1 Caching/storage problems and solutions in wireless sensor network Bin Tang CSE 658 Seminar on Wireless and Mobile Networking

2 Outline Introduction Web caching and replication/placement Caching/storage in sensor network (based on Mobisys’ 03 paper) Our approach and preliminary simulation results

3 Motivation of data placement/ caching in sensor network Collection and delivery sensory data Power conservation of each node is important Communication cost is dominant among sensing, processing and communication cost. Save communication cost - caching

4 Problem and objective Given network topology and user access pattern, decide the number and location of web content replicas Objective function can be minimized clients’ latency, bandwidth, or overall cost function

5 General Approach Map the placement problem onto a graph optimization problem Facility location problem Minimum K-median problem Solve graph optimization problem Using various approximation algorithms

6 Minimum K-Median Problem Given a complete graph G=(V,E), d(j), c(i,j) d(j): # demands c(i,j): distance between node i and j Find a subset V’  V with |V’| = K s.t. it minimizes  v  V min w  V’ d(v)c(v,w) Facility Location problem Given a complete graph G=(V,E), f(i), d(j), c(i,j) f(i): # cost of building i d(j): # demands c(i,j): distance between node i and j Find a subset V’  V s.t. it minimizes  f(i) +  v  V min w  V’ d(v)c(v,w)

7 Algorithms Tree based algorithm underlying topologies are trees, and model it as a dynamic programming problem O(N 3 M 2 ) for choosing M replicas among N potential places Random Pick the best among several random assignments Hot spot Place replicas near the clients that generate the largest load

8 Greedy algorithm Calculate costs of assigning clients to replicas Select replica with lowest cost Super-Optimal algorithm Lagrangian relaxation + subgradient method

9 Data Storage/placement in sensor network What’s the difference with web caching? High density makes topology more flexible Caching service runs on every sensor node Approaches: Data-centric Attribute-based naming Data stored by name Web caching approach (heuristics) “Energy-conserving Data Placement and Asynchronous Multicast in Wireless Sensor Networks” by Sagnik Bhattacharya, et al. (Mobisys’03)

10 Model Multiple observers(sinks) Focus locales, representatives Publish-subscribe model Problem formulation – construction of a minimum-cost Steiner tree, which connects sensor node to observers

11 MS(teiner)T, MS(spanning)T, MK-Median problem. Data placement/tree construction Join the multicast tree Copy creation and migration Leave the multicast tree Simulation – comparison with unicast model, synchronous multicast, directed diffusion

12 Our approach Minimum Steiner tree: N={1, 2, …, n}, S is source node with D dist(i,j), hop numbers b/t i and j U: update frequency of D a(i): access frequency of node i to D Find a set of intermediate nodes M  N, to minimize: U*(optimal cost of a Steiner tree covering S UM) +  i  N min j  M a(i)dist(i,j) O(N 6 M 2 ) for choosing M replicas among N potential places in tree topology.

13 Simplified model Steiner tree cost is replaced by individual unicast cost Update cost is considered as constraint, objective function is t(N, M) =  i  N min j  M a(i)dist(i,j)

14 Greedy algorithm Benefit of A: B(A, M)=t(N,M)-t(N,M U A) Greedy algorithm: In each iteration, select the node with maximum B until the update constraint is reached. Optimal bound for both single and multiple documents storage of each node: 1-1/e

15 Simulations Simulation model: A network of (200 <= N <= 1000) sensor nodes 100m x 100m area Transmission radius (12m <=R <= 25m)

16 Update cost constraint

17 Energy saved from greedy algorithm

18 The effect of update cost

19 The role of transmission rate

20 Future work Multiple documents stored in sensor node Distributed dynamic caching scheme.

21 References “Energy-conserving Data Placement and Asynchronous Multicast in Wireless Sensor Networks” (Mobisys’03) Sagnik Bhattacharya et al. On the placement of Web Server Repilcas” (INFOCOM’01) Lili Qiu et al. “Data-Centric Storage in Sensornets” (WSNA’02) Deborah Estrin et al.

22 Thanks!