A Cluster-Tree-Based Secure Routing Protocol Using Dragonfly Algorithm (DA) in the Internet of Things (IoT) for Smart Agriculture

Abstract

The Internet of Things defines a global and comprehensive network whose task is to monitor and control the physical world by collecting, processing, and analyzing data sensed by IoT devices. This network has succeeded in various areas, and one of its most important applications is in smart agriculture because there are many demands for producing high-quality foodstuff in the world. These demands need new production schemes in the agriculture area. In IoT, communication security is essential due to the extensive heterogeneity of IoT devices. In this paper, a cluster-tree-based secure routing approach using the dragonfly algorithm (CTSRD) is proposed for IoT. The proposed scheme presents a distributed and lightweight trust mechanism called weighted trust (W-Trust). W-Trust reduces the trust value corresponding to malicious nodes based on a penalty coefficient to isolate this node in the network. Furthermore, it improves the trust value of honest IoT devices based on a reward coefficient. Additionally, CTSRD introduces a trust-based clustering process called T-Clustering. In this clustering process, cluster head nodes (CHs) are selected among honest IoT nodes. Finally, CTSRD establishes a routing tree based on the dragonfly algorithm (DA) between CHs. This tree is called DA-Tree. To evaluate the quality of the routing tree, a new fitness function is provided in CTSRD. DA-Tree finds a secure, stable, and optimal routing tree to balance the consumed energy and boost the network lifetime. CTSRD is compared with EEMSR and E-BEENISH with regard to the network lifetime, consumed energy, and packet delivery rate. This comparison shows that our scheme can uniformly distribute the consumed energy in IoT and improves the energy consumption and network lifetime. However, it has a slightly lower packet delivery rate than EEMSR.

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare

“…Not considering link quality when selecting paths, creating unstable paths, long delay for discovering paths SeLeZoR [34] Network clustering, creating unequal clusters, balancing energy consumption in the network, reducing traffic in the network Designing a weak security mechanism AODV [35] Designing an on-demand routing method, creating loop-free paths Not considering energy efficiency, high latency and high bandwidth consumption in the routing process, not designing a security mechanism LEACH-C [36] Clustering, scalability, balancing energy consumption in the network Single point of failure issue, not designing a lightweight security mechanism DRCKDS [37] Considering different security levels in the network, allocating high security level for sensitive data and low security level for low-sensitive data Not defining the routing process, not explaining the trust evaluation process, using symmetric encryption to secure the network CTSRD [38] An energy-aware powerful routing, uniform distribution of energy between nodes, reducing energy consumption, increasing network lifetime, designing a strong defense mechanism against attacks and isolating malicious nodes…”

“…In [ 38 ], a tree-based secure routing method by means of a dragonfly algorithm called CTSRD is introduced for Internet of Things. CTSRD has a weighted trust system (W-Trust) that is distributed and lightweight and attempts to achieve the trust values of IoT devices.…”

Building the Bridge to a Participatory Citizenship: Curricular Integration of Communal Environmental Issues in School Projects Supported by the Internet of Things

“…Still, there are other fields, like education, where it is relatively scarce [ 31 ]. For instance, in the agriculture filed, IoT is often used in the process of crop nutrition monitoring, light, ph, temperature, and humidity, using sensors that send data from node to node to be processed and to make decisions or can change other devices automatically regarding system decisions [ 25 , 31 , 35 , 36 , 37 ].…”

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique

“…The DA method is based on both dynamic (migration) and static (hunting) swarming particles. When it comes to optimization, these two swarming techniques rely on the usage of metaheuristics for both exploitation and exploration [68]. Figure 13 illustrates the flowchart of the Dragonfly algorithm [21,69,70].…”

“…However, the position updating rule of traditional QDA has a lower correlation with the centroid of the preceding generation's population. As a consequence, this may cause problems in locating the global optimum, leading to a solution with poor precision and a tendency to converge too quickly to local minima [24,25,68,69]. Therefore, investigations are urged to discover new methods to update dragonfly locations.…”