Adaptive Optimizations for SurveillanceSensor Network Longevity

R. R. BROOKS and HEMANTH SIDDULUGARI

Holcombe Department of Electrical and Computer Engineering, Clemson University,

Clemson, SC, USA

Sensor networks are typically wireless networks composed of resource-constrainedbattery powered devices. In this paper, we present a criterion for determining whetheror not a surveillance sensor network is viable. We use this criterion to compare methodsfor extending the effective lifetime of the sensor network. The life extension methods weconsider are local adaptations that reduce the energy drain on individual nodes. Theyare communications range management, node repositioning, and data agreement.Simulations of a surveillance scenario quantify the utility of these methods. Ourresults indicate that data agreement provides the most improvement in networklongevity, and communications range management is also useful. Repositioning nodesto reduce the power needed for communications is dependent on the amount ofattenuation experienced by the node’s communications signal and the volume oftraffic between nodes. When these factors are considered, node repositioning is aneffective strategy for network life extension. Synergies between the energy conservationapproaches are also explored.

Keywords Sensor Network; Power Conservation; Distributed Adaptation; Surveillance

1. Introduction

This paper considers the methods for extending the effective lifetime of wireless sensor

networks used for surveillance applications.

The first question we address is ‘‘when is a surveillance network viable?’’ As

explained in section 2, network viability can be determined using the consequences of

the ad hoc network topology model first given in [14] and extended in [6]. This provides a

straightforward technique for determining whether or not a surveillance network can

adequately perform its task.

In our analysis, we will assume that all sensor nodes have the same configuration,

namely:

� There is a generic sensor for target detection.� Computations can be performed locally.

International Journal of Distributed Sensor Networks, 5: 158–184, 2009

Copyright � Taylor & Francis Group, LLC

ISSN: 1550-1329 print / 1550-1477 online

DOI: 10.1080/15501320601062189

A preliminary version of this paper appeared as R. R. Brooks and H. Siddul, ‘‘On Adaptation toExtend the Lifetime of Surveillance Sensor Networks,’’ Innovations and Commercial Applications ofDistributed Sensor Networks Symposium, Bethesda, MD (October 2005).

This work was supported by, or in part by, the U.S. Army Research Laboratory and the U.S.Army Research Office (W911NF-05-1-0226).

Address correspondence to R. R. Brooks, Holcombe Department of Electrical and ComputerEngineering, Clemson University, 313-C Riggs Hall, PO Box 340915, Clemson, SC 29634-0915USA. E-mail: rrb@acm.org

158

� Wireless communication is used for coordination and reporting results.� Global Positioning System (GPS) provides a common clock and accurate localiza-

tion. (This requirement is not strict. As long as the position errors are not correlated,

and clock skew remains within reasonable bounds, and the results given in this

article will hold.)� Nodes are mobile. In this case, we consider nodes with wheels that can move under

their own volition.

In our analysis, target detection is triggered by the Closest Point of Approach (CPA) events

[3]. The sensors are imperfect. We allow for non-trivial false negative (type 1) and false

positive (type 2) error rates. The sensing model is given in section 4.

Section 3 reviews relevant literature on sensor node power issues. We assume nodes

fail when their battery power is exhausted and use a crash-fail model (i.e., when a node fails

it ceases to perform all functions permanently). Nodes can use their last energy to inform

neighbors of their imminent failure. We do not consider node failures for other reasons.

Sensing errors are not considered failures.

The life extension techniques we consider are evaluated using a simulation tool

presented in section 4. The simulation implements the local life extension behaviors

presented in section 5, along with bookkeeping to track node power use. Node behaviors

are reactions to target movement in the region monitored by the network. In the simulation,

target trajectories are spline curves passing through three random points.

In section 5 we discuss the power optimizations:

� Dynamic modification of wireless transmission range.� Node movement to reduce communications range or compensate for node failure.� Local aggregation of sensor readings to reduce network traffic due to type 1 and type

2 sensing errors.

Section 6 considers combinations of these approaches.

The main contributions of this paper to the literature are:

� Presenting a network viability criterion for surveillance applications.� Providing simulation results comparing power conservation approaches for practical

applications.� Illustrating how node mobility can be used to extend network lifetime, contradicting

the disappointing results for node repositioning given in [33].� Exploring the effect of combining multiple node power optimizations.

The paper is structured as follows. Section 2 discusses network viability. Sensor network

power consumption is reviewed in Section 3. Section 4 explains the simulation used to find

the expected sensor network lifetime. In Section 5, we describe two methods for reducing

power consumption and compare them using network simulations. Section 6 looks at node

relocation issues in detail. Section 7 looks at second order effects of integrating power

optimizations. In Section 8 we discuss other network life extension approaches. We conclude

the paper with a discussion of our results and future extensions in Section 9.

2. Network Vialbility Criterion

Consider a surveillance network charged with reporting when a member of a class of

objects (targets) traverses a given surveillance domain (terrain). Reports are sent to a user

community that we assume, for the sake of discussion, is external to the terrain. The

Sensor Network Longevity 159

community is alerted to a detection event by a detection packet reaching the network

periphery. The surveillance network will be viable as long as it assures that:

(i) an object traversing the terrain is detected (with acceptable error rates), and

(ii) the user community is alerted.

These properties must exist almost surely (i.e., with a probability of 1 [2]). Our

criterion is a tautology: the network is viable as long as it can continue to perform its

mission.

To date, the implications of this tautology have been overlooked. For example, the

following methods of determining network viability do not fit the criterion:

� Network connectivity – If full network connectivity is needed, the sensor network is

a giant serial system. Network availability will fall exponentially with the number of

sensor nodes (n), and thus large networks will have an unacceptable mean time to

failure. For networks with any redundancy, some nodes can be isolated from the

network without compromising its application.� Sensing coverage – refers to placing nodes so that sensor detection regions have

little overlap, but the system monitors the entire terrain. Since sensing ranges and

coverage regions are unpredictable, problems with this ‘‘cookie cutter’’ approach

are well known [27]. The problems are often due to environmental influence [24].

Real-world approaches consider distributed surveillance as a tracking problem

using sensors with finite space and time sampling rates [4, 5]. Coverage approaches

ignore sensor errors, background noise, and occlusion. In addition, coverage analy-

sis creates a serial system, which fails when any component fails. Once again, we

have a serial system where dependability falls exponentially with network size.

2.1. Network Connectivity Ignores Sensing Issues. Sensing Coverage Ignores WirelessCommunications Issues

We propose a network viability criterion that is a direct consequence of the network model

in [14] where nodes with a fixed communications range are placed at random in the terrain.

Simulations show that ad hoc networks with range limited communications exhibit phase

change phenomena like those found in the random graph [2] and percolation [23] theories.

The random graph theory is a branch of graph theory that assigns probability distributions

to the existence of edges between vertices. Percolation theory, a branch of physics, studies

fluid flows in random media. Random media are modeled as tessellations of a terrain with

probability distributions for the existence of edges between neighboring vertices.

Reference [6] discusses their common basis.

In these models, network behavior has two phases. In the first phase, the probability of

connection between nodes is small and the network has a large number of isolated

components. As the connection probability grows, the expected size of the largest compo-

nent grows logarithmically. In the second phase, the network is dominated by a unique giant

component that contains most of the system nodes. There are still isolated holes in the

network. The size of the largest hole shrinks logarithmically as the connection probability

increases. The transition between these two phases is extremely steep. For random graphs,

the curve of the maximum component size versus the edge probability takes the form e�e�c

.

In percolation theory, the inflection point of this curve is referred to as the percolation

threshold.

160 R. R. Brooks and H. Siddulugari

The percolation theory has established these properties for systems with a giant

component [23]:

1. For systems above the percolation threshold, a path exists that connects the external

boundaries of the terrain.

2. At the percolation threshold, property 1 is self-similar over scales (i.e., it holds over

samples of the terrain of any size). Readers unfamiliar with self-similarity are

encouraged to refer to [23].

Consider sensor networks with nodes either randomly placed [14] in a regular tessellation

[23] or a weighted combination of the two. Sensor nodes are vertices in a random graph

structure. Edges between vertices represent either an active communications link, or

detection of a target passing between nodes. In practice, the edge probability distribution

is the minimum of the two likelihoods.

Above the phase change (percolation threshold) a single giant component connects

most of the sensor nodes (O(n)) [23]. It has at least one path connecting all the terrain’s

external boundaries (property 1). This property is true for subsets of the system across

scales (property 2). Thus, for a sensor network with a giant component, targets traversing

the network will be detected by at least one node that is able to report the detection to the

user community. Therefore, the network fulfills our viability criterion.

Note the subtle differences between this and the coverage and connectivity criteria.

Coverage is not guaranteed, because small sensing gaps may exist in the terrain surveyed.

But these gaps are of limited size and surrounded by regions that are covered, any target will

be detected before or after it enters a gap. Similarly connectivity is not guaranteed, since

isolated nodes exist that are not connected to the network as a whole. But paths will exist

from other nodes to the user community (in this case the periphery), so the loss of these

isolated nodes does not stop the network from functioning correctly. Above the percolation

threshold, the system is dense enough that the loss of these isolated nodes does not keep the

network from performing its task. Compare this to the criteria used in sensing coverage and

network connectivity studies, where the whole network is incapacitated as soon as any

single node is disabled.

In our approach the network is viable as long as it contains a giant component. In our

simulation, we infer the loss of the giant component from the loss of property 1. When there

is no path between the terrain’s external boundaries, the giant component is fractured.

Consider the worst-case scenarios for networks with initial configurations above the

percolation threshold:

� A target entering the network cannot be detected and/or reported while in a hole. Since

the largest hole above the percolation threshold is O(log n) [2, 23], this is the upper

limit of the target’s ability to avoid detection in the initial network configuration.� As nodes lose power:

(i) maximum hole size grows logarithmically,

(ii) the network becomes sparse, and

(iii) the network approaches the percolation threshold.

As long as we are above the percolation threshold property 1 holds and a target has to

pass through a graph edge to traverse the terrain. Once it does so, a node connected to the

giant component detects the target and notifies the user community. Property 2 says that

property 1 holds for regions inside the terrain up to the percolation threshold.

Sensor Network Longevity 161

A fuller treatment of these issues and how to predict the percolation threshold for

systems is in [6].

This concept is different from the dominating set approaches used in [31] and [32].

While those papers consider both sensor coverage and network connectivity simulta-

neously, they attempt to guarantee coverage over the entire surveillance region. They use

massive redundancy that fields more nodes than needed at any moment. The approach we

suggest allows networks to be fielded that are viable and do not contain massive redun-

dancy; for some applications this could result in significant cost savings.

Also, in [31] and [32], the network lifetime is extended by choosing subsets of nodes to

sleep and conserve energy. These subsets disrupt neither sensing coverage nor network

connectivity. Note that local signal (sensor or communications) disruptions can negate the

results of these approaches by causing local violations of the global policy. The criterion

given here guarantees that with high probability (for very large networks this can be a

probability of 1 [2]) any gaps will be enclosed by regions where surveillance is active.

Disruption of the global network would require jamming signals over a large area, which

would be prohibitively expensive.

Conserving power by allowing nodes to sleep is the equivalent of pre-positioning node

replacements in the field, and the approaches in [31] and [32] require more coordination

between nodes than our approach. These comments do not denigrate the work in [31] and

[32], which is important and largely orthogonal to our work. They are meant to contrast

those solutions, which concentrate on maintaining local attributes, and our solution, which

is willing to sacrifice nodes and local coverage, as long as the global application continues

to function.

3. Review of Sensor Network Power Consumption

We now review sensor node power consumption literature to motivate the simulation

scenario and power conservation techniques. Sensor networks rely on battery power. [20]

researches the use of ambient energy sources to power sensor networks and comes to the

unfortunate conclusion that, although promising, this is not currently feasible. Reliance on

limited, non-renewable battery energy resources means that to achieve a reasonable net-

work lifetime, all aspects of sensor networks must be energy efficient.

Some publications state that computation power needs will decrease until negligible,

and wireless communications energy will dominate the energy consumption by sensor

networks [18, 1]. This logic combines Moore’s law stating that feature sizes halve every

18 months with the fact that smaller feature sizes require less power. Unfortunately, this

ignores important factors:

(i) leakage energy consumption grows as feature size decreases, and

(ii) Moore’s law also states that clock rates increase at the rate feature sizes shrink.

Faster clock rates require more energy. Realistic energy models for sensor net-

works will have to account for all aspects of node behavior.

From the power consumption analysis in [9]:

� For most commercial ARM8 processor instructions, the energy required is 4.3*10-9

joules per bit. Multiplication requires 31.9*10-9 joules per bit.� Berkeley smart dust prototypes consume 0.05* 10-9 joules per bit for most instruc-

tions (multiplication not supported).

162 R. R. Brooks and H. Siddulugari

� Radio frequency ground communications require 10-7 joules per bit for 0–50

meters, and 50* 10-6 joules per bit for 1–10 kilometers.

The mote and radio figures are lower bounds, based on research prototypes. Commercial

products are unlikely to reach these levels of efficiency in the near future:

� Per bit energy consumption for multiple instructions on commercial processors is in the

range 48 (MC68328 DragonBall) to 0.84 (SA-110 StrongARM)* 10–9 joules per bit [7].� Communications require from 40* 10–6 joules (GSM cellular phone) to 1* 10–7

joules (Bluetooth for 10s of meters) per bit [9].� Reception energy needs for GSM are 2*10–6 joules per bit, and 10–7 joules per bit for

Bluetooth. [9].

Energy needs for communications are proportional to r–� where r is the range. The �exponent is in the range 2 to 5. A value of 3.5 is reasonable for many applications [29]. For

commercial and prototype systems, transmitting one bit for one hop is on the order 102

times more expensive than computing one instruction on one bit.

[9] and [19] claim transmission energy is the dominant drain on sensor networks when

per hop communication is over 10 meters. This claim is based on applications with minimal

on-board computation. Two examples in [9] only sample data, do analog-to-digital con-

version, execute a filter, and transmit data. The other example does a least squares estimate

of vehicle velocity from five data samples. This amounts to executing one very small matrix

multiplication. For nodes with minimal to no local data processing, the communications

energy consumption is certain to dominate the computation energy requirements.

Our empirical tests indicate that, for many classes of sensor network applications,

computation dominates energy consumption. In [22, 15, 16] beamforming [8] and CPA

based tracking approaches [3, 4, 5] are compared. The beamforming approach was found to

be more accurate, while requiring 103 times more energy. Communications energy require-

ments were calculated from the bluetooth energy per bit. Computation energy was mea-

sured on an AMD Athlon 4 mobile processor. The communication was responsible for less

than 20% of the total energy drain. Both applications used embedded Linux. Beamforming

is computation intensive, performing cross-correlation over multiple time series to estimate

the signal direction of arrival. The CPA based approach requires minimal computation.

This study of representative sensor network applications supports the view that power

awareness must consider computation and communication.

For secure applications, both [7] and [17] show that encryption, decryption, and secure

hashing are computation intensive, with a large energy overhead. [7] measures the energy

drain of key initialization communications on sensor networks.

4. Simulation Environment

To study the effect of power conservation on network lifetime, a surveillance sensor

network simulation was implemented. The network used topologies based on the model

in [14]. The method in [6] was used to guarantee that initial network configurations were

above the percolation threshold and had a giant component. The simulation environment

resembled the field test we performed at 29 Palms Marine Base [4], where the effective

sensing range was much greater than the wireless communications range. We determined

whether or not the network was viable at any point in the simulation by verifying

empirically that communications paths existed between all four terrain boundaries.

Figure 1 shows a sample display from the simulation.

Sensor Network Longevity 163

We considered a sensor network operating within a 50 m · 50 m square area. Each

node was given an initial energy of 2880 J. Since lithium batteries have better energy

density (2880 J/cc) and longevity than Zinc-air and Alkaline batteries we used them as our

power model [20].

The simulations were started with 100 nodes and increased till 1000 in steps of 100

nodes. The decision to start the simulation with 100 nodes was taken after observing the

phase change graph of the test results. Figure 2 shows where the percolation threshold is

crossed and the giant component starts to form.

Figure 1. Simulation snapshot. Dark points are active nodes. Dull nodes are in a low power state. Light

colored nodes are dead. The star is the target position. Nodes within the circle can detect the target.

Phase change

–5

0

5

10

15

20

25

30

35

0 50 100 150 200

Number of nodes

Net

wor

k lif

e tim

e in

num

ber

of d

ays

Figure 2. Mean network lifetime vs the number of nodes showing a phase change where the giant

component forms.

164 R. R. Brooks and H. Siddulugari

We modeled the nodes after Berkeley Motes, which require 1 pico joule per instruction

computed, 100 nano joules per bit transmitted, and 4 nano joules per sensor sample [9]. The

energy required for data packet reception is constant, as is the energy required for

computation and sensing. This power drains at a constant rate while nodes are active. We

model only the energy consumption due to packet transmission and node movement. The

influence of the other drains can be factored in to our results by using a constant factor times

the time t.

The detection packets broadcast by nodes were 56 bytes long as in [29]. The maximum

communications range is 7.5 m. The energy consumed transmitting packets between nodes

separated by a distance d is:

Ec ¼ �cd� (1)

where,

Ec is the energy required for communication

� is the rate at which the nodes generate packets

c is a constant

d is the distance between the source and the destination nodes

� is the RF attenuation factor

Nodes transmit data at a constant bit rate of 100 kbps. Except where otherwise

indicated, we set parameter � to 3.5, a reasonable value for many applications [29].

Initial node positions were set randomly in the terrain using a uniform distribution.

Movement requires 50 mJ/m; i.e., a node weighing 1 kg required about 5 mJ to move a

distance of 0.1 m.

Targets enter the system at a rate of 15 per hour. The probability of a sensor generating

a false positive (false negative) is 0.2 (0. 2). Each scenario was repeated 35 times. Graphs

showing our results give both the mean and 95% confidence interval.

A transmission power-based (distributed Bellman-Ford with d as metric) routing

algorithm was assumed to be in effect determining the routes at node. We assume that

the shortest path information is available to all the nodes. They forward packets to

neighboring nodes on the lowest energy path to edge nodes. Edge nodes forward packets

to the user community. This approach was chosen for two reasons:

� It is somewhat realistic, being based on systems we have tested for the military.� Placing the data sink inside the network creates a traffic ‘‘hot spot’’ where issues

orthogonal to this study would influence the results.

5. Network Life Extension

5.1. Baseline Simulations

Figure 3 shows simulation results for the baseline case where no energy optimizations

were applied. Both the mean system lifetime and its variance increase notably over

time. All nodes have the same communication range of 7.5 m, which does not

change. The nodes remain at their original location. There is no fusion of information

between nodes.

Sensor Network Longevity 165

5.2. Communications Range Management

Since transmission power is proportional to d�, a natural optimization is to reduce the

communications range when possible.

1. Derivation

In Fig. 4, E is an edge node with neighbors I, A and B. In the top figure, all nodes use

the same amount of energy to broadcast packets. This ignores the unequal distances

between the nodes. In the bottom figure, the communications power is reduced as

much as possible, without affecting network connectivity. This results in an

expected per node power savings rate of:

�E ¼ �c �dð Þ� (2)

with the same variables as in Equation 1, except that �E is energy savings and �d is

reduction in transmission range.

Note that nodes can also increase the communications range as needed by

increasing the energy used to transmit the signal. This increased energy drain is

worthwhile when an intermediate node, like node i in Fig. 4, exhausts its

battery power. The network can continue to function although individual

nodes, in this example node B, will exhaust their power resources more quickly

than would otherwise be the case.

2. Simulation results

Figure 5 shows simulation results obtained by allowing nodes to dynamically adjust

their transmission power. The mean network lifetime increases significantly with

the number of nodes. Over the range of values tested, the mean effective lifetime

seems to be approximately tripled. The network lifetime variance increases more

significantly with the number of nodes than in the baseline. An interesting result is

that the increase in lifetime with increase in nodes is more after 600 nodes because if

there are more number of nodes more and more energy could be conserved.

5.3. Local Data Agreement

We now consider an optimization that reduces the volume of traffic in the network.

Sensors have a non-negligible false positive rate. For every false positive, detection

Baseline Condition

–50

0

50

100

150

200

250

0 200 400 600 800 1000 1200

Number of Nodes

Mea

n lif

etim

e of

net

wor

kin

day

s

Figure 3. Mean network lifetime vs the number of sensor nodes with no power optimizations

applied.

166 R. R. Brooks and H. Siddulugari

packets are forwarded to the user community. Also, every node that detects a target sends

packets to notify the users. This redundant information drains system resources. It is

useful to aggregate information locally and reduce the number of packets traversing the

network.

In addition, we consider the relationship between the detection threshold and signal

power. Just as the transmission power determines the communications range, the detection

thresholds determine the effective sensing ranges and the false positive rates.

Figure 4. (Top) All nodes in the scenario have the same communications range. (Bottom) The

routing algorithm identifies the next hop on the lowest energy path to the outside world.

Communications range is set appropriately. Observe that even now node I is in the communication

range of A & B and E is in the communication range of I.

Sensor Network Longevity 167

Nodes with communications range managementoptimization alone

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200Number of Nodes

Mea

n ne

twor

k Li

fetim

ein

Day

s

Figure 5. Mean network lifetime versus the number of nodes when communications range can vary.

Figure 6. Example ROC from a network security application. The curve is the point (pf, pt) as the

detection threshold varies.

168 R. R. Brooks and H. Siddulugari

1. Derivation

A Receiver Operating Characteristic Curve (ROC), like the one shown in Figure 6, is

defined by the ratio of the true positive and false positive likelihoods as the detection

threshold varies. A normalized ROC is typically used to determine the optimal detection

threshold, which is the point on the ROC closest to point (0,1). This is where the

detection ratio is the closest to the ideal.

Lowering the detection threshold allows the sensor node to detect targets with

weaker signals at the cost of having higher false positive rates. Implicitly, detecting

weaker signals allows sensors to detect targets at a greater distance, since signals

emitted to the target also decay with d�. Note that this attenuation factor is in

general different from the communications attenuation factor. We compensate for

the increased type 2 error rate by combining readings from multiple sensor nodes.

In this approach, neighboring nodes exchange detection packets. The final detec-

tion decision is taken by a majority vote. We use the closest point of approach (CPA)

events for the detection and the space-time clustering approach described in detail in

[3, 5]. When a node receives a target signal, it monitors the signal as long as the signal

strength is increasing. This occurs as long as the target is approaching the sensor. When

the target passes the node, the signal power decreases and a CPA event is declared.

When a node has a CPA event, it broadcasts a detection packet to its neighbors. After a

predetermined time interval, the nodes that have had CPA events look at the detection

packets they have received. The node whose CPA event had the largest power then

attempts to determine whether or not the detection event was a true positive.

To discriminate between true and false positives, we use these four probabilities:

� pt – likelihood of a true positive.� qt – likelihood of a false negative (type 1 error = (1 – pt)).� pf – likelihood of a false positive (type 2 error).� qf – likelihood of a true negative = (1-pf).

In a neighborhood of n nodes, the decision-making node receives k detection

packets.

Nodes with local data agreement optimization

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000 1200Number of nodes

Mea

n ne

twor

k lif

etim

e in

day

s

Figure 7. Mean network lifetime vs the number of nodes when nodes have local data agreement

optimization.

Sensor Network Longevity 169

We now assume that both type 1 and type 2 errors are statistically independentevents. This may not always be the case, (e.g., the background noise signal may resemble

the spectrum of the target). We assume as well that no detection packets are dropped.Given these assumptions, we use a binomial distribution to calculate the group likelihood

Pt (Pf) that the event is a true (false) positive when exactly k detections are reported:

Pt ¼n

k

� �pk

t qn�kt (3)

Pf ¼n

k

� �pk

f qn�kf (4)

Equations (3) and (4) express the problem for cases when nodes have the same

detection threshold, they are relatively close to each other, and the errors arestatistically independent. Should this not be the case, the right hand side may be

modified to be the product of probabilities that are unique for each node.

The decision-making node computes Equations (3) and (4) and accepts the hypoth-

esis that a target is present when the value of Equation (3) is greater than or equal to

the value of Equation (4).

This approach introduces some additional overhead, in the form of one hop for

one packet for every node signaling a detection event. In exchange, it greatly

reduces the network traffic caused by false positives. It also serves to aggregate

detection events within a space-time window, which reduces the volume of infor-

mation transmitted by the network without affecting network reliability.

In [30] authors explained about a binary decision fusion rule based on independentlocal sensor observations and decisions for target detection in sensor networks.Appropriate fusion threshold bounds were derived using Chebyshev’s inequality atthe fusion center to improve system detection performances. In that they consideredtwo different scenarios namely homogeneous and heterogeneous. In a homogeneoussystem, each sensor assumes the same hit rate and false alarm rate regardless of theirdistance to the target. Whereas in a heterogeneous sensor network system, whereevery sensor has its own hit rate and false alarm rate due to various distances to thetarget or different physical properties. We consider only homogeneous networks. Ourwork could be extended to heterogeneous networks by using the methods in [30].

When combined with the ability to dynamically vary the detection threshold, this

approach allows the system to compensate for the loss of sensor nodes by increasingthe effective sensing range of specific nodes as needed without compromising net-

work performance.

2. Simulation results

The simulation, shown in Figure 7, did not explicitly model signals emitted by thetarget. Type 1 and type 2 errors were triggered explicitly using a Bernoulli random

variable. We only considered scenarios where all nodes had uniform errorprobabilities. Note that this approach provided significant improvement in the

network lifetime. It performed much better than either of the two other approaches,

and caused significant improvement at all network sizes.

6. Node Relocation

Since reducing the communications range results in energy savings, we consider reposi-

tioning nodes to reduce the energy required to transmit data. For example, in Fig. 4, if node I

170 R. R. Brooks and H. Siddulugari

were closer to node B, node B would require less energy to transmit packets. On the other

hand, we consider nodes that reposition themselves (in this case the sensor nodes have

wheels). Moving nodes also requires energy.

1. Derivation

Consider an arbitrary node n0 in the network with n neighbors that it commu-

nicates with. Nodes n1 to nn-1 transmit packets to n0 and node nn is the next hop on

n0’s path to the user community. Figure 8 shows an example, where node I moves

to a lower energy position.

From Equation (1), the energy consumed by communications between nodes n0

and ni is:

Ei ¼ �icd�i (5)

where �i is as defined for Equation (1), and di is the distance between nodes n0 and

ni. We, therefore, need to minimize:

E ¼X

i

Ei (6)

Using the Pythagorean Theorem, calculus, and the fact that x and y are orthogonal,

this value is a minimum when the following two equations are satisfied:

Xi

�iðx� xiÞððx� xiÞ2 þ ðy� yiÞ2Þ�=2�1 ¼ 0 (7)

Figure 8. If node I moves to position I1, all the nodes in the neighborhood save energy when

transmitting packets.

Sensor Network Longevity 171

Xi

�iðy� yiÞððx� xiÞ2 þ ðy� yiÞ2Þ�=2�1 ¼ 0

where, xi and yi are the coordinates of ith node among the n nodes around the node n0.

In [33], we considered only the case where � = 2, and the solution is a simple

weighted average. This problem is easy to compute because the factor for x (y) that

contains y (x) becomes a constant factor of 1.

For other values of �, we have two equations with two unknowns. This can be

solved using any number of root finding techniques, including Newton’s method. In

this paper, we used numerical methods to find the optimal position of the node when

� is not 2.

Now, if we denote:

Eoci as the energy required for i’th node to communicate with its next node

considering node ‘no’ in its original position. The sum of these values equals

the remaining node energy, ignoring sensing and computation energy.

Enci as the energy required for i’th node to communicate with its next node

considering node ‘no’ in its expected new position, for the expected node

lifetime in the original position.

Econserved as the energy saved by moving the node to the new position, then

Econserved ¼Xn�1

i¼0

ðEoci � EnciÞ (8)

The energy required for a node to move distance d1 is:

Em ¼ m�d�1 (9)

where,

Em is the energy required for movement

m is the energy required to move 1 unit

d1 is the distance moved by the node in units

� is the RF attenuation factor

Nodes move only if:

Econserved > Em (10)

(i.e., Nodes move only when it leads to an expected net energy savings). Since

intermediate nodes forward packets to the next hop on the path to the user community:

�n ¼Xn�1

i¼0

�i (11)

172 R. R. Brooks and H. Siddulugari

Node repositioning conserves energy in two ways:

� It minimizes the energy needed for communications in a local neighborhood.� It allows nodes to move to positions that mitigate the impact when other nodes die.

There is a natural tendency for nodes to drift towards their data sink (closer to the

edge of the terrain.) To slow this, we allow nodes to move only with probability p.

Figures 9 through 11 illustrate a simulation scenario that shows how node reposi-

tioning implicitly tends to cause nodes to organize themselves into straight lines.

Figure 9. Nodes at the beginning of a simulation.

Figure 10. Over time nodes tend to form straight lines.

Sensor Network Longevity 173

In addition to reducing the migration of nodes to the terrain edge, p also reduces jitter.

Nodes moving simultaneously to lower energy positions would cause oscillations,

with nodes searching for optimal positions in response to neighbor movement. This

would reduce the network lifetime. In some ways, this resembles the use of simulated

annealing in [13]. Empirical testing found that the value of 0.3 for p worked best in

our scenarios. Figure 12 shows results from simulations where p was varied.

Figure 13 shows results obtained using numerical methods to find the optimal

position for a node in a typical neighborhood. The top node is the data sink and

the second node from the top is the node looking for its optimal position. The initial

Figure 11. As nodes die, other nodes move to mitigate the global impact.

Node with different probabilities of movement (0.1,0.3,0.5,0.7)

–100

0

100

200

300

400

500

600

700

800

900

0 200 400 600 800 1000 1200

Number of Nodes

Mea

n ne

twor

k lif

etim

e in

day

s

Figure 12. Mean network lifetime vs the number of nodes when nodes move with a fixed probability.

(From top to bottom at position 1000: p = 0.3, 0.0, 0.1, 0.7, and 0.5).

174 R. R. Brooks and H. Siddulugari

situation is given along with results when � is set to 2, 3, 4, and 5. This diagram

reinforces the natural tendency of nodes to drift towards their data sink, since

communications with the sink accounts for more than half of the total data traffic

for the node. Figure 13 also indicates that the optimal position is not strongly

influenced by �. As � increases the node is pulled more strongly towards the data

sink, but this is not a very strong attraction.

Based on these results we suggest that scenarios where the attenuation factor is large

are best served by approximating the optimal position by computing the weighted

centroid, but weighing the data sink more heavily. This is simpler than solving

equation (7) and likely to produce identical results. Based on these results, we also

ignored the influence of � in the rest of our analysis. The only significant influence

larger values of � will have is increasing the energy cost for data transmissions.

2. Simulation results

Figures 14 and 15 shows simulation results for lifetime extension simulations with p

set to 0.3 and � set to 2. These values are used throughout the rest of this paper,

based on our analysis shown by Figs. 12 and 13. Our model ignored multi-path

fading and other environmental factors that affect communications reception,

except for their implicit use in Equation (1).

In spite of its increased overhead, node relocation can effectively increase the lifetime

of the network. Like data aggregation, this effect is noticeable even when the network

consists of a relatively small number of nodes.

Initial α = 2 α = 3 α = 4 α = 5

Figure 13. The second node from the top moves to its optimal position for varying values of �.

Nodes with mobility (probability 0.3)

0

100

200

300

400

500

600

700

800

0 200 400 600 800 1000 1200Number of nodes

Mea

n lif

etim

e of

net

wor

k in

day

s

Figure 14. Mean network lifetime versus the number of nodes when nodes are allowed to modify

their position.

Sensor Network Longevity 175

Figure 15 compares our current results with the results we presented in [33]. The

difference between the two results is that the position used in the earlier work did not reflect

the data transmission rates between individual nodes. Since the simulations place nodes at

random and have targets move through the surveillance space using randomized trajec-

tories, it seemed reasonable to assume that data rates would not have any discernable

trends. As shown by both Equation (11) and Fig. 15, this assumption was erroneous. It is to

be expected that this effect will be even more pronounced in actual applications where data

traffic correlations are certain to exist. The use of mobile sensing nodes appears to be a

useful energy savings technique that is frequently ignored in the literature.

The results from [33] are still interesting in that, although node repositioning without

considering the data transmission rates appears to be of extremely limited utility, it worked

well in combination with the other optimizations.

7. Combined Approaches

Figure 16 compares the optimization approaches presented in this paper. While data

aggregation clearly has a more profound effect on prolonging network lifetime, the other

two approaches also produce statistically significant results. Our interpretation of Fig. 16 is

that by removing unnecessary communications, data aggregation reduces the energy over-

head for networks of any node density. Repositioning nodes allows them to compensate for

their uneven distribution in the surveillance region, which works well in moderately dense

networks. When the network becomes dense, almost all nodes can use a relatively short

communications range. Only at that point does adjusting the communications range result

in truly significant energy savings.

The rest of this section considers second-order effects. Figure 17 combines node range

management with node movement. The results of this combination appear to have little

synergy. It is almost as if a curve were constructed using the maximum values from the two

individual optimizations.

Figure 18 shows the results of combining node movement and local data agreement.

There appears to be a real synergy that emerges when these approaches are combined. Note

Packet transmission rate effect

0

100

200

300

400

500

600

0 200 400 600 800 1000 1200Number of nodes

Net

wor

k lif

etim

e in

num

ber o

f da

ys

Without Rates

Considering rates

Figure 15. Comparison of the results of this paper with those in [33].

176 R. R. Brooks and H. Siddulugari

that the expected lifetime for large networks is nearly twice (triple) the expected network

lifetime when data aggregation (node movement) is used in isolation. Interestingly, while

the curve for both optimizations in isolation tends to flatten as the network size increases,

over the range tested the network lifetime increase is almost linear.

The most promising pairwise combination occurs when communications range man-

agement and local agreement are combined as shown in Fig. 19. This effectively quadruples

Superimposed graphs

–200

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000 1200

Number of nodes

Mea

n ne

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etim

e in

day

s

Baseline With Movement

Dynamic Comm. Range Local Data Agreement

Figure 16. Mean network lifetimes versus number of nodes for the baseline and optimization

approaches.

Nodes with communication range management and mobility

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000 1200Number of nodes

Mea

n ne

twor

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etim

e in

day

s

Figure 17. Mean network lifetime vs the number of nodes when communications range management

and node relocation approaches are combined.

Sensor Network Longevity 177

the lifetime of the network when compared to either optimization in isolation. As with the

optimization shown in Fig. 18, this synergy leads to a near linear increase in network

lifetime over the range of node densities simulated.

Figure 20 shows the results of combining all three optimizations. The mean values in

Fig. 20 are slightly better than the ones in Fig. 19, but the difference is within the error bars.

While we believe this difference to be real, it is not statistically significant. This is partly

due to the size of the confidence interval in Fig. 20 indicating that the amount of utility of

this approach varies greatly from instance to instance.Figure 21 combines and summarizes

Nodes with mobility and local data agreement

0

500

1000

1500

2000

2500

0 200 400 600 800 1000 1200Number of nodes

Mea

n ne

twor

k lif

etim

e in

day

s

Figure 18. Mean network lifetime vs the number of nodes when we combined local data agreement

and node relocation.

Nodes with communications range management and localdata agreement

0

1000

2000

3000

4000

5000

6000

0 200 400 600 800 1000 1200Number of nodes

Mea

n ne

twor

k lif

etim

e in

day

s

Figure 19. Mean network lifetime vs the number of nodes when we combined local data agreement

and communications range management approaches.

178 R. R. Brooks and H. Siddulugari

these results. The combination of range management and data agreement is clearly more

significant than any other pairwise combination. It also scales better with the network size.

The results from [33] are shown in Fig. 22. The approaches used are identical, except

that the data transmission rates were ignored when computing the optimal position for node

relocation. In contrast with the results for optimizations in isolation, the results for node

movement in combination with other optimizations are not significantly better when data

transmission rates are considered. This answers a question that was posed by our analysis in

[33], why does node relocation result in significant performance improvements only when

Nodes with all the optimizations discussed

0

1000

2000

3000

4000

5000

6000

7000

8000

0 200 400 600 800 1000 1200Number of nodes

Mea

n ne

twor

k lif

etim

e in

day

s

Figure 20. Mean network lifetime vs the number of nodes when the three optimizations are

combined.

Superimposed graphs

–2000

–1000

0

1000

2000

3000

4000

5000

6000

7000

0 200 400 600 800 1000 1200

Number of nodes

Mea

n ne

twor

k lif

etim

e in

day

s

Movement with Range Management Movement & Local Data Agreement

Range Management & Local Data Agreement All the optimizations

Figure 21. Combined approaches superimposed. (From bottom to top: range management with

movement, data agreement with movement, data agreement with range management, and all three

optimizations).

Sensor Network Longevity 179

combined with other optimizations? Node movement in isolation is not sufficient. The node

movement is mainly useful as an enabling technology in combination with other methods to

reduce network power consumption.

8. Comparison with the Literature

The need for sensor networks to be parsimonious with power resources is well established.

A common approach is for nodes to ‘‘sleep,’’ voluntarily transitioning into a semi-dormant

low energy state as in [28]. Nodes interleave their active and dormant time slices extending

the lifetime of the network at the cost of adding additional hardware. In essence, replace-

ment nodes are pre-positioned in the field. We consider this concept orthogonal to the work

presented here.

Much energy awareness research has concentrated on developing efficient networking

technologies, like the efficient MAC protocol in [28]. The same research group also used

data aggregation to reduce the volume of network traffic in [21]. However, the networking

layer does not have sufficient application information to correctly aggregate multiple

detections from multiple nodes into a single event, nor can it filter false positives from

the system. Efficient routing methods have been developed, like the one in [12] that

combines energy awareness and fault tolerance.

Non-network centered work has considered how to reduce power requirements at the

hardware [26], operating system [25], and compiler [10] levels. The concepts presented

here, while conscious of power constraints, are primarily situated at the applications layer

and have possible synergies with all of these research efforts.

Few papers have considered issues involving sensor node movement to minimize

power consumption. The approach in [11] uses integer linear programming to plot the

routes for a node to minimize the combined cost of movement and data transmission.

Unfortunately this approach requires prior knowledge of the node communications pat-

terns, which is not feasible in surveillance applications.

The ideas in [13] are not dissimilar to the mobility approach presented here in that they

look at issues concerning power consumption, network communications, and surveillance.

Superimposed graphs

–2000

–1000

0

1000

2000

3000

4000

5000

6000

7000

0 200 400 600 800 1000 1200

Number of nodes

Mea

n ne

twor

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etim

e in

day

s

Movement and Range management Movement and Local data agreementRange management and Local data agreement All optimizations

Figure 22. Combined approaches superimposed ignoring packet rates at nodes.

180 R. R. Brooks and H. Siddulugari

In addition, the simulated annealing based ideas presented in that paper also consider the

problems of multiple nodes moving simultaneously to optimize their position. Unlike our

paper, [13] assumes that nodes will position themselves to continually survey targets in a

specific location and arrange a store and forward network to support that application. We

assume that targets are mobile and the communications patterns for detection information

will be largely unpredictable.

9. Conclusions

This paper considered how local node adaptations extend the effective lifetime of a

surveillance sensor network. We started by considering the criteria used to determine

whether or not a network is viable. Considerations of network connectivity and sensor

coverage ignore the ability of the network to perform its task. On the other hand, we find

that the presence of a giant component in the system is a practical method for determining

whether or not a surveillance network is functional.

We then consider how local adaptations can extend the effective lifetime of a network.

They are preferable to centralized approaches, since they scale well as the network size

increases. Allowing nodes to determine their own communications power, detection thresh-

olds, and positions integrates communications, sensing, and applications considerations in

our framework.

We demonstrate the performance of the network using each of these optimizations:

� Communications range management reduces the power needed for transmitting

information.� Node relocation reduces transmission power needs.� Data agreement reduces the volume of information transmitted.

We found that local data agreement provides the most significant improvement in network

longevity. Communications range management is also useful, mainly significant for a

larger number of nodes in the network. The combination of the two shows synergy between

the two approaches. Although repositioning nodes to reduce the power needed for com-

munications gives less improvement when compared to other techniques, when combined

with other techniques it does significantly extend the network lifetime. When all optimiza-

tions are applied together, it extends the network lifetime to almost 25 times the baseline.

The data agreement approach could be combined with the ability to modify the

detection threshold. This would allow nodes to increase their effective sensing region

without excessively affecting network performance. In these tests we assumed that the

effective sensing range is much larger than the communications range, which is typically

the case. A fuller understanding is needed of the interactions between these two ranges and

how they determine system performance.

About the Authors

R. R. Brooks is an Associate Professor of Electrical and Computer Engineering at Clemson

University in Clemson, South Carolina. He received a PhD in Computer Science from

Louisiana State University and a B.A. in Mathematical Sciences from The Johns Hopkins

University. Dr. Brooks also studied Operations Research at the Conservatoire National des

arts et Metiers in Paris, France.

Sensor Network Longevity 181

He is a senior member of the IEEE. His books Disruptive Security Technologies with

Mobile Code and Peer-to-Peer Networks and Frontiers in Distributed Sensor Networks

(with S. S. Iyengar) are in press.

Dr. Brooks was PI of the Reactive Sensor Networks Project sponsored by the DARPA

ITO Sensor Information Technology initiative, which explored collaborative signal pro-

cessing to aggregate information moving through the network, and the use of mobile code

for coordination among intelligent sensor nodes. Dr. Brooks was co-PI of a DARPA IXO

JFACC program that used distributed discrete event controllers for air combat C2 planning.

He coordinated a DARPA MURI program that uses cooperating automata in a cellular

space to coordinate sensor network planning and execution. Dr. Brooks is PI of an ONR

URI on cybersecurity issues relating to mobile code and the construction of secure

information infrastructures.

His current research concentrates on adaptation in distributed systems. His research

interests include network security, sensor networks, and self-organizing systems.

His Ph. D. dissertation received an exemplary achievement certificate from the

Louisiana State University graduate school. Dr. Brooks is Associate Managing Editor of

the International Journal of Distributed Sensor Networks. He has a broad professional

background with computer systems and networks. He was head of the Pennsylvania State

University Applied Research Laboratory Distributed Systems Department for over six

years and technical director of Radio Free Europe’s computer network for many years.

His consulting clients include the French stock exchange authority and the World Bank.

Hemanth Siddulugari has an MS degree in Electrical and Computer Engineering from

Clemson University.

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Sensor Network Longevity 183

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