Context-Aware Collaborative Intelligence With Spatio-Temporal In-Sensor-Analytics for Efficient Communication in a Large-Area IoT Testbed

Chatterjee, Baibhab; Seo, Dong-Ho; Chakraborty, S.; Avlani, Shitij; Jiang, Xiaofan; Zhang, Heng; Abdallah, Mustafa; Raghunathan, Nithin; Mousoulis, Charilaos; Shakouri, Ali; Bagchi, Saurabh; Peroulis, Dimitrios; Sen, Shreyas

doi:10.1109/jiot.2020.3036087

Cited by 21 publications

(23 citation statements)

References 35 publications

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“…HDL simulations of ISA in standard 45nm CMOS process resulted in 80 µW power consumption at 100 MHz and a linear increase in computation energy at a rate of ≈2 fJ/bit [5]. The preceding discussion is summarized in Fig.…”

Section: B Theoretical Limits Of Computation and Communication Energymentioning

confidence: 90%

“…A multitude of software techniques have been proposed to prolong the lifetime of battery powered wireless sensor nodes without any energy harvesting modalities. Some of these methods include energy-aware network protocols, dutycycling strategies, redundant placement of nodes, and various in-sensor analytics [5], [6]. A prominent example is an IoT device developed by Intel which implemented multiple energyscavenging techniques like duty cycling to reduce the overall average power, however, duty cycling reduced the overall ontime [7] with an energy/information metric shown in Fig.…”

Section: B Related Workmentioning

confidence: 99%

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EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes with Energy-Information Dynamic Co-Optimization

Avlani¹,

Seo²,

Chatterjee³

et al. 2021

Preprint

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Intensive research on energy harvested sensor nodes with traditional battery powered devices has been driven by the challenges in achieving the stringent design goals of battery lifetime, information accuracy, transmission distance, and cost. This challenge is further amplified by the inherent power intensive nature of long-range communication when sensor networks are required to span vast areas such as agricultural fields and remote terrain. Solar power is a common energy source is wireless sensor nodes, however, it is not reliable due to fluctuations in power stemming from the changing seasons and weather conditions. This paper tackles these issues by presenting a perpetuallypowered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long range communication by dynamically co-optimizing energy consumption and information transfer, termed as Energy-Information Dynamic Co-Optimization (EICO). This energy-information intelligence is achieved by adaptive duty cycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing in-sensor analytics (ISA) to minimize loss of information. This is the first reported sensor node < 35cm 2 in dimension, which is capable of long-range communication over > 1Km at continuous information transfer rates of upto 1 packet/second which is enabled by EICO and ISA.

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Section: B Theoretical Limits Of Computation and Communication Energymentioning

confidence: 90%

Section: B Related Workmentioning

confidence: 99%

EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes with Energy-Information Dynamic Co-Optimization

Avlani¹,

Seo²,

Chatterjee³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The main portion of energy consumption is related to connectivity, typically during the data transmission periods. To decrease data transmission, resource-aware and contextaware solutions are becoming an attractive subject for researchers [137]. • Security and Privacy: LPWAN technologies contain important security and safety vulnerabilities Ṫhese vulnerabilities are exploited by malicious entities and lead to great damages.…”

Section: Discussionmentioning

confidence: 99%

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

et al. 2021

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Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

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“…B. Chatterjee et al To minimize the network energy expenditure, a co-optimization strategy (real-time) has been formulated, achieving total life-cycle maximization for the batteries. The communication–computation energy tradeoffs for a meshed WSN structure are described by Chatterjee et al [ 8 ], which is arranged throughout a college campus2400-acre in size to monitor the water nitrate content and temperature humidity for smart agricultural purposes via multiple sensors. Deng et al develop, implement, and elucidate a hardware platform suitable for nodes of a self-powered WSN and mainly aims to prolong the service life of WSN nodes configured in the field context by offering a system for capturing hybrid energy.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for wireless sensor networks in ginseng field in precision agriculture

Chen

Xie

et al. 2022

PLoS ONE

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In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng.

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Context-Aware Collaborative Intelligence With Spatio-Temporal In-Sensor-Analytics for Efficient Communication in a Large-Area IoT Testbed

Cited by 21 publications

References 35 publications

EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes with Energy-Information Dynamic Co-Optimization

EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes with Energy-Information Dynamic Co-Optimization

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

Algorithm for wireless sensor networks in ginseng field in precision agriculture

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