Routing Algorithms and Algorithms for Lifetime-Optimization in Complex Sensor-Actuator-Networks

7/28/2019 Routing Algorithms and Algorithms for Lifetime-Optimization in Complex Sensor-Actuator-Networks

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International Journal of Engineering Inventions, Nov 2012

ISSN 2278-7461 www.ijeijournal.com 190

Routing Algorithms and Algorithms for Lifetime-Optimization in

Complex Sensor-Actuator-Networks

G. Venkatesh Dr. N. Sambasiva Rao K. Suresh

Asst.Prof. in IT Principal Asst Prof. in ITVCE, Hyderabad VCE, Hyderabad AITS, Rajampet,Kadapa

India. India. India.

AbstractCommunication between sensor nodes is

one of the most energy-consuming activities of a

sensor-actuator-network. Thus, it is important to

use preferably short and energy-efficient

transmission paths for the sensor messages. But

one has also to guarantee that no single node is

overstressed when compared to the others.

Furthermore, the ensuing communication

patterns are usually application-dependent. Thus,they should also be taken into account when

designing a communication structure. A fast

adaptability to changing topologies (i.e. nodes

break done or move) and a distributed planning of

activities are of great importance for almost all

aspects of sensor-actuator-networks. Both are

desirable properties that usually do not arise in

classic scenarios or are only of little importance

there. Thus, we have to be newly develop for our

needs.

Key words Sensor_actuator_network, Energy

Efficient,Topologies.

1. INTRODUCTION

Many wireless sensor network (WSN) Applications

require real-time Communication. For example, a

surveillance System needs to alert authorities of an

intruder within a few seconds of detection. Similarly,

a fire-fighter may rely on timely temperature updates

to remain aware of current fire conditions.

Supporting real-time communication in WSNs is

very challenging. First, WSNs have lossy links that

are greatly affected by environmental factors. As a

result, communication delays are highlyunpredictable. Second, many WSN applications (e.g.,

border surveillance) must operate for months without

wired power supplies. Therefore, WSNs must meet

the delay requirements at minimum energy cost.

Third, different packets may have different delay

requirements. For instance, authorities need to be

notified sooner about high-speed motor vehicles than

slow-moving pedestrians. To support such

applications, a real-time communication protocol

must adapt its behavior based on packet deadlines.

Finally, due to the resource constraints of WSN

platforms, a WSN protocol should introduce minimal

overhead in terms of communication and energy

consumption and use only a fraction of the available

memory for its state.

The rest of the paper is organized as follows.

In section 2 discussed related work, in section 3explained various routing algorithms, and finally

concluded with section 4.

2 .RELATED WORK

There already exists a lot of work in the

field of sensor scheduling and routing with focus on

energy-efficiency and therefore with the goal to

optimize the lifetime of sensor networks. Most often

the presented algorithms either are ad-hoc solutions,

that have only been evaluated empirically without

any theoretical guarantees or they are purelytheoretical works, that base their solutions on models

that are to simple to function in real-world scenarios.

This lack of overlap between theory and practice can

be found throughout this entire field of research.

Our main focus is on algorithms that help to

minimize the energy consumption of sensor-

networks. Generation and storage of energy is still

not solved efficiently on a small scale such as on a

sensor node. Thus, we have to treat the available

energy as a scarce resource and try to optimize the

usage of the remaining energy to extend the lifetime

of the network for as long as possible.


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3. ROUTING ALGORITHAMS

A.Formulation of Power-aware Routing1 The Model

Power consumption in ad-hoc networks can bedivided into two parts: (1) the idle mode and (2)

the transmit/receive mode. The nodes in the

network are either in idle mode or in

transmit/receive mode at all time. The idle mode

corresponds to baseline power consumption.

Optimizing this mode is the focus of. We instead

focus on studying and optimizing the

transmit/receive mode. When messages routed

through the system, all the nodes with the

exception of the source and destination receives

a message and then immediately relay it.

Because of this, we can view the power

consumption at each node as an aggregate

between transmit and receive powers which we

will model as one parameter. More specifically,

we assume an ad-hoc network that can be

represented by a weighted graph G(V;E). The

vertices of the graph correspond to computers in

the network. They have weights that correspond

to the computer's power level. The edges in the

graph correspond to pairs of computers that are

in communication range. Each edge weight is the

power cost of sending a unit message1 between

the two nodes. Our results are independent of the

power consumption model as long as we assume

the power consumption of sending a unit

message between two nodes does not changeduring a run of the algorithm. That is, the weight

of any edge in the network graph is fixed.

Although our algorithms are independent of the

power consumption model, we fixed one model

for our implementation and simulation

experiments. Suppose a host needs power e to

transmit a message to another host who is d

distance away. We use the model of to compute

the power consumption for sending this message:

e = kdC + a;

where k and c are constants for the specificwireless system (usually 2


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Figure 1: The integer solution problem can be

reduced to set partition as follows. Construct a

network based on the given set. The power of xi

is ai for all 1 i n, and the power of y

is the weight of each edge is markedon the network. For any set of integers S = a 1, a2,

a3, a4 an, we are asked to find the subset of

S, A such that Pai2AWecan construct a network as depicted here. The

maximal flow of the network is , andit can only be gotten when the flow of x iy is aifor all ai A, and for all other x iy, the flow is 0.

3. Competitive Ratio for Online Power-aware

Routing

In a system where the message rates are

unknown, we wish to compute the best path to

route a message. Since the message sequence is

unknown, there is no guarantee that we can find

the optimal path. For example, the path with the

least power consumption can quickly saturate

some of the nodes. The difficulty of solving this

problem without knowledge of the message

sequence is summarized by the theoretical

properties of its competitive ratio. The

competitive ratio of an online algorithm is the

ratio between the performance of that algorithm

and the optimal offline algorithm that has access

to the entire execution sequence prior to making

any decisions.

Theorem 1 No online algorithm for

message routing has a constant competitive ratio

in terms of the lifetime of the network or the

number of messages sent.

Theorem 1, whose proof is shown in Figure 2,

shows that it is not possible to compute online anoptimal solution for power-aware routing.

B. Online Power-aware Routing with max-

min zPmin

In this section we develop an approximation

algorithm for online power-aware routing and

show experimentally that our algorithm has a

good empirical competitive ratio and comes

close to the optimal.

We believe that it is important to develop

algorithms for message routing that do not

assume prior knowledge of the message

sequence because for ad-hoc network

applications this sequence is dynamic and

depends on sensed values and goals

communicated to the system as needed. Our goal

is to increase the lifetime of the network when

the message sequence is not known. We model

lifetime as the earliest time that a message

cannot be sent. Our assumption is that each

message is important and thus the failure of

delivering a message is a critical event. Our

results can be extended to tolerate up to m

message delivery failures, where m is a

parameter. We focus the remaining of this

discussion on the failure of the first message

delivery. Intuitively, message routes shouldavoid nodes whose power is low because

overuse of those nodes will deplete their battery

power. Thus, we would like to route messages

along the path

Figure 2: In this network, the power of each

node is 1+ and the weight on each edge is 1.

The First figure gives the network; the center one

is the route for the online algorithm; and the

right one is the route for the optimal algorithm.

Consider the message sequence that begins with

a message from S to T, say, ST. Without loss of

generality (since there are only two possible

paths from S to T), the online algorithm routes

the message via the route SX1X2X3.Xn-1XnT.

The message sequence is X1X2, X2X3, X3X4, .,

Xn-1Xn. It is easy to see that the optimalalgorithm (see right figure) routes the first

message through SY1Y2Y3..Yn-1YnT, then

routes the remaining messages through X1X2,

X2X3, X3X4,., and Xn-1Xn. Thus the optimal

algorithm can transmit n messages. The online

algorithm (center) can transmit at most 1

message for this message sequence because the

nodes X1;X2;. . . ;Xn are all saturated after


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routing the first message. The competitive ratio

is small when n is large. with the maximal

minimal fraction of remaining power after the

message is transmitted. We call this path the

max-min path. The performance of max-min

path can be very bad, as shown by the example

in Figure 3. Another concern with the max-min

path is that going through the nodes with high

residual power may be expensive as compared to

the path with the minimal power consumption.

Too much power consumption decreases the

overall power level of the system and thus

decreases the life time of the network. There is a

trade-off between minimizing the total power

consumption and maximizing the minimal

residual power of the network. We propose to

enhance a max-min path by limiting its total

power consumption.

Figure 3: The performance of max-min path can

be very bad. In this example, each node except

for the source S has the power 20 + , and the

weight of each edge on the arc is 1. The weight

of each straight edge is 2. Let the power of the

source be the network can send 20 messages

from S to T according to max-min strategy by

taking the edges on the arc (see the arc on thetop). But the optimal number of messages

follows the straight edges with black arrows is

10(n - 4) where n is the number of nodes.

The two extreme solutions to power-aware

routing for one message are: (1) compute a path

with minimal power consumption Pmin; and (2)

compute a path that maximizes the minimal

residual power in the network. We look for an

algorithm that optimizes both criteria. We relax

the minimal power consumption for the message

to be zPmin with parameter z 1 to restrict the

power consumption for sending one message to

zPmin. We propose an algorithm we call max-minzPmin that consumes at most zPmin while

maximizing the minimal residual power fraction.

The rest of the section describes the max-min

zPmin algorithm, presents empirical justification

for it, a method for adaptively choosing the

parameter z and describes some of its theoretical

properties.

The following notation is used in the

description of the max-min zPmin algorithm.

Given a network graph (V;E), let P(vi) be the

initial power level of node v i, eij the weight of the

edge vivj, and Pt(vi) is the power of the node vi at

time t. Let

utij = Pt(vi)-eij / p (vi)

P(vi) be the residual power fraction after sending

a message from i to j.

Alg. 1 describes the algorithm. In each

round we remove at least one edge from the

graph. The algorithm runs the Dijkstra algorithm

to find the shortest path for at most |E| times

where |E| is the number of edges. The running

time of the Dijkstra algorithm is O(|E| + |V| log

|V|) where |V| is the number of nodes. Then the

running time of the algorithm is at most O (|E| *

(|E| + |V | log |V |)). By using binary search, the

running time can be reduced to

O(log |E| - (|E|+|V| log |V |)). To find the pure

max-min path, we can modify the Bellman-ford

Algorithm by changing the relaxation procedure.

The running time is O (|V | * |E|).

Algorithm 1 max-min zPmin-path algorithm

1: Find the path with the least power

consumption, Pmin by using the Dijkstra

algorithm

2: while true do

3: Find the path with the least power

consumption in the graph

4: if the power consumption > z * Pmin or no path

is found then

5: the previous shortest path is the solution, stop

6: Find the minimal utij on that path, let it be

umin7: Find all the edges whose residual power

fraction utij umin, remove them from the graph

1.Adaptive Computation for zAn important factor in the max-min zPmin

algorithm is the parameter z which measures the

trade-off between the max-min path and the

minimal power path. Whenz = 1 the algorithm

computes the minimal power consumption path.

When z = it computes the max-min path. We

would like to investigate an adaptive way of

computing z > 1 such that max-min zPmin thatwill lead to a longer lifetime for the network than

each of the max-min and minimal power

algorithms. Alg. 2 describes the algorithm for

adaptively computing z. P is the initial power of

a host. Pt is the residual power decrease at time

t compared to time t - T. Basically,

gives an estimation for the lifetime of that node

if the message sequence is regular with some


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cyclicity. The adaptive algorithm works well

when the message distributions are similar as the

time elapses. We conducted several simulation

experiments to evaluate the adaptive

computation of z. In a first experiment we

generated the positions of hosts in a square field

randomly using the following parameters. The

scope of the network is 10*10, the number of

hosts in the network is 20, the power

consumption weights for transmitting a message

are eij = 0:001 * d3 ij , and the initial power of

each host is 30. Messages are generated between

all possible pairs of hosts and are distributed

evenly. Figure 4 (first) shows the number of

messages transmitted until the first message

delivery failure for different values of z. Using

the adaptive method for selecting z with zinit =

10, the total number of messages sent increases

to 12; 207, which is almost the best performance

by max-min zPmin algorithm.

Figure 4: The effect of z on the maximal number

of messages in a square network space. The

positions of hosts are generated randomly. In the

first graph the network scope is 10 *10, the

number of hosts is 20, the weights are generatedby eij = 0:001 *d

3ij , the initial power of each

host is 30, and messages are generated between

all possible pairs of the hosts and are distributed

evenly. In the second graph the number of hosts

is 40, the initial power of each node is 10, and

all other parameters are the same as the first

graph. In the second experiment we generated

the positions of hosts evenly distributed on the

perimeter of a circle. The radius of the circle is

20, number of hosts 20;

the weight formula: eij = 0:0001 * d3

ij ; and

the initial power of each host is 10. Messages are

generated between all possible pairs of the hostsand are distributed evenly. The performance

according to various z can be found in Figure 5

(first). By using the adaptive method, the total

number of messages sent until reaching a

network partition is 11; 588, which is much

better than the most cases when we choose a

fixed z.

2 Empirical Evaluation of Max-min zPmin

Algorithm

We conducted several experiments for

evaluating the performance of the max-min zPmin

algorithm. In the first set of experiments (Figure

4), we compare how z affects the performance of

the lifetime of the network. In the first

experiment, a set of hosts are randomly

generated on a

Figure 5: The first figure shows the effect of z

on the maximal number of messages in a ring

network. The radius of the circle is 20, the

number of hosts is 20, the weights are generated

by eij = 0:0001* d3 ij , the initial power of each

host is 10 and messages are generated betweenall possible pairs of the hosts and are distributed

evenly. The second figure shows a network with

four columns of the size 1 * 0:1. Each area has

ten hosts which are randomly distributed. The

distance between two adjacent columns is 1. The

right figure gives the performance when z

changes. The vertical axis is the maximal

messages sent before the first host is saturated.

The number of hosts is 40; the weight formula is

eij = 0:001 *d3ij ; the initial power of each host is

1; messages are generated between all possible

pairs of the hosts and are distributed evenly.

square. For each pair of nodes, one message is

sent in both directions for a unit of time. Thus

there is a total of n * (n - 1) messages sent in

each unit time, where n is the number of the

hosts in the network. We experimented with

other network topologies.Figure 5 (first) shows

the results obtained in a ring network. Figure 5

(second) shows the results obtained when the

network consists of four columns where nodes

are approximately aligned in each column. The

same method used in experiment 1 varies the

value of z. These experiments show that

adaptively selecting z leads to superior

performance over the minimal power algorithm

(z = 1) and the max-min algorithm (z = 1).Furthermore, when compared to an optimal

routing algorithm, max-min zPmin has a constant

empirical competitive ratio (see Figure 6 (first)).

Figure 6 (second) shows more data that

compares the max-min zPmin algorithm to the

optimal routing strategy. We computed the

optimal strategy by using a linear programming


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package2. We ran 500 experiments. In each

experiment a network with 20 nodes was

generated randomly in a 10 * 10 network space.

The messages were sent to one gateway node

repeatedly.

We computed the ratio of the lifetime of the

max-min zPmin

algorithm to the optimal lifetime.

Figure 6 shows that max - min zPmin performs

better than 80% of optimal for 92% of the

experiments and performs within more than 90%

of the optimal for 53% of the experiments. Since

the optimal algorithm has the advantage of

knowing the message sequence, we believe that

max-min zPmin is practical for applications where

there is no knowledge of the message sequence.

Figure 6: The first graph compares the

performance of max-min zPmin to the optimal

solution. The positions of hosts in the network

are generated randomly. The network scope is

10*10, the weight formula is eij = 0:0001 *d3ij ,

the initial power of each host is 10, messages are

generated from each host to a specific gateway

host, the ratio z is 100:0. The second figure

shows the histogram that compares max-minzPmin to optimal for 500 experiments. In each

experiment the network consists of 20 nodes

randomly placed in a 10*10 network space. The

cost of messages is given by eij = 0:001 * d3ij .

The hosts have the same initial power and

messages are generated for hosts to one gateway

host. The horizontal axis is the ratio between the

lifetime of the max-min zPmin max-min

algorithm and the optimal lifetime, which is

computed offline.

3 Analysis of the Max-min zPmin Algorithm

In this section we quantify the experimentalresults from the previous section in an attempt to

formulate more precisely our original intuition

about the trade-o_ between the minimal power

routing and max-min power routing. We provide

a lower bound for the lifetime of the max- min

zPmin algorithm as compared to the optimal

solution. We discuss this bound for a general

case where there is some cyclicity to the

messages that flow in the system and then show

the specialization to the no cyclicity case.

Suppose the message distribution is regular, that

is, in any period of time [t1; t1 + ), the message

distributions on the nodes in the network are the

same. Since in sensor networks we expect some

sort of cyclicity for message transmission, we

assume that we can schedule the message

transmission with the same policy in each time

slice we call . In other words, we

partition the time line into many time slots [0; );

[; 2); [2; 3); . Note that is the lifetime

of the network if there is no cyclical behavior in

message transmission. We assume the same

messages are generated in each slot but their

sequence may be different. Let the optimal

algorithm be denoted by O, and the max-min

zPmin algorithm be denoted by M. In M, each

message is transmitted along a path whose

overall power consumption is less than z times

the minimal power consumption for thatmessage. The initial time is 0. The lifetime of the

network by algorithm O is TO, and the lifetime

by algorithm M is TM. The initial power of each

node is: P10, P20, P30, ., P(n-1)0, Pn0. The

remaining power of each node at TO by running

algorithm O is: P1O, P2O, P3O,, Pn-1O, PnO. The

remaining power of each node at TM by running

algorithm M is: P1M, P2M, P3M,, Pn-1M, PnM.

Let the message sequence in any slot be m1;m2;

;ms, and the minimal power consumption to

transmit those messages be P0m1 , P0m2 , P0m3 ,,

P0ms .

Theorem 2 The lifetime of algorithm M satisfies

TM

+

Proof:We have

ko = kM +

Mmk= PM

where MTM is the number of messagestransmitted from time point 0 to TM. PMmk is

the power consumption of the k-th messageby running algorithm M. We also have:

ko = ko +

omk= Po

where MTO is the number of messagestransmitted from time point 0 to TO. POmk isthe power consumption of of the k-thmessage by running algorithm O.


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Since the messages are the same for any two

slots without considering their sequence, we can

schedule the messages such that the message

rates along the same route are the same in the

two slots (think about divide every message into

many tiny packets, and average the message rate

along a route in algorithm O into the two

consecutive slots evenly.). We have:

omk=

. omk=

. omk

And

Mmk=

Mmkj

So we have:

Po= ko+

. omk

And

PO = PM PMmkj is the power consumption of the

k-th message in slot j by running algorithm M.

We also have the following assumption and the

minimal power of P0mk. For any 1 j TM/ and

k, we have only one corresponding l,

Then

PM kM+

. omk

Thus

kM+

. omk

ko+

. omk

We have

TM

+

Theorem 2 gives us insight into how well themessage routing algorithm does with respect to

optimizing the lifetime of the network. Given a

network topology and a message distribution, TO,

, ko, Mk are all fixed in Equation

3. The variables that determine the

actual lifetime are kM and z. The smaller kM 3 is, the better the performance lowerbound is. And the smaller z is, the better the

performance lower bound is. However, a small z

will lead to a large kMThis explains the trade-off between minimal

power path and max-min path.Theorem 2 can be used in applications that

have a regular message distribution without the

restriction that all the messages are the same in

two different slots. For these applications, theratio between and Ps 0mk must be changed

to mk, where P0mk is the minimalpower consumption for the message generated in

a unit of time.

Theorem 3 The optimal lifetime of the

network is at most

where tSPT and

PSPT Ph are the life time of the network and

remaining power of host h by using the least

power consumption routing strategy. Ph is the

initial power of host h.

Proof:

tOPT

= /(

)=

4 CONCLUSIONS

We have described several online algorithms

for power-aware routing of messages in large

networks dispersed over large geographical

areas. In most applications that involve ad-hoc

networks made out of small hand-held

computers, mobile computers, robots, or smart

sensors, battery level is a real issue in the

duration of the network. Power management can

be done at two complementary levels (1) during

communication and (2) during idle time. We

believe that optimizing the performance of

communication algorithms for power

consumption and for the lifetime of the network

is a very important problem. It is hard to analyze

the performance of online algorithms that do notrely on knowledge about the message arrival and

distribution. This assumption is very important

as in most real applications the message patterns

are not known ahead of time. In this paper we

have shown that it is impossible to design an on-

line algorithm that has a constant competitive

ratio to the optimal offline algorithm, and we

computed a bound on the lifetime of a network


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whose messages are routed according to this

algorithm. These results are very encouraging.

We developed an online algorithm called the

max-min zPmin algorithm and showed that it had

a good empirical competitive ratio to the optimal

o_-line algorithm that knows the message

sequence. We also showed empirically that max-

min zPmin achieves over 80% of the optimal

(where the optimal router knows all the messages

ahead of time) for most instances and over 90%

of the optimal for many problem instances. Since

this algorithm requires accurate power values for

all the nodes in the system at all times, we

proposed a second algorithm which is

hierarchical. Zone-based power-aware routing

partitions the ad-hoc network into a small

number of zones. Each zone can evaluate its

power level with a fast protocol. These power

estimates are then used as weights on the zones.

A global path for each message is determined

across zones. Within each zone, a local path forthe message is computed so as to not decrease

the power level of the zone too much. Finally,

we have developed a distributed version of the

max-min zPmin, in which all the decisions use

local information only, and showed that this

algorithm outperforms significantly a distributed

greedy-style algorithm.

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Documents

Routing Algorithms and Algorithms for Lifetime-Optimization in Complex Sensor-Actuator-Networks