Emerging Public Safety Wireless Communication Systems (Artech House Mobile Communications)
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We distinguish two main categories of communication challenges: 1 issues relating to the establishing and operating of the underlying communication network [ ], including ensuring certain topological properties e.
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Communication is an important challenge for UAV swarms; we refer to the surveys [ 30 , 34 ] on UAV-based communication civil use cases and [ 33 ] on UAV communication networks. Communication is identified as one of the integral basic functionalities of a swarm [ 12 ]. Because of their high mobility and capability for rapid deployment [ 34 ], UAVs are often proposed as enabler for aerial mobile communication structures.
Communication networks have certain topological requirements such as e. The deployment and positioning of UAVs to form a cell network [ 55 ] and to dynamically adapt its topology to account for link loss and to avoid outages and connectivity problems [ ] have been addressed in the literature.
Aspects related to operating in the physical environment such as optimizing hovering locations [ 61 ] and handling data processing requirements i. A good survey of important issues in UAV communication networks is offered by [ 33 ].
EMERGING PUBLIC SAFETY WIRELESS COMMUNICATION SYSTEMS By David R. Smith | eBay
A third aspect, previously unmentioned, is the fact that UAVs may have to operate in the absence of—or under the assumption of unreliable or compromised—communication infrastructure. This can be due to the location indoor [ 99 ] or in so-called urban canyons between high rise buildings [ 50 ] disasters such as e.
A significant amount of research [ 7 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , ] is now directed at self-organizing formation control and self-assembly methods especially in the absence of global communication. Above in e. Here we elaborate on this in more detail and consider another distinction within the class of rotary UAVs cf.
Table 3 for a brief overview over these 3. There are already a large number of diverse devices and different device types commercially available and the difference in price can be substantial between them. The differences between fixed wing and multicopter UAVs with respect to e. The—sometime significant—differences in cost makes determining the optimal device and device-type for applications a challenge.
Table 4 illustrates the variations in e.
Next to flight time, maximum payload might be the most crucial aspect when choosing a device type, as e. Applications range from localization of avalanche victims using the signals from specialized wireless enabled devices to using UAV-based ground penetrating radar can be used to scan large areas [ 85 ] to UAVs that carry the hardware required to enable embedded video processing and data acquisition [ 75 ].
Since flight time may depend on the carried fuel or batteries there is a possible trade-off between available payload and maximum air-time. The examples compiled into Table 4 refer specifically to gas sensing operations. These examples include a solar powered UAV [ ] most drones will require recharging or refueling between missions a UAV-based CO2 concentrations sensing system [ ] with a payload of 3.
However, these are examples for relatively low requirements: ref [ , ] use a fixed wing UAV with a 3. In the literature, many of the approaches referenced above perform the respective task using only a single UAV and report on the challenge of off-setting the impact caused by the dynamicity here: wind of the environment. This further motivates the use of swarms to cover entire areas at the same time, enabling the operator to treat the environment as static.
Above we already mentioned radiation detection [ ], ground penetrating radar [ 85 ] ultraviolet and infrared spectrometer [ ] to measure volcanic gases. Another approaches in the literature report using Global Navigation Satellite System Reflectometry GNSS-R [ 79 , , ], sonar sensors or laser range finders [ ], optical and hyper-spectral cameras or Synthetic-Aperture Radar [ 79 ]. Often, a variety of different sensors is required to cover all wave lengths of interest.
In [ ] the author identifies light sensors with sensitivity to various wavelengths in the field of precision agriculture:. Infrared spectrum imaging [ ], and. The more specialized a sensing array is, the more precise applications can be driven by the data generated by it.
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However, specialized equipment normally comes at a high cost and with limited versatility. Since devices such as sensing arrays and fixed wing UAVs with large payload capacity are expensive and require significant maintenance [ 79 ], sharing resource between practitioners, agencies or companies may offer a alternative to otherwise prohibitively large investments by reducing overall cost and increase cost-performance ratio.
Given on-board processing power, UAVs are increasingly able to contribute to problem solving and data processing tasks. This development also continuously makes UAVs potentially more autonomous as algorithms required for e.
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In addition, this reduces the requirements placed in the communication infrastructure since less data has to be communicated to a centralized command. When it comes to the available software, the challenge is either to identify suitable off-the-shelf software or to invest in designing and implementing proprietary solutions, which may require resources and time.
However, the literature is teeming with novel approaches, algorithms and software implementations addressing a vast variety of problems. To name just a few: a geometric correction algorithm to reduce image distortion for UAV-based imagery in disaster scenarios [ 74 ], an algorithm to dynamically adapt the communication network topology to account for link loss and to avoid outages and connectivity problems [ ], an image processing algorithm to process imagery provided from sensors mounted on aerial devices [ ], an approach for optimum hovering locations for multiple drones [ 61 ], an off-line path planning algorithm for heterogeneous UAV swarm-based surveillance scenarios to ensure persistent surveillance [ 87 ], and a self-adapting multi-object evolution algorithm facilitating path planning for UAVs [ ].
Operating UAVs poses a number of challenges as well, such as testing and simulating swarms of UAVs and training operators, which normally require expensive simulators [ 41 ] because the conditions under which UAVs operate are different from those of conventional piloted aircraft [ 93 ]. However, the environment in which UAVs operate itself poses reason for concern, independent of the environment; operating a large number of devices in the same airspace requires a commonly agreed upon traffic management approach. In addition to these practical operational issues, there are of course other concerns regarding the operation of these devices as well as regarding the use of these devices as sensing devices i.
Environmental challenges involve weather conditions rain, wind gusts, humidity, temperature , terrain characteristics presence of high canopy in the vicinity of the area of interest etc. Experiments have shown [ ] that the power consumption may depend significantly more on environmental conditions such as side winds than on either the payload or the altitude. High and changing winds are likely to be an issue when operating in coastal zones [ 41 ]. Operating multiple UAVs in unknown scenarios requires fast and adaptive path planning to avoid collisions and to ensure optimal travel times for the devices.
In [ ] a self-adapting multi-object evolution algorithm to facilitate UAV path planning is proposed and simultaneous localization and mapping based real-time tracking can be used when GPS signals can not be used reliably or are unavailable [ ]. Generally speaking, the choice for device and hardware will be influenced by the environment the device is intended to operate in. The more specialized an application is, the more specialized the operational requirements regarding the environment may get, and it is important to clearly define the specifications of environmental conditions beforehand.
Currently available off-the-shelf multi-rotor UAVs can remain airborne for approximately 15—20 min before needing to recharge [ ], motivating the use of optimization techniques to optimize flight paths. In the context of managing the airspace when large and very large numbers of UAVs are operating in the same theatre, ref [ 80 ] investigates platooning for UAV swarms and proposes an approach that can handle massive fleets and handles device malfunctioning or intrusion well.
In [ 87 ] an off-line path planning algorithm for UAV swarms tasked to provide continuous and uninterrupted surveillance coverage over an area is proposed. Heuristics to solve such problems well known to be of high complexity are needed to increase the efficiency of dispatch and scheduling for UAV swarms. Finally, if UAVs are to be allowed to operate within urban areas, a city-wide traffic management system has to be created to handle the increasing traffic occupying the same airspace. Traffic lights for cars are unmanned and low-cost.
Drones, albeit their almost pervasive presence it is increasingly difficult to find someone who has not seen a drone operating in the wild , are still somewhat of a revolutionary technology in that their use and availability is spreading considerably faster than awareness about potential concerns or legislative frameworks to address these concerns [ 78 ]. For the near future different countries will probably continue to impose different regulations regarding the use of UAVs [ , ], and those regulations may be subject to frequent change and amendment. The use of UAVs in public airspace touches on a number of technical and societal concerns and challenges [ 62 ].
Currently, there are not many certainties with regard to the legal regulations for drones since—as it is frequently the case with new technologies—their rapid adoption outpaces legal, policy, and social ability to cope with issues regarding privacy and interference with well-established commercial air space [ 63 ].
As mentioned in the next section, cybersecurity, privacy, and public safety concerns need to be addressed [ 62 ]. It should be noted though that for applications in the context of emergency or disaster scenarios different regulations may apply to the usage of UAVs [ ] and that currently special authorizations are usually granted to flying devices to help first responders.
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A survey of the main security, privacy, and safety aspects associated with the use of civilian drones in the national airspace is provided by [ 47 ]. With the market for civilian UAVS expanding and the number of devices in operation rapidly increasing poses threats to people, property and privacy rights [ 47 ].
In [ 78 ] the authors analyse the risk drones can pose for privacy and data protection and [ 62 ] surveys aspects of cybersecurity, privacy, and public safety in the context of drones in future smart cities.
In [ 84 ] the use of UAVs to augment the sensing capability of a smart city is proposed but the authors suggest a data broker to manage the privacy and security issues related to sharing data across subscribers. In addition, as discussed above, the communication of the data has to be protected through secure communication protocols e. A survey over the main security, privacy, and safety aspects associated with the use of civilian drones in the national airspace is offered by [ 47 ], but as mentioned above, the legal situation is currently changing quickly and for the foreseeable future the literature reviews are expected to be trailing the actual state of affairs.
Due to an increase in the availability and diversity of affordable hardware, and the performance of virtually all relevant sub-systems, and due to the massive decrease in cost and difficulty to operate, UAVs are rapidly becoming a common part of a variety of applications. In this article we have briefly discussed our view on three application areas which already have and will increasingly continue to do so benefitted from the use of UAVs.
Combining all of the above, what stands between UAVs and the wide-spread use of them is not the lack of application or availability of devices but a number of challenges, identified in Section 4. As we continue to—one by one—overcome these or continuously improve on our solutions to them , UAVs will probably become a more common sight in every-day life applications as well as constitute an increasingly important role in highly specialized infrastructures.
In most cases, the challenges identified have to be addressed not only for individual devices but also for swarms of devices. We furthermore believe that the usefulness of such swarms will dramatically increase with growing autonomy of both the individual swarm member, as well as the entire collective.
Our work in the last 5 years has focused on the design of novel and often nature-inspired [ , , , ] approaches and algorithms to optimize the collective performance of swarms of UAVs cf. Future work will aim to validate these theoretical results using swarms of at least 3 of the drones shown in Figure 4.
As such, this article is meant to serve as a brief overview over the state of the art, but also to offer an outlook over the things that are yet to come and the challenges that we will face along the way. This list is, of course, not comprehensive. Finally, we recommend to keep an eye on the web page of this journal MDPI Drones where new publications are continuously made available for download.
Funding This research received no external funding. Figure 1. Figure 2. For comparison of size, the left panel shows this drone left, top and left, bottom the NEO drone used for full field testing under adverse conditions, cf. Figure 3. Inspired by [ ]. Figure 4. For upcoming projects we will operate swarms of up to sic of these devices. Table 1. For details in areas other than public safety and civil security , we refer to [ 10 ].