Dynamic Data-Driven Active Node Selection for Localization Tasks in IoT and Mobile Crowd Sensing Applications

  • Ahmed N. A. Alagha

Student thesis: Master's Thesis


The Internet of Things (IoT) plays a significant role in realizing the concept of smart environments, such as in environmental, infrastructural, industrial, disaster, or threat monitoring. Several IoT sensing nodes can be deployed within an area to collect regional information for the purpose of achieving a common contextual goal. Mobile Crowd Sensing (MCS) is a subset and a critical component of IoT, where nodes are mobile people carrying smart phones equipped with sensors. For both IoT and MCS systems, an influential aspect that affects their performances is the selection of the set of active nodes to perform a certain task. Selection proves useful in mitigating common IoT-related issues like resource allocation, network lifetime, and the confidence in the collected data, by having the right set of nodes active at a given time. Localization tasks represent an important subset of environmental monitoring, where a source of a certain phenomenon is to be localized using data fusion from multiple sensors. The current active node selection schemes prove inefficient when adapted to localization tasks, as they- (1) are not concerned with the context of the data being gathered (2) do not dynamically exploit data readings in the selection process, and (3) are mostly designed for systems with nodes having sensing ranges. To address these challenges, two frameworks for active node selection for localization tasks are proposed. The first framework is designed for IoT systems with immobile sensing nodes, while the second is designed for MCS systems with mobile participants. The selection frameworks are data-driven ones that- (1) dynamically use data readings from current active nodes to select future ones, (2) assess the area coverage achieved by a group of nodes with considerations to range-free sensors, (3) consider parameters like residual energy, power cost, and data confidence levels in the selection process, (4) combine group-based and individual-based selection mechanisms to enhance the localization process in terms of time and cost, and (5) consider the mobility of participants in the case of MCS systems. The efficacy of the proposed approaches is validated through a running example of radioactive source localization by using real-life and synthetic datasets, and by comparing the proposed approaches to widely known benchmarks. The results demonstrate the ability of the proposed approaches to perform faster localization at low cost, even with smaller number of active nodes.
Date of AwardMay 2019
Original languageAmerican English


  • Localization Tasks
  • Internet of Things
  • Active Node Selection
  • Mobile Crowd Sensing
  • Recruitment.

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