Routing Protocol For Internet Of Things Research Articles

TurboBlom: A light and resilient key predistribution scheme with application to Internet of Things.

In the Internet of Things (IoT), there are often devices that are computationally too constrained to establish a security key using traditional key distribution mechanisms such as those based on the Diffie-Hellman key exchange. To address this, current solution commonly rely on key predistribution schemes (KPSs). Among KPSs, the Blom scheme provably provides the highest resilience against node capture attacks. This, however, comes at high computational overhead, because the Blom scheme requires many multiplications over a large finite field. To overcome this computational overhead, we present TurboBlom, a novel amendment of the Blom scheme. TurboBlom circumvents the need for field multiplications by utilizing specialized generator matrices, such as random zero-one matrices. We demonstrate that, through this approach, TurboBlom can significantly reduce the computational overhead of the Blom scheme by orders of magnitude. In our next key finding, we demonstrate that TurboBlom offers a level of resilience against node capture that is virtually on par with the Blom scheme. Notably, we prove that the gap between the resilience of the two schemes is exponentially small. These features of TurboBlom (i.e., low computational overhead and high resilience) make it suitable for computationally constrained devices. Such devices exist in abundance in IoT, for example, as part of Low Power and Lossy Networks (LLNs). To demonstrate a sample application of TurboBlom, we show how to use it to enable sender authentication in the Routing Protocol for LLNs (RPL), a standard routing protocol for IoT.

Node Localization Using Modified Wild Horse Optimization and Energy Efficient Secured Routing Protocol for IoT Based Wireless Sensor Networks

Internet of Things (IoT) devices are being utilized extensively as a result of the development of information and communication technology. Wireless sensor networks (WSNs), which are formed of inexpensive smart devices for information collection, plays a vital function in the establishment of the IoT. These smart devices are not without limitations, though, whether it comes to processing, memory, computing, and energy usage. In addition to these limitations, the core difficulties facing WSN include node localization, reliability, and data security during transmission in a dangerous environment from hostile nodes. The important and difficult problem for researchers to solve in order to improve network longevity, reliability, scalability, connectivity, throughput is accurate localization and routing. To improve the network period and data trustworthiness, this study intends to design an Energy-Efficient and Secure Routing protocol (EESR) and node localization centered on modified wild horse optimization (LMWHO) for intrusion avoidance in IoT utilizing WSN. Initially the suggested protocol bases its creation of various energy-efficient clusters on the inherent characteristics of nodes. Secondly, the base station (BS) and cluster head are able to reliably and securely share sensory data according to the (k,n) threshold-based Shamir secret sharing method. And finally, node localization centered on modified wild horse optimization (EESR-LMWHO), where the fitness function was formed by the development of residual energy and distance estimate. The suggested EESR-LMWHO utilizes less energy and prolongs the life of wireless networks. Lastly, the simulations are run to evaluate the suggested method's efficiency. The suggested approach, according to the experiments, approximates the location of the unknown node, offers a minimal localization error, and is a lightweight way to deal with intrusions caused by hostile nodes.

Energy and load balancing routing protocol for IoT

SummaryDue to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.

RPL routing protocol over IoT: A comprehensive survey, recent advances, insights, bibliometric analysis, recommendations, and future directions

In the course of time, the Internet of Things (IoT) has attracted significant research interest. However, IoT devices have limited resources available in terms of battery power, processing capacity, memory, bandwidth, etc. In an attempt to provide connectivity and Internet Protocol version 6 (IPv6) support to IoT devices, the IPv6 routing protocol for Low-Power and Lossy Networks (RPL) was officially launched as the standard routing protocol for IoT in 2012. Despite being reputed and used in diverse applications, several recent studies have revealed RPL's drawbacks and limitations. The main objective of this work is to help the IoT research community understand all aspects of RPL. The paper also provides a detailed description of the operation of the RPL protocol. What is more, this work includes novel and thorough examples, thereby gaining practical knowledge of the pros and cons of this protocol. In addition, this paper reviews and summarizes relevant RPL-based protocols and conducts comprehensive comparisons among them from the perspectives of reliability, robustness, energy efficiency, and flexibility. Technically speaking, after studying and reviewing the majority of the proposed RPL solutions, we are ultimately capable, in this work, of highlighting all the challenges faced by IoT researchers while enhancing RPL and providing what is expected to be dealt with professionally. The present work also gives more details about RPL simulation platforms and RPL applications. Not only to this extent, but rather the historical bibliometric analysis of RPL, which shows the trends in the area of research to be focused on, has been professionally analyzed based on RPL challenges over the years 2010 through 2021. To this end, the conclusions and recommendations of this study are presented along with the effective directions for future RPL, and their applicability. As a result, the authors believe that this work will be a valuable reference for all RPL researchers and designers.

Secret Sharing-Based Energy-Aware and Multi-Hop Routing Protocol for IoT Based WSNs

Abstract: Modern advancements in insolent sensors, RFID, Web - based machineries, and technical requirements are made possible by the Internet of Things (IoT). Sensor nodes are often employed to collect and transmit sensed data and are thought of as intelligent devices. In addition to these inherent limitations, sensor nodes are also open to a number of security risks. In order to improve the quality of renewable energy with multi-hop information security versus malicious acts, this work provides (ESMR) protocol. There are three primary components to the suggested procedure. The network field is first divided into inlet and outlet zones depending on the placement of the nodes. Additionally, a large number of clusters are created in each zone based on the proximity of the node neighbourhood. Second, the suggested effective secret sharing mechanism is used to protect the data transfer from cluster members in each zone to the bowl node. In order to reduce routing disruption, the suggested approach examines the mathematical analysis of data connections. The work described offers a multi-hop, compact, secure data analyst solution for limited wireless communications based on the Internet of Things (WSNs). In terms of bandwidth by 38 percent, network available bandwidth by 34 percent, electricity intake by 34 percent, median end-to-end postponement by 28 percent, and high latency by 36 percent when compared to the conventional work, the experimental results of the part of the application and secure number of co routing protocol

THC-RPL: A lightweight Trust-enabled routing in RPL-based IoT networks against Sybil attack.

The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, governmental, and industrial attention in recent years. Since the IoT specifications are quite different from what the Internet can deliver today, many groundbreaking techniques, such as Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSN), have gradually been integrated into IoT. The Routing Protocol for Low power and Lossy network (RPL) is the de-facto IoT routing protocol in such networks. Unfortunately, it is susceptible to numerous internal attacks. Many techniques, such as cryptography, Intrusion Detection System (IDS), and authorization have been used to counter this. The large computational overhead of these techniques limits their direct application to IoT nodes, especially due to their low power and lossy nature. Therefore, this paper proposes a Trust-based Hybrid Cooperative RPL protocol (THC-RPL) to detect malicious Sybil nodes in an RPL-based IoT network. The proposed technique is compared and evaluated with state-of-the-art and is found to outperform them. It detects more attacks while maintaining the packet loss ratio in the range of 15-25%. The average energy consumption of the nodes also remains in the ratio of 60-80 mj. There is approximately 40% more energy conservation at node level with an overall 50% increase in network lifetime. THC-RPL has 10% less message exchange and 0% storage costs.

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

In current days, the domain of Internet of Things (IoT) and Wireless Sensor Networks (WSN) are combined for enhancing the sensor related data transmission in the forthcoming networking applications. Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks. In this view, this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing (EMO-QoSCMR) Protocol for IoT assisted WSN. The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy, throughput, delay, and lifetime. The proposed model involves two stage processes namely clustering and routing. Firstly, the EMO-QoSCMR protocol involves cross-entropy rain optimization algorithm based clustering (CEROAC) technique to select an optimal set of cluster heads (CHs) and construct clusters. Besides, oppositional chaos game optimization based routing (OCGOR) technique is employed for the optimal set of routes in the IoT assisted WSN. The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy, distance to sink node, etc. The proposed EMO-QoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH, PSO-ECHS, E-OEERP, and iCSHS methods have resulted in a lesser NAN of 2, 10, 42, and 51 rounds. The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

Energy Efficient Clustering and Optimized LOADng Protocol for IoT

In recent years, the use of Internet of Things (IoT) devices has increased exponentially due to the advancement of information and communication technologies. Wireless sensor networks (WSNs) are vital in the development of IoT and include low-cost smart devices for data collection. However, such smart devices hold some restrictions in terms of calculation, processing, storage, and energy resources. With such constraints, one of the primary difficulties for the WSN is to achieve the lowest possible energy consumption across the network. This article aims to develop an Energy-Efficient cluster-based Lightweight On-Demand Ad hoc Distance Vector Routing Protocol–Next Generation (LOADng) routing protocol in IoT to maximize network life by reducing the power consumed by each sensor. The proposed approach consists of three stages: cluster formation followed by appropriate selection of cluster head (CH) and finally routing. In this approach, initially, clusters are created using the k-means clustering algorithm. Then CHs are optimally selected using a seagull optimization algorithm (SOA). After the CH selection process, routing is done using the optimal LOADng protocol. The efficiency of the proposed approach is evaluated using different assessment metrics. Also efficiency of this approach is compared with existing algorithms.

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

In recent times, Internet of Things (IoT) has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking applications. Wireless Sensor Network (WSN) is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation, smart buildings, intelligent transportation, disaster management, and other such IoT-based applications. Clustering methods are widely-employed energy efficient techniques with a primary purpose i.e., to balance the energy among sensor nodes. Clustering and routing processes are considered as Non-Polynomial (NP) hard problems whereas bio-inspired techniques have been employed for a known time to resolve such problems. The current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol (EETTC-MRP) for IoT networks. The presented EETTC-MRP technique operates on different stages namely, tentative Cluster Head (CH) selection, final CH selection, and routing. In first stage of the proposed EETTC-MRP technique, a type II fuzzy logic-based tentative CH (T2FL-TCH) selection is used. Subsequently, Quantum Group Teaching Optimization Algorithm-based Final CH selection (QGTOA-FCH) technique is deployed to derive an optimum group of CHs in the network. Besides, Political Optimizer based Multihop Routing (PO-MHR) technique is also employed to derive an optimal selection of routes between CHs in the network. In order to validate the efficacy of EETTC-MRP method, a series of experiments was conducted and the outcomes were examined under distinct measures. The experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

Multi-Population Ensemble Particle Swarm Optimizer based Energy Efficient Clustering Algorithms for IOT Applications

With the growth of wireless sensor networks (WSN), new technologies like the Internet-of-Things (IoT) are being created. There may be challenges that come because when implementing these application areas in practice. The primary issue is energy utilization while data transmission between these resource restricted sensors. In this work, we present a cluster-based routing protocol for IoT to anticipate energy utilization. Furthermore, for cluster head selection and cluster updation, we presented a multi-population ensemble particle swarm optimizer. The simulation was carried out using the MATLAB platform and demonstrates its superiority over different approaches.

Advanced System Control with Traffic Handling for Secure Communication in IoT Routing Protocol

The Internet of Things (IoT) can simply be referred to as the network of things comprising software, sensors, electronics, allowing data to be collected and transmitted. The next step in the field of technology is the Internet of Things, bringing tremendous improvements to manufacturing, medicine, environmental treatment, and urban growth. In shaping this vision, multiple challenges need to be faced, such as technology interoperability problems, protection and data confidentiality standards and, last but not least, the implementation of energy efficient management systems. These devices with minimal human interference are capable of producing, sharing and consuming data. The networking of related as well as heterogeneous devices is often known to be IoT. The Internet of Things allows things to connect and interact with each other, thus minimizing human involvement in simple daily tasks. Addressing protection at all times or at any position for many users, companies, governments, and enterprises is really necessary and responsive. In this paper a secure IOT architecture for routing in a network with RPL Rapid Node Link Routing (RNLR) Model is proposed that performs traffic management and traffic analysis for secure communication using IoT routing protocol. It mainly aims to locate the malicious users in a IOT routing protocols. the proposed mechanism is compared with the state of the art work and compared results shows the proposed work performs well.

Reliable critical nodes detection for Internet of Things (IoT)

The 3D critical node (C-N) detection can play a vital role in algorithm development of security, surveillance, monitoring, topology detection, and situation-aware emergency navigation for the Internet of Things (IoT). However, 3D C-N detection problem in IoT raises some issues and also introduces new challenges. The existing state of the art in 3D C-N detection shows that rely on prior known anchor node, known coordinate, embedding of the 3D situation on a 2D geometrical structure like circles and presence of unreliable node and ignores the energy constraint in Low Power and Lossy Networks IoT. In this paper, we present a practical, distributed, and energy-efficient algorithm for reliable 3D C-N detection. The goal of the proposed mechanism is twofold, firstly a 3D critical nodes (C-N) detection algorithm is proposed which uses only Received Signal Strength Indicator information of neighbor. Secondly, a correlation-based algorithm for the reliability approach is proposed to increases the node resilience against malicious IoT nodes. The complexity of our proposed algorithms has a time complexity of $$\mathcal {O}(\log (N))$$ and computation cost $$\mathcal {O}(\delta (\log N))$$ where N is the number of nodes in networks, and $$\delta $$ is the total number of forward and the backward message from an individual node. To validate our work, we implemented our proposed approach with the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) based IoT routing protocol compare it with RPL and cryptographic approach Version Number and Rank Authentication (VeRa). The result shows that the proposed approach can detect 10–15% more C-N nodes. Result also shows that our proposed algorithm has better PDR than RPL based approach by 12% and less than VeRa (cryptographic approach) by 8% however our proposed approach consumes almost 50% less power than the VeRa.

PCC-RPL: An efficient trust-based security extension for RPL

ABSTRACT RPL is a de facto routing protocol for IoT (Internet of Things). In this paper, an efficient IDS (intrusion detection system) is proposed to solve a major security vulnerability of RPL, which is called fabricated parent change. We show that many well-known attacks and security breaches are carried out via this vulnerability. The proposed method, which is called PCC-RPL (Parental Change Control RPL), prevents unsolicited parent changes by utilizing the trust concept. In PCC-RPL, all parents monitor their children behavior continuously. When a malicious activity is detected by the parent, it decreases the child's trust level and informs the root by sending a suspicion message. Our simulation results indicate that PCC-RPL can detect almost all common RPL attacks with an acceptable accuracy compared to a well-known method. Low control overhead, low energy consumption, short attack detection delay, and high precision are the main features of the proposed scheme.

A new load balancing clustering method for the RPL protocol

Internet of things (IoT) is a network of different interconnected objects that are capable to collect and exchange data without human interaction. IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) is the common IoT routing protocol. One of the main drawbacks of the RPL protocol is lack of support of load balancing leads to unfair distribution of traffic load in the network which may decrease network efficiency. In this paper for load balancing, we proposed a new method called C-Balance based on cluster ranking to increase the network lifetime. In this method, two ranks are calculated for each node. The first rank is used to identify clusters and cluster heads and the second rank is used to select parents of each cluster head to forward packets towards the destination. To calculate these ranks, several metrics are used including Expected Transmission Count, hop count, residual energy and number of children. To investigate the performance of the proposed method, it has been simulated with Cooja simulator in the form of nodes with mobility and non-mobility scenarios plus using a random topology network with 20, 40 and 60 nodes experiments. The results are compared with OF0 and MRHOF standard objective functions as well as the QU-RPL method. The final results in both scenarios show that the proposed method in the field of energy consumption, network lifetime and load balancing has improved compared to the other methods. In terms of end-to-end delay, the proposed method has more delay compared to the standard objective functions and QU-RPL method. The calculation of the mean packet delivery ratio (PDR) of these four methods also shows that the proposed method has an acceptable performance. Final results indicate that on average, there is a 30–45% improvement in energy consumption, 15–23% reduction in average number of children and 22–48% improvement in network lifetime are obtained compared to the other methods. Finally, there is about 12% progress for PDR compared to the OF0.

Detecting Sinkhole Attack in RPL-based Internet of Things Routing Protocol

The Internet of Things (IoT) is a novel paradigm in computer networks which is capable to connect things to the internet via a wide range of technologies. Due to the features of the sensors used in IoT networks and the unsecured nature of the internet, IoT is vulnerable to many internal routing attacks. Using traditional IDS in these networks has its own challenges due to the resource constraint of the nodes, and the characteristics of the IoT network. A sinkhole attacker node, in this network, attempts to attract traffic through incorrect information advertisement. In this research, a distributed IDS architecture is proposed to detect sinkhole routing attack in RPL-based IoT networks, which is aimed to improve true detection rate and reduce the false alarms. For the latter we used one type of post processing mechanism in which a threshold is defined for separating suspicious alarms for further verifications. Also, the implemented IDS modules distributed via client and router border nodes that makes it energy efficient. The required data for interpretation of network’s behavior gathered from scenarios implemented in Cooja environment with the aim of Rapidminer for mining the produces patterns. The produced dataset optimized using Genetic algorithm by selecting appropriate features. We investigate three different classification algorithms which in its best case Decision Tree could reaches to 99.35 rate of accuracy.

Trust-Aware Routing Framework for Internet of Things

Establishing security in internet of things (IoT) is a critical challenge, as it is connected to the network's extremely resource-constrained devices. The RPL is a standard routing protocol for IoT. It is well-suited for low power and lossy networks (LLN). The RPL provides little security in the IoT network against various attacks. However, one needs to strengthen the security concern in RPL. So, this paper proposes a trust-aware, energy-based reliable routing (TAER-RPL) for IoT to enhance security among network nodes. The TAER-RPL is taken into account the routing metrics, namely trust, ETX, RER to pick the optimal parent for data transmission. The simulation is conducted in COOJA simulator. TAER-RPL's efficiency is compared with SecTrust-RPL and RPL. The TAER-RPL increases the lifespan of the network by 15%.

Energy efficient optimal parent selection based routing protocol for Internet of Things using firefly optimization algorithm

AbstractEnergy conservation is a major challenge in the Internet of Things (IoT) as the number of resource‐constrained devices is connected to the network. Routing plays a vital role in IoT to extend the lifespan of the network. Routing protocol for low‐power and lossy networks (RPL) is a standard routing protocol in IoT. The parent selection is a crucial role in the routing process to exchange the data. In RPL, the researchers have introduced a single metric, composite metric, and multiobjective optimization algorithm for parent selection. However, the improper parent selection causes the packet losses, congestion among the network nodes, depletes more energy, and increases the convergence time. To overcome these issues, this article proposes energy efficient optimal parent selection in RPL (EEOPS‐RPL) using firefly optimization algorithm to extend the lifespan of the IoT network. In EEOPS‐RPL, each node in the network is considered to be firefly and also calculates the current location of firefly, attraction of the fireflies, random function, velocity, and the global best values in the network. Residual energy and expected transmission count are attractiveness parameters and distance is a movement parameter to choose the optimum parent in the destination‐oriented directed acyclic graph for data transmission. The simulation is conducted using COOJA. The EEOPS‐RPL provides better performance in comparison to the efficient parent selection for RPL (EPC‐RPL) and the E‐RPL. The EEOPS‐RPL improves the packet transmission ratio and lifespan of the network by 2% to 5%, and 5% to 10%, respectively, compared with EPC‐RPL and E‐RPL.In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time.

Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

IoT integrates and connects intelligent devices or objects with varied architectures and resources. The number of IoT devices is growing exponentially. Due to the massive wave of IoT objects, their diversity and heterogeneity among their architectures, the existing communication protocols for wireless networks become ineffective in the context of IoT. Wireless Sensor Network (WSN) has the potential to be integrated to the internet of things (IoT). The issues of the routing of WSNs impose nearly similar prerequisites for IoT routing technique. Most of the traditional routing protocols are not appropriate for WSNs and IoT because of resource constraints, computational overhead and environmental interference and do not take into account the different factors affecting energy parameter and do not accommodate node mobility. Routing algorithms must ensure the data transmission in an efficient way, having proper knowledge of the IoT system. For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network's dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. The proposed routing algorithm is simulated using MATLAB for performance evaluations. The evaluation results have recorded an improvement in conservation of energy, of almost 50% less consumed energy even with an increase in the number of nodes, by comparing with an existing routing technique based on ant system, a current routing protocol for IoT and the conventional ACO algorithm. © 2020 ASTES Publishers. All rights reserved.

Enhancing Energy Saving in Smart Farming through Aggregation and Partition Aware IoT Routing Protocol.

Internet of things (IoT) for precision agriculture or Smart Farming (SF) is an emerging area of application. It consists essentially of deploying wireless sensor networks (WSNs), composed of IP-enabled sensor nodes, in a partitioned farmland area. When the surface, diversity, and complexity of the farm increases, the number of sensing nodes increases, generating heavy exchange of data and messages, and thus leading to network congestion, radio interference, and high energy consumption. In this work, we propose a novel routing algorithm extending the well known IPv6 Routing Protocol for Low power and Lossy Networks (RPL), the standard routing protocol used for IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). It is referred to as the Partition Aware-RPL (PA-RPL) and improves the performance of the standard RPL. In contrast to RPL, the proposed technique builds a routing topology enabling efficient in-network data aggregation, hence dramatically reducing data traffic through the network. Performance analysis of a typical/realistic precision agriculture case, considering the potato pest prevention from the well-known late blight disease, shows that PA-RPL improves energy saving up to compared to standard RPL.