TY - JOUR
T1 - Data-Driven Dynamic Active Node Selection for Event Localization in IoT Applications - A Case Study of Radiation Localization
AU - Alagha, Ahmed
AU - Singh, Shakti
AU - Mizouni, Rabeb
AU - Ouali, Anis
AU - Otrok, Hadi
N1 - Funding Information:
This work was supported by the Khalifa University Internal Research Fund (KUIRF level 2) under Project 8474000012.
Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - In this paper, the problem of active node selection for localization tasks, on the Internet of Things (IoT) sensing applications, is addressed. 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. Active node 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 sensors active at a given time. Current active node selection schemes prove inefficient when adapted to localization tasks, as they- 1) are usually designed for general monitoring, not localization, 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, we propose a novel Data-driven active node selection approach that- 1) dynamically uses data readings from current active nodes to select future ones, 2) assesses the area coverage achieved by a group of nodes while considering range-free sensors, 3) considers parameters like residual energy, power cost, and data confidence levels in the selection process, and 4) combines group-based and individual-based selection mechanisms to enhance the localization process in terms of time and power consumption. These considerations are integrated into a two-phase active node selection mechanism that uses genetic and greedy algorithms to select optimum groups for localization tasks. The efficacy of the proposed approach is validated through an example of radioactive source localization by using real-life and synthetic datasets, and by comparing the proposed approach to existing benchmarks. The results demonstrate the ability of the proposed approach to performing faster localization at low energy cost, even with a smaller number of active nodes.
AB - In this paper, the problem of active node selection for localization tasks, on the Internet of Things (IoT) sensing applications, is addressed. 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. Active node 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 sensors active at a given time. Current active node selection schemes prove inefficient when adapted to localization tasks, as they- 1) are usually designed for general monitoring, not localization, 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, we propose a novel Data-driven active node selection approach that- 1) dynamically uses data readings from current active nodes to select future ones, 2) assesses the area coverage achieved by a group of nodes while considering range-free sensors, 3) considers parameters like residual energy, power cost, and data confidence levels in the selection process, and 4) combines group-based and individual-based selection mechanisms to enhance the localization process in terms of time and power consumption. These considerations are integrated into a two-phase active node selection mechanism that uses genetic and greedy algorithms to select optimum groups for localization tasks. The efficacy of the proposed approach is validated through an example of radioactive source localization by using real-life and synthetic datasets, and by comparing the proposed approach to existing benchmarks. The results demonstrate the ability of the proposed approach to performing faster localization at low energy cost, even with a smaller number of active nodes.
KW - active node selection
KW - data-driven
KW - IoT
KW - localization
KW - radiation detection
UR - http://www.scopus.com/inward/record.url?scp=85061747568&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2894956
DO - 10.1109/ACCESS.2019.2894956
M3 - Article
AN - SCOPUS:85061747568
SN - 2169-3536
VL - 7
SP - 16168
EP - 16183
JO - IEEE Access
JF - IEEE Access
M1 - 8625410
ER -