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

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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 Deployment, Clustering, and and Routing in WSN

3 Deployment Constraints Sensor characteristics Monitored field characteristics Monitored/probed object 3

4 Deployment Parameters 4

5 5

6 6

7 7

8 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

9 Random Deployment Virtual Force Algorithm 9

10 Virtual Force Algorithm Sensors are initially deployed randomly Objective: To maximize the Coverage Assume no prior knowledge about the monitored field All nodes are mobile Energy and obstacles might present in the field 10

11 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. 11

12 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. 12

13 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. 13

14 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: 14

15 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 = 15

16 Virtual Force Algorithm (Cont.) Energy Constraints 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 16

17 Think….. If some sensors are stationary, does this affect the virtual force algorithm? 17

18 SENSOR REPLACEMENT BASED ENERGY MAPPING 18

19 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. 19

20 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) 20

21 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 21

22 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) 22

23 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 23

24 Think ……. What are the disadvantages of energy mapping algorithm ? 24

25 Movement-Assisted Sensor Deployment 25

26 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 26

27 Voronoi Diagram Definition: Every point in a given polygon is closer to the node in this polygon than to any other node. 27

28 Overview of the proposed algorithm Sensors broadcast their locations and construct local Voronoi polygons Find the coverage holes by examine Voronoi polygons If holes exist, reduce coverage hole by moving VOR : VORonoi-based Pull sensors to the sparsely covered area 28

29 Part of Assignment 2 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. 29

30 30 Deterministic Deployment Deployment Using Circle Packing

31 31 Deployment Using Circle Packing Deployment of homogenous sensors Full Coverage Deployment Deployment of connected heterogeneous sensors

32 32 Deployment of homogenous sensors sSensing rangeDensity

33 33 Full Coverage Deployment sSensor’s sensing range (r)s

34 34 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

35 Sequential Packing-based Deployment Algorithm 35

36 Sequential Packing-based Deployment Algorithm 36

37 37 Potential Points

38 Think ….. 38 How do you guarantee connectivity ?

39 39 Correctness of the Algorithm

40 Sink Placement Problem

41 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

42 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 ?

43 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)

44 Think…. What about putting the sink node initially in the center of all nodes? Does this will be the best position for the sink node? 44

45 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

46 Relay Node Placement in WSN Clustering Algorithms

47 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

48 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

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

50 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 When data is transmitted between nodes

51 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.

52 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…

53 Think more ….. How to modify LEACH to include more parameters such as node degree? 53

54 HEED: Hybrid Energy Efficient Distributed Clustering 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.

55 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.

56 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. Each node stops its protocol execution if it knows that every node in its neighborhood has transmitted. 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. 56

57 Think… How to solve Heed’s problems? 57

58 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,

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

60 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

61 Think… How mobility can affect DECA algorithm? 61

62 Multimodal Limited Similarity Clustering (MFLC)

63 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.

64 MFLC single and multimodal sensor networks Score Equation :

65 Data Similarity Clustering Based Fuzzy Logic (DSBF)DSBF

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

67 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

68 Phase Two: Cluster Head Election

69 Routing in WSN

70

71 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

72 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)

73 Operation process Step1 ADV Step3 DATA Step2 REQ Step4 ADV Step5 REQ Step6 DATA

74 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

75 Think…. In SPIN What about mobile nodes? What about the multimodal Wireless nodes? 75

76 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

77 Directed Diffusion (DD) Basic scheme Sink Source Step 1 : Interest propagation Interests Event Sink Source Step 2 : Initial gradients setup Gradients Event Low rate Sink Source Step 3 : Data delivery along reinforced path Event High rate

78 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 Not easy to realize in a sensor network The overhead involved in recording information Increasing the cost of a sensor node

79 Think…. In DD What about mobile nodes? What about the multimodal Wireless nodes? 79

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

81 Feature Combine query flooding and event flooding Discovering arbitrary paths instead of the shortest path Rumor routing is attractive only when The number of queries is larger than a threshold The number of events is smaller than another threshold Assumption The network is composed of densely distributed nodes Only short distance transmissions Immobile nodes Rumor Routing

82 Basic scheme Each node maintains A lists of neighbors An event table When a node detects an event Generate an agent Let it travel on a random path The visited node form a gradient to the event When a sink needs an event Transmit a query The query meets some node which lies on the gradient Route establishment

83 Rumor Routing The node sensing an event probabilistically generates an agent. In order to propagate directions to the event as far as possible in the network, a straightening algorithm is used The agent maintains a list of recently seen nodes. When picking its next hop, it will first try nodes not in the list.

84 Think…. In Rumor Routing What about mobile nodes? What about the multimodal Wireless nodes? 84

85 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

86 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 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

87 Think…. In Rumor Routing What about mobile nodes? What about the multimodal Wireless nodes? 87

88 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

89 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 89

90 Think once more …. What are the problems of GAF? What about mobile nodes? What about the multimodal Wireless nodes? 90


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