An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT

Tyagi, Sumarga Kumar Sah; Mukherjee, Amrit; Pokhrel, Shiva Raj; Hiran, Kamal Kant

doi:10.1109/jsen.2020.3020889

Cited by 60 publications

(22 citation statements)

References 19 publications

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“…These methods perform badly when dealing with rapidly changing data. The authors of [9] developed a dual prediction-based data reduction technique.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Ahmed

Garg

Rai³

et al. 2022

Electronics

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For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

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“…These methods perform badly when dealing with rapidly changing data. The authors of [9] developed a dual prediction-based data reduction technique.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Ahmed

Garg

Rai³

et al. 2022

Electronics

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“…In [184], the authors indicated SF has the ability to deliver sustainability and beneficial production, which is founded on inventive technology, such as IoT. Sensor-based SF frameworks to offer an attractive solution for the farmer were presented in [185]. The work in [186] proposed IoT advancements can diminish the expense of labor cost, reduce the human effort and enhance the scale of sensor-based farming system by information assortment from sensors.…”

Section: Iot Based Smart Farming Sensor Systemsmentioning

confidence: 99%

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

2021

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The global economy is now under threat due to the ongoing domestic and international lockdown for COVID-19. Many have already lost their jobs, and businesses have been unstable in the Corona era. Apart from educational institutions, banks, privately owned institutions, and agriculture, there are signs of economic recession in almost all sectors. The roles of modern technology, the Internet of things, and artificial intelligence are undeniable in helping the world achieve economic prosperity in the post-COVID-19 economic downturn. Food production must increase by 60% by 2050 to meet global food security demands in the face of uncertainty such as the COVID-19 pandemic and a growing population. Given COVID 19’s intensity and isolation, improving food production and distribution systems is critical to combating hunger and addressing the double burden of malnutrition. As the world’s population is growing day by day, according to an estimation world’s population reaches 9.6 billion by 2050, so there is a growing need to modify the agriculture methods, technologies so that maximum crops can be attained and human effort can be reduced. The urban smart vertical farming (USVF) is a solution to secure food production, which can be introduced at any adaptive reuse, retrofit, or new buildings in vertical manners. This paper aims to provide a comprehensive review of the concept of USVF using various techniques to enhance productivity as well as its types, topologies, technologies, control systems, social acceptance, and benefits. This review has focused on numerous issues, challenges, and recommendations in the development of the system, vertical farming management, and modern technologies approach.

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“…To this end, there has been an increase in the number of works applying MARL to RRM in different types of wireless systems, e.g., unmanned aerial vehicle (UAV) communication [11], multi-user cellular systems [14], Industry 4.0 device-to-device communication [15], multibeam satellite systems [16], integrated access and backhaul networks [17], non-orthogonal multiple access [18], multi-cell networks [19], and joint scheduling of enhanced mobile broadband and URLLC in 5th generation (5G) systems [20]. Other studies have applied RL to wireless resource allocation in sensor networks for smart agriculture [21], smart ocean federated learning-based IoT networks [15], and distributed antenna systems [22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Dynamic Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information

Adeogun

Berardinelli

2022

Sensors

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In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capacity per inXS while minimizing overhead from intra-subnetwork signaling. Since inXSs are expected to be autonomous, selection decisions are made by each inXS based on its local information without signaling from other inXSs. A multi-agent Q-learning (MAQL) method based on limited sensing information (SI) is then developed, resulting in low intra-subnetwork SI signaling. We further propose a rule-based algorithm termed Q-Heuristics for performing resource selection based on similar limited information as the MAQL method. We perform simulations with a focus on joint channel and transmit power selection. The results indicate that: (1) appropriate settings of Q-learning parameters lead to fast convergence of the MAQL method even with two-level quantization of the SI, and (2) the proposed MAQL approach has significantly better performance and is more robust to sensing and switching delays than the best baseline heuristic. The proposed Q-Heuristic shows similar performance to the baseline greedy method at the 50th percentile of the per-user capacity and slightly better at lower percentiles. The Q-Heuristic method shows high robustness to sensing interval, quantization threshold and switching delay.

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An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT

Cited by 60 publications

References 19 publications

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

Multi-Agent Dynamic Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information

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