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1 A Survey on Routing Protocols for Wireless Sensor Networks By Kemal Akkaya & Mohamed Younis Presented by Aggelos Vlavianos Jorge Mena Department of Computer.

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Presentation on theme: "1 A Survey on Routing Protocols for Wireless Sensor Networks By Kemal Akkaya & Mohamed Younis Presented by Aggelos Vlavianos Jorge Mena Department of Computer."— Presentation transcript:

1 1 A Survey on Routing Protocols for Wireless Sensor Networks By Kemal Akkaya & Mohamed Younis Presented by Aggelos Vlavianos Jorge Mena Department of Computer Science and Engineering 02/08/05

2 2 Table of Contents Goals Introduction System Architecture & Designing Protocols for Sensor Networks Summary

3 3 Goals A survey of recent routing protocols for sensor networks Classification of these protocols

4 4 Introduction Sensors are micro-electro-mechanical systems (MEMS) Low power devices Data processing capable Communication capabilities

5 5 Introduction - Usage Gather data locally (Temperature, Humidity, Motion Detection, etc.) Send them to a command center (sink) Applications  Surveillance  Security  Disaster Management  Environmental Studies

6 6 Introduction - Constraints Limitations  Energy Constrains  Bandwidth All layers must be energy aware Need for energy efficient and reliable network routing Maximize the lifetime of the network

7 7 Introduction - Routing No global addressing Redundant data traffic Multiple-source single-destination network Careful resource management  Transmission power  On-board energy  Processing capacity  Storage

8 8 System Architecture & Designing Network Dynamics  Mobile or Stationary nodes  Static Events (Temperature)  Dynamic Events ( Target Detection) Node Deployment  Deterministic – Placed manually  Self-organizing – Scattered randomly

9 9 System Architecture & Designing Energy Considerations  Direct vs Multi-hop communication Direct Preferred – Sensors close to sink Multi-hop – unavoidable in randomly scattered networks Data Delivery Models  Continuous  Event-driven  Query-driven  Hybrid

10 10 System Architecture & Designing Node Capabilities  Homogenous  Heterogeneous  Nodes dedicated to a particular task (relaying, sensing, aggregation) Data Aggregation/Fusion  Aggregation – Combination of data by eliminating redundancy  Data Fusion is Aggregation through signal processing techniques  Aggregation achieves energy savings

11 11 Introduction - Taxonomy Classification of Routing Protocols  Data Centric  Hierarchical  Location-based  Network Flow & QoS Aware

12 12 Data-centric Protocols Sink sends queries to certain regions and waits data from sensors located in that region Attribute-based naming is necessary to specify properties of data

13 13 Data-centric Protocols Flooding Gossiping Sensor Protocols for Information via Negotiation (SPIN) Directed Diffusion Energy-aware Routing Rumor Routing Gradient-Based Routing (GBR) Constrained Anisotropic Diffusion Routing (CADR) COUGAR ACtive QUery forwarding In sensoR nEtworks (ACQUIRE)

14 14 Data-centric Protocols Flooding  Sensor broadcasts every packet it receives  Relay of packet till the destination or maximum number of hops  No topology maintenance or routing Gossiping  Enhanced version of flooding  Sends received packet to a randomly selected neighbor

15 15 Data-centric Protocols – Flooding, Gossiping Problems Problems of Implosion, Overlap, Resource Blindness

16 16 Data-centric Protocols Sensor Protocols for Information via Negotiation (SPIN)

17 17 Data-centric Protocols - SPIN Topological changes are localized - Each node needs to know only its neighbors SPIN halves the redundant data in comparison to flooding Cannot guarantee data delivery SPIN NOT good for applications that need reliable data delivery

18 18 Data-centric Protocols Directed Diffusion  Uses a naming scheme for the data to save energy  Attribute-value pairs for data and queries on- demand (Interests)  Interests are broadcasted by the sink (query) to its neighbors (caching), which can do in-network aggregation  Gradients = reply links to an interest (path establishment)

19 19 Data-centric Protocols – Direct Diffusion Energy saving and delay done with caching No need for global addressing (neighbor-to- neighbor mechanism) Cannot be used for continuous data delivery or event-driven applications

20 20 Data-centric Protocols Energy-aware Routing  Occasional use of a set of sub-optimal paths  Multiple paths used with certain probability  Increase of the total lifetime of the network  Hinders the ability for recovering from node failure  Requires address mechanism  Complicate setup

21 21 Data-centric Protocols Rumor Routing  Variation of Directed Diffusion  Flood the events instead of the queries  Creation of an event  generation of a long live packet travel through the network (agent)  Nodes save the event in a local table  When a node receives query  checks its table and returns source – destination path

22 22 Data-centric Protocols – Rumor Routing Advantages  Can handle node failure  Significant energy savings Disadvantages  Works well only with small number of events  Overhead through adjusting parameters, like the time to live of the agent

23 23 Data-centric Protocols Gradient-Based Routing (GBR)  Slightly changed version of Directed Diffusion  Keep the number of hops to the sink when an interest is created (height of the node)  Node’s height – neighbors height = gradient of the link  Node forward packet to the link with largest gradient

24 24 Data-centric Protocols -GBR Traffic Spreading and Data Aggregation  Balance uniformly the network traffic  Stochastic scheme  Energy-based scheme  Stream-based scheme Outperforms Directed Diffusion in terms of total communication energy

25 25 Data-centric Protocols Constrained Anisotropic Diffusion Routing (CADR)  General form of Directed Diffusion  Query Sensors  Route data in the network  Activates sensors close to the event and dynamically adjusts routes  Routing based on a local information/cost gradient  More energy efficient than Directed Diffusion

26 26 Data-centric Protocols COUGAR  Views the network as a huge distributed database  Declarative queries to abstract query processing from network layer functions  Introduces a new query layer  Leader node performs data aggregation and transmits to the sink

27 27 Data-centric Protocols - COUGAR Disadvantages  Additional query layer brings overhead in terms of energy consumption and storage  In network data computation requires synchronization (i.e. wait for all data before sending data)  Dynamically maintenance of leader nodes to prevent failure

28 28 Data-centric Protocols ACtive QUery forwarding In sensoR nEtworks (ACQUIRE)  Views network as a distributed database  Node receiving a query from the sink tries to respond partially and then forwards packet to a neighbor  Use of pre-cached information  After the query is answered, result is returned to the sink by using the reverse path or the shortest path  If cache information is not up to date  node gathers information from neighbors within look ahead of d hops

29 29 Data-centric Protocols – ACQUIRE Motivation: Deal with one shot complex queries Efficient routing by adjusting parameter d If d equals network size  behaves similar to flooding If d too small the query has to travel more hops

30 30 Hierarchical Protocols Maintain energy consumption of sensor nodes  By multi-hop communication within a particular cluster  By data aggregation and fusion  decrease the number of the total transmitted packets

31 31 Hierarchical Protocols LEACH – Low-Energy Adaptive Clustering Hierarchy Power-Efficient GAthering in Sensor Information Systems (PEGASIS)  Hierarchical PEGASIS Threshold sensitive Energy Efficient sensor Network protocol (TEEN)  Adaptive Threshold TEEN (APTEEN) Energy-aware routing for cluster-based sensor networks Self-organizing protocol

32 32 Hierarchical Protocols LEACH – Low-Energy Adaptive Clustering Hierarchy  One of the first hierarchical routing protocols  Forms clusters of the sensor nodes based on received signal strength  Local cluster heads route the information of the cluster to the sink  Cluster heads change randomly over time  balance energy dissipation  Data processing & aggregation done by cluster head

33 33 Hierarchical Protocols - LEACH Advantages  Completely distributed  No global knowledge of the network  Increases the lifetime of the network Disadvantages  Uses single-hop routing within cluster  not applicable to networks in large regions  Dynamic clustering brings extra overhead (advertisements, etc)

34 34 Hierarchical Protocols Power-Efficient GAthering in Sensor Information Systems (PEGASIS)  Improvement of LEACH  Forms chains from sensors rather than clusters  Data aggregation in the chain  one node sends the data to the base station  Outperforms LEACH  Excessive delay for distant nodes in the chain

35 35 Hierarchical Protocols Hierarchical PEGASIS  Extension of PEGASIS  Decrease the delay for the packets during transmission to the base station  Solution to the delay data gathering problem Simultaneous transmissions of data messages Avoid collisions and possible signal interference  Signal Coding (e.g. CDMA)  Spatially separated nodes can transmit at the same time

36 36 Hierarchical Protocols Threshold sensitive Energy Efficient sensor Network protocol (TEEN)  Good for time-critical applications  Hierarchical along with a data-centric approach  Hierarchical grouping: Close nodes form clusters and this process goes on the second level until sink is reached  Cluster headers broadcast: Hard Threshold Soft Threshold  Not good for applications that need periodic reports

37 37 Hierarchical Protocols

38 38 Hierarchical Protocols Adaptive Threshold TEEN (APTEEN)  Captures both periodic data collection and reacting to time-critical events  APTEEN supports queries: Historical -Analyze past data values One-Time –Take a snapshot of the current network view Persistent monitor an event for a period of time

39 39 Hierarchical Protocols – TEEN & APTEEN Advantages  Outperform LEACH in terms of energy dissipation and total lifetime of the network Disadvantages  Overhead and complexity of: Forming multiple level clusters Implementing threshold-based functions Dealing with attribute-based naming of queries

40 40 Hierarchical Protocols Energy-aware routing for cluster-based sensor networks  Assumptions: Sensors are grouped into clusters prior to network operation Cluster Heads (Gateways) less energy constrained Cluster Heads know the location of the sensors  Known Multi-Hop routing to collect data Communication node (sink) communicates only with gateways

41 41 Hierarchical Protocols Stages of a Sensor inside a cluster  Sensing only  Relaying only  Sensing-Relaying  Inactive

42 42 Hierarchical Protocols Least Cost path used between nodes and gateway Cost function  Energy Consumption  Delay Optimization  Performance Metrics TDMA based MAC is used for nodes to send data to the gateways Protocol performs well for  Energy-based metrics (e.g. network lifetime)  Cotemporary metrics (e.g. throughput)

43 43 Hierarchical Protocols Self-organizing protocol  Architecture supports heterogeneous sensors  Nodes act as routers  backbone of communication Stationary  Sensing nodes forward data to the routers Stationary Mobile  Sensors are a part of the network if they are reachable by a router

44 44 Hierarchical Protocols The architecture requires addressing  Sensor identified by the router is connected to Algorithm for Self-Organizing & Creation of routing table  Discovery phase  Organization phase  Maintenance phase  Self-reorganization phase Utilizes router nodes to keep all sensors connected by forming a dominating set

45 45 Hierarchical Protocols Advantages  Useful for applications which need communication of a specific node (e.g. parking- lot networks)  Small cost of maintaining routing tables  Keeping routing hierarchy strictly balanced  Energy Savings – Utilization of a limited subset of nodes Disadvantages  Organization phase not on demand  Many cuts in the network increase the probability of applying reorganization phase

46 46 Location-based Protocols Distance between two nodes is calculated using location information Energy consumption can be estimated  Efficient energy utilization Protocols designed for Ad hoc networks with mobility in mind  Applicable to Sensor Networks as well  Only energy-aware protocols are considered

47 47 Location-based Protocols MECN & SMECN  Minimum Energy Communication Network GAF  Geographic Adaptive Fidelity GEAR  Geographic and Energy Aware Routing

48 48 MECN & SMECN Utilizes low power GPS Best applicable to non-mobile sensor networks Identifies a relay region Find a sub-network to relay traffic Self-reconfiguring Dynamically adaptive

49 49 SMECN Assumption: broadcasting allowed Obstacles considered Smaller network (number of edges)  Less hops per transmission More energy efficient than MECN Less maintenance cost of links More overhead introduced

50 50 GAF Forms a virtual grid for covered area Nodes use GPS to associate itself to the grid Nodes are allowed to be turned off if are equivalent. Handle mobility

51 51 GAF Three States  Discovery  Active  Sleep Representative Node is always active  No aggregation or fusion performed As good as a normal Ad hoc in terms of latency and packet loss (saving energy)

52 52 GEAR Uses heuristics to route packets  Energy aware neighbor  Geographic informed neighbor Same as Direct Diffusion but restricts interest by region Estimated Cost  Residual energy and distance to destination Learned Cost  Accounts for routing around holes

53 53 Network Flow & QoS-aware Protocols Network Flow: Maximize traffic flow between two nodes, respecting the capacities of the links QoS-aware protocols consider end-to- end delay requirements while setting up paths

54 54 Network Flow & QoS-aware Protocols Maximum Lifetime Energy Routing Maximum Lifetime Data Gathering Minimum Cost Forwarding Sequential Assignment Routing Energy Aware QoS Routing Protocol SPEED

55 55 Maximum Lifetime Energy Routing Maximizes network lifetime by defining link cost as a function of:  Remaining energy  Required transmission energy Tries to find traffic distribution (Network flow problem) The least cost path is one with the highest residual energy among paths

56 56 Maximum Lifetime Data Gathering Maximizes the Data-gathering schedule Maximum Lifetime Data Aggregation  Data aggregation plus setting up maximum lifetime of routes Maximum Lifetime Data Routing  When data aggregation is not possible Computational Expensive (scalability)  Clustering MLDA

57 57 Minimum Cost Forwarding Aims at finding the minimum cost path in a large network, simple and scalable Cost function captures delay, throughput, and energy metrics from node to sink  Back-off based algorithm Finds optimal cost of all nodes to the sink by using only one message per node Does not require addressing or forwarding paths

58 58 Sequential Assignment Routing Table-driven, multi-path protocol Creates trees rooted at immediate neighbors of the sink (multiple paths)  QoS metrics, energy resource, priority level of each packet Failure recoverable (done locally) High overhead to maintain tables and states at each sensor

59 59 Energy Aware QoS Routing Protocol Finds least cost and energy efficient paths that meet the end-to-end delay during connection  Energy reserve, transmission energy, error rate Class-based queuing model used to support best-effort and real-time traffic

60 60 Energy Aware QoS Routing Protocol

61 61 SPEED Each node maintains info about its neighbors and uses geographic forwarding to find the paths Tries to ensure a certain speed for each packet in the network Congestion avoidance

62 62 Summary

63 63 Questions


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