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Performance evaluation of a cartography enhanced OLSR for mobile multi-hop ad hoc networks
Abstract
In this paper, we propose the integration of a cartography gathering scheme to enhance the capacity of the Optimized Link State Routing Protocol (OLSR) to properly track node movements in dynamic networks. We propose an improved version of OLSR called the Cartography Enhanced Optimized Link State Routing Protocol (CE-OLSR), a novel routing protocol designed for mobile multi-hop ad hoc networks. Our contribution is three fold. First, we propose an efficient network cartography collection scheme solely based on OLSR signaling traffic. We show that this cartography is much richer than the mere topology gathered by the seminal OLSR. Second, we designed an enhanced version of OLSR based on the collected cartography. We show that CE-OLSR insures a much better responsiveness and copes appropriately with the mobility of nodes. Third, we conduct an extensive set of simulations to compare the performance of our proposal against that of OLSR. Simulations results show that the proposed CE-OLSR outperforms greatly OLSR in terms of a much better route validity, a much higher throughput and a much lower average delay. For instance, at a speed of 20 m/s, CE-OLSR achieves a route validity beyond 93% while that provided by OLSR barely attains 30%. At high speeds, CE-OLSR delivers more than 3 times the throughput of OLSR with an average end to end delay 21 times smaller. As such, CE-OLSR stands out not only as an appropriate routing protocol for mobile multi-hop ad hoc networks, but also a viable protocol for the transport of time critical data.
... The AN topology evolves with time, which makes the identification of unbroken routes a challenging task [10], [26], [27]. A transmitter node retransmits a unicast packet seven times in the CSMA/CA (carriersense multiple access with collision avoidance) MAC (medium access control) protocol before recognizing a link break, which reduces the channel utilization and increases the queuing delay for the remaining packets at the node [26]. ...
... To prevent the packet transmission over broken routes, each node in [26], [27] includes the GPS locations of itself and its 1-hop neighbor nodes in its control messages in order to create a cartography of the network at each node. Based on these locations, source node selects a route such that links do not break before the reception of new control messages. ...
... Based on these locations, source node selects a route such that links do not break before the reception of new control messages. However, the control packet lengths in [26], [27] increases significantly in a dense network, which results in a higher control overhead and packet collision probability. In addition, periodic reconstruction of the cartography increases the computational overhead at each node, which reduces its residual battery life. ...
An autonomous airborne network (AN) consists of multiple unmanned aerial vehicles (UAVs), which can self-configure to provide seamless, low-cost and secure connectivity. AN is preferred for applications in civilian and military sectors because it can improve the network reliability and fault tolerance, reduce mission completion time through collaboration, and adapt to dynamic mission requirements. However, facilitating seamless communication in such ANs is a challenging task due to their fast node mobility, which results in frequent link disruptions. Many existing AN-specific mobility-aware schemes restrictively assume that UAVs fly in straight lines, to reduce the high uncertainty in the mobility pattern and simplify the calculation of link lifetime (LLT). Here, LLT represents the duration after which the link between a node pair terminates. However, the application of such schemes is severely limited, which makes them unsuitable for practical autonomous ANs. In this report, a mathematical framework is described to accurately compute the \textit{LLT} value for a UAV node pair, where each node flies independently in a randomly selected smooth trajectory. In addition, the impact of random trajectory changes on LLT accuracy is also discussed.
... This section gives the review of Proactive Routing Protocols. As illustrated in Fig. 4, the Proactive Routing Protcols include: "Destination-Sequenced Distance Vector" (DSDV) [75], "Optimized Link State Routing" (OLSR) [76], "Directional Optimized Link State Routing Protocol" (D-OLSR) [77],"Mobile and Load-aware Optimized Connection State Routing Protocol" (ML-OLSR) [78] or "Cartography Enhanced Optimized Link State Routing Protocol" (CE-OLSR) [79] and the brief overview of these protocols has been discussed below. ...
... The ML-OLSR [78] has been addressed to avoid high-speed UAVs from being selected as MPRs. CE-OLSR [79] is a more enhanced form of the OLSR protocol [76] that accommodates for high mobility in extremely complex networks such as FANETs. ...
When disasters such as floods or earthquakes occur, we may not have a support of regular infrastructure based networks. This proves fatal because people who are trapped can not be easily located by search and rescue team. In such cases, airborne network consisting of miniaturized drones can be extremely beneficial in providing quick and effective coverage of the affected area, in an on-demand manner providing instant insights to rescue teams. While the challenges offered by such networks are plenty, the ongoing research and development shows promise to make such a technology more reliable and effective. In this paper, we discuss various disaster events in which network of drones can play a vital role in offering support to rescue operations. Mainly, the article discusses the protocols proposed by researchers for various layers of protocol stack including physical layer, data link layer, network layer, transport layer, application layer along with clustering protocols, time synchronization protocols and localization protocols. Finally, a brief summary of software simulation platforms and testbeds, along with future trends of Flying Ad-hoc networks have been provided.
... DOLSR has the advantage of minimizing end-to-end delay, which is crucial for real-time applications and offers security improvements as it is resistant to jamming. In addition to the protocols explained, there are a variety of protocols with variants that are used in FANETs, such as Predictive-OLSR (P-OLSR) [5], Mobility and Load-Aware OLSR (ML-OLSR) [25], Contention-Based OLSR (COLSR) [26], Modified-OLSR (M-OLSR) [27], Cartography-Enhanced OLSR (CE-OLSR) [28], Topology Broadcast Based on Reverse-Path Forwarding (TBRPF) [29], Fisheye State Routing (FSR) [30], and Babel [31]. Babel builds on the ideas of DSDV, AODV, and other routing protocols to derive a loop-avoiding distance vector routing protocol that is designed to be robust and efficient in both relatively stable and highly dynamic networks. ...
... Topology-Based Static LCAD [11], MLHR [12], DCR [13] Proactive OLSR [16], DSDV [17], BATMAN [18], BATMAN-ADV [23], DOLSR [19], P-OLSR [5], ML-OLSR [25], COLSR [26], M-OLSR [27], CE-OLSR [28], TBRPF [29], FSR [30], Babel [31] Reactive DSR [32], AODV [33], TS-AODV [34], MAODV [35], AODVSEC [36] Hybrid ZRP [37], TORA [38], RTORA [39], HWMP [40], SHARP [41], HRP [42] Position-Based ...
Unmanned aerial vehicles (UAVs) are widely used in our modern society and their development is rapidly accelerating. Flying Ad Hoc Networks (FANETs) have opened a new window of opportunity to create new value-added services. However, the characteristics that make FANETs unique, such as node mobility, node distance, energy constraints, etc., imply that several guidelines need to be considered for their successful deployment. Although numerous routing protocols have been proposed for FANETs, due to the wide range of applications in which FANETs can be applied, not all routing protocols can be used. Due to this challenge, after breaking down and classifying the different types of existing routing protocols for FANET, this paper analyzes and compares the performance of several routing protocols (Babel, BATMAN-ADV, and OLSR) in terms of throughput and packet loss in a real deployment composed of several UAV nodes using 2.4 and 5 GHz WiFi networks. The results show that Babel achieves better performance in the studied metrics than OLSR and BATMAN-ADV, while BATMAN-ADV delivers significantly lower performance. This experimental study confirms the importance of choosing the proper routing protocol for FANETs and their performance evaluation, something that will be extremely important in a few years when this type of network will be common in our day-to-day life.
... For the control messages, overhead is created in the networks [51]. Based on the mechanism of OLSR, several new routing protocols have been proposed, such as D-OLSR [52], M-OSLR [53], and CE-OSLR [54]. ...
... where and are average distances from the CM to their corresponding CH in the free space and multipath models, respectively. For the case of ( ≥ ) from Equation (45), substituting Equation (53), (54), and (55) we get ...
In recent years, unmanned aerial vehicle (UAV) networks have been a focus area of the academic and industrial research community. They have been used in many military and civilian applications. UAV networks have unique features and characteristics that are different from mobile ad hoc networks and vehicular ad hoc networks. In dynamic multi-UAV networks, localization, clustering and routing are the fundamental functions for cooperative control. However, due to the high mobility of UAVs and rapid topology change, UAV localization, clustering and routing are the challenging task. In order to improve the network performance, the clustering approach has been used to address the UAV routing problem. In multi-UAV networks, clustering is used to handle the scalability and stability of networks. Due to energy limitations, network lifetime is a crucial parameter in UAV networks. Furthermore, due to the high mobility of UAVs, topology control is essential to reduce communication interference. Finally, the use of localization with clustering in UAV networks can increase network performance with lower overhead, latency, and energy consumption.
In the first work, we propose a location-aided delay tolerant routing (LADTR) protocol for UAV networks for use in post-disaster operations, which exploits location-aided forwarding combined with a store-carry-forward (SCF) technique. Ferrying UAVs are introduced to enable an efficient SCF, and this is the first attempt at introducing and using ferrying UAVs for routing in UAV networks, to the best of our knowledge. Ferrying UAVs improve the availability of connection paths between searching UAVs and the ground station, thus reducing end-to-end delays and increasing the packet delivery ratio. Future UAV locations are estimated based on the location and speed of UAVs equipped with a global positioning system. The forwarding UAV node predicts the position of the destination UAV node and then decides where to forward. The proposed LADTR ensures that the contact rate between UAV nodes remains high, which enables a high packet delivery ratio, and ensures single-copy data forwarding to avoid replication of each message.
In the second work, we propose swarm-intelligence-based localization and clustering schemes in UAV networks for emergency communications. First, we propose a new three-dimensional (3D) swarm-intelligence-based localization (SIL) algorithm based on particle swarm optimization (PSO) that exploits the particle search space in a limited boundary by using the bounding box method. In the 3D search space, anchor UAV nodes are randomly distributed and the SIL algorithm measures the distance to existing anchor nodes for estimating the location of the target UAV nodes. Convergence time and localization accuracy are improved with lower computational cost. Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for inter-cluster distance, intra-cluster distance, residual energy, and geographic location. For energy-efficient clustering, cluster heads are selected based on improved particle optimization.
In the last work, we propose bio-inspired localization and clustering schemes in UAV networks for wildfire detection and monitoring. First, we propose an energy-efficient bio-inspired three-dimensional localization (BIL) algorithm. The algorithm is based on hybrid grey wolf optimization (HGWO), which can reduce node localization errors and avoid flip ambiguity (FA) in bounded distance measurement errors and achieves high localization accuracy. After measuring the distance between UAV nodes, the HGWO algorithm estimates the locations of the UAVs, which ensures the global convergence of the results. Second, based on the HGWO algorithm, we propose a new energy-efficient bio-inspired clustering (BIC) algorithm to save the energy of UAVs. The BIC algorithm utilizes the grey wolf leadership hierarchy to improve clustering efficiency. Furthermore, we develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose GWO-based compressive sensing (CS-GWO) to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to BS, thereby reducing the routing delay and the number of transmissions.
The performance of each proposed algorithm has been evaluated by computer simulation with the comparison to the existing works. Our performance study shows that the proposed LADTR outperforms the four typical routing protocols reported in the literature in terms of packet delivery ratio, average delay, and routing overhead. The proposed SIC outperforms five typical routing protocols regarding to packet delivery ratio, average end-to-end delay, and routing overhead. Moreover, SIC consumes less energy and prolongs network lifetime. Finally, the proposed BIL and BIC significantly outperform conventional schemes, in terms of various performance metrics under different scenarios.
... Topology change needs link-state knowledge of full network for sharing between all nodes [158][159][160][161][162]. It helps in having an exact idea of network for calculating the shortest path between UAVs. ...
... The event concerning highly dynamic network bandwidth consumption and sharing or exchanging packets could lead to network congestion. Therefore, its suitability in FANET [161] can be only when there is an important update. Table 11 shows a parallel aspect of various routing approaches, whereas Table 12 renders different details on Simulators used in FANET. ...
Rapid expansion and technological advancement of Wireless communication lead by researchers in different fields of engineering have achieved various milestones. Its efficient use has proven that days are not too far when even we think of anything flying sensors, actuators of multiple types surrounded by us process it, and try to bring down or show the best possible result. One of the most talked-about innovations in drones the day it was invented. Group of Drones working in a collectively and Ad hoc manner form a Flying Ad hoc network (FANET). The Mobility of UAVs makes it more accessible, which helps in the mobile revolution that the present world is going through. The use of such a network in different applications across the globe is highly demanding. Most of the traditional study compares it with other Ad hoc networks and coupling, relating to them. However, FANET has its considerations relating to the same class of system. This paper presents a comprehensive survey relating FANET and critical points regarding it, ranging from the categorization of FANET, the architecture of FANET, types of possible communication in FANET, numerous Mobility Models, constraints in FANET, characteristics, and design of FANET, routing protocol, and routing topology. We further present open issues and challenges in FANET relating to essential parameters and research. It endeavored to store all the recommendations to encourage the researchers to function in this particular area and the same class of network. Deterministic jointly with probabilistic points of studies compiled and listed with the belief that deterministic methods should be in association with stochastic approaches for better results in FANET.
... In [8], authors use the network cartography in different ways in order to improve the efficiency of the routing function. In [9] they propose the integration of a cartography gathering scheme to enhance the capacity of the Optimized Link State Routing Protocol (OLSR) to track node movements in dynamic networks accurately. In [3] they discuss problems of OLSR and other routing protocols that are due to the mobility of nodes. ...
The main idea behind the routing protocols based on multi-hop is to find the required path from the source node to the destination. Since those protocols do not consider the node mobility in their mechanism, we propose to enhance the routing protocols benefiting from localization node and predict the time needed for neighbors to go out of range for a node to increase the robustness of the protocols with the mobility. Firstly, we propose an efficient scheme to predict the time needed for the node to quite the neighbor range. Secondly, we conduct a set of simulations to compare the performance of our proposal against OLSR standard.
p>This article is under review and upon acceptance: Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. (Copyright (c) 2015 IEEE.) </p
p>This article is under review and upon acceptance: Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. (Copyright (c) 2015 IEEE.) </p
Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of future communication services. This paper first overviews the key components of NTN while highlighting the significance of emerging UAV networks where for example, a group of UAVs can be used as nodes to exchange data packets and form a flying ad hoc network (FANET). In addition, both existing and emerging applications of the FANET are explored. Next, it provides key recent findings and the state-of-the-art of FANETs while examining various routing protocols based on cross-layer modeling. Moreover, a modeling perspective of FANETs is provided considering delay-tolerant networks (DTN) because of the intermittent nature of connectivity in low-density FANETs, where each node (or UAV) can perform store-carry-and-forward (SCF) operations. Indeed, we provide a case study of a UAV network as a DTN, referred to as DTN-assisted FANET. Furthermore, applications of machine learning (ML) in FANET are discussed. This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks.
Compared with traditional mobile Ad hoc networks (MANET) and vehicular Ad hoc networks (VANET), unmanned aerial vehicle (UAV) networks have two unique characteristics. First, the UAV network topology changes more frequently and unpredictably. Second, link qualities between UAVs vary significantly with time and space. Existing hop-by-hop routing protocols can neither adapt to the highly dynamic network environment nor fully utilize transmission opportunities in all links with various qualities in UAV networks. In this paper, we propose the Geographic Position based Hopless Opportunistic Routing (GPHLOR) Protocol for UAV networks to solve these problems. GPHLOR combines the advantages of the hopless protocols and the position-based protocols. Benefiting from the idea of hopless, compared with hop-by-hop routing protocols, GPHLOR can fully utilize the transmission opportunities of all links regardless of hop distances and link qualities. While different from existing hopless protocols, GPHLOR selects relays according to each node’s geographic position information without caring about the topology, so it can well adapt to the frequent topology change. Moreover, GPHLOR allows each node to calculate its forwarding priority in a distributed manner according to its local position information. It does not rely on frequent network information measurement and exchange, reducing the protocol overhead. Simulation results show that GPHLOR achieves better performance in packet delivery rate, end-to-end delay, throughput, and overhead compared with existing routing protocols in UAV networks.
Scalability is one of the toughest challenges in ad hoc networking. Recent work outlines theoretical bounds on how well routing protocols could scale in this environment. However, none of the popular routing solutions really scales to large networks, by coming close enough to these bounds. In this paper, we study the case of link state routing and OLSR, one of the strongest candidate for standardization. We analyze how these bounds are not reached in this case, and we study how much the scalability is enhanced with the use of Fish Eye techniques in addition to the link state routing framework. We show that with this enhancement, the theoretical scalability bounds are reached.
This paper discusses the link stability estimation for Mobile Ad hoc Networks (MANETs). In this approach the total time for which the link remains connected with the neighboring nodes is estimated. This helps to predict the stability of the route which is required to forward the packets to the destination. This method does not use the selection of the next hop on the basis of shortest distance, but is based on the time period for which the next hop link remains connected. The parameters we use, are the distance and frequency and signal quality. These provide the way for a node to decide the best next hop neighbor and hence a perfect Quality of Service (QoS) is also obtained.
A mobile ad hoc network consists of wireless hosts that may move often. Movement of hosts results in a change in routes, requiring some mechanism for determining new routes. Several routing protocols have already been proposed for ad hoc networks. This paper suggests an approach to utilize location information (for instance, obtained using the global positioning system) to improve performance of routing protocols for ad hoc networks. By using location information, the proposed Location-Aided Routing (LAR) protocols limit the search for a new route to a smaller "request zone" of the ad hoc network. This results in a significant reduction in the number of routing messages. We present two algorithms to determine the request zone, and also suggest potential optimizations to our algorithms.
This paper presents the expected transmission count metric (ETX), which finds high-throughput paths on multi-hop wireless networks. ETX minimizes the expected total number of packet transmissions (including retransmissions) required to successfully deliver a packet to the ultimate destination. The ETX metric incorporates the effects of link loss ratios, asymmetry in the loss ratios between the two directions of each link, and interference among the successive links of a path. In contrast, the minimum hop-count metric chooses arbitrarily among the different paths of the same minimum length, regardless of the often large differences in throughput among those paths, and ignoring the possibility that a longer path might offer higher throughput.
Quality of service (QoS) routing is known to be an NP-hard problem in case of two or more additive constraints, and several exact algorithms and
heuristics have been proposed to address this issue. In this paper, we consider a particular two-constrained quality of service
routing problem maximizing path stability with a limited path length in the quest of improving routability in dynamic multi-hop
mobile wireless ad hoc networks. First, we propose a novel exact algorithm to solve the optimal weight-constrained path problem.
We instantiate our algorithm to solve the most stable path not exceeding a certain number of hops, in polynomial time. This
algorithm is then applied to the practical case of proactive routing in dynamic multi-hop wireless ad hoc networks. In these
networks, an adequate compromise between route stability and its length in hops is essential for appropriately mitigating
the impact of the network dynamics on the validity of established routes. Secondly, we set up a common framework for the comparison
between three families of proactive routing: the shortest path-based routing, the most stable path-based routing and our proposed
most stable constrained path routing. We show then through extensive simulations that routing based on our proposed algorithm
selects appropriate stable paths yielding a very high routability with an average path length just above that of the shortest
paths.
KeywordsMANETS–Constrained-based routing–Quality of service routing–Stability constraint–NP-hard problems–Polynomial algorithms
Optimized link state routing (OLSR) protocol is a table-driven proactive routing protocol for wireless mobile ad hoc networks. The key idea behind this protocol is to reduce the control message overheads by marking subset of neighbors as multi-point relays (MPRs). In this paper, we discuss problems of OLSR or other routing protocols that are due to mobility of nodes. We integrate our solutions to OLSR protocol and call it as Position-based OLSR (P-OLSR). Performance evaluation of OLSR and P-OLSR is done through NS simulation. Simulation results show that P-OLSR outperforms OLSR with respect to packet delivery, throughput, latency and normalized overheads.
Proactive routing in MANETs induces high signaling overhead. Increasing the routing period size, while it reduces such an overhead, prevents to correctly track frequent changes in the topology and impacts the validity of routing as time goes farther from the start of the routing period. Routes' validity plays a central leveraging mission to enhance network performances as forwarding through incorrect routes not only results in traffic wondering inside the network without ever being able to be delivered to their ultimate destinations but also over consumes valuable network resources. In this paper, we propose an autonomic self tuning approach to dynamically gauge the size of the routing period in a way to properly calibrate between the amount of signaling overhead and the routing validity to yield better performances. First, we propose a distributed algorithm to collect the network cartography. We then study the validity of this cartography as a function of time and mobility. The validity of the cartography is then used to dynamically and locally self regulate the routing period size in a way to calibrate the signaling overhead and the routing pertinence. Simulation results show that our proposed scheme not only is capable of correctly tracking changes in network dynamics but also outperforms conventional proactive algorithms by doubling the network throughput at moderate to high workloads.
In this paper, we propose a novel routing algorithm based on the network cartography which is collected using an asynchronous distributed cartography gathering algorithm. Each node senses its own dynamics and chooses locally an appropriate routing size. As such stationary nodes generate little signaling traffic, however fast moving nodes choose small routing periods to mitigate the effect of mobility. Moreover, every node integrates a self regulating process that dynamically regulates the already chosen routing period to track the timely evolution of the node dynamics. The performance of our proposed routing protocol are evaluated and compared to the known OLSR through extensive simulations. First, we show that the collected network cartography maintains over time a validity ratio above 97 percent even for high node speed. Second, while our proposed routing protocol provides around 97 percent routing validity, the OLSR can hardly deliver more than 60 percent. Third, the proposed protocol provides much more throughput than OLSR and much less end to end delay at moderate to high speeds and workloads.