Transcript Power Aware Routing Protocols in Mobile Ad

31st Oct, 2002
Power Aware Routing
in Mobile Ad-Hoc Networks
-Sumit I Eapen
- Joy Ghosh
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Introduction – Power Concerns
 The lifetime of a network is defined as the time it takes for a
fixed percentage of the nodes in a network to die out.
 Portability of wireless nodes being critical its almost mandatory to
keep the battery sizes to a bare necessary
 Since battery capacity is thus fixed, a wireless mobile node is
extremely energy constrained
 Hence all network related transactions should be power aware to
be able to make efficient use of the overall energy resources of
the network
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Traditional routing metrics
• Aims to minimize hop counts and propagation
delay
• Fails to take into account the power usage of
nodes
• Results in poor lifetime of networks
Power Aware Metrics
Intuition
conserve power and share cost of routing packets
to ensure increase in life of node and network
Metrics
1. Minimize energy consumed / packet
2. Maximize time to Network Partition
3. Minimize variance in node power levels
4. Minimize cost / packet
5. Minimize maximum node cost
1. Minimize energy consumed / packet
Definitions:
– T(a,b) = energy consumed in transmitting and receiving one
packet over one hop from a to b
- ej = Σk-1i=1 T(ni, ni+1) = total energy spent for packet j
Goal:
- Minimize ej for all packets j
Note:
- In lightly loaded networks this automatically finds shortest
hop path
- In heavily loaded networks due to contention it might not be
shortest
2. Maximize time to network partition
Definition:
- Cut Set: set of nodes that divide the network into two partitions
As soon as one node in the set dies the delay experienced increases
Goal:
- To balance load of the nodes in the Cut Set to maximize network life
Problems:
- The problem is similar to scheduling tasks to multiple servers so that
the response time is minimized, which is known to be NP-complete
3. Minimize variance in node power levels
Goal:
- To keep all nodes up and running together for as long as possible
Concept:
- Build a route that takes into account the amount of data waiting to be
transmitted in all the intermediate nodes
Merit:
- Achieve some kind of load balancing to ensure similar rates of
dissipation of energy throughout the network
4. Minimize cost / packet
Definition:
Total cost of sending packet j:
cj = Σk-1i=1 fi (xi)
Where,
- xi is the energy dissipated in node i till now
- fi(xi ) is the cost of node i:
fi(xi) = 1 / (1 – g(xi))
Where
g(xi) is the normalized battery capacity
Goal:
- Minimize cj for all packets j
4. Minimize cost / packet (contd.)
Advantage:
- The remaining batter power level is incorporated into the routing
decision
- This also balances load by avoiding usage of weak nodes in presence of
stronger ones
- Network congestion can be taken care of by increasing node cost in
presence of contention.
5. Minimize maximum node cost
Definition:
- Ci(t) = cost of routing a packet through node i at time t
- Ĉ(t) = maximum of the Ci(t)’s
Goal:
- Minimize Ĉ(t), for all t > 0
Side effects:
- Delays node failure
- Reduces variance in node power levels
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
MANET Routing Protocols
Broad Classifications:
 Proactive Protocols
- Table Driven
- Frequent topology updates
- Each node knows about all destinations
- Distance Vector, Link State Routing, etc.
 Reactive Protocols
- On Demand
- A node learns of other nodes through actual communications
- DSR, AODV, etc
Low Power Routing - I
 Transmission Power
- P (i, j) is the Link Cost defined as the power
expended for transmitting and receiving a packet
between two consecutive nodes i and j
- Minimize Σi,jЄpath P (i, j)
o Fixed transmit power
• P(i,j) = b x packet_size + c
Where b = packet size dependent energy consumption
And c = fixed cost for MAC layer control negotiation
o Varying transmit power
• P(i,j) = k x dαij
Where dij = distance between i and j
And α = parameter depending on physical environment
Low Power Routing - II
 Remaining Battery Power
- Ri(t) is the remaining power of node i at time t
 Simple Approach
-
Minimize ΣiЄpath 1/Ri(t)
 Min-Max Approach
- Avoid routes with nodes having minimum battery
capacity among all nodes in all possible routes
 Conditional Min-Max Approach
- Till all nodes in route have energy above a
threshold, choose route with minimum total
transmission power
- As energy falls below threshold, use the min-max
algorithm suggested above
Power-Aware Source Routing (PSR)
 This is a Reactive (On demand) protocol based on DSR
 Cost Function
- The cost of route π at time t is C (π,t)
- C (π,t) = ΣiЄπ Ci(t)
• where Ci(t) is the cost of node i at time t
-
Ci(t) = ρi . [Fi/ Ri(t)]α
- ρi : transmit power of node i
- Fi : full-charge battery capacity of node i
- Ri(t) : remaining battery power of node i at time
time t
- α : a positive weighting factor
 This Cost function takes into account both transmission
power and remaining battery power
PSR – Route Discovery
 RREQ broadcast initiated by source
 Intermediate nodes can reply to RREQ from cache as in DSR
 If there is no cache entry, receiving a new RREQ an
intermediate node does the following:



Starts a timer
Keeps the path cost in the header as Min-cost
Adds its own cost to the path cost in the header and broadcast


The timer for that RREQ has not expired
The new path cost in the header is less than Min-cost
 On receiving duplicate RREQ an intermediate node rebroadcasts it only if the following is true:
 Destination also waits for a specific time after the first RREQ
arrives
 It then replies to the best seen path in that period and ignores
others that come later
 The path cost is added to the reply and is cached by all nodes
that hear the reply
PSR – Route Discovery Illustration
PSR Route Maintenance
 Node mobility
Connections between some nodes on the path are lost due
to their movement. In this case a new RREQ is issued and the
corresponding entry in the cache is purged.
 Energy Depletion
Energy of some intermediate node maybe depleting very
quickly. This can be addressed in two ways:

Semi-global approach
Here the source monitors the remaining battery level of
the path by periodically polling the intermediate nodes

Local approach
Each intermediate node is allowed to send back a route
error at time t if the following condition is met:
PSR Route Cache Invalidation
 Once the cost of a node has increased beyond the
threshold for a particular route, all cache entries
to various destinations are invalidated
 However if a path was newly added to the cache,
the node makes some allowance by lowering the
threshold by some normalized amount for
forwarding packets only in that path
 Invalidated routes are purged from cache after
some time
 A node can use an invalidated route for its own
message initiations but not for relaying other
node’s packets
PSR vs DSR – Simulation on NS(2)
 Test bed of 20 nodes confined in 1000 x 1000 m^2 area
 Range of each node is 250 m
 100 reliable and random ftp connections
 Average duration of connection is 20 sec
 Total simulation time 10000 sec
 Speed of movement is 10 m/s
 Random mobility with pause time of 4 sec
PSR vs DSR – network lifetime
PSR vs DSR – varying threshold
PSR – Points to Ponder
Threshold timers increase latency
Destination has to wait –> blocking nature
The choice of the time-out period is critical
Route invalidation based on the cost increase
threshold is also a sensitive decision


Too low can force frequent route discoveries
Too high can over use a node in a path
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Local Energy-Aware Routing (LEAR)
 Aims to balance energy consumption with shortest
routing delays
 Takes into account a node’s willingness to participate
in the routing path which is based on its remaining
battery power
 Destination does not wait to reply –> non-blocking
 Efficient use of route cache
The basic LEAR Algorithm
• Source uses a sequence number for new request
• If it gets no reply back it increases the sequence number
and re-broadcasts
LEAR – Basic Algorithm
 Problems


Cannot utilize route cache in the basic form since
upstream nodes cannot freely decide on behalf of
downstream nodes
May incur repeated route request messages due to
dropping of requests by intermediate nodes in
cascade
 Solutions: four additional routing control messages




DROP_ROUTE_REQ
ROUTE_CACHE
DROP_ROUTE_CACHE
CANCEL_ROUTE_CACHE
LEAR – DROP_ROUTE_REQ
 The Cascading effect





Say the path is A -> B -> C1 -> C2 -> D
Each of the intermediate nodes say have low energy
On 1st request from A to D, B will drop request and adjust
threshold
On 2nd request from A to D, C1 will drop and adjust, and so on
D will finally get the request on 4th attempt
 DROP_ROUTE_REQ



On 1st attempt from A to D, B drops and adjusts itself and also
forwards DROP_ROUTE_REQ along the path to D
This causes C1 and C2 to adjust their threshold
D will receive the request on the 2nd attempt
LEAR – ROUTE_CACHE
 Destination may receive multiple ROUTE_REQ and
ROUTE_CACHE
 It replies to only the first one
LEAR – DROP_ROUTE_CACHE &
CANCEL_ROUTE_CACHE
On receiving CANCEL_ROUTE_CACHE from C1, B invalidates that entry
LEAR – Complete Algorithm
LEAR – Simulation on GloMoSim
 Test bed of 40 nodes confined in 1000 x 1000 m^2 area
 Range of each node is 250 m
 5 Constant Bit Rate source and destination pair chosen
 1024 byte packets sent every sec for a specified duration
 Total simulation time 500 sec
 Random waypoint mobility
 Speed of movement is 5 m/s
 Pause time is varied from 50 to 400 sec
 Simulation results shown next are average of 100 runs
 Initial Threshold value set to 90% of node’s initial power
 The value of adjustment ‘d’ is taken as 0.1 or 0.4
LEAR – Standard Deviation of energy distribution
• Energy Consumption measured at radio layer
•35% improved energy balance with high mobility (50 sec pause time)
•10% improvement with moderate mobility (400 sec pause time)
•The ‘d’ value does not affect much
LEAR – Ratio of accepted ROUTE_REQ
• Ratio = total route_reqs accepted / total route_reqs received
• Even DSR does not have 100% ratio due to TTL
• ‘d’ = 0.1 drops requests more frequently due to lower adjustment
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Geographical & Energy Aware Routing (GEAR)
 Mostly appropriate for static data-centric sensor
networks
 The basic concept comprises of two main parts:
 Route packets towards a Target region
through geographical and energy aware
neighbor selection
 Disseminate the packet within the region
 The concept of the 1st part can also be applied to
mobile ad-hoc networks
GEAR – Energy aware neighbor computation




Each node N maintains state h(N,R) which is called
learned cost to region R
Each node infrequently updates neighbor of its cost
When a node wants to send a packet, it checks the
learned cost to that region of all its neighbors
If the learned cost of a neighbor to a region is not
available, the estimated cost is computed as follows:
c(Ni, R) = xd(Ni, R) + (1-x)e(Ni)
Where,
x = tunable weight,
d(Ni, R) = normalized distance of neighbor to region
e(Ni) = normalized consumed energy at node i
GEAR – Packet forwarding
 When a node wants to forward a packet to a
destination, it checks to see if it has any neighbor
closer to destination than itself
 In case of multiple choices it aims to minimize the
learned cost h(Ni, R)
 It then sets its own cost to:
h(N, R) = h(Ni, R) + C(N, Ni)
Where,
C(N, Ni) = combination of remaining energy of
N and Ni and the distance between them
GEAR – Forwarding around holes



Incase there are no neighbors closer to destination than itself, the
node forwards to the neighbor with the least learned cost
It updates its own cost accordingly
So next time it wont lie in the route to that region
GEAR – Discussions on hole avoidance

If the length of the path from S to T is n, the learned cost will
converge after S delivers n packets to same target T

Convergence of learned cost only affects efficiency of hole avoidance
not its correctness

Propagating learned cost further upstream through the update
procedure will enable earlier chances to avoid holes
GEAR – Dissemination


Once the target region is reached the packets are disseminated within
the region by recursive geographic forwarding
Forwarding stops when a node is the only one in a sub-region
GEAR – Drawback I
•
Inefficient Transmission
– Recursive geographic forwarding vs. Restricted flooding
GEAR – Drawback II
•
Non-Termination
– When network density is low compared to (sub) target region size
GEAR – proposed solution
•
Node degree is used as a criteria to differentiate low density
networks from high density ones
•
Choice of restricted flooding over recursive geographic
forwarding is made accordingly
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Minimum Energy Wireless Network
• What is “Minimum Energy Network”?
-- It is a network where there is a path from node i to j
that consumes the least transmission power
. Minimum Energy Network Design
--given a set of wireless nodes, for each node find a
selected set of nodes called neighbors, set a directed link
from the node to its neighbor (enclosure graph)
--design an algorithm that will do the above function
--protocol is distributed
• Design first for a stationary wireless network and then
extend it to a mobile scenario
Minimum Energy Network – Power Losses
1. Transmission loss which is proportional to dn where d is the
distance between transmitter and receiver. n >= 2
2. Receiver power loss constant C.
3. CPU computation loss negligible.
 Due to 1 above, it can be seen that relaying packets through
intermediate nodes might save energy instead of directly
transmitting packets.
Relaying Concept
• Relay through b if: tdnab+ tdnbc + C < tdnac
TD^n(bc)
TD^n(ab)
TD^n(ac)
• Relay Region:
R i->r of the transmit-relay node pair (i,r) is
R i->r = {(x,y) | P i->r->(x,y) < P i->(x,y)}
e.g, Ra->b = {c} in the above example
Relay Region
Neighbors
 Neighbors N(i) of a node i
are those nodes that do not
fall in the relay region of
any other node with respect
to i
 Ei = ∩kεN(i) Rc i->k ∩ DN
 N(i) = {n ε N|(xn,yn) ε Ei, n ≠ i}
 Enclosed Node:
A node i is said to be
enclosed if it has
communication links to each
of its neighbors and to no
other node.
Algorithm to find the Enclosure Graph

The distributed protocol to find the enclosure graph consists of two
steps
• for each node i, find its neighbors
• set up directional links from each node to all its neighbors
• This graph is strongly connected
Search for Neighbors (Phase 1)
 A search algorithm is used to determine the above
 Each node sends a signal to its search region. This signal contains the
position of the node.
 The node also listens to signals. When it receives the signals it can
find the relay region of the corresponding node.
Algorithm (contd.)
 Nodes found in the search fall into two
categories.
• Alive nodes
• Dead nodes
 When the search algorithm terminates for node i
then the set of alive nodes is the set of neighbors
for node i.
 The only outgoing communication links from i will
be to these set of alive nodes.
Determining Paths (Phase II)


Apply an algorithm similar to bell ford to enclosure graph
Lets assume that all nodes wish to find the minimum power path to
a particular node called the Master node
Path Determination






Each node broadcasts its cost to its neighbors
The cost of a node i is defined as the minimum power necessary
for it to reach the master node
Each node finds minimum cost it can attain given costs of its
neighbors.
If n ε N(i), when i receives the information cost(n), it computes:
Ci,n = Cost(n) + Ptrans(i,n) + Preceiver(n)
Cost(i) = min nεN(i) Ci,n
Picks the link corresponding to this minimum cost neighbor
Distributed Mobile Network
 Protocol developed so far was for a stationery network
 Localized nature of the search algorithm makes it applicable
to mobile scenarios too
 Here each node periodically executes phase 1 and phase 2.
 This time interval should not be too large or too small
 Thus the protocol can be made self reconfigurable.
 Demerit of Minimum Energy Networks
The remaining battery power is not taken into
consideration.
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Low Energy Adaptive Clustering Hierarchy (LEACH)
In this we consider a micro-sensor network where:
1. The base station is fixed and located far from sensors
2. All nodes are homogeneous and energy constrained
Key features of LEACH
1.
Localized coordination and control for cluster setup and
operation
2.
Randomized rotation of the cluster heads and the
corresponding clusters.
3.
Local compression to reduce global compression
LEACH - Algorithm Details
 Operation of Leach broken into rounds
 Round
 Set-up phase
• Advertisement phase
• Cluster Set-up Phase
• Schedule Creation
• Data transmission
 Steady-state phase
Advertisement Phase


Each node decides whether or not to become a cluster head for a
round based on a threshold.
Each node say node n generates a random number between 0 and 1.
If the random number is less than a threshold T(n) then the node
elects itself to be a cluster head.
T(n) = P / ( 1 – P*(r mod 1/p)) if n ε G
= 0
otherwise
P – desired percentage of cluster heads (P = 0.05)
r – current round
G – is the set of nodes that have not been cluster head in last 1/P
rounds
Advertisement Phase (contd.)
 Each node that elects itself cluster-head for
current round broadcasts a message to the rest
of the nodes
 All cluster-heads transmit their advertisement
with the same transmit energy
 Non cluster heads keep their receivers on
 Based by the received signal strength, each noncluster node decides to which cluster head to
join( assuming symmetric propagation channels)
Cluster Set up Phase
 Each non-cluster-head node informs the clusterhead to whom it wants to join.
 During this phase all heads should keep their
receivers on
Schedule Creation:
Each cluster head based on the number of nodes
in its cluster creates a TDMA schedule which is
broadcasted to its cluster
Data Transmission
 Radios of non-heads are off when its not
transmitting, to preserve energy.
 When all data has been received from all the
nodes the head performs signal processing to
compress the data into a single signal
 This is then send directly to the base station by
a high energy transmission.
Direct Transmission –vs- LEACH
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Sensor Protocols For Information via Negotiation
 A family of adaptive protocols that efficiently disseminate
information among sensors in a energy constrained wireless
sensor network.
 Uses Meta-data : high level data descriptor
 Meta-data negotiations to eliminate redundant information
 Why data dissemination? – classic flooding can be used but
has 3 demerits
• Implosion
• Overlap
• Resource Blindness
Implosion Example
Overlap Example
SPIN – Negotiation & Resource Management
 To overcome the problem of implosion and
overlap, SPIN nodes negotiate before they
transmit data.
 To negotiate in an energy efficient manner metadata is used
 Nodes use a resource manager to find out their
battery reserves
 If low then they cut back on certain activities like
forwarding third party information.
SPIN MESSAGES
 ADV : new data advertisement. When a node has new data
to send it sends an ADV that contains the meta-data
 REQ : this is in response to a ADV. This contains the metadata that it wants
 DATA : data message. This contains the actual sensor data
that the REQ asked for. It has a meta data header.
SPIN1 : 3 way handshake
Energy Dissipation Comparison
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
Hierarchical Power Aware Routing
 Discusses about an online power aware routing algorithm in
large sensor networks
 Path selection takes into consideration both the
transmission power and the minimum battery power of the
node in the path. It tries to compromise
 Makes use of zones to take care of the large number of
sensor nodes
HPAR - Definitions




Pmin : power of the path with minimal power consumption
P(Vi) : initial power of node Vi
Pt(Vi) : power of node Vi at time t
eij : energy to transmit message between node i and j.
 Utij : residual power fraction

Utij = (Pt(Vi) - eij) / P(Vi)
HPAR: max-min zPmin Algorithm
1. Find the path with the least power consumption, Pmin by
using the Dijkstra algorithm
2. Find the path with least power consumption in the graph.
If the power consumption is greater than zPmin or no path
is found, then the previous shortest path is the solution.
3. Find the minimal utij on that path, let it be umin.
4. Find all the edges whose residual power fraction utij is no
greater than umin, remove them from the graph.
5. Goto 1.
HPAR – Empirical Experimental Analysis
HPAR - Zone Based Routing
 Max-min zPmin algorithm requires accurate power
level information for all nodes in the network
 This is not feasible for a large network with lots
of nodes
 So the whole network is divided into a small
number of zones
 Each message is routed across zones using the
information of the power estimate for the zones
HPAR - Zone Power Estimation
 Each zone has a controller node that polls each
node in the zone for their power level
 Power estimation measures the number of
messages that can flow through the zone
 Estimation is done relative to direction of
message transmission
 Once the controller node determines the power
estimate in each direction it broadcasts these to
the other zones
 This is feasible because the number of zones is
small
Zone Power Estimation
HPAR – Power Graph
HPAR – Zone Power Estimation Algorithm
HPAR - Global Path Selection
Local Path Selection
 The max-min zPmin algorithm is used directly to route a
message within a zone.
 There could be multiple entry points into the zone and
multiple exit points. So how are 2 paths in adjacent zones
which are supposed to be part of a common global path
connected.
 For this we associate a count with each node which tells how
many times did a path start from the node when the power
estimation in each direction was done.
 Then whenever we find paths we take the start and end
node in each zone to be the ones the highest count.
HPAR – Path Connection amongst Zones
Contents
Introduction
Metrics for power awareness
Routing Protocols
> Power Source Routing (PSR)
> Local Energy Aware Routing (LEAR)
> Geographical and Energy Aware Routing (GEAR)
> Minimum Energy Mobile Wireless Networks
> Low Energy Adaptive Clustering Hierarchy (LEACH)
> Sensor Protocols for Information via Negotiation
> Hierarchical Power Aware Routing in Sensor Networks
References
References - I
[1]
Power-Aware Routing in Mobile Ad Hoc Networks – Suresh Singh, Mike
Woo, C.S. Raghavendra
[1]
Power-aware Source Routing Protocol for Mobile Ad Hoc Networks –
Morteza Maleki, Karthik Dantu, and Massoud Pedram
[2]
Non-Blocking Localized Routing Algorithm for Balanced Energy
Consumption in Mobile Ad Hoc Networks – Kyungtae Woo, Chansu Yu, Hee Yong
Youn, Ben Lee
[3]
Hierarchical Power-aware Routing in Sensor Networks – Qun Li, Javed
Aslam, Daniela Rus
[4]
Minimum Energy Mobile Wireless Networks – Volkan Rodoplu, Teresa
H. Meng
[5]
A Location-aided Power-aware Routing Protocol in Mobile Ad Hoc
Networks – Yuan Xue, Baochun Li
References - II
[6]
Geographical and Energy Aware Routing: a recursive data
dissemination protocol for wireless sensor networks – Yan Yu, Ramesh Govindan,
Deborah Estrin
[7]
Energy-Efficient Communication Protocol for Wireless Microsensor
Networks - Wendi Rabiner Heinzelman, Anantha Chandrakasan, Hari
Balakrishnan
[8]
Adaptive Protocols for Information Dissemination in Wireless Sensor
Networks - Wendi Rabiner Heinzelman, Joanna Kulik, Hari Balakrishnan
[9]
GPSR: Greedy Perimeter Stateless Routing for Wireless Networks –
Brad Karp, H.T. Kung
[10]
Dynamic Source Routing in Ad Hoc Wireless Networks – David B.
Johnson, David A. Maltz
Thank You!!!
31st Oct, 2002