of some of the more recent works, have been summarized in Table 6. Each approach used in the selected research articles has been classified below:

4.3.1. Proposed Solution

For an efficient DMS, different research articles have provided different types of solutions, whether this is the form of a routing protocol or an efficient algorithm that overall improves the system [9, 26–28, 30–32, 35–38, 40, 41, 43, 44, 48, 51, 64, 65, 72, 73, 75, 76, 81, 89, 93, 96, 99, 101, 103, 106, 108, 111, 114, 117, 123, 127, 130].

4.3.2. Surveys

Some of the reviewed research articles have presented the conduction of either surveys or reviews [10, 12, 13, 15, 18, 21, 24, 29, 33, 39, 41, 52, 53, 55, 56, 61, 66, 67, 77, 88, 91, 92, 94, 98, 100, 102, 107, 109, 112, 115, 116, 118, 120, 125, 126, 129, 132] for the concept of SAGIN overall or in the field of DMS.

4.3.3. Architecture

One of the major approaches in the selected research articles was the formulation of architectures that can be used for the improvement of SAGIN or the scenario of a disaster [11, 16, 17, 20, 22, 23, 34, 35, 42, 46, 47, 54, 58, 59, 62, 63, 82–86, 104, 105, 119, 121, 122, 128, 131].

4.3.4. Framework

Some of the research has been focused on creating a framework that could be used for further research in SAGIN-based structure and its various applications [25, 44, 45, 48, 49, 69, 74, 79, 82, 84, 90, 95, 113].

4.3.5. Model

Similarly, different authors have defined specific models for either the transmission characteristics or other network characteristics for the betterment of data transmission between the different components of SAGIN [19, 30, 50, 51, 57, 60, 71, 78, 80, 87, 97, 110, 114, 119, 124].

4.3.6. Platform

Only 1 research article has focused on creating a platform for the monitoring of the different components or network characteristics of SAGIN [14, 68, 70].

Over the course of the last decade, different research has been conducted using the concept of SAGIN. However, not all the research has made use of the term SAGIN, as some have made use of the term space information network (SIN) or space transmission network (STN). The premise of all the terms is the same which is the use of geostationary satellites and air and ground devices to create a network. The only difference that SIN and SAGIN have is that the former also makes use of sea networks. As such, this research has also included the research that uses these terms. Furthermore, this research has also devised a taxonomy that showcases the use of the different terms of SAGIN, SIN, and STN in relation to the mitigation of a disaster. This devised taxonomy is presented below in Figure 5. The taxonomy presents the different characteristics of disaster mitigation techniques as well as the different types of disasters and their locations where they normally occur.

Some of the selected research work is focused on designing an architecture of SAGIN which can be then used for different types of scenarios and case studies. While some of the research works have made use of existing architecture and instead proposed solutions to a different aspect of SAGIN.

Murtaza and Jianwei [34] present those multiple architectures that have been proposed over the years incorporating the use of SAGIN, however, most of the proposed work is general abstracts or is based on a single dedicated application. The authors present that for there to be an effective SAGIN architecture, the proposed work should be based on the TCP/IP stack. Another issue that the authors discussed was that due to the orbital positions of space nodes, the management of the dynamic network topologies is difficult. Hence, the authors have proposed a SAGIN-based architecture that is based on the TCP/IP stack while also proposing a simple mechanism that would help in maintaining static IPs for all the space nodes (satellites).

Another research approach made was the integration of SAGIN with IoT conducted by [42] which not only provides large satellite coverage but also provides the different characteristics of ground network propagation delay. Another unique research approach conducted by [22] is the integration of artificial intelligence (AI) with SAGIN. The authors made this research in order to resolve the issues of data failures and computation slowdowns in SAGIN. As such, the authors proposed a novel coded storage-and-computation architecture (CSC-AI) that provides reliable transmission of big data as well as fast computational offloads.

Some authors have focused their research on SIN and proposed different architectures. In [16], the authors have proposed an autonomous-based system (AS) architecture that has several components ranging from the use of GEO satellites as the backbone nodes of the network, LEO satellites for the purpose of enhanced coverage nodes, and the use of high-altitude platforms for the sake of meeting service requirements for emergency situations. The authors also proposed a hierarchal AS network model which basically divides the complex SIN network into smaller AS networks for easier control of the topology. Similarly, Yu et al. [58] proposed a system architecture for SIN as well as conducted a preliminary integrated demonstration environment for the proposed architecture.

Other authors have, however, focused their research on improving the architecture of SAGIN by incorporating either the use of vehicular networks [22, 46, 54] or software defined networks (SDN) [11, 17, 20, 47] or mobile edge computing (MEC) or all these terms combined.

Some research has focused on the design or the model of different network protocols or characteristics that would ultimately help in the improvement of SAGIN-based architecture. In [19], the authors have proposed an SDN-based SAGIN model as well as a new load-balancing dynamic routing algorithm for disaster scenarios that is based on the multidimensional resources and energy (LBMRE-OLSR), which through simulation showed a reduction in end-to-end delay and packet loss rate. Another model presented by [30] is based on the scenario of emergency logistics which is then used for a proposed novel scheme of cross-network radio scheduling in SAGIN based on a genetic algorithm and unified resources mapping. Other research articles that propose routing protocols include [9], which proposed a multipath routing algorithm based on network coding for resolving issues of high bit error rate and long propagation delays in SINs.

Another research area that has seen research on SAGIN is the security perspective. Different researchers have either focused on the encryption of data [26, 28, 43] or the trustworthiness of nodes [27, 37] present in the network.

4.4. RQ4: Different Issues and Challenges to Overcome for an Effective SAGIN Based DMS

After a comprehensive review of the selected papers, it can be presented that different solutions and characteristics have been proposed in relation to SAGIN and disaster management systems. However, though some of the papers have discussed the shortcomings and challenges presented in the research area, this paper has identified some additional issues and challenges that need to be resolved for a proper SAGIN-based disaster management system. These different challenges are also presented illustratively in Figure 6.

4.4.1. Hardware Challenges

Hardware-based challenges are one of the biggest as well as the most critical issue present. The reason for this deduction is that even with the introduction or proposal of different architectures or routing/communication protocols. The actual implementation of a SAGIN-based architecture can be difficult due to a critical scenario such as a disaster in a remote or a hilly area where even with a small landslide or snowfall the terrestrial infrastructure needed for a SAGIN can get damaged.

4.4.2. Networking Challenges

Another issue that is also critical is the issue of network-based characteristics and routing/communication protocol. As in a disaster scenario, reliability as well as fast communication of the latest information related to that disaster is a key factor to be used in rescue operations. As such, an effective routing/communication protocol is needed for the effective dissemination of data.

4.4.3. Data Imagery

As in any disaster situation, the first and foremost information required by a rescuer is the availability of images that show the current situation of the disaster area. However, the collection of multiple images as well as the transfer of images to the disaster Head Quarters (HQ) can prove to be difficult as the reliability of these images is very important as they would be used to launch a rescue operation. Any misinformation related to the imagery data can prove a disaster.

4.4.4. Security Challenges

Another issue that poses a difficult challenge is the issue of security of either the entire architecture or structure of the SAGIN-based network or the encryption of data that is captured of the disaster scene and relayed to the HQ. In the situation of any such issue, the privacy of individuals caught in the disaster area would be at risk. From a network point of view, another security issue would be the misinformation provided due to trust issues of nodes being used in the SAGIN architecture. As such, an effective trust management scheme for all the nodes in either SAGIN or SIN is required.

4.4.5. Software Issues

Security issues are not a current problem in the scenario of SAGIN-based DMS, however, this can be an evident issue in the future. Disaster mitigation in terms of SAGIN is an application used by end users for the reporting of any type of disaster. This would be a key aspect to get real-time information regarding a disaster occurrence in a certain location. Another aspect related to software issues is that with the introduction of AI in network architectures, it is also an important challenge to implement such a mechanism of cognition in SAGIN architecture to formulate a rescue operation as quickly as possible while keeping in consideration past disasters.

4.5. Quality Assessment Score

The quality of selected articles is assured by the quality assessment criteria mentioned in the research methodology (subsection VII, Table 4), in the form of scores, as presented in Table 1. Through this assessment result, it would be possible for researchers to select their relevant papers according to their requirements.

Disaster Management is a vast field of study and there are new approaches that are developed in different research studies. Many studies have been conducted to design new frameworks to improve this new paradigm of DM, but few survey studies and literature reviews have been performed to evaluate the research showing the new frameworks and approaches. These approaches are reviewed in this section.

The authors in [10] mainly presented the fundamental concept of SAGIN and 6G and key technologies of UAV and satellite-based communications. They also elaborated the 6G-SAGIN and its orientation towards the aspect of elemental design, as well as the concept of UAV-as-a-service to expand the comprehension of 6G-SAGIN. This research was presented in an elaborative and explanatory manner, however, there was little applicability discussed regarding the DMS.

The aerial platforms and the HSAT networks are surveyed in [29]. They discussed the technologies for disaster management. They outlined the challenges of the HSAT networks. However, the applications of the HSAT for DMS are not elaborated in the article.

SIN is observed to explicate the relationship between the services, security threats, and mechanisms in [41]. Additionally, they presented the future direction of research in the security of SIN. They explained the secure routing and anomaly detection of different security techniques from the perceptions of traditional methods and AI. They proposed a Deep Learning (DL) based anomaly detection scheme. Overall, this paper summarizes the upcoming technologies and delivers inspiration for future exploration, though, the problems related to DMS were not extensively discussed.

In this research study, theuse of UAVs as Aerial Base Stations (ABS) is simulated for communication during such disasterous situation where the communication is uneven and broken [56]. Effective communication probability (ECP) was used to evaluate the performance of a communication network, by measuring connectivity and throughput coverage. They proposed better communication can be acquired through the flexible implementation of ABS. The results show a noticeable increase in ECP when some ABS are deployed in optimal locations. This work is quite related to the scenario of the topic under consideration. However, the survey and comparison of the technique with other related studies can be extensive.

In [61], 6G-SAGIN from the aspect of a service-oriented network is reviewed to propose a service-oriented SAGIN management architecture. They discussed cloud-edge synergy technologies and heterogeneous resource orchestration technologies. Additionally, the prospective directions are presented. A similar study on UAV communication with an aspect of 5G/B5G wireless networks is produced in [100], with a brief introduction of SAGIN, as well as related challenges of integrated networks are also comprehended. This study entails an exhaustive review of several 5G techniques established on UAV platforms and the open issues and future directions for research. Still, the applications of SAGIN in DMS need some more substantial advancement.

To the best of our knowledge, there is no systematic literature review with a discussion of disaster management and SAGIN implementation techniques published yet with taxonomy and analysis. All the related research work on this area is summarized in Table 6, and the detailed comparison of other surveys with this study is summarized in Table 7.

6. Conclusions

The occurrence of disasters, either they be natural or human-made can be damaging. Therefore, there is an imperative need of a suitable and operationally effective disaster management system to alleviate the issues that arise from such disasters. Over the last decade, several investigations have been performed to overcome these concerns using UAV [20, 32], sensor networks [144], and SAGIN [56]. However, remote areas where ground networks are unable to reach can be responsible for more issues related to the provision of emergency response. In such situations, space networks which include the use of satellites and air networks, along with the use of aircraft and UAVs, integrated with a ground network (SAGIN) can solve the mentioned issues. This research provides a systematic literature review of 50 research articles, which constitute a comprehensive review of different research approaches adopted by the researchers. The research gap we have found in this paper indicates that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics, communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues that need to be tackled and their solutions need to be further investigated.

Acronyms

AGIHN:	Air-ground integrated heterogeneous network
ABS:	Aerial base stations
AI:	Artificial intelligence
AS:	Autonomous based systems
CA:	Civil aircrafts
CAA-SAGIVN:	Civil aircrafts, augmented space-air-ground integrated vehicular networks
CORE:	Computing research and education
CSC-AI:	Coded storage-and-computation architecture
DL:	Deep learning
DM:	Disaster management
DMS:	Disaster management system
ECP:	Effective communication probability
GEO:	Geostationary earth orbit
HetNet:	Heterogeneous network
HQ:	Headquarters
HSAT:	Hybrid satellite-aerial-terrestrial
IoT:	Internet of Things
IoV:	Internet of vehicles
IP:	Internet protocol
IT:	Information technology
ITS:	Intelligent transportation system
JCR:	Journal citation report
LBMRE-OLSR:	Load balancing algorithm based on multi-dimensional resources and energy
LEO:	Low Earth Orbit
MEC:	Mobile edge computing
MEO:	Middle Earth Orbit
QA:	Quality assessment
SAGIN:	Space-air-ground integrated network
SAGSIN:	Space-air-ground-sea integrated network
SCPS:	Satellite communication protocol specifications
SDN:	Software defined network
SLR:	Systematic literature review
SIN:	Space information network
STK:	Satellite tool kit
STN:	Space transmission network
TCP/IP:	Transmission control protocol/internet protocol
UAV:	Unmanned aerial vehicles
VANET:	Vehicular ad-hoc network
VM:	Virtual machine
WLAN:	Wireless local area network.

Data Availability

The data that support the findings of this study are available from the author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

M. Sakurai and Y. Murayama, “Information technologies and disaster management–Benefits and issues,” Progress in Disaster Science, vol. 23, Article ID 100012, 2019.
View at: Google Scholar
A. Sinha, P. Kumar, N. P. Rana, R. Islam, and Y. K. Dwivedi, “Impact of internet of things (IoT) in disaster management: a task-technology fit perspective,” Annals of Operations Research, vol. 283, no. 1-2, pp. 759–794, 2019.
View at: Publisher Site | Google Scholar
S. A. Shah, D. Z. Seker, S. Hameed, and D. Draheim, “The rising role of big data analytics and IoT in disaster management: recent advances, taxonomy and prospects,” IEEE Access, vol. 7, pp. 54595–54614, 2019.
View at: Publisher Site | Google Scholar
K. Muhammad, J. Ahmad, and S. W. Baik, “Early fire detection using convolutional neural networks during surveillance for effective disaster management,” Neurocomputing, vol. 288, pp. 30–42, 2018.
View at: Publisher Site | Google Scholar
G. Aiello, F. Hopps, D. Santisi, and M. Venticinque, “The employment of unmanned aerial vehicles for analyzing and mitigating disaster risks in industrial sites,” IEEE Transactions on Engineering Management, vol. 67, no. 3, pp. 519–530, 2020.
View at: Publisher Site | Google Scholar
S. D. Gupta, S. Choudhury, and R. Chaki, “Disaster management system using vehicular Ad hoc networks,” Advanced Computing and Systems for Security, Springer, Singapore, 2019.
View at: Google Scholar
M. A. Lèbre, F. Le Mouël, and E. Ménard, “Efficient vehicular crowdsourcing models in VANET for disaster management,” in Proceedings of the IEEE 91st Vehicular Technology Conference (VTC2020-Spring), pp. 1–5, Antwerp, Belgium, May 2020.
View at: Google Scholar
K. A. Khaliq, O. Chughtai, A. Shahwani, A. Qayyum, and J. Pannek, “An emergency response system: construction, validation,” And Experiments for Disaster Management in a Vehicular Environment Sensors, vol. 19, no. 5, 2019.
View at: Google Scholar
G. Yu, C. Zhong, X. Lan, C. Zhang, L. Wei, and Y. Liu, “Research of multi-path routing based on network coding in space information networks,” Chinese Journal of Aeronautics, vol. 27, no. 3, pp. 663–669, 2014.
View at: Publisher Site | Google Scholar
P. P. Ray, “A review on 6G for space-air-ground integrated network: key enablers, open challenges, and future direction,” Journal of King Saud University-Computer and Information Sciences, vol. 26, 2021.
View at: Google Scholar
X. Huang, Z. Zhao, X. Meng, and H. Zhang, “Architecture and application of SDN/NFV-enabled space-terrestrial integrated network,” in Proceedings of the International Conference on Space Information Network, pp. 244–255, Springer, Singapore, December 2016.
View at: Google Scholar
Y. Ding, H. Y. Yu, H. L. Jiang, Y. Hu, Y. F. Zhu, and L. G. Cong, “Analysis of the difficulties of the earth integrated information network,” in Proceedings of the 2013 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013), pp. 297–300, NY China, June 2013.
View at: Google Scholar
W. U. Huici, L. I. Hanjie, and T. A. O. Xiaofeng, “Green air-ground integrated heterogeneous network in 6G era,” ZTE Communications, vol. 19, no. 1, pp. 39–47, 2021.
View at: Google Scholar
N. Cheng, W. Quan, W. Shi et al., “A comprehensive simulation platform for space-air-ground integrated network,” IEEE Wireless Communications, vol. 27, no. 1, pp. 178–185, 2020.
View at: Publisher Site | Google Scholar
F. Qi, G. Mang, S. Zhang, and L. Liu, “A multi-layer architecture for space-air-ground network and IoT services,” in Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC), pp. 1809–1813, Hong Kong, China, June 2021.
View at: Google Scholar
W. Zhang, D. Bian, Z. Xie, and G. Zhang, “A novel space information network architecture based on autonomous system,” in Proceedings of the 2015 International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1–5, Nanjing, China, December 2015.
View at: Google Scholar
J. Feng, L. Jiang, Y. Shen, W. Ma, and M. Yin, “A scheme for software defined ORS satellite networking,” in Proceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp. 716–721, Sydney, NSW, Australia, December 2014.
View at: Google Scholar
H. Guo, J. Li, J. Liu, N. Tian, and N. Kato, “A survey on space-air-ground-sea integrated network security in 6G,” IEEE Communication Surveys and Tutorials, vol. 13, 2021.
View at: Google Scholar
H. Qu, Y. Luo, J. Zhao, and Z. Luan, “An LBMRE-OLSR routing algorithm under the emergency scenarios in the space-air-ground integrated networks,” in Proceedings of the 2020 Information Communication Technologies Conference (ICTC), pp. 103–107, Nanjing, China, May 2020.
View at: Google Scholar
H. Qu, X. Xu, J. Zhao, and P. Yue, “An SDN-based space-air-ground integrated network architecture and controller deployment strategy,” in Proceedings of the 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET), pp. 138–142, Beijing, China, August 2020.
View at: Google Scholar
Y. Wang, Z. Su, J. Ni, N. Zhang, and X. Shen, “Blockchain-empowered space-air-ground integrated networks: opportunities, challenges, and solutions,” IEEE Communications Surveys and Tutorials, vol. 24, 2021.
View at: Google Scholar
Q. Chen, W. Meng, S. Li, C. Li, and H. H. Chen, “Civil aircrafts augmented space-air-ground integrated vehicular networks: motivation, breakthrough and challenges,” IEEE Internet of Things Journal, vol. 9, 2021.
View at: Google Scholar
S. Gu, Q. Zhang, and W. Xiang, “Coded storage-and-computation: a new paradigm to enhancing intelligent services in space-air-ground integrated networks,” IEEE Wireless Communications, vol. 27, no. 6, pp. 44–51, 2020.
View at: Publisher Site | Google Scholar
B. Shang, Y. Yi, and L. Liu, “Computing over space-air-ground integrated networks: challenges and opportunities,” IEEE Network, vol. 35, no. 4, pp. 302–309, 2021.
View at: Publisher Site | Google Scholar
S. J. H. Pirzada, M. Haris, M. N. Hasan, T. Xu, and L. Jianwei, “Detection and communication of disasters with space-air-ground integrated network,” in Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), pp. 1–6, Bahawalpur, Pakistan, November 2020.
View at: Google Scholar
H. Wang, K. Fan, K. Zhang, Z. Wang, H. Li, and Y. Yang, “Encrypted data retrieval and sharing scheme in space-air-ground integrated vehicular networks,” IEEE Internet of Things Journal, vol. 35, 2021.
View at: Google Scholar
Q. Wang, X. Chen, X. Jin, X. Li, D. Chen, and X. Qin, “Enhancing trustworthiness of internet of vehicles in space-air-ground integrated networks: attestation approach,” IEEE Internet of Things Journal, vol. 2, 2021.
View at: Google Scholar
C. Lai and Z. Chen, “Group-based handover authentication for space-air-ground integrated vehicular networks,” in Proceedings of the ICC 2021-IEEE International Conference on Communications, pp. 1–6, Montreal, QC, Canada, June 2021.
View at: Google Scholar
Y. Wang, Y. Xu, Y. Zhang, and P. Zhang, “Hybrid satellite-aerial-terrestrial networks in emergency scenarios: a survey,” China Communications, vol. 14, no. 7, pp. 1–13, 2017.
View at: Publisher Site | Google Scholar
Z. Li, F. Yang, W. Lin et al., “A novel scheme of cross‐network radio resources scheduling in SAGN based on unified resources mapping and genetic algorithm,” International Journal of Communication Systems, vol. 34, no. 11, Article ID e4844, 2021.
View at: Publisher Site | Google Scholar
Y. Cai, S. Zhang, J. Hou, and H. Jiao, “Internet of things over GEO satellite: a novel space information network solution,” in Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC), pp. 177–181, Harbin City, China, August 2021.
View at: Google Scholar
S. Mao, S. He, and J. Wu, “Joint UAV position optimization and resource scheduling in space-air-ground integrated networks with mixed cloud-edge computing,” IEEE Systems Journal, vol. 15, no. 3, pp. 3992–4002, 2021.
View at: Publisher Site | Google Scholar
Z. Liu, C. Lin, and G. Chen, “Development and trend of space-based information network,” Journal of Physics: Conference Series, vol. 1544, no. 1, Article ID 012180, 2020.
View at: Publisher Site | Google Scholar
A. Murtaza and L. Jianwei, “Multipurpose IP-based space air-ground information network,” Journal of Physics: Conference Series, vol. 1187, no. 4, Article ID 042044, 2019.
View at: Publisher Site | Google Scholar
D. Chen, W. Wang, and T. Jiang, “New multicarrier modulation for satellite-ground transmission in space information networks,” IEEE Network, vol. 34, no. 1, pp. 101–107, 2020.
View at: Publisher Site | Google Scholar
N. Kato, Z. M. Fadlullah, F. Tang et al., “Optimizing space-air-ground integrated networks by artificial intelligence,” IEEE Wireless Communications, vol. 26, no. 4, pp. 140–147, 2019.
View at: Publisher Site | Google Scholar
Z. Liu, J. Weng, J. Guo et al., “PPTM: a privacy-preserving trust management scheme for emergency message dissemination in space-air-ground integrated vehicular networks,” IEEE Internet of Things Journal, vol. 123, 2021.
View at: Google Scholar
L. Bai, R. Han, J. Liu, J. Choi, and W. Zhang, “Relay-Aided random access in space-air-ground integrated networks,” IEEE Wireless Communications, vol. 27, no. 6, pp. 37–43, 2020.
View at: Publisher Site | Google Scholar
J. Zhang, S. Zhu, and C. Li, “Research on key technologies of space integrated information network,” in Proceedings of the 2018 IEEE 18th International Conference on Communication Technology (ICCT), pp. 1104–1108, Beijing China, November 2018.
View at: Google Scholar
L. Wu and W. Wang, “Resource allocation optimization of UAVs-enabled air-ground collaborative emergency network in disaster area” international,” Journal of Performability Engineering, vol. 15, no. 8, p. 2133, 2019.
View at: Google Scholar
M. Zhuo, L. Liu, S. Zhou, and Z. Tian, “Survey on security issues of routing and anomaly detection for space information networks,” Scientific Reports, vol. 11, no. 1, 2021.
View at: Publisher Site | Google Scholar
J. Liu, H. Du, X. Yuan, Y. Zhang, and K. Michel, “Satellite routing in space-air-ground integrated IoT networks,” in Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC), pp. 1534–1538, Harbin City, China, July 2021.
View at: Google Scholar
C. Jiang, X. Wang, J. Wang, H. H. Chen, and Y. Ren, “Security in space information networks,” IEEE Communications Magazine, vol. 53, no. 8, pp. 82–88, 2015.
View at: Publisher Site | Google Scholar
Y. Guo, Q. Li, Y. Li, N. Zhang, and S. Wang, “Service coordination in the space-air-ground integrated network,” IEEE Network, vol. 35, no. 5, pp. 168–173, 2021.
View at: Publisher Site | Google Scholar
G. Wang, S. Zhou, S. Zhang, Z. Niu, and X. Shen, “SFC-based service provisioning for reconfigurable space-air-ground integrated networks,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 7, pp. 1478–1489, 2020.
View at: Publisher Site | Google Scholar
N. Zhang, S. Zhang, P. Yang, O. Alhussein, W. Zhuang, and X. S. Shen, “Software defined space-air-ground integrated vehicular networks: challenges and solutions,” IEEE Communications Magazine, vol. 55, no. 7, pp. 101–109, 2017.
View at: Publisher Site | Google Scholar
Y. Bi, G. Han, S. Xu et al., “Software defined space-terrestrial integrated networks: architecture, challenges, and solutions,” IEEE Network, vol. 33, no. 1, pp. 22–28, 2019.
View at: Publisher Site | Google Scholar
S. J. H. Pirzada, M. N. Hasan, M. Haris, Z. W. Memon, T. Xu, and L. Jianwei, “Space air ground integrated network: communication network for air traffic control,” in Proceedings of the 2020 International Symposium on Recent Advances in Electrical Engineering & Computer Sciences (RAEE & CS), pp. 1–6, Islamabad, Pakistan, October 2020.
View at: Google Scholar
S. J. H. Pirzada, T. Xu, and J. Liu, “Space air ground integrated network: coverage for accident monitoring,” in Proceedings of the 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1–7, Islamabad, Pakistan, June 2021.
View at: Google Scholar
C. Suzhi, W. Junyong, H. Hao et al., “Space edge cloud enabling network slicing for 5G satellite network,” in Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 787–792, Tangier, Morocco, June 2019.
View at: Google Scholar
P. Zhang, C. Wang, N. Kumar, and L. Liu, “Space-air-ground integrated multi-domain network resource orchestration based on virtual network architecture: a drl method,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2798–2808, 2022.
View at: Publisher Site | Google Scholar
J. Liu, Y. Shi, Z. M. Fadlullah, and N. Kato, “Space-air-ground integrated network: a survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2714–2741, 2018.
View at: Publisher Site | Google Scholar
J. Sheng, X. Cai, Q. Li et al., “Space-air-ground integrated network development and applications in high-speed railways: a survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 10, 2021.
View at: Google Scholar
Z. Niu, X. S. Shen, Q. Zhang, and Y. Tang, “Space-air-ground integrated vehicular network for connected and automated vehicles: challenges and solutions,” Intelligent and Converged Networks, vol. 1, no. 2, pp. 142–169, 2020.
View at: Publisher Site | Google Scholar
H. Wang, X. Xia, T. Song, and Y. Xing, “Survey on space-air-ground integrated networks in 6G,” in Proceedings of the 2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops), pp. 315–320, Xiamen, China, July 2021.
View at: Google Scholar
G. Peng, Y. Xia, X. Zhang, and L. Bai, “UAV-aided networks for emergency communications in areas with unevenly distributed users,” Journal of Communications and Information Networks, vol. 3, no. 4, pp. 23–32, 2018.
View at: Publisher Site | Google Scholar
Y. Zhu, R. Liu, M. Sheng et al., “Utilization and analysis of resource mobility in space information networks,” Journal of Communications and Information Networks, vol. 4, no. 1, pp. 67–77, 2019.
View at: Publisher Site | Google Scholar
Q. Yu, J. Wang, and L. Bai, “Architecture and critical technologies of space information networks,” Journal of communications and information networks, vol. 1, no. 3, pp. 1–9, 2016.
View at: Publisher Site | Google Scholar
S. J. H. Pirzada, A. Murtaza, T. Xu, and L. Jianwei, “Disaster management using IP-based space-air-ground information network,” in Proceedings of the 2019 IEEE International Conference on Unmanned Systems (ICUS), pp. 119–123, Beijing, China, November 2019.
View at: Google Scholar
W. Li, Y. Liu, X. Li, and Y. Shen, “Three-dimensional cooperative localization via space-air-ground integrated networks,” China Communications, vol. 19, no. 1, pp. 253–263, 2022.
View at: Publisher Site | Google Scholar
N. Cheng, J. He, Z. Yin et al., “6G service-oriented space-air-ground integrated network: a survey,” Chinese Journal of Aeronautics, vol. 35, no. 9, pp. 1–18, 2022.
View at: Publisher Site | Google Scholar
F. Ahmed, Global IoT Coverage through Aerial and Satellite Network, KTH, StockHolm, Swaden, 2020.
M. Casoni, C. A. Grazia, M. Klapez, N. Patriciello, A. Amditis, and E. Sdongos, “Integration of satellite and LTE for disaster recovery,” IEEE Communications Magazine, vol. 53, no. 3, pp. 47–53, March 2015.
View at: Publisher Site | Google Scholar
S. Khisa and S. Moh, “Priority-Aware fast MAC protocol for UAV-assisted industrial IoT systems,” IEEE Access, vol. 9, pp. 57089–57106, 2021.
View at: Publisher Site | Google Scholar
B. Zeng, Z. Zhang, X. Ding, X. Bu, and J. An, “Predictive decision and reliable accessing for UAV communication in space-air-ground integrated networks,” China Communications, vol. 19, no. 1, pp. 166–185, Jan. 2022.
View at: Publisher Site | Google Scholar
H. Wu, J. Chen, C. Zhou et al., “Resource management in space-air-ground integrated vehicular networks: SDN control and AI algorithm design,” IEEE Wireless Communications, vol. 27, no. 6, pp. 52–60, December 2020.
View at: Publisher Site | Google Scholar
Z. Zhou, J. Feng, C. Zhang, Z. Chang, Y. Zhang, and K. M. S. Huq, “SAGECELL: software-defined space-air-ground integrated moving cells,” IEEE Communications Magazine, vol. 56, no. 8, pp. 92–99, 2018.
View at: Publisher Site | Google Scholar
X. Ding, X. Wang, A. Dou, L. Ding, X. Yuan, and S. Wang, “The development of rapid earthquake disaster assessment system based on space-air-ground integrated earth observation,” in Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 8456–8459, Brussels, Belgium, July 2021.
View at: Publisher Site | Google Scholar
S. Yu, X. Gong, Q. Shi, X. Wang, and X. Chen, “EC-SAGINs: edge-computing-enhanced space–air–ground-integrated networks for internet of vehicles,” IEEE Internet of Things Journal, vol. 9, no. 8, pp. 5742–5754, 2022.
View at: Publisher Site | Google Scholar
X. Yang, M. Jia, J. Wang, X. Yang, P. Wu, and B. Ji, “An integrated space-air-ground monitoring and early warning system of landslide disaster,” in Proceedings of the 2021 7th International Conference on Hydraulic and Civil Engineering & Smart Water Conservancy and Intelligent Disaster Reduction Forum (ICHCE & SWIDR), pp. 846–851, Nanjing, China, November 2021.
View at: Publisher Site | Google Scholar
Y. Liu and J. Zhang, “Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response,” Natural Hazards and Earth System Sciences, vol. 22, no. 1, pp. 227–244, 2022.
View at: Publisher Site | Google Scholar
B. Xianghang, C. Yongxing, D. Chuang, W. Chi, and J. Ke, “Integrated space-air-ground early detection technologies and applicationfor potential landslide of transmission line corridor,” in Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science, pp. 513–519, Hong Kong, China, December 2021.
View at: Google Scholar
Q. Xu, X. Dong, and W. Li, “Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards,” Journal of Wuhan University (Natural Science Edition), vol. 44, no. 7, pp. 957–966, 2019.
View at: Google Scholar
Q. Pan, S. Lin, W. Lu et al., “Space-air-sea-ground integrated monitoring network-based maritime transportation emergency forecasting,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2843–2852, March 2022.
View at: Publisher Site | Google Scholar
X. Yuan, J. Liu, H. Du, Y. Zhang, F. Li, and M. Kadoch, “Machine learning-based satellite routing for SAGIN IoT networks,” Electronics, vol. 11, no. 6, p. 862, 2022.
View at: Publisher Site | Google Scholar
A. Murtaza, S. J. Hussain Pirzada, L. Jianwei, and T. Xu, “Air traffic surveillance using IP-based space information network,” in Proceedings of the 2019 28th Wireless and Optical Communications Conference (WOCC), pp. 1–6, Beijing, China, July 2019.
View at: Publisher Site | Google Scholar
S. Khisa and S. Moh, “Medium access control protocols for the Internet of Things based on unmanned aerial vehicles: a comparative survey,” Sensors, vol. 20, no. 19, p. 5586, 2020.
View at: Publisher Site | Google Scholar
S. Sharma, N. Vishwakarma, and A. S. Madhukumar, “HAPS-based relaying for integrated space–air–ground networks with hybrid FSO/rf communication: a performance analysis,” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 3, pp. 1581–1599, 2021.
View at: Publisher Site | Google Scholar
S. Zhou, G. Wang, S. Zhang, Z. Niu, and X. S. Shen, “Bidirectional mission offloading for agile space-air-ground integrated networks,” IEEE Wireless Communications, vol. 26, no. 2, pp. 38–45, 2019.
View at: Publisher Site | Google Scholar
A. H. Arani, P. Hu, and Y. Zhu, “Re-Envisioning space-air-ground integrated networks: reinforcement learning for link optimization,” in Proceedings of the ICC 2021 - IEEE International Conference on Communications, pp. 1–7, Montreal, QC, Canada, June 2021.
View at: Publisher Site | Google Scholar
G. Xiong, F. Zhu, X. Dong et al., “A kind of novel ITS based on space-air-ground big-data,” IEEE Intelligent Transportation Systems Magazine, vol. 8, no. 1, pp. 10–22, 2016.
View at: Publisher Site | Google Scholar
Z. Wang, Z. Zhou, H. Zhang, G. Zhang, H. Ding, and A. Farouk, “AI-based cloud-edge-device collaboration in 6G space-air-ground integrated power IoT,” IEEE Wireless Communications, vol. 29, no. 1, pp. 16–23, February 2022.
View at: Publisher Site | Google Scholar
Y. Zou, J. Zhu, T. Wu, H. Guo, and H. Wei, “Cooperative drone communications for space-air-ground integrated networks,” IEEE Network, vol. 35, no. 5, pp. 100–106, September/October 2021.
View at: Publisher Site | Google Scholar
X. Hou, J. Wang, Z. Fang, Y. Ren, K.-C. Chen, and L. Hanzo, “Edge intelligence for mission-critical 6G services in space-air-ground integrated networks,” IEEE Network, vol. 10, 2022.
View at: Publisher Site | Google Scholar
J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the space–air–ground–aqua integrated network,” IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5345–5358, 2020.
View at: Publisher Site | Google Scholar
Y. Pang, D. Wang, D. Wang, L. Guan, C. Zhang, and M. Zhang, “A space-air-ground integrated network assisted maritime communication network based on mobile edge computing,” in Proceedings of the 2020 IEEE World Congress on Services (SERVICES), pp. 269–274, Beijing, China, July 2020.
View at: Publisher Site | Google Scholar
T. Yang, Y. Guo, Y. Zhou, and S. Wei, “Joint communication and control for small underactuated USV based on mobile computing technology,” IEEE Access, vol. 7, pp. 160610–160622, 2019.
View at: Publisher Site | Google Scholar
F. A. Dicandia, N. J. G. Fonseca, M. Bacco, S. Mugnaini, and S. Genovesi, “Space-air-ground integrated 6G wireless communication networks: a review of antenna technologies and application scenarios,” Sensors, vol. 22, no. 9, p. 3136, 2022.
View at: Publisher Site | Google Scholar
X. Wang, H. Lin, H. Zhang, D. Miao, Q. Miao, and W. Liu, “Intelligent drone-assisted fault diagnosis for B5G-enabled space-air-ground-space networks,” IEEE Transactions on Network Science and Engineering, vol. 8, no. 4, pp. 2849–2860, 2021.
View at: Publisher Site | Google Scholar
Z. Zhou, J. Feng, L. Tan, Y. He, and J. Gong, “An air-ground integration approach for mobile edge computing in IoT,” IEEE Communications Magazine, vol. 56, no. 8, pp. 40–47, 2018.
View at: Publisher Site | Google Scholar
H. Luo, J. Wang, F. Bu, R. Ruby, K. Wu, and Z. Guo, “Recent progress of air/water cross-boundary communications for underwater sensor networks: a review,” IEEE Sensors Journal, vol. 22, no. 9, pp. 8360–8382, 2022.
View at: Publisher Site | Google Scholar
X. Jiang, M. Sheng, N. Zhao, C. Xing, W. Lu, and X. Wang, “Green UAV communications for 6G: a survey,” Chinese Journal of Aeronautics, vol. 35, no. 9, pp. 19–34, 2022.
View at: Publisher Site | Google Scholar
H. Feng, Z. Cui, C. Han, J. Ning, and T. Yang, “Bidirectional green promotion of 6G and AI: architecture, solutions, and platform,” IEEE Network, vol. 35, no. 6, pp. 57–63, November/December 2021.
View at: Publisher Site | Google Scholar
B. Liu, F. Wang, X. Zheng, and M. Qi, “Suggestions for formulating collaborative remote sensing emergency plan based on case studies,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, 2017.
View at: Google Scholar
F. Yu, H. Chen, and L. Gui, “An integrated framework for mission planning in space information network,” International Conference on Space Information Network, vol. 10, pp. 34–49, 2017.
View at: Google Scholar
L. Zhang, H. Zhang, C. Guo, H. Xu, L. Song, and Z. Han, “Satellite-aerial integrated computing in disasters: user association and offloading decision,” in Proceedings of the ICC 2020 - 2020 IEEE International Conference on Communications (ICC), pp. 554–559, Dublin, Ireland, July 2020.
View at: Publisher Site | Google Scholar
Y. Ren, Z. Ye, G. Song, and X. Jiang, “Space-air-ground integrated mobile crowdsensing for partially observable data collection by multi-scale convolutional graph reinforcement learning,” Entropy, vol. 24, no. 5, p. 638, 2022.
View at: Publisher Site | Google Scholar
W. Li, Z. Su, R. Li, K. Zhang, and Y. Wang, “Blockchain-based data security for artificial intelligence applications in 6G networks,” IEEE Network, vol. 34, no. 6, pp. 31–37, November/December 2020.
View at: Publisher Site | Google Scholar
B. Li, Z. Li, H. Zhou et al., “A system of power emergency communication system based BDS and LEO satellite,” in Proceedings of the 2021 Computing, Communications and IoT Applications (ComComAp), pp. 286–291, Shenzhen, China, November 2021.
View at: Publisher Site | Google Scholar
B. Li, Z. Fei, and Y. Zhang, “UAV communications for 5G and beyond: recent advances and future trends,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2241–2263, April 2019.
View at: Publisher Site | Google Scholar
Z. Wang, B. Lin, L. Sun, and Y. Wang, “Intelligent task offloading for 6G-enabled maritime IoT based on reinforcement learning,” in Proceedings of the 2021 International Conference on Security, Pattern Analysis, and Cybernetics SPAC, pp. 566–570, Chengdu, China, June 2021.
View at: Publisher Site | Google Scholar
G. Xiong, F. Zhu, H. Fan, X. Dong, W. Kang, and T. Teng, “Novel ITS based on Space-Air-Ground collected big-data,” in Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1509–1514, Qingdao, November 2014.
View at: Publisher Site | Google Scholar
Z. Hao and Q. Linjun, “Airborne gateway: key technology of earth observation system in the space-air-ground integration network,” Journal of Physics: Conference Series, vol. 1345, no. 4, Article ID 042001, 2019.
View at: Publisher Site | Google Scholar
D. Yang, J. Liu, Y. Xia, Z. Wang, H. Ding, and S. Meng, “Research on the integrated space-air-ground communication network based on network slicing and its key technologies,” in Proceedings of the 2020 IEEE Sustainable Power and Energy Conference (iSPEC), pp. 2652–2657, Chengdu, China, November 2020.
View at: Publisher Site | Google Scholar
F. Wang, D. Jiang, S. Qi, C. Qiao, and L. Shi, “A dynamic resource scheduling scheme in edge computing satellite networks,” Mobile Networks and Applications, vol. 26, no. 2, pp. 597–608, 2021.
View at: Publisher Site | Google Scholar
X. Zhang, Y. Chen, L. Yu, W. Wang, and Q. Wu, “Three-dimensional modeling and indoor positioning for urban emergency response,” ISPRS International Journal of Geo-Information, vol. 6, no. 7, p. 214, 2017.
View at: Publisher Site | Google Scholar
D. Mishra, A. M. Vegni, V. Loscrí, and E. Natalizio, “Drone networking in the 6G era: a technology overview,” IEEE Communications Standards Magazine, vol. 5, no. 4, pp. 88–95, December 2021.
View at: Publisher Site | Google Scholar
L. Zhong, J. Yu, X. Tang, and S. Pan, “Space-air-ground Co-observation in watershed management: the establishment of system,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, no. 3, 2018.
View at: Google Scholar
D. Li, X. Shen, N. Chen, and Z. Xiao, “Space-based information service in internet plus era,” Science China Information Sciences, vol. 60, no. 10, Article ID 102308, 10 pages, 2017.
View at: Publisher Site | Google Scholar
A. H. Arani, P. Hu, and Y. Zhu, “Fairness-Aware link optimization for space-terrestrial integrated networks: a reinforcement learning framework,” IEEE Access, vol. 9, pp. 77624–77636, 2021.
View at: Publisher Site | Google Scholar
M. Deng, B. Liu, S. Li et al., “A two-phase coordinated planning approach for heterogeneous earth-observation resources to monitor area targets,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 10, pp. 6388–6403, 2021.
View at: Publisher Site | Google Scholar
J. Chen, J. Xiang, P. Tarolli, and Z. Lai, “The method and key technology of dynamic RS-GIS environment monitoring,” EGU General Assembly Conference Abstracts, vol. 18, pp. EPSC2016–1926, 2016.
View at: Google Scholar
Y. Guo and S. Wang, “Challenges and opportunities in space service computing,” in Proceedings of the 2021 IEEE International Conference on Services Computing (SCC), pp. 44–51, Chicago, IL, USA, September 2021.
View at: Publisher Site | Google Scholar
J. Zhao, F. Gao, G. Ding, T. Zhang, W. Jia, and A. Nallanathan, “Integrating communications and control for UAV systems: opportunities and challenges,” IEEE Access, vol. 6, pp. 67519–67527, 2018.
View at: Publisher Site | Google Scholar
D. Li, X. Shen, and L. Wang, “Connected Geomatics in the big data era,” International Journal of Digital Earth, vol. 11, no. 2, pp. 139–153, 2018.
View at: Publisher Site | Google Scholar
M. Giordani and M. Zorzi, “Non-terrestrial networks in the 6G era: challenges and opportunities,” IEEE Network, vol. 35, no. 2, pp. 244–251, 2021.
View at: Publisher Site | Google Scholar
Z. Na, Y. Liu, J. Shi, C. Liu, and Z. Gao, “UAV-supported clustered NOMA for 6G-enabled internet of things: trajectory planning and resource allocation,” IEEE Internet of Things Journal, vol. 8, no. 20, pp. 15041–15048, 2021.
View at: Publisher Site | Google Scholar
S. Zhang, D. Zhu, and Y. Wang, “A survey on space-aerial-terrestrial integrated 5G networks,” Computer Networks, vol. 174, Article ID 107212, 2020.
View at: Publisher Site | Google Scholar
Y. Li, H. Wei, L. Li, Y. Han, J. Zhou, and W. Zhou, “An extensible multi-layer architecture model based on LEO-MSS and performance analysis,” in Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), pp. 1–6, Honolulu, HI, USA, November 2019.
View at: Publisher Site | Google Scholar
F. Guo, F. R. Yu, H. Zhang, X. Li, H. Ji, and V. C. M. Leung, “Enabling massive IoT toward 6G: a comprehensive survey,” IEEE Internet of Things Journal, vol. 8, no. 15, pp. 11891–11915, 2021.
View at: Publisher Site | Google Scholar
S. Cao, H. Han, J. Wei, Y. Zhao, S. Yang, and L. Yan, “Space cloud-fog computing: architecture, application and challenge,” in Proceedings of the 3rd International Conference on Computer Science and Application Engineering, pp. 1–7, Sanya, China, October 2019.
View at: Google Scholar
Z. Qu, G. Zhang, T. Hong, H. Cao, and W. Zhang, “Architecture and network model of time-space uninterrupted space information network,” IEEE Access, vol. 7, pp. 27677–27688, 2019.
View at: Publisher Site | Google Scholar
A. Murtaza, S. J. H. Pirzada, F. Fatima, T. Xu, and L. Jianwei, “IP-Based space air ground information network for air traffic control communication,” in Proceedings of the 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), pp. 1–6, Victoria, BC, Canada, July 2019.
View at: Publisher Site | Google Scholar
M. Ndong, M. Hayajneh, N. A. Ali, and S. Alkobaisi, “Towards a 3-tiered space-air-ground network with reinforcement learning,” Journal of King Saud University-Computer and Information Sciences, vol. 14, 2022.
View at: Google Scholar
Z. Wang, F. Zhang, Q. Yu, and T. Qin, “Blockchain-envisioned unmanned aerial vehicle communications in space-air-ground integrated network: a review,” Intelligent and Converged Networks, vol. 2, no. 4, pp. 277–294, 2021.
View at: Publisher Site | Google Scholar
Bo Cong, Y. Liu, H. Shi, and X. Bai, “User access of space communication networks: challenges and system analysis,” in Proceedings of the 2016 15th International Conference on Optical Communications and Networks (ICOCN), pp. 1–3, Hangzhou, September 2016.
View at: Publisher Site | Google Scholar
Y. Zhang, J. Zhu, Q. Zhu et al., “The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks,” International Journal of Digital Earth, vol. 13, no. 12, pp. 1637–1655, 2020.
View at: Publisher Site | Google Scholar
X. Zhang, P. Dong, X. Du, H. Zhang, and M. Guizani, “Space-ground integrated information network enabled internet of vehicles: architecture and key mechanisms,” IEEE Communications Standards Magazine, vol. 4, no. 4, pp. 11–17, December 2020.
View at: Publisher Site | Google Scholar
X. Zhang, Y. N. Zhou, and J. Luo, “Deep learning for processing and analysis of remote sensing big data: a technical review,” Big Earth Data, vol. 10, pp. 1–34, 2021.
View at: Google Scholar
R. Liu, Y. Zhu, Y. Zhang, W. Wu, D. Zhou, and K. Chi, “Resource mobility aware hybrid task planning in space information networks,” Journal of Communications and Information Networks, vol. 4, no. 4, pp. 107–116, 2019.
View at: Publisher Site | Google Scholar
X. Wang, S. Zhao, J. Zhao, Y. Li, H. Zhao, and Y. Jiang, “Service customized software-defined airborne information networks,” in Proceedings of the International Conference on Space Information Network, pp. 256–265, Kunming, China, August 2016.
View at: Google Scholar
W. Wang and S. Wang, “Overview of the international satellite-based COSPAS-SARSAT system,” in Proceedings of the International Conference on Space Information Network, pp. 52–61, Shenzhen, China, December 2018.
View at: Google Scholar
W. Unisdr, Terminology on Disaster Risk Reduction, United Nations Office for Disaster Risk Reduction, Geneva, Switzerland, 2009.
T. Pultarova, “Telecommunications-space tycoons go head-to-head over mega satellite network [news briefing],” Engineering and Technology, vol. 10, no. 2, 2015.
View at: Google Scholar
V. P. Hubenko, R. A. Raines, R. F. Mills, R. O. Baldwin, B. E. Mullins, and M. R. Grimaila, “Improving the global information grid’s performance through satellite communications layer enhancements,” IEEE Communications Magazine, vol. 44, no. 11, pp. 66–72, 2006.
View at: Publisher Site | Google Scholar
Intelligent Transportation System, 2022, https://en.wikipedia.org/wiki/Intelligent_transportation_system.
I. A. Alimi, A. O. Mufutau, A. L. Teixeira, and P. P. Monteiro, “Performance analysis of space-air-ground integrated network (SAGIN) over an arbitrarily correlated multivariate FSO channel,” Wireless Personal Communications, vol. 100, no. 1, pp. 47–66, 2018.
View at: Publisher Site | Google Scholar
S. Keele, Guidelines for Performing Systematic Literature Reviews in Software Engineering Citeseer, Elsevier, Amsterdam, Netherlands, 2007.
T. Dybå and T. Dingsøyr, “Empirical studies of agile software development: a systematic review,” Information and Software Technology, vol. 50, no. 9-10, pp. 833–859, 2008.
View at: Publisher Site | Google Scholar
A. Fernandez, E. Insfran, and S. Abrahão, “Usability evaluation methods for the web: a systematic mapping study,” Information and Software Technology, vol. 53, no. 8, pp. 789–817, 2011.
View at: Publisher Site | Google Scholar
E. Mehmood and T. Anees, “Challenges and solutions for processing real-time big data stream: a systematic literature review,” IEEE Access, vol. 8, pp. 119123–119143, 2020.
View at: Publisher Site | Google Scholar
R. Sardar and T. Anees, “Web of things: security challenges and mechanisms,” IEEE Access, vol. 9, pp. 31695–31711, 2021.
View at: Publisher Site | Google Scholar
M. Shaheen, M. S. Farooq, T. Umer, and B. S. Kim, “Applications of federated learning; taxonomy, challenges, and research trends,” Electronics, vol. 11, no. 4, p. 670, 2022.
View at: Publisher Site | Google Scholar
A. Anum, T. Ali, S. Akbar et al., “Angle adjustment for vertical and diagonal communication in underwater sensor networks,” IJACSA Int. J. Adv. Comput. Sci. Appl, vol. 11, pp. 604–616, 2020.
View at: Google Scholar

Copyright

Copyright © 2023 Muhammad Junaid Anjum et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

1525

Downloads

849

Citations

Applied Computational Intelligence and Soft Computing

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Abstract

1. Introduction

1.1. Motivation

1.2. Contribution

1.3. Organization of Article

2. Background

2.1. Disasters

2.2. Space-Air-Ground Integrated Network

3. Research Methodology

3.1. Research Objectives

3.2. Research Questions

3.3. Search String

3.4. Screening for Relevant Results

3.5. Quality Assessment

4. Analysis

4.1. RQ1 Assessment: Key Publication Channels Targeted for DMS

4.2. RQ2 Assessment: Frequency of Change in Research with Respect to Time

4.3. RQ3 Assessment: Approaches Used for Design of SAGIN Based DMS

4.3.1. Proposed Solution

4.3.2. Surveys

4.3.3. Architecture

4.3.4. Framework

4.3.5. Model

4.3.6. Platform

4.4. RQ4: Different Issues and Challenges to Overcome for an Effective SAGIN Based DMS

4.4.1. Hardware Challenges

4.4.2. Networking Challenges

4.4.3. Data Imagery

4.4.4. Security Challenges

4.4.5. Software Issues

4.5. Quality Assessment Score

5. Related Work

6. Conclusions

Acronyms

Data Availability

Conflicts of Interest

References

Copyright