A transmission model for diffusion based molecular communication nano networks

Molecular communication has been an attractive topic for researchers afterthe introduction of the nanoscale communications. Numerous potential applications of nanonetworks make molecular communication even more appealing.These potential applications range from biomedical applications, such as intelligent drug delivery and health monitoring systems, to military and environmentalapplications such as air pollution monitoring. Nanonetworks is a communication between nanodevices, called nanomachines or molecular machine. A nanomachine can be definedas an artificial device that relies on nanometer-scale components.Molecular communication is one of the types of nanonetworks in which nanomachines are able to sense, calculate, actuate and intercommunicate. Transmission, propagation and reception are the three main stages to provide information exchange between sender and receiver in molecular communication. Diffusion based molecular communication that is an essential type of molecular communication, relies on propagation of molecules between sender and receiver nodes through free diffusion. There are three different phases in diffusion based molecular communication. The first phase is named emission process. In this phase, the transmitter nanomachine or the sender node emits particles into the medium. The second phase is composed of diffusion process. In this part, emitted particles by the transmitter node are subject to Brownian motion. Finally, the last phase, the reception process in which the receiver nanomachine or receiving node absorb or capture all particles within its range. Throughout this thesis, the harvester and receiver nodes have been considered as absorbers that capture particles which are in contact with their range. For the working operating environment is a bounded confined one, no particle escapes from it. Furthermore, particles released by the transmitter node during transmission process, circulate in the bounded operating environment and when a particle reaches the edge or boundary of the working environment, it is bounced back into the working environment. Therefore, the total number of particles within the bounded operating environment remains unchanged along the time. The amount of particles to be released by the transmitter node at a certain time t represents the input or the signal of the model. Afterwards, the signal of the model propagates in the bounded fluidic space via diffusion. In this work, the diffusion of particles is achieved via random walk. Therefore, the propagation of particles between transmitter and receiver nodes is uniquely accomplished via Brownian motion. Harvester nodes have been assigned a limited harvesting capacity that cannot exceed a certain amount of particles in order to investigate their impact upon the signal reception at the receiving node. For example, if the harvesting node reservoir capacity is full, a new particle reaching its range will result to hitting one particle from the harvester reservoir. A particle has a kinetic energy along each axis, which is the half of the product of the absolute temperature and Boltzmann's constant. And also it has a mass and speed or velocity that has been assumed to be unchanged and independent of time. The model is to investigate the impact of the harvester nodes on signal reception at the receiver node. The receiver node measures the concentration within its range, which is subject to the signal strength. This thesis investigates how harvesting nodes can degrade the signal strength of the receiver node in a confined space. The analytic expression of the received signal at the receiver node has been drawn; the effects of the increasing number of harvesting nodes and the harvesting rate have been discussed in this work.Scientists in diffusion based molecular communication may use that analytic expression proposed in this current paper when dealing with nanonetworks composed of more than two receiving nodes. The signal received in terms of concentrations at the receiver node has been approximated using finite difference methods and the accuracy of each numerical scheme (Explicit, Implicit and Crank Nicolson) has been computed and compared to the analytic solutions. The rest of the thesis is organized as follows. The introduction is given in section I. The model description is presented in section II. In section III, the analytic expression that computes the concentration of receiver node in a scenario of multiple harvesters is proposed. The results are discussed in section IV. The section V provides the approximations on the signal received at the receiver nanomachine. Finally, a conclusion has been drawn based on the results obtained at sections IV and V in section VI.