Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review

Jawad, Haider Mahmood; Nordin, Rosdiadee; Gharghan, Sadik Kamel; Jawad, Aqeel Mahmood; Ismail, Mahamod

doi:10.3390/s17081781

Cited by 463 publications

(290 citation statements)

References 163 publications

Supporting

Mentioning

274

Contrasting

Unclassified

Order By: Relevance

“…The Internet of Things (IoT) will consist of billions of connected devices that have a large number of applications, such as smart metering, logistics [1], and localization and tracking [2]. Other potential uses of IoT devices include health monitoring [3] and massive sensor networks for smart farming and environmental monitoring [4]. Since these devices will mostly be low-power and they are expected to connect wirelessly with each other (or with centralized gateways), several specialized communications protocols have been proposed for IoT applications.…”

Section: Introductionmentioning

confidence: 99%

On the Error Rate of the LoRa Modulation With Interference

Afisiadis

Cotting

Burg

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

LoRa is a chirp spread-spectrum modulation developed for the Internet of Things. In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an existing interference model, which assumes perfect alignment of the signal of interest and the interference, to the more realistic case where the interfering user is neither chip-nor phase-aligned with the signal of interest and we derive an expression for the error rate. We show that the existing aligned interference model overestimates the effect of interference on the error rate. Moreover, we prove two symmetries in the interfering signal and we derive low-complexity approximate formulas that can significantly reduce the complexity of computing the symbol and frame error rates compared to the complete expression. Finally, we provide numerical simulations to corroborate the theoretical analysis and to verify the accuracy of our proposed approximations. ). layer, which uses an ALOHA-based channel access scheme in which collisions are not explicitly avoided. These collisions lead to same-technology inter-user interference which may ultimately become the capacity-limiting factor in massive IoT scenarios [13]. For this reason, it is of great interest and importance to study the performance of LoRa under sametechnology interference.The authors of [14] present a mathematical network model for LoRa that includes the capture effect, i.e., the fact that a LoRa packet can be correctly decoded even under interference from another LoRa packet. A stochastic geometry framework for modeling the performance of a single gateway LoRa network is used in [15]. An investigation of the latency, collision rate, and throughput for LoRaWAN under duty-cycle restrictions is performed in [16]. Several real-world deployments of LoRa have been tested, but in order to assess the network scalability of LoRaWAN to future network densities that are expected to be orders of magnitude larger, evaluations through network simulators need to be performed. For this reason, the works of [17], [18] added LoRa functionality to the well-known ns-3 network simulator. A simpler Python-based network simulator for the LoRa uplink was first described in [19], and later extended for the LoRa downlink in [20]. The impact of the downlink feedback on LoRa capacity was also studied in [21]. A general overview and performance evaluations of LoRaWAN can be found in [13], [22].The impact of interference coming from different technologies on the performance of the LoRa modulation has received some attention in the literature. Specifically, [23] studies the co-existence of LoRa with IEEE 802.15.4g, while [24] studies the co-existence of LoRa with ultra-narrowband technologies, such as Sigfox. The impact of interference coming from other LoRa nodes has also received some attention. Specifically, the work of [25] extended the simulator of [19] in order to study the impact of imperfect orthogonality between different LoRa spr...

show abstract

Section: Introductionmentioning

confidence: 99%

On the Error Rate of the LoRa Modulation With Interference

Afisiadis

Cotting

Burg

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Sensors are created to work autonomously for prolonged periods of time in months or years after deployment task. It is not easy to recharge or replace the sensors batteries [22]. Therefore, many aspects that affect the energy management should be examined to minimize the energy consumption of the sensor battery.…”

Section: Wireless Sensor Network Challengesmentioning

confidence: 99%

The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

et al. 2018

View full text Add to dashboard Cite

Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.

show abstract

“…They provide a vast amount of online information in the deployment field. However, due to constraints on the signal processing and communication capabilities [1][2][3][4][5] of WSNs, some unusual data (called outliers) may result from sensor malfunction, process disturbances, human-related errors, and/or a sudden change in the state of the environment [6][7][8][9][10]. The outliers might seriously affect the accuracy of data analysis, which leads to model misspecification.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spatial outlier detection in wireless sensor networks

Nguyen

Thai

2019

ETRI Journal

View full text Add to dashboard Cite

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph‐based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state‐of‐the‐art methods in terms of accuracy and work well with various data types.

show abstract

Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review

Cited by 463 publications

References 163 publications

On the Error Rate of the LoRa Modulation With Interference

On the Error Rate of the LoRa Modulation With Interference

The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

Temporal and spatial outlier detection in wireless sensor networks

Contact Info

Product

Resources

About