2016/2/19 H igh- S peed N etworking L ab. Using Soft-line Recursive Response to Improve Query Aggregation in Wireless Sensor Networks High-Speed Networking Lab. Dept. of CSIE, Fu-Jen Catholic University Adviser: Jonathan C. Lu, Ph.D. Speaker: Tzung-Lin Yu

2016/2/19 H igh- S peed N etworking L ab. Outline AbstractAbstract IntroductionIntroduction Related WorkRelated Work SRR protocolSRR protocol EvaluationEvaluation ConclusionConclusion ReferenceReference

2016/2/19 H igh- S peed N etworking L ab. I. Abstract BS Query for k-type data Response BS query for k-type dataBS query for k-type data sensors response the k-type packetssensors response the k-type packets waiting with a delay time k k k

2016/2/19 H igh- S peed N etworking L ab. In WSNs, aggregated or compressed data along the way toward the BS is desirable for saving energy.In WSNs, aggregated or compressed data along the way toward the BS is desirable for saving energy. Each hop might incur varying delays due to medium access contention, transmission and computation delays.Each hop might incur varying delays due to medium access contention, transmission and computation delays. The common method to aggregate data uses a hard-line precomputed timer.The common method to aggregate data uses a hard-line precomputed timer. We develop a novel method, Soft-line Recursive Response (SRR), that bases response-wait on actual response times to previous queries using a history buffer.We develop a novel method, Soft-line Recursive Response (SRR), that bases response-wait on actual response times to previous queries using a history buffer. I. Abstract

2016/2/19 H igh- S peed N etworking L ab. 21 transmissions transmissions I. Abstract An example to motivate SRR Once aggregation misses, downstream data will not be able to catch up with the upstream traffic.Once aggregation misses, downstream data will not be able to catch up with the upstream traffic.

2016/2/19 H igh- S peed N etworking L ab. II. Introduction Sensors in some areas might have different transmission abilities from sensors in other areas therefore introducing different transmission delay.Sensors in some areas might have different transmission abilities from sensors in other areas therefore introducing different transmission delay. Under the presence of network failure or delay fluctuations, we want to maximize the amount of traffic going back to the sink in the least amount of time.Under the presence of network failure or delay fluctuations, we want to maximize the amount of traffic going back to the sink in the least amount of time. Our goal is to have query responses flow from the network edge towards the BS while maximizing aggregation using shortest paths.Our goal is to have query responses flow from the network edge towards the BS while maximizing aggregation using shortest paths. – the event of failure or temporal delay, the aggregation process should skip those nodes for more aggregation

2016/2/19 H igh- S peed N etworking L ab. III. Related Work - iBubble Infrastructure SRR adapt to the iBubble infrastructure with minimal modification.SRR adapt to the iBubble infrastructure with minimal modification. iBubble routing protocol allows efficient query in heterogeneous networksiBubble routing protocol allows efficient query in heterogeneous networks –Keywords for describing sensor data type and sending the keywords towards the BS to guide and restrict queries. –original: not consider aggregation

2016/2/19 H igh- S peed N etworking L ab. III. Related Work - iBubble Infrastructure iBubble in Heterogeneous WSN since N7 has received keyword k from downstream  when BS queries for k, N7 will forward the query to its children (N3, N6) announced k  waits for both nodes to replysince N7 has received keyword k from downstream  when BS queries for k, N7 will forward the query to its children (N3, N6) announced k  waits for both nodes to reply N3, N4: forward the queryN3, N4: forward the query N5, N6: send data upstream immediatelyN5, N6: send data upstream immediately hopcount can sense type k {type of sensing device} {keywords} 8

2016/2/19 H igh- S peed N etworking L ab. A. When Network Connection Is Perfect (using Basic Recursive Response) 1.Homogenous Network 1) edge nodes  respond to a query immediately 2) others  wait for responses from all of its downstream 2.Heterogeneous Network using iBubble Infrastructureusing iBubble Infrastructure IV. SRR protocol

2016/2/19 H igh- S peed N etworking L ab. IV. SRR protocol B. When the Network is Not Perfect) (Using the Soft-line Threshold to Override Recursion) (Using the Soft-line Threshold to Override Recursion) When a node receives a query request, it will calculate how long it should wait for the downstream response using the α– percentile of the previous downstream response delay values it tracks in its two history buffers.When a node receives a query request, it will calculate how long it should wait for the downstream response using the α– percentile of the previous downstream response delay values it tracks in its two history buffers. –1 st buffer : »store all history response-times »record even time-out last time »for calculating the next » if buffer is full: FIFO queue –2 nd buffer : » same values as 1 st but a sorted list » binary search to insert a new response time

2016/2/19 H igh- S peed N etworking L ab. IV. SRR protocol Ex: node x, sorted list (1, 2, 3, 4) the next response comes in  x computes the response-time = 2.5 (response - query)  list = (1, 2, 2.5, 3, 4) a new query comes in (with α– percentile = 80%)  5 (elements) × 80% = 4,  index = 4, it will wait for downstream query responses for 3 seconds insertion and deletion into FIFO (1 st buffer): constant timeinsertion and deletion into FIFO (1 st buffer): constant time insertion and deletion into sorted list (2 st buffer): logarithmic timeinsertion and deletion into sorted list (2 st buffer): logarithmic time computation of α–percentile: constant timecomputation of α–percentile: constant time  SRR operation times at most O (log b) computation time (b= history buffer size)

2016/2/19 H igh- S peed N etworking L ab. V. Evaluation 500 nodes randomly in a WSN 1000 × 1000m 2 area BS centered at the middle of the area. Node’s radio transmission range: 240m

2016/2/19 H igh- S peed N etworking L ab. V. Evaluation Larger buffer reduce more aggregation missesLarger buffer reduce more aggregation misses fixing buffer size: α ↑, aggregation miss↓fixing buffer size: α ↑, aggregation miss↓ α=10% α=100%

2016/2/19 H igh- S peed N etworking L ab. V. Evaluation SRR’s two advantage over hard-line when α increases  SRR: aggregation miss tolerance increases & end to end delay decreasingwhen α increases  SRR: aggregation miss tolerance increases & end to end delay decreasing RSS response delays of three distributions (buffers) –Average end to end node response delay to BS –Average response delays from nodes situated at network edge to BS –Contrast aggregation miss between the hard-line and SRR Miss Saving Delay Saving Soft / Hard Saving

2016/2/19 H igh- S peed N etworking L ab. VI. Conclusion Our simulations show that SRR can improve aggregation opportunities up to 120% over the hard- line approach, while increasing response delay less than 5%.Our simulations show that SRR can improve aggregation opportunities up to 120% over the hard- line approach, while increasing response delay less than 5%. SRR reduces query response traffic and data redundancy in both homogeneous and heterogeneous static and mobile WSNs with a maximum O(N) transmission overhead in large WSNs of N nodes and O (log b) update cost. (b= history buffer size)SRR reduces query response traffic and data redundancy in both homogeneous and heterogeneous static and mobile WSNs with a maximum O(N) transmission overhead in large WSNs of N nodes and O (log b) update cost. (b= history buffer size)

2016/2/19 H igh- S peed N etworking L ab. VII. Reference Using Soft-Line Recursive Response to Improve Query Aggregation in Wireless Sensor Networks Xiaoming Lu; Spear, M.; Levitt, K.; Matloff, N.S.; Wu, S.F.; Communications, ICC '08. IEEE International Conference on May 2008 Page(s): Digital Object Identifier /ICC Communications, ICC '08. IEEE International Conference on