Received Signal Strength for Randomly Distributed Molecular Nanonodes

We consider nanonodes randomly distributed in a circular area and characterize the received signal strength when a pair of these nodes employ molecular communication. Two communication methods are investigated, namely free diffusion and diffusion with drift. Since the nodes are randomly distributed, the distance between them can be represented as a random variable, which results in a stochastic process representation of the received signal strength. We derive the probability density function of this process for both molecular communication methods. Specifically for the case of free diffusion we also derive the cumulative distribution function, which can be used to derive transmission success probabilities. The presented work constitutes a first step towards the characterization of the signal to noise ratio in the considered setting for a number of molecular communication methods.Comment: 6 pages, 6 figures, Nanocom 2017 conferenc

Indoor Localisation Using Received Signal Strength

Inimeste lokaliseerimine siseruumides on viimase aastakümne jooksulkõvasti populaarsust kogunud. Selleks on välja pakutud ning testitud erinevaid viise.Käesolev töö proovib ennustada inimese asukohta SPHERE testmajas. SPHERE ontervisehoiuga seotud projekt, mille eesmärgiks on kasutada sensortehnoloogiat, et näiteks varakult tuvastada erinevaid haiguseid, jälgides inimese tegevusi tema kodus. Täpne inimese asukoha määramine võib selleks olulist infot anda. Lokaliseerimiseks kasutatakse siin töös vastuvõetud signaali tugevuse (ingl received signal strength indicator - RSSI) väärtuseid fikseeritud vastuvõtjate ja mobiilse sensori vahel. Selleks kasutatakse kahte masinõppe meetodit: peidetud Markovi ahelaid (ingl hidden Markov model - HMM) ning k-lähima naabri (ingl k-nearest neighbor - k-NN) algoritmi. Antakse ka detailne ülevaade mõlema meetodi implementatsiooni protsessist kasutades SPHERE andmestikku. Viimaseks esitatakse mõlema meetodiga saadud tulemused ning võrreldakse neid. Leiti, et k-NNi võimekus peale tunnuste eeltöötlemist oli oodatust parem, saavutades ruumisiseseid täpsusi 90% ümber. Esialgne HMMi võimekus oli sarnane k-NNi omaga, kuid meie pakutud HMMi muudatustega suudeti viimaks saavutada täpsuseid kuni 96%.Indoor localisation of people has gained a lot of interest during the last decade. Different approaches have been proposed and tested in various environments. This thesis tries to predict a person’s location in the SPHERE testing house. SPHERE is a project with an aim to use sensor technology for healthcare, such as early diagnosis of different illnesses by monitoring person’s activity in their homes. Accurate localisation of the person can provide useful information for this purpose. We use the received signal strength indicator (RSSI) values between the receivers with fixed positions and one mobile node to perform the localisation. For this we use two machine learning methods: hidden Markov models (HMMs) and k-nearest neighbors algorithm (k-NN). A detaileddescription of the implementation process of both models used on the SPEHRE dataset is also given. Finally, we provide the results and the comparison of both approaches.We found that after feature pre-processing, the k-NN performed surprisingly well by achieving room-level accuracies around 90%. The initial performance of the HMM was found to be similar to k-NN’s but with our modifications to the HMM, we finally achieved accuracies up to 96%

Parametric Estimation of Handoff

The efficiency of wireless technology depends upon the seamless connectivity to the user at anywhere any time.Heterogeneous wireless networks are an integration of different networks with diversified technologies. The most essential requirement for Seamless vertical handover is that the received signal strength should always be healthy. Mobile device enabled with multiple wireless technologies makes it possible to maintain seamless connectivity in highly dynamic environment.Since the available bandwidth is limited and the number of users is growing rapidly, it is a real challenge to maintain the received signal strength in a healthy stage.In this work, the proposed, cost effective parametric estimation for vertical handover shows that the received signal strength maintains a healthy level by considering the novel concept.Comment: 5 Pages,3 figures, NCCCS-12,ISBN:978-1-4673-2837-

Real-time localization using received signal strength

Locating and tracking assets in an indoor environment is a fundamental requirement for several applications which include for instance network enabled manufacturing. However, translating time of flight-based GPS technique for indoor solutions has proven very costly and inaccurate primarily due to the need for high resolution clocks and the non-availability of reliable line of sight condition between the transmitter and receiver. In this dissertation, localization and tracking of wireless devices using radio signal strength (RSS) measurements in an indoor environment is undertaken. This dissertation is presented in the form of five papers. The first two papers deal with localization and placement of receivers using a range-based method where the Friis transmission equation is used to relate the variation of the power with radial distance separation between the transmitter and receiver. The third paper introduces the cross correlation based localization methodology. Additionally, this paper also presents localization of passive RFID tags operating at 13.56MHz frequency or less by measuring the cross-correlation in multipath noise from the backscattered signals. The fourth paper extends the cross-correlation based localization algorithm to wireless devices operating at 2.4GHz by exploiting shadow fading cross-correlation. The final paper explores the placement of receivers in the target environment to ensure certain level of localization accuracy under cross-correlation based method. The effectiveness of our localization methodology is demonstrated experimentally by using IEEE 802.15.4 radios operating in fading noise rich environment such as an indoor mall and in a laboratory facility of Missouri University of Science and Technology. Analytical performance guarantees are also included for these methods in the dissertation --Abstract, page iv

Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network

Device-free localization (DFL) based on the received signal strength (RSS) measurements of radio frequency (RF)links is the method using RSS variation due to the presence of the target to localize the target without attaching any device. The majority of DFL methods utilize the fact the link will experience great attenuation when obstructed. Thus that localization accuracy depends on the model which describes the relationship between RSS loss caused by obstruction and the position of the target. The existing models is too rough to explain some phenomenon observed in the experiment measurements. In this paper, we propose a new model based on diffraction theory in which the target is modeled as a cylinder instead of a point mass. The proposed model can will greatly fits the experiment measurements and well explain the cases like link crossing and walking along the link line. Because the measurement model is nonlinear, particle filtering tracing is used to recursively give the approximate Bayesian estimation of the position. The posterior Cramer-Rao lower bound (PCRLB) of proposed tracking method is also derived. The results of field experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that the tracking error of proposed model is improved by at least 36 percent in the single target case and 25 percent in the two targets case compared to other models.Comment: This paper has been withdrawn by the author due to some mistake

Received signal strength indication for movement detection

© 2015 IPSJ. Wireless networks are spreading continuously, filling our homes and the world around us. By using a ZigBee network we will show that a person can be detected by analyzing the fluctuations of signal strength inside the network. The simplicity of our approach means that it could be extended to all wireless networks. This work shows both implications on privacy as well as promising advances in fields like home automation and smart devices by localizing people as they go about their daily lives