Mobile Crowd-Sensing for E-Health Monitoring and Hot-Spot Detection. Basem Shihada Assistant Professor of Computer Science and Electrical Engineering

Transcription

1 Mobile Crowd-Sensing for E-Health Monitoring and Hot-Spot Detection Basem Shihada Assistant Professor of Computer Science and Electrical Engineering Bell Canada Invited Seminar Tuesday Dec. 9 th, 2014

2 Computing and Networking Trends q Bell's Law * : Every decade sees a change in the class of computing devices q 1990s saw the emergence of the laptop; q saw the mobile phones q Next decade, desktops will disappear. End-user computers will be almost entirely laptops & tablets. q Data and applications will live in the cloud. q Ubiquitous high-speed wireless connectivity is a must. q Dense, small-range wireless access points (AP) will become more important than today. q Spectrum is limited, neighboring APs will likely have to operate on the same spectrum. q Interference between neighboring APs will become the dominating factor. q Small cells connectivity and load will fluctuate rapidly, both in space and time. * Bell's Law of Computer Classes formulated by Gordon Bell in 1972, describes how types of computing systems (referred to as computer classes) form, evolve and may eventually die out.

3 NetLab: Research Domains Applications Resource & Energy Allocation

4 Cellular and Wireless Packet Networks

5 Delays in Wireless Mesh Networks The objective is to optimize buffer sizes to balance the throughput and delay requirements 1,2,3. Proposed a distributed, neighborhood buffer, that is sized for saturating the channel while minimizing queueing delays. We proved that using small buffers (1-4 packets) improves delay by up to 10x 1 We implemented our scheme in a Linux testbed WMN deployed on the 4 th floor of Building A. Showail, K. Jamshaid, and B. Shihada, "WQM: An Aggregation-aware Queue Management Scheme for IEEE n based Networks", in Proc. ACM Sigcomm Capacity Sharing Workshop (CSWS), pp , B. Shihada and K. Jamshaid, "Buffer Sizing for Balancing Throughput and Delay Requirements in Multi-hop Wireless Networks," U.S. Patent No. 8,638, A. Showail, K. Jamshaid, B. Shihada, "An Empirical Evaluation of Bufferbloat in IEEE n Wireless Networks", in Proc. IEEE Wireless Communications and Networking Conference (WCNC), Accepted, 2014.

6 Energy Optimization in Wireless Multi-Hop Networks The objective is to minimize the energy consumption at the energycritical nodes and the overall network transmission delay 1,3. The transmission rates of energycritical nodes are adjusted according to its local packet queue size. We proved that there exists a threshold type control which is optimal 1. We implemented a decentralized algorithm to control the packets scheduling of these energy-critical nodes 2. 1 L. Xia and B. Shihada, Decentralized Transmission Scheduling in Energy-Critical Multi-Hop Wireless Networks" IEEE American Control Conference, L. Xia, B. Shihada, Max-Min Optimality of Service Rate Control in Closed Queueing Networks," IEEE Transactions on Automatic Control, Vol. 58, No. 4, pp , L. Xia, B. Shihada, and P-H. Ho, Power and Delay Optimization for Multi-Hop Wireless Networks", International Journal of Control, 2014.

7 Wireless Cognitive Radio Systems

8 CogWNet System Layers 1 1. Communication Layer 2. Decision-Making Layer (Repository and Parameter Mapper) 3. Policy Layer Application Transport Network Link Physical Requirements From CAPRI Interface ULLA Interface CCC Repository Sensory Input Form GENI Interface Configuration Sensory Input Form Configuration Shared info Command Utility Manager -KERNEL Space- Utility Registration (AAL) <<notify>> Store Flow Manager Cgwindow, Flow Type <<notify>> Store Physical Bandwidth/ Center Frequency/ delay/ BER <<notify>> Store Negotiator 2 push data 4 Push data Push data 1 -User Space- Sensory Processing <<AB. Push.data >> <<notify>> (Cross layer data) With unique identifier from each channel) (data stored from future prediction) SP 3 Action Module Data Base for Future Parameter Prediction <<Schedule Action >> <<notify>> Receive configuration parameters after Policy check AM Policy Architecture: Admin Push_data To Parameter Mapper (AB) Schedule Action From Parameter Mapper (AB) From Repository (Policy Server) Server Manager Data Base Copy of sensory data (Sensory Processing) SP2 1 (CCC) 4 Trigger Manager Detection Module Rules evaluation: <<push_data>> <<check_power>> <<Primary user signal>> <<If detected: trigger>> DM (Policy Engine) Data Base Engine Manager If detection trigger AB.pushdata Reasoner Policy Module <<PE. pushdata>> <<notify>> PM Policy Interface 3 2 TO PM Notification (Yes or No) 3 Decision Parameters from PM (AB) Action Schedule (Rep) if Policy reply is Yes Policy Query Policy Reply TM Sensory Dara (Rep) Receiver data from Repository (SP1): -Transfer sensory data to storage. -Request decision Algorithm. -Check Policy. -Schedule Action Store <<push_data>> <<Read>> Storage Parameter Mapper Reply (Config Parameters) Action Broker Algorithm Request (run) + Policy Feedback if No -Receive Running Request from AB. -Run (Algorithm) => decision Tree -Notify AB about decision (to check with policy) Parameter Mapper Decision Module 1 I. Qerm, B. Shihada, and K. Shin, "CogWnet: A Resource Management Architecture for Cognitive Radio Networks", in Proc. IEEE International Conference on Computer Communications and Networks (ICCCN), 2013.

9 Energy Efficient Cognitive Radio Utilizing Sensing Information The objective is to minimize the EPG metric subject to 1,2 : 1. Rate constraint. 2. Interference constraint. 3. Peak Power constraint. The optimal resource allocation is done while combining the soft sensing information. Soft sensing information is the knowledge we obtain about the PT before making the detection decision. We generalized the targeted scenarios into PU Exists and Absent with a certain probability 2. We proved the convexity of the problem. We utilized the calculus of variation theory to obtain an optimal and sub-optimal solutions. 1 A. AlAbbasi, Z. Rezki, and B. Shihada, "Energy Efficiency and SINR Maximization Beamformers for Spectrum Sharing with Sensing Information", IEEE Transactions on Wireless Communications, Vol. 13, No. 9, pp , A. AlAbbasi, Z. Rezki, and B. Shihada, "Energy Efficiency and SINR Maximization Beamformers for Cognitive Radio Utilizing Sensing Information", in Proc. IEEE International Symposium on Information Theory (ISIT), pp , 2014

10 Wireless Sensor Systems

11 Towards Optimal Event Detection and Localization We identify and solve the problem of Event Detection and Localization in Acyclic Flow Networks 1,2. We proved that Event Detection problem is NP hard and provide a heuristic algorithm to solve it. We presented Beacon Placement and Event Localization algorithms for Event Localization. We evaluated our algorithms in simulation. We developed a flow learning algorithm, and evaluated the algorithms on bigger graphs 1 M. Suresh, R. Stolern, E. Zechman, and B. Shihada, "On Event Detection and Localization in Acyclic Flow Networks", IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 43, No. 3, pp , M. Suresh, R. Stolern, R. Denton, E. Zechman, and B. Shihada, "Towards Optimal Event Localization in Time-Varying Acyclic Flow Networks", in Proc. International Conference on Distributed computing and Networking (ICDCN), pp , 2012.

12 Green-Frag Energy efficient scheme that combines a frame fragmentation technique with an adaptive power mechanism 1,2. Green-Frag achieves the least energy consumption in all environment situations. It is capable of selecting the best transmit power according to the channel conditions. Data CRC 12 Bytes 1 Byte Time % Normal Channel Condition Combination of Different Block Modes Bytes Bad 1m Distance between Sender and Receiver 1 A. Showail, A. El-Rasad, A. Meer, A. Daghistani, K. Jamshaid, and B. Shihada, "ifrag: Dynamic Partial Packet Recovery for Sensor Systems", ACM Journal of Wireless Networks, Vol. 20, No. 4, pp. 1-18, A. Daghistani and B. Shihada, "Green-Frag: Energy-Efficient Frame Fragmentation Scheme for Wireless Sensor Networks", in Proc. IEEE International Conference on Wireless and Mobile Computing (WiMob), pp , (Received the Best Paper Award). Data 24 Bytes Block 4 25 Bytes Block 8 13 Bytes Data 48 Bytes Block 2 49 Bytes CRC 1 Byte Data 96 Bytes Block 1 97 Bytes CRC 1 Byte TailMap 1 Byte Data Frame 112 Bytes BlockMap 4 Bytes ACK Frame 6 Bytes CRC 1 Byte CRC 1 Byte Tail 14 7 Bytes 2.5m CRC 1 Byte 0 dbm 3 dbm 7 dbm 15 dbm 25 dbm

13 Smart Phone GPU

14 Energy Efficiency of Smartphone GPUs Adding GPUs to smartphones provided a big leap in the graphical experience: 3D Games, interactive and rich UI, high quality graphics applications. Smartphones resolution is rapidly increasing. Smartphone operating systems such as Android are fully hardware-accelerated. GPUs in smartphones are becoming more and more powerful by the day. Energy consumption of smartphone GPUs should be thoroughly investigated!

15 GPU Governors Total Energy Saving

16 Internet of Things: Crowdsourcing

17 What is Crowdsourcing? "The practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers "

18 What is Crowdsourcing? An example of crowdsourcing all know and use? It has over 17M articles written collaboratively by the community and is the most popular reference site on the internet.

19 What is Mobile CrowdSourcing? The rise of mobile devices expands Crowdsourcing to be real-time, location-based, and contextual. Mobile application users are no longer passive consumers, they can share GPS data, photos, surveys, sensor data, and much more!

20 Mobile Crowd-Sensing (MCS) Mobile Crowd-Sensing (MCS) proposes a new framework for data collection based on the power of sensors installed in smartphones, wearable devices, and sensor-equipped vehicles. Taking advantage of such sensor-enabled devices which reside at the edge of the Internet in more real life disciplines will cause an evolution in the concept of Internet of Things (IoT). MCS enables collecting massive location-based sensor data in a feasible distributed manner for monitoring a specific phenomena.

21 Successful Examples Non-profit environmental and governmental causes Crises, disasters, and emergency management Traffic, Navigation, and Transportation Management

22 And Many More...

23 Why Would People Share Information? Acquiring similar information Common good Reward system (money, phone credit...)

24 Multidisciplinary Research Data Sensing, Collection, and Streaming (Networks) Real-Time Data Analysis and Prediction (AI) Data Storage and Processing (Cloud Computing) Energy Efficiency and Awareness (Mobile Computing) Privacy, Security, and Trust Issues (Security) Architecture/Framework Design (Systems)

25 Our Project! Mobile Crowd-Sensing for Real-Time Mass Pedestrian Monitoring and Hot-Spot Detection Managing crowds in large-scale mass gatherings can be a very critical and challenging task. History have shown, failing to manage pedestrian crowds could lead to injuries and even lost lifes.

26 Previous Solutions Computer vision techniques Stationary sensors Drawbacks: 1. Limited node coverage. 2. Inability to scale to reach every part of the crowd. 3. The need pre-hand deployment, which is not efficient in the case of emergencies and evacuations. 4. High deployment and maintenance cost. 5. Provide no interactivity with the crowds in terms of offering real-time instructions or guidelines.

27 Proposed Solution Why not using the crowd themselves in monitoring? We use opportunistic mobile crowd sensing to collect information from within the crowds in a distributed manner using the crowds themselves as mobile nodes in a spatialtemporal dynamic network.

28 Proposed Solution Rather than aiming to have a precise density estimation, this project monitors the environmental conditions and surrounding of crowds, such as humidity, temperature, CO 2 emission, speed of movement, and noise, to infer the crowd level and predict any future incident.

29 Proposed Solution Our incentive for participatory sensing: The framework will perform a full circle by providing a heat map and real-time location based recommendations for participants to show less crowded areas, shortest path to reach there, and the best time to perform a certain task when there are the least crowd.

30 Methodology: Mobile Application We developed Ssense 1.0 * an android application that logs the following information every defined interval: Time, Air Pressure, Temperature, Relative Humidity, Absolute Humidity, Longitude, Latitude, Speed, GPSAccuracy, BatteryLevel * Ssense v1.0, free BSD license,

31 Methodology: Mobile Application Obtaining Ground Truth: For data collection purposes, one of three crowd levels should be dynamically chosen on every change based on the Jacob's method. An illustration is provided for volunteers to help in visualizing.

32 Methodology: Data Collection Test Case: Al Mataf- Makkah

33 Methodology: Humidity factor Out of all measured features only two were chosen that showed significant deviation from the context: Humidity and Temperature

34 Methodology: Temprature Factor Out of all measured features only two were chosen that showed significant deviation from the context: Humidity and Temperature

35 Methodology: Spatial-Temporal Modeling Our problem changes over both Time and Space. We model the crowd nodes as a spatial temporal-network where topology and attributes change over time. We model the temporal dependency in each node using first order hidden Markov model (HMM). We train the HMM to use the transition probability between different crowd levels, and the emission probability of the observed data to predict a hotspot of an overcrowd. We model the spatial dependency between nodes using Markov Random Fields (MRFs) to represent the relations between adjacent nodes. Combining both models would generate a full spatial-temporal detection framework.

36 Methodology: Temporal Dependency Modeling & Detection The problem is modeled as HMM to detect the temporal dependence in each sensor reading and detect the crowds hidden state: Hidden states = {Normal CORWD, MEDIUM CROWD, OVERCROWDED} Observation 1 (Humidity Deviation)={High, Medium, Low} Observation 2 (Temperature Deviation)={High, Low} * Assume that the two observations (Humidity and Temperature) are conditionally independent given the crowd state:

37 Methodology: HMM Supervised learning To learn the parameter of the HMM transition, emission, and initial matrices, we trained the HMM using supervised learning which uses Maximum- Likelihood Estimation (MLE).

38 Methodology: HMM Supervised learning

39 Methodology: HMM State Prediction No available tool support HMM with multiple observation variable. Therefore, we modified HMMLearn library of SciKit to support HMM models of two observation variables. When a hotspot is detected, the GPS coordinates of that node is retrieved to get the exact location of the overcrowded incident. Appropriate measures can then be taken according to the congestion location.

40 Spatial Dependency Modeling & Detection (MRF) Split targeted area into a grid of patches, each patch would represent a node in a network. When a temporal event is detected in one patch, adjacent patches in the same clique are checked before declaring a spatial-temporal event to eliminate noise, sensor errors, and outliers.

41 Future Work Building a cloud server for processing and storing the sensing data, thus taking advantage of the cloud infrastructure for our online data analysis. Include participatory sensing as well the opportunistic, where users can upload images and related information, such as how much time it took them to stand in a queue or to perform a certain task to further more identify the rush hours and pedestrian patterns. Generate real time heat maps and recommendations provided to users depending on their current location.

42 Thanks!