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# Gupta datamule

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• Becoming to be used in more and more various applications Mobility is one of the aspects that have been diversifying sensor networks deal with various forms of “mobility” Now, we focus on mobility
• One of the most characteristic features of this application is to use a UAV to collect data from the sensors
• We can consider this path as one-dimensional location axis
• For convenience, we introduce some terminology and definitions
• “ Location on the path”; We can consider this path as one-dimensional location axis
• If it’s too fast, data mule may not be able to finish all the jobs. &quot;How should we change the speed of data mule so that it can collect the data from all the sensor nodes in the shortest amount of time?&quot; &quot;Also, in that travel, from what node should the data mule collect data at what time?&quot;
• We first talk about 1-D DMS problem and then about path selection
• change speed instantaneously
• One interesting observation is that the 1-D DMS problem is related to DVS problem, and we can use the optimal algorithm for DVS for solving DMS problem
• ... except this (variable-simple-online) case which we can use EDF-based algorithm
• constant rate acceleration is represented as a quadratic curve in location-speed profile
• constant rate acceleration is represented as a quadratic curve in location-speed profile
• Not clear until we solve each of these 1-D DMS problems Finding continuous paths is difficult + Not clear which path leads to shorter travel time Edge (1,3) passes through the communication range of nodes 1,2,3.
• bottom: pink: DM goes to node’s exact location regardless of size of comm range
• In multihop forwarding approach, energy consumption is higher and latency is presumably lower than DM approach
• Base station is the bottleneck The result suggests that, in this case, multihop forwarding approach is not optimal and we can further improve the data delivery time by using hybrid approach.
• ### Gupta datamule

1. 1. Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego DAC 2011, San Diego. Controlled Mobility for Scalability and Efficiency in CPS Spatio-temporal Processing
2. 2. Thought-line 1. Embedded systems when scaled to societal levels become Cyber-Physical Systems 2. (Spatiotemporal) data gathering and processing are central to CPS 3. Processing and communication architectures are central to scalability and efficiency of data gathering. 2 Sensor Localization [INFOCOM 2011] Controlled Mobility for Scale & Efficiency [TOSN 2010, TMC 2010, INFOCOM 2009]
3. 3. 3 Mobility Often Differentiates Sensor Networks Habitat monitoring [Mainwaring et al., 2002] Environmental monitoring [Batalin et al., 2004] Emergency response [Malan et al., 2004] ZebraNet [Juang et al., 2002] Farm management [Sikka et al., 2006] [Kansal et al., 2004] [Todd et al., 2007][Vasilescu et al., 2005] None Predictable Random Voluntary? Controlled Either sensor data moves through a network of sensor nodes (Either sensor data moves through a network of sensor nodes (MonitoringMonitoring), or), or Sensed object moves through a network of sensor nodes (Sensed object moves through a network of sensor nodes (TrackingTracking))
4. 4. 4 Our Pet Project with Los Alamos National Labs http://www.lanl.gov/projects/ei/ • SHM (Structural Health Monitoring) application – Post-event assessments for large-scale civil structures – Passive sensors; measuring peak displacement • Data collection by UAV (Unmanned Aerial Vehicle) – Communication with sensors via ZigBee radio – GPS-based autonomous control • Quick data collection is required – Limited fuel
5. 5. 5 A network is not always the best way to move data • Sensor nodes must form a network • Network needs to be connected • Need non-trivial resources for the network – Networking and communications drains nodes (some more than the others) – Need to make sure that the nodes stay alive and stay reachable Base station Sensor nodes
6. 6. Data Mule Advantages • Reduced hops – Less congestion – Less retransmissions, higher network capacity – Less synchronization errors • May even be faster to send large data over constrained bandwidths • Simpler nodes, energy harvested sensors • DM as a resource delivery platform. 6 Base station Data mule Data mule approach Sensor nodes Optimize the motion (path, speed) of the mule to improve data delivery latency Sensor nodes
7. 7. 7 About an eight-year old problem • Data Mobile Ubiquitous LAN Extensions by Shah+Roy, WSN 2003 – Random mobility • “Mobile Router” by Kansal+Srivastava, MobiSys 2004: Controlled mobility along a fixed path – Controlled mobility on periodic routes, builds upon Directed Diffusion. Problems Assumptions RemarksComm. only at node Speed of data mule Instant move/stop [Zhao et al., 03] Path + Speed Variable Heuristic algorithm [Kansal et al., 04] Speed Variable Adaptive heuristic algorithm [Somasundara et al., 04] Path Constant + Stop NP-hardness; Heuristic algorithm [Ma, Yang, 06] Path Constant Heuristic; Assume negligible comm. time [Ma, Yang, 07] Path Constant + Stop Heuristic; Stop to communicate [Xing et al., 07] Path Constant + Stop Heuristic; MANETs with mobile nodes: Epidemic Routing, Message Ferrying, Many-to-many comms.
8. 8. Data Mule Scheduling (DMS) Problem • Path Selection Problem (1D DMS) • Energy-latency tradeoffs with DMS • DMS as a proxy for other important CPS problems – scale-parameterized scheduling problems (DVFS) 8
9. 9. 9 B Idea of DMS formulation • Assumptions – Communication is possible only within the intervals – Communication takes fixed amount of time – Node location, communication range/time are given A C Data mule Communication range Data mule's path Location A B C eB eC eA Communication timeNode
10. 10. 10 Terminology and definitions Time A B eB C eC eA Jobs Release time Deadline Feasible interval Execution timeSimple jobs Location A B C eB eC eA Location jobs Simple location jobs General location jobs In real-time scheduling … In DMS (Data Mule Scheduling) problem … D eD General jobs D eD Execution time Release location Deadline location Feasible location interval
11. 11. 11 B Idea of DMS formulation (1/2) • Consider communication as a location job – Location constraint: Feasible location intervals – Time constraint: Execution time A C Data mule Communication range Data mule's path Location A B C eB eC eA Execution timeLocation jobs
12. 12. 12 Idea of DMS formulation (2/2) Location A B C eB eC eA Location jobs Time A' B' C' eB eC eA Jobs + Time-speed profile (i.e., change of speed over time) Set of Location jobs Set of (real-time) jobs Time A' B' C' eB eC eA Jobs Faster speed • Location job is mapped to "job" when the speed is given – Real-time scheduling problem
13. 13. 13 Data Mule Scheduling (DMS) Problem • Three subproblems – Path selection • Which trajectory the data mule follows – Speed control • How the data mule changes the speed along the path – Job scheduling • From which sensor the data mule collects data at certain time • Objective – Minimize the total travel time (≈ data delivery latency) 1-D DMS Path selection Communication range node A node B node C Location job Speed control Speed Time Location A B C Execution time e(A) e(B) e(C) Job scheduling Time Execution time A’ B’ C’ Job Time A’ B’ C’ e(A) e(B) e(C)
14. 14. 14 1-D DMS: Closer look A B Location C Location Speed Time Time A B C Input: Set of location jobs Time-Speed profile (Solution for the problem) Corresponding Real-time Scheduling problem Time Time-Location profile (determined by Time-Speed profile) Execution time Execution time Location job Job e(A) e(B) e(C) e(A) e(B) e(C)
15. 15. 15 1-D DMS: Job scheduling • Simple jobs (i.e., one feasible interval) – Earliest deadline first (EDF) is an optimal online scheduling algorithm [Liu, Layland, 1973] • “Always execute the job with the earliest deadline” • General jobs (i.e., multiple feasible intervals) – No optimal online algorithm: Proof by an adversary argument • An adversary can make any online schedulers fail by releasing a new job – Offline: Linear programming (LP) formulation 2z Time 1e 2e 3e 25x 11x 12x 22x 23x 13x 14x 15x 33x 34x 35x 36x 26x 27x
16. 16. 16 1-D DMS: Speed control • Three different cases: – Simple cases • Constant speed • Variable speed – General case • Variable speed with acceleration constraint
17. 17. 17 Speed control: Simple cases • “Processor demand” (for simple jobs) – Sum of execution time of the jobs that are completely contained in the interval • Feasibility test [Baruah et al., 1993] [Yao et al., 1995] – Optimal offline algorithm from processor speed scaling by YDS. – Find minimum maximum speed from all ‘tight’ intervals. – Move any faster, there exists at least one infeasible interval. timet1 t2 job 1: e1 job 2: e2 job 3: e3 job 4: e4 (Const. speed, Variable speed) Pr : set of release time P : set of deadline A set of simple jobs is feasible Proc. demand for any Feasible interval of job
18. 18. 18 Complexity of 1-D DMS problem Simple jobs General jobs Offline Online Offline Online Real-time scheduling EDF [Liu, Layland, 1973] LP Non-existent 1-D DMS Simple case Constant speed Non-existent LP Non-existent Variable speed (vmin = 0) LP Non-existent (vmin > 0) Non-existent General case Variable speed with acceleration constraint Open Non-existent (fixed k ≥ 2) NP-hard Non-existent(k arbitrary) NP-hard in the strong sense Contributions Hard problems Design heuristic algorithm
19. 19. 19 Heuristic algorithm for general case (1/2) • Simplify – Convert all general location jobs to simple location jobs – Proportionally distribute the execution time • Maximize – Increase the speed until a tight interval is found • Tight interval: Processor demand = interval length Location Speed Full accel/decel at the maximum rate Location Speed Tight interval Accel interval Decel interval Plateau interval Proc. demand = time duration General location job A Execution time Execution timeSimple location job A1 A2 A3
20. 20. 20 Heuristic algorithm for general case (2/2) • Trim – Eliminate all fixed intervals from remaining jobs • Fixed interval: Intervals that the speed is already determined • Execution time of each location job is changed accordingly • Recursion – Repeat from “Maximize” for the remaining intervals Location Speed Recursively maximize Accel interval Location Decel interval LocationLocation Tight interval Location Speed Tight interval Accel interval Decel intervalPlateau interval
21. 21. 21 Example: General case Locationjob JobJob Speed control Job scheduling (Variable speed w/ accel. constraint) heuristic solution Not only works very well in practice but subject to convex optimization via SDP.Not only works very well in practice but subject to convex optimization via SDP.
22. 22. 22 1-D DMS Path selection Communication range node A node B node C Location job Speed control Speed Time Location A B C Execution time e(A) e(B) e(C) Job scheduling Time Execution time A’ B’ C’ Job Time A’ B’ C’ e(A) e(B) e(C) Data Mule Scheduling (DMS) Problem • Three subproblems – Path selection • Which trajectory the data mule follows – Speed control • How the data mule changes the speed along the path – Job scheduling • From which sensor the data mule collects data at certain time • Objective – Minimize the total travel time (≈ data delivery latency)
23. 23. 23 Path selection problem • Objective: – Find a path s.t. the induced 1-D DMS problem has the minimum travel time – Problem: Difficult to find such a path • Interrelation between path and travel time is unclear • Infinite choices of path • Idea: Simplify the problem as a graph problem – Find the shortest tour that covers all labels s 1 2 3 4 5 Location 1 2 3 4 5 start end Location 1 2 3 4 5 endstart Location 1 2 3 4 5 start end
24. 24. 24 Example (40 nodes) r = 100 r = 200 r = 300 r = 1 r = 20 r = 50
25. 25. 25 Experiments • Methods and parameters – 20 nodes randomly deployed in circular area (radius: 500m) – Each node has data that needs 10 secs for transmission – Data mule movement: m/s – Average of 100 trials Proposed algorithm successfully exploits larger communication rangeProposed algorithm successfully exploits larger communication range Totaltraveltime(sec) Communication range 0 100 200 300 400 500 600 700 0 20 40 60 80 100 120 No remote communication, constant speed e.g., [Ma, Yang, 2006], [Xing et al., 2007] Remote communication, constant speed, stop to transmit e.g., [Ma, Yang, 2007] Remote communication, visit all nodes, variable speed e.g., [Zhao et al., 2003] Label Covering TSP Formulation [Sugihara TOSN 2010]
26. 26. 26 Energy-Latency Tradeoff • Objective: – Add flexibility to energy-latency trade-off • Idea: – Combine multihop forwarding and data mule approach Energy consumption Data delivery latency Data mule Multihop forwarding
27. 27. 27 Hybrid approach • “Forwarding” subproblem – Determine where to gather data under energy constraint – Objective: • Find a forwarding strategy s.t. the induced DMS problem has the minimum travel time – Difficult • Simplified: Forward as close to the base station as possible
28. 28. 28 Results: Connected network 0 50 100 150 200 250 300 350 0 5 10 15 20 Energy consumption limit Time(sec) Travel time Lower bound of forwarding time
29. 29. 29 Preliminary result: Disconnected network 0 100 200 300 400 500 600 700 0 5 10 15 20 Time(sec) Travel time Lower bound of forwarding time Energy consumption limit
30. 30. 30 DMS with uncertainty Location Known comm. range Unknown comm. range Data mule’s path • Idea: Semi-online scheduling – Assumption: “Communication is possible in the vicinity of node” • ... plus unknown communication range – Strategy • Offline scheduling with known communication range • Opportunistically exploit unknown communication range – As in online scheduling
31. 31. Idea of 2-D Semi-online Algorithm A B C D E P Node 1 Known comm. range Unknown comm. range Data mule’s path Node 2 Job execution in offline schedule Actual job execution
32. 32. BS Example (Simulation on Matlab, 20 nodes
33. 33. Closing Thoughts • DMS provides a framework for solving spatiotemporal data collection problems • Scale-parameterized scheduling – Real-time scheduling where some parameters are scaled by a factor • Discretize location, speed and time • Find an appropriate step size for each of these three that guarantees an approximation ratio • In the discretized configuration space, use dynamic programming to find the optimal trajectory • Similarity with speed scaling – Inverse relationship between data mule's speed and processor speed • Ongoing work addresses assumptions made… 33