Searching the Physical World: Distributed Protocols for Data Coverage and Caching in WSNs

Searching the Physical World: Distributed Protocols for Data Coverage and Caching in WSNs @ Dept. of Computer & Communication Engineering, University of Thessaly Dimitrios Katsaros, Ph.D. Nicosia, June 17th, 2008

Outline of the talk • WSNs – A working reality • What is the “Sensory Web”? • Data Coverage issues in WSNs • Cooperative Caching for WSNs • Concluding remarks

Wireless Sensor Networks (WSNs) Wireless Sensor Networks features • Homogeneous devices • Stationary nodes • Dispersed network • Large network size • Self-organized • All nodes acts as routers • No wired infrastructure • Potential multihop routes

WSNs - Applications

More exotic applications of WSNs

What’s special about WSNs ? • Resource constraints • sensor nodes are battery-, memory- and processing-starving devices • Variable channel capacity • multi-hop nature of WSNs implies that wireless link capacity depends on the interference level among nodes • Multimedia in-network processing • sensor nodes store rich media (image, video), and must retrieve such media from remote sensor nodes with short latency

Challenges … • Huge network size • Unknown/variable network topology • Agnostic users • Fault tolerance • Sensor readings are simply votes

IN-NETWORK INTELLIGENCE Research areas: Ultimately  ??? Sensory Web Mobile/Pervasive Computing Overlay Nets Web Mobile Ad Hoc Wireless Sensors Networks Information Retrieval

Search Engines for the Physical World • Cooperating Sensors • Distributed Protocols • Energy-efficient Communication • Short-latency Data Retrieval • Unknown Network Topology • Topology Control • Storage in Flash Devices

Querying WSNs … • Simple queries,e.g., “Report the value of the humidity” • Aggregate queries,e.g., “Report the average humidity of all sensors in region X” • Approximate queries,requiring datasummarization to perform holistic dataaggregationin the form of histograms, contour maps, e.g., “Report the contour of toxic chemicalgas in region X” • Complex queries, which, if expressed in SQL, would involve joins nested or conditioned-based sub-queries, e.g., “Among regions X and Y, report the average humidity of the region with the highest temperature” • Advanced queries, such as top-k queries, e.g., “Report the k data objects with the highest temperature”

Qyerying limitations (1/2)… Report the k smallest values of humidity within region X along with the sensors that sensed them What about sensor failures?

Qyerying limitations (2/2)… Report the k smallest values of humidity across the whole sensornet along with the sensors that sensed them What about small shifts in the region boundaries?

The concept of Data Coverage … Report the sensor(s) whose humidity value is not covered by any other humidity value across the whole sensornet Sensor with max humidity value

The concept of k-Data Coverage … Report the sensor(s) whose humidity value is covered by at most k (e.g., k=2) other humidity values across the whole sensornet Sensor with max value Sensor with 2nd max value Sensor with 3rd max value

Feature Distribution Maps Still, we can not find out what happens in neighborhoods, i.e., local minima, local maxima, etc. These are not network-wide (global)

The concept of d-hop k-Data Coverage … Depict the points (i.e., sensors) with the largest, relative to their neighboring sensors, humidities • localized definition of neighborhoods • no region prespecification • define d to be the sensornet diameter • Network-wide k-coverage

The d-hop k-Data Coverage problem • Generalizes • The k-skyband query • The top-k query • The d-hop dominating set formation problem • Deals with • Any number of readings by a sensor node • Any number of measured quantities, e.g., humidity, temperature, etc. • More generic predicates, not only maximum, minimum

Data Coverage in Neighborhoods-DaCoN • Distributed protocol for processing d-hop k-data coverage queries in WSNs • Runs localized in neighborhoods • No network spanners, e.g., aggregation tree, spanning tree • No demanding initialization phase to construct the spanner • Uniform energy consumption, no hot spots of communication • Runs in 3 phases

DaCoN’s execution • In a 2-dimensional space, assume that we wish the maximization of the first dimension and the minimization of the second one • v_i.d_x denotes the x-th dimension of value v_i • v_i covers a value v_j, if it holds • v_i.d_1 > v_j.d_1 and v_i.d_2 < v_j.d_2

PHASE 1. First d-rounds • Each sensor sends its k-th larger values to all its 1-hop neighbors • It finds the k-th larger values taking account its own values and the values that has received from its neighbors • It forms a message with these values and it stores the message into a buffer frb • In the next d-1 rounds, the above procedure is repeated with the difference that now each sensor considers as its k-th larger values, the values of the last message of the frb

PHASE 2. Next d-rounds • Similarly to the previous rounds, but … • Each sensor finds its k-th values by taking into account the previous message and the messages that has received from its neighbors as follows: each v_i value (1 ≤ i ≤ k) is selected by keeping the smaller i-th value of these messages • These values form a message that is stored into a buffer srb

PHASE 3. Answer of query • Each value v_i (1 ≤ i ≤ k) of the answer is selected as follows: the sensor compares the messages of frb and srb and tries to find pairs of values in the first i-th values of each message After the identification of all pairs of values, the sensor selects the minimum pair as the i-th value of its answer If a pair of values does not exist, the sensor selects the maximum of the first i-th values of the messages of frb

DaCoN evaluation • No competing methods • Network topologies, • existence and “strength” of clusters of sensors • density of sensor nodes, etc • Sensor data generator

Impact of sensornet size: messages

Impact of sensornet size: activated sens

Impact of assortativity: messages

Impact of assortativity: activated sens

Impact of k (500 sensors): activated sens

Impact of k (1000 sensors): activated sens

d-hop k-data coverage • Feature Distribution Maps • Fully distributed solution: DaCoN • Little overhead • Little storage • Light computational load • Few messages & no hotspots in communication • How do we improve upon the latency, when the sensors need data from other sensors? • Cooperative Caching

Our proposal … • Cooperative Caching: NICOCA protocol • multiple sensor nodes share and coordinate cache data to cut communication cost and exploit the aggregate cache space of cooperating sensors • Each sensor node has a moderate local storage capacity associated with it, i.e., a flash memory • Jim Gray predicted that flash memories will replace hard disks

Relevant work Protocols that deviated from such approaches: • CacheData: intermediate nodes cache the data to serve future requests instead of fetching data from their source • CachePath: mobile nodes cache the data path and use it to redirect future requests to the nearby node which has the data instead of the faraway origin node • Amalgamation of them: the champion HybridCache cooperative caching for MANETs

NICoCa consists of … • A metric for estimating the importance of a sensor node, which will imply short latency in retrieval • A cooperative caching protocol which strives to achieve uniform energy consumption • Datum discovery and cache replacement component subprotocols • Performance evaluation of the protocol and comparison with the state-of-the-art cooperative caching for MANETs, with J-Sim

Terminology and assumptions • WMSN is abstracted as a graph G(V,E) • edge e=(u,v) exists iff u is in the transmission range ofv and vice versa (bidirectional links) • The network is assumed to be connected • N1(v) : the set of one hop neighbours of v • N2(v) : the set of two hop neighbours of v • N12(v) : combined set of N1(v) and N2(v) • LNv : is the induced subgraph of G associated with vertices in N12(v) • dG(v,u) : distance between v and u

A measure of sensor importance • σuw= σwu : number of shortest paths from uV towV (σuu=0) • σuw(v) : number ofshortest paths from u to w that some vertex vV lies on • Node importance indexNI(v) of a vertex v is:

13 6 8 12 15 5 7 14 20 18 2 16 4 9 11 19 3 17 10 1 Y X T A U P V C B R W Q The NI index in sample graphs

13 (0) 6 (0) 8 (26) 12 (0) 15 (0) 5 (0) 7 (156) 14 (233) 20 (0) 18 (97) 2 (0) 16 (131) 4 (96) 9 (0) 11 (0) 19 (0) 17 (1) 3 (68) 10 (0) 1 (0) Y (0) X (0) T (1,33) A (6,67) U (54) P (41) V (1,33) C (0) B (13) R (9,33) W (3,33) Q (8) The NI index in sample graphs • Nodes with large NI: • Articulation nodes (in bridges), e.g., 3, 4, 7, 16, 18 • With large fanout, e.g., 14, 8, U

Centralized solution ??? • Create a broadcast tree to coordinate the identification of NI’s • lot of messages • larger latency • Hot-spots in communication (nodes with large NI) • Localized Algorithms are preferable • NI’s in neighborhoods …

13 6 8 12 15 5 7 14 20 18 2 16 4 9 11 19 3 17 10 1 The NI index in a localized algorithm 2-hop neighbors of node 8 node 8 calculates the NI of its 2-hop neighbors

13 (0) 6 8 (14) 12 (0) 15 (0) 5 7 (0) 14 (65) 20 18 (0) 2 16 (23) 4 9 (0) 11 (0) 19 3 17 10 (0) 1 The NI index in a localized algorithm nodes 14 and 16 are more important than the others from the viewpoint of node 8 Each node can identify its own “important” nodes

Housekeeping information in NICoCa • Ultimate source of multimedia data: Data Center • Each node is aware of its 2-hop neighborhood • Uses NI to characterize some neighbors as mediators • Can be either a mediator or an ordinary node • Each sensor node stores • the dataID, and the actual datum • the data size, TTL interval • for each cached item • characterized either as O (i.e., own) or H (i.e., hosted) • the timestamps of the K most recent accesses

The cache discovery protocol (1/2) A sensor node issues a request for a multimedia item • Searches its local cache and if it is found (local cache hit) then the K most recent access timestamps are updated • Otherwise (local cache miss), the request is broadcasted and received by the mediators • These check the 2-hop neighbors of the requesting node whether they cache the datum (proximity hit) • If none of them responds (proximity cache miss), then the request is directed to the Data Center

The cache discovery protocol (2/2) When a mediator receives a request, searches its cache • If it deduces that the request can be satisfied by a neighboring node (remote cache hit), forwards the request to the neighboring node with the largest residual energy • If the request can not be satisfied by this mediator node, then it does not forward it recursively to its own mediators, since this will be done by the routing protocol, e.g., AODV • If none of the nodes can help, then requested datum is served by the Data Center (global hit )

The cache replacement protocol • Each sensor node first purges the data that it has cached on behalf of some other node • Calculate the following function for each cached datum i • The candidate cache victim is the item which incurs the largest cost • Inform the mediators about the candidate victim • If it is cached by a mediator, the metadata are updated • If not, it is forwarded and cached to the node with the largest residual energy

Evaluation setting (1/2) • We compared NICOCA to: • Hybrid, state-of-the-art cooperative caching protocol for MANETs • Implementation of protocols using J-Sim simulation library

Searching the Physical World: Distributed Protocols for Data Coverage and Caching in WSNs