1. Communication Protocol Engineering Lab. Mobile Cloud Sensing, Big data, and 5G Networks make an intelligent and smart world IEEE network March/April 2015 Qilong Han, Shouang Liag, and Hongli Zhang 2015. 7.11 Chan Uk, Kang (chanukkun@kw.ac.kr)chanukkun@kw.ac.kr Kwangwoon Univ. Communication Protocol Engineering Lab. IEEE network March/April 2015 1/24

2. Communication Protocol Engineering Lab. Contents  Introduction  Applications of mobile cloud sensing Health/Fitness monitoring Combined with social sensing to predict trends  Mobile cloud sensing overview Sensing platforms Preprocessing of sensing data Mobile cloud sensing architecture  Limitations of mobile cloud sensing Limited network resources and interfaces Handle unprecedented workload in the cloud  The promise of 5G and Big data 5G networks ad the infrastructure for mobile cloud sensing Employing Big data in mobile cloud sensing  Conclusion IEEE network March/April 2015 2/24

3. Communication Protocol Engineering Lab.  Mobile sensors are the wheels of this “intelligent” revolution. -With these sensors, mobile devices can fetch several data of an ambient environment. -Collected data from these sensors can be used to infer the context information and detect activity of the device.  Mobile devices are constrained by their limited memory, computing resources and own battery. -The combination of a mobile device along with cloud computing has been proposed to form mobile cloud computing. Introduction IEEE network March/April 2015 3/24

4. Communication Protocol Engineering Lab.  Through widely available online in App stores, mobile sensing Apps can reach large numbers of people from all around the world. -To handle and interpret such large scale data from mobile sensors, new data processing mechanisms have to be explored. -Big data analysis is the technique used for processing large scale data. -Mobile cloud sensing is an idea that combines mobile sensing, cloud computing, and big data analysis to acquire, process, and predict mobile sensor data. Introduction IEEE network March/April 2015 4/24

5. Communication Protocol Engineering Lab.  This article is organized as follows. -Discuss the applications of mobile cloud sensing, in which we also summarize the taxonomy of mobile cloud computing. -Review the architecture of mobile cloud sensing and details about each building block. -Discuss the limitations of mobile cloud sensing -Discuss how 5G networks and big data make the idea of large scale mobile cloud sensing a possibility. -Conclude this article. Introduction IEEE network March/April 2015 5/24

6. Communication Protocol Engineering Lab.  Before talking about the applications of mobile cloud sensing, we should know the taxonomy of the applications. -Individual sensing: Data collected for personal use only, that is, not shared with other people. -Group sensing: Data collected for sharing with a group of people with common interests. -Community sensing: Data collected by people from communities and integrated together to predict global trend. -Opportunistic sensing: Data collection activity is highly automatic with very little user involvement. -Participatory sensing: Users of mobile devices participate in the data collection activity, which can be conducted in a very complex environment with high quality results. Applications of mobile cloud sensing IEEE network March/April 2015 6/24

7. Communication Protocol Engineering Lab.  Health/Fitness monitoring -Health examination and fitness monitoring were done in hospitals before it could be done with mobile sensors and the cloud. Health monitoring App installed on the phone, sleep data can be collected using the accelerometer sensor and microphone. Runners can also upload their workout data to a server, based on the smartphone GPS and the heart rate sensor that can be attached to the runner’s chest. Medical care, a non-contact electrocardiogram has been widely used to capture biomedical signals from users. -We believe that more health related sensors will be integrated into smartphones. Applications of mobile cloud sensing IEEE network March/April 2015 7/24

8. Communication Protocol Engineering Lab.  Combined with social sensing to predict trends -The social sensing data could be complementary to mobile sensing data and reveal high resolution views of the real world. Social networking Apps such as Facebook, Twitter, Myspace and G+ have tons of data generated everyday. Researchers use data-mining technologies to interpret billions of tweets and map the tweets to mood patterns of global twitter users These social sensing data are used and interpreted as the way we collect and interpret mobile sensing data. And can be applied to stock market trend predications and global event monitoring. -This is not the end of the story; we can still expect more from sensing data. Fusing mobile sensing data with social sensing data can give more precise and comprehensive observations of the real world. -The correlation of mobile sensing data and social sensing data can help us better understand context, as well as help us mine for more valuable information from sensing data. Applications of mobile cloud sensing IEEE network March/April 2015 8/24

9. Communication Protocol Engineering Lab.  Mobile cloud sensing inherits both cloud computing services and mobile sensing features. Cloud computing by the definition of NIST (National Institute of Standards and Technology) is as follows: Cloud computing is a model for enabling convenient, on demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Mobile cloud sensing overview IEEE network March/April 2015 9/24

10. Communication Protocol Engineering Lab.  From the NIST definition, cloud computing is highly efficient, scalable, and accessible to various devices.  These features compensate for the shortcomings of mobile devices.  Mobile cloud sensing provides mobile devices access to cloud computing resources and enables a cloud computing infrastructure to have the ability to obtain real world data from mobile sensing devices. Mobile cloud sensing overview IEEE network March/April 2015 10/24

11. Communication Protocol Engineering Lab.  Sensing platforms -Sensing platforms are the probes to the physical world. -From wireless sensor networks to recently popular mobile devices and wearable mobile sensing platforms exist everywhere around us in various forms. -Wireless sensor networks : The sensor nodes have a small footprint and are very cheap. Typical sensors that are built into the node are temperature, humidity, barometer, 3-axis accelerometer, microphone, and camera. Each node has a radio transmitter and receiver to communicate to other nodes and the center node in short range. The battery on the node can last for years. Mobile cloud sensing overview IEEE network March/April 2015 11/24

12. Communication Protocol Engineering Lab. -Mobile Devices : Smartphones and tablets fall into this category. they come with a very rich sensor set that includes ambient light, proximity, accelerometer, gyro, digital compass, GPS, microphone, and camera. Data collected from these sensors can be sent to an outside network via Wi-Fi or cellular network from the mobile devices. -Wearable Devices : The sensors on these devices are dedicated to a specific use or the applications of the corresponding device. Example ;  Smart watches - Used as a replacement to phone functionality, Motion sensors, GPS, voice, and image built inside.  Smart bracelets - Used as a personal health managing device, so they usually come with a pulse and motion sensor. Most of these wearable devices have Bluetooth LE (Low Energy) interface to communicate with mobile devices through Bluetooth. Mobile cloud sensing overview IEEE network March/April 2015 12/24

13. Communication Protocol Engineering Lab.  Preprocessing of Sensing Data -Before the data is sent to the cloud, we need to answer two questions: 1. Do we have sufficient bandwidth to upload the raw data to the cloud? -No, we need to preprocess the raw sensor data before transmitting to the cloud. We can do the following processing with the sensor data to keep the data footprint small: Data compression: Employ some data compressing algorithms to reduce the data size. Compressed sensing technique can reduce significant amounts of communication data by using some simple compression algorithms. Feature extraction: Conduct transformation of the raw data to extract the simplified features of the original raw data. Mobile cloud sensing overview IEEE network March/April 2015 13/24

14. Communication Protocol Engineering Lab. 2. How can have access to my data and what if we don’t want to share this data with other people or parties? The potential for privacy leakage and reverse-engineering attacks keeps many mobile users from participating in mobile sensing projects. To address the privacy issue, besides resorting to cryptography, researchers have been proposing many different ways to keep data leakage risk to a minimum. Examples of these efforts include using cloaking techniques to make your location data anonymous to third parties, and server side authentication to keep illegal data requests from being granted. Mobile cloud sensing overview IEEE network March/April 2015 14/24

15. Communication Protocol Engineering Lab.  Mobile Cloud Sensing Architecture Mobile cloud sensing overview IEEE network March/April 2015 15/24

16. Communication Protocol Engineering Lab.  Data Sensing Unit: -Physical sensing probes ; smartphones, tablets, wearable devices. Data generated by the physical sensing unit is raw format accelerometer data, ambient light strength, pulse rate, digital images, and audio data. -Social sensing probes ; posts on social networks. Exception of data that generated by physical sensing unit. Obtain everyday posts from Facebook or fetch tweets from Twitter. Sensor data and social data work complementarily as a sensing data source.  Data Preprocessing Unit: -Raw data takes too much bandwidth and explores user data without any protection, raw sensing data goes through this component to be preprocessed and sent to the cloud. 1.Examines the raw data from sensors and social networks, extracting corresponding features for each sensor data type. 2.Extracted features will be encrypted and compressed to minimize the data bandwidth and protect the data from leakage. Mobile cloud sensing overview IEEE network March/April 2015 16/24

17. Communication Protocol Engineering Lab.  Network Management Unit: -It is the gate the data goes through to get to the cloud. -We isolate the network management unit from the other parts of the mobile operating system since mobile networking has high cohesion with the preprocessed data. -The network management unit can be further optimized to make sensing data throughput larger and make the integration of other network interfaces (5G networks) much easier and faster.  Cloud Data Mining and Storage: -Highly powerful in computing and have sufficient storage for sensing data. -Data from all kinds of sensing sources converges here. -The features and data processing results are to be stored on the cloud for accessing. Mobile cloud sensing overview IEEE network March/April 2015 17/24

18. Communication Protocol Engineering Lab.  Cloud Data Authentication and Service Interfaces: -enforces only that authorized users can get access to the data and the service. -Authentication can be implemented using secure tokens or more secure two-factor authentication. -Multi-biometric authentication using biometric information recognize users could be applied to the cloud platforms to provide fast and robust authentication services. Mobile cloud sensing overview IEEE network March/April 2015 18/24

19. Communication Protocol Engineering Lab.  Limited Network Resources and Interfaces -Current network infrastructures cannot ensure network availability continuously. Compared with millions of new mobile users each year, the radio spectrum will be unable to support such a large user base. In order to access the cloud service, mobile devices must be connected to the network without congestion or network failure. -Compatibility of different mobile network interfaces and standards. The mobile cloud has WiFi, LTE, WiMax, and other radio interface seamlessly. Current 4G networks cannot handle this and need emerging communication technologies to address this problem. Limitations of mobile cloud sensing IEEE network March/April 2015 19/24

20. Communication Protocol Engineering Lab.  Handle Unprecedented Workload on the Cloud -The capability to handle dramatically large amounts of data will be the key task to optimize the mobile cloud’s ability to accommodate the fast increase in mobile sensing data. -Managing the explosion of mobile data could be a tough task but would totally revolutionize the way we live. Limitations of mobile cloud sensing IEEE network March/April 2015 20/24

21. Communication Protocol Engineering Lab.  5G Networks as the Infrastructure for Mobile Cloud Sensing -5G networks are designed for more “intelligent” and “smart” applications. They support wireless and mobile network interoperability better, based on an all-IP network (AIPN) model. 5G network infrastructure supports more network interfaces by using the autonomous radio access technologies. The large amount of sensing data is from all kinds of mobile devices with various wireless interfaces, so the feature of autonomous radio access technologies makes data acquisition from various mobile devices available. -Bandwidth for the fifth generation network is more than 1 Gb/s, which is sufficient for most sensing applications. With the fast and large bandwidth access of 5G networks, the acquisition velocity of sensing data is able to satisfy the requirement of big data analysis systems. Also provides services dedicated to wearable devices with AI (artificial intelligence) capabilities. The promise of 5G and Big data IEEE network March/April 2015 21/24

22. Communication Protocol Engineering Lab.  Employing Big Data in Mobile Cloud Sensing -Volume: Monthly mobile data traffic will surpass 15 exabytes by 2018. Thanks to the fast growing mobile phone market, mobile devices now generate unprecedented data volume. -Velocity: As an application developer and hardware engineer, we want to give the best user experience to smartphone users. To that end, mobile applications are now able to fetch sensor data in real time and continuously. The promise of 5G and Big data IEEE network March/April 2015 22/24

23. Communication Protocol Engineering Lab. -Variety: The ability to handle data with so many sources is one of the key features of big data. The variety feature of big data can handle the various sources of mobile cloud sensing well. -Value: The large amount of data volume, extremely rapid increase in velocity, and the number of forms of data, make big data unique from previous data storage. Moreover, we can find correlations of the data with real world incidents, which will help us predict the future and develop strategies for the future using the data. The promise of 5G and Big data IEEE network March/April 2015 23/24

24. Communication Protocol Engineering Lab.  This article focuses on the technologies that make an intelligent and smart world possible through mobile devices.  This article also discussed the architecture of mobile cloud sensing and gave details about each of the building blocks.  Ultimately, we presented the limitations and issues of mobile cloud sensing from the perspective of a network infrastructure and the data from mobile cloud sensing.  Also, we discussed how 5G and big data technologies are promising techniques to make mobile cloud sensing possible. Conclusion IEEE network March/April 2015 24/24