Low Cost Efficient Deliverying Video Surveillance Service to Moving Guard for Smart Home
Abstract
:1. Introduction
- Coverage deficiencies in LTE (especially in emergent countries).
- Range coverage and radio channel interferences in WiFi [16], prolonged access to the channel (long periods of contention) of WiFi terminal.
- Bad planificafication of dense WiFi networks.
- Inefficient behavior of transport protocols of Internet in WiFi and LTE.
- Inefficient behavior of video servers (they usually do not detect efficiently if the wireless terminal effectively will receive video frames).
- Inefficient behavior of video clients that cannot process the deadlines associated to the reception and visualization of video frames.
2. Background and Related Work
- Intelligent image processing. In [20,21] video surveillance systems are focused mainly on the detection of activities such as fights, assaults or excesses that may occur within an environment. The system contains external sensors that allow capturing information from the environment using cameras, microphones and motion detectors. When a security alert occurs, a centralized broker, for example will call the police, or blocks doors.... It does not support smart phones so it does not mitigate adverse effects of service interruptions. In [22] authors proposed a prototype whose intrusion alerts allows the detection of movement and location of objects in a determined area by processing the video coming from different cameras. Our system detects movement of the thief but we are not interested in his exact localization. In [23] a system capable of processing images was presented to obtain relative positions of objects supporting environmental adversities and combining it with IoT to improve the acquisition of information. Kavi and Kulathumani [24] are able to detect orientation of objects. We are not interested in the segmentation of objects or the affectation of environmental conditions, but in a real-time reception of images that facilitates a first vision on the part of the guard who observes the intrusion in the Smart Home. In [25] is treated the problem of intelligent recognition of objects in nigthtime using visible light cameras. They proposed an interesting image recognition system for near infrared cameras that can operate in daytime and nighttime. Whichever type of camera is used the problem of video service disruptions still exists. That is the reason why we only considered visible light cameras and full delivery of video. In [26] authors proposed a solution through the design and development of a video surveillance system, which uses semantic reasoning and ontologies. This system is able to work with small and cheap cameras, reduce required bandwidth and optimize the processing power. Our system also is able to use ontologies over an embedded processor.
- Smart codification and compression of objects. The main idea is to reduce the needed communication bandwidth between server (that processes a high amount of videos) and client, sending only relevant information to the client (user) [27]. Due to the low economic cost we imposed on our system, we consider a small number of video cameras, so we did not need to implement a system of this type. In [28] was presented a system to extract metadata of important objects to avoid the impossible to solve problem of monitoring large number of cameras. We did not treat with this problem due to the reduced number of cameras we considered.
- High number of video streams synchronization. Pereira et al. [29] proposed a window strategy together with a correntropy function to synchronize video streams of line applications that require a low computational power. We did not focus in synchronization of video streams; on the contrary, we focused on the adequated reception of frames of a video stream. However, the application of that technique to the synchronization of several video streams in our system requires a deep study to solve the mitigation of adverse effects of several video streams at the same time.
- Usage of low economic cost and embedded computers for hosting the video streaming server, which connects to a video camera through Universal Serial Bus (USB), and uses a mobile network for communicating the video streaming [30]. In [31] is used a built-in system based on the ARM9 processor (freely available), a 3G mobile network card, a USB camera that captures video using the H.264 standard and sends it to a video server. The user accesses it with an Android smart phone. The construction of an embedded system as a server and video processor is cheap because there are low economic cost freely distributed hardware and software to do it. However, in [31] they do not focus, as we did in real time streaming video. We used a general-purpose embedded computer (Raspberry Pi) for the good performance it offers and its low economic cost.
- Based on the regulation of the transmission rate. They estimate the highest possible rate of video transmission [33].
- Based on adaptive video buffer. They seek the relationship between the occupation of the buffer and the selected video bit rate and the available bandwidth [34]. Latency would increase for real-time video. Our system must minimize the latency so this scheme would be limited. In [35] the starting latency, the reproduction fluidity, the average reproduction quality, the smoothness of the reproduction and the cost of real time video are improved. In [36] was managed the bandwidth tolerance of the QoS degradation.
- Based on prediction of QoS parameters, which optimizes the allocation of resources and related variables in a control model such as the Markov Chains [37].
3. System Architecture and Operation
- The sensors are continuously sending sensed data (with a certain sampling period) to the alarm processor.
- The video camera starts operating once the Alarm processor fuses the sensor data and determines that an intrrusion has occurred. At that moment, the video will be stored in the video server memory (a file that works as a buffer) and simultaneously an instant message (Telegram application) and/or an e-mail are sent to the Client (guard). The file containing the recorded video can be used before the judge.
- The Client will receive a Telegram instant message and/or an e-mail. Whenever he wants, he can start a video streaming session on the Video Server, clicking a link (containing the Internet Protocol Address (@IP) of that server) in the instant message or in the e-mail text. If the Client receives the video while the server is producing it, the video will be delivered in real time. However, the Client could start an on demand video Streaming session whenever he wants, once the Video Server finished recording that video.
- Video service disruption: The Video Server registers the last set of frames sent to the Client continuously and has not been consumed by the Client. When the Client experiments a service disruption, the Video Server will continue sending from the last set of video frames previously sent. There is no need to restart the video session because the Video Server also maintains the original session opened during at most 10 min (the time that lasts ussually an intrusion). In that simple way, nested disruptions can be easily managed.
3.1. Software Design Pattern Specification
- Model (Server): it is located in the Video Server (video buffer that stored temporally the pending video still not sent to the Client and other stored videos). The Model consists in an Observer software pattern in charge to control de videos. An external Observer software design pattern abstracts the data model corresponding to the sensed data. That Observer is constantly testing the sensor data communicated to the Controller.
- View (Client or user): It consists of several views: the video display, the e-mail and Telegram messages interfaces and the links that allow starting the video streaming. Other simple view is the warning of disruptions.
- Controller: It is the most important software pattern. It is in charge to do all the control of video service disruptions, the control of the wireless channel to advertise possible disruptions and the cooperation with the sensor alarms. It consists in the cooperation among different standard software patterns that we present next. We distinguished internal software patterns to the Controller (inside the MVC) and other external software patterns to the Controller (outside the main MVC pattern).
3.2. Low Cost Hardware Implementation
- Ligth: Dimensions 65 × 11 × 13 mm, series Photoresistor-BH1750, measurements 1-65535 LX, sampling 2 s.
- Digital temperature (Ds28b20): Each device has a unique 64-bit serial code stored in its Read Only Memory (ROM), multipoint capability that simplifies the design of temperature detection applications, can be powered from the data line. The power supply range is 3.0 V to 5.5 V. Measures temperatures from −55 °C to +125 °C (−67 °F to +257 °F) ± 0.5 °C to the nearest −10 °C at +85 °C. The resolution of the thermometer is selectable by the user from 9 to 12 b and converts the temperature into 12 b codes in 750 ms (maximum).
- Infrared Barrier: 10.2 × 5.8 × 7 mm phototransistor, peak operating distance: 2.5 mm, operating range for collector current variation greater than 20%: 0.2 mm to 15 mm, typical output current under test: 1 mA, ambient light blocking filter, emitter wavelength: 950 nm.
3.3. Software Implementation Based on Agents, Web Services and Free Platforms
4. High Level Abstraction Model of Performance
4.1. Formal Model for Video Streaming without Service Disruptions Mitigation
4.2. Formal Model for Video Streaming Mitigating Service Disruptions
5. Experimental Results: Discussion
5.1. Discussion about QoE Performance
- Q1.
- How would you rate the design of the user interface of the application?
- Q2.
- How would you rate the usability of the application?
- Q3.
- How would you rate the interactivity of the application?
- Q4.
- Did you have problems accessing the application?
- Q5.
- Did the application have an execution error?
- Q6.
- Did the application reconnect the video without reloading the page?
- Q7.
- Did the application send e-mail and Telegram notification messages?
5.2. Discussion about QoS Performance
- WTerm 1: Smart phone Celular Sony Xperia Z3 Compact; 127 × 64.9 × 8.6 mm; 1280 × 720 pixels (4.6′′); Android 5.0; processor of 4 cores (2.5 GHz), Graphic Processor Unit Adreno; Battery capacity 2.600 mAh; 2 GB RAM; LTE and WiFi (IEEE 802.11 g/n/ac). This terminal was used in the blki (i = 1..3).
- WTerm 2: Laptop Mac Book Pro 15′′; 1920 × 1200 pixels (15.4′′); Mac OS High Sierra; 2.2 GHz quad-core Intel Core i7, Turbo Boost up to 3.4 GHz, with 6 MB shared L3 cache; 4 GB de SDRAM DDR3 (two moduls SO-DIMM de 2 GB) to 1.333 MHz; WiFi (IEEE 802.11 g/n/ac). This terminal was used in the blki (i = 4..6).
5.3. Notification Messages, Handover and Battery Consumption
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Actors | Alert Intrusion | Person that Invades the Home and Provokes an Intrusion |
---|---|---|
Use Cases | User | Guard in charge of process alert messages and visualize real time intrusion video |
Start Sensor Provider | Initiates the processing of the alarm after the sensors, asynchronously, have captured the information inside the Smart Home. Tests the sensors. Evaluates the range values. Triggers alarm. | |
Start Message Procesor | Initiates the alert message processing, which constructs the messages to be sent to the User. | |
Activate Telegram Bot Agent | Send the Telegram instant message to the User. | |
Activate Mail Agent | Sends an e-mail to the User. | |
Start Video Server | Initiates the Video Server to start the communication when requested by the User. | |
Activate Recording Agent | Activates the agent that records the offline video. | |
Activate Transmission Agent | Activates the agent that communicates the video to the User. Request the activation of the camera. | |
Start video service | Starts the video session from the User. Visualize the video. |
Actors | Video Interruption | Represents the Disruption Causes. |
---|---|---|
Use Cases | Recording Agent | Manages video recording during disruptions. |
Client | User or Entity that request to visualize the video. | |
Transmission Agent | Manages video transmission and monitors the channel. | |
Video Service | Video deployment to the User or Client. | |
Test Transmission Channel | Evaluates the wireless channel state. When a video service disruption occurs an alarm is triggered, it will store the last not received video frames. | |
Interrupt Video Service | Tests if a video Service interruption occured. | |
Stop Transmission | Stops the video streaming service to the Client. | |
Start Recording Offline Video | Once a video service interruption was detected, the video storing will start. | |
Restart Transmision | Once the service interruption finalizes, the previous stored video will start to be sent to the Client. | |
Stop Recording Offline Video | When video service interruption finalizes offline video will stop to be stored. | |
Send Offline Video | Sends the video stored offline. |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Gualotuña, T.; Macías, E.; Suárez, Á.; C., E.R.F.; Rivadeneira, A. Low Cost Efficient Deliverying Video Surveillance Service to Moving Guard for Smart Home. Sensors 2018, 18, 745. https://doi.org/10.3390/s18030745
Gualotuña T, Macías E, Suárez Á, C. ERF, Rivadeneira A. Low Cost Efficient Deliverying Video Surveillance Service to Moving Guard for Smart Home. Sensors. 2018; 18(3):745. https://doi.org/10.3390/s18030745
Chicago/Turabian StyleGualotuña, Tatiana, Elsa Macías, Álvaro Suárez, Efraín R. Fonseca C., and Andrés Rivadeneira. 2018. "Low Cost Efficient Deliverying Video Surveillance Service to Moving Guard for Smart Home" Sensors 18, no. 3: 745. https://doi.org/10.3390/s18030745