2,340 research outputs found

    Reference Nodes Selection for Anchor-Free Localization in Wireless Sensor Networks

    Get PDF
    Dizertační práce se zabývá návrhem nového bezkotevního lokalizačního algoritmu sloužícího pro výpočet pozice uzlů v bezdrátových senzorových sítích. Provedené studie ukázaly, že dosavadní bezkotevní lokalizační algoritmy, pracující v paralelním režimu, dosahují malých lokalizačních chyb. Jejich nevýhodou ovšem je, že při sestavení množiny referenčních uzlu spotřebovávají daleko větší množství energie než algoritmy pracující v inkrementálním režimu. Paralelní lokalizační algoritmy využívají pro určení pozice referenční uzly nacházející se na protilehlých hranách bezdrátové sítě. Nový lokalizační algoritmus označený jako BRL (Boundary Recognition aided Localization) je založen na myšlence decentralizovaně detekovat uzly ležící na hranici síti a pouze z této množiny vybrat potřebný počet referenčních uzlu. Pomocí navrženého přístupu lze znažně snížit množství energie spotřebované v průběhu procesu výběru referenčních uzlů v senzorovém poli. Dalším přínosem ke snížení energetických nároku a zároveň zachování nízké lokalizační chyby je využití procesu multilaterace se třemi, eventuálně čtyřmi referenčními body. V rámci práce byly provedeny simulace několika dílčích algoritmu a jejich funkčnost byla ověřena experimentálně v reálné senzorové síti. Navržený algoritmus BRL byl porovnán z hlediska lokalizační chyby a počtu zpracovaných paketů s několika známými lokalizačními algoritmy. Výsledky simulací dokázaly, že navržený algoritmus představuje efektivní řešení pro přesnou a zároveň nízkoenergetickou lokalizaci uzlů v bezdrátových senzorových sítích.The doctoral thesis is focused on a design of a novel anchor free localization algorithm for wireless sensor networks. As introduction, the incremental and concurrent anchor free localization algorithms are presented and their performance is compared. It was found that contemporary anchor free localization algorithms working in the concurrent manner achieve a low localization error, but dissipate signicant energy reserves. A new Boundary Recognition Aided Localization algorithm presented in this thesis is based on an idea to recognize the nodes placed on the boundary of network and thus reduce the number of transmission realized during the reference nodes selection phase of the algorithm. For the position estimation, the algorithm employs the multilateration technique that work eectively with the low number of the reference nodes. Proposed algorithms are tested through the simulations and validated by the real experiment with the wireless sensor network. The novel Boundary Recognition Aided Localization algorithm is compared with the known algorithms in terms of localization error and the communication cost. The results show that the novel algorithm presents powerful solution for the anchor free localization.

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

    Get PDF
    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Target localization in wireless sensor networks for industrial control with selected sensors

    Get PDF
    This paper presents a novel energy-based target localization method in wireless sensor networks with selected sensors. In this method, sensors use Turbo Product Code (TPC) to transmit decisions to the fusion center. TPC can reduce bit error probability if communication channel errors exist. Moreover, in this method, thresholds for the energy-based target localization are designed using a heuristic method. This design method to find thresholds is suitable for uniformly distributed sensors and normally distributed targets. Furthermore, to save sensor energy, a sensor selection method is also presented. Simulation results showed that if sensors used TPC instead of Hamming code to transmit decisions to the fusion center, localization performance could be improved. Furthermore, the sensor selection method used can substantially reduce energy consumption for our target localization method. At the same time, this target localization method with selected sensors also provides satisfactory localization performance

    Resource aware distributed detection and estimation of random events in wireless sensor networks

    Get PDF
    In this dissertation, we develop several resource aware approaches for detection and estimation in wireless sensor networks (WSNs). Tolerating an acceptable degradation from the best achievable performance, we seek more resource efficient solutions than the state-of-the-art methods. We first define a multi-objective optimization problem and find the trade-off solutions between two conflicting objectives for the distributed detection problem in WSNs: minimizing the probability of error and minimizing the total energy consumption. Simulation results show that Pareto-optimal solutions can provide significant energy savings at the cost a slight increase in the probability of error from its minimum achievable value. Having detected the presence of the source, accurate source localization is another important task to be performed by a WSN. The state-of-the-art one-shot location estimation scheme requires simultaneous transmission of all sensor data to the fusion center. We propose an iterative source localization algorithm where a small set of anchor sensors first detect the presence of the source and arrive at a coarse location estimate. Then a number of non-anchor sensors are selected in an iterative manner to refine the location estimate. The iterative localization scheme reduces the communication requirements as compared to the one-shot location estimation while introducing some estimation latency. For sensor selection at each iteration, two metrics are proposed which are derived based on the mutual information (MI) and the posterior Cramer-Rao lower bound (PCRLB) of the location estimate. In terms of computational complexity, the PCRLB-based sensor selection metric is more efficient as compared to the MI-based sensor selection metric, and under the assumption of perfect communication channels between sensors and the fusion center, both sensor selection schemes achieve the similar estimation performance that is the mean squared error of the source location gets very close to the PCRLB of one-shot location estimator within a few iterations. The proposed iterative method is further extended to the case which considers fading on the channels between sensors and the fusion center. Simulation results are presented for the cases when partial or complete channel knowledge are available at the fusion center. We finally consider a heterogenous sensing field and define a distributed parameter estimation problem where the quantization data rate of a sensor is determined as a function of its observation SNR. The inverse of the average Fisher information is then defined as a lower bound on the average PCRLB which is hard to compute. The inverse of the average Fisher information is minimized subject to the total bandwidth and bandwidth utilization constraints and we find the optimal transmission probability of each possible quantization rate. Under stringent bandwidth availability, the proposed scheme outperforms the scheme where the total bandwidth is equally distributed among sensors
    corecore