Chapter 1
Cartography applications
for autonomous sensory
agents
Sarjoun Doumit and Ali Minai
University of Cincinnati, Cincinnati
sdoumit@ececs.uc.edu, aminai@ececs.uc.edu
This paper proposes a coverage scheme for the rapid mapping of an area’s characteristics by a group of mobile and autonomous sensory agents. It is assumed that
the agents utilize the wireless medium for communication, and have limited computational, storage and processing capabilities. These wireless sensory agents collaborate
among each other in order to optimize their coverage tasks and communications. In
this paper, we present a scheme that helps maximize the system’s efficiency through
an adaptive coverage algorithm, where agents share information and collectively build
a spatio-temporal view of the activity patterns of the area. Our scheme is particularly useful in applications where the events of interest exhibit an oscillatory behavior.
Relevant applications include distant scouting and exploratory missions where the size
and number of the scouting agents are crucial to the success of the mission.
1.1
Introduction
1.1.1
Preliminaries
In some scientific explorations the goal is to locate, quantify and analyze the
magnitude of some local events or phenomena, and then keep looking for new
events. This information allows the involved scientists to draw conclusions about
the significance of an area with respect to a certain criteria. For example, the
analysis of the locations and emission rates of geysers (such as the California
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Cartography application for autonomous sensory agents
geysers) warns seismologists about upcoming earthquakes [9]. In another application, NASA [6] scientists are hoping to find life and other bio-sources on the
frozen moon Europa [6]. The success of these missions hinges on first discovering areas of volcanic vents or hot water sources, underneath the frozen oceans
of the planet, since these sources can provide the much needed warmth and nutrients for potential life forms, before sending in specialized underwater robots
to collect life samples. These vents are the result of natural geological forces,
and can exhibit either uniform or nonuniform oscillatory patterns of activity.
Activity is identified by the emission of particles or liquids that becomes intense
for some period of time, and then recedes back to a more tranquil state. In
certain location or spot, the amount of particles emitted during a period of time
is used as a criterion for determining how active or hot that spot is. Identifying
the spatio-temporal pattern of activity of the different hot spots or hs, allows us
to determine the seasonal behavior of the terrain. In other words, which parts
of the terrain are going to be active and at what times. The physical constraints
of the agents ranging from the limited battery supply to limited computational
capability force us to adopt minimalistic and energy-efficient designs. Finally,
the main advantage of using these exploratory scout agents is that they allow
for following specialized agents to come and explore the best area, hence maximizing the quality of valuable scientific information yield vis-à-vis energy and
time spent.
1.1.2
Challenges
The challenges for distant exploratory missions stem mainly from the fact that
no human intervention is possible once the agents are deployed. This requires the
agents to be autonomous, i.e. they must be able to self-organize into network(s),
study the surroundings, discover the hs, relay the information in a timely manner
and collaborate for keeping constant coverage of the area. Another important
challenge is due to the tight physical space available in the transport ship that
imposes restrictions on the physical size of the agents in addition to their possible
deployable number. This minimalist physical design makes the agents resourcechallenged devices, especially in terms of battery, memory, computational and
communicational capabilities. Hence all proposed schemes for these types of applications have to take all these restrictions into consideration for energy efficient
communicational, storage and computational algorithms.
1.2
Related work
There is a lot of work in the area of coverage and map-drawing for sensory
agents, and the literature seems to be divided into two main approaches. The
first approach views the coverage area from a mobility perspective where the
deployment area is divided into a large grid-based framework. The network’s
performance is based on the actual physical location/coverage of all the grid’s
parts. The second approach considers the area in terms of control-based laws
Cartography application for autonomous sensory agents
3
and computational geometry of the spatial structures found in the area, using
techniques such as Voronoi diagrams in order to direct the nodes in the proper
direction. An interesting work is reported in [7] where the authors propose using
Voronoi diagrams and the Delaunay triangulation method in order to configure
the network based on agent locations in the network. Then they define an
algorithm that calculates the maximal breach path, which represents the path
that a target can take and be least covered by the sensor nodes, and another
maximal support path as its contrary where it is most covered by sensor nodes.
In [8] the authors define a mathematical model based on the agent’s sensing
abilities and then display results after using their model on tracking a target
using multiple amount of sensors. They calculate the sensors’ placement versus
the breach and support paths presented in [7]. In [3] the authors present a gridbased analysis of the sensor’s density relationship to the physical area coverage
and how much sensor nodes have their areas covered by other sensor nodes. In [4]
the authors present a dynamic clustering framework for the sensor networks
with emphasis on putting a bound on finding an available path. A random-walk
based mobility model is also presented. In [5] the authors study the effect of
the range of radio transceivers on the general network connectivity and power
consumption. They present an algorithm to determine the minimum required
radio range and study the effects of random mobility on these values. In [10]
a vision-based mobility approach is presented for robotic coverage tasks where
the metrics are the percentage of area covered and distance traveled by the
robot. The mobility is based on a zigzag pattern after the robots subdivide the
area into smaller areas and landmarks. Finally, a good work on coverage and
mobility is presented in [1] where the authors present a mathematical model for
decentralized control laws that seek to coordinate between mobile sensor nodes
covering an area. They define a node/agent sensing model and rely on randomly
generated manifold formations and Voronoi diagrams in order to relocate the
sensor nodes in positions that would give the sensor agent a good coverage and
at the same time avoid agents crossing each other’s paths.
1.3
1.3.1
Outline of the mobility algorithm
Architecture
In our system, ANTS [2] autonomous network of tiny sensors, we consider two
types of agents: Workers and Leaders. Workers are smaller with less computational and battery powers than Leaders. Leaders act as data storage and process the information gathered by their Workers. Workers form clusters around
a Leader, and the Leaders collectively act as a 2nd tier network for the whole
system. The reason for such an architecture is to decrease the load of computation and communication at the Worker level in order to increase the longevity
of the network. In simple comparisons with other systems, this specialization
architecture has proven to increase the life of the network when compared to
other architectures such as flat networks for similar scenarios [2].
4
1.3.2
Cartography application for autonomous sensory agents
Communication
Every Worker in the system defines its coverage area by its sensing radius’ R s,
which, for simulation purposes, is limited to the its coordinate location or spot.
The leader node on the other hand, defines its area of coverage by its radius
(or range) of communication, Rc. As is the common practise for simplification,
we consider both areas to have a square shape rather than a circle. The Leader
has the capability of communicating with all Workers in its cluster. Inter-cluster
and intra-cluster communication occur at the Leader-Leader and Worker-Worker
levels respectively. Leaders use a combination TDMA and FDMA to communicate with cluster members, and CDMA to communicate with other Leaders.
The frequency used by the cluster is determined by every cluster’s Leader after
negotiation with nearby Leaders to avoid interference.
1.3.3
Shingling
After the deployment of all the agents and the establishment of clusters in the
network, every Leader agent collects information about every Worker node in its
cluster including its location coordinates. Then it calculates and broadcasts back
to all its Workers the value of the smallest distance separating any two Workers.
This value is used by each agent to define the sides of a virtual square, centered
at the agent’s location. Every worker plans for its own route, but can broadcast
to its Leader the coordinates for the plan, to receive back the step-by-step route
details.
The agents view the deployment area in terms of a grid where each point
coordinate corresponds to a triplet (N, E, T ). N stands for the value of the
sensed phenomenon, E represents the geological characteristic such as elevation
and terrain type, and its value defines the needed mobility energy and time cost
for this location. Finally, T represents the time stamp when N and E’s values
were valid. The sensed phenomenon could represent a multitude of natural
occurences, so the type of data value could be temperature, pressure, humidity
and or chemical composition. Choosing the smallest distance between any two
agents as the starting virtual square, ensures the creation for the first contiguous
coverage area formed by the fusion of the two closest agents’s coverage/virtual
squares. It also sets a common minimum starting cycle coverage area for all the
agents. This fact helps in the coordination and time synchronization of all the
agents so that the Leader can have a sense of when all the nodes should cover
their respective squares. Time delays resulting from the E factor found in the
terrain is incorporated in future route planning.
Every Worker agent picks a starting location on its virtual square and proceeds to cover every coordinate location in the square following the order found
in its route-coordinates list. At every coordinate location, the agent records the
triplet values. An hs is discovered when the sensed phenomenon’s value at a
location exceeds a certain threshold of normalcy. The time to cover the initial
virtual square will be then used as the standard cycle to discover the phases
and periods of all the other hs’ cycles. The first fused covered area that emerges
Cartography application for autonomous sensory agents
5
would contain at least two Worker agents and these Workers would belong to a
sub-cluster. In subsequent cycles more fused areas will emerge and more subclusters will start to appear and grow and fuse together until the sub-cluster’s
membership becomes the same as the orginal cluster’s. Simulations have shown
us that when this happens, almost the entire target area would have been covered
in its entirety at least once.
Before a Worker starts moving, it first checks first with its memory to see
if the new proposed route has been recently covered by itself or any member
of its sub-cluster. A Worker just needs to store in its memory the contents of
two virtual square’s triplet values, which can be easily stored as a doubly-linked
list data structure. This way a Worker is guaranteed not to cross into its own
recently covered area. Workers communicate with each other and Workers of
the same sub-cluster add another identification variable to their transmissions.
Workers negotiate among each other in order to avoid redundancy by simply
checking if part of the route proposed by the neighbor Worker is found in their
memories. This guarantees a minimum span of one cycle difference before the
same agent returns to the same area. The Workers do transmit their findings
after every cycle to their Leader for storage and spatio-temporal analysis. The
challenge of discovering a varied oscillatory phenomena is to cover the area like
shingles which usually overlap slightly. Once any hs is discovered during a cycle
and its location made known, the T factor is noted, and if subsequently other
Workers pass onto the same location, then the current N state of the location
is noted to see when it was active and when it was not. The Leader node can
then determine at which cycles or seasons it is most likely to be active. Refer
to the following Figure 1.1 for a more illustrative explanation of our algorithm.
(a)
(b)
(c)
Figure 1.1: Mobility pattern of agents of a cluster of 4 agents.
(a) Shows the first mobility coverage with respect to the original agents’
locations, (b) Shows the location of the agents after the first cycle, (c) Shows
the second cycle coverage area .
The planned routes, communication and standard shingling between the
Workers allow for the same sub-cluster members to have an estimate about
the separating distance between them. Hence they can tailor their transmis-
6
Cartography application for autonomous sensory agents
sion variables to accomodate the needed amount of power to transmit efficiently
across to other Workers. Also, since the Leader acts as a time-data repository,
Workers can always query their Leader for information without the need of attaching vast amounts of data to their packets when communicating with each
other. Finally the internal decision mechanism for Workers to decide on the
location of their new starting point and the direction of their new coverage is
based upon the need to go in the direction of newly discovered hs, other shingle
areas and to the borders of the Leader’s Rc area.
1.4
Simulation Results
In our paper, we are considering the Europa scenario, where we assume that
the agents have been deployed on an ocean floor and the sensed phenomenon
is temperature. In this section we will focus on two different cluster sizes for
the same sub-area. In Figure 1.2 we show a (20x20) portion of the (100x100)
locations map, where a 5-member and 15-member cluster are deployed with 30
hidden hotspots. The figures show the coverage progress of each cluster size. In
Figures 1.3 and 1.4 we show average results, for a size 5 and 15 cluster members
regarding the time it takes to discover what percentage of hs and total coverage
as well. Note that only a fraction of these hs is active at any period of time.
Note how the network quickly makes a complete sweeping coverage of the terrain
first.
(a)
(b)
Figure 1.2: Showing clusters of sizes 5 & 15 around leader A, x represent hotspots.
The y-axis & x-axis both range from 30 7→ 70
(a) Shows the coverage progress of a 5-member cluster after a few cycles, (b)
Shows the coverage after few cycles of a 15-member cluster after same few
cycles.
Cartography application for autonomous sensory agents
1.5
7
Conclusion
In this paper, we have described an approach to exploring and mapping an
unknown environment’s events especially when they exhibit oscillatory activity,
and where the agents are resource-challenged. We have provided a real-world
application scenario where these challenges are likely to arise. Our approach is
based upon exploiting a fast overlapping mobility pattern, similar to shingling,
that allows the agents to quickly cover the bulk of the target region area and
also to create a contiguous coverage area. The use of a standardized area for
mobility allows for communication cost reduction, and cyclic pattern discovery
of hotspots. The information gathered by these agents, in addition to the E
factor provides future missions with important information regarding the data
cost and value of different locations at different times for path planning purposes.
Figure 1.3: Showing the time taken v.s. rate of spots and hotspots discovered for a
size 5 cluster.
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Cartography application for autonomous sensory agents
Figure 1.4: Showing the time taken v.s. rate of spots and hotspots discovered for a
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