Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Fariborz Ahmadi
 -- 1859 2024-01-18 23:21:22 |
2 format correct
Catherine Yang
 Meta information modification 1859 2024-01-19 01:52:19 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Ahmadi, F.; Abedi, O.; Emadi, S. Enhancing Smart Agriculture Monitoring. Encyclopedia. Available online: https://encyclopedia.pub/entry/54071 (accessed on 01 May 2024).
Ahmadi F, Abedi O, Emadi S. Enhancing Smart Agriculture Monitoring. Encyclopedia. Available at: https://encyclopedia.pub/entry/54071. Accessed May 01, 2024.
Ahmadi, Fariborz, Omid Abedi, Sima Emadi. "Enhancing Smart Agriculture Monitoring" Encyclopedia, https://encyclopedia.pub/entry/54071 (accessed May 01, 2024).
Ahmadi, F., Abedi, O., & Emadi, S. (2024, January 18). Enhancing Smart Agriculture Monitoring. In Encyclopedia. https://encyclopedia.pub/entry/54071
Ahmadi, Fariborz, et al. "Enhancing Smart Agriculture Monitoring." Encyclopedia. Web. 18 January, 2024.
Enhancing Smart Agriculture Monitoring
Edit
This entry is adapted from the peer-reviewed paper 10.3390/inventions9010010

The evolution of agriculture towards a modern, intelligent system is crucial for achieving sustainable development and ensuring food security. In this context, leveraging the Internet of Things (IoT) stands as a pivotal strategy to enhance both crop quantity and quality while effectively managing natural resources such as water and fertilizer.

smart agriculture remote sensing IoT-based agriculture dynamic clustering connectivity restoration

1. Introduction

Remote sensing plays a vital role in smart agriculture. Using sensors, it collects information about soil conditions, weather conditions, humidity, and crop health, and contributes to food security and sustainable development [1][2]. Smart agriculture uses a series of equipment, such as agricultural sensors, actuators, and drones, which are connected through wireless communication. WSN is the most significant component in smart agriculture, and is used in soil analysis, weather monitoring, determining yield productivity, the early detection of disease, and crop monitoring. Figure 1 shows an example of this architecture. In these systems, agricultural sensors should cover the entire environment during network activity to fully monitor crops. Thus, network connectivity is the primary challenge in this area. Each node failure can cause the disconnection of a series of other sensors from the network, so fault tolerance in WSN-based smart agriculture is a solution for high network connectivity. Additionally, other significant challenges, such as data traffic unbalancing, energy consumption, node damping near sinks, and environment factors lead to failure in these networks [3]. All the above challenges regarding high network connectivity should be solved so they do not negatively affect the efficiency of the smart agriculture system.
Figure 1. Smart agriculture based on heterogenous wireless sensor network.
The preceding challenges can be overcome by developing a wireless sensor and actuator network (WSAN), where supernodes serve as alternative gateways that are the core of the WSAN [4]. In addition to broader transmission ranges and more excellent batteries, supernodes perform the decision making process and make specific reactions based on their decisions. In many cases, data delivery from nodes to these supernodes is sufficient to ensure the network functions correctly [5][6]. According to [7], optimizing the placement of supernodes can extend the network lifetime by a factor of five. These networks can also be used for different purposes, e.g., recognizing combustion in agriculture [8], underground precision agriculture [9], and olive grove monitoring [10].
Studies indicate that traffic load and energy consumption distribution pose challenges for heterogeneous wireless sensor networks. Inefficient distributions result in a situation where, following the failure of the initial network node, 90% of the energy in live nodes remains unused [11]. Many studies attempted to balance the energy consumption of nodes so that they would discharge at a specific interval through different methods, e.g., by using a dynamic transmission range and adjusting the transmission range [12]. Earlier works reported that balancing energy consumption can improve the network’s lifetime by 30% since it helps balance the data traffic in the nodes adjacent to the sink [13]. Consequently, the proposed path construction method considers the residual energy of path-forming nodes as a crucial parameter, with nodes of a lower energy contributing to fewer paths.
Hotspots, or bottlenecks, are nodes that exist at a one-step distance from supernodes. Their energy discharges much more rapidly than that of other nodes since they serve as relay nodes for other nodes in addition to sending sensed data. Thus, hotspot failure is a significant challenge for sensor networks since it disconnects many nodes from supernodes [14][15][16]. Numerous studies have analyzed supernode mobility [14][15][17][18] and clustering [19][20][21][22][23][24][25] to overcome this challenge. The energy consumption of relay nodes can be balanced through supernode mobility and the periodic change of relay nodes. However, a mobile supernode imposes a new challenge on the network since a new movement and settlement change the network topology. Control messages are required to arrange a new topology to provide network coverage and connectivity. Therefore, handling the premature damping of relay nodes leads to the control message overhead in the network. Furthermore, finding optimal settlement points for a supernode is an NP-hard problem [26]. In [27], a suboptimal heuristic algorithm was assessed to find settlement points. The preceding techniques have the drawback of focusing primarily on the relay nodes while ignoring the other nodes’ energy consumption.
Agriculture sensors may fail for various reasons, such as energy discharge, hardware faults, and severe weather. This failure can disconnect a series of nodes from the network. It is essential to design fault-tolerant methods for network re-connectivity. In the disjoint path vector (DPV) method [6], each node is connected to a set of supernodes through k-disjoint paths to enable a node to select other paths to transmit the sensed data in case of node failure. DPV is aimed at reducing the total transmission range and maximum transmission range in order to lengthen the network’s lifetime. It can maintain supernode connectivity at a node failure rate of up to 5% [5][28]. In DPV, a node failure reduces k-vertex connectivity, whereas a supernode failure disconnects a large number of nodes. In [5], the adaptive disjoint path vector (ADPV) utilized r-restoration paths to maintain k-vertex connectivity. Despite improved supernode connectivity, it had two significant disadvantages: (1) the supernode layer was not fault-tolerant, and supernode failure disconnected a large number of nodes; (2) hotspots were required to be much more than k in number to avoid bottlenecks and premature death. In other words, ADPV was heavily dependent on the network structure and node locations in an operating environment.

2. Enhancing Smart Agriculture Monitoring via Connectivity Management Scheme and Dynamic Clustering Strategy

Based on the predictions, the world population will reach one billion people by 2050 [29]. This population growth requires a sustainable proportional increase in crops. Also, it is estimated that the number of people with cancer will be about 26 million by 2030 [30], and 17 million people will die from this disease. Food security is one of the ways to prevent this disease. Remote sensing plays a vital role in modern agriculture since it can effectively provide and improve food security and sustainable crop growth by monitoring the quality and quantity of crops. Moreover, the application of remote sensing, especially in its water-related contexts, has the potential to furnish sustainable resolutions for addressing the imminent challenge of irrigation water scarcity [31]. The infrastructure of these systems is wireless sensor networks. The challenges of WSN-based smart agriculture should be solved for better crop management. This section presents an overview of the recent studies on fault tolerance topology control, clustering methods, mobile supernodes, heterogeneity, and connectivity restoration.
Fault tolerance is an essential task in WSNs, ensuring uninterrupted data exchange. In recent years, many studies have been conducted on fault tolerance topology control [32][33][34][35] to reduce the residual energy consumption of nodes by adjusting the transmission power. In [36], a distributed topology control method was proposed for a WSN to change its topology dynamically through network coding. In [34], the game algorithm was employed to design a fault-tolerant topology control scheme for underwater WSNs by reducing unnecessary links and energy consumption. In [37], clustering was combined with fault tolerance to reduce energy consumption by applying fault tolerance to inter-cluster links. Fault tolerance and clustering were integrated in [38], using particle swarm optimization (PSO) to connect the members of a failed cluster to the new cluster head. These methods mainly focus on topology control and clustering to reduce node energy consumption. They ensure fault tolerance by lowering energy consumption.
Designing clustering algorithms is a method of reducing energy consumption in WSNs. Cluster head selection has been recently discussed in many studies. In [39], residual energy, node density, and node distances from sinks were integrated, and a fuzzy system was employed to calculate the probability of node selection as cluster heads. In addition to these parameters, link lifetime was considered in [40], assigning specific weights to each parameter. The residual energy had the highest weight, whereas the sink distance had the lowest weight. In [41], a PSO-based method was adopted to find the optimal cluster head by combining residual energy and sink distance to minimize the message overhead. In [42], nodes were clustered before using energy-based paths to connect clusters. In [43], the adaptive selfish optimization algorithm was utilized to select cluster heads, and the k-medoids technique was used to determine the nodes of each cluster to lengthen the network’s lifetime by preserving node energy. In [44], each UAV was considered a cluster head by default, and hierarchical clustering was employed to transmit data to UAVs. In actuality, clustering algorithms distribute network nodes over various zones; hence, cluster heads and inter-cluster routes should include fault tolerance to prevent network disconnectivity. These techniques only considered energy usage and lacked fault tolerance.
Mobile sinks are a standard method to distribute relay jobs between nodes since such mobility can periodically change the relay nodes. In [45], the main focus was on reducing the sink travel distance, and path planning was used to shorten the traveled distance of sinks. In [46], the primary purpose was to lengthen the network’s lifetime. To balance traffic load distribution, the relay nodes were periodically changed by utilizing sink movement between clusters.
In [47], mobile sinks and clustering were integrated; the sink was mounted on the cluster head with the highest traffic load in the subsequent migration. In [48], two important parameters were measured: movement time and stop time. The sink moved to the next migration at the movement time and remain there for the stop time. In [49], an ant colony optimization-based algorithm was proposed, where each node selected a data-gathering point via a random function. These data-gathering points determined the sink settlement points. In [50], a bipartite graph was created to divide sensor nodes into two sets, and the mobile sink calculated the nearest neighboring node using the breadth-first traversal algorithm once it entered each set. Then, it visited the node in the next movement. These methods focused mainly on collecting sensor data, decreasing energy consumption, and neglecting fault tolerance.
Regarding connectivity restoration in WSAN, the DPV algorithm [6] is designed to decrease the total transmission power in heterogeneous wireless sensor networks by maintaining the k-vertex disjoint paths from each sensor to a group of supernodes. Generally, this algorithm’s input is a k-vertex supernode-connected graph, and its output is a subgraph with fewer edges composed of the same collection of sensors. This method finds the edges that satisfy the following conditions:
  • Each node has a k-disjoint path to the supernode set;
  • ∑𝑛𝑖=0 p𝑖 is minimized (pi is the weight of the maximum weighted edge).
ADPV [5] extends the DPV algorithm and uses the residual energy of sensor nodes to generate a fault-tolerant topology. This method improves the network’s lifetime by balancing the energy consumption of sensor nodes and involving initialization and restoration phases. In the first phase, the necessary information is collected, and the initial topology is constructed. In ADPV, whenever a node failure disrupts the connectivity of the k-vertex supernode, other k-disjoint paths are extracted for each sensor node during the restoration phase. At the end of the restoration phase, the transmission power of each node is adjusted in the generated topology. The disadvantage of the studies reviewed by [5][6] is that they considered only node failures; however, network connectivity may also be affected by supernode failures. In these methods, sensor nodes and supernodes are static; hence, they are ineffective in prolonging the network’s lifetime. The suggested approach to increasing network lifetime involves mobilizing supernodes and considering their failures.

References

  1. Kamilaris, A.; Prenafeta-Boldú, F.X. Deep Learning in Agriculture: A Survey. Comput. Electron. Agric. 2018, 147, 70–90.
  2. Glaroudis, D.; Iossifides, A.; Chatzimisios, P. Survey, Comparison and Research Challenges of IoT Application Protocols for Smart Farming. Comput. Netw. 2020, 168, 107037.
  3. Liu, X.; Zeng, X.; Ren, J.; Yin, S.; Zhou, H. Region-Different Network Reconfiguration in Disjoint Wireless Sensor Networks for Smart Agriculture Monitoring. ACM Trans. Sens. Netw. 2023, 3614430.
  4. Akyildiz, I.F.; Kasimoglu, I.H. Wireless Sensor and Actor Networks: Research Challenges. Ad Hoc Netw. 2004, 2, 351–367.
  5. Deniz, F.; Bagci, H.; Korpeoglu, I.; Yazıcı, A. An Adaptive, Energy-Aware and Distributed Fault-Tolerant Topology-Control Algorithm for Heterogeneous Wireless Sensor Networks. Ad Hoc Netw. 2016, 44, 104–117.
  6. Bagci, H.; Korpeoglu, I.; Yazici, A. A Distributed Fault-Tolerant Topology Control Algorithm for Heterogeneous Wireless Sensor Networks. IEEE Trans. Parallel Distrib. Syst. 2015, 26, 914–923.
  7. Yarvis, M.; Kushafnagar, N.; Singh, H.; Rangarajan, A.; Liu, Y.; Singh, S. Exploiting Heterogeneity in Sensor Networks. In Proceedings of the IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Miami, FL, USA, 13–17 March 2005; Volume 2, pp. 878–890.
  8. Shafi, U.F.; Bajwa, I.S.; Anwar, W.; Sattar, H.; Ramzan, S.; Mahmood, A. Sensing Spontaneous Combustion in Agricultural Storage Using IoT and ML. Inventions 2023, 8, 122.
  9. Holtorf, L.; Titov, I.; Daschner, F.; Gerken, M. UAV-Based Wireless Data Collection from Underground Sensor Nodes for Precision Agriculture. AgriEngineering 2023, 5, 338–354.
  10. Tsipis, A.; Papamichail, A.; Koufoudakis, G.; Tsoumanis, G.; Polykalas, S.E.; Oikonomou, K. Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves. AgriEngineering 2020, 2, 175–205.
  11. Xu, Z.; Chen, L.; Liu, T.; Cao, L.; Chen, C. Balancing Energy Consumption with Hybrid Clustering and Routing Strategy in Wireless Sensor Networks. Sensors 2015, 15, 26583–26605.
  12. Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA, 4–7 January 2000; Volume 1, p. 10.
  13. Xu, Z.; Long, C.; Chen, C.; Guan, X. Hybrid Clustering and Routing Strategy with Low Overhead for Wireless Sensor Networks. In Proceedings of the 2010 IEEE International Conference on Communications, Cape Town, South Africa, 23–27 May 2010; pp. 1–5.
  14. Chauhan, S.S.; Gore, M.M. Balancing Energy Consumption across Network for Maximizing Lifetime in Cluster-Based Wireless Sensor Network. CSIT 2015, 3, 83–90.
  15. Jafari Kaleibar, F.; Abbaspour, M.; Aghdasi, H.S. An Energy-Efficient Hybrid Routing Method for Wireless Sensor Networks with Mobile Sink. Wirel. Pers. Commun. 2016, 90, 2001–2015.
  16. Khalilpour Akram, V.; Akusta Dagdeviren, Z.; Dagdeviren, O.; Challenger, M. PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks. Sensors 2021, 21, 6418.
  17. Koç, M.; Korpeoglu, I. Controlled Sink Mobility Algorithms for Wireless Sensor Networks. Int. J. Distrib. Sens. Netw. 2014, 10, 167508.
  18. Koç, M.; Korpeoglu, I. Traffic- and Energy-Load-Based Sink Mobility Algorithms for Wireless Sensor Networks. IJSNET 2017, 23, 211.
  19. Shankar, R.; Ganesh, N.; Čep, R.; Narayanan, R.C.; Pal, S.; Kalita, K. Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization. Processes 2022, 10, 616.
  20. Ganesh, N.; Shankar, R.; Čep, R.; Chakraborty, S.; Kalita, K. Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm. Appl. Sci. 2023, 13, 3223.
  21. Ganesh, N.; Shankar, R.; Kalita, K.; Jangir, P.; Oliva, D.; Pérez-Cisneros, M. A Novel Decomposition-Based Multi-Objective Symbiotic Organism Search Optimization Algorithm. Mathematics 2023, 11, 1898.
  22. Narayanan, R.C.; Ganesh, N.; Čep, R.; Jangir, P.; Chohan, J.S.; Kalita, K. A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications. Mathematics 2023, 11, 2301.
  23. Joshi, M.; Kalita, K.; Jangir, P.; Ahmadianfar, I.; Chakraborty, S. A Conceptual Comparison of Dragonfly Algorithm Variants for CEC-2021 Global Optimization Problems. Arab J. Sci. Eng. 2023, 48, 1563–1593.
  24. Dai, Z.; Ma, Z.; Zhang, X.; Chen, J.; Ershadnia, R.; Luan, X.; Soltanian, M.R. An Integrated Experimental Design Framework for Optimizing Solute Transport Monitoring Locations in Heterogeneous Sedimentary Media. J. Hydrol. 2022, 614, 128541.
  25. Haq, M.Z.U.; Khan, M.Z.; Rehman, H.U.; Mehmood, G.; Binmahfoudh, A.; Krichen, M.; Alroobaea, R. An Adaptive Topology Management Scheme to Maintain Network Connectivity in Wireless Sensor Networks. Sensors 2022, 22, 2855.
  26. Tomlinson, I. Doubling Food Production to Feed the 9 Billion: A Critical Perspective on a Key Discourse of Food Security in the UK. J. Rural Stud. 2013, 29, 81–90.
  27. Thun, M.J.; DeLancey, J.O.; Center, M.M.; Jemal, A.; Ward, E.M. The Global Burden of Cancer: Priorities for Prevention. Carcinogenesis 2010, 31, 100–110.
  28. Bogdanov, A.; Maneva, E.; Riesenfeld, S. Power-Aware Base Station Positioning for Sensor Networks. In Proceedings of the IEEE INFOCOM 2004, Hong Kong, China, 7–11 March 2004; Volume 1, pp. 575–585.
  29. Youssef, W.; Younis, M. Intelligent Gateways Placement for Reduced Data Latency in Wireless Sensor Networks. In Proceedings of the 2007 IEEE International Conference on Communications, Glasgow, UK, 24–28 June 2007; pp. 3805–3810.
  30. Deniz, F.; Bagci, H.; Korpeoglu, I.; Yazıcı, A. Energy-Efficient and Fault-Tolerant Drone-BS Placement in Heterogeneous Wireless Sensor Networks. Wirel. Netw. 2021, 27, 825–838.
  31. Preite, L.; Solari, F.; Vignali, G. Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review. Sustainability 2023, 15, 5975.
  32. Du, Y.; Xia, J.; Gong, J.; Hu, X. An Energy-Efficient and Fault-Tolerant Topology Control Game Algorithm for Wireless Sensor Network. Electronics 2019, 8, 1009.
  33. Mazumdar, N.; Nag, A.; Nandi, S. HDDS: Hierarchical Data Dissemination Strategy for Energy Optimization in Dynamic Wireless Sensor Network under Harsh Environments. Ad Hoc Netw. 2021, 111, 102348.
  34. Wei, L.; Han, J. Topology Control Algorithm of Underwater Sensor Network Based on Potential-Game and Optimal Rigid Sub-Graph. IEEE Access 2020, 8, 177481–177494.
  35. Singla, P.; Munjal, A. Topology Control Algorithms for Wireless Sensor Networks: A Review. Wirel. Pers. Commun. 2020, 113, 2363–2385.
  36. Khalily-Dermany, M. A Decentralized Algorithm to Combine Topology Control with Network Coding. J. Parallel Distrib. Comput. 2021, 149, 174–185.
  37. Wu, H.; Han, X.; Yang, B.; Miao, Y.; Zhu, H. Fault-Tolerant Topology of Agricultural Wireless Sensor Networks Based on a Double Price Function. Agronomy 2022, 12, 837.
  38. Rani, K.P.; Sreedevi, P.; Poornima, E.; Sri, T.S. FTOR-Mod PSO: A Fault Tolerance and an Optimal Relay Node Selection Algorithm for Wireless Sensor Networks Using Modified PSO. Knowl.-Based Syst. 2023, 272, 110583.
  39. Mehra, P.S.; Doja, M.N.; Alam, B. Fuzzy Based Enhanced Cluster Head Selection (FBECS) for WSN. J. King Saud Univ.-Sci. 2020, 32, 390–401.
  40. Rawat, P.; Chauhan, S. Probability Based Cluster Routing Protocol for Wireless Sensor Network. J. Ambient. Intell. Hum. Comput. 2021, 12, 2065–2077.
  41. Wang, C. A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks. Sensors 2023, 23, 6699.
  42. Wang, Z.; Zhang, M.; Gao, X.; Wang, W.; Li, X. A Clustering WSN Routing Protocol Based on Node Energy and Multipath. Clust. Comput. 2019, 22, 5811–5823.
  43. Cherappa, V.; Thangarajan, T.; Meenakshi Sundaram, S.S.; Hajjej, F.; Munusamy, A.K.; Shanmugam, R. Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks. Sensors 2023, 23, 2788.
  44. Shah, S.L.; Abbas, Z.H.; Abbas, G.; Muhammad, F.; Hussien, A.; Baker, T. An Innovative Clustering Hierarchical Protocol for Data Collection from Remote Wireless Sensor Networks Based Internet of Things Applications. Sensors 2023, 23, 5728.
  45. Temene, N.; Sergiou, C.; Georgiou, C.; Vassiliou, V. A Survey on Mobility in Wireless Sensor Networks. Ad Hoc Netw. 2022, 125, 102726.
  46. Chang, J.-Y.; Jeng, J.-T.; Sheu, Y.-H.; Jian, Z.-J.; Chang, W.-Y. An Efficient Data Collection Path Planning Scheme for Wireless Sensor Networks with Mobile Sinks. J. Wirel. Commun. Netw. 2020, 257.
  47. Prasanth, A.; Pavalarajan, S. Zone-Based Sink Mobility in Wireless Sensor Networks. Sens. Rev. 2019, 39, 874–880.
  48. Abu Taleb, A. sink mobility model for wireless sensor networks using kohonen self-organizing map. Int. J. Commun. Netw. Inf. Secur. 2022, 13, 1.
  49. Wu, X.; Chen, Z.; Zhong, Y.; Zhu, H.; Zhang, P. End-to-End Data Collection Strategy Using Mobile Sink in Wireless Sensor Networks. Int. J. Distrib. Sens. Netw. 2022, 18, 155013292210779.
  50. Abu Taleb, A.; Abu Al-Haija, Q.; Odeh, A. Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs. Future Internet 2023, 15, 182.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Computer Science, Interdisciplinary Applications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Fariborz Ahmadi
,
Omid Abedi
,
Sima Emadi
View Times: 110
Revisions: 2 times (View History)
Update Date: 19 Jan 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback