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31 st 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)

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Power aware routing in mobile ad hoc networks l.jpg

31st Oct, 2002

Power Aware Routing in Mobile Ad-Hoc Networks

Sumit I Eapen

- Joy Ghosh


Contents l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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


Contents4 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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


Contents13 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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


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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)

  • 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

  • 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 l.jpg

Low Power Routing - II

  • Remaining Battery Power

    • Ri(t) is the remaining power of node i at time t

  • Simple Approach

    • Minimize ΣiЄpath1/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 l.jpg

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) = ΣiЄπCi(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 l.jpg

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 l.jpg

PSR – Route Discovery Illustration

5

12

5

7

t2

11 @ t1

t1

3

13, 11

2

8

9 @ t3

8

S

t3

D

6

reply to 11

2

4

2


Psr route maintenance l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

PSR vs DSR – network lifetime


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PSR vs DSR – varying threshold


Psr points to ponder l.jpg

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


Contents26 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

LEAR – DROP_ROUTE_CACHE & CANCEL_ROUTE_CACHE

On receiving CANCEL_ROUTE_CACHE from C1, B invalidates that entry


Lear complete algorithm l.jpg

LEAR – Complete Algorithm


Lear simulation on glomosim l.jpg

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 l.jpg

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 l.jpg

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


Contents37 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

GEAR – Drawback I

  • Inefficient Transmission

    • Recursive geographic forwarding vs. Restricted flooding


Gear drawback ii l.jpg

GEAR – Drawback II

  • Non-Termination

    • When network density is low compared to (sub) target region size


Gear proposed solution l.jpg

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


Contents47 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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


Slide49 l.jpg

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.


Slide50 l.jpg

b

TD^n(bc)

TD^n(ab)

c

a

TD^n(ac)

Relaying Concept

  • Relay through b if:tdnab+ tdnbc + C < tdnac

  • 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 l.jpg

Relay Region


Neighbors l.jpg

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.


Slide53 l.jpg

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.


Slide54 l.jpg

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.


Slide55 l.jpg

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 l.jpg

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.


Contents57 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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

  • Localized coordination and control for cluster setup and operation

  • Randomized rotation of the cluster heads and the corresponding clusters.

  • Local compression to reduce global compression


Leach algorithm details l.jpg

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 l.jpg

Advertisement Phase

  • Each nodedecides 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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

Direct Transmission –vs- LEACH


Contents65 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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 l.jpg

Implosion Example


Overlap example l.jpg

Overlap Example


Spin negotiation resource management l.jpg

SPIN – Negotiation & Resource Management

  • Toovercome 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 l.jpg

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 l.jpg

SPIN1 : 3 way handshake


Energy dissipation comparison l.jpg

Energy Dissipation Comparison


Contents73 l.jpg

Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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 l.jpg

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 l.jpg

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 l.jpg

HPAR: max-min zPmin Algorithm

  • Find the path with the least power consumption, Pmin by using the Dijkstra algorithm

  • 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.

  • Find the minimal utij on that path, let it be umin.

  • Find all the edges whose residual power fraction utij is no greater than umin, remove them from the graph.

  • Goto 1.


Hpar empirical experimental analysis l.jpg

HPAR – Empirical Experimental Analysis


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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 l.jpg

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 l.jpg

Zone Power Estimation

D

C

A

B

E


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HPAR – Power Graph


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HPAR – Zone Power Estimation Algorithm


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HPAR - Global Path Selection


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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.


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HPAR –Path Connection amongst Zones


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Contents

Introduction

Metrics for power awareness

Routing Protocols

> Power SourceRouting (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


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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


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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


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Thank You!!!

31st Oct, 2002


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