scholarly journals A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT

2021 ◽  
Vol 21 (4) ◽  
pp. 45-61
Author(s):  
T. C. Jermin Jeaunita ◽  
V. Sarasvathi
Keyword(s):  
Machine Learning ◽  
Routing Protocol ◽  
Routing Algorithm ◽  
Maintenance Phase ◽  
Second Phase ◽  
Route Discovery ◽  
Lossy Networks ◽  
Route Maintenance ◽  
Q Learning ◽  
Multi Agent

Abstract The Routing Protocol for Low power and lossy networks (RPL) is used as a routing protocol in IoT applications. In an endeavor to bring out an optimized approach for providing Quality of Service (QoS) routing for heavy volume IoT data transmissions this paper proposes a machine learning-based routing algorithm with a multi-agent environment. The overall routing process is divided into two phases: route discovery phase and route maintenance phase. The route discovery or path finding phase is performed using rank calculation and Q-routing. Q-routing is performed with Q-Learning reinforcement machine learning approach, for selecting the next hop node. The proposed routing protocol first creates a Destination Oriented Directed Acyclic Graph (DODAG) using Q-Learning. The second phase is route maintenance. In this paper, we also propose an approach for route maintenance that considerably reduces control overheads as shown by the simulation and has shown less delay in routing convergence.

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

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2760 ◽  
Author(s):  
Karim Fathallah ◽  
Mohamed Abid ◽  
Nejib Ben Hadj-Alouane
Keyword(s):  
Low Power ◽  
Energy Saving ◽  
Routing Protocol ◽  
Precision Agriculture ◽  
Routing Algorithm ◽  
High Energy ◽  
Sensor Nodes ◽  
Personal Area Networks ◽  
Lossy Networks ◽  
Smart Farming

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 40 % compared to standard RPL.

scholarly journals Pengaruh Penerapan Algoritma Clustering COEEC Terhadap Kinerja Protokol Routing DSR di Jaringan MANET

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Lalu Ikhwan Rosadi ◽  
Andy Hidayat Jatmika ◽  
Sri Endang Anjarwani
Keyword(s):  
Routing Protocol ◽  
Source Node ◽  
Destination Node ◽  
Delivery Ratio ◽  
Route Discovery ◽  
Alternative Communication ◽  
Neighboring Node ◽  
Route Maintenance ◽  
End To End Delay ◽  
Dynamic Source

MANET can be used as an alternative communication solution because it is mobile, meaning that it does not have a fixed infrastructure. MANET implementation can use routing protocols, one example of which is Dynamic Source Routing (DSR). DSR routing protocol works when there is a request for sending packets from the source node to the destination node using two main mechanisms namely, route discovery and route maintenance. Route discovery occurs when the process of sending data from the source node to the destination node by performing a flooding mechanism to the neighboring node. If there is an excessive flooding mechanism, it can cause an increase in routing overhead on the DSR routing protocol. So to overcome these problems clustering is done by utilizing the Overhead Energy Efficient Cluster (COEEC) algorithm on the DSR routing protocol. The results of the addition of clusters in the DSR routing protocol with the COEEC algorithm can increase throughput performance by 43% and 31%, then an increase in the average end to end delay of 0.005% and 0.01%, then the packet delivery ratio parameter increases

scholarly journals A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-32
Author(s):  
Ali Seyfollahi ◽  
Ali Ghaffari
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Low Power ◽  
Routing Protocol ◽  
Directed Acyclic Graph ◽  
Intrusion Detection Systems ◽  
Lossy Networks ◽  
Detection Systems ◽  
Information Object ◽  
Network Router

IPv6 routing protocol for low-power and lossy networks (RPL) has been developed as a routing agent in low-power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low-power, high-throughput IPv6 routing protocol that uses distance vectors. Each sensor-to-wire network router has a collection of fixed parents and a preferred parent on the path to the Destination-oriented directed acyclic graph (DODAG) graph’s root in steady-state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.

Modeling and proving dynamic behaviors of a routing protocol: A tutorial

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110586
Author(s):  
Agnieszka Paszkowska ◽  
Konrad Iwanicki
Keyword(s):  
Internet Of Things ◽  
Routing Protocol ◽  
Packet Routing ◽  
Lossy Networks ◽  
Route Maintenance ◽  
Proof Techniques ◽  
Distance Vector ◽  
Common Problems ◽  
Control Modeling ◽  
Open Issues

With the increasing adoption of Internet of Things technologies for controlling physical processes, their dependability becomes important. One of the fundamental functionalities on which such technologies rely for transferring information between devices is packet routing. However, while the performance of Internet of Things–oriented routing protocols has been widely studied experimentally, little work has been done on provable guarantees on their correctness in various scenarios. To stimulate this type of work, in this article, we give a tutorial on how such guarantees can be derived formally. Our focus is the dynamic behavior of distance-vector route maintenance in an evolving network. As a running example of a routing protocol, we employ routing protocol for low-power and lossy networks, and as the underlying formalism, a variant of linear temporal logic. By building a dedicated model of the protocol, we illustrate common problems, such as keeping complexity in control, modeling processing and communication, abstracting algorithms comprising the protocol, and dealing with open issues and external dependencies. Using the model to derive various safety and liveness guarantees for the protocol and conditions under which they hold, we demonstrate in turn a few proof techniques and the iterative nature of protocol verification, which facilitates obtaining results that are realistic and relevant in practice.

scholarly journals Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3351 ◽  
Author(s):  
Xinlu Li ◽  
Brian Keegan ◽  
Fredrick Mtenzi
Keyword(s):  
Routing Protocol ◽  
Large Scale ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Route Discovery ◽  
Artificial Fish Swarm Algorithm ◽  
Hybrid Routing ◽  
Large Scale Network ◽  
Artificial Fish Swarm ◽  
Scale Network

Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime.

scholarly journals Sink-to-Sink Coordination Framework Using RPL: Routing Protocol for Low Power and Lossy Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Meer M. Khan ◽  
M. Ali Lodhi ◽  
Abdul Rehman ◽  
Abid Khan ◽  
Faisal Bashir Hussain
Keyword(s):  
Low Power ◽  
Routing Protocol ◽  
Network Performance ◽  
Task Force ◽  
Life Time ◽  
Network Size ◽  
Traffic Load ◽  
Lossy Networks ◽  
Route Maintenance ◽  
Coordination Framework

RPL (Routing Protocol for low power and Lossy networks) is recommended by Internet Engineering Task Force (IETF) for IPv6-based LLNs (Low Power and Lossy Networks). RPL uses a proactive routing approach and each node always maintains an active path to the sink node. Sink-to-sink coordination defines syntax and semantics for the exchange of any network defined parameters among sink nodes like network size, traffic load, mobility of a sink, and so forth. The coordination allows sink to learn about the network condition of neighboring sinks. As a result, sinks can make coordinated decision to increase/decrease their network size for optimizing over all network performance in terms of load sharing, increasing network lifetime, and lowering end-to-end latency of communication. Currently, RPL does not provide any coordination framework that can define message exchange between different sink nodes for enhancing the network performance. In this paper, a sink-to-sink coordination framework is proposed which utilizes the periodic route maintenance messages issued by RPL to exchange network status observed at a sink with its neighboring sinks. The proposed framework distributes network load among sink nodes for achieving higher throughputs and longer network’s life time.

Energy-Balanced Distance-Based Routing Algorithm in Wireless Sensor Networks

2014 ◽  
Vol 539 ◽  
pp. 229-233
Author(s):  
Qiang Xian ◽  
Wan Ting Zhang
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Routing Algorithm ◽  
Maintenance Phase ◽  
Routing Algorithms ◽  
System Model ◽  
Wireless Sensor ◽  
Primary Parameter ◽  
Route Maintenance ◽  
Individual Distance

In routing process, individual distance is regarded as the primary parameter in order to adjust the energy consumption. In this paper, we build a time and distance-based system model, and effectively design route setup and route maintenance phase. An Energy-balanced Distance-based Routing Algorithm (EDRA) is put forward to maximize network lifetime. Simulation results demonstrate that the EDRA effectively prolongs the network lifetime and reduces the energy consumption than other routing algorithms.

Simple Ant Routing Algorithm for WSNs Used in Power Line Monitoring

2013 ◽  
Vol 401-403 ◽  
pp. 1981-1985
Author(s):  
Li Fen Li
Keyword(s):  
Transmission Lines ◽  
Power Transmission ◽  
Routing Algorithm ◽  
Maintenance Phase ◽  
Sensor Nodes ◽  
Local Optimum ◽  
Power Transmission Lines ◽  
Route Maintenance ◽  
Search Mechanism ◽  
A Chain

The special class of wireless sensor networks for monitoring power transmission lines may extend for hundreds of miles in distances. The sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a chain-type topology. Thus routing protocols in such environments must be kept as simple as possible. In this paper, we present the Simple Ant Routing Optimizing Algorithm (SAROA) to offer a low overhead solution in optimizing the routing process. Four improved strategies were used in our approach. During the route discovery we have used a new local search mechanism, in which each node broadcasts a control message (FANT) to its neighbors, but only one of them broadcast this message again. During the route maintenance phase, we only use data packets to refresh the paths of active sessions. Finally, the route repair phase is also enhanced, by using a deep search procedure as a way of restricting the number of nodes used to recover a route. A broadest search is only executed when the deeper one fails to succeed. The simulation results show that the enhance algorithm can effectively jump out of the local optimum and satisfy the tolerable delay in network-wide data collection.

scholarly journals ANFIS Based Optimal Routing using Group Teaching and Adaptive Equilibrium Optimization based Trust Aware Routing Protocol in MANET

2021 ◽  
Author(s):  
R. Hemalatha ◽  
R Umamaheswari ◽  
S Jothi
Keyword(s):  
Routing Protocol ◽  
Packet Transmission ◽  
Delivery Ratio ◽  
Route Discovery ◽  
Route Selection ◽  
Optimal Route ◽  
Trust Evaluation ◽  
Route Maintenance ◽  
Multiple Routes ◽  
Maintenance Process

Abstract Recently, routing is considered the main problem in MANET due to its dynamic nature. The route discovery and the optimal route selection from the multiple routes are established for the efficient routing in MANET. The major objective of this research is to select the optimal route for packet transmission in MANET. In this paper, four stages namely trust evaluation, route discovery, optmal route selection and route maintanance are elucidated. Initially, the trust evaluation is made by using ANFIS where the primary trust values are evaluated. The next stage is the route discovery scheme, in which the routes are established by Group teaching optimization algorithm (GTA). From the route discovery scheme, multiple routes are found. The optimal route for the transmission is selected with the help of the Adaptive equilibrium optimizer (AO) algorithm. Finally, the route maintenance process is established; if any of the routes fails for the broadcast it immediately selects the alternate optimal route from the multi-zone routing table for efficient packet transmission. The proposed approach is evaluated by various performance measures like throughput, energy consumption, packet delivery ratio, end-to-end delay, packet loss rate, detection rate, and routing overhead. This result describes that the proposed approach outperforms other state-of-art approaches.

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