Topology Management
Ad hoc and Sensor Networks
The Need for Topology Management
– What is it?
o The physical or logical interconnection pattern of a network
– Topology schemes in wired networks:
o Bus
o Star
o Ring
– Why do we need different schemes in sensor networks?
o location of sensors is not deterministic
o resource constraints
The Need for Topology Management
– Energy/Power consumption
– Interference
– Throughput
– Connectivity
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
[Basagni+ 2001]
Motivation for Backbone Architecture
 essential for management of large ad hoc networks
 helps generate the minimum possible overhead for construction
and maintenance of the backbone network
 can provide efficient solution for mobility and node/link failures in
very large ad hoc networks
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
Proposed B-Protocol Description
B-Protocol
• Also known as backbone protocols
• Sets up and maintains a connected network (B-network)
• B-network convey the time-sensitive network management information
•
from every node in the network with minor overhead and in a
timely manner
• Comprises two major tasks:
(a) B-nodes selection
(b) B-links establishment
Nodes that are not selected as B-nodes are termed F- nodes that belong to the
flat network
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
B-nodes Selection
 Executed at each node based on a node’s own weight
 Weight is computed based on what is most critical to that node for the
specific network application
 The highest weight of a node is more suitable to be a B-node
 A node knows
 Its own identifier (ID) and weight
 Ids, weights and roles (B-node or F-node) of one-hop neighbors
 Once a node b determines its role as B-node, all its neighbors may
become the F-nodes served under b unless they have decided to join
another node
 B-nodes selection is adaptive to node mobility and changes in its local
status
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
B-nodes Selection
Illustrative Example:
6(1)
2(3)
4(9)
1(6)
5(8)
7(5)
3(2)
8(1)
Numbers represent node IDs and numbers within parentheses represent the node weights
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
B-nodes Selection
Illustrative Example:
6(1)
2(3)
4(9)
B-node
1(6)
7(5)
3(2)
5(8)
B-node
8(1)
B-node
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
B-links Establishment
 Determines the inter-B-nodes links to be established in order for the
network to be connected
 Two types of B-links:
 Physical – when a direct link between B-nodes at most three hops
away can be established without involving intermediate F-nodes
(via power control or directional antenna)
 Virtual – when a direct link between B-nodes at most three hops
away cannot be established. In this case, virtual link is implemented
among two B-nodes by a corresponding physical path with at most
three links
 The rules stated follow the theorem proven in [Chlamtac ’96]
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
B-links Establishment
Theorem 1 [Chlamtac ’96]:
Given a set B of network nodes such that no two of them are neighbors
and every other node has a link to a node in B, then a connected
backbone is guaranteed to arise if each node in B establishes links to all
other nodes in B that are at most three hops away. Moreover, these links
are all needed for the deterministic guarantee in the worst case, in the
sense that if any of them is left out then it is not true anymore that the
arising backbone is connected for any underlying flat network.
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
Properties of B-protocol
1.
Each node in flat network knows only its one-hop neighbors. This
induces the minimum possible overhead
2.
B-link establishment is run at each B-node only with no knowledge of
the surrounding B-nodes. Again, this induces the minimum overhead
3.
Every B-node serves a number of F-nodes each of which is at most
three-hops away. B-node selection protocol guarantees that all the F-
nodes are served by only one neighboring B-node
4.
There are no two B-nodes that are neighbors in the flat network. This
guarantees that B-nodes are evenly distributed in the network
5.
B-node selection is based on the node’s current status (weight)
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
Properties of B-protocol
6.
The B-network is always connected provided that the underlying flat
network is connected
7.
B-protocols takes into account different technologies and mechanisms
that can be used to link the B-nodes in the network. Two types of Blinks are provided; namely physical and virtual links. Physical links are
used when there is a direct link between B-nodes at most three hops
away and virtual links are used when there is a direct link cannot be
established
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
Simulation Environment

A simulator used for an ad hoc network of nodes ranges 100 - 1000

Nodes can freely move around in a rectangular region (a grid)

Node movements are discretized to grid units of 1 meter

A node determines its direction randomly by choosing between its
current direction (with 75% probability) and uniformly among all other
directions (with 25% probability)

When a node hits a grid boundary, it bounces back into the region with
an angle determined by the incoming direction

Fixed transmission range of each node (250 m) and the grid size have
been chosen to obtain a good network connectivity

Each packet contains the time-stamped, node identified weight of the
sending node. All packets are sent for the one-hop neighbors only
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
Simulation Results

k is the total number of Fnodes a B-node can serve
at any point in time

Three cases:



k < n (where n is total
number of nodes in
network)
k<4
k<8
Figure 1: Number of B-nodes (% w.r.t the number of the network nodes)
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
Simulation Results

k is the total number of Fnodes a B-node can serve
at any point in time

Three cases:



k < n (where n is total
number of nodes in
network)
k<4
k<8
Figure 2: Number of B-links (%) when a physical link between any
two B-nodes can be established directly.
Mobility-Adaptive Protocols for Managing Large Ad
hoc Networks
Simulation Results

k is the total number of Fnodes a B-node can serve
at any point in time

Three cases:



k < n (where n is total
number of nodes in
network)
k<4
k<8
Figure 3: Number of B-links (%) when a link between B-nodes is implemented
by a physical path with at most three hops away
Optimizing Sensor Networks in the EnergyLatency-Density Design Space
[Schurgers+ 2002]
–
–
–
Describes
o
a topology management technique that is power efficient
o
energy, Latency and Density trade-offs
Provides
o
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
o
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)
Optimizing Sensor Networks in the EnergyLatency-Density Design Space
[Schurgers+ 2002]
–
–
Two states for sensor nodes:
o
Monitoring State
o
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
Optimizing Sensor Networks in the EnergyLatency-Density Design Space
[Schurgers+ 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
Optimizing Sensor Networks in the EnergyLatency-Density Design Space
[Schurgers+ 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
Optimizing Sensor Networks in the EnergyLatency-Density Design Space
[Schurgers+ 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
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
o
Signals when it detects high message loss, requesting additional nodes to
join the network to continue relaying messages
o
Reduces its duty cycle if high messages losses are detected due to
collisions
o
Probes local communication environment and only joins to the multi-hop
routing infrastructure if it is useful
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
o
These systems must be long-lived and operate without manual intervention
o
They need to self-configure and adapt to environmental dynamics and some
terrain conditions may result regions with non-uniform communication density
o
These issues can be addressed by deploying redundant nodes and designing
algorithms to use redundancy to extend the system lifetime
o
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
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
–
o
Ad-hoc deployment
o
Energy constraints
o
Unattended operation under dynamics
If we use too few nodes initially:
o
the distance between neighboring nodes will be too far
o
packet loss rate may increase
o
energy required to transmit over larger distances may be prohibitive
ASCENT: Adaptive Self-Configuring sEnsor
Networks Topologies
[Cerpa+ 2002]
–
If we use all deployed nodes simultaneously:
o
system will expand unnecessary energy
o
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:
o
Carrier Sense Multiple Access (CSMA) MAC protocol

o
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
ASCENT: Adaptive Self-Configuring sEnsor
Networks Topologies
[Cerpa+ 2002]
–
Two essential contributions of ASCENT design are:
1.
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
2.
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
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
Help
Messages
Data Message
Passive Neighbor
Source
Sink
Figure 2(a): Communication Hole
Active Neighbor
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
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
Neighbor
Announcements
Messages
Data
Message
Source
Source
Sink
Sink
Figure 2(b-c): Self-configuration transition and final state
ASCENT: Adaptive Self-Configuring sEnsor
Networks Topologies
[Cerpa+ 2002]
ASCENT State Transactions
Test
neighbors < NT and
• loss > LT
• loss < LT & help
after Tt
Active
neighbors > NT (high ID for ties);
or
loss > loss T0
after Tp
Passive
after Ts
NT: neighbor threshold
LT: loss threshold
T?: state timer values (p: passive, s: sleep, t: test)
DL: Data loss rate
Sleep
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
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
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
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
SPAN: An Energy-Efficient Coordination Algorithm
for Topology Maintenance in Ad hoc Wireless
Networks
[Chen+ 2002]
–
SPAN is a power saving technique for multi-hop ad hoc networks that
reduces energy consumption with the consideration of maintaining the
capacity or connectivity of the network
–
It is distributed and randomized algorithm in which the nodes make
local decisions on whether to sleep or join to the backbone
–
Each node decides based on an estimate of how many neighbors will
benefit from it being awake and the energy available to it
–
Improvement in the system lifetime increases along with the ratio of
idle-to-sleep energy consumptions
–
Non-coordinators remain in power saving mode and periodically check
to see if they should wake up and become coordinators
SPAN
–
A good power saving technique for ad hoc networks should have the following:
1.
Allow as many as nodes to turn their radios off most of the time
2.
Forward packets between any source and destination with the minimum
possible delay than if all nodes were awake
3.
Distributed algorithm where having each node making local decisions
4.
Backbone formed by the awake nodes should provide close to total capacity as
the original networks such that congestion can be avoided
5.
Do not make many assumptions about the link layer’s facilities for sleeping and
work with any link layer that provides sleeping and periodic polling
6.
Inter-operate correctly with any routing algorithm being used
–
SPAN fulfills all the above requirements
–
Each node makes periodic local decisions to sleep or stay awake as a coordinator
and participate in the forwarding backbone topology
SPAN
–
In order to keep the same level of capacity of the original network, a node may
volunteer to become a coordinator if it figures out from the local information
gathering that two of its neighbors cannot communicate directly or through one or
two existing coordinators
–
In order to keep the number of coordinators low and rotate this role amongst all
nodes, each node delays sending message about its desire to become a
coordinator by a random time interval
–
The decision is based on two factors:
1.
the remaining battery energy
2.
the number of pairs of neighbors it can connect together
–
This allows SPAN to maintain capacity-preserving backbone at any time with the
nodes consuming about the same level of energy
–
SPAN also scales well with the number of nodes
SPAN
SPAN Design
–
The goals of SPAN includes:
1.
Ensures enough coordinators to be elected such that each node is in radio
range of at least one coordinator
2.
Rotates coordinators to ensure that all nodes provides equal support to
achieve global connectivity
3.
Increases the network lifetime, preserves the capacity with minimum latency by
minimizing the number of elected coordinators
4.
Coordinators are elected based on local available information
–
SPAN is proactive such that each node periodically broadcasts HELLO messages
which contains the node’s status (coordinator or not), its current coordinators, and
its current neighbors
–
From these HELLO messages, each node keeps tracks of a list of the node’s
neighbors and coordinators, and for each neighbor, a list of its neighbors and
coordinators
SPAN
Coordinator Announcement
–
Each non-coordinator node periodically determines whether it should become a
coordinator or not
–
The coordinator eligibility rules ensures that the network is covered with sufficient
number of coordinators
Coordinator Eligibility Rule
A non-coordinator node should become a coordinator if it figures out from the local
information gathering that two of its neighbors cannot communicate directly or through
one or two existing coordinators
–
If many nodes are willing to become coordinators, SPAN solves this contention by
delaying coordinator announcement with a randomized backoff delay
–
Each node selects a delay value and delays sending HELLO message indicating the
desire to become coordinator for that amount of time
–
At the end of the delay, the node reevaluates its eligibility based on the HELLO
messages received from neighbors and if it is still eligible, it makes announcement
SPAN
Coordinator Announcement
–
At the end of the delay, the node reevaluates its eligibility based on the HELLO
messages received from neighbors and if it is still eligible, it makes announcement
–
Consider a case where all the nodes have the same level of energy which means
that only topology is considered in the decision of becoming a coordinator
Eq. 1
–
Consider a case where the nodes have unequal energy left
Eq. 2
Er = energy remaining at node
Em = maximum amount of energy
Ci = number of new connections through node i
Ni = number of neighbors for node i
T = round-trip delay for packet
R = random number in [0, 1]
SPAN
Coordinator Announcement
–
In Eq. 1, if nodes with high Ci become coordinators, total number of coordinators
needed would be less to ensure that every node in the network is covered
–
Therefore, the nodes with a high Ci values should volunteer for coordinator position
quicker than those with smaller Ci
–
In Eq. 2, the node with large value of (Er/Em) is expected to volunteer quicker to
become a coordinator than the nodes with smaller ratio in order to assure the
fairness
SPAN
Coordinator Withdrawal
–
Each node periodically checks whether it should withdraw as a coordinator
–
A node withdraws if all of its neighbors can reach each other directly or with one or
more coordinators
–
For fairness, after some period of time, a coordinator withdraws and declares itself
as a tentative coordinator if all neighbors can reach each other via other neighbors,
even if these are not coordinators (allows neighbors to act as coordinators)
–
A tentative coordinator is still used to forward packets and described coordinator
announcement algorithm treats tentative coordinators as non-coordinator nodes
–
A coordinator nodes gives its neighbors the opportunity to become coordinators by
declaring itself as tentative coordinator
–
A coordinator maintains its position as tentative for WT time, where WT is the
maximum value of Eq. 2 which is
W T = 3 x Ni x T
SPAN
Coordinator Withdrawal
–
If the coordinator has not withdrawn within WT time period, it clears its tentative bit
–
In order to prevent node to drain its battery completely, the amount time a node acts
as a coordinator before turning on its tentative bit is proportional to the amount of
energy it has, indicated as (Er/Em)
SPAN
Simulation Results
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links
[Liu+ 2003]
–
–
Topology Control
o
Does not describe a new topology
o
Provides a mechanism to build certain topology
Distributed
o
–
No central control or central source of information
Asymmetric Links
o
Due to the presence of heterogeneous devices
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
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
o
Pi = Transmission Power of Node i
o
Pimax = Maximum Transmission Power of Node i
o
Pij = Transmission Power required for node i to reach j
o
Lij = Asymmetric link from i to j
o
G = (V, L) : directed graph of topology with max power
o
G is strongly connected
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:
–
o
Same bi-directional reachability
o
Consumes minimum power
Phases
o
Establishing the vicinity topology
o
Deriving the minimum power vicinity tree
o
Propagation of transmission powers
o
Optimizations
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}
–
o
If Pjmax > Pij , j can reach I with single hop Lji
o
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)
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)
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
Distributed Topology Control in Wireless Sensor
Networks with Asymmetric Links
[Liu+ 2003]
Optimizations
–
–
Achieved via discarding PR messages when
o
A node already has run the algorithm to find its shortest vicinity tree
o
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
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:
o
No synchronization required
o
No central control node with network information
o
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
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
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor Networks
[Melodia+ 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
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor Networks
[Melodia+ 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
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor Networks
[Melodia+ 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
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor Networks
[Melodia+ 2004]
–
The contributions of this work are:
o
Introduction of a novel analytical framework to evaluate the energy
consumption of geographical routing algorithms for sensor networks
o
Integer Linear Programming (ILP) formulation of the topology
Knowledge Range optimization problem
o
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)
o
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
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor Networks
[Melodia+ 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
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor Networks
[Melodia+ 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
Optimal Local Topology for Energy Efficient
Geographical Routing in Sensor Networks
[Melodia+ 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
References
[Basagni+ 2001] S. Basagni, D. Turgut, and S.K. Das, Mobility-Adaptive Protocols for Managing Large
Ad hoc Networks, Proceedings of IEEE International Conference on Communications (ICC),
Helsinki, Finland, June 11-14, 2001, pp. 1539-1543.
[Chen+ 2002] B. Chen, K. Jamieson, R. Morris, and H. Balakrishnan, SPAN: An Energy-Efficient
Coordination Algorithm for Maintenance in Ad hoc Wireless Networks, To appear in ACM Wireless
Networks Journal, Vol. 8, No. 5, September 2002.
[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.
References
[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.
[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.