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Smart Wireless Sensor Networks

model specifies the topology, i.e. the structure of how the nodes are organized, there are different topologies to a WSN such as square, star, ad-hoc, irregular Piedrahita et al. (2

­ig. . ardware,  pplication ayers and €omplete ‚odel Proposal

6.2.2 Middle Layer
ƒhe middle layer is responsile to attach a WSN with the needed agents for a specific application. ence this layer has two agents that perform control and resources manage.
•‚anager resources  gent (‚ … †t is a specialized moile agent that ta‡es decisions aout controlling resources of memory and power. †t is aware of required charge for an agent performs a tas‡, and denies or admits to eˆecute an agent. ƒhis is an agent that ta‡es decisions ased on a ‰Š† model ‹eorgeff et al. (ŒŒŽ. ‚oreo‘er, it says if a group of tas‡s can e eˆecuted in ‡eeping with the specified hardware.

•Capturing Agent of physical variables (CA): It is a mobile agent that is aware of physical variables according to a specific application. It takes decisions about propagation and transmitting of these variables.
6.2.3 Application Layer
he application layer represents specific study case or application for which the  is going to be deployed. herefore this layer has agents that perform application reuired tasks.
•Coordinator Agent (CoA): It is an agent aware of reuired tasks by a study case so it has a ueue of application tasks. ence  it manages  organi­es and negotiates them  for being e€ecuted by a A successfully. Also  it takes decisions based on a ‚ƒI model.
•asks Agent (A): It is a reactive agent that performs tasks assigned by a CoA  as long as CoA said it had to be.
•ƒeliberative Agent (ƒA): It is a mobile agent that takes decisions based on a ‚ƒI model too. It does not need that a CoA manages  organi­es and negotiates its tasks  it does by its own. Accordingly  it performs a set of tasks to achieve its own goal or a goal established by a „A which it belongs to.
It is a specific treatment for an application multi…agent system  due to not all sensor nodes platforms can perform a rational agent i.e. for a simple application there is a group of A with a CoA that manages and coordinates entire system  and for a comple€ application there is a group of ƒA that interact to achieve a global goal.
6.3 Interaction Process
†irst of all  the CoA(or a ƒA  depending of reuired type agents) starts the process for assign… ing a task  it has the belief that a task needs to be done  it has this belief because there is a tasks list related to the application. Its desire consist of ensure that a task is done successfully by a A. hen  its first intention is to interact with „A and to ask task feasibility.
ow  „A beliefs about its hardware characteristics and charge task  and its desire consist to inform if there are enough resources to do the task  for this reason its intention is reasoning if charge task processing fits on available resources. It informs true or false.
If „A answer is true  CoA second intention is to create an instance of a A  and assign this task. †inally  its last intention is to be sure that the task was done then it asks to A  if it is done and depending on this answer it starts with another task or the same.
In the case of ƒA multi…agent system any ƒA starts the interaction process with agents in the middle layer. „A beliefs about its hardware characteristics and charge on a plan (task group). If „A confirms available resources  the ƒA starts its process  otherwise it waits until get an affirmation from „A.
aking into account above process  we introduce some theoretical formula to determinate global battery discharge (see ‡uation ˆ and ‰) and memory usage (see ‡uation Š and ‹)  for a time period in the simulation.
B(t) = B(t−ˆ) − P(CoA)(MA) − P(TA)L(−ˆ)

and P(MA) are the processing of DA and MA agents and L(t−1) is the plan charge. These tasks and plans are negotiated in a specified order, and constantly repeating.
For Memory usage (M(t)), the formula required to perform or not a task or a plan,
M(t) = M(t−1) − P(CoA)(MA) − P(TA)L(t−1) + P(TA)L(t−)

7. Conclusions and future work
The principles, algorithms and application of Distriuted Artificial ntelligence can e used to optimie a net ork of distriuted  ireless sensors. The Multi­Agent €ystem approach permits ‚€ƒ optimiation using rational agents to get this achie„ement.
t is possile to implement a solution that enales a sensor net ork to eha„e as an intelligent multi­agent system through the proposed model due to it utilies multi­agent systems together  ith layered architecture to facilitate intelligence and simulate any ‚€ƒ, all needed is to kno  the final application,  here the ‚€ƒ is going to e deploy. Also, a layered architecture can pro„ide modularity and structure for a ‚€ƒ system. Moreo„er, proposed model emphasies aout ho  a ‚€ƒ  orks and ho  to make it intelligent.
From a perspecti„e of multi­agents, artificial societies and simulated organiations, a distriuted sensor net ork can e installed in an efficient manner and achie„e the proposed o…ecti„es of taking measures of physical „ariales y itself  ith different types of rational agents that can e reconfigured to fit any kind of application and measures, also to fit the most appropriate strategy to achie„e requirements of physical „ariales monitoring.
Further  ork to do is testing model using a real ‚€ƒ. €ome study cases of multi­agent systems for specific applications are required to do a complete testing. A useful tool to use is the
€olarium €un€†‡T emulator. This emulator makes a„ailale a realistic testing to de„elop and test €un€†‡T de„ices  ithout requiring hard are platform. After this testing finishes, the model could e performed o„er a real ‚€ƒ of €un€†‡T de„ices.
8. Acknowledgments
This  ork presents the results of the researches carried out y ˆDA (Artificial ntelligence ‰esearch Š De„elopment ˆroup) and ˆ‹Œ (€cientific Š ndustrial nstrumentation ‰esearch ˆroup) at the ƒational Žni„ersity of ‹olomia ­ ‹ampus Medellin, as ad„ance of t o research pro…ects co­sponsored y DMŒ (‰esearch Direction of ƒational Žni„ersity of ‹olomia at Medellin ‹ampus) and ‹‡‘‹Œƒ‹A€ (‹olomian nstitute of €cience and Technology) respecti„ely entitled’“ntelligent ”yrid €ystem Model for Monitoring of †hysical „ariales using ‚€ƒ and Multi­Agent €ystems“  ith code ••1••–1 and “De„elopment of a model of intelligent hyrid system for monitoring and remote control of physical „ariales using distriuted  ireless sensor net orks“  ith code ••1••–•–.
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