Sage Journals: Discover world-class research

Abstract

Smart City Internet of Things will become a fundamental infrastructure to support massive machine-type communications between the widely deployed sensors serving big cities. Since there exists many location constraints for the existing terrestrial Internet of Things, the non-terrestrial Internet of Things sheds light on breaking these limits. Therefore, this article conducts a comprehensive survey on non-terrestrial Internet of Things technologies for Smart City, which is an important complement to terrestrial Internet of Things. We first present the application scenarios of Internet of Things and point out where the existing terrestrial Internet of Things cannot work perfectly. Two non-terrestrial Internet of Things technical proposals are then introduced, namely satellite Internet of Things and unmanned aerial vehicle Internet of Things. However, the focuses of these non-terrestrial Internet of Things are distinct, that is, the major problems of satellite and unmanned aerial vehicle Internet of Things are the high dynamic nature of channel and high maneuverability of unmanned aerial vehicles, respectively. The key technologies for satellite and unmanned aerial vehicle Internet of Things are then reviewed separately. Both physical and non-physical layer technologies are surveyed for satellite Internet of Things, and the route planning is mainly investigated for the unmanned aerial vehicle Internet of Things. Finally, we draw a conclusion and give some potential research directions of non-terrestrial Internet of Things.

Keywords

Internet of Things Smart City non-terrestrial satellite unmanned aerial vehicle

Introduction

With the digital transformation of traditional economy, Internet of Things (IoT), as a new generation of communication infrastructure, is the key enabling factor of ubiquitous connectivity. It realizes ubiquitous social services, builds a beautiful vision of a Smart City, and creates a brand new network environment for human development and civilization evolution through ubiquitous access and information sharing. By 2025, IoT applications are projected to grow dramatically, reaching 27 billion connections worldwide. Therefore, as an IoT application scenario, Smart City is bound to become the direction of future urban development. The deployment of Smart City in large cities will eliminate the information islands and information chimneys existing in traditional urban development and realize the great collaboration and development of the whole industry and society.

Wireless communication technology is the core technology of enabling IoT. As a summary of existing technologies, Table 1 summarizes the current research directions of IoT, which mainly focuses on the standards, security, scenarios, protocols, data computing, and other aspects of terrestrial IoT, and the research has been very extensive. However, with the emergence of some new type of business for the IoT, such as hydrological monitoring and environmental protection, terrestrial wireless communication has been unable to meet requirements. Therefore, new ideas are urgently needed to supplement the deficiency of terrestrial wireless communication. The wide coverage and low cost of non-terrestrial wireless communications complement terrestrial wireless communications. However, there is little research literature on non-terrestrial IoT.

Table 1.

Summary of existing surveys about IoT.

Classification	Direction
Territorial IoT	Standard¹
	Overview^2,3
	Security^4–6
	Data collection^7–9
	Scenarios^10–12
	Protocols^13,14
	Data calculation^15–17
	Integrating with 5G¹⁸
Non-territorial IoT	UAV¹⁹
Non-territorial IoT	Satellite²⁰

IoT: Internet of Things; UAV: unmanned aerial vehicle.

Different from existing literature, this article starts with the IoT-oriented application scenarios, and leads to non-territorial IoT for scenarios that are not suitable for territorial IoT deployment. Then, we conducted an extensive survey, as given in sections “Physical layer technologies of satellite IoT,”“Non-physical layer technologies of satellite IoT,” and “Communication technologies of UAV IoT,” by dividing the non-territorial IoT into satellite IoT and UAV IoT. Finally, this article summarizes the whole article and looks forward to the possible future development directions. Figure 1 illustrates the broad outline of this review.

Figure 1.

The outline of this article.

Overview on Internet of Things

IoT is primarily for device-to-device (D2D) communication. Different from cellular communication, IoT requires a lower transmission rate, a higher number of device connections, and requires low-power transmission due to limited power of the terminal device. In general, IoT application scenarios can be divided into two parts in Smart City: (1) small range and short-distance transmission scenarios, (2) large range and long-distance transmission scenarios. For the terminals in the first scenario, they are usually covered by local area network (LAN), such as WiFi, ZigBee, and so on. The terminals in the latter scenario are covered by low-power wide-area network (LPWAN). Typical applications of these scenarios are shown in Table 2.

Table 2.

Typical applications of scenarios.

Scenarios	Typical application	Appropriate technology
Small range and short-distance transmission	Smart Home, E-health	LAN
Wide range and long-distance transmission	Smart City, smart grid, disaster warning	LPWAN

LAN: local area network; LPWAN: low-power wide-area network.

IoT technologies for different scenarios have different characteristics. Figure 2 illustrates the relationship between coverage and throughput for different technologies. Figure 3 shows the relationship between coverage and equipment life for LAN and LPWAN. As can be seen from these two figures, LPWAN has low throughput but long terminal battery life, and LAN requires higher throughput and does not require strict power dissipation limits. Since this article focuses on Smart City, LPWAN is the key point.

Figure 2.

Relationship between coverage and throughput.

Figure 3.

Relationship between coverage and life duration.

Table 3 shows the relevant typical technologies and important indicators contained in LPWAN.

Table 3.

Technical indicators of LPWAN.

Feature	LoRa	NB-IoT (R13+)	LTE-M (R13)
Modulation	Chirp SS	OFDMA	OFDMA
Rx bandwidth	500–125 KHz	200 KHz	20–1.4 MHz
Max output power	20 dBm	20 dBm	23/30 dBm
Data rate	290 bps to 50 Kbps	~ 20 Kbps	200 Kbps to 1 Mbps
Link budget	154 dB	150 dB	146 dB
Power efficiency	No.1	No.2	No.3

LPWAN: low-power wide-area network; OFDMA: orthogonal frequency-division multiple access; LoRa: long range; SS: spread spectrum; NB: narrowband; LTE-M: long-term evolution for machines.

The application scenario of LPWAN can be divided into four parts, as shown in Table 4. To cover a wide range of scenario with a small number of terminals in some big mountain cities, considering the relatively limited coverage of the terrestrial base station, the complex geographical conditions, and the limited and dispersed equipment, the base station is not only difficult to implement, but also can only access fewer IoT equipment, which makes the construction and communication costs surge. Significant contrast with the terrestrial wireless communications, the non-terrestrial wireless communications has unique advantages, as follows:

Wide coverage, which can realize global coverage, and sensor laying is not limited by space;

Not affected by extreme terrain;

Strong destruction resistance of the system.

Table 4.

Subdivided scenes of LPWAN.

Scenarios	Application
Small number of terminals and large-scale distribution	Tsunami warning, Landslide warning
Small number of terminals and small range distribution	Military application
Large number of terminals and large-scale distribution	Warehousing, dock
Large number of terminals and small range distribution	Gas, water

LPWAN: low-power wide-area network.

These advantages totally fit into the scenario of large coverage and few terminals. In General, non-terrestrial IoT communication platforms can be considered to include satellite platforms and UAV platforms. However, the characteristics of satellite IoT and UAV IoT are different, and so are the technical requirements. Therefore, we will study these two technical approaches separately in the following article.

Satellite IoT

Since the features of satellite orbit make different kinds of satellites having different characteristics, it is very important to choose the right kind of satellites to meet the needs of IoT. The geostationary Earth orbit (GEO) satellite is stationary to the Earth and can provide continuous service to an area. However, it is in a higher orbit resulting in large round-trip delay and signal attenuation. In contrast to GEO, The low Earth orbit (LEO) satellite moves relative to the Earth, has a lower orbit, lower signal delay, and relatively small signal attenuation.

Due to the low cost and small size of terrestrial terminals facing Smart City, compared with GEO satellites, LEO satellites have less signal attenuation and are easier to meet the characteristics of terrestrial terminals. Therefore, there is more extensive research on the LEO satellite IoT. On the other hand, GEO satellites have the advantage of ensuring continuous service within designated coverage and providing global access beyond the polar regions with just three satellites.

However, to meet IoT requirements, satellite IoT still faces many problems:

As IoT requires a large number of terminal access systems, wireless access technologies that avoid/tolerate collisions are required;

Low power consumption and high energy efficiency design;

Designed for coping with large dynamic channels;

Deal with the scarcity of spectrum;

Design the antenna required by satellite IoT;

Satellite IoT protocols, standards, and so on.

Oriented by requirements, the following sections describe technologies of satellite IoT in two parts: physical and non-physical, with an emphasis on technologies of physical layer.

UAV IoT

UAVs are highly maneuverable. Therefore, the flexible layout and mobility of UAVs are the most important features of UAV IoT. However, there are still many problems to overcome:

Flexible UAV deployment to achieve efficient coverage and data collection;

Low power consumption design;

Coping with data collision;

Design for dynamic channel characteristics such as Doppler frequency shift;

Others: such as throughput optimization, low delay design, and so on.

In the section “Communication technologies of UAV IoT,” we focus on the flexible deployment of UAVs and summarize the technical status quo of UAV IoT oriented by requirements.

Physical layer technologies of satellite IoT

A typical satellite IoT system model is shown in Figure 4. The terrestrial IoT device first uploads data to a satellite relay, which then sends the data to a terrestrial data processing center for unified processing. Based on this typical system model, this article summarizes the current research achievements on physical layer of the satellite IoT in Table 5 according to the technical requirements mentioned in the previous section. These technologies of physical layer mainly focus on modulation, multiple access, channel coding, frame structure design, resource scheduling, spectrum, and so on, which are subsequently described in the sections “Physical layer technologies of satellite IoT” and “Non-physical layer technologies of satellite IoT” later.

Figure 4.

Typical system model of satellite IoT.

Table 5.

Summary of physical layer technology of satellite IoT.

Requirements	Reference	Physical layer technology
Wireless access technology	²¹	Modulation
	^22–29	Access
	^30–37
	^38–45
	⁴⁶	Access control
	⁴⁷	Performance analysis
High efficiency	⁴⁸	Frame structure
High efficiency	⁴⁹	Resource allocation
Large dynamic channel	^50–52	Modulation
	^53,54
	^55,56	Channel coding
	^57,58	Receiver design
	⁵⁹	Resource allocation
	⁶⁰	Scheduling
Scarce spectrum	^61,62	mm-Wave
Scarce spectrum	⁶³	Performance analysis
Other enabling technologies	^64–68

IoT: Internet of Things; mm-Wave: millimeter wave.

Wireless access technologies

To deal with the large number of terrestrial terminals access to satellites, related scholars put forward a variety of access technologies to avoid or tolerate collisions. For IoT based on GEO satellite, Hofmann et al.²¹ proposed a new chirp-spread spectrum (CSS)-based modulation and signaling scheme. Unipolar codes are used in the proposed transport structures in a new way, which allows a large number of devices to access a common channel randomly.

Non-orthogonal multiple access (NOMA) is an important means of terrestrial wireless access and a hotspot of B5G/6G research and standardization. Furthermore, in IoT access for integrated air–ground network, it is still a key research direction.²² Ding et al.²³ proposed a multiple input multiple output (MIMO)-NOMA scheme for IoT, which uses precoding to make one user strictly meet the service quality, and the other user gets the services through NOMA opportunistically. Hu et al.²⁴ proposed constellation coding for multiuser reuse, which improves spectral efficiency and the number of users’ access by transmitting multiple users signals simultaneously on the same frequency band. Specifically, Hu et al. first give each user a specific number and corresponding constellation, and then they map each combination of user constellation points to a higher-order constellation point, and the schematic diagram is shown in Figure 5. At the receiver, after the user detects the high-order modulation signal, each user applies the corresponding constellation decoding to get its own signal.

Figure 5.

Example of constellation mapping.²⁴

Random access technology is a kind of multiple access technology based on competition. Multiple users can occupy channel resources in a competitive way without signaling scheduling, which is an access method suitable for the IoT. ALOHA is the first random-access technology proposed by Abramson,²⁵ which has the problem of low channel utilization. Later, Roberts²⁶ introduced the concept of “time slot” based on ALOHA and proposed the slotted ALOHA, which reduced the collision probability of packets. On the basis of slotted ALOHA, researchers in recent years have studied the repeated transmission of packets to find the optimal balance between packet collision and reliability. They introduced the idea of time/frequency diversity and interference cancelation, and proposed diversity slotted ALOHA (DSA),²⁷ resolution DSA (RDSA),²⁸ and so on. Irregular repetition slotted ALOHA (IRSA)²⁹ improved RDSA to further increase throughput. In addition, asynchronous contention resolution diversity slotted ALOHA (ACRDSA),³⁰ spread slotted ALOHA (SSA),³¹ and coded slotted ALOHA (CSA)³² is also an improved scheme based on ALOHA. ACRDSA overcomes the disadvantage of multi-terminal synchronization based on CRDSA. SSA introduces spread spectrum technology in slotted-ALOHA; CSA introduces the combination of packet erasure correcting codes and successive interference cancelation (SIC) into slotted ALOHA. Irregular repetition spatially coupled slotted ALOHA(IRSC-SA)³³ introduces the concept of spatial coupling in slotted ALOHA and applies a new density evolution (DE) method to address the unequal protection of different users. Subsequently, R-CSA³⁴ further improved CSA to cope with the ALOHA collision problem in the multi-receiver satellite IoT. Bai and Ren³⁵ proposed a new adaptive packet-length assisted slotted ALOHA scheme to cope with the large dynamic satellite channel environment. Ren’s team introduced the ideas of non-orthogonal multi-access and polarized transport into slotted ALOHA, and proposed two random-access methods, namely non-orthogonal slotted ALOHA (NOSA)³⁶ and polarized MIMO slotted ALOHA (PMSA),³⁷ to achieve higher throughput. Zhao et al.³⁸ proposed a random-access method called random mode multiplexing, which implements multi-user random mode access by mapping packets to a resource block (RB) composed of multiple resource elements (REs). Herrero and De Gaudenzi³⁹ proposed a multi-access scheme to provide machine-to-machine (M2M) communication services for a large number of low-cost satellite-borne terminals, which improved the spectrum efficiency in the case of power imbalance. Zhao et al.⁴⁰ proposed a cooperative random-access scheme for multiple satellites. By using a packet structure based on single-carrier interleave frequency division multiple access (SC-IFDMA), the influence of user transmission delay on received signals of satellite nodes was overcome and the synchronization of received signals was ensured. Zhen et al.⁴¹ proposed an enhanced spatial group-based random-access scheme from the perspective of preamble design to accommodate massive and concurrent M2M random access requests and ensure human-to-human communication, which significantly reduces the collision probability. Time/frequency ALOHA, a random-access scheme suitable for ultra narrow band (UNB), is introduced by Anteur et al.⁴² Due to the large number of devices in IoT, an overloaded random-access channel (RACH) may cause a service outage. Therefore, based on the background of satellite IoT system, Chelle et al.⁴³ proposed a dynamic calculation method of load control parameters based on access class barring (ACB). In the article by Gan et al.,⁴⁴ the performance of an uncoordinated code domain NOMA protocol as shown in Figure 6 is discussed to solve the pilot collision of massive machine type communications (mMTC) in space information network (SIN). In addition, SIC and successive joint decoding (SJD) were used to recover collision information under the satellite and ground channel model of shadow fading and path loss. Due to the characteristics of non-terrestrial IoT business, signaling interaction is reduced and grant-free transmission is required. To solve the collision problem caused by grant-free transmission, a rate-adaptive multiple access (RAMA) scheme was proposed.⁴⁵

Figure 6.

The process of uplink uncoordinated code domain NOMA protocol.⁴⁴

Aiming at the problem that a large number of territorial IoT terminals upload data that may easily lead to data collision, Kawamoto et al.⁴⁶ divided IoT terminals into groups and allocated satellite bandwidth control access in a divide and conquer manner. In the article by Cluzel et al.,⁴⁷ an abstract estimation method of bit error rate (BER) and packet error rate (PER) using physical layer is studied under a time-frequency random scheme.

High efficacy

Based on the modified Zadoff-Chu sequence, Doré and Berg⁴⁸ proposed an efficient channel synchronization frame structure, which has low-level peak-to-average power ratio (PAPR) and is suitable for large levels Doppler.

It is worth noting that the power resources and storage resources of the satellite are limited, inefficient resource allocation may lead to interruption events, and the limited storage resources may overflow. For the communication between satellite and ground station, a low-cost source coding scheme is needed to compress the image information efficiently. Distributed source coding of hyperspectral images based on low-complexity discrete cosine transform (DCT) can effectively reduce the complexity of the coding side and is suitable for on-star signal processing. In the article by Sun et al.,⁴⁹ the authors first established a long-term joint power distribution and rate control scheme for satellite IoT NOMA downlink system. The optimal SIC decoding sequence is difficult to express because queue states and channel states constantly change from one slot to another. Therefore, deep-learning-based long-term power allocation (DL-PA) is adopted to approximate SIC decoding order by training a large amount of data. The framework of the DL-PA scheme is shown in Figure 7, where $Q_{i} (t)$ represents the queue backlog status of UE_i at slot $t$ , $g_{i} (t)$ represents the channel gain of i-th downlink between $S$ and UE_i at time slot $t$ , $s_{i} (t)$ represents the index of $UE$ with the i-th largest power level at time slot $t$ , and $p_{i} (t)$ represents the transmit power allocated to $U E_{i}$ at time slot $t$ .

Figure 7.

The framework of DL-PA scheme.⁴⁹

Large dynamic channel

Regarding the Doppler effect, since LoRa has not established relevant standards for the Doppler effect of fast-moving satellite communications, Doroshkin et al.^50,51 discussed the feasibility of LoRa modulation in CubeSat radio communication systems. Since chirp signal has good anti-Doppler shift performance, Qian et al.⁵² studied the application of LoRa technology in LEO satellite, placing emphasis on CSS modulation and introduced symmetry CSS (SCSS) into LEO satellite IoT. Asymmetric chirp signal (ACS) is therefore more applicable to LEO satellite IoT. Similarly, Qian et al.⁵³ proposed ACS. Compared with symmetric chirp signal (SCS), ACS has better auto-correlation and better cross-relation in time domain and frequency domain. Yang et al.⁵⁴ proposed a folded chirp-rate shift keying (FCrSK) modulation technique, which has a strong ability to resist Doppler shift. Compared with traditional chirp-rate shift keying, its bandwidth and symbol length are consistent among chirp-rate.

Since the traditional fixed rate coding is difficult to satisfy the communication service of high-speed moving objects, the rateless coding can solve this problem to some extent. Spinal codes are a kind of rateless codes, which first converts message bits into pseudo-random sequences using a hash function and maps them to dense constellation points for transmission.^55,56 The coding process of spinal codes is shown in Figure 8, where $M_{i} (t)$ represents a message block, $h$ is a random hash function, $s_{i}$ represents a $v$ -bit state.

Figure 8.

Encoding process of spinal codes.^55,56

Based on the NB-IoT standard of 3GPP, Sylvain Cluzel et al.⁵⁷ designed a kind of IoT for LEO satellite, and proposed a set of detection algorithms for Doppler shift. Similarly, Colavolpe et al.⁵⁸ mainly studied LoRa waveform characteristics and signal detection for LEO satellite IoT application. Then, Colavolpe et al. proposed a new receiver structure that exploits interference cancelation to deal with the Doppler shift.

NB-IoT was used to enable LEO satellite IoT.⁵⁹ In this system, Kodheli et al. found the problem of high differential Doppler between different user channels. Aiming at this problem, Kodheli et al. put forward a resource allocation scheme. In addition, Kodheli et al.⁶⁰ proposed an uplink scheduling technique to keep differential Doppler within NB-IoT acceptable limits.

Scarce spectrum

At present, service demands on throughput and service quality are rising, and the traditional frequency band is difficult to meet the practical demand. Therefore, Wang et al.⁶¹ introduced the millimeter-wave (mm-Wave) frequency band. Combined with the massive MIMO assisted by beamforming, Wang et al. proposed an effective adaptive random-selected multi-beamforming (ARM) estimation scheme. For hybrid satellite-terrestrial relay network (HSTRN), X Liang et al.⁶² analyzed the system performance of mm-Wave, and further studied the influence of rainfall on mm-Wave communication under non-line-of-sight conditions. In view of the common channel interference between systems caused by the spectrum sharing of terrestrial IoT and non-terrestrial IoT, Xu et al.⁶³ analyzed various situations of common channel interference.

Other enabling technologies

Sanil et al.⁶⁴ studied and discussed the use of satellite facilities in C-band and X-band in IoT, and designed a multi-band microstrip antenna with three different frequencies in C-band and X-band. In order to respond flexibly to traffic demand, Takahashi et al.⁶⁵ used beamforming technology to allocate power resources, and introduced a new power resource allocation method based on transmit power and multi-beam directivity fusion control. Jin et al.⁶⁶ analyzed the special application scenarios and traffic distribution characteristics of LEO satellite IoT, and then proposed a traffic simulation method based on LEO satellite IoT. To solve the satellite downlink replanning problem, Song et al.⁶⁷ proposed a combination method based on improved genetic algorithm, and took advantage of back propagation (BP) neural network to optimize the initial population of genetic algorithm. In addition, Roy et al.⁶⁸ proposed a symmetric chirp signal for LEO satellites, namely symmetric chirp with multi-chirp rate (SC-MCR), which improved the cross-correlation level of SC waveform and achieves better anti-interference performance than symmetric chirp (SC) signals. In addition, time domain multiplexing (SC-MCR) waveform is designed to improve the transmission rate.

Non-physical layer technologies of satellite IoT

This section summarizes the contribution of network protocol, MAC layer protocols, network architecture, and satellite constellation model to technical requirements of satellite IoT.

High efficacy

As the current IoT protocol is not suitable for LEO satellite IoT, Wang et al.⁶⁹ proposed a low-complexity satellite IoT protocol, which simplifies the signaling interaction process and random-access process, and achieves efficient LEO satellite IoT communication. Huang et al.⁷⁰ considered the problem of collecting data from IoT gateways via LEO satellites using an energy-saving method under time-varying uplink. In LoRa-based satellite IoT, Qian et al.⁷¹ made a critical study on how to capture SCS, and proposed a new SCS acquisition method that can balance complexity and performance.

Network architecture

From the perspective of network architecture, considering the future development direction of satellite network, IoT network and mobile network, Wei-Che Chien et al.⁷² proposed a potential heterogeneous space and terrestrial integrated network (H-STIN) architecture for the purpose of integrating various system architectures and different wireless communication protocols. To achieve a satellite-ground integrated network, network function virtualization (NFV) and software defined network (SDN) can be used to improve the capacity of wireless communication systems.⁷³ To cope with the problem of unbalanced traffic demand and frequent link congestion of satellite IoT, Z Liu et al.⁷⁴ proposed a routing scheme for low-earth orbit satellite network, aiming to maintain global and local load balance and optimize the data transmission of IoT. To reduce the connection delay of nanosatellite constellation, Marcano and Jacobsen⁷⁵ studied random linear-network coding (RLNC) and proposed an RLNC transmission scheme. Chelle et al.⁷⁶ analyzed M2M communication extensively from the perspective of satellite, and defined a new traffic modeling for M2M communication from the perspective of the satellite.

Other enabling technologies

The European Space Agency considered the constrained limited application protocol (CoAP) as the IoT application protocol suitable for collecting IoT data through satellites in machine-type communication satellite networks. Therefore, Soua et al.⁷⁷ studied the effective configuration of CoAP, and put forward the relevant optimal design according to the characteristics of satellite link. Similarly, Bacco et al.⁷⁸ compared two common protocol stacks, CoAP and MQTT, based on the DVB-RCS2 standard. Based on the satellite VHF data exchange system (S-VDES) MAC protocol, the detection probability of ships was analyzed and derived from the Satellite VDES by Wong et al.⁷⁹ Furthermore, this team also proposed an asynchronous multichannel pure collective ALOHA MAC protocol based on a decollision algorithm (MC-CA-SA).⁸⁰ In order to improve energy efficiency and reduce the network delay, Wang et al.⁸¹ proposed a joint TDMA MAC protocol (SL-MAC) applicable to LEO satellite IoT. Bacco et al.⁸² analyzed the main problems hindering M2M interconnection, and proposed a M2M/IoT communication protocol stack based on oneM2M standard.

Communication technologies of UAV IoT

UAVs are now widely used in many scenarios as low-altitude platforms. However, as a low-altitude communication platform, UAV is different from traditional terrestrial communication, such as high dynamic channel environment and severe non-stationarity.⁸³ In view of the technical requirements faced by UAV IoT, this article makes a list of related technologies as shown in Table 6.

Table 6.

Summary of UAV IoT technical requirements.

Requirements	References
Flexible deployment	^84–96
Low power consumption design	^97–105
Data collision elimination	^106–110
Large dynamic channel	^111–116
Other enabling technologies	^{117–119,133}
	^120–124
	^125,126,134
	^127–132

UAV: unmanned aerial vehicle; IoT: Internet of Things.

Flexible deployment and route planning

Due to the high maneuverability of UAV, trajectory planning is an important optimization point for UAV to achieve air-to-ground communication. For wireless sensor network scenario, as in the article by Yang and Yoo,⁸⁴ UAV first obtains data collection points from the whole sensor network and then uses the proposed joint genetic algorithm and ant colony optimization algorithm to determine the best route between adjacent collection points. For the task of selecting an appropriate UAV for a specific IoT task, Motlagh et al.⁸⁵ designed two solutions based on the standard of optimal energy consumption, called energy-aware selection (EAS) of UAVs and delay-aware selection (DAS) of UAVs. Similarly, Mozaffari et al.⁸⁶ mainly considered saving energy for IoT devices and realizing reliable uplink communication. Therefore, a new method for optimal mobility of UAV is proposed, which reduces the total transmission power by 56%. In terms of system construction, according to the limited energy characteristics of UAVs, Liu et al.⁸⁷ established a multi-hop D2D link in Figure 9 to extend the coverage. Lyu and Zhang⁸⁸ attempted to realize UAVs’ uplink and downlink communication in three-dimensional (3D) space by using cellular networks. Therefore, a new 3D system model for UAVs is constructed, and a framework for analyzing UAVs’ uplink/downlink 3D coverage performance is proposed. Shi et al.⁸⁹ proposed a 3D UAV trajectory design idea: multiple DC periodically fly over the IoT devices and forward the data of the IoT to base stations (BSs). Compared with static UAV deployment, the proposed scheme reduces the average road loss by 10–15 dB. Qi et al.⁹⁰ developed a 5G IoT network based on the imagination of future Smart City. The article particularly emphasized the UAV enabling the IoT in the air to achieve 3D connectivity and connected the whole world through heterogeneous intelligent devices. Technically, a layered multi-UAV architecture for team coordination and trajectory tracking is proposed. In order to integrate different UAVs into 5G network, Huo et al.⁹¹ proposed a multi-layer and distributed UAVs hierarchical structure. Jiang et al.⁹² focused on cache-enabled UAVs. In order to meet the requirements of multimedia data transmission scenario on system throughput, a collaborative IoT network structure assisted by cache-enabled UAV was proposed. Ye et al.⁹³ studied the UAV-assisted full duplex wireless IoT network under the scenario of sparse sensor distribution, in which UAV is equipped with full-duplex hybrid access point (HAP). It is assumed that the signals sent by UAV can only be received by neighboring sensors. Therefore, the Ye et al. proposed a new UAV-assisted IoT network line model to optimize system throughput. The model structure is shown in Figure 10. The model shows a dynamic time division multiple access (TDMA) frame structure, which means that each sensor can only transmit information in its allocated time slot thereby avoiding interference from other sensors. Abouzaid et al.⁹⁴ proposed an analysis model of the UAV flying mesh network, which works cooperatively in multi-hop mode to provide connectivity, data acquisition and forwarding for the terminal system, balancing end-to-end throughput, and stability. Liu et al.⁹⁵ proposed a system model for UAV-enabled wireless sensor network data acquisition. In order to maximize the uplink decoding success ratio and minimize the flight time of UAV, Farajzadeh et al.⁹⁶ adopted the power domain NOMA in the uplink, and proposed an optimization framework to determine the trade-off between numerous network parameters.

Figure 9.

Architecture of the UAV and D2D communication system. UAV: unmanned aerial vehicle; D2D: device to device.⁸⁷

Figure 10.

Equivalent line model and frame structure for energy harvesting and information transmission.⁹³

Low power consumption design

Al-Turjman et al.⁹⁷ took UAV as the aerial base station in a specific dangerous area to enable 5G network. By optimizing the number and location of UAV, higher energy efficiency can be achieved when considering data rate, delay, throughput, and other parameters. Considering the low cost and long service life of IoT devices, battery-free sensing devices based on scattering communication are a possible choice.⁹⁸ From the perspective of the system framework, a new framework was proposed by Mozaffari et al.⁹⁹ for the joint optimization of UAVs, device–UAV association, and uplink power control, which enables the uplink reliable transmission of IoT devices with the minimum comprehensive transmission power. In the case of multi-user overlay transmission, the mutual interference is reduced and the receiving performance is improved by designing multi-user constellation map under the simple serial interference elimination technology.¹⁰⁰ Du et al.¹⁰¹ studied a kind of UAV as a marginal cloud to minimize UAV energy by optimizing UAV’s hovering time, scheduling, and resource allocation. Feng et al.¹⁰² adopted multi-antenna UAV and multi-antenna IoT device cluster communication to form virtual MIMO link. The transmission duration and transmission power of all devices in the system are designed jointly, which greatly improves the efficiency of data acquisition of the whole flight. Considering the energy consumption and operation time of UAVs to ensure the efficient operation of value-added IoT services (VAIoTSs), Motlagh et al.¹⁰³ proposed three complementary solutions, namely energy-sensing UAV selection (EAUS), delay sensing UAV selection (DAUS) and fair weighing UAV selection (FTUS). Zhan and Lai¹⁰⁴ studied the data collection of IoT system enabled by UAV with limited energy. In order to meet the energy budget of UAV, Zhan and Lai focused on the energy of mobile propulsion system of UAV, and introduced the energy propulsion model of UAV to minimize the maximum energy consumption of all IoT devices. Aiming at the requirement of efficient data collection in dense wireless sensor networks, Ebrahimi et al.¹⁰⁵ proposed a projection-based compressive data gathering (CDG) method. This method aggregates data on selected projection nodes, reducing the number of data-required transmissions, consequently, reducing energy consumption and extending network life.

Collision resolution design

The access of a large number of IoT devices in the region leads to a large amount of data traffic, and the spectrum is also very crowded, which makes data collisions occur from time to time. Almasoud and Kamal¹⁰⁶ studied cognitive UAV for data transmission, and data collision is effectively avoided by using spectrum sensing technology. Introducing controllable interference between multiple users to realize the increase of the number of access users is the core idea of NOMA technology.¹⁰⁷ Therefore, the application of NOMA technology in UAV IoT is a feasible solution to deal with multi-user collision. Liu et al.¹⁰⁸ used the characteristics of tolerating data collision of power domain NOMA (PD-NOMA), introducing PD-NOMA into UAV-assisted heterogeneous IoT emergency communication, so that air-to-ground (A2G) and ground-to-ground (G2G) communication are compatible in the same spectrum, which solved the data collision problem and further proposed a distributed SIC-free NOMA (DSF-NOMA) solution. Also using the power domain NOMA, buffering-aided (BA) relay selection was applied to the uplink of NOMA network where both user and device exist by Nomikos et al.,¹⁰⁹ and a relay selection strategy based on dynamic decoding sequence was proposed, namely flex-NOMA. This strategy avoided the need for transmitter channel state information (CSI) and reduced the probability of packet collision and delay. In addition, Duan et al.¹¹⁰ combined UAV and NOMA to build a high-capacity uplink transmission system that can tolerate collisions. By jointly optimizing UAV flight height, transmission power and sub-channel allocation, the system capacity is maximized.

Large dynamic channel

Xu et al.¹¹¹ introduced coherent/incoherent spatial modulation (SM) and space-time block coding using index shift keying (STBC-ISK) to deal with Doppler correspondence, where coherent SM and coherent STBC-ISK structure diagrams are shown in Figure 11. In the complex channel environment, NOMA can be used to optimize access efficiency, reduce mutual interference, and improve transmission robustness.¹¹² In terms of channel modeling, with the introduction of von-mises-fisher (VMF) scattering distribution, a new 3D MIMO channel model was proposed by Bi et al.¹¹³ Simulation results have shown that the model can accurately evaluate A2G UAV channel. Ding and Xiu¹¹⁴ proposed a scheme called block turbo-coded orthogonal frequency-division multiplexing (OFDM) for high-speed UAV data link, which combines block turbo codes (BTCs) with OFDM. The traditional channel coding relies on the estimation of CSI and the active bit rate selection, which cannot adapt to the rapidly changing channel conditions. Rateless codes can achieve almost optimal bit rates under rapidly changing channel conditions without CSI estimation and explicit rate selection. Zhang and Hranilovic¹¹⁵ and Pang et al.¹¹⁶ studied the applications of rateless raptor codes and spinal codes in UAV IoT, respectively. In addition, polar codes are also a promising channel-encoding method for higher throughput performance in UAV IoT systems.¹¹⁷

Figure 11.

Schematics of SM (a) and STBC-ISK (b).¹¹¹

Other enabling technologies

In this subsection, the content is divided into four parts: low-latency design, joint optimization, data acquisition technology, and other technologies, which will be explained in turn below.

For the UAV cluster-service scenario, a UAV management system needs to be designed due to the system’s demand for real-time data delivery. Choi et al.¹¹⁸ designed a procedure that can realize real-time data transmission for oneM2M message flow. Aiming at the challenge posed by the high time-sensitive business of UAV IoT to effective routing, Zhang et al.¹¹⁹ proposed a layered UAV swarm network structure and designed a low-latency routing algorithm (LLRA) based on partial location information and network structural connectivity. Considering the ultra-low latency requirements of the tactile IoT, grant-free NOMA is a feasible solution that can take advantage of its non-orthogonal and unlicensed transmission to achieve low-latency access.¹²⁰

Based on the reinforced barriers and collision avoidance characteristics of the heterogeneous UAV, Kim and Ben-Othman¹²¹ conducted joint optimization of the UAV’s flight path without collision. Aiming at the problem that dense network signaling interactions may cause aggregation interference to terminal nodes, Ji et al.¹²² proposed a joint optimization scheme that can effectively improve system throughput, interrupt probability and lawlessness by considering such parameters as transmission power, scaling factor, and UAV relay selection. To ensure timely delivery of information, Abd-Elmagid and Dhillon¹²³ constructed an optimization problem that takes into account UAV’s flight trajectory, energy allocation, and service time allocation for packet transmission, then proposed an effective iterative algorithm. Spectrum sharing in heterogeneous networks leads to cross-layer interference, which makes the power association problem in three-layer networks of satellite, UAV and macro-cellular become a key problem. A two-stage joint hovering altitude and power control scheme was proposed by Wang et al.,¹²⁴ which effectively improved the system throughput. In the UAV-assisted IoT communication system, Wang et al.¹²⁵ maximized the sum rate by jointly optimizing UAV altitude, user equipment uplink transmission power, and user equipment–UAV scheduling.

Chakareski¹²⁶ investigated the immersive data acquisition technology under the remote virtual reality scene based on UAV IoT, and designed the scalable source channel viewpoint coding, so as to maximize the reconstruction fidelity of the data captured at each UAV position at the ground convergence point. To realize accurate and efficient data sampling and reconstruction, Yu et al.¹²⁷ proposed a spatial data sampling scheme of UAV using a denoising autoencoder (DAE) neural network. Autoencoder (AE) is a neural network model for feature extraction, which has the ability to process nonlinear data. DAE evolved from basic AE, which can extract features from corrupted data and reconstruct the original data. The structure of DAE is shown in Figure 12, where $x$ represents original data, $\tilde{x} = q_{D} (x)$ represents the data corrupted by $x$ , $q_{D}$ is corruption function. As shown in Figure 12, $y = f_{θ} (\tilde{x})$ is coded data from $\tilde{x}$ , $z = g_{θ'} (y)$ represents decoded data form $y$ . Qin et al.¹²⁸ designed a novel protocol using existing ultra-low-power physical and asynchronous media access control mechanisms, as well as a lightweight application layer for data collection.

Figure 12.

Structure of DAE.¹²⁷

To realize the communication among various systems that may not contain the available electrical volume to support the traditional transmission of required signals, Santos et al.¹²⁹ proposed a new small electric antenna realized by direct antenna modulation (DAM). Handouf et al.¹³⁰ studied the problem of activity energy optimization of UAV, introduced the constraint optimization method of activity cycle adjustment, finally improved the system coverage and rate performance. Jingcheng et al.¹³¹ proposed a UAV-positioning method, which uses the range resolution ability of wide-band radar to infer the target position, and uses time-frequency analysis to analyze the Doppler effect generated by rotor rotation to determine whether it is a UAV. Gaur et al.¹³² proposed an efficient vertical handover mechanism between different networks, which improves the communication reliability of beyond line of sight (BLOS) and reduces the cost. In order to achieve the optimal summation rate of UAV, Wang et al. conducted joint optimization of IoT equipment-UAV scheduling, uplink transmission power of IoT equipment and UAV altitude. Yuan et al.¹³³ mainly studied the super-reliable communication system of UAV swarm, designed the software protocol stack and radiofrequency (RF) hardware, and developed the open-source UAV cluster platform, namely Easy- Swarm. In the UAV-assisted IoT communication network, it is necessary to study the access selection of UAVs and the bandwidth allocation of BS to balance the network performance. Therefore, Yan et al.¹³⁴ proposed a layered game framework, in which they modeled the bandwidth allocation problem as a non-cooperative game. According to the ergodic rate analysis, this article proposed a game framework, that is, a hierarchical Stackelberg game framework. The framework consists of the follower evolution game for UAVs access selection and the leader non-cooperative game for BS bandwidth allocation, as shown in Figure 13.

Figure 13.

Hierarchical Stackelberg game framework.¹³⁴

Conclusion and future prospects

As a complement to the territorial IoT platforms in extreme geographic environments, non-territorial IoT platforms can effectively improve IoT coverage and enhance network reliability and availability. The article mainly discusses two non-territorial IoT technical paths, satellite IoT, and UAV IoT. This article studies and sorts out the status quo of both technologies according to the perspective of technical requirements. In terms of satellite IoT, we divide existing technologies into physical and non-physical layers. As for UAV IoT, most of the articles focus on UAV layout optimization.

However, there are still some defects in the present study. As for the satellite IoT, it is observed that the research is mainly conducted under the LoRa system, while the OFDM system is less studied. In terms of UAV IoT, there is relatively little research on physical layer technology. In general, the research on physical layer technology for large dynamic and low power consumption is still insufficient, and the research on networking technologies for non-territorial IoT platform is relatively superficial.

Specifically, according to the current technological progress, some possible future research directions are as follows:

In order to achieve the integration of the non-territorial IoT and the territorial IoT in the B5G era, we need to unify the two systems. In terms of physical layer, the waveform of non-territorial link and territorial link will be unified. Considering that territorial IoT adopts OFDM waveform, and OFDM is easy to be combined with other multiple access methods, OFDM waveform is a possible development direction. In terms of network layer, it is a possible research direction to combine non-territorial and territorial IoT systems considering the large link delay and space link instability of non-territorial IoT.

In order to cope with the high dynamic channel of non-territorial communication, there may be two technical approaches as follows: In terms of channel coding, we could consider using the rateless characteristic of spinal codes to automatically adjust the code rate according to the real-time channel characteristic to cope with the large dynamic characteristic of satellite channel. In terms of multi-access, asynchronous multi-access is also a possible research direction considering the problem of multi-user time-frequency asynchrony easily caused by large dynamic channels.

To solve the problem of frequent switching of user connection relation, which arises from the change of network topology caused by the rapid movement of non-territorial platform, we can consider simplifying the user access, registration signaling process, and corresponding signal design of physical layer at the signaling process level. In addition, in the physical layer, multi-user multi-satellite/multi-station non-orthogonal connection communication system is also a feasible research direction.

Considering that the hardware resources of non-territorial platform are severely limited, we can consider the antenna and power constraints on the transmitting side to study the NOMA scheme with low peak average power ratio and high energy efficiency. The receiving side should be based on the general platform to realize the multi-user equalization and receiving technology with low complexity and updating. In addition, the neural network can be introduced to optimize the problem that cannot be expressed in closed form by data-driven method, and offline training is used to realize intelligent updates.

Footnotes

Handling Editor: Bo Rong

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported by Advance Research Projects of 13th Five-Year Plan of Civil Aerospace Technology (B0105) and the National Natural Science Foundation of China (61771051).

ORCID iD

Peisen Wang

References

Meddeb

. Internet of Things standards: who stands out from the crowd? IEEE Commun Mag 2016; 54(7): 40–47.

Al-Fuqaha

Guizani

Mohammadi

, et al. Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tut 2015; 17(4): 2347–2376.

Lin

Zhang

, et al. A survey on Internet of Things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things 2017; 4(5): 1125–1142.

Zhao

. A survey on the Internet of Things security. In: Proceedings of 2013 ninth international conference on computational intelligence and security, Leshan, China, 14–15 December 2013, pp.663–667. New York: IEEE.

Hassija

Chamola

Saxena

, et al. A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 2019; 7: 82721–82743.

Granjal

Monteiro

Silva

. Security for the Internet of Things: a survey of existing protocols and open research issues. IEEE Commun Surv Tut 2015; 17(3): 1294–1312.

Tsai

Lai

Chiang

, et al. Data mining for Internet of Things: a survey. IEEE Commun Surv Tut 2013; 16(1): 77–97.

Pattar

Buyya

Venugopal

, et al. Searching for the IoT resources: fundamentals, requirements, comprehensive review, and future directions. IEEE Commun Surv Tut 2018; 20(3): 2101–2132.

Alam

Mehmood

Katib

, et al. Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access 2017; 5: 9533–9554.

10.

Zanella

Bui

Castellani

, et al. Internet of Things for smart cities. IEEE Internet Things 2014; 1(1): 22–32.

11.

Ahmed

Yaqoob

Gani

, et al. Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wirel Commun 2016; 23(5): 10–16.

12.

Islam

Kwak

Kabir

, et al. The Internet of Things for health care: a comprehensive survey. IEEE Access 2015; 3: 678–708.

13.

Rajandekar

Sikdar

. A survey of MAC layer issues and protocols for machine-to-machine communications. IEEE Internet Things 2015; 2(2): 175–186.

14.

Palattella

Accettura

Vilajosana

, et al. Standardized protocol stack for the Internet of (important) Things. IEEE Commun Surv Tut 2012; 15(3): 1389–1406.

15.

Liang

, et al. A survey on the edge computing for the Internet of Things. IEEE Access 2017; 6: 6900–6919.

16.

Chiang

Zhang

. Fog and IoT: an overview of research opportunities. IEEE Internet Things 2016; 3(6): 854–864.

17.

Mouradian

Naboulsi

Yangui

, et al. A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tut 2017; 20(1): 416–464.

18.

Akpakwu

Silva

Hancke

, et al. A survey on 5G networks for the Internet of Things: communication technologies and challenges. IEEE Access 2017; 6: 3619–3647.

19.

Motlagh

Taleb

Arouk

. Low-altitude unmanned aerial vehicles-based Internet of Things services: comprehensive survey and future perspectives. IEEE Internet Things 2016; 3(6): 899–922.

20.

Chen

Thombre

Järvinen

, et al. Robustness, security and privacy in location-based services for future IoT: a survey. IEEE Access 2017; 5: 8956–8977.

21.

Hofmann

Knopp

. Ultra-narrowband waveform for IoT direct random multiple access to GEO satellites. IEEE Internet Things 2019; 6: 10134–10149.

22.

Yang

, et al. Non-orthogonal multiple access: achieving sustainable future radio access. IEEE Commun Mag 2019; 57(2): 116–121.

23.

Ding

Dai

Poor

. MIMO-NOMA design for small packet transmission in the internet of things. IEEE Access 2016; 4: 1393–1405.

24.

. A novel forward-link multiplexed scheme in satellite-based Internet of Things. IEEE Internet Things 2018; 5(2): 1265–1274.

25.

Abramson

. The ALOHA system: another alternative for computer communications. In: Proceedings of the AFIPS ‘70 (fall) joint computer conference, Houstan, TX, 17–19 November 1970, pp.281–285. New York: ACM.

26.

Roberts

. ALOHA packet system with and without slots and capture. SIGCOMM Comput Commun Rev 1975; 5(2): 2842.

27.

Choudhury

Rappaport

. Diversity ALOHA —a random access scheme for satellite communications. IEEE T Commun 1983; 31(3): 450–457.

28.

Casini

De Gaudenzi

Herrero

ODR

. Contention resolution diversity slotted ALOHA (CRDSA): an enhanced random access scheme for satellite access packet networks. IEEE T Wirel Commun 2007; 6(4): 1408–1419.

29.

Ghanbarinejad

Schlegel

. Irregular repetition slotted aloha with multiuser detection. In: Proceedings of 2013 10th annual conference on wireless on-demand network systems and services (WONS), Banff, AB, Canada, 18–20 March 2013, pp.201–205. New York: IEEE.

30.

De Gaudenzi

del Ro Herrero

Acar

, et al. Asynchronous contention resolution diversity ALOHA: making CRDSA truly asynchronous. IEEE T Wirel Commun 2014; 13(11): 6193–6206.

31.

Makrakis

Murthy

KMS

. Spread slotted ALOHA techniques for mobile and personal satellite communication systems. IEEE J Sel Area Comm 1992; 10(6): 985–1002.

32.

Paolini

Liva

Chiani

. Coded slotted ALOHA: a graph-based method for uncoordinated multiple access. IEEE Trans Inf Theor 2015; 61(12): 6815–6832.

33.

Fei

Cao

, et al. Analysis of irregular repetition spatially-coupled slotted ALOHA. Sci China Inf Sci 2019; 62(8): 80302.

34.

Wang

Cui

, et al. Cooperative slotted ALOHA with reservation for multi-receiver satellite IoT networks. In: Proceedings of 2018 IEEE/CIC international conference on communications in China (ICCC), Beijing, China, 16–18 August 2018, pp.593–597. New York: IEEE.

35.

Bai

Ren

. Adaptive packet-length assisted random access scheme in LEO satellite network. IEEE Access 2019; 7: 68250–68259.

36.

Wang

Ren

Gao

, et al. A framework of non-orthogonal slotted aloha (NOSA) protocol for TDMA-based random multiple access in IoT-oriented satellite networks. IEEE Access 2018; 6: 77542–77553.

37.

Bai

Ren

. Polarized MIMO slotted ALOHA random access scheme in satellite network. IEEE Access 2017; 5: 26354–26363.

38.

Zhao

Ren

Zhang

. Random pattern multiplexing for random access in IoT-oriented satellite networks. IEEE Syst J. Epub ahead of print 22 July 2019. DOI: 10.1109/JSYST.2019.2927319.

39.

Herrero

ODR

De Gaudenzi

. High efficiency satellite multiple access scheme for machine-to-machine communications. IEEE T Aero Elec Sys 2012; 48(4): 2961–2989.

40.

Zhao

Ren

Zhang

. Multisatellite cooperative random access scheme in low earth orbit satellite networks. IEEE Syst J 2019; 13(3): 2617–2628.

41.

Zhen

Qin

Zhang

, et al. Optimal preamble design in spatial group based random access for satellite-M2M communications. IEEE Wirel Commun Le 2019; 8(3): 953–956.

42.

Anteur

Deslandes

Thomas

, et al. Ultra narrow band technique for low power wide area communications. In: Proceedings of 2015 IEEE global communications conference (GLOBECOM), San Diego, CA, 6–10 December 2015, pp.1–6. New York: IEEE.

43.

Chelle

Crosnier

Dhaou

, et al. Adaptive load control for IoT based on satellite communications. In: Proceedings of 2018 IEEE international conference on communications (ICC), Kansas City, MO, 20–24 May 2018, pp.1–7. New York: IEEE.

44.

Gan

Jiao

, et al. Performance analysis of uplink uncoordinated code-domain NOMA for SINs. In: Proceedings of 2018 10th international conference on wireless communications and signal processing (WCSP), Hangzhou, China, 18–20 October 2018, pp.1–6. New York: IEEE.

45.

Wang

, et al. Rate-adaptive multiple access for uplink grant-free transmission. Wirel Commun Mobile Comput 2018; 2018: 8978207.

46.

Kawamoto

Nishiyama

Fadlullah

, et al. Effective data collection via satellite-routed sensor system (SRSS) to realize global-scaled Internet of Things. IEEE Sens J 2013; 13(10): 3645–3654.

47.

Cluzel

Dervin

Radzik

, et al. Physical layer abstraction for performance evaluation of LEO satellite systems for IOT using time-frequency ALOHA scheme. In: Proceedings of 2018 IEEE global conference on signal and information processing (GlobalSIP), Anaheim, CA, 26–29 November 2018, pp.1076–1080. New York: IEEE.

48.

Doré

Berg

. TURBO-FSK: a 5G NB-IoT evolution for LEO satellite networks. In: Proceedings of 2018 IEEE global conference on signal and information processing (GlobalSIP), Anaheim, CA, 26–29 November 2018, pp.1040–1044. New York: IEEE.

49.

Sun

Wang

Jiao

, et al. Deep learning-based long-term power allocation scheme for NOMA downlink system in S-IoT. IEEE Access 2019; 7: 86288–86296.

50.

Doroshkin

Zadorozhny

Kus

, et al. Experimental study of LoRa modulation immunity to Doppler effect in CubeSat radio communications. IEEE Access 2019; 7: 75721–75731.

51.

Zhang

. Research on LoRa adaptability in the LEO satellites Internet of Things. In: Proceedings of 2019 15th international wireless communications & mobile computing conference (IWCMC), Tangier, Morocco, 24–28 June 2019, pp.131–135. New York: IEEE.

52.

Qian

Liang

. Symmetry chirp spread spectrum modulation used in LEO satellite Internet of Things. IEEE Commun Lett 2018; 22(11): 2230–2233.

53.

Qian

Liang

. The performance of chirp signal used in LEO satellite Internet of Things. IEEE Commun Lett 2019; 23(8): 1319–1322.

54.

Yang

Wang

Zheng

, et al. Folded chirp-rate shift keying modulation for LEO satellite IoT. IEEE Access 2019; 7: 99451–99461.

55.

Perry

Iannucci

Fleming

, et al. Spinal codes. In: Proceedings of the ACM SIGCOMM 2012 conference on applications, technologies, architectures, and protocols for computer communication, Helsinki, 13–17 August 2012, pp.49–60. New York: ACM.

56.

Perry

Balakrishnan

Shah

. Rateless spinal codes. In: Proceedings of the 10th ACM workshop on hot topics in networks, Cambridge, MA, 14–15 November 2011, p.6. New York: ACM.

57.

Cluzel

Franck

Radzik

, et al. 3GPP NB-IoT coverage extension using LEO satellites. In: Proceedings of 2018 IEEE 87th vehicular technology conference (VTC spring), Porto, 3–6 June 2018, pp.1–5. New York: IEEE.

58.

Colavolpe

Foggi

Ricciulli

, et al. Reception of LoRa signals from LEO satellites. IEEE T Aero Elec Sys 2019; 55(6): 3587–3602.

59.

Kodheli

Andrenacci

Maturo

, et al. Resource allocation approach for differential Doppler reduction in NB-IoT over LEO satellite. In: Proceedings of 2018 9th advanced satellite multimedia systems conference and the 15th signal processing for space communications workshop (ASMS/SPSC), Berlin, 10–12 September, pp.1–8. New York: IEEE.

60.

Kodheli

Andrenacci

Maturo

, et al. An uplink UE group-based scheduling technique for 5G mMTC systems over LEO satellite. IEEE Access 2019; 7: 67413–67427.

61.

Wang

Jiao

, et al. ARM: adaptive random-selected multi-beam forming estimation scheme for satellite-based Internet of Things. IEEE Access 2019; 7: 63264–63276.

62.

Liang

Jiao

Feng

, et al. Performance analysis of millimeter-wave hybrid satellite-terrestrial relay networks over rain fading channel. In: Proceedings of 2018 IEEE 88th vehicular technology conference (VTC-Fall), Chicago, IL, 27–30 August 2018, pp.1–5. New York: IEEE.

63.

Zhang

Ding

. Analysis of co-channel interference in low-orbit satellite Internet of Things. In: Proceedings of 2019 15th international wireless communications & mobile computing conference (IWCMC), Tangier, Morocco, 24–28 June 2019, pp.136–139. New York: IEEE.

64.

Sanil

Venkat

PAN

Ahmed

. Design and performance analysis of multiband microstrip antennas for IoT applications via satellite communication. In: Proceedings of 2018 second international conference on green computing and Internet of Things (ICGCIoT), Bangalore, India, 16–18 August 2018, pp.60–63. New York: IEEE.

65.

Takahashi

Kawamoto

Kato

, et al. Adaptive power resource allocation with multi-beam directivity control in high-throughput satellite communication systems. IEEE Wirel Commun Le 2019; 8(4): 1248–1251.

66.

Jin

Ding

. Trafﬁc analysis of LEO satellite Internet of Things. In: Proceedings of 2019 15th international wireless communications & mobile computing conference (IWCMC), Tangier, Morocco, 24–28 June 2019, pp.67–71. New York: IEEE.

67.

Song

Zhang

, et al. The satellite downlink replanning problem: a BP neural network and hybrid algorithm approach for IoT Internet connection. IEEE Access 2018; 6: 39797–39806.

68.

Roy

Nemade

Bhattacharjee

. Symmetry chirp modulation waveform design for LEO satellite IoT communication. IEEE Commun Lett 2019; 23(10): 1836–1839.

69.

Wang

Cui

, et al. Design and implementation of NS3-based simulation system of LEO satellite constellation for IoTs. In: 2018 IEEE 4th international conference on computer and communications (ICCC), Chengdu, China, 7–10 December 2018, pp.806–810. New York: IEEE.

70.

Huang

Guo

Liang

, et al. Green data-collection from GEO-distributed IoT networks through low-earth-orbit satellites. IEEE Trans Green Commun Netw 2019; 3(3): 806–816.

71.

Qian

Liang

. The acquisition method of symmetry chirp signal used in LEO satellite Internet of Things. IEEE Commun Lett 2019; 23: 1572–1575.

72.

Chien

Lai

Hossain

, et al. Heterogeneous space and terrestrial integrated networks for IoT: architecture and challenges. IEEE Network 2019; 33(1): 15–21.

73.

Sun

Kadoch

Gong

, et al. Integrating network function virtualization with SDR and SDN for 4G/5G networks. IEEE Network 2015; 29(3): 54–59.

74.

Liu

Wang

, et al. HGL: a hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks. Int J Distrib Sens N 2017; 13(3): 1550147717692586.

75.

Marcano

NJH

Jacobsen

. On the delay advantages of a network coded transport layer in IoT nanosatellite constellations. In: Proceedings of ICC 2019-2019 IEEE international conference on communications (ICC), Shanghai, China, 20–24 May 2019, pp.1–6. New York: IEEE.

76.

Chelle

Crosnier

Deslandes

, et al. Modelling discontinuous LEO satellite constellations: impact on the machine-to-machine traffic and performance evaluation. In: Proceedings of 2016 8th advanced satellite multimedia systems conference and the 14th signal processing for space communications workshop (ASMS/SPSC), Palma de Mallorca, 5–7 September 2016, pp.1–7. New York: IEEE.

77.

Soua

Palattella

Engel

. IoT application protocols optimisation for future integrated M2M-satellite networks. In: Proceedings of 2018 global information infrastructure and networking symposium (GIIS), Thessaloniki, 23–25 October 2018, pp.1–5. New York: IEEE.

78.

Bacco

Colucci

Gotta

. Application protocols enabling Internet of remote things via random access satellite channels. In: Proceedings of 2017 IEEE international conference on communications (ICC), Paris, 21–25 May 2017, pp.1–6. New York: IEEE.

79.

Wong

DTC

Chen

Peng

, et al. Detection probabilities for satellite VHF data exchange system with decollision algorithm and spot beam. In: Proceedings of 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 5–8 February 2018, pp.326–331. New York: IEEE.

80.

Wong

DTC

Chen

Peng

, et al. Multi-channel pure collective ALOHA MAC protocol with decollision algorithm for satellite uplink. In: Proceedings of 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 5–8 February 2018, pp.251–256. New York: IEEE.

81.

Wang

Liu

, et al. SL-MAC: a joint TDMA MAC protocol for LEO satellites supported Internet of Things. In: Proceedings of 2018 14th international conference on mobile ad-hoc and sensor networks (MSN), Shenyang, China, 6–8 December 2018, pp.31–36. New York: IEEE.

82.

Bacco

Boero

Cassara

, et al. IoT applications and services in space information networks. IEEE Wirel Commun 2019; 26(2): 31–37.

83.

Khuwaja

Chen

Zhao

, et al. A survey of channel modeling for UAV communications. IEEE Commun Surv Tut 2018; 20(4): 2804–2821.

84.

Yang

Yoo

. Optimal UAV path planning: sensing data acquisition over IoT sensor networks using multi-objective bio-inspired algorithms. IEEE Access 2018; 6: 13671–13684.

85.

Motlagh

Bagaa

Taleb

. UAV selection for a UAV-based integrative IoT platform. In: Proceedings of 2016 IEEE global communications conference (GLOBECOM), Washington, DC, 4–8 December 2016, pp.1–6. New York: IEEE.

86.

Mozaffari

Saad

Bennis

, et al. Mobile Internet of Things: can UAVs provide an energy-efficient mobile architecture? In: Proceedings of 2016 IEEE global communications conference (GLOBECOM), Washington, DC, 4–8 December 2016, pp.1–6. New York: IEEE.

87.

Liu

Zhao

, et al. Transceiver design and multihop D2D for UAV IoT coverage in disasters. IEEE Internet Things 2019; 6(2): 1803–1815.

88.

Lyu

Zhang

. Network-connected UAV: 3D system modeling and coverage performance analysis. IEEE Internet Things 2019; 6(4): 7048–7060.

89.

Shi

Cheng

, et al. 3D multi-drone-cell trajectory design for efficient IoT data collection. In: Proceedings of ICC 2019-2019 IEEE international conference on communications (ICC), Shanghai, China, 20–24 May 2019, pp.1–6. New York: IEEE.

90.

Zhu

Mang

, et al. UAV network and IoT in the sky for future smart cities. IEEE Network 2019; 33(2): 96–101.

91.

Huo

Dong

, et al. Distributed and multi-layer UAV networks for next-generation wireless communication and power transfer: a feasibility study. IEEE Internet Things 2019; 6(4): 7103–7115.

92.

Jiang

Yang

, et al. Multimedia data throughput maximization in Internet-of-Things system based on optimization of cache-enabled UAV. IEEE Internet Things 2018; 6(2): 3525–3532.

93.

Kang

Joung

, et al. Optimal time allocation for full-duplex wireless-powered IoT networks with unmanned aerial vehicle. In: Proceedings of ICC 2019-2019 IEEE international conference on communications (ICC), Shanghai, China, 20–24 May 2019, pp.1–6. New York: IEEE.

94.

Abouzaid

Sabir

Errami

, et al. A queuing theoretic framework for flying mesh network assisted IoT environments. In: Proceedings of 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, 15–18 April 2019, pp.882–887. New York: IEEE.

95.

Liu

Wei

Guo

, et al. Performance analysis of UAVs assisted data collection in wireless sensor network. In: Proceedings of 2018 IEEE 87th vehicular technology conference (VTC Spring), Porto, 3–6 June 2018, pp.1–5. New York: IEEE.

96.

Farajzadeh

Ercetin

Yanikomeroglu

. UAV data collection over NOMA backscatter networks: UAV altitude and trajectory optimization. In: Proceedings of ICC 2019-2019 IEEE international conference on communications (ICC), Shanghai, China, 20–24 May 2019, pp.1–7. New York: IEEE.

97.

Al-Turjman

Lemayian

Alturjman

, et al. Enhanced deployment strategy for the 5G drone-BS using artificial intelligence. IEEE Access 2019; 7: 75999–76008.

98.

Gao

Niyato

Wang

, et al. Contract design for time resource assignment and pricing in backscatter-assisted RF-powered networks. IEEE Wirel Commun Le 2020; 9(1): 42–46.

99.

Mozaffari

Saad

Bennis

, et al. Mobile unmanned aerial vehicles (UAVs) for energy-efficient Internet of Things communications. IEEE T Wirel Commun 2017; 16(11): 7574–7589.

100.

Wang

, et al. On constellation rotation of NOMA with SIC receiver. IEEE Commun Lett 2018; 22(3): 514–517.

101.

Wang

Yang

, et al. Energy-efficient resource allocation in UAV based MEC system for IoT devices. In: Proceedings of 2018 IEEE global communications conference (GLOBECOM), Abu Dhabi, UAE, 9–13 December 2018, pp.1–6. New York: IEEE.

102.

Feng

Wang

Chen

, et al. UAV-aided MIMO communications for 5G Internet of Things. IEEE Internet Things 2018; 6(2): 1731–1740.

103.

Motlagh

Bagaa

Taleb

. Energy and delay aware task assignment mechanism for UAV-based IoT platform. IEEE Internet Things 2019; 6(4): 6523–6536.

104.

Zhan

Lai

. Energy minimization in Internet-of-Things system based on rotary-wing UAV. IEEE Wirel Commun Le 2019; 8: 1341–1344.

105.

Ebrahimi

Sharafeddine

, et al. UAV-aided projection-based compressive data gathering in wireless sensor networks. IEEE Internet Things 2018; 6(2): 1893–1905.

106.

Almasoud

Kamal

. Data dissemination in IoT using a cognitive UAV. IEEE T Cogn Commun Netw 2019; 5: 849–862.

107.

Han

Zhao

, et al. Uplink nonorthogonal multiple access technologies toward 5G: a survey. Wirel Commun Mobile Comput 2018; 2018: 6187580.

108.

Liu

Yang

Gui

. DSF-NOMA: UAV-assisted emergency communication technology in a heterogeneous Internet of Things. IEEE Internet Things 2019; 6(3): 5508–5519.

109.

Nomikos

Michailidis

Trakadas

, et al. Flex-NOMA: exploiting buffer-aided relay selection for massive connectivity in the 5G uplink. IEEE Access 2019; 7: 88743–88755

110.

Duan

Wang

Jiang

, et al. Resource allocation for multi-UAV aided IoT NOMA uplink transmission systems. IEEE Internet Things 2019; 6(4): 7025–7037.

111.

Bai

Zhang

, et al. Adaptive coherent/non-coherent spatial modulation aided unmanned aircraft systems. IEEE Wirel Commun 2019; 26(4): 170–177.

112.

, et al. DeepNOMA: a unified framework for NOMA using deep multi-task learning. IEEE T Wirel Commun 2020; 19(4): 2208–2225.

113.

Zhang

, et al. Performance analysis of three-dimensional MIMO antenna arrays for UAV channel. In: Proceedings of 2018 IEEE/CIC international conference on communications in China (ICCC Workshops), Beijing, China, 16–18 August 2018, pp.142–146. New York: IEEE.

114.

Ding

Xiu

. Block turbo coded OFDM scheme and its performances for UAV high-speed data link. In: Proceedings of 2009 international conference on wireless communications signal processing, Nanjing, China, 13–15 November 2009, pp.1–4. New York: IEEE.

115.

Zhang

Hranilovic

. Short-length raptor codes for mobile free-space optical channels. In: Proceedings of 2009 IEEE international conference on communications, Dresden, 14–18 June 2009, pp.1–5. New York: IEEE.

116.

Pang

Liu

, et al. Trust function based spinal codes over the mobile fading channel between UAVs. In: Proceedings of 2018 IEEE global communications conference (GLOBECOM), Abu Dhabi, UAE, 9–13 December 2018, pp.1–7. New York: IEEE.

117.

Wang

Liu

. A novel puncturing scheme for polar codes. IEEE Commun Lett 2014; 18(12): 2081–2084.

118.

Choi

Ahn

Park

, et al. Towards real-time data delivery in oneM2M platform for UAV management system. In: Proceedings of 2019 international conference on electronics, information, and communication (ICEIC), Auckland, New Zealand, 22–25 January 2019, pp.1–3. New York: IEEE.

119.

Zhang

Jiang

Feng

, et al. IoT enabled UAV: network architecture and routing algorithm. IEEE Internet Things 2019; 6(2): 3727–3742.

120.

, et al. Deep learning aided grant-free NOMA toward reliable low-latency access in tactile internet of things. IEEE T Ind Inform 2019; 15(5): 2995–3005.

121.

Kim

Ben-Othman

. A collision-free surveillance system using smart UAVs in multi domain IoT. IEEE Commun Lett 2018; 22(12): 2587–2590.

122.

Zhou

, et al. Performance analysis of UAV relay assisted IoT communication network enhanced with energy harvesting. IEEE Access 2019; 7: 38738–38747.

123.

Abd-Elmagid

Dhillon

. Average peak age-of-information minimization in UAV-assisted IoT networks. IEEE T Veh Technol 2018; 68(2): 2003–2008.

124.

Wang

Jiang

Wei

, et al. Joint UAV hovering altitude and power control for space-air-ground IoT networks. IEEE Internet Things 2018; 6(2): 1741–1753.

125.

Wang

Feng

Chen

, et al. Sum rate maximization for mobile UAV-aided Internet of Things communications system. In: Proceedings of 2018 IEEE 88th vehicular technology conference (VTC-Fall), Chicago, IL, 27–30 August 2018, pp.1–5. New York: IEEE.

126.

Chakareski

. UAV-IoT for next generation virtual reality. IEEE T Image Process 2019; 28(12): 5977–5990.

127.

Wang

Shami

. UAV-enabled spatial data sampling in large-scale IoT systems using denoising autoencoder neural network. IEEE Internet Things 2018; 6(2): 1856–1865.

128.

Qin

Boyle

Yeatman

. A novel protocol for data links between wireless sensors and UAV based sink nodes. In: Proceedings of 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 5–8 February 2018, pp.371–376. New York: IEEE.

129.

Santos

Fereidoony

Hedayati

, et al. High efficiency bandwidth electrically small antennas for compact wireless communication systems. In: Proceedings of 2019 IEEE MTTS international microwave symposium (IMS), Boston, MA, 2–7 June 2019, pp.83–86. New York: IEEE.

130.

Handouf

Sabir

Sadik

. Energy-throughput tradeoffs in ubiquitous flying radio access network for IoT. In: Proceedings of 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 5–8 February 2018, pp.320–325. New York: IEEE.

131.

Jingcheng

Xinru

Zongkai

, et al. UAV detection and identification in the Internet of Things. In: Proceedings of 2019 15th international wireless communications & mobile computing conference (IWCMC), Tangier, Morocco, 24–28 June 2019, pp.1499–1503. New York: IEEE.

132.

Gaur

Budakoti

Lung

, et al. IoT-eq1 UAV communications with seamless vertical handover. In: Proceedings of 2017 IEEE conference on dependable and secure computing, Taipei, Taiwan, 7–10 August 2017, pp.459–465. New York: IEEE.

133.

Yuan

Jin

Sun

, et al. Ultra-reliable IoT communications with UAVs: a swarm use case. IEEE Commun Mag 2018; 56(12): 90–96.

134.

Yan

Peng

Cao

. A game theory approach for joint access selection and resource allocation in UAV assisted IoT communication networks. IEEE Internet Things 2018; 6(2): 1663–1674.