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1
Topology Management In Sensor Networks
2
The Need for Topology Management

• What is it?
• The physical or logical interconnection pattern
  of a network
• Topology schemes in wired networks
• Bus
• Star
• Ring
• Why do we need different schemes in sensor
  networks?
• location of sensors is not deterministic
• resource constraints

3
The Need for Topology Management

• Energy/Power consumption
• Interference
• Throughput
• Connectivity

4
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Topology Control
• Does not describe a new topology
• Provides a mechanism to build certain topology
• Distributed
• No central control or central source of
  information
• Asymmetric Links
• Due to the presence of heterogeneous devices

5
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Objective
• Reachability between any two nodes is guaranteed
  to be like initial topology
• Nodal power consumption is minimized

6
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Model
• Network of heterogeneous sensors (called nodes)
• Nodes deployed in a two dimensional plane
• Each node equipped with omni-directional antenna
  with adjustable transmission power
• Nodes have different maximum power
• Pi Transmission Power of Node i
• Pimax Maximum Transmission Power of Node i
• Pij Transmission Power required for node i to
  reach j
• Lij Asymmetric link from i to j
• G (V, L) directed graph of topology with max
  power
• G is strongly connected

7
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Algorithm
• Fully distributed with no synchronization
  required
• Takes G as input and produces G where G has
• Same bi-directional reachability
• Consumes minimum power
• Phases
• Establishing the vicinity topology
• Deriving the minimum power vicinity tree
• Propagation of transmission powers
• Optimizations

8
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Establishing the vicinity topology
• Node i broadcasts initialization request (IRQ)
  with Pimax
• Vi is the set of nodes that receive the message
  i.e. locationi, Pimax
• Each node j in Vi sends initialization reply
  (IRP) message i.e. locationj, Pjmax
• If Pjmax gt Pij , j can reach I with single hop
  Lji
• Otherwise find a multi-hop path to reach i
• Given the knowledge of location and max power of
  itself and all vicinity nodes, node i can
  determine the vicinity edges
• Node i establishes a vicinity topology , Gi (Vi,
  Ei)

9
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Deriving Minimum Power Vicinity Tree
• Derive Minimum power path in Gi, to reach from a
  node i to node j using Dijkstra or Bellman-Ford
  algortihms based on sum of power consumption on
  that path.
• Compute it for each node in Vi to obtain
  minimum-power vicinity tree,
• Gis (Vi, Eis)

10
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Propagation of transmission powers
• Node i computes minimum power requirement for
  itself and others nodes in Vi
• Node i sends a power request (PR) message to each
  node in Vi, describing the minimum power required
  for that node to reach farthest hop
• A node j in Vi, receiving the PR message
  increases its power requirement if the requested
  power in PR is greater than current one
• Otherwise, it discards the PR message

11
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Optimizations
• Achieved via discarding PR messages when
• A node already has run the algorithm to find its
  shortest vicinity tree
• A node receives a PR message for a node in its
  vicinity
• Example A asks B for PBC while B already has PBD
  to reach node C

Figure 1 A scenario of further optimized nodal
transmission range
12
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Advantages
• Guarantees same bi-directional interconnection
  while reducing per node power consumption
• Distributed algorithm
• No synchronization required
• No central control node with network information
• Easy to add/remove nodes from the network
• Uses existing well known algorithms to obtain
  minimum power consumption
• Works on network with asymmetric links, which
  seem more realistic
• Assumption of asymmetric links, makes it possible
  to obtain minimum power path via multi-hop rather
  than using a single hop high power link

13
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links Liu 2003

• Disadvantages
• Computationally expensive to be run on network
  with mobile sensors
• Overhead of sending IRQ, IRP and RP in a large
  network of sensors
• Time to converge for the algorithm is large
• Suggestions/Improvements/Future Work
• More details on how multi-hop paths will be
  discovered
• Detailed example covering more complex scenarios

14
Optimizing Sensor Networks in the
Energy-Latency-Density Design SpaceSchurgers
2002

• Describes
• a topology management technique that is power
  efficient
• energy, Latency and Density trade-offs
• Provides
• a theoretical analysis of these techniques,
  including a mathematical formulation that can be
  used to design a network with required energy,
  latency and density configuration
• a hybrid solution with existing topology
  management scheme (GAF) to provide energy saving
  of over two order of magnitude
• The proposed new topology management scheme is
  called
• STEM (Sparse Topology and Energy Management)

15
Optimizing Sensor Networks in the
Energy-Latency-Density Design SpaceSchurgers
2002

• Two states for sensor nodes
• Monitoring State
• Transfer State
• Most of the time a sensor remains in monitoring
  state (i.e. sensing environment)
• When an event occurs, nodes come into transfer
  mode and transfer their data

16
Optimizing Sensor Networks in the
Energy-Latency-Density Design SpaceSchurgers
2002

• Issues
• Nodes must listen periodically for call to duty
  (i.e transfer)
• But if they poll periodically on the same
  frequency, it will collide with other data
  transfer
• Solution
• Use two radios, one for polling while the other
  for data transfer
• STEM-B (Beacon Approach)
• Initiator sends a stream of beacon packets to
  poll a target with initiator and target MAC
  addresses
• Target node sends acknowledgement on receiving
  the packet
• Target node turns its transfer radio on

17
Optimizing Sensor Networks in the
Energy-Latency-Density Design SpaceSchurgers
2002

• STEM-T (Tone Approach)
• Initiator sends a wake up tone
• Every node receiving that tone starts its data
  transfer radio
• No need to send acknowledgement
• Every node in the neighborhood of initiator wakes
  up
• STEM/GAF Hybrid
• GAF proposes a scheme in which a sensor network
  is divided in a grid
• One node in a region has its radio on, others
  have it turned off
• Nodes alternate the responsibility of being the
  active node
• GAF uses network density to conserve energy
• Assuming the active node to be the virtual node,
  STEM can be used on the virtual node to manage
  whole network

18
Optimizing Sensor Networks in the
Energy-Latency-Density Design SpaceSchurgers
2002

• Advantages
• Highly efficient in environments where events are
  rare
• Flexible in term of design trade-off for energy,
  latency and density
• Transition from monitoring state to transfer
  state is easily achieved
• No synchronization required
• Can be use with other topology management schemes
  like GAF
• Disadvantages
• Continuous polling consumes energy
• Not suitable for highly reactive environments
• Requires extra radio on sensor nodes
• Suggestions/Improvements/Future Work
• Analysis of STEM with clustered networks

19
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• In ASCENT, the nodes coordinate to exploit the
  redundancy provided by high density to extend the
  overall system lifetime
• Nodes achieve self-configuration to establish a
  topology that provides communication and sensing
  coverage under energy constraints
• Each node examines its connectivity and adapts
  its participation in the multi-hop network
  topology based on the operating region
• The node
• Signals when it detects high message loss,
  requesting additional nodes to join the network
  to continue relaying messages
• Reduces its duty cycle if high messages losses
  are detected due to collisions
• Probes local communication environment and only
  joins to the multi-hop routing infrastructure if
  it is useful

20
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• Sensor nodes do local processing to reduce
  communication and energy costs
• Challenges arises from the increased level of
  dynamics (systems and environmental)
• One of the most important challenge arises from
  energy constraints imposed by unattended systems
• These systems must be long-lived and operate
  without manual intervention
• They need to self-configure and adapt to
  environmental dynamics and some terrain
  conditions may result regions with non-uniform
  communication density
• These issues can be addressed by deploying
  redundant nodes and designing algorithms to use
  redundancy to extend the system lifetime
• Scaling challenges are associated with spatial
  coverage and robustness
• Central vs. Distributed
• When energy is constraint and environment is
  dynamic, distributed approaches are preferable
  and practical

21
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• Scalable wireless sensor networks require to
  avoid large amounts of data being transmitted
  over long distances
• ASCENT applies well-known techniques from MAC
  layer protocols to the problem of distributed
  topology formation
• Imagine a habitat monitoring sensor network that
  is deployed in remote forest
• The deployed systems must confer with the
  following conditions
• Ad-hoc deployment
• Energy constraints
• Unattended operation under dynamics
• If we use too few nodes initially
• the distance between neighboring nodes will be
  too far
• packet loss rate may increase
• energy required to transmit over larger distances
  may be prohibitive

22
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• If we use all deployed nodes simultaneously
• system will expand unnecessary energy
• nodes interfere with each other by congesting the
  channel
• ASCENT does not use localized distributed
  algorithm to find a single solution
• Adaptive self-configuration using localized is
  suited to problem spaces which have a vast number
  of possible solutions (in this case, large
  solution spaces means dense node deployment)
• ASCENT has the following two assumptions
• Carrier Sense Multiple Access (CSMA) MAC protocol
• Possibilities for resource contention when too
  many neighboring nodes participate in the
  multi-hop network
• Reacts when links have high packet loss
• Does not detect or repair network partitions and
  assumes that there is enough node density to
  connect the entire region

23
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• Two essential contributions of ASCENT design are
• Adaptive techniques that allow applications to
  configure the topology based on the needs while
  saving energy to extend network lifetime. The
  techniques do not assume a specific model or
  fairness, degree of connectivity, or capacity
  required
• Self-configuring techniques that react to
  operating conditions are measured locally. It
  does not assume any specific radio propagation
  model, geographical distribution of nodes, or
  routing mechanisms used
• ASCENT Design
• Adaptively elects active nodes from all the nodes
• Active nodes stay awake always and participate in
  routing while the other nodes remain passive and
  periodically checks if they should become active

24
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• ASCENT Design
• Initially, only some nodes are active while other
  are passively listening to packets but not
  transmitting
• When source starts transmitting data packets
  towards the sink, the sink gets high message loss
  from the source due to limited radio range,
  called communication hole
• The receiver gets high packet loss due to poor
  connectivity with the sender

Figure 2(a) Communication Hole
25
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• ASCENT Design
• Sink start sending help messages to neighbors
  that are in listen-only mode, called passive
  neighbors, to join the network
• When a neighbor receive a help message, it
  decides to join the network or not
• If the node joins, it becomes an active neighbor
  and signals the existence of a new active
  neighbor to other passive neighbors by sending a
  neighbor announcement message
• It continues until the number of active nodes
  stabilizes on a certain value and the cycle stops

26
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• ASCENT Design
• When the process is completed, the newly joined
  nodes participate in the data delivery process
  from source to sink more reliably
• The process will be repeated in the case of
  network event (e.g., node failure) or
  environmental effect (e.g., new obstacle) causes
  message loss

Figure 2(b-c) Self-configuration transition and
final state
27
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002
ASCENT State Transactions
NT neighbor threshold LT loss threshold T?
state timer values (p passive, s sleep, t
test) DL Data loss rate
28
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• ASCENT State Transactions
• Initially, a random timer turns on the nodes to
  avoid synchronization
• Node initializes to test state
• Sends data and routing control messages
• Sets up a timer, Tt and sends neighbor
  announcement messages
• Moves into passive state if the conditions are
  met before Tt expires
• When Tt expires, it enters to active state
• The reasoning behind the test state is to probe
  the network to decide whether the addition of a
  new node would improve connectivity
• On entering the passive state, node
• Sets up a timer Tp and when Tp expires, it enters
  to sleep state
• If before Tp expires, it enters to test state
  only if the conditions are met
• Nodes in passive state can hear all packets
  transmitted, but no routing or data packets are
  forwarded in this state since this is listen-only
  state

29
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• ASCENT State Transactions
• The reasoning behind the passive state is to
  gather information about the state of the network
  without causing interference with other nodes
• Nodes in passive and test states update the
  number of active neighbors and data loss rates
• In passive states, the nodes still consume energy
  since the radio is on
• The nodes in sleep state turns the radio off,
  sets up timer Ts and goes to sleep
• When Ts expires, the nodes moves into passive
  state
• A node in the active state continuous to forward
  data and routing packets until it runs out of
  energy

30
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• ASCENT Parameters Tuning
• ASCENT has many parameters and the choices are
  left to the applications such as a particular
  application may trade energy savings for greater
  sensing coverage
• Neighbor Threshold (NT)
• Determines the average connectivity if the
  network
• Tradeoff between energy consumed and/or level of
  interference (packet loss) vs. desired sensing
  coverage
• Loss Threshold (LT)
• Determines the maximum amount of data loss an
  application can tolerate before requesting help
  to improve network connectivity
• This value is highly application dependent

31
ASCENT Adaptive Self-Configuring sEnsor Networks
Topologies Cerpa 2002

• ASCENT Parameters Tuning
• Test timer (Tt), Passive timer (Tp), Sleep timer
  (Ts)
• Determines the maximum time a node remains in
  test, passive, sleep states
• Tradeoff between power consumption vs. decision
  quality

32
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor NetworksMelodia
2004

• The primary design constraints of the sensor
  network algorithms and protocols are
  energy-efficiency, scalability and localization
• The improved energy efficiency can be achieved by
  designing protocols and algorithms with
  cross-layer approach, i.e., considering
  interactions between different layers of the
  communication process such that overall energy
  consumption is minimized
• A scalable algorithm performs well in a large
  network
• The scalability for an algorithm is related to
  that of localization in a scalable algorithm
  each node exchanges information only with its
  neighbors (localized information exchange) in a
  very large wireless network

33
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor NetworksMelodia
2004

• This paper considers the interaction between
  topology control and energy efficient
  geographical routing
• The question to answer is How extensive should
  be the Local Knowledge of the global topology in
  each sensor node, so that an energy efficient
  geographical routing can be guaranteed?
• This question is related to the degree of
  localization of the routing scheme
• If each sensor node have the complete knowledge
  of the topology, it could compute the global
  optimal next hop to minimize the energy
  consumption
• However, the knowledge of complete topology
  information has an associated cost, i.e., energy
  used to exchange the signaling traffic

34
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor NetworksMelodia
2004

• An analytical framework is developed to capture
  the tradeoff between the topology information
  cost, which increases with the Knowledge Range of
  each node, and the communication cost, which
  decreases when the knowledge becomes more
  complete
• This analytical framework is then applied to
  different position based forwarding schemes and
  demonstrated by using Monte Carlo simulations
  that a limited knowledge is sufficient to make
  energy efficient routing decisions
• A neighbor for a certain sensor node is another
  node which falls into its topology Knowledge
  Range, denoted as KR

35
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor NetworksMelodia
2004

• The contributions of this work are
• Introduction of a novel analytical framework to
  evaluate the energy consumption of geographical
  routing algorithms for sensor networks
• Integer Linear Programming (ILP) formulation of
  the topology Knowledge Range optimization problem
• Detailed comparison of the leading existing
  forwarding schemes Takagi 1984, Hou , Finn
  1987, Kranakis 1999, Nelson 1984 and
  introduced a new scheme called Partial Topology
  Knowledge Forwarding (PTKF)
• Introduction of PRobe-bAsed Distributed protocol
  for knowledge rAnge adjustment (PRADA) for the
  on-line solution of the problem that allows nodes
  to select near-optimal Knowledge Ranges in a
  distributed way

36
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor NetworksMelodia
2004

• Advantages
• No need for knowing the global topology of the
  network
• PRADA can be run independently in the nodes, thus
  the nodes do not require time synchronization
• Demonstrates a limited amount of topology
  knowledge is sufficient in order for energy
  conserving routing protocols to be implemented
• The nodes periodically update their knowledge
  range, thus the algorithm could be implemented
  in sensor networks where the nodes are mobile
• Draws a fine line between topology information
  cost and communication cost

37
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor NetworksMelodia
2004

• Disadvantages
• No mentioning about the sensitivity towards
  location error of their proposed protocol
• For a pair of source-destination path, the most
  optimal path is always chosen however, this
  would lead to a starvation of some of the nodes
  that would not get any traffic
• The performance evaluation of protocol does not
  consider the lower layers, such as MAC

38
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor NetworksMelodia
2004

• Suggestions/Improvements/Future Work
• Extending the optimization objectives to include
  not only power but also battery level of each
  node (thus improving network lifetime)
• Implementing the proposed routing protocol within
  a simulator which considers routing and MAC layer
  together to draw a more convincible conclusion

39
References

• Cerpa 2002 A. Cerpa and D. Estrin, ASCENT
  Adaptive Self-Configuring Sensor Networks
  Topologies, Proceedings of the Twenty First
  International Annual Joint Conference of the IEEE
  Computer and Communications Societies (INFOCOM
  2002), New York, NY, USA, June 23-27 2002.
• Finn 1987 G.G. Finn, Routing and Addressing
  Problems in Large Metropolitan-Scale
  Internetworks, ISI res. rep ISU/RR- 87-180, Mar.
  1987.
• Hou T.C. Hou and V.O.K. Li, Transmission
  Range Control in multihop packet radio networks,
  IEEE Transactions on Communications, Vol. 34,
  No.1, pp. 38-44.
• Kranakis 1999 E. Kranakis, H. Singh, and J.
  Urrutia, Compass routing on geometric networks,
  Proceedings of the 11th Canadian Conference on
  Computational Geometry, Vancouver, Canada, August
  1999.
• Liu 2003 J. Liu and B. Li, Distributed
  Topology Control in Wireless Sensor Networks with
  Asymmetric Links, GLOBECOM 2003.
• Melodia 2004 T. Melodia, D. Pompili, and I.F.
  Akyildiz, Optimal Topology Knowledge for Energy
  Efficient Geographical Routing in Sensor
  Networks, Proceedings of the Twenty First
  International Annual Joint Conference of the IEEE
  Computer and Communications Societies (INFOCOM
  2004), Hong Kong, P.R., China, March 2004.
• Nelson 1984 R. Nelson and L. Kleinrock, The
  spatial capacity of a slotted ALOHA multihop
  packet radio network with capture, IEEE
  Transactions on Communications, Vol. 32, No.6,
  pp. 684-694, 1984.

40
References

• Takagi 1984 H. Takagi and L. Kleinrock,
  Optimal Transmission Ranges for Randomly
  Distributed Packet Radio Terminals, IEEE
  Transactions on Communications, Vol. 32, No.3,
  pp. 246-57, 1984.
• Schurgers 2002 C. Schurgers, V. Tsiatsis, S.
  Ganeriwal, and M.B, Srivastava, Optimizing Sensor
  Networks in the Energy-Latency-Density Design
  Space, IEEE Transactions on Mobile Computing,
  Vol. 1, No.1, pp. 70-80, January-March 2002.