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Efficient Data Aggregation in Sensor Networks: Routing Strategies & Performance Analysis

Explore data aggregation and convergecast in sensor networks, optimal routing techniques, aggregation heuristics & performance evaluation.

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Efficient Data Aggregation in Sensor Networks: Routing Strategies & Performance Analysis

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  1. Chapter 4Routing Protocols(Part II)

  2. Outline • 4.1 Routing Challenges and Design Issues in WSNs • 4.2 Flat Routing • 4.3 Hierarchical Routing • 4.4 Location Based Routing • 4.5 QoS Based Routing • 4.6 Data Aggregation and Convergecast • 4.7 Data Centric Networking • 4.8 ZigBee • 4.9 Conclusions

  3. Chapter 4.6 Data Aggregation and Convergecast

  4. Aggregation in Sensor Networks • Traditional Address-Centric routing • IP address routing • Not suitable in large scale sensor networks • Data-Centric Routing • Content-based routing • Enhance the data aggregation opportunity Source 1 Source 2 Source 1 Source 2 2 1 Data Aggregation 2 1 B A B A 1+2 1 2 Sink Sink a) Address-Centric (AC) Routing b) Data-Centric (DC) Routing

  5. Theoretical Results on Aggregation • Let there be k sources located within a diameter X, each a distance di from the sink. Let NA and ND be the number of transmissions required with AC and optimal DC protocols, respectively. 1. The following are bounds on ND: 2. Asymptotically, for fixed k, X, as d = min(di) is increased, 3. Although the problem is NP-hard in general, the optimal data aggregation tree can be formed in polynomial time when the sources induce a connected sub-graph on the communication graph.

  6. Aggregation Techniques • In general the formation of the optimal aggregation tree is NP-hard. Some suboptimal DC routing heuristics as follows: • Center at Nearest Source (CNSDC) • All sources send the information first to the source nearest to the sink, which acts as the aggregator. • Shortest Path Tree (SPTDC) • Opportunistically merge the shortest paths from each source wherever they overlap. • Greedy Incremental Tree (GITDC) • Start with path from sink to nearest source. Successively add next nearest source to the existing tree. • Address Centric (AC) • No aggregation, distinct shortest paths from each source to sink.

  7. Performance Study Event-Radius model Random Sources model

  8. Performance Study (cont.) Energy Costs Event-Radius model

  9. Performance Study (cont.) Energy Costs Random Sources model

  10. Conclusions • Data aggregation can result in significant energy savings for a wide range of operational scenarios. • The gains from aggregation are paid for with potentially higher delay. It should be possible to design routing algorithms for sensor networks in which this tradeoff is made explicitly.

  11. Reference • B. Krishnamachari, D. Estrin, and S. B. Wicker, "The impact of data aggregation in wireless sensor networks," In Proceedings of the 22nd International Conference on Distributed Computing Systems Workshops (ICDCSW'02), pp. 575-578, Vienna, Austria, July 02-05 2002.

  12. Chapter 4.7 Data centric networking

  13. 4.7.2 Data-centric Storage • Data centric storage • Data is stored inside the network. • All data with the same name (or data range) will be stored at the same sensor network location • Why data centric storage? • Energy efficiency • Robustness against mobility and node failures • Scalability

  14. One-dimensional Data Storage • Data-Centric Storage in Sensornets with GHT, a Geographic Hash Table (GHT [Ratnasamy et al. 2003]) • Data Storage and Retrieval • Perimeter Refresh Protocol • Structured Replication

  15. data response query One-dimensional Data Storage • GHT • Put(k, v)- stores v (observed data) according to the key k • Get(k)- retrieve whatever value isassociated with key k • Hash function • Hash the key into the geographic coordinates • Put() and Get() operations on the same key “k” hash k to the same location (12,24) user Get (“elephant”) Put (“elephant”, data) Hash (‘elephant’)=(12,24) Hash (‘elephant’)=(12,24) An example for GHT

  16. Perimeter Refresh Protocol • Assume key k hashes at location L • A is closest to L so it becomes the home node E Replica Replica D L F A home C B

  17. Structured Replication • Augment event name with hierarchy depth • Given root r and given hierarchy depth d • Compute 4d – 1 mirror images of r Example of structured replication with a 2-level decomposition

  18. Conclusions • Data centric storage entails naming of data and storing data at nodes within the sensor network • GHT uses Perimeter Refresh Protocol and structured replication to enhance robustness and scalability • DCS is useful in large sensor networks and there are many detected events but not all event types are Queried

  19. Multi-dimensional Data Storage • Multi-Dimensional Range Queries in Sensor Networks (DIM [Li et al. 2003]) • Building Zones • Data Insertion • Query Propagation

  20. L[0, 1/2) L[1/2, 1) T[3/4, 1) 3 6 5 8 4 1 7 9 2 T[1/2, 1) T[1/2, 3/4) T[1/4, 1/2) T[0, 1/2) T[0, 1/4) L[1/4, 1/2) L[1/2, 3/4) L[3/4, 1) L[0, 1/4) Building Zones • Divide network into zones. • Each node mapped to one zone. • Encode zones based on division. • Each zone has a unique code. • Map m-d space to zones. • Zones organized into a virtual binary tree. 010 0111 110 1111 1110 0110 0001 10 10 001 0000 L: Light, T: Temperature

  21. L[0, 1/2) L[1/2, 1) 1111 010 0111 110 T[3/4, 1) T[1/2, 1) E1= <0.8, 0.7> 1110 T[1/2, 3/4) 2 8 1 5 4 7 9 3 6 0110 Store E1 T[1/4, 1/2) 0001 T[0, 1/2) 10 T[0, 1/4) 001 0000 10 L[1/4, 1/2) L[1/2, 3/4) L[3/4, 1) L[0, 1/4) Data Insertion • Encode events • Compute geographic destination • Hand to GPSR • Intermediate nodes can refine the destination estimation L: Light, T: Temperature

  22. L[0, 1/2) L[1/2, 1) 1111 010 0111 110 Q11= <.5-.75, . 5-1> T[3/4, 1) T[1/2, 1) Q12= <.75-1, .75-1> 1110 T[1/2, 3/4) 8 1 2 7 4 9 6 3 5 0110 Q10= <.75-1, .5-.75> T[1/4, 1/2) 0001 T[0, 1/2) 10 Q1= <0.5-1, 0.5-1> T[0, 1/4) 001 0000 10 L[1/4, 1/2) L[1/2, 3/4) L[3/4, 1) L[0, 1/4) Query Propagation • Split a large query into smaller sub-queries. • Encode each sub-query. • Process sub-queries separately, resolving locally or forwarding to other nodes based on their codes. L: Light, T: Temperature

  23. Conclusions • DIM resolves multi-dimensional range queries efficiently. • Work that still needs to be done • Skewed data distribution • These can cause storage and transmissionhotspots. • Existential queries • Whetherthere exists an event matching a multi-dimensional range. • Node heterogeneity • Nodes with larger storage space assert larger-sized zones for themselves.

  24. Chapter 4.8ZigBee 24

  25. The ZigBee Standard 25 ZigBee is a low cost, low power, low complexity, and low data rate wireless communication technology at short range. Based on IEEE 802.15.4, it is mainly used as a low data rate monitoring and controlling sensor network

  26. The Network Layer 26 • ZigBee identifies three device types • The ZigBee coordinator (one in the network) is an FFD managing the whole network • A ZigBee router is an FFD with routing capabilities • A ZigBee end-device corresponds to a RFD or FFD acting as a simple device • The ZigBee network layer supports three types of network configurations: • Star topology • Tree topology • Mesh topology

  27. The Network Layer (cont.) (a) Star network (b) Tree network (c) Mesh network ZigBee end device ZigBee coordinator ZigBee router 27

  28. Network Formation and Address Assignment 28 • Before forming a network, the coordinator determines • Maximum number of children of a router (Cm) • Maximum number of child routers of a router (Rm) • Depth of the network (Lm) • Note that a child of a router can be a router or an end device, so Cm ≥ Rm • The coordinator and routers can each have at most Rm child routers and at least Cm − Rm child end devices

  29. Network Formation and Address Assignment (cont.) 29 For the coordinator, the whole address space is logically partitioned into Rm + 1 blocks The first Rm blocks are to be assigned to the coordinator’s child routers and the last block is reserved for the coordinator’s own child end devices From Cm, Rm, and Lm, each router computes a parameter called Cskip to derive the starting addresses of its children’s address pools

  30. Network Formation and Address Assignment (cont.) 30 • The coordinator is said to be at depth d = 0, and d is increased by one after each level • Address assignment begins from the ZigBee coordinator by assigning address 0 to itself • If a parent node at depth d has an address Aparent , the n-thchild router is assigned to address • Aparent + (n − 1) × Cskip(d) + 1 • n-th child end device is assigned to address • Aparent + Rm × Cskip(d) + n

  31. Network Formation and Address Assignment (cont.) Cm = 5 Rm = 4 Lm = 2 Addr = 9 Addr = 10 Addr = 8 A2 Addr = 7Cskip = 1 Addr = 12 • Aparent + (n − 1) × Cskip(d) + 1 Addr = 24 A4 Addr = 19Cskip = 1 Addr = 6 Addr = 0Cskip = 6 A1 Addr = 1Cskip = 1 • Aparent + Rm × Cskip(d) + n Addr = 25 Addr = 3 A3 Addr = 13Cskip = 1 Addr = 2 ZigBee end device ZigBee coordinator ZigBee router 31

  32. ZigBee Routing Protocol 32 In a ZigBee network, the coordinator and routers can directly transmit packets along the tree When a device receives a packet, it first checks if it is the destination or one of its child end devices is the destination If so, this device will accept the packet or forward this packet to the designated child. Otherwise, it forwards the packet to its parent

  33. ZigBee Routing protocol (cont.) 33 If a device n receives a packet with destination Adest . Assume that the depth of the device n is d and its address is A. This packet is for one of its descendants if the destination address Adestsatisfies A < Adest < A+ Cskip(d− 1), and this packet will be relayed to the child router with address If the destination is not a descendant of this device, this packet will be forwarded to its parent

  34. ZigBee Routing protocol (cont.) Addr = 64 Cskip = 1 Cm = 6 Rm = 4 Lm = 3 Addr = 125 Addr = 92 Addr = 30 Addr = 63 Cskip = 7 Addr = 0 Cskip = 31 Addr = 126 Addr = 1 Cskip = 7 ? A Addr = 40 Cskip = 1 ? Addr = 31 C Addr = 32 Cskip = 7 Addr = 33 Cskip = 1 A < Adest < A+ Cskip(d− 1) B Z Addr = 38 ZigBee end device ZigBee coordinator ZigBee router 34

  35. Route Discovery Routing Table in ZigBee 35

  36. Route Discovery (cont.) Route Discovery Table 36

  37. Route Discovery (cont.) RREQ message RDT entry exists for this RREQ ? Create RDT entry and record fwd path cost Yes No Does RREQ report A better fwd path cost ? Update RDT entry with better fwd path cost Yes Is RREQ for local node or one of end-device children ? Yes No Drop RREQ Send RREP No Create RT entry (Discovery_Underway) And rebroadcast RREQ The RREQ processing 37

  38. Route Discovery (cont.) Discard route request B C route req. A route req. route reply S T route req. route req. D Unicast Broadcast Without routing capacity route req. 38

  39. References 39 P. Baronti, P. Pillai, V. Chook, S. Chessa, and F. Gotta, A. andFun Hu. Wireless sensor networks: a survey on the state of the art and the 802.15.4 and zigbee standards. Communication Research Centre, UK, May 2006. J. Bruck, J. Gao and A. A. Jiang, “MAP: Medial Axis Based Geometric Routing in Sensor Network,” in Proceedings of ACM MobiCom, 2005. Q. Fang, J. Gao, L. Guibas, V. de Silva, and L. Zhang. GLIDER: Gradient landmark-based distributed routing for sensor networks. In Proc. of the 24th Conference of the IEEE Communication Society (INFOCOM’05), March 2005. B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris. Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. In International Conference on Mobile Computing and Networking (MobiCom 2001), pages 85–96, Rome, Italy, July 2001. Y. Xu, J. Heidemann, and D. Estrin. Geography-informed energy conservation for ad hoc routing. In Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking, pages 70–84, Rome, Italy, July 2001.

  40. Conclusions • Routing in sensor networks is a new area of research, with a limited but rapidly growing set of research results • We highlight the design trade-offs between energy and communication overhead savings in some of the routing paradigm, as well as the advantages and disadvantages of each routing technique • Overall, the routing techniques are classified based on the network structure into four categories: flat, hierarchical, and location-based routing, and QoS based routing protocols. • Although many of these routing techniques look promising, there are still many challenges that need to be solved in sensor networks

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