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

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.

Localization Context-Aware Models for Wireless Sensor Network

Wireless sensor networks (WSNs) are emerging as the key technology to support the Internet of Things (IoT) and smart objects. Small devices with low energy consumption and limited computing resources have wide use in many applications and different fields. Nodes are deployed randomly without a priori knowledge of their location. However, location context is a fundamental feature necessary to provide a context-aware framework to information gathered from sensors in many services such as intrusion detection, surveillance, geographic routing/forwarding, and coverage area management. Nevertheless, only a little number of nodes called anchors are equipped with localization components, such as Global Positioning System (GPS) chips. Worse still, when sensors are deployed in an indoor environment, GPS serves no purpose. This chapter surveys a variety of state-of-the-art existing localization techniques and compares their characteristics by detailing their applications, strengths, and challenges. The specificities and enhancements of the most popular and effective techniques are as well reported. Besides, current research directions in localization are discussed

Localization Algorithm for Mobile Sensor Nodes Using 3D Space in Wireless Sensor Network

On the problem of wireless sensor network localization; few true three - dimensional (3D) methods have been developed to satisfy the practical needs. In this work we proposed range - based 3D localization algorithm that is accurate, anchor - free, scalable and physical position available. A novel combination of distance and direction measurement techniques introduced to estimate ranges between neighbours. Based on this information local coordinate systems are constructed and then converge to form a global network wide coordinate system ,which finally leads to nodes absolute positions. Simulation results have shown that our algorithm achieves good trade - off between localization percentage and precision

Localization by decreasing the impact of obstacles in wireless sensor networks

In sensor networks ,Localization techniques makes use of small number of reference nodes, whose locations are known in prior, and other nodes estimate their coordinate position from the messages they receive from the anchor nodes. Localization protocol can be divided into two categories: (i) range-based and (ii) range-free protocols. Range-based protocols depend on knowing the distance between the nodes. Where as, range-free protocols consider the contents of message sent from the anchor node to all other sensor node. Previous range-free based localization methods requires at least three anchor nodes ,whose positions already known ,in order to find the position of unknown sensor node and these methods might not guarantee for complete solution and an infeasible case could occur. The convex position estimation method takes the advantage of solving the above problem. Here different approach to solve the localization problem is described. In which it considers a single moving anchor node and each node will have a set of mobile anchor node co-ordinates. Later this algorithm checks for the connectivity between the nodes to formulate the radical constraints and finds the unknown sensor node location. The nodes position obtained using convex position estimation method will have less location error. However, Network with obstacles is most common. Localizing these networks, some nodes may have higher location error. The new method is described to decrease the impact of obstacle, in which nodes near or within the obstacle that fail to get minimum of three anchor node position values get the anchor position set from its neighbor nodes, applies the convex position estimation method and gets localized with better position accuracy. The Convex position estimation method is range-free that solves localization problem when infeasible case occurs and results in better location accuracy