Power Aware Routing in Mobile Ad-Hoc Networks -Sumit I Eapen - Joy Ghosh 31 st Oct, 2002

Power Aware Routing Power Aware Routing in Mobile Ad-Hoc Networksin Mobile Ad-Hoc Networks

Power Aware Routing Power Aware Routing in Mobile Ad-Hoc Networksin Mobile Ad-Hoc Networks

-Sumit I EapenSumit I Eapen- Joy Ghosh- Joy Ghosh

3131stst Oct, 2002 Oct, 2002

ContentsContentsContentsContents

IntroductionIntroduction

Metrics for power awarenessMetrics for power awareness

Routing ProtocolsRouting Protocols

> Power Source Routing (PSR) > > Local Energy Aware Routing (LEAR)Local Energy Aware Routing (LEAR) > > Geographical and Energy Aware Routing (GEAR)Geographical and Energy Aware Routing (GEAR) > > Minimum Energy Mobile Wireless NetworksMinimum Energy Mobile Wireless Networks > > Low Energy Adaptive Clustering Hierarchy (LEACH)Low Energy Adaptive Clustering Hierarchy (LEACH) > > Sensor Protocols for Information via NegotiationSensor Protocols for Information via Negotiation > > Hierarchical Power Aware Routing in Sensor NetworksHierarchical Power Aware Routing in Sensor Networks

ReferencesReferences

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







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

Intuitionconserve power and share cost of routing packets to ensure increase in life of node and network

Metrics1. Minimize energy consumed / packet2. Maximize time to Network Partition3. Minimize variance in node power levels4. Minimize cost / packet5. 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 / packetDefinition: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 costDefinition:

- 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







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)oFixed transmit power

• P(i,j) = b x packet_size + cWhere b = packet size dependent energy consumptionAnd c = fixed cost for MAC layer control negotiation

oVarying transmit power• P(i,j) = k x dα

ij

Where dij = distance between i and jAnd 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 On receiving duplicate RREQ an intermediate node re-

broadcasts it only if the following is true: The timer for that RREQ has not expired The new path cost in the header is less than Min-cost

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 latencyDestination has to wait –> blocking natureThe choice of the time-out period is criticalRoute 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







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







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 regione(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







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

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.

Minimum Energy Network – Power Losses

• Relay through b if: tdnab+ tdn

bc + C < tdnac

Relaying Concept

TD^n(ab) TD^n(bc)

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.

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 to find the Enclosure Graph

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.

Algorithm (contd.)

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

Determining Paths (Phase II)

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.







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 LEACH1. Localized coordination and control for cluster setup

and operation2. 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

non-cluster node decides to which cluster head to join( assuming symmetric propagation channels)

Cluster Set up Phase

Each non-cluster-head node informs the cluster-head 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







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 meta-data 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 meta-data 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







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







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!!!Thank You!!!Thank You!!!Thank You!!!

3131stst Oct, 2002 Oct, 2002

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Power Aware Routing in Mobile Ad-Hoc Networks -Sumit I Eapen - Joy Ghosh 31 st Oct, 2002