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Data Fusion in Sensor Networks

Data Fusion in Sensor Networks. Asheq Khan. Outline. Introduction Key concepts Three schemes Cluster based data fusion Synchronization among nodes Resistance against attacks Conclusion. Introduction. A sensor network comprises of sensor nodes and a base station.

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Data Fusion in Sensor Networks

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  1. Data Fusion in Sensor Networks Asheq Khan

  2. Outline • Introduction • Key concepts • Three schemes • Cluster based data fusion • Synchronization among nodes • Resistance against attacks • Conclusion Asheq Khan

  3. Introduction • A sensor network comprises of sensor nodes and a base station. • Each sensor node is battery powered and equipped with: • Integrated sensors • Data processing capabilities • Short-range radio communications • Due to their limited power and shorter communication range, sensor nodes perform in-network data fusion. Asheq Khan

  4. Data Fusion Process • A data fusion node collects the results from multiple nodes. • It fuses the results with its own based on a decision criterion. • Sends the fused data to another node/base station. • Advantages: • Reduces the traffic load. • Conserves energy of the sensors. Asheq Khan

  5. Key Concepts in Data Fusion • Three questions needs to be addressed: • First, at what instance does a node report a sensed event? • Second, how does a node fuse multiple reports into a single one? • Third, what data fusion architecture to use? Asheq Khan

  6. Reporting • Periodical reporting: Sensor nodes periodically send reports to the base station. • Base station inquiry response reports: the BS queries sensors in specific regions for current sensed information. • Event triggered reports: The occurrence of a certain event can trigger reports from sensors in that particular region. Asheq Khan

  7. Fusion Decision • Voting: the oldest and most widely used fusion decision method. • Fusion node arrives at a consensus by a voting scheme like: • Majority voting • Complete Agreement • Weighted voting • The popularity of voting arises from its simplicity and accuracy. • Other fusion decision algorithms include probability-based Bayesian Model and stack generalization. Asheq Khan

  8. Fusion Architecture • Centralized: • Simplest • A central processor fuses the reports collected by all other sensing nodes. • Advantage: Erroneous report(s) can be easily detected. • Disadvantage: inflexible to sensor changes and the workload is concentrated at a single point. Asheq Khan

  9. Fusion Architecture (2) • Decentralized : • Data fusion occurs locally at each node on the basis of local observations and the information obtained from neighboring nodes. • No central processor node. • Advantages: • scalable and tolerant to the addition or loss of sensing nodes or dynamic changes in the network. Asheq Khan

  10. Fusion Architecture (3) • Hierarchical: • Nodes are partitioned into hierarchical levels. • The sensing nodes are at level 0 and the BS at the highest level. • Reports move from the lower levels to higher ones. • Advantage: • Workload is balanced among nodes Asheq Khan

  11. Cluster Based Data Fusion Asheq Khan

  12. Problem • Due to their energy constraints, sensors need to perform efficient data fusion to extend the lifetime of the network. • Lifetime of a sensor network is the number of rounds of data fusion it can perform before the first sensor drains out. • This is known as the “Maximum Lifetime Data Aggregation” (MLDA) problem. Asheq Khan

  13. Goal • Given: the location & energy of each sensor and the BS. • Find an efficient manner to collect & aggregate reports from the sensors to the BS. • [Dasgupta, WCNC’03] propose a cluster based heuristic (CMLDA) to solve the problem. Asheq Khan

  14. System Model • n sensor nodes(1..n) • Base station(n+1) • Fixed data packet size: k bits • Initial energy of a sensor i: εi • Receive energy, RXi = εelec * k • Transmission energy, TXi,j = εelec *k + εamp*d2i,j*k Asheq Khan

  15. Algorithm • Two phases. • Phase 1: • Sensors are grouped into clusters called “super-sensors”. • Each super sensor consists of a minimum no. of sensors. • The energy of a super sensor is the sum of the energy of all the sensors within it. • Distance between two super sensors is the maximum distance between two sensors where, each reside in a different super sensor. • Apply the MLDA algorithm. Asheq Khan

  16. MLDA Algorithm • ILP is employed to find a near-optimal admissible flow network. • Objective: maximize the lifetime of network (T) under the energy constraints. • Generate schedule(s) from the admissible flow network. Asheq Khan

  17. Example 1 1 75 3 3 25 75 25 2 2 Schedule 2 Schedule 1 Asheq Khan

  18. Algorithm (2) • Phase Two: • Initialize {Aggregation Schedule} = Ø • Life Time, T = 0 • Choose a Scheduler from phase 1 • Initialize Aggregation tree, A with the BS • Visit each super clusters and add the nodes to the tree such that, the residual energy at each edge is maximized. • Add A to the Aggregation Scheduler • Increment T by 1 • Repeat steps 3-7 until a node drains out. Asheq Khan

  19. Comments • Provides a set of data fusion schedules that maximize the lifetime of the network. • Clustering of nodes reduces the time needed to solve the ILP. Asheq Khan

  20. Synchronization Among Nodes Asheq Khan

  21. Problem • During data fusion, internal nodes at each level wait for a certain period of time before they fuse the received reports. • If nodes at each level wait for the same period of time then an internal node may timeout before receiving reports from all of its children. • With insufficient reports, the credibility of a sensed event is questionable. Asheq Khan

  22. Example Base Station Level 3 Report D T = .5 sec B Level 2 TIMEOUT T = .5 sec C D Level 1 Senses E F Level 0 Senses Senses Asheq Khan

  23. Solution • An efficient data fusion protocol with following characteristics: • Synchronizes the nodes at different levels. • Nodes at higher levels wait longer before fusing data. • A fixed time period is assigned from the sensing of an event to the time it is received by the base station. • Provide a balance between latency & accuracy. Asheq Khan

  24. Multi-level Fusion Synchronization (MFS) Protocol • [Yuan,GLOBECOM’03] propose the MFS protocol. • The parameters: • MAX: time BS waits before fusing the received data • Δ: difference in waiting period at consecutive levels • K: the distance (in hops) from the sink Asheq Khan

  25. Algorithm • Upon detection of an event, a leaf node reports to its parent node. • This triggers the timer of the parent node. • Then the parent node sends a START message to trigger the timer of its neighboring nodes. • The timer at a node expires after (MAX – K*Δ) seconds. Asheq Khan

  26. An Example Base Station T = 1.0 sec Max = 1 sec Δ = 0.2sec Level 3 Report C+D T = (1-(1*0.2)) = 0.8 sec B Level 2 START T = (1-(2*0.2)) = 0.6 sec C D Level 1 Senses E F Level 0 Senses Senses Asheq Khan

  27. Latency • Best case: • Assuming: • START messages do not collide • No propagation delay in triggering the timer • MAX • Worst case: • Assuming: • None of the internal nodes receive the START message • L =∑(MAX – j*Δ) = D*MAX – ((D-1)*D*Δ)/2 D-1 j=0 {D = depth of propagation tree} Asheq Khan

  28. Setting the parameters • If the BS knows the depth of the fusion tree then it can compute the values of MAX and Δ. • Otherwise, in a learning phase, the BS queries the sensors with different values of MAX and Δ. • And adjust the values based on the reports credibility and application requirements. Asheq Khan

  29. Result: No. of reports vs. Δ MAX=1.2s • Similar performance with both BFS (balanced tree) & ODMRP (unbalanced tree).Very small or large Δ performs worst. Asheq Khan

  30. Result(2): Latency vs. Δ • Small Δ incurs large waiting period whereas large Δ incurs small waiting period.In BFS, latency for each Δ < 2* MAX. Asheq Khan

  31. Pros and Cons • Pros: • Synchronizes nodes at different levels. • MAX and Δ can be tuned • Cons: • Reports arriving after timeout is discarded. • Collision if START messages will cause a latency greater than MAX. Asheq Khan

  32. Resistance Against Attacks Asheq Khan

  33. Problem • Previously, it is assumed that the nodes conducting the data fusion are secured. • But, a malicious data fusion node can send bogus reports to the BS. • The BS is incapable of detecting the bogus information since the sensor nodes do not directly send the reports to the BS. Asheq Khan

  34. Witness Based Data Assurance • [Du GLOBECOM’03] present a witness based scheme to ensure that the BS accepts only valid data fusion results. • To prove the validity of a report, the fusion node is required to provide proofs from several witnesses. • A witness is a node that also performs data fusion but does not send its report to the BS. Asheq Khan

  35. Algorithm • Let there be m witnesses + 1 data fusion node. • Each witness wi share an unique key with the BS, ki • After receiving reports from the sensor nodes, each witness performs data fusion and obtains the result ri. • It then sends a MAC (Message Authentication Code) to the data fusion node: MACi = MAC(ri, wi, ki) • The data fusion node computes its result and sends its MAC key with its witnesses to the BS. • The BS exercises a voting scheme to determine the validity of the report. • If the report is corrupted, the BS discards it and polls one of the witness nodes for the correct report. Asheq Khan

  36. Voting Schemes • The Base Station can employ two voting schemes to determine the validity of the fused report. • m+1 out of m+1: the result is valid if supported by all the witnesses. • n out of m+1: (1=<n<=m+1) the result is valid if supported by at least n witness. Asheq Khan

  37. m+1 out of m+1 voting scheme • After receiving all the MAC’s from the witness nodes, the data fusion node computes: • MACF = MAC(SF,F,KF, MAC1 xor …xor MACm) • F then sends (SF,F, w1,.., wm, MACF) to the BS. • The BS then computes the MACi = MAC(SF, wi, ki) for each w • Finally computes: MAC’F = MAC(SF,F,KF, MAC1 xor …xor MACm) 5. If (MACF = MAC’F) then accepts the report Asheq Khan

  38. n out of m+1 voting scheme • The disadvantage of the previous approach is that a corrupt witness node can always send invalid MAC and achieve Denial of service attack. • To prevent that, F should not merge all the MACi’s but instead forward them all: R = (SF,F, MACF, w1, MAC1,..wm,MACm) • If at least n out of m+1MAC’s match, then the result SF is accepted. • Otherwise the result is dropped. Asheq Khan

  39. Pros & Cons • Pros • Provides a scheme that ensures that only valid reports are accepted by the BS. • Cons • Redundancy: multiple copies of similar reports are fused by the witnesses. • No energy efficient Asheq Khan

  40. Conclusion • This talk attempted to give an overview of the data fusion process in sensor networks. • Different data fusion architectures, voting schemes architecture are presented. • Three important aspects of efficient data fusion are presented: energy efficiency, synchronization among sensors and resistance against attacks. • Obviously, an ideal data fusion will be one that can incorporate all the three characteristics. Asheq Khan

  41. References • K. Dasgupta, K. Kalpakis and P. Namjoshi, “An Efficient Clustering-based Heuristic for Data Gathering and Aggregation in Sensor Networks,” IEEE WCNC, 2003. • K. Kalpakis, K. Dasgupta and P. Namjoshi, “Maximum Lifetime Data Gathering and Aggregation in Wireless Sensor Networks,” IEEE ICN, 2002. • Wei Yuan, Srikanth V. Krishnamurthy, and Satish K. Tripathi, “Synchronization of Multiple Levels of Data Fusion in Wireless Sensor Networks,” In Proceedings of GLOBECOM, 2003. • W. Du, J. Deng, Y. S. Han and P. K. Varshney, “A Witness-Based Approach for Data Fusion Assurance in Wireless Sensor Networks,” In Proceedings of GLOBECOM, 2003. Asheq Khan

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