Efficient Data Gathering in Distributed Hybrid

Sensor Networks using Multiple Mobile Agents

Piyush Shah1, Krishna M. Sivalingam1,2 and Prathima Agrawal3

1 Dept of CSEE, University of Maryland, Baltimore County, Baltimore, MD 21250, USA2 Dept of CSE, IIT Madras, Chennai - 600 036, INDIA

3 Dept of ECE, Auburn University, Auburn, AL 36849, USA

Email: piyushs1@cs.umbc.edu, skrishnam@cse.iitm.ac.in, pagrawal@eng.auburn.edu

Abstract—Mobile agent based data gathering in Dis-tributed Sensor Networks (DSN) has been a topic ofsignificant research in recent years. In this paper, weconsider a network architecture for DSN that makes useof hybrid nodes with RF and Ultra-wideband (UWB) com-munication. It uses a cluster based approach with reducedconnectivity constraint between cluster heads. Each clusterof sensor nodes (and the corresponding cluster head) willbe working as an independent unit, with mobile agents suchas unmanned aerial vehicles (UAV) or mobile robots used togather the data from the cluster heads. Intra-cluster datagathering will be done using traditional RF communication.Cluster heads will also be equipped with UWB transmittersto transfer data with high rate to mobile agents. Further, wealso propose a multiple mobile agent based data gatheringalgorithm (MMDG) that takes into account the priority ofdata and life time of the cluster heads. Simulation basedanalysis is used to show the effectiveness of algorithm interms of time of tour, network coverage and life-time ofthe cluster heads.

I. INTRODUCTION

A distributed sensor network (DSN) consists of sensor

nodes spread across a region of interest and intercon-

nected via some form of communication network [1].

The current generation sensors’ limited power and com-

munication capabilities lead to fundamental problems

such as bandwidth and operational lifetime of the sensor

network. Narrowband RF-based schemes typically offer

very low data rate of the order of few Kbps and results

in poor performance as data size increases. In such con-

ditions, ultra wide band (UWB) communications which

offers very high data rates becomes a potential choice

for use in wireless sensor networks [2], [3]. In addition

to high data rates, advantages of the in-built localization

and low power consumption of UWB technology make

it attractive for wireless sensor networks.

In a traditional client-server paradigm for data gath-

ering, all sensor information is sent to the central base

station using multi-hop routing. However, this results in

creating hotspots in the network leading to imbalance

of the energy consumption in the network. As a result,

mobility has been explored to reduce the problem. The

use of mobile data gathering agents avoids multihop and

also removes the relaying overhead of nodes near the

base station [4]. Advantages of using mobile agents in

WSN can be summarized as reduced bandwidth, scala-

bility, extensibility, stability and security [5], [6]. In this

paper, we consider an architecture for distributed hybrid

sensor networks that combines mobility and UWB com-

munications. The network consists of small disconnected

clusters, each of which has a resource-rich cluster head.

The network is hybrid since it makes use of few energy

rich nodes and several simple sensor nodes, and because

it uses both narrowband RF radio and UWB radios.

Within each cluster, the sensor data will be sent to cluster

head using narrowband RF communication. To reduce

the overall cost of the network, the clusterheads will be

powered with only UWB transmitters and data will be

collected from them using multiple mobile agents. The

mobile agents are equipped with both narrowband RF

and UWB transceivers. The connectivity among cluster

heads is not required as we no longer need multihop

communication to reach base station. The architecture

also takes advantages of UWB’s localization feature.

We present a data gathering algorithm that utilizes the

multiple mobile nodes. The mobile agents in our problem

could be either unmanned aerial vehicles (UAVs) or

mobile robots, equipped with UWB. The problem is

mapped to the Vehicle Routing problem with Time win-

dow (VRPTW) problem [7].The proposed algorithm uses

a tour construction algorithm followed by use of Tabu

search. It also incorporates life-time based scheduling

for cluster heads by computing priorities that depends

on data and battery status.

The rest of the paper is organized as follows. Sec-

tion II contains the detail study of work done in use of

mobile agents for wireless sensor networks and summary

of various algorithms used for solving VRP problem.

Section III describes our proposed architecture of hybrid

sensors and details out the network model for the same.

The data gathering model containing problem formula-

tion and our proposed algorithm is covered in Section IV.

Section V describes the simulation work and results are

analyzed. Section VI summarizes the paper.

II. RELATED WORK

Recently, various types of mobility have been explored

to solve the data gathering problem in wireless sensor

networks. In [4], mobility is classified into three types

as random, predictable and controlled. The use of data

mules with random walk was first proposed by [8]. Other

work done in the field of random mobility is presented in

[8], [9] where animals or humans were used as agents

and data was collected opportunistically. The problem

with random mobility is that worst-case delay for entire

data gathering cannot be bounded. This can further cause

buffer overflow in the agents and reducing the reliability

of data collection.

Predictable mobility involves a fixed track moving

strategy. The work in [10] involved an access point

mounted on a bus that goes through a preset route and

sensors periodically awake when the bus is close to

them. However, the limitations of such schemes are that

they are not flexible and cannot account for the failures,

change of topology, or different energy constraints of the

sensor nodes.

One variant of controlled mobility tries to combine

the advantages of mobility of sink with multihop routing.

This avoids the hotspot problem and balances the energy

consumption in the network [4], [8]. In another variant,

a single mobile data gathering agent is used, while the

sink and sensor nodes remain static [11]. In this work, a

robot-driven agent explores the network efficiently using

a traveling salesman problem and making use of Voronoi

diagram for selecting pickup locations. Related work on

using mobile data collection agents has been explored in

[12]–[15].

Another way of using mobile agents to solve the data

gathering is use of hierarchical organization of sensor

nodes in the network. Sensors within communication

network form a cluster and cluster heads can commu-

nicate with each other forming a connected graph [16].

A three-tier architecture proposed by [9] also consists of

lower tier sensors, middle tier mobile elements (Mules)

and upper tier of access points. The advantage with such

schemes is that data aggregation can be done effectively,

while avoiding buffer overflows at mobile agents. The

limitations of these kinds of models are larger latency

and scalability.

There are very few studies that investigate the use

of multiple mobile agents. By use of multiple agents,

we can explore the network better than a single agent

[13]. By aggregating the collected data, the mobile

agents can achieve better energy efficiency [17]. In [18],

the problem of network exploration has been worked

upon and a genetic algorithm is used to obtain the

number of mobile agents needed to achieve the desired

frequency/density of visits.

The problem of data gathering using multiple mobile

agents has been mapped to various flavors of the class of

algorithms, which are NP-complete. One way of solving

this problem is Mobile Element Scheduling problem

[14]. Other set of problems in the literature include Trav-

eling salesman problem, Vehicle Routing problem with

time window, Periodic Vehicle Routing problem, Orien-

teering problem and multiple traveling sales person prob-

lem. Of all these, the Vehicle Routing Problem (VRP)

is one of the well-known integer programming problems

in the operational research science community. It falls

into the category of NP-hard problems. Consequently,

heuristic mechanisms dominate the solution techniques

of VRP. As described in [7] and [19], we classify

the heuristic techniques for VRP as Tour Construction

Algorithms, Tour Improvement Algorithms, Two Phase

Algorithms and Meta Heuristics. Most commonly used

include Ant Algorithms, Deterministic Annealing, Ge-

netic Algorithms, Simulated Annealing and Tabu Search.

For the sake of space, the details of these are left out in

this paper.

III. SYSTEM DESIGN

In this section we present our system model consisting

of hybrid sensors and our scheme of using multiple

mobile agents for data gathering.

A. System Architecture

We consider a distributed sensor network architec-

ture similar to the hierarchical heterogeneous network

approach presented in [20]. The resource constrained

sensor nodes called Lite Nodes (LN) will collect the

data and forward it to sophisticated nodes (SN). The

SNs are special nodes with more computation, energy

and communication capabilities. The SNs will essentially

act as clusterheads for the LN nodes that are in its

cluster. The SNs will communicate with the mobile

data gathering agents using high bandwidth Ultra Wide

Band (UWB) communication and with neighboring LNs

using narrowband RF interface. Note that the high-speed

communication can be some suitable technology besides

UWB. The inter-cluster connectivity constraint is slightly

relaxed in our architecture, in that the SNs are not

required to be connected to each other. The data sink

is the base station and has maximum computational

power. Mobile data gathering agents are specialized

nodes mounted on a moving entity such as a robot or

an unmanned aerial vehicle (UAV) whose motion can

be controlled. These nodes are equipped with UWB

transceivers to communicate with both SNs and the base

station. Figure 1 presents an example network structure.

Fig. 1. Network Architecture, showing Lite Nodes, Cluster Heads(SN), Mobile nodes and the base station.

The base station should be able to locate the SN

through single or multiple hops. A SN link may get

disconnected after its position is known to the base

station. The SN then has to remain in contact only with

the local cluster of LN nodes through RF communi-

cation. The mobile agent will be able to approach the

SN with data fed from the base station. This relaxation

in connectivity gives the system better fault tolerance

and its distributed nature. When applying the concept

of mobility of data agents to sensor networks, it is

difficult to make the mobile agent visit every node in

the network, for various reasons. These include terrain

movement limitations, keeping track of every static node

would be an overhead for mobile agent, and increased

MAC collisions and congestion when the mobile agent is

within communication range of a group of sensor nodes.

Hence, it is better to form small clusters (consisting of

an SN and several LNs) such that data is aggregated at

cluster heads before mobile agents reach them. This will

result in saving effective time for data gathering and is

called as local multihops or pre-caching [16], [21]. In

particular for our system model, this will also reduce

the overall cost of the system.

B. Cluster Formation

In this method, we assume that the Lite Nodes (LN)

are randomly scattered over the network. The cluster

formation for our system and SN node placement can

be done using the Hybrid method of Genetic Algorithm

[20], as briefly explained below.

Greedy Algorithm: In the greedy algorithm, we start

by clustering and placing first SN at the cluster center

that can serve maximum LN traffic. Then over all next

iterations, we use k-means clustering algorithm to obtain

the clusters. After every iteration, we try to optimally

place SN such that the traffic limit of the cluster is

maintained.

BILP Algorithm: In this method, we formulate the prob-

lem of SN location without connectivity as a Binary

Integer Linear Programming (BILP) problem. We then

obtain the standard BILP solution called “pure” solution

for it.

Hybrid Algorithm: In this method we use the basic

Genetic Algorithm [22] as a heuristic. In the traditional

GA approach, many solutions are considered and after

each generations the one that are better act as parents

for the next generation solution. The hybrid approach

drastically improves the performance of GA by applying

the solutions obtained in the above two methods as input

to the first generation of GA. This also ensures that

hybrid approach always converges unlike the original

GA.

C. Data transfer

The data transfer from a LN node to the mobile agent

happens in two phases. Mobile agents at the end of tour

will then transfer collected data to base station using

UWB.

1) Communication between LN and SN: Once the

clusters are formed, till the time when mobile agent

arrives at the location of the SN, it can collect data from

within its cluster. This phenomenon is known as pre-

caching. We propose to use directed diffusion protocol

[23] for the communication between the lite nodes (LN)

of the cluster and the SN, which will also be acting as

the cluster head. The communication will be done using

simple RF communication.

2) Communication between SN and mobile agent:

To take the advantages of UWB technology while reduc-

ing the cost of sophisticated nodes (SNs), we propose a

transmit-only sensor node architecture. As discussed in

[11], the sensor nodes with UWB transmitters only will

be much cheaper as compared to UWB transceiver. This

is because the transmitters just have to generate short

pulses intermittently and hence they are simple to imple-

ment and inexpensive. We can afford to have UWB and

RF transceivers with every mobile agent as they will be

fewer in the system and their number can be controlled

in the data gathering model. RF communication will be

used for exchanging control signals between the SN and

mobile agent while data transfer will take place using

UWB signals.

D. Energy Analysis

As described in [21], we can consider the energy

consumption for data gathering in a multi-hop network

against the one with mobile base station. For sending

data from leaf sensor node to base station in a static

network, the energy consumption is the sum of energy

required for transmission of each sender sensor node, en-

ergy required for receptions of these bits by intermediate

nodes and the wake-up cost of nodes. On the other hand,

energy consumed in collecting same data using mobile

base station will include motion cost of base station,

communication cost for data transfer, wake up cost and

energy consumed in writing to flash. It has been shown

that energy used in the network with mobile base station

is lower beyond a particular density.

We can now improve this model in two ways. Firstly,

the base station remains static and the external mobile

agents, which can be easily recharged, will be used. As

such, the motion cost of base station is eliminated. Along

with that, the problem of lower density and writing to

flash due to larger delay and buffer overflows can be

solved by means of using multiple mobile agents. This

will also help in scaling the scheme of mobile agents to

sparse networks deployed over larger areas.

IV. DATA GATHERING MODEL

One of the main problem in current routing protocols

is that they always try to find and use an optimal route

that consumes lowest energy. However, it is identified

that this is not always the best choice for network

lifetime. Energy of the nodes along the optimal path will

deplete quickly if they are used frequently and may lead

to network partition.

Deriving our scheme and parameters from the work

done by [17], we choose the next hop for the mobile

data agent by taking into account both routing cost and

residual energy at the node. By doing so, the mobile

data agents will spread their traffic more evenly and will

lead to energy that is more balanced consumption in the

network as well.

A. Problem Formulation

The problem of designing the routes for the mobile

agent can be formulated as a variant of vehicle routing

problem with time window. However, we remove the

capacity constraint out of the problem as we can assume

that the sophisticated nodes have enough memory to

hold the data and perform aggregation on it. Along with

that, the memory, if under constrained, could be made

as parameter of feedback that determines the next time

window for the mobile agent. We define the problem

formally as follows. Let a graph G(E,V) represent a set

of cluster-heads connected by routes for mobile agent.

The following notations are defined:

• V = {v0, v1, ..., vn} is a vertex set representingbase station v0 and all the cluster heads

• V ′ = V −{v0} represent the set of n cluster heads.• C is the matrix of non-negative costs between vi

and vj

• d is the vector of demands at cluster head

• Ri is the route of mobile agent i

• m is the number of mobile agents

The problem is said to be symmetric if the cost cij =

cji, for all (vi, vj) and in such cases the set of edges is

considered to be an arc set.

Each cluster head has a service time si, a time window

defined by earliest arrival ei and latest departure li. A

mobile agent arriving early may wait with waiting time

wi at cluster head i, equal to the earliest arrival time ei

minus the actual arrival time. Let bi denote the beginning

of service at cluster head. Provided that the mobile agent

travels to the next cluster head as soon as it has finished

service at the current cluster head, bi can be recursively

computed as:

bi = max[ei, bi−1 + si−1 + ci−1,i],

with b0 = e0 and s0 = 0Also, it is required that total duration of any mobile

agent should not surpass its capacity defined by D. Thus,

for a route Ri to be feasible, the following must hold

true:

ei ≤ bi ≤ li

1 ≤ i ≤ m

bm + sm + cm,0 ≤ D

Thus, the waiting time is given as: wi = max[0, bi −bi−1 − si − ci−1,i]. The cost of route Ri is given as:

C(Ri) =m

∑

i=0

ci,i+1 +m

∑

i=1

si +m

∑

i=0

wi

For a solution S with routes R1, R2, . . . , Rm, the cost

of S is given as: F (S) =m

∑

i=0

Ci.

S is said to be feasible if all routes belonging to S

are feasible and its cluster head is visited by exactly one

route. We assume that initially all mobile agents leave the

base station at the earliest possible time. Having obtained

a solution of the VRPTW, we adjust the departure time

of each agent to eliminate any unnecessary waiting time.

B. Proposed Algorithm

To solve the above formulated problem, we propose

to use the following two techniques.

a) Solomon’s Insertion Heuristic: The work in

[24] presents a benchmark problem set for the vehicle

routing problem with time windows and conducts a

computational study of several heuristic algorithms using

this set. In the Insertion Heuristic, two methods are

provided for initializing tours. The first method initially

routes to the farthest customer, and the second method

initially routes to the customer with earliest deadline.

Once the tour is initialized, remaining customers are

inserted until either the vehicle is at maximum capacity

or no other customers can be added without violating

the time window feasibility or vehicle endurance. At

this point, another tour is initialized and the process

continues.

b) Tabu Search: The concept of Tabu Search (TS)

was first introduced by Glover [25]. The basic principle

of TS is to move, at each iteration, from a solution s to

the best solution in a subset of its neighborhood N(s).Cycling back to previously visited solutions is prevented

by the use of memories, called Tabu lists that record the

recent history of the search. Typically, the initial solution

is constructed using some heuristics. After creating an

initial solution, an attempt is made to improve it using

local search with one or more neighborhood structures

and a best-accept strategy. This improves the quality of

solution and the speed of algorithm.

C. Multiple Mobile Agents based Data Gathering

(MMDG) Algorithm

We now present our MMDG algorithm that uses

Solomon’s Insertion heuristic to construct the tour. We

then apply the Tabu Search heuristic with intensification

to get near optimal solution for our defined problem.

c) High Level Algorithm:

1) If not first tour, get the feedback from previous

tour about the cost parameters from the previous

tour.

2) Calculate the cost and time matrix with priorities

for the cluster heads.

3) Set all-cluster heads as unallocated.

4) If there is no cluster head unallocated

then go to step 7

else

start a new tour for UAV m.

5) Repeat insertion of unallocated cluster heads to the

current route R using Solomons heuristic with time

window constraints only until there is no feasible

cluster head.

6) When the tour is full, go to step 4.

7) Add the current solution to a candidate list.

8) Apply the First Best Local Search on the solution.

9) Check for more insertion improvements using

Global Best Local Search.

10) Improve the solution using Global Best Tabu

Search.

11) Find the best solution.

d) Cost and Time Matrix Computation: We pro-

pose to use UWB’s localization capabilities to locate the

cluster heads. Once the location of all the cluster heads is

obtained, the cost matrix can be computed as a measure

of the distance between these cluster heads. The time

matrix for the VRPTW is computed using the feedback

of residual energy and priorities of the data.

e) Solomon’s Insertion Heuristic: Solomon’s inser-

tion heuristic inserts the cluster head u between cluster

heads i and j based on two criteria:

Cs(i(u), u, j(u)) = min C(ip, u, jp+1),

p = 1, 2, ...,m (1)

C(ip, u, jp+1) = α1(diu + duj − µdij) +

α2(bju − bj) (2)

where dij is the distance between cluster heads i and

j, bju is the beginning service time of cluster head j

with cluster head u inserted before it, bj is the beginning

service time of cluster head j without cluster head u

inserted and p is the position, 1 to m, within the current

tour. Parameters α1 and α2 must be positive and sum toone, with parameter µ also being a positive value.

Equation 1 attempts to minimize the cost of inserting

cluster head u into an emerging tour in terms of distance

added (diu + duj − µdij) and delay to the following

cluster head (bju − bj). Parameter µ determines how

much the original distance between cluster heads i and

j has to be subtracted from the distance between i to

cluster head u to cluster head j. Parameters α1 andα2 balance the relative importance of distance and timewindow feasibility. All these three parameters can be set

by the user.

Next, the algorithm chooses which cluster head needs

to be inserted based on the criteria in Equation 2 as

C2(i(u∗), u∗, j(u∗)) = max(λdou−C(i(u), u, j(u)), forall unrouted cluster heads u. This equation inserts the

unassigned cluster head u with the largest ‘savings’

compared to the distance between the cluster head and

the base station. Parameter λ is the multiplier for the

distance. The insertion algorithm is run until the tour is

completed with maximum possible capacity or all cluster

heads have been assigned.

Time window feasibility is always maintained

throughout the algorithm. If a cluster head is inserted

in a tour that is time window feasible, it remains time

window feasible if the insertion does not result in a delay

to the following cluster head. Therefore, when we insert

a new cluster head, we need to only check from the

insertion point until we find a cluster head within time

window or we find a time window violation or we reach

end of tour.

f) First Best Local Search: The first best local

search (FBLS) makes the first improvement by finding

a local optimum. For this algorithm, we consider only

single cluster head insertion moves. A single cluster

head can be removed from one spot in the tour and

inserted into a different spot in the same tour. Only

feasible insertions are considered. The algorithm can be

summarized as below:

1) Compute the improvement for each cluster head.

The cost of gathering data from a cluster head is

the function of two parameters - distance traveled

and wait time. For a cluster head u between two

cluster heads i and j, these parameters can be

calculated as: diu + duj − dij , wiu + wuj − wij ,

where dij is the distance between i and j, wij is

the wait time when j follows i.

Using the above equations, we determine the cost

of gathering from each cluster head starting from

first position in the tour.

2) If the new cost of gathering is less than current

cost, we check for time window and vehicle ca-

pacity.

3) If the move is feasible, we place the cluster head

into the new position and look for the next one.

4) If we cannot find any feasible improvements in the

current tour, we check the next tour.

5) If all tours are checked, return the solution

g) Global Best Local Search: The Global best

local search (GBLS) is similar to FBLS, but attempts

to check for the improvement in moves in the current

neighborhood. Using the equations given above in FBLS,

we evaluate the current cost of gathering the data from

cluster head. We then evaluate the new cost of service

by placing it in some other tour. If the amount saved

is positive and greater then the best savings found so

far, we check the time window and vehicle capacity. If

the move is feasible, we retain the move as the best

found so far. When we have checked all insertion moves

between tours, we make the best-found move. If we do

not find a feasible improvement, we have reached the

local optimum and return the solution.

h) Global Best Tabu Search: As discussed earlier,

the global best Tabu search (GBTS) is an extension

to local search(LS) methods that helps them overcome

the local optima. The algorithm considers the same

single cluster head insertion moves as done above. At

every iteration, all possible moves are evaluated and

the best feasible non-Tabu move is chosen. The best

move in this case may actually be non-improving. The

algorithm continues until 10 iterations are performed

without improving the best solution.

V. PERFORMANCE EVALUATION

This section describes the system design of simulation

model developed for MMDG algorithm. The simulation

model was implemented in native Java.

A. Simulation Model

The system developed was based on the solution

model of [19]. In this system, a solution is represented

in terms of tour where each tour is assigned a mobile

agent and a set of cluster heads to be visited. The

advantage of using such a model is that it not only

captures the standard tour, but it also captures binary

and sub-tour breaking constraints. The priorities and time

window frames for each cluster head are computed after

taking feedback from previous tour. The following are

the assumptions in the simulation model:

1) The distribution of the sensor nodes including

cluster heads over the region of interest is random.

2) The placement of the sophisticated nodes (SN)

is done using the hybrid genetic algorithm as

discussed earlier.

3) The locations of the the cluster heads have been

determined using any UWB-based localization

technique prior to the execution of the MMDG

algorithm.

4) The sensor nodes are spread over an area of 150

X 150 Km; the speed of mobile node (UAV in this

study) was assumed to be 100 knots with no wind

opposition.

B. Evaluation Parameters

To develop a diverse set of initial solutions, we

used various set of parameters in the tour construction

algorithm using Solomon’s insertion heuristics. The pa-

rameter values were derived from [26] and included the

following ranges:

• α1 = 0.0, 0.1, ..., 1.0; α2 = 1 − α1

• λ =

{

1.25, 1.5, . . . , 2.0 if µ = λ − 10.0, 0.5, . . . , 1.5 if µ = 1

These parameters along with two types of initialization

methods will yield us around 176 combinations for initial

solution. Of these combinations, the actual number of

parameters taken will depend on the solution effort taken

for tour construction algorithm.

The system parameters varied were the number of

cluster heads, number of UAVs, and priority of the clus-

ter heads. The performance of the system was evaluated

on the following metrics:

1) Number of Vehicles: is defined as the number

of vehicles that are required to gather the data

from entire network without loss of time window

feasibility.

2) Network Coverage: is defined as the percentage of

the total number of cluster heads from whom data

is collected at the end of the algorithm.

3) Time of Tour: is defined as the time from the

instant the first UAV leaves the base station to the

instant last UAV returns back to the base station.

It also includes the wait time and service time.

C. Performance Results

As discussed above, the first step in evaluating the

system performance was the number of vehicles used

by the algorithm. With a constant time of 10 minutes as

time required to collect data i.e service time for all the

nodes, we evaluated the system with varying number of

cluster heads in the network. The data sets had 30, 60,

100, 150, 200 and 400 cluster heads. The dimensions

of the network were varied as well between 100 x 100

Km to 150 x 150 Km depending on the co-ordinates in

RC1 data set. The demand column was replaced to have

constant value of 10 minutes for all the cluster heads.

With UWB based communication, a data of around

200 MBytes could be transferred between the UAV and

clusterhead (assuming a conservative data channel rate

of 40 Mbps - we realize that we can use higher data

rates for UWB).

Table I presents the results in terms of number of

mobile vehicles needed, for varying number of cluster

heads. In spite of enabling 15 vehicles for entire set

of rounds, we could see from table that the number

of vehicles used by algorithm remained much less than

actual enabled vehicles. Also as expected, with increase

in number of clusterheads, the number of vehicles re-

quired also increases. It could also be observed that

as number of cluster heads increases, the number of

vehicles required does not increase in proportionately.

This indicates that earlier vehicles also get used further

when number of cluster head increases.

To further investigate the distribution of workload

for each vehicle, we carried out the network coverage

test for increasing number of vehicles. This was again

done for 30, 60, 100, 150 and 200 cluster heads. These

results are presented in Figure 2. Fig. 2(a) shows that

with increase in number for vehicles, there is a gradual

increase in the network coverage. This also assures us

of fair load distribution to each of the vehicle and that

vehicle capacity was obeyed. Also, after reaching 100

percent coverage, no more vehicles are used. This shows

that the minimum number of vehicles are getting used

by the system, while still avoiding any loss of data due

to missing time window.

Finally, the system was tested to see the effect of prior-

ities on the length of tour and number of vehicles. For the

first tour, a default priority of 3 is assumed for all cluster

heads. After first round of data gathering, randomly 10

percent of total number of cluster heads were selected

as nodes to die soon. Their lifetime were calculated and

accordingly time-window and higher priorities were set

for such nodes.

As seen in the Figure 2(b), the cost of tour for same

number of cluster heads with different priorities is same

or more than the one with same priority for all cluster

heads. This assures us that to account for cluster head

with higher priorities, the algorithm generates different

routes with slightly higher cost. This will help in pre-

venting the loss of data due from dying nodes.

To further strengthen our claim about priorities being

taken into consideration, we conducted similar exper-

iments with fewer number of vehicles. With varying

priorities between 1 to 5 and having 1 less vehicle than

actually required to complete tour, we ran the algorithm

to figure out the highest skipped priority cluster head.

As shown in the graph, for the network of all sizes, the

highest skipped priority is 4 or 5 as per the data set used.

VI. CONCLUSIONS

This paper proposed an UWB based distributed hybrid

sensor network architecture, with multiple mobile nodes

for data gathering. The problem was formulated as a

Vehicle Routing problem with Time Window (VRPTW).

A solution that combined the use of Solomon’s insertion

in tour construction followed by meta heuristic of Tabu

Search, was presented. This algorithm also takes into

account node life-time and priority of the data. The

simulation results show that algorithm meets the needs

of using minimal number of vehicles for data gathering,

with equitable load distribution on these vehicles and use

of priority based model for attending cluster heads with

critical data or limited life time.

VII. ACKNOWLEDGMENTS

Part of the research was supported by a grant from

Air Force Office of Scientific Research (AFOSR) grant

No. FA9550-06-1-0103.

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(a) Number of UAVs used by MMDG

Number of Cluster

Heads

30 60 100 150 200

Nearly optimalNumber of UAVs

required

5 7 10 12 14

(b) Priority and Non-Priority based MMDG

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0 2 4 6 8 10

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