Efficient Event Detection by Collaborative Sensors and Mobile Robots

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Efficient Event Detection by Collaborative Sensors and Mobile Robots Aditya Kumar Gupta, Sandhya Sekhar, and Dharma P. Agrawal OBR Center for Distributed and Mobile Computing, Department of Electrical and Computer Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45221-0030 {guptaay, sekhars, [email protected]} Abstract - This paper focuses on coalescing sensor networks and mobile robots to provide a significant leap forward in technology by maintaining a self-organizing, ad hoc wireless network for environmental monitoring, military applications and rescue operations. Low-power sensor nodes are used to detect an event and guide mobile robots to such locations. These robots are used to transport resources within the network. The symbiosis of two independently powerful spheres leads to the overall efficiency of the network. The goal of this research work is to enable quick and efficient detection of events using sensors and achieve resource transportation using mobile robots.

collaboration between mobile robots and sensor networks is a key factor towards achieving efficient transmission of data, network aggregation, quick detection of events and timely action by robots. This paper is organized as follows. In Section II, we discuss some of the related work. In Section III, we describe possible applications of our scheme. In Section IV we explain our procedure for speedy and efficient transportation of resources within the network. In Section V, we present some simulation results of the prototype system. We conclude in Section VI with some future possibilities.

Keywords: Ad hoc Sensor Networks, Event Detection, Mobile Robots, Multi-Robot Cooperation, Resource Transportation

II. RELATED WORK

I. INTRODUCTION The enhancements in the field of robotics are paving the way for industrial robots to be applied to a wider range of tasks. Advances in materials and technology have made modern robots much smaller, lighter and more precise, which means that there can be more applications of these robots than was previously envisioned. However, harnessing their full efficiency also depends on how accurately they understand their environment. To measure physical parameters of the surroundings, different sensor devices can be applied so that useful information can be procured. Also, to get a global view, each robot needs to retrieve and aggregate information from sensors, while sensors themselves can exchange information using wireless devices. Such radio-interconnected sensors are usually known as Sensor networks [1]. These nodes can sense various physical phenomena such as light, temperature, humidity, chemical vapors or sound. They are deployed in a large quantity, perhaps randomly through aerial drop, covering a large area and constituting a single network. Any sensor node detecting an interesting data, reports it to a predetermined location that receives information. This location is termed a sink. Excellent communication between robots and sensor network is desirable for precise movement and actions. The

Coordination between multiple robots for resource transportation has been explored for quite some time. Transporting various types of resources for different applications like defense, manufacturing process, etc. has been suggested in [2]. In these schemes, time taken to detect an event depends entirely on the trail followed by the robots. Though the path progressively gets better with the use of an ant-type algorithm as in [2] [3], the whole process has to be started anew when the position of the event changes. Suitable algorithms for the detection of event at any point in the network have not been formulated. Another drawback in these systems is that there are no sensors that guide them towards the event. In [4], two robots that are visually guided simultaneously carry resources from place to place. Constant information exchange is mandatory here and use of a single or multiple robots (more than two) has been over looked. In [5], a sensor network is used to guide the motion of a robot, an autonomous helicopter, to an event location. Here, the helicopter acts as a reservoir of sensors, dispersing them while flying above the network and the sensors in turn report events to it. Robots have also been used for deploying and calibrating sensors, detecting and reacting to sensor failures, and maintaining the overall efficiency of the sensor network [6] [7] [8]. A centralized approach where a discrete controller reacts to events sensed by mobile robots has been discussed in [9]. Wireless

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sensors mounted onto mobile robots have been used to sense events in [10] [11]. No work has been done where robots have been used to transport resources to and from event location with sensors guiding their motion. III. COALITION NETWORK In terrains where human ingress is difficult, we use mobile robots to imitate the human’s chore [12]. Typical resource-carrying robots have been depicted in Figure 1. Here we have shown a possible means how a robot could transfer its resources to another. These robots have the capability to carefully transport their contents and transfer their resources to another. Once depleted of their resource, they may get themselves refilled from the sink, which is a local reservoir of resources. The resource in demand could be water or sand (to extinguish fire), oxygen supply, medicines, bullets, clothes or chemicals to neutralize hazardous wastes, etc. Here, the target region that is in need of these resources is called an event location. The robots can also detect the presence of each other as well as sensors and adjust their steering accordingly. Whether it is a sensor or another robot within collision distance, it is considered an obstacle and the robot proceeds in a direction opposite to it. Robots with similar properties that can detect another’s presence with the help of sonar sensors have been described in [13]. The choice of resource is limited only by the carrying capacity of the robots. The nature of the resource depends on the application that the network supports. In rescue operations, sensors could guide the motion of the robots with the help of temperature sensors that sense the body heat of victims in the vicinity; or sound sensors that can perceive sound, such as when one is yelling for help; or motion sensors. The robots could carry resources that provide immediate assistance to the victims. They could also help in efficiently reporting a victim’s location to the sink. In military operations, robots could act as resource carriers and help in speedy transport of the resources requested by the soldiers. Robots could play a major role in defense and military applications,

Fig. 1. Resource Carrying Robots

because human time and life is so precious and robots could be easily used for mundane tasks like resource carrying. Other applications, like fire fighting, can also be envisioned where these robots would be carriers of fire extinguishers. When the sensors trigger an event, robots would move to the location of fire and help extinguish it. In autonomous waste disposal, robots could play a major role because handling these wastes could be hazardous to human beings. Here again, the robots would act as carriers of wastes and dispose it at a dumping area (sink). The only change is that the direction is reversed here; robots would carry wastes from the event to the sink. In other cases, they carry resources from the sink to the event. Hence, we see that there are a number of future applications where sensors and robots could work together. In all these cases, the important factor is that the whole process is self-organizing without any external surveillance. Sensors detect events autonomously and the mobile robots take appropriate actions based on the nature of the event. Coordination between the mobile robots is critical in achieving better resource distribution and information retrieval. IV. NETWORK SCENARIO We introduce a distributed scheme to channel the motion of multiple mobile robots in a given sensor network. In our scheme, there are two different entities – low power sensors and mobile robots, both powered with wireless radio for information exchange. The mobile robots can themselves be used to monitor the network as in [9] [10]. However, they can then detect an event only when it falls within their sensing range and the entire network may not be continuously monitored. Hence, we use sensor nodes in our scheme that continuously sense events and promptly direct a mobile robot to the event location. A. Some Definitions As mentioned earlier, a sink is a predetermined location that is a local reservoir of the network resource. All robots in the network are aware of this location and hence can move towards it from any position in the network taking the shortest possible path. We define two types of robots - Random and Bounded. The difference lies in the nature of their motion although they have the same hardware capabilities. Random Robots: These robots monitor the entire sensor network in a random manner. Bounded Robots: These robots monitor only a specific region of the entire sensor network. Thus, a bounded robot is in charge of event detection in its

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domain. This is done to ensure that there is an upper bound or limit on the event detection time. Another reason for including bounded robots is to guarantee that no region of the network is unattended for a very long time. Figure 2 illustrates this motion of the robots. In this figure, the entire network has been divided into four sub-regions. Each sub-region is monitored by its bounded robot and the random robots move around the entire network. The actual number of sub-regions needed depends on the limit that we set on the event detection time as explained in Section V. As we can see from the figure, no part of the network remains unmonitored for a long time. Further, based on their functionalities, we assign these robots three different roles: Master: The random robot or the bounded robot that first detects an event (with the help of the sensor nodes) assumes the role of a ‘master’. Pal: The robots that the master uses in transporting resources from the sink to the event are termed its pals. These pals help the master in traveling lesser distance towards the sink by offering their resources on its way (to the sink). Slave: The slave robots are used to provide more support to the event location, in the form of more resources. These robots move towards the event location as ordered by the master robot. B. Proposed Scheme We now explain the proposed scheme with reference to rescue operations using illustrative diagrams. Bounded robots monitor their subsection while random ones move about the entire network, both waiting for the occurrence of an event. While there are no events, the robots move around the network at a nominal speed to conserve their energy. When an event is detected, the sensors promptly pass on this

information through one–hop neighbors until it reaches the first robot (bounded or random). This process has been illustrated in Figure 3. As soon as a robot receives information about an event, it becomes the ‘master’ of that event. The master now moves towards the event with an increased speed. Only when an appropriate action has been taken, it reverts to its original lower speed waiting for the next event to occur. The master robot that detected the event also aggregates the data from the sensors that fall within its range and passes it on to the sink. The master now supports this event (the victim) with required resources, like water or oxygen. When the master is depleted of its resources, it moves towards the sink in order to replenish itself. On its journey towards the sink, if the master meets another resource rich random robot, it can get its fill from this robot rather than proceeding to the sink. This new robot termed its ‘pal’ is now depleted of its resource and so proceeds towards the sink. The master returns to the event location to continue its aid to the victim. Again, if the pal meets another resource rich random robot on its way, it gets its fill from this new pal and moves back to its initial position where it met the master. The new pal now proceeds towards the sink, refills itself and returns to its original position. This results in their simultaneous motion as depicted in Figures 4(a) and 4(b). In Figure 4(a), A is the master robot and B its pal. B moves towards the sink and A returns to the event location. In Figure 4(b), C, the pal of B proceeds to the sink while B moves to its original position. Thus, the master and its pals form a line from the event to the sink as shown in Figure 5. The idea behind the formation of a line from the event to the sink is that we anticipate periodic occurrence of the event and the next time the master returns, after serving the event, it finds its pal waiting for it with the required resource. This motion of the master and pals between the event and the sink is similar to assembly line systems that transfer resources between two points using a bucket brigade algorithm [14].

Sensor

Robot

Event

Sensor

Random Robot

Bounded Robot

Fig. 2. Bounded Robots Monitoring Smaller Domains and Collecting Sensor Data

(a) Sensors broadcast event information till it reaches a bounded robot.

(b) A robot moves towards the event as soon as it gets event information

Fig. 3. Event Detection Using Sensors.

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Sink Simultaneous Motion B A

Event (a) Master with one Pal

Sink

use of an example scenario as in Figure 6. Robots A, B, C, D, E, F and G have been used to explain a typical scenario that captures the essence of the scheme. For simulation purpose, we restrict the number of slave robots required to four. Their positions are on a concentric circle around the master. In Figure 6(a), C detects D and sends it towards A. In Figure 6(b), B detects E. In Figure 6(c), robot E detects F and sends it towards A. In Figure 6(d), there are 4 slaves around the master to assist it, after D detected G and sent it towards A. Figure 6(d) is a typical scenario in the

Simultaneous Motion C

D

B

Original position of B

A

C

B

A

Event (b) Master with two Pals

(a) C sends D to Master

A: Master, B: Pal (of A), C: Pal (of B) Fig. 4. Simultaneous Motion of Robots

C. Tasks Allocation

D

We can further enhance the basic algorithm so that the overall efficiency of the network can be increased. While the master and pals wait in their positions along the line shown in Figure 5, they are constantly on the lookout for resource rich random robots that may fall within their communication range. Once detected, these robots position themselves on a circle around the master robot and augment the support to the event location. The slave robots can also detect other robots that fall within their range. These new robots are also the slaves of the master robot and position themselves on the circle around the master. This is better illustrated with the

B C A

E

(b) B sends E to Master

D A B C E

Sink

F

C (Pal) (c) E sends F to Master

B (Pal) G

D A

Line Formation

B

A (Master)

C E

Event (d ) Master with 4 Slaves Fig. 5. Line Formation from Event to Sink

Fig. 6. Overall Scheme - Master, Pals, Slaves

F

5

1

network where the full potential of scheme is utilized. Here, B and C are Pals, D, E, F and G are slave robots. After expending their energy, the slaves move towards the sink to replenish themselves of the resource. On their way to the sink, if they find other resource rich robots, they get their fill from these robots. Once refilled, they return to their original position around the master robot to continue their support to the event location. Figures 7(a), 7(b) and 7(c) describe the chief functionalities of a master, pal and slave robot. From these figures, it is evident that any robot has the potential to assume any role since all robots have the same hardware requirements. They just execute different algorithms based on their chosen roles. Some differences between slaves and pals have been discussed in Table I.

Start Refill from Pal 2

Monitor the area

Take a step towards Original Position

Detected by Master ?

No

Another Robot in range? No

Yes Provide resource

Send towards master Gather Information; Store current position as Original Position No Reached Original Position? Take a step towards Sink No

They provide their resources to the master

Check for master and/or other robots

Reached Sink?

Yes

Make it a Pal

TABLE I PALS vs SLAVES

Yes

No

Another Robot in range? Yes

Pals Pals help in line formation

Yes

Give readings to Sink; Refill

2

1

Slaves Slaves help in concentric circle formation They provide their resources directly to the event location

(b) Flowchart for Pal.

Start Take a step towards the position given by Master No

1 2

Start

Reached Position ?

Refill from Pal Yes

2

Monitor the area

Take a step towards Event

Check for data and/or other robot

No

Have data ? Event notified by sensors?

Another Robot in range?

No

No

Another robot in range?

Yes

Yes

Yes Send to Master

Yes No

Provide resource

Make it a Slave; Give position

No

Gather Information

Refill from the robot

1

Provide resource

Gather Information; Store current position as Original Position

Take a step towards Original Position

Take a step towards Sink

No

Reached Event?

2

Take a step towards Sink No

Another Robot in range?

No

Yes

Reached Sink?

Yes

Check for sensor readings and/or other robots

Reached Sink?

No

Another Robot in range?

Yes

Make it a Pal Give readings to Sink; Refill

Yes 2

Give readings to Sink; Refill

1

(c) Flowchart for Slave (a) Flowchart for Master Fig. 7. Flowcharts for Master, Pal and Slave

Yes 1

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In our scheme, bounded robots do not leave their domain and so do not perform the role of pals or slaves in order to ascertain that all parts of the network are monitored continuously. This is important because occurrence of simultaneous or multiple events can then be handled effectively. This is also the reason why we do not flood the entire network in search of random robots and instead direct only those that fall within the communication range of the master and pals to support the event. We have not simulated instances of multiple events in our current work, but it will definitely be a future enhancement to the scheme. The contribution of this paper lies in the reduction of time taken for event detection with the help of sensor nodes that guide mobile robots to the event, formation of a line by these mobile robots to help in speedy transfer of resource from the sink to the event and including slave robots to increase resource concentration to the event location. V. SIMULATION RESULTS To model this scheme, we used a simulator called MobotSim 2.1. Two hundred sensors were simulated over a total area of 20m x 20m. At first, we determine an optimum size of the sub-region monitored by bounded robots. The purpose of having bounded robots is to detect an event (within its domain) at the earliest. In our scheme, we fix an upper limit of 1 minute for event detection, i.e. any event is detected within a minute after its occurrence. In Figure 8, bounded regions of various sizes are analyzed and a graph is plotted with the area of the region against time taken for event detection. From, the graph we see that time taken for areas of size 36 sq. m and 49 sq. m are well over a minute and so do not suit our purpose. Our obvious choice is a bounded region of area 25 sq. m because this would need fewer bounded robots than an area of 16 sq. m and hence lesser hardware costs. The graph gives the upper limit on the time taken for event detection. Each reading in all the graphs is an average of 50 individual readings. Thus, the size of the bounded region is a user-controlled parameter that can be varied according to the event detection time required. For all further results, this value of bounded region has been used. Table II summarizes the network parameters used in our simulation. Here, the number of bounded robots can be calculated as: Total network area: 20m x 20m = 400 m2 Area covered by each bounded robot = 25m2 Number of bounded robots needed = 400/25 = 16

Fig. 8. Bounded Detection Time

The following graphs emphasize the effectiveness of our scheme, although we are unable to compare our proposed scheme with any existing system because there is no suitable benchmark for the same. Figure 9 shows the time taken to report an event to the sink and return to the event location, once the event has been detected. This does not take into account the time taken for detecting an event. The X-axis gives the distance between the event and the sink in meters and the Y-axis gives the corresponding time to cover that distance in minutes. The first curve gives the time the master takes to report the event to the sink, refill itself and return to the event location, when it finds no pals on its way. The second curve depicts the time taken when the master meets one pal on its way to the sink. The graph clearly shows that the average time taken to report to the sink almost halves. This shows the advantage of pals and the efficiency of the scheme. The last curve shows the case where the master detects a robot, which in turn detects another one. It is seen that the average reporting time for this case is TABLE II NETWORK SIMULATION PARAMETERS Simulator

MobotSim 2.1

Network area

20m x 20m

Bounded Sub-region

5m x 5m

No. of Bounded Robots

16

Event detection time

1.0 min (Upper limit)

Concentric Circle positions

4

No. of sensors in network

200

Collision range

0.5m

Communication range

3m

Low-power speed

0.25 m/s

Increased speed

0.5 m/s

7

1 Pal

2 Pals

3 Pals

2.5

Time (min)

2

1.5

1 Robot 1

3Robots 0.5

2 Robots 0 0

5

10

15

20

Distance Between Event and Sink (m)

Fig. 9. Network Reporting Time

further reduced. Thus the event is reported faster depending on the number of pals the master detects. The simultaneous motion of these robots ensures faster communication compared to a single robot performing the entire task. Figure 10 shows the total in-network processing time. It includes the time taken for detecting an event, reporting it to the sink and returning to the event location. This combines the results of Figures 8 and 9. This gives us an overall picture of the effectiveness of our scheme. We notice that the time taken for reporting an event gradually decreases proportionate to the total number of robots in the network. We vary the number of random robots in the network and find the time taken to report an event to the sink. The first curve shows the time taken when the number of random robots is five. We observe that there is a trough in this curve for x = 15. This is because here the master detected a pal on its way to the sink. The time taken at the point of the trough is less because of the simultaneous motion of the master and the pal. Next we plot the curve for seven random robots. Here there are a couple of valleys in the curve as compared to the first one. This signifies that the master finds pals on more occasions here than in the previous case. The curve for ten random robots lies well below that for five and seven robots. Further, the curve for thirteen random robots clearly shows that the master finds more pals on an average here and hence smaller reporting time. Determining an optimal number of random robots could be an interesting future work to consider. Figure 11 gives an estimate of the number of slave robots that the master and its pals detect on their way to the sink. Slave robots found in this manner form a concentric circle around the event to assist the master. We see that the probability of

finding slaves increases with increase in the number of robots in the system. The number of slave robots on the concentric circle has been limited to 4 for simulation purpose. Table III summarizes the data for this graph. Consider the reading for the case where the total number of random robots is five. We have plotted readings for 13 different cases and the random robots found four slaves in three out of the total 13 simulations. Four simulations found three slaves and four more found two slaves and so on. Two inferences can be drawn from this graph. In case of 13 robots in the network, four slaves are found in all 13 cases. With increasing distance between the event and sink, the probability of finding a slave increases too. But the location of the event and sink are parameters that we cannot control. However, we also notice that the probability of finding slaves increases with increasing number of random robots in the system. Thus, by controlling the number of random robots in the system we can increase the efficiency of the scheme.

3

Robo7

Robo10

R obo5

R obo13

2.5

7 Robots 2

Time (min)

3

1.5

5 Robots 1

13 Robots 0.5

10 Robots 0 0

2

4

6

8

10

12

14

16

18

Distance B/W sink and source (m)

Fig. 10. Total In-network Processing Time

TABLE III SIMULATION READINGS FOR FIGURE 10 Random Robots 13 10 7 5

Slaves 4 13 10 7 3

Slaves 3 0 3 5 4

Slaves 2 0 0 0 4

Slaves 1 0 0 1 2

8

[6]

5

5 robots

7 robots

10 robots

13 robots

[7]

4

No of Slaves

13 Robots 10 Robots

[8]

3

2

5 Robots

7 Robots

[9] 1

0 0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

Distance B/w Source and Sink (m )

[10]

Fig. 11. Task Division

VI. CONCLUSION Initial results show that coalition of robots and wireless sensor network has a potential to achieve unprecedented levels of system independence. Yet, there is much scope for enrichment in this arena. Crafting an innovative method of dispersing data within a sensor network can advance this scheme. We intend on extending the simulations to predict the number of the random robots needed for a given area of the network. We have restricted ourselves to a regular shape of the network while we could extend our work to irregular ones as well. We could also work on a predetermined motion (rather than a random one) that would enhance the performance of the mobile robots. Handling simultaneous events is also a possible improvement to the scheme. Finally, finding a suitable benchmark for scaling the effectiveness of our scheme would also be a fruitful undertaking. REFERENCES [1] [2]

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[5]

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