September technical meeting slovenia september 22 25 2008
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Networking for Communications Challenged Communities: Architecture, Test Beds and Innovative Alliances Grant Agreement: 223994. September Technical Meeting Slovenia September 22-25 2008. Session: Application 2. Animal Tracking Santiago Zazo – Universidad Politécnica de Madrid

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September Technical Meeting Slovenia September 22-25 2008

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September technical meeting slovenia september 22 25 2008

Networking for Communications Challenged Communities: Architecture, Test Beds and Innovative AlliancesGrant Agreement: 223994

September Technical MeetingSlovenia September 22-25 2008

Session: Application 2. Animal Tracking

Santiago Zazo – Universidad Politécnica de Madrid

[email protected]



Stages of this research line


Tasks Walk-through

Preliminary considerations

Software simulator

HW implementations

Real trials, presentation









Release 1

First HW





Release 2

Second HW





with animals

(few equip.)






D6.4 (Month 24): Mesh Networking for mobile ultra low power radio links

D6.5 (Month 30): Update of D6.4 based on experience from field tests

M6.4 (Month 24): Prototype of the lower layers of the ultra low power radio system

Preliminary considerations 1

Preliminary considerations (1)

Definition of potential scenarios

Summer group: 15000 animals, 80x150 Km2.

Winter group: 3000 animals, 20x50 Km2

Related information

Animal movement models: groups of around 10 animals, 1- 2 Km/h, interchange of animals among the groups when crossing.

Unkown information

Acceptable density of hot spots in the area and in the perimeter.

Description of the movement of the head (for the kinetic generator)

Expected (desired) features of the application

Number of times (per day) the information must be retrieved by the application

Accuracy: in the position and in the number of animals that are tracked

Mean time between batteries replacement

Performance of other existing solutions

Consumption, accuracy, cost per unit, autonomy…

Preliminary considerations 2

Preliminary considerations (2)

Definition of the nodes participating in the network

Secondary nodes: simplest equipment carried by most of the animals. They use a kinetic generator (no batteries required!!) to transmit their identification randomly.

Primary nodes: more complex equipment carried by some selected animals with GPS that listen to the secondary nodes and merge identifications and location information. From time to time (depending on proximity) they upload this information to the base stations.

Base stations (hot-spots) spread over the surveillance area and perimeter to collect positioning information. Could be fixed (portable) or mobile (maybe we can use the term nomadic station to emphasize this fact…). They may be equipped with two transceivers: one to listen to the primary nodes and other to communicate with other hot-spots or control center in much longer links.

Preliminary considerations 3

Preliminary considerations (3)

Combined functionality

Primary and secondary nodes designed by UPM following minimum consumption principles.

Base stations are nodes of a hybrid network forwarding DTN messages and positioning information via longer links (WiFi, WiMAX…). An interface must be agreed







Animal Tracking

DTN Application

Mobile nodes

Base Station

UPM responsibility

INTEL / TCD / IPN responsibility

Preliminary considerations 4

Preliminary considerations (4)

HW design primary constraint: optimize the chips selection and connections to minimize consumption although with enough capacity to guarantee the expected performance.

SW design: ad-hoc programing of the electronic devices (mprocessor,…) and specific communication protocol to minimize transmitted power and messages transmission from the animal to the hot spot through the network nodes.

Integration of animal tracking application and dtn networks

Integration of animal tracking application and DTN networks


Software simulator 1

Software simulator(1)

It will be the key tool to select the set of optimum parameters to minimize consumption while maximizing connectivity

Software simulator 2

Software simulator (2)

Objective: define ONE representative scenario to obtain the optimum set of parameters

Inputs: Number of animals, density of hot spots in the area and in the perimeter, dimensions of the area, radius of coverage of the link secondary to primary, radius of coverage of the primary to the base station, mean number of times the secondary node transmits its identification.

Outputs: secondary to primary nodes ratio, period where the primary node is active for listening, period of activation of GPS, interval of transmission of the primary node to the base station.

This design will determine the characteristics of the HW: size of memory, expected consumption…

Choice of the scenario: 200 animals, 10x10 Km2. Different terrains characteristics. Design criteria will be to determine the optimum density of hot-spots depending on tracking capabilities, positioning accuracy…

Hw real tests presentation and data privacy

HW, real tests, presentation and data privacy

HW first release for design validation. It will be defined a set of controlled situations to assess the performance. Very important to test the efficiency of the mechanical part (ad-hoc design for the movement of the head of the animal..)

HW second release. Few miniaturized prototypes will be delivered to be included in a bag or collar for some scaled tests using real animals. Ex: 1Km2, 2 primary nodes, 10 secondary nodes, 4 cornered hot-spots (links between the hot-spots will not be considered).

A presentation tool will be developed to make sure that every owner just has information of its animals. This information will be presented in a graphical way (tracks) and with distressing situations detection (animal not detected in days, animals that crossed the perimeter…)

Criteria to measure success

Criteria to measure success

Selection of the most suitable set of parameters is an optimization problem. Success is related to the implementation of the simulation tool and its capability to manage large number of variables.

HW implementation success will be related to measured performance and comparison with the simulation tool prediction

Fair comparison with existing HWs in terms of accuracy, consumption, manufacturing cost…

Enviromental related achivements: avoidance of using thousands of batteries…

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