Routing Metrics Research Articles

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.

Secure and Optimal LOADng Routing for IoT with Composite Routing Metric

Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things (IoT) network. To overcome this challenge, a secure Lightweight On-demand Ad hoc Distance-vector—Next Generation (LOADng) Routing Protocol is proposed in this paper. As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector (AODV) protocol, it retains most of the basic functionality and characteristics of AODV. During the route discovery process, the cyclic shift transposition algorithm (CSTA) is used to encrypt the control packets of the LOADng protocol to improve its security. CSTA approach only derives transposition and substitution without product cipher with respect to input data. Besides this, for choosing the best probable path between the source and destination, routing metrics such as link quality Indicator (LQI), hop count (HC) and queue length (QL) are included in the control packets. The data is then securely sent using CSTA using the optimal secure path selected. Experimental Results depict that the proposed secure and optimal LOADng (SO-LOADng) using CSTA encryption obtains better throughput, delivery ratio encryption time and decryption time than the existing state-of-art approaches.

An improved RPL algorithm for low-power and lossy networks

The routing protocol for low-power and lossy networks (RPL), standardized by Internet Engineering Task Force (IETF), is mainly designed to use for Low-power and Lossy Networks (LLNs). To solve the problems of several important routing metrics are not evaluated, the optimal path may contain long single hop links, lack of scientific multi-routing metrics evaluation method and mechanism to balance the parent child number (especially the parent with one hop away from root), this paper proposes an improved RPL algorithm for LLN (I-RPL). First of all, we propose the evaluated routing metrics: child number of parent, candidate parent number, hop count, ETX and energy consumption index. Meanwhile, we improve the path ETX calculation method to avoid selecting optimal path containing long single hop links. Then we design a novel lexical method to synthetically evaluate candidate parents. Meanwhile, based on the evaluation results of candidate parents, we design a novel objective function and a new calculation node rank method which can also be used for selecting the optimal path. Finally, evaluation results show that I-RPL outperforms ETXOF and several other improvements in terms of packet delivery ratio, latency, etc.

Additive Metric Composition-Based Load Aware Reliable Routing Protocol for Improving the Quality of Service in Industrial Internet of Things

The Internet of Things (IoT) is the collection of low-power devices deployed in real-time applications like industries, health care and agriculture. The real-time applications must quickly sense, analyze and react to the data within a time frame. So the data’s should be transmitted without any delay. The Routing Protocol for Low-power and Lossy Networks (RPL) is used to route the data by finding the optimal path. RPL forward the data packets from source to destination based on the objective functions. The objective functions can be designed using different routing metrics and most of the existing objective functions are not designed based on the characteristics of IoT applications. The Industrial Internet of Things (IIoT) environment with real-time data transfer characteristic is considered for this proposed work. Packet loss, power depletion and load balancing are the problems faced by real-time environment. Neighbor Indexed based RPL (NI-RPL) is implemented in two steps to improve efficiency of RPL. First, based on the Received Signal Strength Indicator (RSSI) and path-cost the preferred-parent set is formed from the set of neighboring nodes. Second, the rank of the nodes from the preferred-parent set is calculated based on the Neighbor Index (NI), Expected Transmission count (ETX) and Residual Energy (RE), and then the best route is selected based on the rank. The NI is used to avoid congestion, the ETX and RE helps in improving the Quality of Service (QoS) and lifetime of the network. The proposed objective function, NI-RPL is compared with other objective functions. NI-RPL guarantees the delivery of real –time data with better QoS, because it has improved the packet delivery ratio by 3% to 5% and decreases latency by 7 to 12 seconds

ML-RPL: Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks

This research explores the potential of Machine Learning (ML) to enhance wireless communication networks, specifically in the context of Wireless Smart Grid Networks (WSGNs). We integrated ML into the well-established Routing Protocol for Low-Power and Lossy Networks (RPL), resulting in an advanced version called ML-RPL. This novel protocol utilizes CatBoost, a Gradient Boosted Decision Trees (GBDT) algorithm, to optimize routing decisions. The ML model, trained on a dataset of routing metrics, predicts the probability of successfully reaching a destination node. Each node in the network uses the model to choose the route with the highest probability of effectively delivering packets. Our performance evaluation, carried out in a realistic scenario and under various traffic loads, reveals that ML-RPL significantly improves the packet delivery ratio and minimizes end-to-end delay, making it a promising solution for more efficient and responsive WSGNs.

Ob-EID: Obstacle aware event information dissemination for SDN enabled vehicular network

With rapid increase in traffic density in urban areas, the accident rate is also becoming a serious problem which results in traffic congestion, inconvenience and sometimes life loss. The rate of accidents can be minimized by sharing real-time event information among vehicles on the road through safety application. To realize the safety applications, it is paramount to disseminate event information in real-time through a reliable path which is extremely challenging due to unique vehicular characteristics. To ensure reliable path, there are majorly two critical issues: (i). It is very difficult to form the stable connections due to varying vehicular density and dynamic topology. (ii) the dynamic obstacles affect the signal intensity and hinder the communication between vehicles. Indeed, majority of dissemination schemes are based on distance and dissemination time only, while neglecting the effect of dynamic obstacles. Also, the localized traffic view is considered in such schemes lack in providing the real time vehicular dynamics and network status information. To overcome these issues, a Software Defined Network (SDN) based vehicular network model has been presented to ensure real time vehicular dynamics awareness in urban environment to share event information. For reliable event information dissemination, a heuristic based reliable path selection (HRPS) algorithm is proposed to disseminate event messages between a source and destination vehicle in real-time that will minimize the hindrance due to dynamic obstacles. The proposed algorithm selects a multi-modal path reliability function that depends on multiple routing metrics (connectivity, link reliability and obstacle impact probability) and selects the reliable path by optimizing the path reliability function. The proposed algorithm is simulated for varying urban traffic conditions to analyze its performance. The proposed algorithm shows packet delivery ratio between 90%–95% for varying vehicular density. The simulation results show the supremacy of proposed algorithm as compared to existing compared routing scheme. The proposed algorithm ensures real time event information dissemination for delay sensitive event information that minimizes the impact of dynamic obstacles.

IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

Low-Delay Opportunistic Routing with Reducing Overhead in Asynchronous Duty-Cycled Wireless Sensor Networks

The Internet of Things (IoT) and cloud computing are well developed and applied in different services. Among these services, some of them take latency and overhead as the metrics to evaluate the quality of services. This paper introduces how an opportunistic routing (OR) protocol reduces latency and overhead when it is applied in wireless sensor network (WSN) application among lightweight devices. OR mitigates the delay problem and balances the energy consumption and delay of the nodes in WSNs. However, OR, with the nature of broadcasting, can easily cause heavy overhead such as redundant data and control packets in the forwarding process. In the current solution, the number of candidates (potential forwarders) is not limited by the dynamics of forwarding process. Also, the overhead of establishing the candidate sets is high due to global information calculation. Therefore, a low-delay OR protocol with reducing overhead which designs the dynamic candidate area (DCA) to establish candidate sets is first proposed in this paper. The main work is as follows. (a) The duty cycle of a node is adjusted according to the distance between the node and sink node in the initialized network. (b) A method to establish dynamic candidate sets is proposed based on the adaptive duty cycle. Before and during the forwarding process, the dynamic candidate area is adjusted in time. (c) According to the feature of the candidate area, the corresponding routing metric is proposed to complete the cooperative communication among candidates. Through further theoretical analysis and simulations, the results indicate that this protocol achieves better performance in terms of transmission delay, data and control overhead, and network lifetime compared to the state-of-the-art solutions.

A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0

Industry 5.0 requires intelligent self-organi- zed, self-managed, and self-monitoring applications with ability to analyze and predict the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for Internet of Things applications in Industry 5.0. Knowledge-defined networks (KDN) bridge this gap by extending software-defined networking architecture with knowledge plane, which learns the network dynamics to avoid suboptimal decisions. Cognitive routing leverages the sixth-generation (6G) self-organized networks with self-learning feature. This article presents a self-organized cognitive routing framework for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most reliable path with minimal probability of route-flapping. The proposed framework precalculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry-stranded link-state and distance-vector routing algorithms SPF and DUAL, respectively.

An innovative RPL objective function for broad range of IoT domains utilizing fuzzy logic and multiple metrics

The indispensable part of IoT is the Low-power and Lossy Network (LLN), which is required to connect plenty of resource-constrained (e.g., power and memory) wireless devices. Interestingly, the IPv6 Routing Protocol for LLNs (i.e., RPL), which is the standard routing protocol for IoT, relies primarily on constructing a destination-oriented directed acyclic graph governed by a variety of routing metrics that help in choosing the Objective Function (OF), which is liable for selecting the best preferred parent of each node which in turn gets involved in the route establishment towards the destination. In fact, establishing an OF based on a single metric, as the majority of research considers, has flaws or drawbacks, as it only benefits a few IoT applications. To put it another way, the network lifetime is improved for a few IoT apps but degraded for the majority. In this article, we address not only the limitation of using a single metric, but also the limitations of those works employed a composite metric by putting forward a cross-layer design and accordingly developing a fuzzy logic system that brings together four input metrics, namely, hop count, energy consumption, latency, and received signal strength indicator as a new OF, abbreviated as FL-HELR-OF. The simulation findings, acquired by the Cooja simulator, prove the effectiveness of our new OF, which particularly outperforms other existing studies concerning the packet delivery ratio, control message overhead, latency, energy consumption, and average hop count.

Routing optimization in a wireless network using genetic algorithm and ILA routing metric

Wireless access technology is a popular means of delivering various telecommunication services to users in Nigeria. Wireless Mesh Networking and Wireless Sensor Networking have found useful applications in wireless access systems, surveillance and infrastructure Management. A key problem associated with this technology is performance degradation in terms of Quality of Service (QoS). Achieving good QoS in Wireless Networks design involves implementing a robust routing optimization technique. Genetic Algorithm (GA) technique in NS2 network simulator was used to implement routing optimization in order to solve the performance issues experienced in a campus-based Voice over Internet Protocol (VoIP) communication system. The Interference and Load Aware (ILA) routing metric was chosen as the fitness function which helps to select the best route from a set of identified traffic paths that best guarantees low delay, high throughput and low packet losses suitable for real time communication. Results show that the GA based route optimization produced a 23% improvement in Throughput, 11% less Delay and 16% less Packet loss in comparison to the non-GA route. This work shows that route optimization in wireless multi-hop networks based on GA and ILA improves its QoS.

A New Objective Function Based on Additive Combination of Node and Link Metrics as a Mechanism Path Selection for RPL Protocol

Since its development by IETF, the IPv6 routing protocol for low power and lossy networks (RPL) remains the subject of several researches. RPL is based on objective function as a mechanism selection of paths in the network. However, the default objective functions standardized selects the routes according to a single routing metric that leads to an unoptimized path selection and a lot of parent changes. Thus, we propose in this paper weighted combined metrics objective function (WCM-OF) and non-weighted combined metrics objective function (NWCM-OF) that are based both on additive link quality and energy metrics with equal weights or not to achieve a tradeoff between reliability and saved energy levels. The proposed objective functions were implemented in the core of Contiki operating system and evaluated with Cooja emulator. Results show that the proposed objective functions improved the network performances compared to default objective functions.

Queue Length and Mobility aware Routing Protocol for Mobile Ad hoc Network

A mobile ad-hoc network (MANET) is different from other wireless networks in many ways. One of the key differences is that a MANET is a multihop wireless network,i.e., a routing path is composed of intermediate mobile nodes and wireless links connecting them. In this paper, heterogeneous Mobile Ad-hoc Networks (H-MANETs) are considered. H-MANETs are composed of nodes with different transmission range. We propose an improvement of AODV protocol called AMAODV (Adaptative Mobility aware AODV). This protocol is based on new metric combine more routing metrics (distance, relative velocity, queue length and hop count) between each node and one hop neighbor. Which permits to avoid losing route. Through the simulation, it is confirmed that this improvement has higher packet delivery ratio and less average end-to-end delay than basic AODV protocol.

Optimal Cluster Head in DTN Routing Hierarchical Topology (DRHT)

In delay tolerant networking (DTN), nodes are autonomous and behave in an unpredictable way. Consequently, a control mechanism of topology is necessary. This mechanism should ensure the overall connectivity of the network taking into account nodes’ mobility. In this paper, we study the problem of data routing with an optimal delay in the bundle layer, by exploiting: the clustering, the messages ferries and the optimal election of cluster head (CH). We first introduce the DTN routing hierarchical topology (DRHT) which incorporates these three factors into the routing metric. We propose an optimal approach to elect a CH based on four criteria: the residual energy, the intra-cluster distance, the node degree and the head count of probable CHs. We proceed then to model a Markov decision process (MDP) to decide the optimal moment for sending data in order to ensure a higher delivery rate within a reasonable delay. At the end, we present the simulation results demonstrating the effectiveness of the DRHT. Our simulation shows that while using the DRHT which is based on the optimal election of CH, the traffic control during the TTL interval (Time To Live) is balanced, which greatly increases the delivery rate of bundles and decreases the loss rate.

Link Quality and MAC-Overhead aware Predictive Preemptive Multipath Routing Protocol for Mobile Ad hoc Networks

In Ad Hoc networks, route failure may occur due to less received power, mobility, congestion and node failures. Many approaches have been proposed in literature to solve this problem, where a node predicts pre-emptively the route failure that occurs with the less received power. However, these approaches encounter some difficulties, especially in scenario without mobility where route failures may arise. In this paper, we propose an improvement of AOMDV protocol called LO-PPAOMDV (Link Quality and MAC-Overhead aware Predictive Preemptive AOMDV). This protocol is based on new metric combine two routing metrics (Link Quality, MAC Overhead) between each node and one hop neighbor. Also we propose a cross-layer networking mechanism to distinguish between both situations, failures due to congestion or mobility, and consequently avoiding unnecessary route repair process. The LO-PPAOMDV was implemented using NS-2. The simulation results demonstrate the merits of our proposed LO-PPAOMDV with approximately 10-15% increase in the packet delivery ratio while average end-to-end delay is reduced by 20%, and normalized routing load is reduced about 45%, also with 7% increase in the throughput, when compared with PPAOMDV.

Evaluating an Application Aware Distributed Dijkstra Shortest Path Algorithm in Hybrid Cloud/Edge Environments

To increase the flexibility and the dynamism of communication networks, Software Defined Networking (SDN) has emerged as a challenging approach to decouple control and data planes, using a logically centralized controller able to manage the underlying network resources. However, traditional network solutions can not be always used in SDN. In this paper, we deal with routing issues in the setup of dynamic SDN spanning Fog/Edge and IoT systems for supporting the new generation of applications. In particular, we present a modified version of Dijkstra's routing algorithm that can optimize complex routing metrics and uses MapReduce to speed up the configuration of routers in a software-defined network. The system can optimize the packet routing accordingly to different parameters including, e.g., hops, latency, and energy efficiency policies. To show the effective benefits of the proposed solution, we performed evaluations on the revised MapReduce version of the Dijkstra routing algorithm considering a highly scalable network topology with thousands of virtual nodes.

An agent-based algorithm for dynamic routing in service networks

Service networks consisting of components and links are regarded as significant infrastructures to provide all kinds of services for users. They are supposed to select the best route for service requests in case of time-varying demand and different service patterns. In addition, it is necessary to choose appropriate routing metrics so as to satisfy user requirements for a variety of services. Multiple quality of service (QoS) requirements must be considered in the dynamic environment as a result of high demand for service networks. To that end, we propose an agent-based algorithm to address the routing problem in service networks. We construct a multi-layer network model to figure out component behaviors and complicated relationship between components under uncertainty. In order to reflect various service requirements, QoS metrics are defined from the perspectives of component and link. We also put forward an improved deep Q-learning method to achieve global convergence and enhance the efficiency of the routing algorithm. The numerical results on a case study illustrate the proposed algorithm finds high-quality solutions at acceptable costs, which routes service requests properly in the dynamic network environment. The proposed algorithm achieves outstanding performance compared with state-of-the-art routing algorithms in terms of delay and service factor.

Performance analysis of 6LoWPAN protocol for a flood monitoring system

The internet of things is a disruptive technology that has been applied as a solution to problems in many fields of monitoring environmental variables. It is supported by technologies such as wireless sensor networks, which offer many protocols and hardware platforms in the market today. Protocols such as 6LoWPAN are novel, so this work focuses on determining whether its implementation on TelosB mote is feasible; these would be placed on an experimental deployment for a particular scenario of flash floods in a sector known as “La Brigada”, in the city of Barranquilla. This proposal has not been evaluated in Colombia for this type of application, and no similar work has been done for this type of scenario. For the evaluation of 6LoWPAN, a deployment with two end nodes and a sink node has been designed, due to the monitoring section under study; 5-min tests are proposed where through round trip time traffic PINGv6 packets are generated back and forth (Echo) between a sink node and two end nodes. The results are based on the evaluation of metrics such as delay and ping packet request/response rate. The performance of these metrics is subject to test scenarios that vary according to distance, packet size, and channel scan time. Two routing options, static or dynamic, are also proposed for this application case. The tests performed yielded results in terms of better performance in the test scenarios for packets with an average size of 120 B and channel monitoring times of 1024 ms. Likewise, the use of the TelosB platform was validated as a viable and innovative option for a monitoring scenario to flash floods in short stretches of the city of Barranquilla—Colombia. This study is important because it can provide information on the use of the TelosB platform as a valid solution for similar application scenarios; furthermore, the tests performed can be replicated in similar studies to evaluate congestion, power consumption, routing, topologies, and other metrics. This study is providing a road map for the research community to follow the simulation scenario to apply the test to their own studies. This work also provides the guidelines for similar researchers to monitor the flood in their own regions and then compare their results with this study.

Balancing the Electromagnetic Field Exposure in Wireless Multi-Hop Networks: An EMF-Aware Routing Scheme

This work is situated at the conjunction of the fields of distributed systems, telecommunications, and mathematical modeling, aiming to offer solutions to the problem of people’s overexposure to electro-magnetic fields (EMF) radiation. In this paper, we propose a new routing scheme for wireless multi-hop networks, which seeks a fairer distribution of the exposure to electromagnetic fields, by leveraging a combination of the transmitted power and the accumulated exposure as a routing metric. We carry out a holistic approach, including: (1) an algorithmic study, (2) an analytical model of the aforementioned novel routing metric, and (3) the specification of a routing protocol. We make a performance assessment of our novel routing protocol and the corresponding algorithm, by means of an extensive simulation campaign over the NS-3 simulator. The obtained results yield that the proposed novel solution is able to not only fairly distribute the exposure, but also to reduce its average value, thus enhancing the user experience. We also show that the power consumption using the EMF-aware proposed solution, based on Cycle Canceling Algorithm (CCA), and that observed with an approach seeking power reduction are alike. Indeed, even if there exist key-differences from the user experience’s point of view between both routing approaches, there is no statistically relevant power increase between them. Thus, our solution manages to keep the consumed power at a low level, and at the same time it reduces the overall nodes’ exposure to EMF.

Cooperative and feedback based authentic routing protocol for energy efficient IoT systems

AbstractThe open communication medium of the Internet of Things (IoT) is more vulnerable to security attacks. As the IoT environment consists of distributed power limited units, the routing protocol used for distributed routing should be light‐weighted compared to other centralized networks. In this situation, complex security algorithms and routing mechanisms affect the generic data communications in IoT platforms. To handle this problem, this proposed system develops a cooperative and feedback‐based trustable energy‐efficient routing protocol (CFTEERP). This protocol calculates local trust value (LTV) and global trust value (GTV) of each node using node attributes and K‐means‐based feedback evaluation procedures. The K‐means clustering algorithm leaves out the distorted node routing metrics and misbehaving node metrics for all channels. This proposed CFTEERP uses the nearest secure node costs to increase the network lifetime without selecting the nearest nodes for routing the data. In this work, secure routing is initiated using multipath routing strategy that analyses LTV, GTV, next trustable node, average throughput, energy consumption, average packet delivery ratio (PDR) and traffic various metrics of entire IoT communication. The technical aspects of proposed system are implemented to solve different existing techniques' limitations. In the comparative experiment, the proposed method provides 90% of PDR and a minimal energy consumption rate of 25% lesser than the existing systems against different malicious attacks.