Funamental of Information Theoritic Approaches

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Running head: FUNDAMENTALS OF INFORMATION THEORETIC APPROACHESBASED ON SENSOR NETWORKSFundamentals of Information theoretic approaches based on sensor networksName of the studentName of the UniversityAuthor note
1FUNDAMENTALS OF INFORMATION THEORITIC APPROACHES BASED ONSENSOR NETWORKSIntroductionIn the modern times, the sensor networks are usually comprised of various numbers ofdevices that are powered using low power communication devices. The capability of the sensorbased network is the main difference between the network and its associated sensors mainly dueto the fact of coordination and working. The utilization of sensor networks must be done inaccordance to the special design based parameters which helps in performing any associatedfunctions through it. This paper will also list the fundamental limits of sensors required fordetecting the environment state with maximum accuracy. Moreover, a discussion of sensorselection capacity is also listed in this paper.Literature surveyThis section of the paper discusses about the associated literature which is related to thetopic. This paper includes a discussion about the sensing capacity measurements and the numberof devices utilized for this process.Fundamental limits of sensor networksThe main involvement of sensor based networks includes various actions like whichdirection to move according to parameters and which inputs to display and perform actionsaccordingly [1]. They are also involved in the transmitting and the reception of waveforms fromthe sources in case they are made active. Utilization of sensor network management is mainlydone to facilitate the sensors available to perform task in a dynamic manner such that it can beused in a variety of applications.
2FUNDAMENTALS OF INFORMATION THEORITIC APPROACHES BASED ONSENSOR NETWORKSA target vector v of the discrete level is used for representing the state of theenvironment. Utilizing a fixed sensor configuration can help in encoding the state of the signal ina similar manner where a sensor with no noise output which forms the codeword associatedwhich is denoted as x. The noise related sensor measurements are termed as y which is thenutilized by the detection algorithm to evaluate the state of the environment vector v.Sensing capacityThis section of the paper discusses about the various frameworks to include for sensordetection. This includes the joint multi-target probability density (JMPD) and the Particle Filter(PF) implementation. The use of the JMPD is mainly involved in capturing the entity pointsrelated in a surveillance based region [2]. This basically involves the uncertainties involved inthe region including the kinematic state, mode and class involved. The involvement of the JMPDis being computed in a recursive manner by using fusing measurements, sensor modeling andtarget modeling. This is mainly termed as the non-linear approach of filtering signals from thesensors and the adoption of the Gaussian assumptions are not adopted in this approach. Thesensing capacity of the networks can be determined by considering the spatial and the temporaldependence of the network.The sample space involved in the JMPD process is very large as it is involved with all theconfigurations possible with the state vectors X (k). For this reason, the utilization of thecomputation tractability can be done by using a sophisticated approach for approximation. Thiscan be done by implementing the Particle Filter implementation algorithm by emphasizing on theadaptive importance density.
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