Power Conservation in wireless sensor network using receiver switch-off

(1)

Goel, Apurva A. Power Conservation in Wireless Sensor Networks using Receiver Switch-off.

(Under the direction of Dr. Rudra Dutta).

A wireless sensor network is a network of nodes equipped with sensors and capable

of relaying their data to the monitoring station using multi-hop communication. Essentially,

they operate using the ad hoc paradigm due to the unpredictable and dynamic topology.

The lifetime of the nodes is limited to that of its battery. Thus in this work we try to

conserve the battery by putting the nodes to sleep when they are not involved in any

communication process.

Sensor nodes produce readings every fixed time interval and we try to leverage the

predictability of the packet inter-arrival time to obtain the sleep periods. We model the

inter-arrival times that a node observes in the form of a probability distribution function.

A node may be forwarding packets from multiple sources and it associate this knowledge

to every stream it is carrying. After a node captures a packet, using its knowledge it can

predict the approximate arrival time of the next packet. It can try to sleep during this

period. It thus conserves power.

In our study we show that simply forwarding the packet when they are received

causes the predictability needed for this approach to be lost. We then propose an alternative

approach, which is to maintain some local periodicity. This can be accomplished by delaying

the packets. The multi-hop nature of the network causes these delays to accumulate and

thus we have to consider the tolerances of the data packets to these delays. We then discuss

the relation between the sleep durations possible and the delay tolerances of the packets.

Finally, we explore the inter-dependence between packet loss, sleep duration, delay and the

(2)

by

Apurva A Goel

A Thesis submitted to the Graduate Faculty of North Carolina State University

in partial satisfaction of the requirements for the Degree of

Master of Science

Department of Computer Science

Raleigh

2003

Approved By:

Dr. Mihail Sichitiu Dr. David Thuente

(3)

To my parents,

(4)

Biography

Apurva Ajay Goel is a graduate student in the Department of Computer Science

at North Carolina State University. He was born and brought up in Bombay, India. In 2001,

he received his Bachelor of Engineering Degree in Computer Engineering from University

of Bombay, India. After graduating from there he joined North Carolina State University

in 2001. He is currently working towards the completion of his Master of Science degree in

(5)

Acknowledgements

I acknowledge the efforts of my thesis advisor,Dr. Rudra Dutta, in providing me

direction and guidance. Working with him has been a an honor and an invaluable learning

experience. I am also grateful to him for the financial support he accorded.

I thank Dr. Thuente and Dr. Sichitiu for serving on my thesis advisory committee

and providing me with invaluable advice.

I thank my family for encouraging me and supporting me in my decisions. In this

endeavor, they have put in just as much effort as me. And a special thanks to my friends

(6)

List of Figures

3.1 Representing the stream in terms of a PDF and CDF . . . 25

4.1 Time line of Dropping a packet . . . 29

4.2 Impact of one and two hops on CDF/PDF of inter-arrival times . . . 36

4.3 A “y” shaped network . . . 37

4.4 Impact of variance on merging . . . 38

5.1 The decision tree . . . 46

6.1 Comparing Static and Moving PDF estimations . . . 52

6.2 Sleep for different Robustness Factor . . . 53

6.3 Packets lost under different Robustness Factor . . . 54

6.4 Delay experienced for different Robustness factor . . . 55

6.5 Extra transmissions for different Robustness factors . . . 56

6.6 Sleep fraction for different number of nodes . . . 58

6.7 Packets lost for different number of nodes . . . 59

6.8 Delay experienced for different number of nodes . . . 60

6.9 Extra transmissions on increasing the number of nodes . . . 61

6.10 Sleep fraction when delays are unbounded . . . 62

6.11 Packet loss when Delays are unbounded . . . 63

6.12 Delay experienced when delays are unbounded . . . 64

6.13 Extra transmissions when delays are unbounded . . . 65

6.14 Effect of varying Standard Deviation on sleep . . . 66

6.15 Effect of varying Standard Deviation on loss . . . 67

6.16 Effect of varying Standard Deviation on the delay . . . 67

(10)

Chapter 1

Introduction

Ad hoc networks are self organizing and self configuring networks of peer nodes.

They have been envisioned for deployment in scenarios where it may not be possible or

feasible to set up a network infrastructure. A special type of network which is based on

the ad hoc paradigm is the wireless sensor network. It is made up of nodes with sensors on

board and capable of communicating with other nodes in its vicinity. These nodes may be

deployed in such a manner that the topology may not be predictable. Thus they need to

organize themselves in an ad hoc manner, set up the routing and start the communication

process. Although node mobility is not a consideration in our work, the topology will be

dynamic. This is because nodes may consume their entire power and die or new nodes may

be deployed. Thus the network has to be adaptable to these changes. The operation of

the sensor network is based on using the communication capability of nodes for multi-hop

forwarding of the sensor data to a monitoring station. These sensor nodes usually have

a limited power source in the battery that they carry. It may be difficult or infeasible to

replace these batteries or the nodes themselves. Thus it becomes necessary to conserve

power to ensure a longer lifetime of the nodes and ultimately that of the network.

One of the power conserving approaches is to switch off the transceivers of the

nodes when they are not communicating. This is because putting the nodes to sleep ensures

that the power consumed in the idle mode and in overhearing packets, not meant for that

particular node, is saved. Both these factors can lead to significant power savings. However,

(11)

to formulate a model for determining these sleep schedules.

We base our model on the probabilistic inter arrival times of the packets from an

upstream nodes. For this purpose we introduce the concept of streams. We show that every

node can independently infer the streams that it is carrying and the distribution of their

inter-arrival times. Each node can then use this knowledge to make its own sleep decisions.

The node could immediately go to sleep on receiving a packet. Then based on its knowledge

it could calculate the arrival time of the next packet with the attached probability value.

Thus, it could wake up once this attached probability has crossed some threshold value.

This implies that there will be some loss of packets. It is possible to configure the threshold

probability value and thus the packet loss than can be expected. We study the impact that

this loss has on the probability density function of the inter-arrival times of the packets.

We then consider this approach for a networks carrying multiple streams and then gauge

the impact on the sleep time of the nodes.

We show that the approach mentioned so far in itself is not sufficient for a real

sensor network. We then formulate an alternate approach based on the manipulation of the

inter-arrival times. The nodes that forward the packets, manipulate the inter-arrival times

by delaying the packets so that they conform to some periodicity. Each node makes the

decision regarding the periodicity that it wishes to enforce based on the characteristics of

the incoming stream that it observes. There is no centralized decision making involved in

this process. The most important characteristic of the stream, in this work, is the delay that

the sensor data can tolerate. We study the scenarios when this delay tolerance is bounded

and when it is unbounded. The delay tolerance dictates for how long the nodes can hold the

packets and thus it is crucial to their shaping capability. We enumerate some of the other

policy decisions that can be made while designing a protocol based on the sleep approach

with traffic shaping.

The rest of this thesis is arranged as follows. In the next chapter we briefly describe

the background and the literature in the field of ad hoc and wireless sensor networks. In

Chapter 3 we present the definition of the problem. The two different approaches, without

traffic shaping and with traffic shaping, have been discussed in Chapter 4 and Chapter 5.

Numerical experimentation for the traffic shaping approach has been presented in Chapter

6. Chapter 7 discusses the possible directions for future research and present our conclusion

(12)

Chapter 2

Context

2.1 Ad hoc networks

2.1.1 Introduction

Since the first wireless packet radio networks were implemented in the 1970’s

[20] [22], they have grown tremendously. Today, wireless networks can be classified into

two basic types. The first type are the infrastructured networks like the wireless LAN.

Ubiquitous in offices and campuses, they require a set of infrastructure components to be

present. The other type are the ad hoc networks which are an infrastructure-less network of

wireless nodes. These Ad hoc networks are expected to operate in a multi-hop manner, with

the other nodes forwarding the packets to facilitate the communication. In infrastructured

networks like the wireless LAN, there is usually one hop communication as the wireless

node communicates directly with the base station. Ad hoc networks have been conceived

for situations where it is not possible to set up the required infrastructure. Some example

of such situations are combat zones, emergencies and remote areas.

2.1.2 Architecture and Properties

The nodes of an ad hoc network are usually battery powered and are expected to

(13)

Therefore a nodes may communicate its own data to any other node or may assist some

other node in doing so.

The basic principle that governs a truly ad hoc network is: self-organization and

self-configuration of peer nodes. They are called peer nodes because all the nodes are

capable of essentially the same functionality.

The topology in an ad hoc network cannot be predicted in advance. The nodes

may move in and out of transmission range, they may stop functioning due to loss of power

or new nodes may be deployed in an already operational ad hoc network. This makes the

topology highly dynamic. The network has to be able to absorb these changes and should

show only a degradation of performance in the lack of resources rather than a complete

collapse.

Because of their very nature the most core functions of ad hoc networks have a

distributed implementation. A central entity in any functionality is a single point of failure

and it does not agree with the basic principle of ”peer nodes” mentioned for ad hoc networks.

There may be exceptions to this aspect. In some networks it may be necessary to deploy

specialized nodes like servers [22].

As technology grows smaller, smarter and more sophisticated these nodes are

be-coming more complex and even more capable, alleviating some of the difficulties in meeting

the principles of an ad hoc network. Nevertheless, limitations remain. Some of the smaller

nodes may be incapable of carrying out complex computation efficiently due to limited

pro-cessor and memory capacity. Their wireless interfaces have a higher error rate and lower

bit rate than regular wired interfaces. They have only limited energy at their disposal in

the batteries that they carry. These factors make them more of a challenge to operate then

their wired counterparts.

2.1.3 Challenges

Ad hoc networks are faced with several difficulties in their operation. Some of

these are inherently present in all networks while many are introduced or worsened due to

the specific nature of ad hoc networks. Some of the most fundamental problems in ad hoc

network operations have been discussed below.

(14)

power source may not be convenient or possible. Consequently the lifetime of the

network itself is heavily dependent on the battery life of its nodes.

There may be nodes in an ad hoc network without which the network may get

parti-tioned. These nodes are likely to be highly loaded in the normal network operation.

In such a situation, they will lose power at a higher rate and thus the network may

lose these important nodes the earliest.

• Bandwidth: The wireless medium is unreliable and error prone. The nodes have

to compensate for this in the form of slower transmit speeds and retransmissions of

packets.

• Dynamic Topology: Links to the neighbors may be broken or new links may be

established due to node mobility, powering up-down of nodes or due to nature of the

wireless medium. The network has to adapt to the new topology as efficiently as

possible.

In this work we focus on the power consumption of the networks. We identify a

source of power wastage and discuss the possible ways to stem this waste.

In the following section we shall introduce routing in ad hoc networks. We shall

introduce their classification and briefly discuss the operations of a few.

2.2 Literature Review for Routing in Ad hoc networks

2.2.1 Introduction

Routing in fixed wired networks is a well studied subject. A number of routing

protocols have been developed for wired networks. But due to the limitations mentioned in

Section 2.1.3 and the characteristics of ad hoc networks, those protocols cannot be used

without modification in ad hoc networks.

Wired networks were designed with the assumption that routers would go down

only occasionally and link failures would be rare. The dynamic topology of ad hoc networks

makes these assumptions invalid. Routing algorithms in ad hoc networks are accountable

(15)

has to limit the control messages and the bandwidth that it uses. Also, it should not impose

a burden on the limited memory and computing capacity of the nodes.

2.2.2 Classification and Discussion of Routing Algorithms

The routing protocols proposed for ad hoc sensor networks can be broadly

clas-sified into two types. This classification is based on the methodology and timing of route

computation.

• Table Driven protocols

In the protocols falling in this category, the routes are pre-computed and stored. The

nodes use periodic updates to maintain routing tables. Thus the route to every other

node in the network is always available.

These protocols may differ from each other in the way and the kind of information

that they maintain and how changes in the network topology are propagated. In

protocols using Distance Vector approach the nodes advertise their hop count from

each node. Each node updates it’s routing table based on the vector that it received

from its neighbors.

In Link state based protocols each node tests the status of its links with its neighbors

and send this information to all the nodes in the network. As each node receives

this link-state information, it build a complete map of the network and computes the

routing table.

• On Demand

The other approach to routing is to obtain the route on an as-needed basis. Thus,

when a route to a destination is required, the node can initiate the protocol to discover

the route. This discovered route can then be maintained for as long as it is in use.

On comparing the two approaches some of the trade offs become apparent. Table

driven protocols maintain routes irrespective of whether they are needed or not. This occurs

at the cost of periodic updates and, consequently, more control overhead. On demand

protocols on the other hand expend resources in discovering and maintaining only the

(16)

In a network using a table driven protocol, a packet can be dispatched immediately

when it ready to be sent. Whereas in an on demand protocol the route discovery process

can be time consuming and this the initial packets may experience latency.

2.2.3 Some Common Protocols

2.2.3.1 Destination-Sequenced Distance Vector Routing

Destination-Sequenced Distance Vector Routing (DSDV), is a distance vector

pro-tocol using a modified version of the Distributed Bellman Ford(DBF) Algorithm [13] [6].

The modification is the introduction of sequence numbers to tag route table entries so that

stale routes can be detected. This modification is necessary to avoid routing loops in the

network.

Each node transmits periodic updates of its routing information and immediate

updates when changes in the topology are observed. All the destination entries in the

updates will have a sequence number generated by that destination node. The entries with

the more recent sequence numbers are always preferred for making decisions.

When a node receives a new update of the routing information it will compare the

sequence numbers of this new information with that of the information it already has. The

information bearing the more recent sequences numbers will be used. In case the sequences

numbers are the same then then the “better” metric is taken into consideration. In case of

establishment of new routes at a node the information is immediately advertised. But in

case of just an improvement in the metric, the information can be sent in the next periodic

update.

2.2.3.2 Global State Routing

Link State is the preferred algorithm in the internet type of environment over

distance vector [19]. This is due to link state’s shorter convergence time and because it

makes it easier to prevent routing loops. But restrictions on the flooding mechanism become

necessary especially in an ad hoc network environment. Global State Routing(GSR) [4] is

an attempt to combine LS accuracy with the dissemination method of distance vector

(17)

size for the MAC layer and limiting control packets themselves.

The network Model: For each node i, there is a list , Neighbor list A_i and 3 tables:

(1) Topology Table TT, T T_i.LS(j), gives the link state as reported by j and T T_i.SEQ(j)

is the time stamp of the link state. (2) A next hop table, N EXT_i(j), gives the next hop

to forward the packets destined for j. (3)Distance Table,D_i(j), denotes the distance of the

shortest path from i to j

The Algorithm: I. Initially the neighbor list is empty. All the packets received by

i are placed in an queue PktQueue. The senders of these packets are then placed in the

neighbor list. Thus all nodes which can be heard by i are listed as its neighbor.

II. The routing packets bear the LS information. They are used to update the LS

information of a node only if the time stamp of the packet is later than the time stamp in

the table in the node. Then a new routing table is built.

Information dissemination:The important characteristic of GSR is that GSR does

not flood the LS packets but like DBF, LS packets are periodically exchanged with the

neighbors only.

Shortest Path Computation: The shortest path can be computed by any Algorithm

like Dijkstra’s and this information in used to populate the N EXT_i(j) and D_i(j) fields in

the respective tables.

Update interval plays an important part in the routing overhead and inaccuracy.

Inaccuracies in routing would decrease but the overhead would increase as the update

intervals are made shorter for DBF and GSR. This effect is more pronounced in higher

mobilities.

Thus GSR attempts to combine the best of both worlds in the domain of proactive

routing approaches.

2.2.3.3 Fisheye State Routing

Fisheye State Routing(FSR) [12] is an implicit hierarchial protocol. The name

of the protocol is such cause the node maintains accurate information about its immediate

neighborhood and progressively less details as the distance increases.

The FSR originates for the Global State Routing GSR. The GSR can be

consid-ered a special case of FSR [12]. In GSR the information is disseminated by periodically

(18)

is exchanged with the neighbor which is considerable overhead. What FSR attempts to do

is exchange the link state information of the nodes that are closer more frequently then the

information about the nodes that are further away. Thus GSR is nothing but FSR with

scope equal to infinity as the entire topology map is exchanged between the neighbors.

Thus the route for far away nodes may not be accurate at a given time but the

routing becomes more and more accurate as the packet approaches the destination.

The algorithm is very similar to the GSR except that the link state packets are

synthesized in a different manner. The topology table is scanned and if D_i(x) is within

the fisheye level l then the link state of x is included in the update message. The update

interval is not the same as it was in GSR, it is different for different scopes.

As the scope increases there is more overhead as the number of link states

ex-changed also increases but there is also a decrease in the inaccuracy as the protocol can be

though of as approaching full fledged Link State. FSR’s advantage over GSR in overhead

becomes clear as the network size becomes larger as GSR has to deal with larger link state

packets.

FSR provides the flexible routing solution to a larger ad hoc network with higher

mobility as low bandwidth. As it provides the accuracy of link state protocol with incurring

as much overhead

2.2.3.4 Dynamic Source Routing

The Dynamic Source Routing(DSR)protocol presented in [8] is an example of the

On demand routing protocol. Thus its approach is such that it avoids sending periodic

updates. Each node maintain a cache of the routes that it has learnt. Each entry in this

cache has an expiration period after which it is deleted. When sending a packet it consults

this cache first and then tries the route discovery mechanism. The node may buffer this

packet and continue processing other packets. In case of a route breaking down route

maintenance kicks in.

A route discovery packet identifies the target host for which the route is requested.

The route discover packet maintains the route record , which accumulates the ID’s of the

nodes through which the request propagated. Also contained in the packet is the

request-ID, a locally maintained sequence number. The initiator address along with the request ID

(19)

On receiving a request the following steps are taken :

1. If this request has been recently seen, discard it without processing

2. If the host is already listed in the route record ,discard it without processing

3. If the node is the target then send the route reply

4. Otherwise append its own address and re-broadcast the request packet.

The route reply may be send using the route from the hosts cache or by reversing

the recorded route.

If there is a hop by hop acknowledgement then a transmission problem can be

discovered immediately and that host that discovered the problem , can send back a route

error packet to the source of the message. The hosts will then remove the routes that have

the erroneous hop in them.

If the hop by hop acknowledgement mechanism is not implemented then the other

option is that end to end acknowledgments can be used. But in this case the node will not

be able to find out the hop at which error occurred.

The basic operation described so far can be further optimized. An intermediate

node is able to observe the entire route in a data or a route reply packet and thus it can

enrich its route cache. Also the broadcast property can be used to learn about more routes

by nodes listening in the promiscuous mode.

Also intermediate nodes can reply to a route request from its own route cache

provided that this does not cause the formation of a loop. The overhead of a route request

can be reduced by introducing a maximum hop field. The route request can be sent

itera-tively with increasing max hop values, this avoids over propagation of the requests. There

are a number of other optimizations like data piggybacks on route requests and explicitly

remembering a non-working hop.

Thus, as expected from an On demand protocol, this protocol provides maximum

advantage on periods of infrequent or no movement.

2.2.3.5 Ad-hoc On-Demand Distance Vector Routing

Ad-hoc On-Demand Distance Vector Routing(AODV) in puts forth the concept

of “pure on-demand route acquisition system” [14]. In this system the aim is that unless a

nodes is actually involved in a communication directly or as a intermediary, it should not

(20)

section 2.2.3.4. It attempts to establish routing entries in the nodes along the entire path

unlike DSR which required the source to provide the entire path.

When a node needs to forward a packet to which it does not possess a valid route,

it will initiate the route discovery by broadcasting the route request, RREQ. The neighbors

of the requesting node will broadcast the RREQ to their neighbors if they do not have a

valid route themselves. If however a node is the destination node itself or it possesses a

valid route it has to reply with a RREP. This process continues with neighbors that forward

RREQs storing the path from which the RREQ came in. This is used later to set up the

reverse route when the RREP ultimately comes through. The node handing down the

RREP is recorded to set up the forward path.

The basic approach mentioned above is not sufficient and it is enriched with the

introduction of the source sequence number and the destination sequence number. The

destination sequence number is included in the RREQ. It is used to tell the intermediate

nodes how “fresh” its route should be before it can reply to the RREQ. Each RREQ is

uniquely identified by the combination of the initiating node’s IP address and the broadcast

ID. Each nodes increments the broadcast ID for every RREQ that it sends out. Thus the

destination sequence number not only ensures freshness of the route that the destination

receives but also prevents routing loops. The source sequence number is included in the

RREQ to maintain the freshness information about the reverse path that is set up to the

source.

AODV also provides for a path maintenance mechanism. If a node becomes

un-available then the node upstream from it propagates an unsolicited RREP to all the active

upstream source. This RREP has a new sequence number and a hop count of infinity. Thus

it will reach the active sources and the can repeat the entire procedure of setting up the

route again.

2.2.3.6 DREAM

Distance Routing Effect Algorithm for Mobility(DREAM) [2] belong to the

cate-gory of protocols which depend on the knowledge of the geographic location of nodes. [6]

The core concept to this protocol is that the routing tables store the geographic

location of the nodes. When a node wants to send a packet to the destination D, it finds the

(21)

This continues till the packet eventually reaches D. There is no large amount of information

exchanged to maintain the routing table, only node IDs and their co-ordinates. Also there

is no route discovery so there is no initial latency.

To maintain the routing table the node has to broadcast its location. Majority

of these broadcasts have a short lifespan so they update only the closer neighbors. The

remaining have a longer lifespan but are sent less frequently thus the far away nodes are

updated less frequently. This approach is based on the “distance effect”, according to

which a far away node would appear to move slowly as compared to a nearby node for

an observing node. Another optimization is that a node sends its location updates at a

frequency proportional to its mobility rate. This is cause slower moving nodes will generally

have to update their locations less frequently.

Thus DREAM has a proactive approach to maintaining its location table but it is

also reactive as it seeks routes only when needed.

2.3 Literature Review for Power Conservation

We have already pointed out that power constraint is one of the most fundamental

problems in the ad hoc network operation. A review of the literature suggests that the efforts

devoted to address this problem have lagged behind some of the other aspects like routing.

The emergent approaches in power conservation can be broadly seen to possess two different

perspective. Both these perspectives have a common goal are different in how they try to

go about it.The following sub sections introduce and discuss these two perspective.

2.3.1 Non-switching-off, Network Lifetime Approaches

As the name suggests these approaches try to consider the network as a whole

without paying attention to the individual nodes. In these nodes the nodes are not switched

off and the thinking is that the overall network should be consuming less power and attention

should be paid to factors that can ensure that the network remains connected and operating

for as long as possible. Most of the approaches that operate at the network layer fall under

(22)

2.3.1.1 Maximum Battery life Routing

The routing protocol used by the network may attempt to route a packet so that

the overall power consumed is minimized. Thus the lifetime of the network may be

pro-longed. Four different route selections schemes have been proposed to this end in [21].

They are,

Minimum total transmission power routing (MTPR): The minimum power at

which a node has to transmit to be received successfully depends on the distances between

the sender and receiver, the bit-error-rate and the interfering noise. A simple approach

would be to try to find a path such that the sum of all the required transmission power

is minimum. However this tends to create longer paths then necessary. This may not be

desired as longer paths are usually more unstable due to more number of points of failure.

Thus an alternative approach is to not only consider the transmission power but also the

reception power. This will tend to push the selected path towards the shorter one.

Minimum Battery Cost Routing (MBCR): MTPR does not take into account that

a single node may be getting over utilized. Thus the remaining battery life should also

be considered as a metric in routing. Thus we can define a parameter f which is inversely

proportional to the battery capacity at time t. Now define the battery cost R, for a route i

consisting of D nodes as the sum of the parameter f for all the D nodes. Thus the routing

protocol may attempt to find a route with the minimum battery cost R.

Min-max Battery cost Routing(MMBCR): If we follow MBCR, the protocol will

avoid a route with the minimum total battery capacity node in it. The individual nodes

are not paid attention to. Thus we may decide to look at trying to avoid the route which

is the lowest single battery capacity node.

Conditional max-min battery capacity routing(CMMBCR): If we follow MBBCR

without modification, the protocol will avoid a route with the minimum battery capacity

node in it, but it may increase the overall battery consumption of the network.

Thus a slight modification can be incorporated. When all nodes have battery

capacity over a certain threshold then routes with the minimum total power are chosen.

However when routes have nodes below the threshold then routes with the lowest battery

(23)

2.3.1.2 Power aware source routing protocol

The route in DSR [8] tended to be the shortest routes to the destination. It

did not attempt to take the power consumption of the route or the remanding battery

capacity of the nodes along that route. The Power aware source routing protocol (PSR)

brings in sensitivity to both these factors by introducing a cost function C_i [9]. The cost

function is directly proportional to the transmission power required at a node and inversely

proportional to the remaining battery capacity of that node.

The basic operation remains the same as was mentioned for the DSR in section

2.2.3.4. The new feature is that the node will calculate the link cost mentioned above and

add it to the path cost in the header of the RREQ packets. The destination on reception of

the first RREQ packet will start a timer t and keeps the cost in that header as the minimum

cost. When additional RREQ’s make their way to the destination, their header’s path cost

is compared to the minimum cost already stored in the destination. For time t it will receive

all the RREQ’s and then choose the one with the minimum path cost. This is the RREQ

to which the reply will be sent.

Another interesting aspect is how energy depletions are handled in PSR. Two

approaches have been put forth here too. In the semi-global approach the source monitors

all the nodes. The source becomes responsible for purging the route when it increases by a

certain percentage. Whereas in the local approach, when a single nodes capacity falls below

a certain level it can simply send back a route error message.

2.3.1.3 Common Power Protocol

The common power protocol [10], COMPOW, attempts to meet three different

goals. These are to maximize the battery life, to increase the traffic carrying capacity and

to reduce the contention at the MAC layer. For this, the protocol proposes that all nodes

transmit at a low common power level.

With common power levels it tries to ensure bi-directionality of links. Also since

they are transmitting at a lower power it will conserve power. Lower transmission range

means lesser number of neighbors and thus there will be lesser number of collisions. Nodes

that may have been unable to communicate among themselves may now communicate cause

there is less interference from surrounding nodes, this increases the capacity.

(24)

network still remains connected. To do this, COMPOW maintains multiple routing table,

one for each transmit power level available. This table can be formed by sending hello

packets at the different power levels. Once the table has been formed, the optimal power

level is the smallest power level whose routing table has the same number of entries as in

the routing table if the node were operating without the COMPOW protocol.

2.3.2 Node Switch-off Approaches

The power conservation methodology may be focused on each node individually. It

attempts to increase the lifetime of individual nodes by switching them off, usually operating

in the MAC layer. Thus the objective becomes to get the work done that is required of the

node by the network but with a lower power consumption at the nodes.

2.3.2.1 Benefits

The communication equipment draws a significant portion of the power [18] of a

node and the idle and receive components form a major part of this power consumption [5].

Energy is wasted when the node is waiting to receive something. Not only this, not all

receptions are useful too, nodes receive packets from neighboring nodes that were meant

for some other node. These overheard packets have to be discarded.

Thus switching off the communication equipment when a node is not involved in

any communication is an attractive proposal. It could significantly reduce the power wasted

in the idle mode and in overhearing the packets not meant for it.

2.3.2.2 Costs

Switching off nodes offers substantial savings in nodes but there are some trade-offs

involved in this process. Switching off the transceiver and then switching it back on is not

a cost free operation. This transition consumes power which must be taken into account.

The consumption may be significant in terms of impact that it has on the total savings [3].

In any algorithm allowing the node to power off, it will expend energy and resources

in maintaining and updating its knowledge or executing the mechanism that allows it to

(25)

this knowledge may be imperfect and some loss of packets may also occur. The node will

remain unaware of this loss unless a mechanism is incorporated to inform it.

2.3.2.3 Example: Using RTS/CTS

In a simple approach presented in [3] to sleeping and conserving power is the one

that attempts to take advantage of the RTS/CTS mechanism of the MAC layer. When

two nodes are communicating, another node in the transmission range of the transmitter

overhears the entire packet and expends its energy on a packet which it has to discard.

Thus if the node switches off while it knows that the packet it is overhearing is not meant

for it, it can save power in the node.

The suggested approach is for a node that is not involved in the communication to

simply power off. The node knows from the RST/CTS mechanism that it is not involved.

It can simply capture only the header of the packet and enter the sleep state. The header

is captured so that the node can obtain the length of the packet and calculate how long it

should be asleep.

2.3.2.4 Example: PAMAS

The Power Aware MultiAccess Protocol with Signalling [17], PAMAS, add on

to the idea of switching off by taking advantage of the RTC/CTS mechanism. However

in PAMAS, a separate signalling channel is used to determine when and for how long the

nodes can sleep. The medium access mechanism is now executed in this signalling channel.

The nodes in the network can be in one of the six sates:

• Idle: When the node has no packet to send and it is not involved in any communication

it can be in the idle state

• AwaitCTS: After a node has sent a RTS, it enters the AwaitCTS state

• Binary Exponential Backoff(BEB): If the node does not the expected CTS it enters

the BEB state

• Await packet: After sending the CTS, the node enters this state as it is expecting to

(26)

• Receive packet: When a receiver start receiving, it signals a busy tone over the sig-nalling channel and enters this state

• Transmit packet: After receiving a CTS, the node enters this state and starts

trans-mitting the packet

In the normal operation of the protocol, the procedure is pretty close to the IEEE

MACA protocol. When a node receives a RTS, it can send the CTS if the data channel is

sensed as being free and no neighbor is in the awaitCTS State if no noise is heard on the

control channel for sometime interval τ. where τ is equal to one round trip time plus the

transmission time for RTS/CTS.

When a node sends RTS and a neighbor is in the receiving state, it will send a

busy tone over the control channel which will collide with the incoming CTS. Now the node

enters the BEB state and does not transmit. On the other hand, if a node is receiving and

it hears a RTS or some other noise in the control channel, it will transmit a busy tone. This

ensures that the node that sent the RTS does not receive a CTS.

The operation described so far is the normal operating mechanism. The power

conservation mechanism can be seamlessly integrated into it. As we have already discussed

that it is beneficial to switch off the receiver when a neighboring node is transmitting. But

the problem is how long should the node power off for.

Initially the duration of the neighbor’s transmission is known and a node can power

of for that long. But while the node is asleep, some other transmissions may have started.

In this case the node on waking up will sense a busy channel. In this case it can go back

to sleep but it does not know for how long. To alleviate this problem the probe packet is

introduced. The node on waking up sends the t-probe(l) over the control channel, where l

is the maximum packet length. All transmissions completing in the interval [l/2 ,l] respond

with the t-probe-response(t) packet. Then the node can power down till t. However, if there

was a collision in response to the probe packet then the node can probe with the interval

[3/4l,l]. On no response to this probe the node can probe with the interval [l/2, 3/4l]. On

silence to the first probe the node can search the [0,l/2]. This is basically a binary search

to determine when this transmission gets over.

If a node has a packet to send when it comes back up from sleep. It will simply

send a RTS. On receiving a busy tone or a colliding RTS/CTS it probes the receivers and

(27)

last receiver finishing and t is the time till the last transmitter finishes.

2.3.2.5 Example: Geography informed Energy Conservation

As presented in [25], Geographical Adaptive Fidelity (GAF) capitalizes on the

existence of redundant nodes from a routing perspective. To this end the protocol uses

both application and system level information to maintain fair usage of all nodes while

trying to minimize overall power consumption.

GAF defines virtual grids such that all the nodes in one grid are capable of

com-municating with all the nodes in the adjacent grid. Now nodes in each grid are considered

to be “equivalent”. The nodes in each grid can now co-ordinate the sleep cycles among

themselves. After a node has remained active for sometime it gives a chance to other nodes

to become active by switching to the “discovery” state. In the discovery state messages

are exchanged and the node with a higher rank moves to the active state while the other

can go to sleep. They can remain asleep till the estimated time that the node that just

became active will remain active. The key is the choice to the ranking methodology. The

natural choice is that the nodes with most remaining power get the higher ranks and thus

become more likely to remain active. Applications are allowed to vary the parameters of

the protocol to tailor it to their specific needs.

Thus GAF attempts to exploit the redundant nodes in a relatively dense network

to achieve a fair usage and save power in the overall network.

2.4 Wireless Sensor Networks

A form of ad hoc wireless networks that in recent times has gained much attention

is the wireless sensor network. The ever expanding capabilities and the shrinking size and

cost of computer and communication technologies has made the construction of tiny ”sensor

nodes” possible. The sensor nodes are capable in both computation and communication.

The potential for their application is tremendous. From battlefield to

environ-mental protection, they can be used for some form of monitoring or the other. They could

be used to monitor the environment for chemicals or temperature or in a combat situation

(28)

2.4.1 Architecture

Each node in the sensor network can be an originator of sensing data and router

of such data from the neighboring nodes. To accomplish its tasks the nodes in the sensor

networks have five components. These are the sensor, the processor, the memory,

commu-nication equipment and the power supply [23] to power it all. Due to their large numbers

and the method used for disseminating these nodes, it may not be always possible to place

them in a fixed position with respect to each other. Thus it becomes necessary to give them

self-organizing capability. This is where the parallel from the more “traditional” mobile ad

hoc networks(MANETS) comes in.

After they are scattered, like MANETS they have to organize themselves, establish

some form of routing and start sending data to the designated nodes. This designated node

is called the sink. In a deviation from MANET architecture, which envisages communication

capabilities from any node to any other node, sensor networks may need to only send their

data to the sink [23] [1].

2.4.2 Properties and Issues

The MANET challenges, mentioned in section 2.1.3, of power, bandwidth and

dynamic topology are present in sensor networks too. However there are some distinct

characteristics that make some of the protocols and mechanisms that were developed for

MANETs infeasible for sensor networks.

Scalability may be an issue since sensor nodes may be deployed in larger numbers.

The existence of a universally unique ID for each node cannot be assumed. This was

an assumption for majority of the previous MANET related research. Mobility is not

a major consideration factor, because once the nodes are deployed they are usually not

moved intentionally. However the topology still remains dynamic because nodes may die

out and new nodes may be deployed to compensate for them. Nodes in the sensor networks

often are even more constrained then other form of ad hoc networks in memory, power and

(29)

2.4.3 Power consumption in wireless sensor networks

The power consumed in a node in the sensor network can be categorized into

two: the communications-related power and the non-communications related power. The

five components of a node in a sensor network have been discussed in section 2.4.1. For

the purpose of our discussion we shall consider the power used by the communication

equipment as communications-related power and we term the power used by the remaining

components as the non-communications related power. (Although this is not strictly true

cause communication requires some computation in general.) The communication related

power consumption has been observed to be a significant part of the total power consumed

in wireless devices [5] [18].

The communication equipment can be in one of the various states of operation. It

may be transmitting, receiving, idle or asleep. The power consumed can be further classified

according to the state of the communication equipment.

It has been observed that in a wireless network interface the highest power is

consumed in the transmit mode, followed by the receive and idle mode and while sleep

mode power consumption has a much smaller value [5]. However the receive and idle mode

power consumption approaches that of the transmit mode.

2.4.4 Power Conservation by switching off in ad hoc sensor networks

2.4.4.1 Sensor-MAC

The sensor-MAC (SMAC) descibed in [26], is a proposed medium access approach

tailored for ad hoc sensor networks. This protocol is tailored to allow switching off of nodes

to achieve power saving as discussed in sections 2.3.2.1. Thus it attempts to cut down on

the expensive overhearing and the idle modes. In addition it attempts to minimize control

overhead and collisions.

In order to avoid over hearing transmissions not meant for it, the nodes use a

control signalling technique similar to PAMAS discussed in section 2.3.2.4. Except that in

this case the signalling is done in-band. To save the power consumption of idle state the

nodes try to implement a scheme of periodic listen and sleep. The idea is to cause a local

synchronization so that all the nodes listen at the same time and switch off at the same

(30)

2.4.4.2 Cross layer Scheduling

In [16] the approach is to use the knowledge of the deterministic period between

the transmission of data to form a sleep schedule. The nodes are tightly time synchronized

and this enables the formation of sleep schedules that allow the nodes to remain awake only

when needed. The nodes are thus able to form a schedule that dictates when they should

wake up to transmit, when they are expecting a new packet and when they should generate

a sensor reading.

In the operation of this protocol, after the routes have been setup by the routing

algorithm, a control packet is sent from the data originating node to the base station. Now

these control packets are responsible for setting up a schedule for the flow from this node

in every node that they traverse. The node receiving the control packet has to schedule

a reception for this flow so that it does not collide with another flow from a neighbor.

After a successful scheduling, a transmit entry is made for the node that will transmit

and a corresponding reception entry is made for the receiving node. This scheduling is

considered temporary until an route acknowledgement is sent back from the base station to

the originating node.

Also, to ensure optimality, the protocol tries not to disturb the existing flows in the

network when control packets have to be sent. Giving the control packets a lower priority

ensures this. They are given low priority by first sensing the channel and then sending them

when there is a relatively low chance of a data packet being scheduled.

2.5 Our contribution

Our work falls in the category of power conserving protocols for sensor networks

that advocate sleeping. Our work is the closest in approach to [16]. Like [26] and [16],

we try to base sleep schedules on some form of synchronization. In our work, we use

the knowledge of the data generation process. This knowledge allows us to identify the

latent loose synchronization in the sensor network nodes. We then introduce the concept of

streams and probability density functions to model the inter-arrival times of data packets.

Once the streams have been defined and the knowledge associated with them in

the form of the probability density function, the nodes can leverage this to formulate a sleep

(31)

In our approach the node uses the PDF to predict this time while maintaining the risk of

losing the packet below a certain level. The node could be put to sleep in this time to save

the power that is drained in the idle waiting and in overhearing packets meant for other

nodes.

We further show that it is necessary to manipulate the periods of the streams.

(32)

Chapter 3

Problem Definition

3.1 The setup

3.1.1 Defining a Stream

Our approach is to put the sensor nodes to sleep while the probability of receiving

a packet is less than some predefined value. To apply this approach to sleep it becomes

necessary to introduce the concept of streams. This is necessary so that the probabilities

can with associated to them. We define “streams” here to mean packets from a common

source. We have two options for the definition of the common source and consecutively two

definitions for the stream.

• Per-source streams: We could take the common source to mean the common

orig-inating source. In this case, all the data packets from one origorig-inating node would

constitute a stream.

• Per-upstream neighbor stream: All the packets coming from the same upstream source

neighbor, irrespective of their originating node, could be taken as a stream.

Sensor networks are expected to have a large number of nodes. Potentially all of

them will be originating data. In this case, a node close to the sink would likely be carrying

(33)

used, then such a node would have to keep track of many streams. The overhead in this

case would be very large. As the number of nodes increases so would the overhead. Thus,

the scalability issue effectively rules out this possibility. We settle on defining a stream as

the packets being delivered from a common upstream neighbor node.

The nodes in a sensor network will have a limited number of upstream neighbors.

This implies that a node would have to keep track of only a limited number of streams.

This would make the protocol operation more manageable for the nodes.

3.1.2 Representing the stream

A node may produce and send a reading every fixed interval of time according

to its own clock. But the clocks of the nodes are not in exact synchronization with each

other. They may be slightly slower or faster when compared to each other. This variation

with respect to one another also differs with time. Also once the application has handed

down the data to be transmitted to the communication process, the process may take

variable amount of time to send out the data packet. Considering these factors, we choose

to represent the inter arrival times statistically rather than deterministically. Thus, we

express the inter-arrival time that the down stream node sees for the packets belonging

to a stream with the mean observed inter-arrival time and the observed variance. This is

best defined by a Probability Density Function(PDF) and the corresponding Cumulative

Distribution Function(CDF).

3.1.3 The approach : Switching on-off for streams

Now each node has knowledge associated with the stream that it carries in the

form of a PDF. The nodes can use this knowledge to predict that upon receiving a packet

after what interval will the next one arrive with some attached probability value. The nodes

can use this knowledge to obtain the sleep periods. The node could switch off after receiving

a packet. It will switch on when there is some “significant probability” of a packet coming

in according to its knowledge of the stream. We define this significant probability as the

robustness factork.

The concept mentioned above has been illustrated in Fig. 3.1. If a packet arrives

(34)

0 50 100 150 200 250 300 350 400 450 500 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 Time t f(t)

mean = 250 variance = 30

sleep time t

(a) PDF of packet Inter-arrival times

0 50 100 150 200 250 300 350 400 450 500

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 Time t F(t)

k = 0.05

(b) Corresponding CDF

Figure 3.1: Representing the stream in terms of a PDF and CDF

PDF,f(), of the arrival time of the next packet. Since the robustness factor isk, the node

could sleep till the time that the arrival CDF remains less than k. Let the time at which

it has to wake up be t_k, such that, F(t_k) = k. After the packet is captured the node can

sleep and the same procedure repeats. Thus, in this simple scenario, the node manages to

switch off for some time before every packet.

From Fig. 3.1 it becomes apparent that higher the value of k, greater will be the

value oft_kand longer the node will be able to sleep. But it also means that the probability

of the packet being dropped increases.

A node will, in general, have multiple streams to forward. In that case, it will

maintain separate knowledge for each stream. It will treat each stream independently of

the other and will schedule its sleep in between the expected times of the streams. The

alternative would have been for the node not to distinguish between the streams. We have

shown later in Sec. 4.2 that this approach is not feasible.

3.2 Assumptions

In this section, we present the basic assumptions and try to analyze some

(35)

• There has to be some sort of predictability to the flow for a node to be able to switch on and off. This predictability in our case is expressed in the form of a PDF/CDF

for packet inter-arrival times. Thus our very base assumption is that for each flow

it is possible to predict, with an attached probability, the intervals between packet

arrivals. In other words, there exists some unimodal PDF for each of the flows.

• We assume that the scale of the period of the packet inter-arrival is much greater than

the variability that can be introduced by clock drifts and the contention for channel

access. Or, mean >> variance, in the PDF.

• The concept of flow is limited to packets from a unique upstream neighbor for our

discussion and does not conform to the Internet standard definition of a flow. This

concept may be extended to include, a unique source and unique destination or

alter-natively, a unique upstream neighbor and unique downstream neighbor. Advantages

and disadvantages of each are beyond the scope of this work.

• Some packet loss is expected and this is not a critical failure for this approach. The

benefit of power saving comes with the penalty of dropped packets. As the node

strives for more power saving the chances of dropping a packet increases. We study

the loss of packets due to the power saving algorithm and do not consider the loss of

packets due to routing and other factors in our work.

• The CDF is time invariant. So, if some packet arrived earlier than the expected time,

it does not indicate that the next packet will arrive early too.

• The CDF/PDF used by the nodes is a close approximation of the inter-arrival times

that will be encountered by the node. We expect that the nodes would remain awake

initially to infer the PDF/CDF of the inter-arrival times. After this, when the nodes

implement the sleep cycles, the CDF/PDF would be continually updated by the nodes,

thus making this a feasible assumption.

• When a node fails to capture a packet because it is asleep, it remains unaware of the

loss. After it wakes up, it will remain awake till it receives the next packet.

• Routing decisions are left to the network layer and the power saving algorithm has no

(36)

3.3 An ad hoc sleep schedule

In the previous sections we have introduced the problems, its components and the

assumptions for this work. Now, we shall state the problem using the concepts introduced

above.

The problem that we wish to address is of trying to conserve power in a wireless

ad hoc sensor by formulating a sleep schedule based on the sensing period characteristic.

To do this, we define streams and through them try to exploit the periodicity of the sensor

nodes.

In this approach, each node works independently and there is no central decision

making entity. The approach is completely distributed and we try to ensure its scalability.

Each node infers the streams that it is carrying and their characteristics on its own. It

independently calculates the mean and the variance according to its own observations and

ultimately makes the sleep decisions locally.

This work is a first study of the sleep approach based on the stream concept. We

do not attempt to optimize the savings in this study. Our focus remains to enumerate

the issues involved in this approach and to gain an understanding of the interdependence

(37)

Chapter 4

A simple first approach

As a first approach we propose that nodes simply switch on and off according to

the observed PDF of the stream. This approach does not attempt to alter the characteristics

of the stream. It remains transparent to the monitoring station as the data packets are not

delayed in any manner.

4.1 Single Stream Study

4.1.1 Impact of Switching-off on the PDF/CDF of inter-arrival times

Some packet loss is an expected cost for the opportunity to save power by sleeping.

Consider a node i using this approach. After receiving a packet i will calculate the time

for which it can sleep and then go to sleep. If a packet arrives in this period it will not be

captured. When i wakes up after some time, there is no means in our approach for it to

learn that it missed a packet. It will simply stay awake till the next packet comes in. When

icaptures this packet it will infer the inter-arrival period. The observed inter-arrival time

is the period between the previous packet it received and this packet that it just received.

In reality this is not the case, since there was another packet in-between these two packets,

which imissed. Thus the PDF of the inter-arrival times observed by the receiving node is

(38)

packet received t0

calculate d sleep time

scheduled wake up tk

packet arrival ta

packet arrival tb actual inter−arrival time

*Observed inter−arrival time

Figure 4.1: Time line of Dropping a packet

In the Fig. 4.1 a packet is successfully received at t₀. The node goes to sleep and

it is scheduled to wake up att_k. The next packet comes in at time t_a, earlier than t_k, and

it is missed. When the node does wake up it receives the packet at time t_b. Thus for it

the inter-arrival time for this packet is t_b −t₀, where as it should have been just t_b−t_a.

Thus it can be easily deduced that, in this kind of scenario, the observed inter-arrival time

is always greater than the actual value.

The over-all impact of this skewing is discussed in further detail in the rest of this

section. First, we try to formulate the transformation equation for the PDF/CDF and then

run simulations to gain further insight.

4.1.2 Theory and Equations

Let f() represent the PDF for the inter-arrival times of the packets that are

gen-erated by the source. For an inter-arrival timex, we can say that the packets may have an

inter-arrival time ofx with a probabilityf(x).

Now, due to the skewing phenomenon illustrated in Fig. 4.1, there are two effects

attempting to alter the value off(x). One of the effects is an increase in the value off(x).

This is because some of the packets that had an inter-arrival interval of less than x, may

appear to have an inter-arrival time of x because of the missed packets. Thus this causes

the density from the left of x in the pdf to move to the right and gather atx.

At the same time, the missed packets may also cause some of the packets that did

have an inter-arrival ofx, to appear to have a value greater thanx. This would cause some

(39)

f(x).

We are attempting to define a transformed functiong(x), such thatg(x) represents

the PDF of the packets as observed by the receiving node.

We have,

• k= Robustness factor

• t_k is the corresponding time value Thus,F(t_k) =k

4.1.2.1 Approach I : Using the PDF

As we have seen in the previous discussion, there are two opposing effects

influenc-ing the value of the density function at every point to the right oft_k. Every point to the left

of t_k in the PDF will have no density. We shall now attempt to state the net effect of the

forces in the form of an equation. The equation can be broken into its components. First we

state the components which represent the increase of the density ofx. This increase occurs

as packet misses cause some other arrivals to be thought of as if they had an inter-arrival

of time x. These components are denoted byinc₁(x), inc₂(x), .. and so on.

inc₁(x) =

Z _t_k

y=0

f(y)×f(x−y)dy (4.1.1)

Eqn. 4.1.1 represents the probability of just one packet being missed and the second

one being captured and thought of as arriving after an interval ofx.

inc₂(x) =

Z _t_k

y1=0

Z _t_k₋_y₁

y2=0

f(y1)×f(y2)×f(x−(y1 +y2))dy1dy2 (4.1.2)

Eqn. 4.1.2 is the probability that two packets are missed since they both arrive

before the wake up and the third one arrives in time to be captured. The third packet

is now thought of as having an inter-arrival time of x. Similar argument can be carried

further to obtain the Eqn. 4.1.3. This pattern can continue till infinity but the probability

is continuously decreasing.

inc₃(x) =

Z _t_k

y1=0

Z _t_k₋_y₁

y2=0

Z _t_k₋₍_y₁₊_y₂₎

y3=0

f(y1)×f(y2)×f(y3)×f(x−(y1 +y2 +y3))dy1dy2dy3

Power Conservation in wireless sensor network using receiver switch-off

Contents

List of Figures

Chapter 1

Introduction

Chapter 2

Context

2.1

Ad hoc networks

2.2

Literature Review for Routing in Ad hoc networks

2.3

Literature Review for Power Conservation

2.4

Wireless Sensor Networks

2.5

Our contribution

Chapter 3

Problem Definition

3.1

The setup

3.2

Assumptions

3.3

An ad hoc sleep schedule

Chapter 4

A simple first approach

4.1

Single Stream Study