Presentation is loading. Please wait.

Presentation is loading. Please wait.

Introduction to Sensor Networks Rabie A. Ramadan, PhD Cairo University 2.

Similar presentations

Presentation on theme: "Introduction to Sensor Networks Rabie A. Ramadan, PhD Cairo University 2."— Presentation transcript:

1 Introduction to Sensor Networks Rabie A. Ramadan, PhD Cairo University 2

2 Do not think how hard the problem you are solving Just, “keep your eyes on the prize” 2

3 Hardware Platforms Augmented General Purpose PCs Embedded PCs (PC104), PDAs, etc.. Usually have O.S like Linux and wireless device such as Bluetooth. Dedicated Sensor Nodes Commercially off the shelf components (e.g. Berkeley Motes) System-on-chip Sensor Platform like Smart dust, PicoNode 3

4 Software Platforms Operating Systems and Language Platforms Typical Platforms are: TinyOS, nesC, TinyGALS, and Mote’ TinyOS Event Driven O.S. Requires 178 bytes of memory Supports Multitasking and code Modularity Has no file system – only static memory allocation Simple task scheduler nesC – extension of C language for TinyOS- set of language constructs TinyGALS - language for TinyOS for event triggered concurrent execution. Mote’ - Virtual machine for Berkeley Mote 4

5 Wireless Sensor Network Standards IEEE 802.15.4 Standard Specifies the physical and MAC Layers for low-rate WPANs Data rates of 250 kbps, 40 kbps, and 20 kbps. Two addressing modes: 16 - bit short and 64 - bit IEEE addressing. Support for critical latency devices, for example, joysticks. The CSMA - CA channel access. Fully handshaking protocol for transfer reliability. Power management to ensure low - power consumption. 5

6 6 CSMA-CA Protocol How it works?

7 Wireless Sensor Network Standards IEEE 802.15.4 Standard The physical layer is compatible with current wireless standards such as Bluetooth MAC layer implements synchronization, time slot management, and basic security mechanisms. 7

8 –“the software” –Network, Security & Application layers –Brand management IEEE 802.15.4 –“the hardware” –Physical & Media Access Control layers Wireless Sensor Network Standards IEEE 802.15.4 & ZigBee In Context PHY 868MHz / 915MHz / 2.4GHz MAC Network Star / Mesh / Cluster-Tree Security 32- / 64- / 128-bit encryption Application API ZigBee Alliance IEEE 802.15.4 Customer Silicon Stack App 8

9 ZigBee Utilization 9 RESIDENTIAL/ LIGHT COMMERCIAL CONTROL INDUSTRIAL CONTROL ZigBee Wireless Control that Simply Works CONSUMER ELECTRONICS TV VCR DVD/CD remote security HVAC lighting control access control lawn & garden irrigation PC & PERIPHERALS asset mgt process control environmental energy mgt PERSONAL HEALTH CARE BUILDING AUTOMATION security HVAC lighting control access control mouse keyboard joystick patient monitoring fitness monitoring

10 Applications Example 10

11 Put tripwires anywhere—in deserts, other areas where physical terrain does not constrain troop or vehicle movement—to detect, classify & track intruders [Computer Networks 2004, ALineInTheSand webpage, ExScal webpage] Project ExScal: Concept of operation 11

12 ExScal scenarios Border Monitoring: Detect movement where none should exist, Decide target classes, e.g., foot traffic to tanks Ideal when combined with towers, tethered balloons, or UAVs 12

13 WSN Research Fields Sensors HW and Software Deployment Physical, MAC, Routing, Applications Data Aggregation and Data Mining Artificial Intelligence and data handling Self Healing Web Integration Heterogeneity Security Software Engineering (Simulators ) Cloud Computing and Sensor Networks Mobility Issues and Localization 13

14 Assignment 1 Report the main security considerations of IEEE 802.15.4 ? 14

15 Deployment, Clustering, and and Routing in WSN 15

16 Deployment Constraints Sensor characteristics Monitored field characteristics Monitored/probed object 16

17 Deployment Parameters 17

18 Deployment Parameters 18 Diffraction: passing the signal through small opening and spreading it after passing the opening Scattering: scatter the coming signal Reflection : send the signal back towards the sender

19 Deployment Parameters 19

20 Deployment Parameters 20

21 Deployment Problems and Solutions Random Deployment Virtual force Algorithm Deterministic Deployment Circle Packing Energy Mapping Movement-Assisted Sensor Deployment Sink Placement Problem Single node Multiple sink deployment Relay Node Placement in WSN 21

22 Random Deployment Virtual Force Algorithm 22

23 Virtual Force Algorithm Sensors are initially deployed randomly Objective: To maximize the Coverage Assumptions: Assume no prior knowledge about the monitored field All nodes are mobile Energy and obstacles might present in the field 23

24 Virtual Force Algorithm (Cont.) Attractive and Repulsive forces Sensors do not physically move A sequence of virtual motion paths is determined for the randomly placed sensors. Once the effective sensor positions are identified, a one-time movement is carried out to redeploy the sensors at these positions. 24

25 Virtual Force Algorithm (Semi Distributed.) Assumptions: Clustered network All clustered heads are able to communicate with the sink node The cluster head is responsible for executing the VFA and managing the one-time movement of sensors to the desired locations. 25

26 Virtual Force Algorithm (Cont.) Each sensor behaves as a “Source of force” for all other sensors. This force can be either positive (Attractive) or negative (Repulsive). The closeness and wide distance between two sensors are measured using a predefined threshold. 26

27 Virtual Force Algorithm (Cont.) Sensor Binary Model Consider an n by m sensor field grid and assume that there are k sensors deployed in the random deployment stage. s i Each sensor has a detection range r. Assume sensor s i is deployed at point (x i, y i ). s i For any point P at (x, y), we denote the Euclidean distance between s i and P as d(s i, P), The coverage of a Grid Point P can be expressed by: 27

28 Virtual Force Algorithm (Cont.) Virtual Forces Attraction force  F12 Repulsive force  F13 Zero Force  F14 Obstacle Force  preferential coverage Force  Total Force on node i = 28

29 Virtual Force Algorithm (Cont.) Using such forces, the cluster head runs the VFA After stability occurs, Sensors are ordered to move to the new positions Energy and Obstacles might be problems Any sensor will not be able to move the required distance, the moving order is discarded Obstacles need an obstacle avoidance algorithm 29

30 Think….. If some sensors are stationary, does this affect the virtual force algorithm? What other problems you see in the algorithm? Coverage might not be satisfied due to the limitation in the energy since some nodes might not be able to move to the specified place. Mobility assumption might not be the case for all WSNs 30


32 The problem A set of sensors S is deployed in a monitored field F(A)for a period of time T. The field is divided into a grid of cells A. Each cell is assigned a weight where represents the importance of the cell i. The location of each sensor is assumed known; More than one sensor could be deployed in one cell. Sensors are assumed heterogeneous in terms of their energy and mobility. 32

33 Assumptions A sensor could be in different states; it could have its sensing off or on based on the field monitoring requirements. Sensing off, radio off – (sleep mode) Sensing off, radio receiving – (Receiving mode) Sensing off, radio transmitting – (Routing mode) Sensing on, radio receiving – (Sensing and Receiving mode) Sensing on, radio transmitting – (Sensing and Transmitting mode) Sensing on, radio off - (Sensing mode) 33

34 The main idea Knowing the energy map of the network : Knowing the energy map of the network : May lead to early detection to the uncovered areas. Redeploy new sensors Turn off some of the sensors due to their coverage redundancy Wake up some of the nodes when needed Move one or mobile nodes to cover the required uncovered spots 34

35 Redeployment based Energy map Step 1: Step 1: Energy dissipation rate prediction Each sensor predicts its own energy rate based on its history (e.g. Markov Chain..) Step 2: Step 2: sensors send their initial energy and the location, predicted energy dissipation rate to the sink node through a cluster head. Sensors update their energy dissipation rate based on a specific threshold (if the new dissipation rate increased more than the given threshold, the node sends the new dissipation rate) 35

36 Redeployment based Energy map Step 3 Step 3: the sink node constructs the energy map based on the received dissipated energy rate from the sensors. The sink may move one of the mobile sensors to the uncovered spot or wake up one of the sleeping sensors 36

37 Think ……. What are the disadvantages of energy mapping algorithm ? Sensor network is an event based network. Therefore, events are not frequently or based on specific pattern. Thus, the amount of messages to be transmitted to report the energy mapping will not be expected and might play a role in sensors energy dissipation. Centralized algorithm 37

38 Movement-Assisted Sensor Deployment 38

39 The problem of sensor deployment Given the target area, how to maximize the sensor coverage with less time, movement distance and message complexity The importance of the problem Distributed instead of centralized 39

40 Voronoi Diagram Definition: Every point in a given polygon is closer to the node in this polygon than to any other node. 40

41 Overview of the proposed algorithm Sensors broadcast their locations and construct local Voronoi polygons Find the coverage holes by examining Voronoi polygons If holes exist, reduce coverage hole by moving VOR : VORonoi-based Pull sensors to the sparsely covered area 41

42 Part of Assignment 1 (on CD and a printed report) Implement both Virtual Force algorithm and Voronoi based algorithm ? Report your experience and algorithms efficiency? Given a set of sensors with limited amount of energy. Some of these sensors are assumed mobile and others are assumed stationary. Assume similar sensing and communication ranges for all sensors. Sensors are allowed to move from one place to another iff they have enough energy to move to the required destination. In addition, the borders of the monitored area is assumed known in terms of 2D coordinates. Borders may be found in the monitored area. Advice a suitable deterministic deployment algorithm for efficient deployment to the sensors given that the deployed sensors have to be connected and important areas in the field are covered. In addition, your algorithm must guarantee the coverage of the monitored field for certain period of time. You may look for an already given solution or come up with a convincing one. 42

43 43 Deterministic Deployment Deployment Using Circle Packing

44 44 Deployment Using Circle Packing Deployment of homogenous sensors Full Coverage Deployment Deployment of connected heterogeneous sensors 44

45 45 Deployment of homogenous sensors sSensing rangeDensity 10.5000000000000.785398163397 20.2928932188130.539012084453 30.2543330950300.609644808741 40.2500000000000.785398163397 50.2071067811870.673765105566 60.1876806011470.663956909464 70.1744576301870.669310826841 80.1705406887010.730963825254 90.1666666666670.785398163397 140.1293317937100.735679255543 160.1250000000000.785398163397 250.1000000000000.785398163397 360.0833333333330.785398163397 These results were based on the information presented at “introduction to circle packing” book 45

46 46 Full Coverage Deployment sSensor’s sensing range (r)s 10.70710678118654752440160.16942705159811602395 20.55901699437494742410170.16568092957077472538 30.55901699437494742410180.16063966359715453523 40.35355339059327376220190.15784198174667375675 50.32616058400398728086200.15224681123338031005 60.29872706223691915876210.14895378955109932188 70.27429188517743176508220.14369317712168800049 80.26030010588652494367230.14124482238793135951 90.23063692781954790734240.13830288328269767697 100.21823351279308384300250.13354870656077049693 110.21251601649318384587260.13176487561482596463 120.20227588920818008037270.12863353450309966807 130.19431237143171902878280.12731755346561372147 140.18551054726041864107290.12555350796411353317 150.17966175993333219846300.12203686881944873607 46

47 47 Sequential Packing-based Deployment Algorithm (SPDA) Given Sensors Sensing Ranges Sensors Communication Ranges Bounded Monitored Field Objective Best Connected Deployment Scheme Max. Coverage. Min. Overlapped Areas Benefit from the properties learned from the optimal deployment using circle packing 47

48 Sequential Packing-based Deployment Algorithm 48

49 Sequential Packing-based Deployment Algorithm 49

50 50 Potential Points 50

51 Think ….. 51 How do you guarantee connectivity ?

52 52 Correctness of the Algorithm 52

53 Sink Re-Placement Problem 53

54 Potential benefits of sink relocation Increased network longevity: shortened data paths can safe the total energy consumed to data collection and extend the life of relaying nodes. Improved timeliness: involves fewer relays leading to avoidance of large packet backlogs Enhanced safety: moves the sink away from harmful events without damaging network performance 54

55 Energy-Based Relocation -- Motivation Sink node Inactive Sensor Active Sensor One hop Sensor Dead Sensor Can repositioning the sink node help? Normal Operational Mode: Sensors pursue multi-hop paths to communicate with the sink node Issues: When the sink is stationary, nearby sensors get involved in heavy packet forwarding and die quickly Nodes further away are picked as substitute relays Consequence : Increase in total transmission power  rapid energy depletion Effect grows spirally outward To where ? 55

56 Moving the Sink Where to go  Towards the region, whose sensors generate the most number of packets  Centroid of the last-hop nodes that route the largest traffic (use a distance * traffic metric) 56

57 Think…. What about putting the sink node initially in the center of all nodes? Will this be the best position for the sink node? No, because sensor networks again are event based networks 57

58 Part of your assignment Device an algorithm for Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks? You may look at : E. Ilker Oyman and Cem Ersoy, Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks,, IEEE International Conference on Communications, 2004Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks, IEEE International Conference onCommunications2004 58

59 Relay Node Placement in WSN Clustering Algorithms 59

60 Clustering Facts Clustering plays a dominant role in delaying the first node death, while aggregation plays a dominant role in delaying the last node death In each cluster one node acts as a cluster head which is in charge of coordinating with other cluster heads 60

61 LEACH Algorithm The LEACH Network is made up of nodes, some of which are called cluster-heads The job of the cluster-head is to collect data from their surrounding nodes and pass it on to the base station LEACH is dynamic because the job of cluster-head rotates LEACH is considered as clustering and routing protocol 61

62 The Amount of Energy Depletion This is the formula for the amount of energy depletion by data transfer: 62

63 LEACH’s Two Phases The LEACH network has two phases: the set-up phase and the steady-state The Set-Up Phase Where cluster-heads are chosen The Steady-State The cluster-head is maintained Data is transmitted between nodes 63

64 Stochastic Threshold Algorithm  Cluster-heads can be chosen stochastically (randomly based) on this algorithm:  If n < T(n), then that node becomes a cluster-head  The algorithm is designed so that each node becomes a cluster-head at least once. 64

65 Deterministic Threshold Algorithm A modified version of this protocol is known as LEACH-C (or LEACH Centralized) This version has a deterministic threshold algorithm, which takes into account the amount of energy in the node… 65

66 Think more ….. How to modify LEACH to include more parameters such as node degree? 66

67 67/103 HEED: Hybrid Energy Efficient Distributed Clustering

68 HEED was designed to select different cluster heads in a field according to the amount of energy that is distributed in relation to a neighboring node. Four primary goals: Four primary goals: prolonging network life-time by distributing energy consumption terminating the clustering process within a constant number of iterations/steps minimizing control overhead producing well-distributed cluster heads and compact clusters. 68

69 Heed Algorithm Each node performs neighbor discovery, and broadcasts its cost to the detected neighbors. Each node sets its probability of becoming a cluster head, Chprob, as follows: Where, Cprob is the initial percentage of cluster heads among n nodes (it was set to 0.05), Eresidual and Emax are the residual and the maximum energy of a node (corresponding to the fully charged battery), respectively. The value of CHprob is not allowed to fall below the threshold pmin. 69

70 Disadvantage (LEACH and HEED) – think…. Nodes’ score is computed based on node identifiers, and each node holds its message transmission until all its neighbors with lower IDs have done so. It is assumed that the network topology does not change during the algorithm execution, and it is thus valid for each node to wait until it overhears every higher-scored neighbor transmitting. 70

71 Think… How to solve Heed’s problems? 71

72 HEED Assignment Previous Algorithm is used with homogenous sensors (all have the same characteristics ). Device another clustering algorithm for heterogeneous WSN (nodes with different capabilities). You may have a look at the following paper Harneet Kour and Ajay K. Sharma, “Hybrid Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network, ” International Journal of Computer Applications (0975 – 8887) Volume 4 – No.6, July 2010Hybrid Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network, 72

73 Mobility Resistant Clustering in Multi-Hop Wireless Networks --- Distributed Efficient Clustering Approach (DECA) ---DECA 73

74 DECA Each node periodically transmits a Hello message to identify itself, and based on such Hello messages, each node maintains a neighbor list. Define for each node the score function as: Where E stands for the node residual energy, C stands for the node connectivity, I stands for the node identifier, and the weights follow The computed score is then used to compute the delay for this node to announce itself as the cluster head. The higher the score, the sooner the node will transmit. The computed delay is normalized between 0 and a certain upper bound D max 74

75 Think… How mobility can affect DECA algorithm? The connectivity parameter changes with mobility and the node might be selected as a cluster head multiple times 75

76 Multimodal Limited Similarity Clustering (MFLC) 76

77 MFLC for single and multimodal sensor networks A single feature sensor network is a network with each sensor node reports only one feature. Multimodal sensor network is a network with nodes report more than one feature. MFLC adapts LEACH clustering technique to support the multimodal sensor networks. MFLC differs from the LEACH on the criteria used for a node to decide to be a cluster head or not. 77

78 MFLC single and multimodal sensor networks Score Equation : 78

79 Data Similarity Clustering Based Fuzzy Logic (DSBF)DSBF 79

80 DSBF Phase One: Computing Node Degrees Phase Two: Cluster Head Election Phase Three: Data Reporting 80

81 Phase One: Computing Node Degrees The node degree based similarity feature is computed The node degree in this context means the number of similar sensors around 81

82 Phase Two: Cluster Head Election 82

83 Fuzzy C-Means Clustering for Efficient Operations in WSNs 83/103

84 Main idea 84/103 Instead of one cluster per node use multiple clusters with different membership functions

85 Multilayer clustering example 85/103

86 Semi Distributed Clustering Monitoring Nodes Clustering 86/103

87 Think Can the percentage more than 100% ? 87/103

88 Routing in WSN 88

89 89

90 Flat Routing Each node plays the same role Data-centric routing Due to not feasible to assign a global id to each node Save energy through data negotiation and elimination of redundant data Protocols Sensor Protocols for Information via Negotiation (SPIN) Directed diffusion (DD) Rumor routing Minimum Cost Forwarding Algorithm (MCFA) Gradient-based routing (GBR) Information-driven sensor querying/Constrained anisotropic diffusion routing (IDSQ/CADR) COUGAR ACQUIRE Energy-Aware Routing Routing protocols with random walks 90

91 Sensor Protocols for Information via Negotiation (SPIN) 91/103

92 Features Negotiation to operate efficiently and to conserve energy using a meta-data Resource adaptation To extend the operating lifetime of the system monitoring their own energy resources SPIN Message ADV – new data advertisement REQ – request for ADV data DATA – actual data message ADV, REQ messages contain only meta-data Sensor protocols for information via negotiation (SPIN) 92

93 Sensor protocols for information via negotiation (SPIN) Operation process Step1 ADV Step3 DATA Step2 REQ Step4 ADV Step5 REQ Step6 DATA 93

94 Sensor protocols for information via negotiation (SPIN) Resource adaptive algorithm When energy is plentiful Communicate using the 3-stage handshake protocol When energy is approaching a low-energy threshold If a node receives ADV, it does not send out REQ Energy is reserved to sensing the event Advantage Simplicity Each node performs little decision making when it receives new data Need not forwarding table Robust to topology change Drawback Large overhead Data broadcasting 94

95 Think…. In SPIN What about mobile nodes? What about the multimodal Wireless nodes? 95

96 Directed Diffusion (DD) 96/103

97 Directed Diffusion (DD) Feature Data-centric routing protocol A path is established between sink node and source node Localized interactions The propagation and aggregation procedures are all based on local information Four elements Interest A task description which is named by a list of attribute-value pairs that describe a task Gradient Path direction, data transmission rate Data message Reinforcement To select a single path from multiple paths 97

98 Interest Propagation Flooding Constrained or Directional flooding based on location. Directional Propagation based on previously cached data. Source Sink Interest Gradient

99 Data Propagation Reinforcement to single path delivery. Multipath delivery with probabilistic forwarding. Multipath delivery with selective quality along different paths. Source Sink Gradient Data

100 Directed Diffusion (DD) Advantage Small delay Always transmit the data through shortest path Robust to failed path Drawback Imbalance of node lifetime The energy of node on shortest path is drained faster than another Time synchronization technique To implement data aggregation- paths change with interests Not easy to realize in a sensor network The overhead involved in recording information Increasing the cost of a sensor node 100

101 Think…. In DD What about mobile nodes? What about the multimodal Wireless nodes? 101

102 Comparison between SPIN, LEACH & Directed Diffusion SPINLEACHDirected Diffusion Optimal Route No Yes Network Lifetime GoodVery goodGood Resource Awareness Yes Use of meta-data YesNoYes 102

103 Minimum Cost Forwarding Algorithm (MCFA) Objective Establish the cost field Transmit the data through the minimum-cost path Feature Optimality Minimum cost path criteria : hop count, energy consumption, delay etc. Simplicity Need not to maintain forwarding table Need not to know an ID for a neighbor node 103

104 Minimum Cost Forwarding Algorithm (MCFA) Operation process Each node stores its cost to the sink The sink broadcasts an ADV message containing its own cost (0 initially) Each node receiving the message transmits neighbor node Add the cost in ADV message to its own cost The cost field is set up after the ADV message propagates through the network The source transmits an information through using the cost field Drawback Limited network size The time to set the cost field is directly proportional to the size of the network Load is not balanced 104

105 Think…. What about mobile nodes? What about the multimodal Wireless nodes? 105

106 Geographic Adaptive Fidelity (GAF) Forms a virtual grid of the covered area Each node associates itself with a point in the grid based on its location Nodes associated with same point in grid are considered equivalent Some nodes in an area are kept sleeping to conserve energy Nodes change state from sleeping to active for load balancing 106

107 Creating a Virtual Grid Use location information (GPS) to create a virtual grid All nodes in a grid are equivalent Only one node from a grid point is active at a time All nodes in a grid point is within the radio range of nodes in adjacent grids Virtual grid results in hierarchical clusters of nodes 107

108 Think once more …. What are the problems of GAF? What about mobile nodes? What about the multimodal Wireless nodes? 108

Download ppt "Introduction to Sensor Networks Rabie A. Ramadan, PhD Cairo University 2."

Similar presentations

Ads by Google