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Routing Techniques in Wireless Sensor Networks: A Survey

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1 Routing Techniques in Wireless Sensor Networks: A Survey

2 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

3 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

4 Introduction (1/2) Routing protocols in WSNs Differ depending on the application and network architecture Classified into three categories based on the underlying network structure: Flat: Nodes are assigned equal roles Hierarchical: Nodes will play different roles Location-based: Nodes’ positions are exploited to route data Classified into multipath-based, query-based, negotiation-based, QoS-based, and coherent-based depending on the protocol operation Trade-offs between energy and communication overhead savings

5 Introduction (2/2)

6 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

7 Challenges (1/2) Due to the relatively large number of sensor nodes, it is not possible to build a global addressing scheme for the deployment of a large number of sensor nodes as the overhead of ID maintenance is high Applications of sensor networks require the few of sensed data from multiple sources to a particular BS Sensor nodes are tightly constrained in terms of energy, processing, and storage capacities

8 Challenges (2/2) In most application scenarios, nodes in WSNs are generally stationary after deployment except for maybe a few mobile nodes. Sensor networks are application-specific Position awareness of sensor nodes is important since data collection is normally based on the location Data collected based on common phenomena, so there is a high probability that this data has some redundancy

9 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

10 Design Issues (1/4) The main design goals of WSNs is to carry out data communication while trying to prolong the lifetime of the network and prevent connectivity degradation by employing aggressive energy management techniques

11 Design Issues (2/4) Node deployment: application-dependent
Manual (deterministic): data is routed through predetermined paths Randomized: nodes are scattered randomly, creating an ad hoc routing infrastructure Distribution of nodes is not uniform, optimal clustering becomes necessary Energy consumption without losing accuracy Use up their limited supply of energy The malfunctioning of some sensor nodes Data reporting method Time-driven: for application requiring periodic data monitoring Event-driven: react due to a certain event (time-critical ap) Query-driven: response to a query (time-critical ap) Hybrid

12 Design Issues (3/4) Node/link heterogeneity Fault tolerance
For example, hierarchical protocols designate a cluster head node Fault tolerance The failure of sensor nodes should not affect the overall task of the sensor network Scalability Any routing scheme must be able to work with huge number of sensor nodes Network dynamics Nodes can be mobile The phenomenon can be mobile Transmission media The required bandwidth is low(1-100 kb/s) TDMA-based protocols conserve more energy than contention-based protocols (like CSMA)

13 Design Issues (4/4) Connectivity Coverage Data aggregation
Density in sensor networks Depends on the possibly random distribution of nodes Coverage A sensor’s view of the environment is limited in both range and accuracy Data aggregation Sensor nodes may generate significant redundant data To reduce the number of transmissions Quality of service Network lifetime often is considered more important Bounded latency for data delivery is a condition for time-constrained applications

14 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

15 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

16 Sensor protocols for information via negotiation (SPIN)
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

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

18 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

19 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

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

21 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

22 Rumor Routing Feature Assumption
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

23 Rumor Routing Basic scheme Each node maintain
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

24 Rumor Routing The node sensing an event probabilistically generates an agent. The probability of generating an agent is an algorithm parameter… 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.

25 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

26 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

27 Minimum Cost Forwarding Algorithm (MCFA)
The direction of routing is always known – toward the fixed external BS The BS broadcasts a message with the cost set to zero, while every node initially sets its least cost to the BS to infinity To check if the estimate in the message plus the link on which it is received is less than the current estimate. 110

28 Gradient-based routing
Memorize the number of hops when the interest is diffused Minimum the number of hops to reach the BS To obtain balanced traffic and prolong lifetime: A stochastic scheme An energy-based A stream-based scheme

29 Information-driven sensor querying and constrained anisotropic diffusion routing (IDSQ/CADR)
Key idea Routing data in a network so that information gain is maximized while power and bandwidth consumption is minimized CADR: Aims to be a general form of directed diffusion Diffuses queries by using a set of information criteria to select which sensors can get the data IDSQ: Does not specifically define how the query and information are routed The querying node can determine which node can provide the most useful information with the additional advantage of balancing the energy cost

30 Information-driven sensor querying and constrained anisotropic diffusion routing (IDSQ/CADR)
with global knowledge of sensor positions optimal position to route query to is given by xo = argx [Mc = 0] note: Mc = Mu – (1 - )Ma The routing is directly addressed to the sensor node that is closest to the optimal position

31 COUGAR View the network as a huge distributed database system
Use declarative queries Abstract query processing from the network layer Disadvantages May add extra overhead – query layer Synchronization Leader nodes maintenance

32 COUGAR

33 ACQUIRE Views the network as a distributed DB where complex queries can be divided into several subqueries The BS sends a query, which is then forwarded by each node receiving the query Each node tries to respond to the query partially bye using its precached information Triggered update obtaining information from all neighborhood within a look-ahead of d hops Query is returned back to the querying node as a completed response

34 ACQUIRE Update only if current information is obsolete
Active Query Update Messages Complete Response Update only if current information is obsolete Randomly select next node to forward Complete response is routed back directly to the original querier

35 Energy-Aware Routing A destination-initiated reactive protocol
It maintains a set of paths Choosing paths by means of certain probability depending on how low the energy consumption is

36 Energy-Aware Routing Setup Phase Directional flooding Local Rule
10 nJ 30 nJ p1 = 0.75 p2 = 0.25 Local Rule Sensor Controller

37 Energy-Aware Routing Data Communication Phase
Each node makes a local decision Controller Sensor 0.3 0.7 0.6 0.4 1.0 1.0

38 Routing protocols with random walks
A routing protocol for WSN that tries to do load balancing among intermediate nodes. Making use of multiple paths that exist from source to destination by making local packet forwarding decisions Current algorithm is only valid for grid-topology sensor network Advantages Archiving load balancing Maintaining little state information Disadvantages Topology may not be practical

39 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

40 Hierarchical Routing Nodes will play different roles
Advantages related to scalability and efficient communication Mainly two-layer routing Select cluster heads Routing Protocols Low Energy Adaptive Clustering Hierarchy (LEACH) Power-Efficient Gathering in Sensor Information Systems (PEGASIS) Threshold-Sensitive Energy Efficient Protocols Small Minimum energy communication network (MECN) Self-organizing protocol (SOP) Virtual grid architecture routing Hierarchical power-aware routing Two –Tier Data Dissemination (TTDD)

41 Low-energy adaptive clustering hierarchy (LEACH)
Randomly select sensor nodes as cluster-heads, so the high energy dissipation in communicating with the base station is spread to all sensor nodes in the sensor network. Set-up phase each sensor node chooses a random number between 0 and 1 If this random number is less than the threshold T(n), the sensor node is a cluster-head.

42 Low-energy adaptive clustering hierarchy (LEACH)
Set-up phase The cluster-heads advertise to all sensor nodes in the network The sensor nodes inform the appropriate cluster-heads that they will be a member of the cluster. (base on signal strength) Afterwards, the cluster-heads assign the time on which the sensor nodes can send data to the cluster-heads based on a TDMA approach.

43 Low-energy adaptive clustering hierarchy (LEACH)
Steady phase the sensor nodes can begin sensing and transmitting data to the cluster-heads. The cluster-heads also aggregate data from the nodes in their cluster before sending these data to the base station. After a certain period of time spent on the steady phase, the network goes into the set-up phase again and enters into another round of selecting the cluster-heads.

44 “Thanks for the time slot, Here’s my data” “Here’s your time slot”
“Me Head !!!” (CSMA-MAC) “I am with you” (CSMA-MAC) Cluster Set up Phase Advertisement Phase Every node chooses a random number (R) and compute a threshold T(n). T(n) = P/(1-P*(r mod(1/p)) if n element of G, = else P – desired percentage of cluster heads (e.g. 5%) r – the current round G – set of nodes that have not been cluster head in the last 1/P rounds It elects itself as a cluster-head if R < T(n) Every cluster-head broadcast an advertisement message, with the same transmit energy. Non-cluster-head node decide which cluster it joins in this round based on the received signal stregth. Largest strength  closet  minimal enery needed for communication. After decide which cluster it joins, each node informs the cluster-head To reduce energy consumption non- cluster-head nodes: Use minimal amount of energy chosen based on the strength of the cluster-head advertisement. Can turn off the radio until their allocated transmission time. Based on the number of nodes in the cluster, the cluster-head node creates a TDMA schedule telling each node when it can transmit. This schedule is broadcast back to the nodes in the cluster. “Thanks for the time slot, Here’s my data” “Here’s your time slot” Schedule Creation Phase Data Transmission Phase (TDMA) Modified from

45 Low-energy adaptive clustering hierarchy (LEACH)
= 0.05/(1-0.05*0) = 0.05/(1-0.05*1) = 0.05/(1-0.05*2) = 0.05/(1-0.05*3) = 0.05/(1-0.05*4) = 0.05/(1-0.05*5) = 0.05/(1-0.05*6) = 0.05/(1-0.05*7) = 0.05/(1-0.05*8) = 0.05/(1-0.05*9) = 0.05/(1-0.05*10) = 0.05/(1-0.05*18) = 0.05/(1-0.05*19) Number of clusters may not fixed in any round. To avoid the case that there is no cluster-head in a round…(PE-WASUN’04, Oct. 7, 2004) Simply skips the round which has no cluster-heads elected

46 Power-Efficient Gathering in Sensor Information Systems (PEGASIS)
Assumption All nodes have location information about all other nodes Sensor nodes are immobile Feature Chain-based power efficient protocol The chain construction by greedy algorithm Each node has global knowledge Dynamic leader selection To evenly distribute the energy load Data fusion

47 Power-Efficient Gathering in Sensor Information Systems (PEGASIS)
Performance PEGASIS Outperforms LEACH By eliminating the overhead of dynamic cluster formation By minimizing the total sum of transmission distances By limiting the number of transmissions Problem To obtain a global knowledge is difficult It is not suitable for sensor networks Scalability problem Very long delay

48 Threshold-Sensitive Energy Efficient Protocols
Terminology Hard Threshold (HT) A threshold value for the sensed attribute The absolute value of the attribute Soft Threshold (ST) A small change in the value of the sensed attribute which triggers the node to switch on its transmitter Feature Cluster-based routing protocol based on LEACH Time critical application The user can control the trade-off between energy efficiency and accuracy A smaller value of the ST more accurate picture of the network increased energy consumption

49 Threshold-Sensitive Energy Efficient Protocols
Basic scheme A gain of sensing value Decision whether to report it or not Based on the values of HT and ST Data are reported only When the sensed value exceeds HT When the value’s change is bigger than ST Drawback Cannot allocate the time slot Each node turn on its transmitter all the time Cannot distinguish a node which does not sense a “big” change from a dead or failed node Collision occurrence in the cluster

50 Small Minimum energy communication network (MECN)
Use small subgraph to communication The energy required to transmit data from node u to all its neighbors in subgraph G is less than the energy required to transmit to all its neighbors in graph G’ G’ G u v MECN SMECN

51 Self-organizing protocol (SOP)
To build architecture to support heterogeneous sensor SOP Discovery Phase: discovery neighbors Organization Phase: organize a hierarchy which is height balanced Maintenance Phase: keep track alive and routing table Self-Reorganization Phase: when group partitions or node failures

52 Sensor aggregates routing
The objective: To collectively monitor target activity in a certain environment (target tracking applications) Sensors are divided into clusters according to their sensed signal strength To elect a leader, information exchanges between neighboring sensors Three algs: DAM, EBAM, EMLAM

53 Sensor aggregates routing

54 Sensor aggregates routing
DAM algorithm: Goal : elect local cluster leaders. One peak may represent one target Compare with one-hop neighbors Broadcasts “qualification” Downward only

55 Sensor aggregates routing
12 13 10 12 14 15 12 10 12 11 10

56 Sensor aggregates routing
EBAM algorithm: Provides a solution to count a targets within each sensor cluster Consider the energy level of target signal sources The energy level is estimated by computing the signal impact area, combining a weighted form of the detected target energy at each “impacted” sensor. To convert the energy level into the corresponding target density: assume roughly constant source energy output for the targets.

57 Sensor aggregates routing
MLAM algorithm: removes the constant and equal target energy level assumption. estimates the target positions and signal energy using received signals, uses the resulting estimates to predict how signals from the targets may be mixed at each sensor.

58 Virtual grid architecture routing
Utilizes data aggregation and in-network processing to maximize the network lifetime In side each zone, a node is optimally selected to act as CH. Data aggregation is performed at two levels: Local: the set of CHs performing local aggregation Global: the selection of global aggregation points is NP-hard Strategies for the selection of MAs: Exact alg: ILP Approximate algs: genetics-based, k-means, greedy-based

59 Hierarchical power-aware routing
Goal To optimize the lifetime of the network. We develop an approximation algorithm called max-min zPmin. To ensure scalability, we introduce a hierarchical algorithm, which is called zone-based routing

60 Hierarchical power-aware routing
Max-min zPmin algorithm : Maximal minimal fraction of remaining power after transmission

61 Hierarchical power-aware routing
Adaptive computation for z : lifetime estimate

62 Hierarchical power-aware routing
Max-min zPmin: requires accurate power level information for all nodes Zone-based: a hierarchical approach Zone power estimation Routing across zones (Globe path routing) Routing within each zone (local path selection)

63 Hierarchical power-aware routing
Zone power estimation P: maximal number of messages

64 Hierarchical power-aware routing
Globe path routing: Modified Bellman-Ford algorithm Local path selection: Max-min zPmin algorithm is used directly to route a message within a zone

65 Two –Tier Data Dissemination (TTDD)
Excessive Power Consumption Increased Wireless Transmission Collisions State Maintenance Overhead

66 Two –Tier Data Dissemination (TTDD)
Assumption Sensor nodes are stationary and location-aware Sinks may change their location dramatically Sensor nodes are aware of their missions Feature Scalable and efficient data delivery protocol to multiple mobile sinks Mobile sensor nodes are not allowed in the network Location information is required to set up the grid structure Sensitive to the topology change

67 Two –Tier Data Dissemination (TTDD)
Basic scheme Grid construction Lower-tier query flooding Each node selects its own dissemination points Higher-tier query grid forwarding From dissemination point of sink to source node Communication between dissemination points Higher-tier data grid forwarding From source node to dissemination point of sink Transmission of actual data Lower-tier data trajectory forwarding Dissemination point transmits data to sink through PA, IA PA (Primary agent), IA (Intermediate agent)

68 Two –Tier Data Dissemination (TTDD)
Dissemination Node Data Announcement Source Data Sink Query Immediate Dissemination Node

69 Two –Tier Data Dissemination (TTDD)
Source Dissemination Node Trajectory Forwarding Data Announcement Source Immediate Dissemination Node Data

70 Two –Tier Data Dissemination (TTDD)
Grid maintenance issues: Handle unexpected dissemination node failures Efficiency Solutions: Source sets the Grid Lifetime in Data Announcement DN replication: each DN recruits several sensor nodes from its one-hop neighbor, replicates the location of the upstream DN DN failure detected and replaced on-demand by on-going query and data flows

71 Two –Tier Data Dissemination (TTDD)
Dissemination Node Source X Immediate Dissemination Node Data

72 Two –Tier Data Dissemination (TTDD)
Dissemination Node Source X Immediate Dissemination Node Data

73 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Hierarchical vs. Flat Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

74 Hierarchical vs. Flat

75 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

76 Location-Based Routing Protocols
Nodes’ positions are exploited to route data Sensor nodes are addressed by means of their locations Distance can be estimated on the basis of incoming signal strengths Protocols: Geographic Adaptive Fidelity Geographic and Energy Aware Routing MFR, DIR and GEDIR The Greedy Other Adaptive Face Routing SPAN

77 Geographic Adaptive Fidelity
Core idea Turn off a node if it is equivalent from a routing perspective Adaptively adjust routing fidelity use node deployment density What’s fidelity Uninterrupted connectivity between communicating nodes

78 Geographic Adaptive Fidelity
Determine routing equivalence What’s fidelity Uninterrupted connectivity between communicating nodes

79 Geographic Adaptive Fidelity
Use GPS information to decide virtual grid ID 3-state transition Discovery (Td) Active (Ta) Sleep (Ts) Node ranking Active node wins High energy node wins Adapting to mobility With GPS information

80 Geographic and Energy Aware Routing
Motivation: Reduce overhead of interest and low rate data flooding in directed diffusion Basic ideas: Leverage geographical information to restrict flooding, and recursively disseminate data inside the target region. Extend overall network lifetime using local techniques to balance energy usage Reuse routing information across multiple user queries.

81 Geographic and Energy Aware Routing
Forward the packets towards the target region: Greedy mode: minimizing cost function (f=mix function of distance and energy) Route around “communication holes” with energy aware neighbor estimation Disseminate the packet within the target region: Geographic Recursive Forwarding recursively re-send packets to sub-regions of the original geographic region

82 Geographic and Energy Aware Routing
Each node has a learned cost (historical cost) and an estimated cost (present state cost) to decide the next forwarding node Learned cost Estimated cost

83 MFR, DIR and GEDIR MFR – most forward with progress A B C A’ D S E F
Minimize DS.DA = |DS||DA’|

84 MFR, DIR and GEDIR MFR – loop-free An A1 A2 An-1 D A3 A1  A2

85 MFR, DIR and GEDIR DIR – best direction A D S Closest direction

86 MFR, DIR and GEDIR E DIR – not loop-free H D F G Transmission radius

87 MFR, DIR and GEDIR GEDIR – closest to destination
B Closest neighbor to D

88 MFR, DIR and GEDIR GEDIR – loop-free Assume A1 closest to D
A2 sends to A3 - contradiction

89 MFR, DIR and GEDIR MFR vs GEDIR may choose different node
B MFR vs GEDIR A S D A’ B’ may choose different node choice is same most of time! GEDIR wins in power efficiency AD<BD

90 The Greedy Other Adaptive Face Routing
Problem with greedy: Holes Stuck at X: No neighbor of X is closer to D than X. S X D

91 The Greedy Other Adaptive Face Routing
Route through the sequence of faces that intersect the line segment [S,D]. Go around each face. Switch to the next face at a common edge. D S

92 The Greedy Other Adaptive Face Routing
Simple face routing can be very bad

93 The Greedy Other Adaptive Face Routing
Bound Searchable Area s t

94 The Greedy Other Adaptive Face Routing
What is the correct size of the bounding area? Start with a small searchable area Grow area each time you cannot reach the destination In other words, adapt area size whenever it is too small → Adaptive Face Routing AFR

95 The Greedy Other Adaptive Face Routing
GOAFR: Combine Greedy and (Other) Adaptive Face Routing Route greedily as long as possible. If stuck, do face routing. Switch to greedy, from the “best point” in the current face. Starting at s, GOAFR proceeds in greedy mode until reaching the local minimum n1. The algorithm switches to face routing mode and explores the boundary of face F to find n2, the node closest to t on F's boundary. GOAFR falls back to greedy mode and finally reaches t.

96 SPAN Goal Turn off nodes without significantly diminishing the capacity or connectivity of the network Core concept Coordinator Forwarding backbone Non-coordinator

97 SPAN Rule1: periodically broadcasts HELLO message
Current Status (coordinator or not) Current coordinator Current neighbors Rule2: coordinator announcement A node decides to volunteer to be a coordinator if it discovers that two of its neighbors cannot communicate with each other directly or via one or two coordinators Avoid coordinator contention: delayed announcement Rule3: coordinator withdrawal If every pair of its neighbors can reach each other either directly or via some other coordinators To archive fairness, if one node has been a coordinator for some period of time and every pair of neighbor nodes can reach each other via some other neighbors (even if they are not coordinators yet)

98 SPAN Announcement contention 1 3 5 6 7 2 4 Coordinator contention
Initial configuration All the nodes are eligible And try to be a coordinator at the same time Boo

99 SPAN Resolving announcement contention using backoff utility

100 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

101 Routing Protocols Based on Protocol Operation
Multipath Routing Protocols Query-Based Routing Negotiation-Based Routing Protocols QoS-based Routing Coherent and Noncoherent Processing

102 Multipath Routing Protocols
Use multiple paths in order to enhance network performance Fault tolerance Balance energy consumption Energy-efficient Reliability

103 Query-Based Routing Destination nodes propagate a query for data
Usually theses queries are described in natural language or high-level query language E.g. Directed diffusion Rumor routing protocol

104 Negotiation-Based Routing Protocols
Use high-level data descriptors in order to eliminate redundant data transmissions through negotiation Communication decisions are also made based on the resources available to them E.g. SPIN

105 QoS-based Routing Has to balance between energy consumption and data quality E.g. SPEED (congestion avoidance)

106 Coherent and Noncoherent Processing
Nonocoherent: Locally process the raw data before it is sent Low data traffic loading Coherent: The data is forwarded to aggregators after minimum processing Energy efficiency is achieved by path optimality

107 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

108 Future Directions (1/2) QoS Nodes mobility Exploit redundancy
Tiered architectures Exploit spatial diversity and density of sensor nodes Achieve desired global behavior with adaptive localized algorithms

109 Future Directions (2/2) Leverage data processing inside the network and exploit computation near data sources to reduce communication Time and location synchronization Localization Self-configuration and reconfiguration Secure routing Integration of sensor networks with wired networks

110 Outline Introduction Challenges Design Issues Flat Routing
Hierarchical Routing Flat vs. Hierarchical Location-based Routing Routing Protocols Based on Protocol Operation Future Directions Conclusions

111 Conclusions They have the common objective of trying to extend the lifetime of network Trade-off energy and communication overhead There are still many challenges that need to be solved

112 The End Thanks for Listening…


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