Smart cities, first proposed in the early 1990s, continue to face new opportunities, such as the prevalence of UAVs, and new challenges, such as the need to provide hard real-time communications with UAVs with delays of sub-10-milliseconds and little jitter or even no jitter. Although existing highly reliable, low-latency wireless networks are capable of meeting these challenges, there is a general agreement that classical transport protocols cannot effectively meet such stringent requirements. This paper investigates UAV-borne WebRTC-based IoT for its ability to provide low jitter in an outdoor, highly reliable network environment. The results of field experiments conducted under various network conditions show that WebRTC was able to ensure little fluctuation of end-to-end delays. Moreover, in the case of error-free transmissions, fluctuations were so small that jitter, expressed in milliseconds, was close to zero. The significant advantage of the WebRTC Data Channel over the classic web logical channel, i.e. WebSocket, in terms of jitter was also demonstrated.

The currently observed development of time-sensitive applications also affects wireless communication with the IoT carried by UAVs. Although research on wireless low-latency networks has matured, there are still issues to solve at the transport layer. Since there is a general agreement that classical transport solutions are not able to achieve end-to-end delays in the single-digit millisecond range, in this paper, the use of WebRTC is proposed as a potential solution to this problem. This article examines UAV-borne WebRTC-based IoT in an outdoor environment. The results of field experiments conducted under various network conditions show that, in highly reliable networks, UAV and WebRTC-based IoT achieved stable end-to-end delays well below 10 ms during error-free air-to-ground transmissions, and below 10 ms in the immediate vicinity of the retransmitted packet. The significant advantage of the WebRTC data channel over the classic WebSocket is also demonstrated.

The proliferation of new services, either interpersonal or machine-oriented, has generated new demands concerning the flexibility and efficiency of transmission. The ubiquity of multimedia communication in the current internet is seamlessly and successfully supported by the WebRTC concept. This paper reports on the study of the usage of a solution employing a proxy transmission unit for air-to-ground delivery of video streaming multiplexed with sensor data in the UAV-IoT system when using the WebRTC protocol stack. The comparative experiments were carried out for two cases employing the 802.11ac network with WebRTC: the first scenario (S1) without an external transceiver and the second scenario (S2) with an external transceiver working as a proxy of the ground receiver. The presented results compare the transmission conditions without (scenario S1) and with (scenario S2) the external transceiver in terms of the RSSI, the available data rate, and total throughput of transmission of multimedia data (video stream from the UAV camera and bursty data coming from employed sensors. The usefulness of the external transceiver used in a wide range of transmission conditions is clearly proven.

This study proposes a conference bridge that cooperates with both the WebRTC layered simulcast and the Software Defined Networking architecture in order to improve WebRTC video streaming. The proposed bridge divides the functionality of a classic Selective Forwarding Unit into two parts. The selection of layers is performed by the SDN controller and the forwarding of layered video is still accomplished by the bridge. The bridge and the SDN controller exchange data on the state of the transmitted video stream and the state of the network. The proposed solution was implemented in the Jitsi Videobridge and tested in the GEANT testbed network. The results showed that our solution significantly reduces problems related to available throughput overshooting, which is typical for layered simulcast.

Nowadays, we observe a great interest in air pollution, including exhaust fumes. This interest is manifested in both the development of technologies enabling the limiting of the emission of harmful gases and the development of measures to detect excessive emissions. The latter includes IoT systems, the spread of which has become possible thanks to the use of low-cost sensors. This paper presents the development and field testing of a prototype pollution monitoring system, allowing for both online and off-line analyses of environmental parameters. The system was built on a UAV and WebRTC-based platform, which was the subject of our previous paper. The platform was retrofitted with a set of low-cost environmental sensors, including a gas sensor able to measure the concentration of exhaust fumes. Data coming from sensors, video metadata captured from 4K camera, and spatiotemporal metadata are put in one situational context, which is transmitted to the ground. Data and metadata are received by the ground station, processed (if needed), and visualized on a dashboard retrieving situational context. Field studies carried out in a parking lot show that our system provides the monitoring operator with sufficient situational awareness to easily detect exhaust emissions online, and delivers enough information to enable easy detection during offline analyses as well.

Energy efficiency is important in the case of devices which has strictly limited energy resources, such as battery powered sensors. Cluster wireless sensor networks enable the sensors within the cluster to select the most appropriate node at a given moment for long-distance transmission (Cluster Head) and use it to communicate with the base station. This paper proposes the use of the Traffic Flow Description option for the IP protocol to improve the selection of Cluster Heads. The simulation results showed that the benefits of using TFD-assisted selection of Cluster Heads outweigh the costs of having to transmit additional signaling information using the TFD option.

Smart-city management systems use information about the environment, including the current values of weather factors. The specificity of the urban sites requires a high density of weather measurement points, which forces the use of low-cost sensors. A typical problem of devices using low-cost sensors is the lack of legalization of the sensors and the resulting inaccuracy and uncertainty of measurement, which one can attempt to solve by additional sensor calibration. In this paper, we propose a different approach to this problem, i.e., the two-stage selection of sensors, carried out on the basis of both the literature (pre-selection) and experiments (actual selection). We formulated the criteria of the sensor selection for the needs of the sources of weather information: the major one, which is the fast response time of a sensor in a cyber-physical subsystem and two minor ones, which are based on the intrinsic information quality dimensions related to measurement information. These criteria were tested by using a set of twelve weather sensors from different manufacturers. Results show that the two-stage sensor selection allows us to choose the least energy consuming (due to the major criterion) and the most accurate (due to the minor criteria) set of weather sensors, and is able to replace some methods of sensor selection reported in the literature. The proposed method is, however, more versatile and can be used to select any sensors with a response time comparable to electric ones, and for the application of low-cost sensors that are not related to weather stations.

The classic approach to the Web of Things in embedded systems consists of the use of embedded web servers that transmit data from sensors to external entities (web clients). In this paper we present a browser-driven sensor service, in which an embedded web browser shares data with external entities: collects them, pre-processes them and sends them to their recipients. The paper shows the framework software of such a system and a simple, prototype implementation with the use of this framework.

Thanks to IoT, Internet access, and low-cost sensors, it has become possible to increase the number of weather measuring points; hence, the density of the deployment of sources that provide weather data for the needs of large recipients, for example, weather web services or smart city management systems, has also increased. This paper presents a flying weather station that carries out measurements of two weather factors that are typically included in weather stations (ambient temperature and relative humidity), an often included weather factor (atmospheric pressure), and a rarely included one (ultraviolet index). In our solution, the measurements are supplemented with a visual observation of present weather phenomena. The flying weather station is built on a UAV and WebRTC-based universal platform proposed in our previous paper. The complete, fully operational flying weather station was evaluated in field studies. Experiments were conducted during a 6-month period on days having noticeably different weather conditions. Results show that weather data coming from the flying weather station were equal (with a good approximation) to weather data obtained from the reference weather station. When compared to the weather stations described in the literature (both stationary weather stations and mobile ones), the proposed solution achieved better accuracy than the other weather stations based on low-cost sensors.

Reports on the history, development, and applications supported by NEC's V20 microprocessor product line.

The WebRTC technology was intended mainly for audio/video transmissions with, if necessary, associated data. The use of full-stack WebRTC transmissions, which merges media and non-media streams and flows, enables natural integration of different types of data in one session, common cryptographical protection of the session, and multi-platform applications development. As was shown in our previous papers, these features are essential for building efficient WebRTC-based IoT brokers. This paper is focused on the widely understood adaptability of dual-stack WebRTC-based IoT transmissions. During experiments, conducted in a real environment, the UAV-based IoT system, composed of an air station and a ground station, was transmitted video from a 4K camera and data from sensors through IEEE 802.11ac WLAN. Results show that full-stack WebRTC communication assures good adaptability to network circumstances. The full-stack congestion control is able to do a good job of protecting high priority data coming from sensors, even at the cost of the QoS parameters of the associated video transmission.

Nowadays, we are observing a rapid development of UAV-based monitoring systems, which are faced with more and more new tasks, such as high temporal resolution and high spatial resolution of measurements, or Artificial Intelligence on board. This paper presents the open universal framework intended for fast prototyping or building a short series of specialized flying monitoring systems able to work in urban and industrial areas. The proposed framework combines mobility of UAV with IoT measurements and full-stack WebRTC communications. WebRTC offers simultaneous transmission of both a real-time video stream and the flow of data coming from sensors, and ensures a kind of protection of data flow, which leads to preserving its near-real-time character and enables contextual communication. Addition of the AI accelerator hardware makes this system AI-ready, i.e., the IoT communication hub, which is the air component of our system, is able to perform tasks of AI-supported computing. The exemplary prototype of this system was evaluated in terms of the ability to work with fast-response sensors, the ability to work with high temporal and high spatial resolutions, video information in poor visibility conditions and AI-readiness. Results show that prototypes based on the proposed framework are able to meet the challenges of monitoring systems in smart cities and industrial areas.

In many urban and industrial areas, there exist wireless network infrastructures - usually complex, covering large public buildings (often with adjacent parking lots and green areas). In the case of emergency situations, such infrastructure could be used as a production network (i.e. a network dedicated to the transmission of user data) for creating ad-hoc flying monitoring systems, composed of one or more air stations (drones equipped with specialized sensors and detectors, as well as a high resolution camera), and corresponding ground station(s). This paper proves that the existing network architecture is able to play a significant role in the casual assurance of suitable air-to-ground transmission of monitoring data. Transmissions are carried out between two WebRTC applications of IoT brokers, placed on the air station, and on the ground one. The stations are connected through the IEEE 802.11ac (Wi-Fi) production network. During experiments, two different wireless local area networks were used as a production network. The first one was dedicated to transmissions coming from the flying monitoring system. The second one was the private network of the AGH University of Science and Technology, available for the academic community. Results of experiments show that although a dedicated network better fits the needs of the flying monitoring system, a well-dimensioned public network that has good coverage of the monitored area is able to effectively replace it in an emergency.

The state of network paths is of great importance for the quality and reliability of the transmission of information coming from the IoT devices. Changing network conditions cause the state of a single path to be highly unpredictable, especially when large-scale IoT networks, connecting thousands of specialized devices, are in use. The solution to this problem is to perform trials in a real environment, with the use of real hardware. The rationalization of the design, the implementation and the exploitation of the process will be the main benefit of this approach. In this demonstration, a testbed infrastructure for the estimation of the state of path parameters in wired and wireless networks is presented. The estimation is based mainly on active measurements performed with the use of the Probing Packet Trains method. The key elements of the testbed infrastructure are locally and remotely controlled energy-efficient single-board computers, working under the control of the Linux operating system, which are equipped with software able to track (in manual or automatic mode) changing conditions of a given transmission path.

A simple signalling server, written in JavaScript and running in node.js run-time environment, was presented in the previous paper of the Authors [3]. In this paper, principles of building of WebRTC signalling client are presented, as well as exemplary communication between the client and the server, captured with the use of WireShark software tool during experimental WebRTC sessions. The paper includes also an example of application of Internet chat, that uses the simple signalling server [3]. The chat was build in two versions, which uses the same user interface and their signalling systems were build according to the same methodology, but differs in method of transmission of chat messages. The first version transmits user data (text messages of the chat) via signalling server, with the use of signalling channel. This version uses WebSocket interface for transmission of chat messages. In the case of the second version, user data are transmitted directly between web browsers, using WebRTC's channel dedicated for transmission of nonmedia data. This version transmits chat messages with the use of WebRTC's RTCDataChannel interface.

The DCCP complements the UDP with three TCP-friendly congestion control modes. The most important are the CCID 2 mode, which uses the TCP’s congestion control mechanism, and the CCID 3 mode, which implements the TFRC congestion control. The CCID 3 is intended for general multimedia transmission, although the authors of the DCCP specification notes that it may caused problems in the case of high-bandwidth video transmissions. This paper presents a prototype implementation of the modified TFRC congestion control, designed for multimedia transmission, that uses the RTP linear throughput equation (instead of the TCP one, originally used by the TFRC). This solution was introduced by the Authors in their previous work. The prototype implementation in the Linux kernel includes both a new congestion control module and updates to the DCCP kernel API. The proposed solution causes the DCCP to be not fully TCP-friendly, but still remains TCP-tolerant and does not cause unnecessary degradation of competing TCP flows. As a result, this method of congestion control can be used wherever it is necessity to use a TCP-tolerant protocol and when the rate of the media stream cannot be limited in a way typical for the TCP.

Multimedia communication in the Internet shows evolution from SIP-based to WebRTC architecture which is also related to migration of QoS solutions from RSVP-based to DiffServ-based, supported also in IEEE 802.11. In order to figure out the efficiency of WEbRTC implementation in mobile network, the QoS parameters were used: throughput of media streams, error rate, delay and jitter. During experiments, 4K video streams were transmitted with the use of WebRTC technology via an IEEE 802.11ac network. The results of the experiments were compared with the ones obtained for non-responsive (not congestion controlled) UDP streams. WebRTC transmissions were live transmissions from a web camera, and UDP transmissions were emulated with the use of the iPerf tool. Results of the comparative analysis show that if the total throughput of simultaneously transmitted video streams exceeds the available throughput of the wireless link, the WebRTC is able to scale down the transmitted stream to preserve the real-time character of the stream and significantly reduce the error rate. As a result, the observed bit error rate achieved by the WebRTC was about three orders of magnitude lower than the bit error rate of non-responsive UDP streams. However, this improvement of the quality of service of the WebRTC media streams is at a cost of quality of experience (lower frame rate).

Streszczenie: Jednym z problemów występujących w dużych systemach Internetu Rzeczy, złożonych z tysięcy urządzeń IoT, są przeciążenia sieci w pobliżu urządzeń pełniących rolę hubów komunikacyjnych (brokerów danych, chmur obliczeniowych)

WebRTC uses two simulcast schemes: the older, based on the replication of streams encoded to different qualities of the original signal (typically: video), and the newer, fully introduced this year to one of the more popular browsers, based on layered encoding. While the first solution has a well-established position in the WebRTC technology, the layered simulcast still remains in an early stage of development. The goal of this paper was to compare behavior of the two WebRTC simulcast schemes in terms of the key feature of the simulcast, i.e. its adaptability to network conditions. To achieve this goal, we wrote the WebRTC application (a simple videomonitoring software) that act as simulcast sender. The adaptability of the simulcast schemes was investigated for the case of video traffic transmitted in a heterogeneous test network (wired and wireless). As adaptability indicators, such as the throughput and packet error rate were used. Results of the simulcasting of 4K video show that in the case of infrastructural limitations only there are no significant differences between the adaptability of the stream replication simulcast and the layered simulcast. Adaptability for dynamic limitations (introduced by background traffic) was better in the case of the layered simulcast.

In this paper we propose a flying IoT system designed to monitor parking lot. The system is composed of a flying mobile station and a terrestrial station. The mobile station consists of visual monitoring camera associated with air quality monitoring sensors, mounted on a Turnigy SK450 quad copter controlled with Pixhawk autopilot hardware. Data gathered from the monitoring system are transmitted to an IoT broker running on a Raspberry Pi 3+ microcontroller, also mounted on a drone, that aggregates data into a common flow and retransmits them to the terrestrial station. The transmission system was build according to WebRTC architecture. In order to assure separation of the production network (for transmission of application data) and the management network (for management and control purposes), two different transmission channels are used: one for the WebRTC transmission and the other for controlling the drone. The paper includes a description of the system and the results of field trials carried out on the parking lot of the AGH University of technology.