scholarly journals Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

2019 ◽  
Vol 9 (8) ◽  
pp. 1582 ◽  
Author(s):  
Valmik Tilwari ◽  
Kaharudin Dimyati ◽  
MHD Hindia ◽  
Anas Fattouh ◽  
Iraj Amiri
Keyword(s):  
Ad Hoc ◽  
Learning Algorithm ◽  
Residual Energy ◽  
Quality Parameters ◽  
Link Quality ◽  
Convergence Time ◽  
Mobile Nodes ◽  
Q Learning ◽  
Routing Scheme ◽  
End To End

To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network.

scholarly journals FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 576
Author(s):  
Ali Alshehri ◽  
Abdel-Hameed A. Badawy ◽  
Hong Huang
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocols ◽  
Opportunistic Routing ◽  
Link Quality ◽  
Q Learning ◽  
Routing Scheme ◽  
End To End ◽  
Geographic Opportunistic Routing ◽  
Asymmetric Link ◽  
Network Metrics

The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.

scholarly journals A proposed routing scheme for power reduction in wireless networks

2013 ◽  
Vol 3 (1) ◽  
pp. 20 ◽  
Author(s):  
Dalia Elewely ◽  
Marwa Areed ◽  
Hesham Ali
Keyword(s):  
Wireless Networks ◽  
Ad Hoc ◽  
Power Saving ◽  
Ad Hoc Wireless Networks ◽  
Mobile Nodes ◽  
Routing Scheme ◽  
Routing Strategy ◽  
Network Operation ◽  
Dynamic Source ◽  
Hoc Networks

Ad-hoc networks consist of a set of mobile nodes with a restricted power supply resources that can communicate with each other without any established infrastructure or centralized administration. The loss of some nodes may cause significant topological changes, undermine the network operation, and affect the lifetime of the network. This paper discusses the energy consumption problem and summaries the existing power saving techniques in ad-hoc wireless networks. The main objective of this paper is to introduce a new power aware multi-path node disjoint routing scheme based on the Dynamic Source Routing protocol (DSR), which can prolong MANETs lifetime, reduce routing delay and increase the reliability of the packets reaching its destination. Therefore, a comprehensive study of DSR protocol has been drawn using NS-2 simulator, to evaluate the performance of DSR as a routing strategy and investigate its efficiency in saving wireless networks resources, as a prelude to avoid its performance shortcomings in our proposed routing scheme. Keywords: Power aware protocol, node disjoint, network simulation 2, multipath routing, Dsr protocol, ad-hoc network.

scholarly journals LINK QUALITY, DELAY AND ENERGY AWARE ROUTING PROTOCOL (LQDEARP) FOR MOBILE AD HOC NETWORKS

2015 ◽  
pp. 212-217
Author(s):  
ARAFAT S.M. QAED ◽  
T. DEVI
Keyword(s):  
Ad Hoc Networks ◽  
Mobile Ad Hoc Networks ◽  
Ad Hoc ◽  
Residual Energy ◽  
Link Quality ◽  
Delivery Ratio ◽  
Energy Aware ◽  
Routing Optimization ◽  
Mobile Ad Hoc ◽  
Hoc Networks

Routing Optimization in mobile ad hoc networks is an ever-demanding task. Mobile ad hoc networks are highly dynamic topology natured and hence several routing protocols meet the challenge of link quality, delay and energy conscious routing. This paper proposes a link quality, delay and energy conscious routing approach based on ant colony optimization. Based on the estimated link quality, delay and residual energy of the nearby nodes, Adaptive node stability (ANS) mechanism is mathematically modeled to make the routing strategy. LQDEARP selects the efficient node based on the ANS mechanism and sends the data packets through that node. Simulation results proved that LQDEARP reduces delay and energy consumption and increases packet delivery ratio than that of the AODV and DECRP protocol.

scholarly journals Modifying the MANET Routing algorithm by GBR CNR-Efficient Neighbor Selection Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 912-917
Keyword(s):  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Routing Algorithm ◽  
Packet Transmission ◽  
Mobile Nodes ◽  
Routing Overhead ◽  
End To End Delay ◽  
Stay Time ◽  
End To End ◽  
Neighbor Selection

In today’s worlds, Mobile Ad-Hoc Network (MANET) plays most important role in the field networks technology in the world. The MANET has been rapidly rising and becoming significant from the last decade. A MANET is a kind of wireless network which has been set-up without requirement of fixed infrastructure where mobile nodes are connected over wireless link. Due to moving nature of the devices, the network topology is unstable and will change dynamically. That’s why stable routing in MANET cannot work properly. In this research paper, a new routing algorithm is proposed to get better routing performance in the MANET. The proposed algorithm designed based on the number of neighbors in the network. Planned algorithm is the improvement of GBR-CNR-LN (GBR-CNR with less neighbors) by calculating the stay time between the selected neighbor nodes and the transmission nodes. If the stay time of sender node is more than the packet transmission time then the selected node is the efficient neighbor selection. The algorithm is implemented and results are analyzed. The results of this paper show the usefulness of the proposed algorithm. The Evaluation of AODV protocol was carried out using Python and outcome of this evaluation showed that proposed Algorithm gave better results than GBR-CNR with less neighbor in terms of End-to-End delay, Number of control message transferred(Routing Overhead) and Network Load. The proposed Algorithm (GC-ENS) decrease Average End-to-End delay 52.54 %, reduce Average Routing Overhead 60.54% and decline the Average load on Network 61.17%.

scholarly journals Delay-Constrained Routing Based on Stochastic Model for Flying Ad Hoc Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shaojie Wen ◽  
Chuanhe Huang
Keyword(s):  
Ad Hoc Networks ◽  
Stochastic Model ◽  
Ad Hoc ◽  
Transition Probability ◽  
Relay Node ◽  
Link Quality ◽  
Mathematical Form ◽  
End To End Delay ◽  
End To End ◽  
Hoc Networks

This paper aims at solving the end-to-end delay-constrained routing problem in a local way for flying ad hoc networks (FANETs). Due to the high mobility, it is difficult for each node in FANETs to obtain the global information. To solve this issue, we propose an adaptive delay-constrained routing with the aid of a stochastic model, which allows the senders to deliver the packets with only local information. We represent the problem in a mathematical form, where the effective transmission rate is viewed as the optimization objective and the link quality and end-to-end delay as the constraints. And, some mathematical tools are used to obtain the approximate solutions for the optimization problem. Before designing the routing scheme, the senders calculate the transition probability for its relay node by jointly considering local delay estimation and expected one-hop delay. Then, the sender transmits the packets to their relay node with transition probability. Finally, we prove the convergence of the proposed routing algorithm and analyse its performances. The simulation results show that the proposed routing policy can improve the network performance effectively in terms of throughput, loss rate, and end-to-end delay.

scholarly journals Resource allocation in clustered M2M networks: a q-learning approach

2021 ◽  
Author(s):  
Fatima Hussain
Keyword(s):  
Resource Allocation ◽  
Learning Algorithm ◽  
Random Access ◽  
Cumulative Distribution ◽  
Convergence Time ◽  
Mixed Integer ◽  
M2m Communication ◽  
Q Learning ◽  
Threshold Levels ◽  
Slot Assignment

Machine to machine (M2M) communication has received increasing attention in recent years. A M2M network exhibits salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small sized packets, energy-constrained and low or no mobility. A large number of M2M terminals may exist in a small area with many trying to simultaneously and randomly access for channel resources - which will result in overload and access problem. This increased signaling overhead and diverse requirements of machine type communication devices (MTCDs) call for the development of flexible and efficient scheduling and random access techniques. In this thesis, we first review and compare various scheduling and random access techniques in LTE-based cellular networks for M2M communication. We also discuss how successful they are to fulfill the unique requirements of M2M communication and networking. Resource management in M2M networks with a large number devices is also reviewed from the access point of view. We propose a multi-objective optimization based solution to the problem of resource allocation in interference-limited M2M communication. We consider MTCDs in a clustered network structure, where they are divided into clusters and the devices belonging to a cluster communicate to cluster head (or controller). We maximize the number of admitted MTCD controllers and throughput with least interference caused to conventional primary users. We formulate the problem as a mixed-integer non-linear problem with multiple objectives and solve it using meshed adaptive direct search (MADS) algorithm. Simulation results show the effects of varying different parameters on cumulative throughput and the number of admitted iii MTCD controllers. We then formulate the slot selection problem in M2M networks with admitted MTCDs as an optimization problem. We present a solution using the Q-learning algorithm to select conflict-free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyze the performance of the proposed algorithm considering different parameters related to its operation. We also compare it with simple ALOHA and channel-based scheduled allocation and show that the proposed Q-learning based technique has a higher probability of assigning slots compared to these techniques. We then present a block based Q-learning algorithm for the scheduling of MTCDs in clustered M2M communication networks. At first centralized slot assignment is done and an algorithm is proposed for minimizing the inter-cluster interference. Then we propose to use an Q-learning algorithm to assign slots in a distributed manner and comparison is made between the two schemes. Afterwards, we show the effects of distributed slot-assignment with respect to varying signal-to-interference ratio on convergence rate and convergence probability. Cumulative distribution function is used to study the effect of various SIR threshold levels on the convergence probability. With the increase in SIR threshold levels, increase in convergence time and decrease in convergence probability are observed, as less block configuration fulfills the required threshold in the M2M network.

Quality of Service (QoS) Routing in Mobile Ad Hoc Networks

2010 ◽  
pp. 464-496
Author(s):  
R. Asokan ◽  
A. M. Natarajan
Keyword(s):  
Quality Of Service ◽  
Ad Hoc ◽  
Qos Routing ◽  
Qos Provisioning ◽  
Mobile Nodes ◽  
Delay Jitter ◽  
End To End Delay ◽  
Mobile Ad Hoc ◽  
End To End

A Mobile Ad hoc NETwork (MANET) consists of a collection of mobile nodes. They communicate in a multi-hop way without a formal infrastructure. Owing to the uniqueness such as easy deployment and self-organizing ability, MANET has shown great potential in several civil and military applications. As MANETs are gaining popularity day-by-day, new developments in the area of real time and multimedia applications are increasing as well. Such applications require Quality of Service (QoS) evolving with respect to bandwidth, end-to-end delay, jitter, energy etc. Consequently, it becomes necessary for MANETs to have an efficient routing and a QoS mechanism to support new applications. QoS provisioning for MANET can be achieved over different layers, starting from the physical layer up to the application layer. This chapter mainly concentrates on the problem of QoS provisioning in the perception of network layer. QoS routing aims at finding a feasible path, which satisfies QoS considering bandwidth, end-to-end delay, jitter, energy etc. This chapter provides a detailed survey of major contributions in QoS routing in MANETs. A few proposals on the QoS routing using optimization techniques and inter-layer approaches have also been addressed. Finally, it concludes with a discussion on the future directions and challenges in QoS routing support in MANETs.

scholarly journals Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jeevaa Katiravan ◽  
D. Sylvia ◽  
D. Srinivasa Rao
Keyword(s):  
Ad Hoc Networks ◽  
Power Control ◽  
Wireless Ad Hoc Networks ◽  
Ad Hoc ◽  
Residual Energy ◽  
Link Quality ◽  
Route Discovery ◽  
Route Selection ◽  
Routing Overhead ◽  
Hoc Networks

In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network.

scholarly journals Hybrid Differential Evolution Based Probabilistic Model to Support Quality Aware Best Path Routing Scheme for Mobile Adhoc Networks

2020 ◽  
Vol 8 (4S5) ◽  
pp. 250-258
Keyword(s):  
Ad Hoc Networks ◽  
Differential Evolution ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Multipath Routing ◽  
Traffic Load ◽  
Destination Node ◽  
Mobile Nodes ◽  
Routing Scheme ◽  
Hoc Networks

The Mobile Ad hoc Networks (MANETs) today represent a new system containing wireless mobile nodes to dynamically and freely organize the topologies of the network without having any communication infrastructure. Owing to the traits such as temporary topology and the absence of a proper centralized authority routing can be a very important issue faced by the ad hoc networks. This multipath routing algorithm has established various paths between the source node and its destination node thus spreading traffic load along various routes. This will be able to alleviate congestion of traffic on a particular path. Thus, the multipath routing algorithms were able to provide route resilience that ensured data transmission reliability. For the purpose of this work, there was a multipath routing scheme which is called the Ad hoc On-demand Multipath Distance Vector Routing (AOMDV) Protocol. This was proposed by means of employing a hybrid Fire Fly (FF) with Differential Evolution (DE) algorithm. This approach was able to achieve better, as well as, reciprocal advantages in a dynamic and hostile network situation. Thus, the proposed scheme of routing was a very powerful method to find effective solutions to MANET routing problems. The proposed method is a well-known probabilistic metaheuristic algorithm to improve the quality aware best path routing protocol. Simulation results indicate that the proposed method FF-DE achieves better performance than AOMDV and FF.

scholarly journals Levenberg–Marquart logistic deep neural learning based energy efficient and load balanced routing in MANET

2021 ◽  
Vol 23 (2) ◽  
pp. 1002
Author(s):  
A. Sangeetha ◽  
T. Rajendran
Keyword(s):  
Energy Efficient ◽  
Ad Hoc ◽  
New Technologies ◽  
Activation Function ◽  
Mobile Node ◽  
Residual Energy ◽  
Node Mobility ◽  
Mobile Nodes ◽  
Neural Learning ◽  
Load Balanced

As the advent of new technologies grows, the deployment of mobile ad hoc networks (MANET) becomes increasingly popular in many application areas. In addition, all the nodes in MANET are battery operated and the node mobility affects the path stability and creates excessive traffic leads to higher utilization of energy, data loss which degrades the performance of routing. So, in this paper we propose Levenberg–Marquardt logistic deep neural learning based energy efficient and load balanced routing (LLDNL-EELBR) which is a machine learning method to deeply analyze the mobile nodes to calculate residual load and energy and it also uses logistic activation function to select the mobile node having higher residual energy and residual load to route the data packet. Experimental evaluations of three methods (LLDNL-EELBR, multipath battery and mobility-aware routing scheme (MBMA-OLSR) and opportunistic routing with gradient forwarding for MANETs (ORGMA)) were done and the result reveals that LLDNL-EELBR method is able to increase the through put and minimizes the delay and energy consumption in MANET when compared to works under consideration.<br /><div> </div>

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