Many-Objective Sensor Selection in IoT Systems

Lin, Chun‐Cheng; Deng, Der-Jiunn; Lu, and Liang-Yi

doi:10.1109/mwc.2017.1600409

Cited by 20 publications

(2 citation statements)

References 13 publications

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“…Heuristic approaches were employed in the two algorithms, instead of analytical solutions due to the nature of the IoT device selection problem, which is NP-hard, as demonstrated in other works focused on IoT performance optimization [29]- [32].…”

Section: ) Re-clustering Of Low-performing Objects Algorithmmentioning

confidence: 99%

REMOS-IoT-A Relay and Mobility Scheme for Improved IoT Communication Performance

Simiscuka

Muntean

2021

IEEE Access

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The Internet of Things (IoT) can avail from device-to-device (D2D) communication techniques to increase object data exchange performance. IoT networks aim to offer a massive number of services at high quality levels, and many of the devices providing these services are mobile. Devices such as wearables, sensors, drones and smart vehicles need constant connectivity despite their moving patterns and therefore, an IoT architecture should consider both Quality of Service (QoS) and mobility. D2D allows devices to communicate directly to share content and functionality, such as access to the Internet. This paper proposes REMOS-IoT -A RElay and MObility Scheme for improved IoT communication performance in support of increased QoS for the data exchange services between mobile IoT devices. Simulation-based testing showed how performance of devices increased in several scenarios, demonstrating the efficiency of the proposed architecture and algorithms.

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Section: ) Re-clustering Of Low-performing Objects Algorithmmentioning

confidence: 99%

REMOS-IoT-A Relay and Mobility Scheme for Improved IoT Communication Performance

Simiscuka

Muntean

2021

IEEE Access

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“…Wireless sensor networks (WSN) have been gaining increasing interest in the last few years due to their role in emerging technologies such as Internet of things (IoT). Advanced sensor networks are needed in order to meet the increasing needs of IoT applications, such as automated surveillance, environmental monitoring, smart cities, and so on [3,4,5]. To guarantee a durable autonomous sensor network, sensing nodes should be capable of processing and communicating data with restricted energy harvesting (EH) and consumption budgets.…”

Section: Introductionmentioning

confidence: 99%

Sensor placement and resource allocation for energy harvesting IoT networks

Bushnaq

Chaaban

Chepuri

et al. 2020

Digital Signal Processing

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The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors to the fusion center. In the digital transmission case, an information theoretic approach is used to model the transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the digital system outperforms the analog system with a possibly different sensor selection.Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.

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