Coverage, Connectivity and Mobility in Wireless Mobile Sensor Robots

1. Coverage, Connectivity and Mobility in Wireless Mobile Sensor Robots Youn-Hee Han yhhan@kut.ac.kr Korea University of Technology and EducationLaboratory of Intelligent Networks http://link.kut.ac.kr

2. Introduction

3. Review: Sensor Node Architecture • System architecture of a typical wireless sensor node • i) a computing subsystem consisting of a microprocessor or microcontroller • ii) a communication subsystem consisting of a short range radio for wireless communication • iii) a sensing subsystem that links the node to the physical world and consists of a group of sensors and actuators • iv) a power supply subsystem, which houses the battery and the dc-dc converter, and powers the rest of the node.

4. Mobile Sensors • Mobile Sensor Capabilities [1,2] • Sensing • Communication • Computation • Locomotion • Self-deploy function • Mobile Robots with Sensors Static Sensor’ Capabilities [Similar to a Tank] [Eight Legged Robot of LEGO mindstorm] [www.thinkbotics.com]

5. Mobile Sensor Robots Single Sensor vs. Distributed Multiple Sensors Single Robot vs. Distributed Multi-Robots Mobile Sensor Robots : Distributed Multi-Robots with Sensing Capability

6. What Issues in Mobile Robots? • Issues in Distributed Multi-Robots [3] • Biological Inspirations • Use of the local control rules of biological societies, such as ants, bees, and birds to the development of similar behaviors in multi-robot systems. • behavior-based robotics • robot architectures are built on activity-generating building blocks rather than on centralized representations and deductive logic. • Communication • Network robotics and Inter-robot interaction • How to handle non-deterministic time delays in communications and achieve robust performance in faulty communication environment • E.g., the remote tele-operation of space exploration robots • Connectivity Issues

7. What Issues in Mobile Robots? • Issues in Distributed Multi-Robots [3] • Localization, Mapping, and Exploration • Enables robot team members to track positions of autonomously moving objects • Navigate between places of interest in an initially unknown environment • Motion Coordination • Multi-robot path planning, formation generation • Reconfigurable Robotics • Architecture, Task Allocation, and Control • Object Transport and Manipulation

8. What Issues in Mobile Sensor Robots? • Then, what issues in Mobile Sensor Robots ? • Environmental Robotics • the deployment of distributed sensors and supported mobile sensor robots to observe, monitor, and assess the state of complex environmental processes. • It involves many different types of distributed sensing in land, sea, and air, and the coordination of mobile sensors through adaptive redeployment and adaptive sampling of environmental phenomena. • Coverage Issues [2004 WTEC ROBOTICS WORKSHOP]

9. Mobile Sensor Robots [조선일보 2008-09-22] 떼지어 군사작전 '로봇' 나왔다 정찰·독성물질 탐지 수행… 英 내년 상용화 벌이나 개미처럼 무수한 소형로봇들이 하나의 군사작전을 수행하는 '로봇떼(swarm of robots)'가 곧 현실화한다. 영국 국방부가 16~18일 영국 솔즈베리에서 개최한, 새 군사 기술의 경연대회인 '그랜드 챌린지'에서 특히 '소형로봇떼' 개념이 떠오르는 신기술로 주목을 받았다고 BBC 방송이 보도했다. 전체 11개 팀 중에서 3개 팀이 '로봇떼'를 선보였다. 작은 곤충로봇들이 땅에서 움직이는 '마인드시트(Mindsheet)', 날아다니는 비행로봇들의 집단인 '로커스트(Locust)', 그리고 미니 헬리콥터 8개가 나는 '아울스(Owls)'등이다. 영국 국방부는 '아울스'의 기술을 참가 팀들 중 '가장 혁신적인 아이디어'로 선정했다. 아울스는 8개의 소형 헬리콥터 로봇이 한 팀이 돼 움직인다. 로봇 1개당 프로펠러 4개가 달려 있고 무게는 1㎏ 미만. 이 로봇떼는 다양한 각도에서 고해상도의 영상을 찍어 적의 위협을 감지한다. 대기에 뿌려진 독성물질을 탐지할 수도 있다. 8개 중 일부가 파괴되거나 고장 나도, 나머지 로봇들이 없어진 로봇들의 임무를 대신하도록 프로그램 돼 있다. 내년에 '새떼'의 움직임을 모방한 알고리즘까지 아울스에 내장된다. 영국 일간지 가디언은 "아울스는 내년쯤 상용화해 영국군에 배치될 전망"이라고 보도했다. 이 밖에, 현재 미군이 개발 중인 '마이크로 자동 시스템기술(MAST)' 프로그램은 병사 1명에게 하나의 로봇떼를 제공하는 것이 목표다. MAST의 로봇떼는 시가전(市街戰)상황에서 건물이나 모퉁이 너머로 몰래 다가가 적의 동태를 살피는 '정찰병' 역할을 수행한다.

10. Change of Research Issues in Sensor Networks • Hardware (2000) • CPU, memory, sensors, etc. • Protocols (2002) • MAC layers • Routing and transport protocols • Applications (2004) • Localization and positioning applications • Management (2005) • Coverage and connectivity problems • Power management • Etc. FromDr. Yu-Chee Tseng(Associate Dean),College of Computer Science, National Chiao-Tung University

11. Study of Coverage Problem • Coverage Problem • In general, determine how well the sensing field is monitored or tracked by sensors. • Objectives of the problem • Determine the coverage hole (or targets) • Minimize the number of sensors deployed • Make the whole area covered by three or more sensors • Location determination by “Triangulation” • Maximize the network lifetime • [Def.] Sensor Network Lifetime • The time interval that all points (or targets) in the given area is covered by at least one sensor node. • Etc.

12. Review: Art Gallery Problem • Victor Klee (1973) • Place the minimum number of cameras such that every point in the art gallery is monitored by at least one camera • Chvátal's art gallery theorem (1975) • guards (cameras) are always sufficient and sometimes necessary to guard a simple polygon with vertices 42 vertices  upper bound:

13. Tx Rx * 2Mb/s IEEE 802.11 Wireless LAN Energy Consumption Idle Sleep Modes Review: Power Saving • Make the sensor node sleep!!! [13] • Rockwell’s WINS Nodes • Medusa II Nodes It is highly recommended to “schedule” the wireless sensor nodes to alternate between active (Tx, Rx, Idle) and sleep mode http://www.inf.ethz.ch/personal/kasten/research/bathtub/energy_consumption.html

14. Review: Power Saving • Make the sensor node intelligent!!! [13] • The ratio of the energy spent in sending one bit of information to the energy spent in executing one instruction. • 1500~2700 for Rockwell’s WIN nodes • 220~2900 for the MEDUSA II nodes • 1400 for the WINS NG 2.0 • So, local data processing, data fusion and data compression are highly desirable.

15. Problem Design Methodology • Algorithm Characteristics • 1) Centralized • 2) Distributed • 3) Self-* • Self-determination • free choice of one’s own acts without external compulsion • Self-organization (Self-configuration) • a process of evolution where the effect of the environment is minimal, i.e. where the development of new, complex structures takes place primarily in and through the system itself • Self-healing • For example, a mobile sensor can move to an area with a coverage hole or routing void and significantly improve network performance.

16. Coverage

17. Problem Design Criteria (1/2) • Sensor Deploy Method • Deterministic (planned) vs. Random • Coverage Types • Area coverage vs. Target (Point) coverage t1 S1 S3 S4 t2 t3 S2 1 8 R 2 7 6 3 4 5

18. Problem Design Criteria (2/2) • Coverage Modeling • Binary Model vs. Probability Model • Communication Range ( ) & Sensing Range ( ) • vs. vs. • Homogeneous vs. heterogeneous? Probabilistic sensing model Binary, unit disc sensing model

19. Coverage Modeling • Binary Model [1] • Each sensor’s coverage area is modeled by a disk • Any location within the disk is perfectly monitored by the sensor located at the center of the disk; otherwise, it is not monitored by the sensor. • Probability Model [2] • An event happening in the coverage of a sensor is either detected or not detected by the sensor depending on a probability distribution • Hence even if an event is very close toa sensor, it may still by missed by the sensor.

20. BinaryModel: K-coverage in 2-D • K-coverage (only within Binary Model) • [Definition] covered • A location in an area is said to be covered by if it is within 's sensing range. • [Definition] k-covered (location or area) • A location in an area is said to be k-covered if it is within at least K sensors' sensing ranges. • “k” is called coverage level • WhyK>1? • Fault-tolerance in case of the dismissal of some sensors • Power saving and enlarge network lifetime • Triangulation: getting location of a targeted object • Uplift the confidence level on gathering information

21. BinaryModel: K-coverage in 2-D • Problems about K-coverage [1] • [Definition] k-NC problem • Given a natural number k, the k-Non-unit-disk Coverage (k-NC) problem is a decision problem whose goal is to determine whether all points in an area are k-covered or not. • [Definition] k-UC problem • Given a natural number k, the k-Unit-disk Coverage (k-UC) Problem is a decision problem whose goal is to determine whether all points in an area are k-covered or not, subject to the constraint that r1 = r2 = · · · = rn. k-UC (k=1) k-NC (k=1)

22. BinaryModel: K-coverage in 2-D Is this area 1-covered? So this area is not 1-covered! This region is not covered by any sensor! This area is not only 1-covered, but also 2-covered! What is the coverage level of this area? 1-covered means that every point in this area is covered by at least 1 sensor 2-covered means that every point in this area is covered by at least 2 sensors Coverage level = k means that this area is k-covered

23. BinaryModel: K-coverage in 2-D • Algorithm to determine coverage level, k, in a given sensor network? [1] • [Definition] k-perimeter-covered • Consider any two sensors si and sj. A point on the perimeter of si is perimeter-covered by sj if this point is within the sensing range of sj • [Theorem] • An area A is k-covered iff each sensor in A is k-perimeter-covered. • 2차원 문제를 1차원 문제로 바꾸어 해결 • Partially self-determination, but a central node determines the coverage level (k) finally.

24. BinaryModel: Coverage Configuration in 2-D • Coverage Configuration Protocol (CCP) [3] • 1) a coverage level (k) is allocated to all sensors • 2) all sensors are deployed randomly at the target area • 3) Each sensor makes itself sleep or active to achieve the coverage level • [Theorem] • A given area is “k-covered” if the following conditions are satisfied 1) All intersection points between each pair of sensors are "k-covered" 2) All intersection points between each sensor and boundary of the area are "k-covered” Active nodes Intersection points

25. BinaryModel: Coverage Configuration in 2-D • Coverage Configuration Protocol (CCP) [3] • A node becomes “sleep” if all intersection points inside its coverage is already K-covered by other active nodes in its neighborhood. • A node becomes “active” if there exists an intersection point inside its sensing circle that is not K-covered by other active nodes. active? Active nodes Sleeping nodes Intersection points

26. BinaryModel: K-coverage in 3-D • K-coverage in 3-D [4] • [Definition] k-BC Problem • Given a natural number k, the k-Ball-Coverage (k-BC) Problem is a decision problem whose goal is to determine whether all points in a 3-D cuboid sensing area are k-covered or not. • How to determine k? • (3D2D) Determine whether the sphere of a sensor is sufficiently covered • (2D1D) Determine whether the circle of each spherical cap of a sensor intersected by its neighboring sensors is covered

27. Probability Model • Why Probability Coverage Model? [2] • Quality of sensor surveillance may be much affected by sensing distances, signal propagation characteristics, obstacles, and environmental factors. • Probability coverage model may be more realistic! • Methodology • Simple Model [5] • Signal-strength-based Model [2] 임의의 센서와 가까운 지역이 특수한 요인 (장애물)에 의하여 센싱이 되지 않을 수 있거나 그 센서와 먼 지역이 특수한 요인 (다수의 센서의 감지)에 의하여 센싱이 될 수도 있다.

28. Probability Model • Simple Model [5] • : the probability that a sensor can sense a event happened at a location • : the detection probability contributed by the sensors

29. Coverage and Scheduling

30. Scheduling • Basic Policy • Sensor should be active or sleep? • Scheduling (related to the coverage issue) • An interval: is active • Another interval: is active • So, the battery power can be saved

31. Scheduling • Scheduling Type • Centralized • All sensors send “their location information” to the centralized sink node. • The sink node performs “its scheduling algorithm” for the sensors • The sink node broadcasts “the scheduling information” to all sensor nodes • Each sensor becomes active or sleep according to the information • Distributed • Each sensor self-determies its scheduling time • # of messages reduced

32. Centralized Scheduling • MDSC (Maximum Disjoint Set Covers) [9] [Definition] Maximum Disjoint Set Covers Problem

33. Centralized Scheduling • MDSC (Maximum Disjoint Set Covers) [9] • For example, • C={S1, S2, S3, S4}, TARGETS={t1, t2, t3} • A sensor’s battery lifetime: 1 • Network Lifetime without any scheduling: 1 • By MDSCScheduling • Two Set Covers, C1 and C2 • C1={S1, S2} with active time=1 • C1={S3, S4} with active time=1 • So that, network lifetime: 2 t1 s1 s1 s3 t1 s2 s4 t2 t2 s3 t3 s2 t3 s4

34. Centralized Scheduling • MSC (Maximum Set Covers) [10] [Definition] Maximum Set Covers Problem removed! MSC MDSC MDSC problem is a special case of MSC problem.!

35. Centralized Scheduling • MSC (Maximum Set Covers) [10] • For Example, • By MSCScheduling • Network Lifetime: 2.5 t1 s1 s3 s4 t2 t3 s2 active time=0.5 active time=0.5 active time=1 active time=0.5

36. Centralized Scheduling • MSC (Maximum Set Covers) [10, 11] • Existing Algorithms • Linear Programming [10] • Greedy [10] (Complexity: ) • Branch-and-Bound [11] i: # of setcovers, m: # of targets, n: # of sensors

37. Centralized Scheduling • MSC (Maximum Set Covers) [10, 11] • Existing Algorithms • Linear Programming [10] • Greedy [10] (Complexity: ) • Branch-and-Bound [11] i: # of setcovers, m: # of targets, n: # of sensors

38. Distributed Scheduling • 1-Coverage Preserving Scheduling (1-CP) [12] • For Example Init Phase: 1) Each sensor exchange its location and Ref. value 2) Each sensor get its schedule (active) time The set of intersection points within ‘s area Trnd=20 The set of sensorscovering the target p Ref1=2, Ref2=9, Ref3=11

39. Distributed Scheduling • 1-Coverage Preserving Scheduling (1-CP) [12] 2 16.5 5.5 9 11

40. Connectivity

41. Connectivity • Why Connectivity? • Any sensing data should be sent to gateway (sink, base station) node • Multi-hop routing Base Station Sink

42. K-Connectivity • Connected Graph of Sensor Networks • Vertex: each sensor nodes • Edge: direct communication path for pairs of sensors • there exists an edge between two vertices iff the distance between them is less or equal to the transmission range r.

43. K-Connectivity • [Definition]k-connectivity • The network will remain connected after removing any arbitrary k-1 sensors from network. • It is also called “vertex k-connectivity” (not “edge k-connectivity”) • k-connected:  any pair of nodes are connected by k indep. paths • Independent paths:

44. K-Connectivity • Examples 2-connected 4-connected

45. K-Edge-Connectivity • [Definition]k-edge-connectivity • The network will remain connected after removing any arbitrary k-1 edges from network. • k-edge-connected:  any pair of nodes are connected by k disjoint paths • disjoint paths:

46. Min-Power Connectivity Problem • Connectivity & Transmission Power • Nodes in the network correspond to transmitters • More power  larger transmission range  More Edges  More Connectivity • transmitting to distance r requires rpower • Battery operated  power conservation critical • [Definition]Min-Power Connectivity Problems • Find min-power range assignment so that the resulting communication network satisfies prescribed properties (k-connectivity)

47. e e d d f f c c g g b b a a Min-Power Connectivity Problem Range assignment Communication network

48. K-Connectivity & K-Coverage • Relationbetween K-Coverage and K-Connectivity [3] • Communication Range: • Sensing Range: [Theorem] • If the given region is continuous and , “The region is k-covered” means “The region is k-connected” • For example, k=1 • Assume that the requested coverage level, k, is one and • If The sensors covers the whole region completely, then • Any sensing data produced by a sensor can be delivered to the sink node.

49. Sensing and Communication Ranges • Real Products’ Ranges [7]

50. Self-deployment I