Agenda for Networking Session Chairs: S. Shyne

1
Agenda for Networking SessionChairs S. Shyne
M. Srivastava

• D. Estrin (USC) Scalable Directed Diffusion
  Methods
• L. Zhang (UCLA) Gradient Broadcast for Sensor
  Nets
• D. J. Van Hook (MIT/LL) Publish/Subscribe
  Methods
• W. Kaiser (Sensor.com) WINS NG Networking
• Break
• M. Srivastava (UCLA) Sensor Networking Issues
• C. Chien (RSC) Latency, and Security Trade-offs
  in Wireless Communications
• D. Carman (NAI Labs) Security Architecture
  Techniques
• Discussion

2
Some Issues for Discussion

• Relationship between diffusion, gradient, and
  publish-subscribe routing models
• Above vs. traditional multihop routing with
  spatial and power awareness
• is one sufficient, or are both needed?
• how do they compare
• Interaction of routing with distributed signal
  processing algorithms
• Interaction of routing with sensor task
  distribution and scheduling
• Interaction of networking with mobile code /
  scripting
• Management of sensor network centralized,
  distributed
• Trade-offs latency, power, survivability,
  accuracy

3
Sensor Networking Issues

• DSN (ISI) SensorWare (RSC) Projects at UCLA

4
Power Issues in Sensor Networking

• Problems
• routing is power unaware focus on topology
  changes
• metrics such as shortest hop, shortest delay,
  link quality etc.
• power loss in quiescent state signaling in
  proactive protocols
• lack of routing and MAC coordination, e.g.
• large number of collisions with CSMA MAC during
  broadcasts used by routing protocols for probing
• TDMA slot scheduling, and clustering overheads
  lead to sub optimal route selection in algorithms
  such as DSR
• What can be done?
• obvious one power-based routing metrics
• leverage location information during routing
• intelligently combine and filter replies at
  intermediate nodes
• exploit path diversity
• tightly coupled routing TDMA MAC
• soft-state slot schedule, locally proactive
  globally reactive

5
Exploiting Path Diversity for Power

• Idea is distribute traffic over alternative paths
  to increase network lifetime and coverage
• packet disperser and combiner entities
• Works with DSR as well as gradient based routing
• Evaluation metrics
• time to breakdown
• of depleted nodes
• RMS energy distribution
• A problem do not know which nodes are important
  as it depends on future target traffic pattern
  and user movement pattern

6
Path Diversity Scenario
B
C
A
user

• A and B generate 1 packet every 100 ms until 5s
• C generates 1 packet every 100 ms from 5s till 15s

7
of Nodes with gt 10 Battery
Packets received by t150 Normal
127 Stochastic 133 Energy Disperse
160 Stochastic ED 161 Divert streams 175
8
RMS Battery Energy Consumption
Lower Bound 2
Lower Bound 1
9
MAC and Routing Interaction

• With DSR Route A-gtH
• Route request path A,B,C,E,G,H
• Paths depend on slot assignment
• With DSDV Route A-gtG
• 3 routes with equal length ABCEG,AJOHG, AJIHG
• These will fluctuate depending on the route
  updates
• Tightly coupled MAC and routing layers can help
  resolve these issues and increase flexibility

• TDMA MAC Routing
• simulations in ns-2 are
• now in progress

10
The Networking Viewpoint Alone is Inadequate

• Notion of quality of service is quite different
  and task-specific
• Hunt for the best protocol for sensor nets is
  inadequate
• e.g. different tasks on sensor network work best
  with different routing
• boundary between networking other layers fuzzy
  in sensor net.

11
Distributed Computing Viewpoint

• A Distributed Computing viewpoint is better
  suited
• distributed algorithms with application-specific
  protocols
• application-specific routing helps with power,
  latency etc.
• dynamic, resource-constrained environment
• nodes coordinate to do tasks such as target
  detection, target tracking, distributed signal
  processing, network monitoring
• Approach a minimal substrate or middleware
  with support for mobile scripts
• easily write and incrementally in situ deploy
  distributed apps with application-specific
  networking
• administer and manage the network
• accommodate transient users with specialized
  tasks etc.

12
Our Architecture

• Small set of common (local as well as
  distributed) services
• Scriptable, lightweight runtime at each node on
  top of a RTOS
• compact, platform independent sensor node control
  scripts
• used primarily for control flow (protocols) and
  not data crunching
• Node Object Model (SP, communication, sensor
  resources and services)
• Scripts can replicate

13
Implementation Approach

• Scripts based on subset of Tcl
• Tcl interpreter ported to Rockwell nodes on uC/OS
• Also, in ns via external Unix processes attached
  to ns nodes
• Model (events, state) actions
• actions events data processing state change
• signal processing, communication, and sensing
  services available as persistent native code
  objects
• Mechanisms
• API spawn, migrate, replicate, kill, script
  state maintenance
• resource-based script admission control
• future script compression, authentication

14
Implementation on RSC Nodes
. . wait e1 e2 if (e1v1) . . .
App1
App1
Apps
Script Manager
Script
Sensor Stack
Network Stack
Other Drivers/Services
Base RTOS
Hardware Abstraction Layer
15
Implementation on ns nodes
Sensor Model
UNIX process
ns-2 environment
Sensor Agent
DSR
Custom Routing
802.11
Sensor Model
NetIf
wireless channel
16
Example Application 1Custom Network Status
Queries

• E.g. which node has the minimum battery level?

• No scripts and with knowledge of global topology.
  Ask every node, wait for a reply.
• Contention around central node. Not scalable.
• With scripts. Populate the script so an optimal
  multicast/gathercast tree is created.
• Less contention around central node. Scalable!

of messages received
of messages transmitted
Node running a script
17
Example Application 1(contd.)

• No scripts, no knowledge of topology. Ask every
  node for neighbors, wait for a reply, then ask
  for battery level.
• The same problems are sharpened
• With scripts. The processes of discovering the
  topology and the min value are combined.
• Still scalable.

of messages received
of messages transmitted
18
Script Example

• set node localNode_memory_read 0 set minenergy
  localNode_memory_read 1
• set neighbors localNode_memory_read 2
• set send_node Agent_memory_read 0 set
  send_node_neighbors Agent_memory_read 1
• Agent_memory_write 0 node Agent_memory_write 1
  neighbors
• set remaining_nodes neighbors set n lsearch
  neighbors send_node
• lreplace remaining_nodes n n
• foreach i send_node_neighbors set n lsearch
  neighbors i lreplace remaining_nodes n n
• Agent_replicate remaining_nodes
• foreach i remaining_nodes set result wait -msg
  -for 1000 set minenergy (minenergy lt
  result) ? minenergy result
• send send_node1 minenergy
• set minenergy
• Script size
• Uncompressed verbose 840 bytes
• Potential compression (byte code) 224 bytes

19
Example Application 2Mission-specific Target
Tracking

• Resident application (or initial script flooded
  to all nodes) sends message to user informing a
  potential target
• User downloads a tracking script to the
  appropriate node
• script encodes a custom tracking mechanism, e.g.
  calculate new position every 10s and send it to
  user
• Script spreads to form an initial sensing cluster
• Script does data fusion or simple beamforming
  (using signal processing modules resident at the
  node)
• Script arranges for the active sensor cluster to
  migrate as the target moves
• motion prediction using history (e.g. movement
  direction)
• sentry scripts spawned around the cluster
• avoids polling and cluster management by the
  distant user, which is needed if nodes only
  support simple forms of query

20
Tracking Scenario
21
Tracking Scenario
22
Tracking Scenario
23
Tracking Scenario
24
Initial Script

• Runs on all nodes within the area of interest
• Straightforward approach where each node sends a
  message to the user is energy inefficient with
  multihop
• polling-based, interrupt-based
• Clustering approach
• first node that detects the target becomes a
  clusterhead
• other nodes join the cluster when they detect the
  target
• after MAX_LATENCY the clusterhead sends a message
  to the user, then waits for the ACK and
  distributes the ACK to other members of the
  cluster
• ideally, only one message is sent to the user
  from one cluster,
• however due to unreliable protocols, a node
  maintains soft-state waiting for an ACK
• if an ACK is not received within an interval,
  cluster dissolves into smaller clusters whose
  clusterheads send messages to the user

25
Tracking Script

• To determine an approximate location of a target,
  a node has to receive messages from several other
  nodes that detected the target
• when a node acquires certain number of messages,
  either simple intersection or potentially
  beamforming is used
• Due to power constraints in sensor nodes, they
  should refrain from sending repeated messages
  about the target
• if one node detects the target and sends a
  message to the neighbors, the nodes in certain
  area around that node do not add significant
  information concerning the location of the target
• Solution when a node senses a target it sends a
  report to other nodes only if none of the other
  nodes within some area generated a report during
  certain time interval
• similar to IGMP mechanism on LANs

26
Tracking Script (contd.)

• When a node acquires certain number of messages,
  it determines target position and sends to the
  user
• That message is broadcast to other nodes within
  an area so that all nodes that want to determine
  the location and send a message to the user will
  refrain for a certain time
• area where that message is broadcast may be
  asymmetric, if a motion prediction algorithm is
  used
• Messages containing target location is generated
  in periodic intervals except when a target moves
  fast enough and leaves an area before the time
  interval expires
• if target moves fast, location messages generated
  more often

27
Simulation Results

• The ratio of consumed energy by the initial
  script as a function of MAX_LATENCY compared to
  the approach where each node sends a message
  directly to the user
• 200x200m area, 50 nodes, 50m transmission range,
  35m sensing range, 10 m/s target speed

28
Simulation Results

• The ratio of consumed energy by the initial
  script compared to the approach where each node
  sends a message directly to the user as a
  function of the number of nodes
• area is 200x200, MAX_LATENCY for initial script
  is 3s

29
Simulation Results

• The ratio of consumed energy by the initial
  script as a function of the covered area compared
  to the approach where each node sends a message
  directly to the user
• 80 nodes, MAX_LATENCY - 3s

30
Services for Network Management

• Queries not just about the target, but also about
  the network status
• Traditional centralized approach
• a Network Operations Center where nodes report
  their status
• users query the NOC
• Alternate distributed approach
• support users asking queries about the network
• e.g.
• which nodes have energy lt threshold ?
• which is the weakest path (maximal breach)
  through the network?

31
Maximal Breach Path

• Given A field S instrumented with sensors areas
  I and F corresponding to initial (I) and final
  (F) locations of the agent
• Problem Identify the path of maximal breach of
  surveillance in S, starting in I and ending in F.
• Solution
• 1) Generate Voronoi Diagram
• 2) Apply Graph-Theoretic Abstraction
• 3) Use Binary-Search, Breadth-First-Search
  to find the solution

32
Enabling Step Voronoi Diagaram
By construction, each line-segment maximizes
distance from the nearest point
(sensor). Consequence Path of Maximal Breach of
Surveillance in the sensor field lies on the
Voronoi diagram lines. Source Rockwell Science
Center
33
Graph-Theoretic Formulation

• Given Voronoi diagram D with vertex set V and
  line segment set L and sensors S
• Construct graph G(N,E)
• Each vertex vi?V corresponds to a node ni ?N
• Each line segment li ?L corresponds to an edge ei
  ?E
• Each edge ei?E, Weight(ei) Distance of li from
  closest sensor sk ?S
• Formulation Is there a path from I to F which
  uses no edge of weight less than K?

34
Sample Results(50, 200, 1000 nodes)