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Routing protocols for Internet of Things (IoT) play a major role in the performance of the network. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) suffers from a number of limitations including congestion of higher-level nodes and unbalanced topology. This paper proposes a novel Objective Function called Load Balanced Minimum Rank with Hysteresis Objective Function (LB_MRHOF), which assigns child nodes to the most suitable parent in the topology. The Objective Function utilizes a weight of the Expected Transmission Count (ETX) and number of children to calculate the Composite ETX and Number of Children (CENOC) which estimates the load on each node. The attained CENOC is used to select the optimum parent for each node in the topology, where nodes with high CENOC are avoided in the parent selection process. The proposed Objective Function has been evaluated under random and hierarchical network topologies. In addition, the evaluation has investigated the influence of the number of nodes by testing for small, medium and large-scale networks. Results have shown that the proposed Objective Function outperforms MRHOF, OF_FUZZY and OF-EC in terms of Packet Delivery Ratio (PDR) and reduces nodal hop-count under all tested scenarios, with no compromise in energy consumption. They have also revealed that the best performance achieved by LB_MRHOF is attained under large-scale networks. The resulting network topology which is formed by the proposed Objective Function has shown improved balance and more depth. [ABSTRACT FROM AUTHOR]

The growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks always operate within the context of diverse and constrained characteristics of the devices. Low-Power and Lossy Networks (LLNs) constitute a network architecture commonly utilized in IoT application deployments, facilitating networking and the establishment of paths for data transmission. The Routing Protocol for Low-Power and Lossy Networks (RPL) demonstrates promising capabilities for LLN network operations, supporting IPv4 and IPv6-enabled services. The RPL protocol constructs a Destination Oriented Directed Acyclic Graph (DODAG) logical routing topology based on defined Objective Function (OF) metrics. Routing operations within the DODAG utilize these metrics and constraints to select parent nodes and calculate optimal routes between two nodes. Standardized OFs have traditionally focused on either parent node selection or routing objectives within the DODAG, often treating load balancing and bottleneck optimization separately. However, their combined impact on RPL's effectiveness has been overlooked. This paper introduces an Adaptively Composite Objective Function (AC-OF) approach that considers the combined objectives of DODAG load balancing and optimized routing operations. Through simulation evidence, the paper presents improved network parameters. The AC-OF implementation brings out significant results in the form of a balanced DODAG topology and it has good impacts on data transmission, control overhead messages, parent switching, delay, energy consumption, and node lifetime. [ABSTRACT FROM AUTHOR]

The 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) architecture for wireless sensor networks merges the time-slotted channel hopping (TSCH) at the medium access control (MAC) layer with the routing protocols tailored for low-power and lossy networks (RPL). However, research often neglects the incorporation between TSCH MAC and RPL. Standard RPL strategies rely on an objective function (OF) using the expected transmission count (ETX) metric, which does not adequately reflect the traffic dynamics. Moreover, RPL’s hysteresis function employs a static threshold to control parent change decisions. This static setting disregarded the diverse traffic patterns within the network, leading to unnecessary parent node changes and preventing the node from selecting a better parent. To overcome these shortcomings, we introduce 3 advancements to standard RPL. First, an adaptive parent-changing mechanism based on cooperative Q-learning. Second, a cell usage and traffic load aware objective function. Third, an improved initial transmission cell allocation. Those methods are collectively termed ACI-RPL. We evaluated the performance of the proposed method through simulations using the 6TiSCH simulator and real-hardware tests on the FIT IoT-Lab testbed with OpenWSN firmware. The experiment result indicates that ACI-RPL performs better than the benchmark algorithms. In comparison to the standard RPL, ACI-RPL improves the packet delivery ratio and the total received packets by 12% and 17%, respectively. Additionally, ACI-RPL reduces energy consumption and latency by 23% and 9%.

Internet of Things (IoT) has seen a lot of traction over the last few years and is expected to play a major role in controlling devices and communication. IoT network involves sensors with constrained memory, limited processing and power capabilities. The process of network formation largely decides the Quality of service in these networks. IPv6 Routing Protocol for LLNs (RPL) is specially designed by the IETF ROLL working group to cater to routing requirements in such networks. In RPL, an Objective function specifies a set of metrics or constraints which can be used as criteria for best parent and path selection, ensuring a faster route to the destination. In our work, we propose a novel objective function OF-FZ, which uses four metrics to make routing decisions. Fuzzy Logic is used to combine the metrics hop count (HC), ETX, delay and node residual energy (RE) to obtain a single decision metric termed as Quality Assurance (QA) score. During routing, a neighbor node having the highest QA score is chosen as the best parent. The proposed Objective Function leads to significant savings in energy consumption (EC) (9% lower), Packet Delivery Ratio (PDR) (3% higher), End-to-end Delay (8% lower) as compared to existing OFs. Controlling advertisement messages circulating in the network leads to a significant reduction in control overhead to the tune of 45%. Performance evaluation and deep dive of OF-FZ is done in three configurations: network scalability, increasing packet transmission rates and multi-sink scenarios using Contiki OS and COOJA network simulator. [ABSTRACT FROM AUTHOR]

Routing is the linchpin of the LLNs due to their scarcity of memory, energy and computing resources. Efforts have been made in the past, to optimize the individual components of the RPL namely optimization of Trickle algorithm, objective function, network topology, etc. E-Trickle is one of the successful methods. Similarly, the standard MRHOF, at times suffers from the bottleneck in the network due to long hops. This problem has been significantly addressed by the Sigma routing metrics. Our proposed work, E-Sigma routing, suggests a new routing metric that combines both, the Sigma routing and the E-Trickle. In this proposed work, efforts have been taken to improve and enhance RPL by (i) eliminating the short listen problem (ii) eliminating the long hop problem and (iii) use of a reliable routing method. This proposed E-Sigma routing suggests a new composite routing metric that combines both the Sigma routing and the E-Trickle. This new routing metric has been tested with Cooja simulator over Contiki OS, and the results are compared with the inbuilt objective functions OF0, MRHOF, Sigma, and E-Trickle. The proposed E-Sigma routing method not only solves the short listen and long hop problem but also gives better packet delivery ratio, energy consumption, network convergence time, control traffic overhead and network latency, compared with the rest. [ABSTRACT FROM AUTHOR]

Routing Protocol (RPL) is treated as a standard protocol for Low power and Lossy Networks (LLNs). It was introduced by the Internet Engineering Task Force (IETF) Routing over Low Power and Lossy network (ROLL) working group to deal with routing challenges that occur in LLNs. It is noted that RPL permits optimization at several levels in the networks. RPL uses an objective function that helps in evaluating network performance. The objective function can be created using single or composite metrics. Literature reveals that single metrics-based objective function showed poor performance whilst composite metrics have demonstrated excellent performance, but still there is plenty of scope for further improvement. This paper shows an extension of the composite metrics. The real problem of RPL concerning IoV is that the heterogeneous network undergoes extreme packet loss and congestion which disables the full utilization of network capacity. Thus, this paper presents an enhanced fuzzy-based objective function and analyzes its impact on the Internet of Vehicles (IoV) network. The objective function aims at reducing the Control Traffic Overhead (CTO) in the network and providing a high Packet Delivery Ratio (PDR).The contribution of this paper is as follows: First, network scalability is analyzed for (a) random and grid configurations; (b) random and Self-similar Least Action Walk (SLAW) mobility models. Second, the performance of the proposed E-MOF is validated with the standard objective functions for both networks. Third, extensive computer simulations are performed for performance analysis. Simulation result reveals that the proposed E-MOF outperform OF-EC concerning the PDR, CTO and comparable latency at the expense of high energy consumption for both configurations and mobility models. Finally, it is remarked that E-MOF extends the applicability of the RPL for IoV networks. Further, it commits a better PDR Quality of Service (QoS) and high network reliability. The results and discussion reported in this paper are outstanding, therefore, they will motivate other researchers to develop a novel approach in the future. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides a common platform to connect the heterogeneous devices over the internet. Hence, the number of devices connected via the internet and consequently congestion is exponentially increased. Congestion reduces the network performance and negatively affects QoS. RPL (routing protocol for low-power and lossy networks) significantly has responded to IoT routing needs, but controlling congestion and QoS needs have been failed to be considered. Therefore, this paper improved the RPL protocol based on the congestion control and QoS requirements and proposed a protocol called Congestion and QoS-Aware RPL for IoT applications (CQARPL). This protocol selects parents based on multi-metric evaluation, considering the conditions of routes to the root. In addition, it somehow controls the parent selection and the children's acceptance that a balanced DODAG graph is formed. Furthermore, it provides the ability to predict congestion and prevents it from occurring as much as possible. The proposed protocol is implemented by the Cooja simulator and evaluated with different scenarios compared to CLRPL and RPL. The simulation results showed that CQARPL maintains the quality of transmissions and controls congestion in IoT. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a self-structured network containing small, energy-constrained wireless nodes that act together to accomplish difficult tasks. Wearable sensors, one of the WSNs play a significant role in healthcare applications, especially patient monitoring. With a miniature size, wearable sensors have less space dedicated for energy sources. So it is important for wearable sensors to be manufactured as energy efficient and reliable and it must ensure quality of service in providing the data. Remote health care monitoring has two limitations such as adoption of mobility and the usage of low power consumption devices. To overcome these limitations, appropriate routing protocol can be used in Low Power Lossy Networks (LLNs). IPV6 Routing Protocol for Low Power Lossy Networks (RPL) is one of the routing protocols standardized to be applied in Internet of things network with wireless sensors. The current research article investigates the performance of RPL with three Objective Functions (OF), Minimum Rank with Hysteresis Objective Function (MRHOF) with Energy as metric, MRHOF with Expected Transmission count (ETX) as metrics and Objective Function zero(OF0) with hop count as metric, in elderly health care monitoring system. The study considered two scenarios case 1 has all static nodes while case 2 has few dynamic nodes. The performance was evaluated in terms of metrics control overhead, convergence time, Packet Delivery Ratio (PDR), Latency and energy consumption and the OF optimum for reliability, mobility and energy consumption is determined. The results of the simulation showed that, in mobile scenario OF0 converged at a fast rate than the MRHOF, which increases the life time. OF0 also consumed the least energy and it increased the life time of the node. As far as PDR is concerned, OF0 had low PDR when the nodes were mobile and ETX performed well. [ABSTRACT FROM AUTHOR]

Standardized by Internet Engineering Task Force (IETF) working group, RPL (Routing Protocol for Low Power and Lossy networks) stands as an effective IPV6 Routing Protocol that establishes a path between any sensor and the sink wich typically collect the information generated by a wireless sensor network. For this purpose, RPL constructs a Destination Oriented Directed Acyclic Graph (DODAG), using an objective function (OF) that implements the metric being used. In particular, RPL supports the point-to-multipoint (P2MP) mode of traffic, and optionally the multipoint-to-point (MP2P) and point-to-point traffic ranges. This paper evaluates the performance of RPL considering three key parameters: network density, throughput and sink localization. We further consider the three following metrics: Expected Transmission count (ETX), Hop Count (HC) and Energy. Our simulations reveal that the number of nodes affects the parameters regardless of the scenarios. In addition, the ETX metric performs well in all scenarios in terms of Packet Delivery Ratio (PDR) and Energy metric, which turns out to have the highest energy consumption with respect to all the implemented scenarios. [ABSTRACT FROM AUTHOR]

Summary: Routing protocol for low‐power and lossy networks (RPL) is a proactive routing protocol that automates the path development process using objective functions (OFs). The IETF defines two types of RPL objective functions, minimum rank with hysteresis objective function (MRHOF) and OF0. OF0 is based on the minimum hop count metric while the MRHOF is based on the expected transmission count (ETX) metric. Because of the single metric, both MRHOF and OF0 will suffer from long hops when selecting the routs to the sink, and select inefficient routes maybe paths contain nodes with small residual energy, which may cause consume the nod's energy faster than other nodes. The other problem of RPL is unbalanced chosen by parents that make bottleneck nodes that cause more network delay and high packet loss ratio because of the high congestion nodes, especially the nodes that locate near the sink node. In this article, we proposed an improvement RPL objective function that considers three metrics (load, residual energy, and ETX metrics) instead of one metric, to solve the MRHOF problems. The performance of the proposed protocol shows the enhancement in the RPL performance in terms of PDR, total energy consumption, and many other parameters. [ABSTRACT FROM AUTHOR]

Routing Protocol for Low Power and Lossy Networks (RPL) is the only standard protocol that assists the routing process in Low Power and Lossy Networks (LLNs) - based Internet of Things (IoT) applications. However, due to the resource-constrained nature, lack of physical protection and security requirements of the inherent protocol, LLNs are vulnerable to attacks that primarily alter the working of the RPL protocol and disrupt the ongoing communication. Therefore, it is a high profit and desperate target of many attackers. Our work offers a detailed overview of one such significant threat to RPL known as the rank attack. Considering the resource-constrained nature of the IoT devices, we propose and evaluate a lightweight and efficient method to mitigate and isolate the rank attack. Specifically, our approach uses a novel Echelon Metric Based Objective Function (EMBOF) over the default RPL to calculate and check the legitimacy of the advertised rank. The Echelon value is additively determined by the root node and the corresponding parent node(s) in the RPL network topology. Our approach not only detects the attacker node(s) but also isolates it immediately. We implement and evaluate our approach through extensive simulations in Cooja, a Contiki operating system (OS) based simulator. The results of our experiments show the viability of our proposal concerning detection accuracy, isolation latency, packet delivery ratio, end-to-end delay, power consumption and memory overhead. [ABSTRACT FROM AUTHOR]

Abstract In a real network deployment, the diverse sensor applications generate a heterogeneous traffic pattern which may include basic sensing measurements such as temperature readings or high-volume multimedia traffic. In a heterogeneous traffic network, the two standardized objective functions (OFs), i.e., objective function zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF) for routing protocol for Low-Power and Lossy Networks (RPL) perform poor routing decisions by selecting an already congested parent node and cause more re-transmissions across the network. Therefore, careful consideration is required in designing a new OF for heterogeneous traffic scenarios. In this study, we examine the RPL protocol under a heterogeneous traffic pattern and proposed a new protocol based on queue and workload-based condition (QWL-RPL). The aim of the proposed protocol is to achieve a reliable path with better overall performance. The proposed OF model considers the link workload in addition to mapping the congestion status of the node using the packet queue. We implement the proposed routing model in the Contiki operating system (OS) Cooja environment to compare with the existing technique. The simulation results show that QWL-RPL can improve the performance of a heterogeneous traffic network as compared with both OF0 and MRHOF, specifically in terms of the amount of overhead, packets reception ratio (PRR), average delay, and jitter. Final results indicate that on average, there is a 5%–30% improvement in PRR, 25%–45% reduction in overheads, 12%–30% reduction in average delay, and 20%–40% reduction in jitter.

Faced to challenges of low power and lossy networks (LLN's) as well as the nature of these networks without infrastructure, it remains the subject of several researches to deal with routing constraints and thus propose adapted routing protocols according to different applications. In this context, a protocol based on Ipv6 RPL has been standardized to meet the requirements of low power and lossy networks. RPL centralizes traffic to one or more nodes through the construction of a Destination Oriented Directed Acyclic Graph (DODAG) based on a specific objective function. Nevertheless, the objective function influences the behavior of the routing protocol, so that the design must follow the prerequisites of each application. The problem of standardized objective functions is found in their routing paths selection, they are not based on different criteria which leads to unoptimized choices of routes which affects the network quality of services. Thus, we propose in this paper a new objective function IRH-OF based on designed combined metric cmIRH for rank calculation. Our proposed method, injected on the core of Contiki Operating System, ensures a better quality of services compared to other objective functions. The results showed that the new objective function can maintain a packet delivery ratio higher than 98% regardless of density, decreases approximately with 45% the average power consumption of the network, achieves a less convergence time and latency. [ABSTRACT FROM AUTHOR]

The diverse applications of the internet of things (IoT) require adaptable routing protocol able to cope with several constraints. Thus, RPL protocol was designed to meet the needs for IoT networks categorized as low power and lossy networks (LLN). RPL uses an objective function based on specific metrics for preferred parents selection through these packets are sent to root. The single routing metric issue generally doesn’t satisfy all routing performance requirements, whereas some are improved others are degraded. In that purpose, we propose a hybrid objective function with empirical stability aware (HOFESA), implemented in the network layer of the embedded operating system CONTIKI, which combines linearly three weighty metrics namely hop count, RSSI and node energy consumption. Also, To remedy to frequent preferred parents changes problems caused by taking into account more than one metric, our proposal relies on static and empirical thresholds. The designed HOFESA, evaluated under COOJA emulator against Standard-RPL and EC-OF, showed a packet delivery ratio improvement, a decrease in the power consumption, the convergence time and DIO control messages as well as it gives network stability through an adequate churn. [ABSTRACT FROM AUTHOR]

The Routing over Low Power and Lossy Networks (RPL) protocol is an important standardized routing solution for the Internet of Things (IoT), characterized by significant benefits, including IPv6 support and efficient low-power operation over noisy channels, especially for many-to-one communication scenarios (i.e., a set of deployed devices gathering periodic measurements destined to a single sink node). However, the RPL faces performance issues in networks with mobility or point-to-point communication requirements, raising applicability constraints in a number of real-world IoT applications, spanning from environmental monitoring to industrial and urban networks. In this paper, we approach RPL from the Software-Defined Networking (SDN) perspective, exploiting its high customization features to address the above inefficiencies. We apply an evolutionary methodology, i.e., building over the widely deployed RPL protocol, while maintaining compliance with its standard. More precisely, we investigate two routing control strategies exploiting the global view of the network: (i) the Moderate RPL control that enables dynamic reconfigurability of crucial protocol parameters to improve its operation in mobile environments; and (ii) the Deep RPL control that utilizes a new RPL Objective Function (OF) we proposed that enforces direct point-to-point paths through link-coloring. We implemented and evaluated the two strategies based on a novel centralized routing control facility. Our experimental analysis considers hybrid scenarios with both fixed and mobile nodes, as well as many-to-one and point-to-point communication. Compared to the standard RPL in mobile topologies, the proposed solution achieves improved packet delivery ratio of up to 33 percent and 21 percent for the mobile nodes and for the whole network, respectively, while maintaining RPL compliance. In case of point-to-point communication in a random topology, the improvements rise up to 32.7 percent for the trip-time and 42 percent for the round-trip time, while the packet loss ratio for the same experiment is improved up to 75 percent in the non-storing mode.

The Internet of Things networks comprise a large number of resource-limited nodes, which often use multi-hop communications to transfer packets to the gateway. Thus, finding the proper forwarding path and managing limited resources have always been important. In this paper, the aim is to improve the approach of selecting the parent nodes to extend the network lifetime and also prevent congestion in nodes' queues to prohibit network excessive traffic load. We address these issues in two ways. First, a linear combination equation is used to combine three crucial network metrics: ETX, delay, and node residual energy. Second, the "Node's Traffic Intensity" metric is used to detect and prevent congestion in nodes' queues. Results showed that by choosing appropriate nodes with higher battery-level for the paths toward the gateway and by avoiding numerous retransmissions, the nodes' energy and network lifetime increased up to about 65%. Furthermore, by preventing congestion and queue overflow in network nodes, the amount of packet-loss decreased up to 81%. Also, through decreasing packet retransmissions and consequently increasing the number of original packets, the total number of sent original packets was improved. The proposed method is implemented and simulated by the NS-2. [ABSTRACT FROM AUTHOR]

In a real network deployment, the diverse sensor applications generate a heterogeneous traffic pattern which may include basic sensing measurements such as temperature readings or high-volume multimedia traffic. In a heterogeneous traffic network, the two standardized objective functions (OFs), i.e., objective function zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF) for routing protocol for Low-Power and Lossy Networks (RPL) perform poor routing decisions by selecting an already congested parent node and cause more re-transmissions across the network. Therefore, careful consideration is required in designing a new OF for heterogeneous traffic scenarios. In this study, we examine the RPL protocol under a heterogeneous traffic pattern and proposed a new protocol based on queue and workload-based condition (QWL-RPL). The aim of the proposed protocol is to achieve a reliable path with better overall performance. The proposed OF model considers the link workload in addition to mapping the congestion status of the node using the packet queue. We implement the proposed routing model in the Contiki operating system (OS) Cooja environment to compare with the existing technique. The simulation results show that QWL-RPL can improve the performance of a heterogeneous traffic network as compared with both OF0 and MRHOF, specifically in terms of the amount of overhead, packets reception ratio (PRR), average delay, and jitter. Final results indicate that on average, there is a 5%–30% improvement in PRR, 25%–45% reduction in overheads, 12%–30% reduction in average delay, and 20%–40% reduction in jitter. [ABSTRACT FROM AUTHOR]

This paper presents the adaptation and implementation of the objective function in routing protocol RPL (Routing Protocol for Low Power and Lossy Networks) restating in terms of the status of the connection for better performance quality of service offered. In this way, a new routing metric that improves the indicators quality of service was established: network lifetime, packet delivery and energy savings of LLN networks outlined in the framework of the Internet of Things (IoT); all within the functional specifications for these networks of Internet and Engineering Working Group (In English: Internet Engineering Task Force - IETF). It was performed a series of tests and different simulations in Cooja, where BF-ETX generates better results in terms of network latency and power consumption in densely deployed networks and large-scale front MRHOF. This way, it can achieve a better performance of LLNS to large scale in terms of quality of service compared to routing problems. [ABSTRACT FROM AUTHOR]

This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption. [ABSTRACT FROM AUTHOR]

The IPv6 routing protocol for low power and lossy networks (RPL) was designed to satisfy the requirements of a wide range of Internet of Things (IoT) applications, including industrial and environmental monitoring. In most scenarios, different from an ordinary environment, the industrial monitoring system under emergency scenarios needs to not only periodically collect the information from the sensing region, but also respond rapidly to some unusual situations. In the monitoring system, particularly when an event occurs in the sensing region, a surge of data generated by the sensors may lead to congestion at parent node as data packets converge towards the root. Congestion problem degrades the network performance that has an impact on quality of service. To resolve this problem, we propose a congestion-aware routing protocol (CoAR) which utilizes the selection of an alternative parent to alleviate the congestion in the network. The proposed mechanism uses a multi-criteria decision-making approach to select the best alternative parent node within the congestion by combining the multiple routing metrics. Moreover, the neighborhood index is used as the tie-breaking metric during the parent selection process when the routing score is equal. In order to determine the congestion, CoAR adopts the adaptive congestion detection mechanism based on the current queue occupancy and observation of present and past traffic trends. The proposed protocol has been tested and evaluated in different scenarios in comparison with ECRM and RPL. The simulation results show that CoAR is capable of dealing successfully with congestion in LLNs while preserving the required characteristics of the IoT applications.

RPL (routing protocol for low-power and lossy networks) is an important candidate routing algorithm for low-power and lossy network (LLN) scenarios. To solve the problems of using a single routing metric or no clearly weighting distribution theory of additive composition routing metric in existing RPL algorithms, this paper creates a novel RPL algorithm according to a chaotic genetic algorithm (RPL-CGA). First of all, we propose a composition metric which simultaneously evaluates packet queue length in a buffer, end-to-end delay, residual energy ratio of node, number of hops, and expected transmission count (ETX). Meanwhile, we propose using a chaotic genetic algorithm to determine the weighting distribution of every routing metric in the composition metric to fully evaluate candidate parents (neighbors). Then, according to the evaluation results of candidate parents, we put forward a new holistic objective function and a new method for calculating the rank values of nodes which are used to select the optimized node as the preferred parent (the next hop). Finally, theoretical analysis and a series of experimental consequences indicate that RPL-CGA is significantly superior to the typical existing relevant routing algorithms in the aspect of average end-to-end delay, average success rate, etc.

The Smart Grid (SG) aims to transform the current electric grid into a “smarter” network where the integration of renewable energy resources, energy efficiency and fault tolerance are the main benefits. This is done by interconnecting every energy source, storage point or central control point with connected devices, where heterogeneous SG applications and signalling messages will have different requirements in terms of reliability, latency and priority. Hence, data routing and prioritization are the main challenges in such networks. So far, RPL (Routing Protocol for Low-Power and Lossy networks) protocol is widely used on Smart Grids for distributing commands over the grid. RPL assures traffic differentiation at the network layer in wireless sensor networks through the logical subdivision of the network in multiple instances, each one relying on a specific Objective Function. However, RPL is not optimized for Smart Grids, as its main objective functions and their associated metric does not allow Quality of Service differentiation. To overcome this, we propose OFQS an objective function with a multi-objective metric that considers the delay and the remaining energy in the battery nodes alongside with the dynamic quality of the communication links. Our function automatically adapts to the number of instances (traffic classes) providing a Quality of Service differentiation based on the different Smart Grid applications requirements. We tested our approach on a real sensor testbed. The experimental results show that our proposal provides a lower packet delivery latency and a higher packet delivery ratio while extending the lifetime of the network compared to solutions in the literature.

The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [1] was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility. In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency. [ABSTRACT FROM AUTHOR]

The RPL routing protocol for low power and lossy networks uses the objective function (OF) to build a Destination Oriented Directed Acyclic Graph (DODAG) based on a set of metrics and constraints. The OF has as the main function to select and specify the best parent or the optimal path to reach the destination. However, proposing an adequate objective function in Low Power and Lossy Networks (LLNs) presents a substantial challenge. In this paper, we propose a survey on existing objective functions in LLNs based on a set of metrics. These metrics can define a node or/and link characteristics. We highlight the advantages and the shortcoming of each studied solution. Furthermore, we propose a classification of the used metrics and the criteria of choice. Then, we present a comparative study of the existing OFs in terms of the required performances of the RPL protocol and we provide a deep statistical analysis of all reviewed papers. Finally, we conclude our contribution by highlighting the different issues and challenges that can be exploited for future works. We believe that this survey will help LLNs researchers' community to easily understand the objective function concept and contributes to improving RPL in this context for further relevant research works. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is expected to have a significant impact on city's service provisioning and make a smart city more accessible and pragmatic since the deployment of heterogeneous smart devices in each infrastructure of cities is increasing. So far, the IPv6 routing protocol for low power and lossy networks (RPL) is considered to fit on IoT infrastructure for achieving the expected network requirements. While RPL meets the IoT network requirements quite well, there are some issues that need to be addressed, such as adaptability to network dynamics. This issue significantly limits the use of RPL in many smart city application scenarios, such as emergency alerts with high traffic flows. As part of a smart city vision, IoT applications are becoming more diverse, which requires context-awareness in routing protocols to support the behavior of the network. To address this issue, we design an objective function that performs the route selection based on fuzzy logic techniques while using contextual information from the application. For this, we present a new context-oriented objective function (COOF) that comprises both nodes as well as link metrics. Further, we suggest two new routing metrics, known as queue fluctuation index (QFI) and residual energy index (REI), which consider the status of queue utilization and remaining energy, respectively. The metrics used are designed to respond to the dynamic needs of the network. The proposed approach has been examined and evaluated in different scenarios when compared to other similar approach and default RPL objective functions. Simulation experiments are conducted in Cooja network simulator for Contiki OS. The evaluation results show that COOF can cope with network dynamics and IoT-based smart city application requirements. [ABSTRACT FROM AUTHOR]

The IPv6 routing protocol for low power and lossy networks (RPL) was designed to satisfy the requirements of a wide range of Internet of Things (IoT) applications, including industrial and environmental monitoring. In most scenarios, different from an ordinary environment, the industrial monitoring system under emergency scenarios needs to not only periodically collect the information from the sensing region, but also respond rapidly to some unusual situations. In the monitoring system, particularly when an event occurs in the sensing region, a surge of data generated by the sensors may lead to congestion at parent node as data packets converge towards the root. Congestion problem degrades the network performance that has an impact on quality of service. To resolve this problem, we propose a congestion-aware routing protocol (CoAR) which utilizes the selection of an alternative parent to alleviate the congestion in the network. The proposed mechanism uses a multi-criteria decision-making approach to select the best alternative parent node within the congestion by combining the multiple routing metrics. Moreover, the neighborhood index is used as the tie-breaking metric during the parent selection process when the routing score is equal. In order to determine the congestion, CoAR adopts the adaptive congestion detection mechanism based on the current queue occupancy and observation of present and past traffic trends. The proposed protocol has been tested and evaluated in different scenarios in comparison with ECRM and RPL. The simulation results show that CoAR is capable of dealing successfully with congestion in LLNs while preserving the required characteristics of the IoT applications. [ABSTRACT FROM AUTHOR]