Showing papers on "Geographic routing published in 2022"

Cooperative routing for improving the lifetime of wireless ad-hoc networks

Abstract: In Wireless Ad-hoc Networks (WANET), route detection is the main issue. In the usual route detection method, the sender itself discovers the route to the receiver based on the shortest path. In this path, the sender node does not require knowledge of the in-between nodes, and the sender node transmits the information to the in-between nodes. The in-between nodes transmit the data to the near node that receives it. This procedure will be maintained till the information reaches the receiver node. The main disadvantage of usual route detection is that the node is highly moved; thus, the transmitted data packet will be dropped. A Cooperative Routing for Improving Lifetime (CRIL) in WANET is introduced to solve these issues. This approach aims to enhance the WANET lifetime and minimize the cost of route discovery. This approach uses the fresher encounter algorithm with energy-efficient routing to improve network lifetime. It is a simple algorithm to efficiently discover the routes in WANET.

Implementation of Hidden Node Detection Scheme for Self-Organization of Data Packet

Abstract: The mobile nodes are infrequent movement in nature; therefore, its packet transmission is also infrequent. Packet overload occurred for routing process, and data are lossed by receiver node, since hackers hide the normal routing node. Basically, the hidden node problem is created based on the malicious nodes that are planned to hide the vital relay node in the specific routing path. The packet transmission loss occurred for routing; so, it minimizes the packet delivery ratio and network lifetime. Then, proposed enhanced self-organization of data packet (EAOD) mechanism is planned to aggregate the data packet sequencially from network structure. The hacker node present in routing path is easy to separate from network with trusty nodes. In order to secure the regular characteristics of organizer node from being confirmed as misbehaving node, the hidden node detection technique is designed for abnormal routing node identification. This algorithm checks the neighboring nodes that are hacker node, which hide the trust node in the routing path. And that trust nodes are initially found based on strength value of every node and assign path immediately. It increases network lifetime and minimizes the packet loss rate.

A position-based reliable emergency message routing scheme for road safety in VANETs

Abstract: Reliable emergency message (EM) transmission in vehicular adhoc networks (VANETs) necessitates an effective routing scheme. Position-based routing is considered more suitable for VANETs for not having to maintain any routing table or sharing connection states with neighbors. However, position-based routing is challenging in VANETs because vehicles change their positions instantly, and the next-hop can often go out of the communication range in greedy forwarding mode. This unstable behavior of the next-hop triggers route redundancy and leads to a high end-to-end delay (ED) and lower packet delivery ratio (PDR). Moreover, routing decisions based on a next-hop (relay) vehicle may be less optimal if we do not consider the stability and predict the position of a next-hop vehicle in such dynamic environments. To that end, we propose a position-based reliable emergency message routing (REMR) scheme based on our mobility metrics, which exploits the vehicle moving behaviors to enhance EM delivery. We describe how the choice of next-hop in greedy forwarding can be enhanced by leveraging neighbor’s future location information. By taking into account the Euclidean distance and position information, REMR predicts the relative positions of neighbor vehicles to exclude unstable neighbors from the list of candidate next-hops. In addition, REMR employs the vehicles’ movement information (e.g., position, speed variation, and moving angle) to minimize a possible link disruption and to choose an optimal next-hop for robust routing of EMs. REMR also offers a beaconing control strategy to enhance message reliability and to deal with the problem of beacons congestion. To minimize beacons congestion, REMR adjusts the beacon interval based on the neighborhood density. By consolidating mobility metrics and beacon control strategy, REMR can respond adequately to variation in the network traffic and frequent topology changes as validated by our simulation results.

Deep-Learning-Aided Packet Routing in Aeronautical <i>Ad Hoc</i> Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multiobjective Optimization

Abstract: Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

A New Method of Fuzzy Multicriteria Routing in Vehicle Ad Hoc Network

Abstract: The internet of vehicles (IoVs) provides delay-sensitive services, but high-latency communication with roadside units causes service failures and high costs. Mobile edge computing (EC) is migrating cloud computing platforms from the core network to the edge of mobile networks, giving vehicles local access to numerous computing resources. The classic greedy boundary stateless routing (GPSR) method is widely used to meet the communication requirements of vehicle self-organizing networks. In order to solve the problems of GPSR’s neighbor node position acquisition lag and single routing criterion, a new routing method based on the fuzzy logic system Geographic Routing method based on Velocity, Angle, and Density (GRVAD) is proposed. This method gets the relative velocity between the nodes and the angle between the current node, the neighbor node and the target node, and the node density of the neighbor node as the input of fuzzy logic, and the unscented Kalman filter is used to predict the location of the neighbor node to obtain more accurate location information of neighbor nodes. Simulation results show that the routing method compensates for some of the shortcomings of the GPSR method and reasonably considers the delivery rate and end-to-end delay of data packets and is more in line with the communication requirements of the vehicle ad hoc network.

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

Abstract: Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.

ARPLR: An All-Round and Highly Privacy-Preserving Location-Based Routing Scheme for VANETs

Abstract: Location-based routing is a widely adopted message transmission mechanism in Vehicular Ad Hoc Networks (VANETs). While the existing location-based routing schemes of VANETs ignore the location privacy protection of vehicles, leading that the drivers to be tracked, and further threaten the safety of their life and property. To address the above issue, we propose an All-Round and Highly Privacy-Preserving Location-Based Routing for VANETs (called ARPLR). Specifically, ARPLR first proposes a road side unit assisted location management with location privacy protection that prevents the destination vehicle’s location from being leaked by the arbitrary query. Then, a message routing based on location ciphertext with highly privacy protection is designed by order revealing encryption, in which a multi-hop routing between the source and destination vehicle is established only by comparing the encrypted locations between intermediate vehicles. Security analysis shows that, ARPLR can not only effectively provide location privacy protection for the intermediate and the destination vehicles in the whole routing process, but also ensure end-to-end secure communication between the source and destination vehicles. Extensive experiments based on real-road map indicate that, compared with two state-of-the-art solutions, the average transmission delay of ARPLR is respectively reduced by about 18% and 60%, meanwhile the average packet delivery rate also increases about 30% and 2%, respectively.

Enhanced Ant-Based Routing for Improving Performance of Wireless Sensor Network

Abstract: Routing packets from the source node to the destination node in wireless sensor networks WSN is complicated due to the distributed and heterogeneous nature of sensor nodes. An ant colony system algorithm for packet routing in WSN that focuses on a pheromone update technique is proposed in this paper. The proposed algorithm will determine the best path to be used in the submission of packets while considering the capacity of each sensor node such as the remaining energy and distance to the destination node. Global pheromone update and local pheromone update are used in the proposed algorithm with the aim to distribute the packets fairly and to prevent the energy depletion of the sensor nodes. Performance of the proposed algorithm has outperformed three (3) other common algorithms in static WSN environment in terms of throughput, energy consumption and energy efficiency which will result to reduction of packet loss rate during packet routing and increase of network lifetime.

QMIX Aided Routing in Social-Based Delay-Tolerant Networks

Abstract: Delay-tolerant network (DTN) is a network that’s designed to operate effectively in heterogeneous networks that may lack continuous network connectivity. It is characterized by their lack of instantaneous end-to-end paths, resulting in difficulties in designing effective DTN routing protocols. Traditional routing algorithms largely rely on greedy schemes. Such schemes can not guarantee the packets will be eventually transmitted to their destinations, thereby presenting a poor transmission efficiency. Recently, the social-based method has attracted a large amount of attention in wireless network routing. It can use the community and the centrality information to increase the delivery rate of the whole network. Therefore, in this article, we introduce the social-based mechanism to our DTN routing design. Besides, how the distributed nodes can learn the collaboration strategies is another challenge. Inspired by the recent success of multi-agent learning in online control, we adopt a centralized training and distributed execution learning paradigm and design a hierarchical social-based DTN architecture. Based on this, we propose a collaborative multi-agent reinforcement learning (termed as QMIX) aided routing algorithm.

A Wireless Mesh Opportunistic Network Routing Algorithm Based on Trust Relationships

Abstract: To solve the problem of low message delivery rate and high network resource consumption when forwarding messages in opportunistic networks, an opportunistic routing algorithm based on trust relationships for wireless mesh networks is proposed. Firstly, the wireless mesh network is analyzed and the opportunistic routing model is constructed; By analyzing the security mechanism and security threat of communication entities, then measuring the trust degree of links and nodes, establishing the trust relationship between nodes, and defining and quantifying a new security measurement method based on the trust model; Finally, according to the security measurement method defined by the model, select the node with high trust value to participate in the message forwarding process. At the same time, give priority to the node with greater trust with the destination node as the relay node, and allocate the message copy according to the trust degree to make the message pass along the direction of increasing trust, to complete the design of opportunistic routing algorithm in wireless mesh networks. Experimental results show that the routing algorithm can effectively improve the message delivery rate, up to about 95%, and reduce the consumption of network resources.

Ring-based forwarder selection to improve packet delivery in ultra-dense networks

Abstract: This paper proposes a novel protocol-agnostic approach to optimize the performance of routing protocols in ultra-dense networks through a careful selection of the forwarders in a multi-hop transmission. In an ultra-dense wireless network, nodes have hundreds of neighbours, and existing routing protocols which require neighbourhood information are unable to operate efficiently. Using our method, each node wanting to transmit first selects forwarders that fall in a ring near the border of the communication range of the transmitting node, which makes up a subset of all the node's neighbours. This significantly reduces the number of nodes contending for the wireless channel yet ensures that there are sufficient forwarders to deliver packets successfully. We validate our approach using two routing schemes, one flooding and one unicast, augmented with our forwarder selection method and applied to an electromagnetic nanonetwork scenario as a novel incarnation of ultra-dense networks. Simulations using an enhanced propagation model show that our forwarder selection method drastically reduces the number of forwarders while still allowing packets to reach the intended destinations.

Intertwine Connection-Based Routing Path Selection for Data Transmission in Mobile Cellular Networks and Wireless Sensor Networks

Abstract: In a network setting, a sensor node's round-trip delay time over hostile nodes compromises the node's ability to transmit data from the sender node to the destination node. Minimum distance path discovery causes the path failure, since aggressive nodes are available. Node connectivity is poor which should cause the packet loss; it does not control more energy consumption, since packet broadcasting is repeated for many times using that path. So, the proposed intertwine connection-based routing path selection (ICBRPS) technique allows only energy efficient routing path, path connectivity is important, and routing path is damaged because of the presence of aggressive nodes. It hacks the information from sensor and operates unpredictable manner. The objective of this presented ICBRPS scheme is to improve the routing path in efficient manner. If any damages occur during the transmission of data, then the alternate best node connectivity path is created by energetic route discovery method. The performance metrics of parameters are delay, network overhead, energy consumption, packet loss, packet delivery ratio, and connectivity ratio. It enhances the connectivity rate and reduces the energy consumption.

Efficient routing for spatial networks.

Abstract: In many complex networks, the main task is to transfer load from sources to destinations. Therefore, the network throughput is a very important indicator to measure the network performance. In order to improve the network throughput, researchers have proposed many effective routing strategies. Spatial networks, as a class of complex networks, exist widely in the real-world. However, the existing routing strategies in complex networks cannot achieve good results when applied in spatial networks. Therefore, in this paper, we propose a new degree-location ( D L) routing strategy to improve the throughput of spatial networks. In this routing strategy, the load transmitted from sources to destinations will bypass the nodes with high degrees and the nodes located close to the center of region. Simulations on homogeneous and heterogeneous spatial networks show that the D L routing strategy proposed in this paper can effectively improve the throughput of the network. The result of this paper can help the routing design of spatial networks and may find applications in many real-world spatial networks to improve the transmission performance.

Enhanced Geographic Routing with One- and Two-Hop Movement Information in Opportunistic Ad Hoc Networks

Abstract: Opportunistic ad hoc networks are characterized by intermittent and infrastructure-less connectivity among mobile nodes. Because of the lack of up-to-date network topology information and frequent link failures, geographic routing utilizes location information and adopts the store–carry–forward data delivery model to relay messages in a delay-tolerant manner. This paper proposes a message-forwarding policy based on movement patterns (MPMF). First, one- and two-hop location information in a geographic neighborhood is exploited to select relay nodes moving closer to a destination node. Message-forwarding decisions are made by referring to selected relay nodes’ weight values obtained by calculating the contact frequency of each node with the destination node. Second, when relays in the vicinity of a message-carrying node are not qualified due to the sparse node density and nodal motion status, the destination’s movement and the location information of a one-hop relay are jointly utilized to improve the message-forwarding decision. If the one-hop relay is not closer to the destination node or moving away from it, its centrality value in the network is used instead. Based on both synthetic and real mobility scenarios, the simulation results show that the proposed policy performs incomparable efforts to some typical routing policies, such as Epidemic, PRoPHETv2, temporal closeness and centrality-based (TCCB), transient community-based (TC), and geographic-based spray-and-relay (GSaR) routing policies.

Distributed Cooperative Reinforcement Learning for Wireless Sensor Network Routing

Abstract: In this work we examine a specific case of wireless sensor networks (WSN) we call peer-to-peer WSN where source and destination are both dynamic and each is subject to constraints of low bandwidth, limited energy storage, and limited computational resources. Peer-to-peer WSN require the consideration of data computation time as a limiting constraint on information availability unlike a standard WSN that can rely on the unconstrained sink to perform the necessary computation of the raw sensor data into usable information. To effectively manage and improve upon peer-to-peer WSN routing, and WSN routing in general, we present a deep reinforcement learning algorithm known as distributed cooperative reinforcement for routing (DCRL-R) which uses a neural network and expanded state space parameters to learn routing policies for WSN. DCRL-R also incorporates an increased action space for determining when and where to perform in-network computation of the raw sensor data. We perform tests of DCRL-R on a physical network utilizing measured node state parametric data and show its viability in future WSN applications compared to a baseline routing algorithm using shortest path decisions with no computational offloading.

An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks

Abstract: In wireless sensor networks, tree-based routing can achieve a low control overhead and high responsiveness by eliminating the path search and avoiding the use of extensive broadcast messages. However, existing approaches face difficulty in finding an optimal parent node, owing to conflicting performance metrics such as reliability, latency, and energy efficiency. To strike a balance between these multiple objectives, in this paper, we revisit a classic problem of finding an optimal parent node in a tree topology. Our key idea is to find the best parent node by utilizing empirical data about the network obtained through Q-learning. Specifically, we define a state space, action set, and reward function using multiple cognitive metrics, and then find the best parent node through trial and error. Simulation results demonstrate that the proposed solution can achieve better performance regarding end-to-end delay, packet delivery ratio, and energy consumption compared with existing approaches.

Deep Reinforcement Learning based Routing in an Air-to-Air Ad-hoc Network

Abstract: This paper studies the Multiple Sources and Multiple Destinations (MSMD) routing problem in a dynamic Air-to-Air Ad-hoc Network (AAAN). We consider a spectrum-limited scenario where multiple links have to share the same frequency channel so that co-channel interference becomes inevitable. As a result, routing decisions and spectrum access are coupled and must be jointly considered. This paper proposes a deep Q-learning-based algorithm to find an optimal routing and channel selection strategy that minimizes the end-to-end communication delay. Specifically, under the assumption that only local information is available to every node, the Deep Q-Network (DQN) is trained offline to learn the optimal routing and channel selection strategy. After the trained DQN is implemented in every node, multiple relay nodes can simultaneously determine their next-hop relay and channel selections in real-time. Simulation results demonstrate the efficacy of our proposed algorithm.

A Software-Defined Directional Q-Learning Grid-Based Routing Platform and Its Two-Hop Trajectory-Based Routing Algorithm for Vehicular Ad Hoc Networks

Abstract: Dealing with the packet-routing problem is challenging in the V2X (Vehicle-to-Everything) network environment, where it suffers from the high mobility of vehicles and varied vehicle density at different times. Many related studies have been proposed to apply artificial intelligence models, such as Q-learning, which is a well-known reinforcement learning model, to analyze the historical trajectory data of vehicles and to further design an efficient packet-routing algorithm for V2X. In order to reduce the number of Q-tables generated by Q-learning, grid-based routing algorithms such as the QGrid have been proposed accordingly to divide the entire network environment into equal grids. This paper focuses on improving the defects of these grid-based routing algorithms, which only consider the vehicle density of each grid in Q-learning. Hence, we propose a Software-Defined Directional QGrid (SD-QGrid) routing platform in this paper. By deploying an SDN Control Node (CN) to perform centralized control for V2X, the SD-QGrid considers the directionality from the source to the destination, real-time positions and historical trajectory records between the adjacent grids of all vehicles. The SD-QGrid further proposes the flows of the offline Q-learning training process and the online routing decision process. The two-hop trajectory-based routing (THTR) algorithm, which depends on the source–destination directionality and the movement direction of the vehicle for the next two grids, is proposed as a vehicle node to forward its packets to the best next-hop neighbor node in real time. Finally, we use the real vehicle trajectory data of Taipei City to conduct extensive simulation experiments with respect to four transmission parameters. The simulation results prove that the SD-QGrid achieved an over 10% improvement in the average packet delivery ratio and an over 25% reduction in the average end-to-end delay at the cost of less than 2% in average overhead, compared with two well-known Q-learning grid-based routing algorithms.

Location based Routing Protocols and its Performances in Wireless Sensor Networks: An Investigation

Abstract: A Wireless Sensor Network (WSN) is a network that comprises of independent sensor nodes which communicate with one another to share data. The routing techniques, regarded as a fundamental component of WSNs, govern how a node can transfer data in real-time. Location-based routing is one of the routing strategies used in wireless sensor networks to convey data by utilizing the position information of the node. In this article, a variety of location-based routing protocols such as GAF, GEAR, GPSR, and DREAM used in wireless sensor networks are considered for the study. In addition to that, the performance of selected location-based protocols is investigated using simulations based on several network parameters such as network delay, data loss ratio, data delivery ratio, and network throughput against different sizes of the network. The simulation results prove that, based on only the network parameter, picking up the best protocol is critical because the performance of each protocol also depends on the size of the network.

Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network

Abstract: Routing is a key function in Wireless Sensor Networks (WSNs) since it facilitates data transfer to base stations. Routing attacks have the potential to destroy and degrade the functionality of WSNs. A trustworthy routing system is essential for routing security and WSN efficiency. Numerous methods have been implemented to build trust between routing nodes, including the use of cryptographic methods and centralized routing. Nonetheless, the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node activities. At the moment, there is no effective way to avoid malicious node attacks. As a consequence of these concerns, this paper proposes a trusted routing technique that combines blockchain infrastructure, deep neural networks, and Markov Decision Processes (MDPs) to improve the security and efficiency of WSN routing. To authenticate the transmission process, the suggested methodology makes use of a Proof of Authority (PoA) mechanism inside the blockchain network. The validation group required for proofing is chosen using a deep learning approach that prioritizes each node's characteristics. MDPs are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting messages. According to testing data, our routing system outperforms current routing algorithms in a 50% malicious node routing scenario.

Adaptive Time-Varying Routing for Energy Saving and Load Balancing in Wireless Body Area Networks

Abstract: —Routing plays an essential role in ensuring normal and lasting operation of wireless body area networks (WBANs). However, existing routing schemes cause inefficient and unbalanced energy dissipation, which contributes to premature death of some nodes and high temperature within a small area of the body. In this article, we propose an adaptive time-varying routing (ATVR) protocol to address these issues. Unlike in conventional routing solutions, in our protocol a node may act as different roles (source node or relay node) and select different paths in disparate periods. This dynamic routing pattern helps to achieve a globally optimal routing solution. In ATVR, a node evaluation function and a path evaluation function are designed to reflect the node state and the path state respectively. Then, the path selection problem is transformed into a Hitchcock transportation problem, in which the nodes with worse node state act as source nodes (i.e., producers) and the nodes with better node state act as relay nodes (i.e., consumers). Then, this Hitchcock transportation problem is addressed by the AlphaBeta algorithm, in which the paths with less energy consumption and less path loss are selected to forward data. The experimental results show that our protocol has better performance in terms of energy consumption, network lifetime, and node temperature.

An Analytical Study of Energy Efficient Routing Approaches in Wireless Sensor Network

Abstract: . This article discusses energy-efficient routing approaches in wireless sensor networks (WSNs). This follows characterization and exploration with another proposed scientific classification recognizing certain classes of protocols as specific: delay-based routing and power efficiency, next-hop selection, network architecture, link initiators, network topology, protocol operation, delivery. mode, installation path, and application type. We analyze these classes, discuss representative routing protocols (mechanisms, advantages, disadvantages..) and compare them based on various parameters within the respective class. advancement of low-power steering conventions in WSN. This article proposes an Energy Efficient Layered Routing Protocol (EELRP). The proposed strategy isolates the organization into a few concentric circles of various radii. The circles are separated into eight equivalent regions. Areas are made by crossing points among layers and districts. Each segment comprises of a few hubs, and the most appropriate of them is chosen as a specialist. The hubs in each segment send sensor information to their representatives. The specialist then, at that point, gathers the information, performs blunder identification and remedy as indicated by the equilibrium's repetitive number framework, and afterward sends the data to the specialist at the lower part of a similar field. The interaction go on until the data arrives at the base station. Assuming you contrast the presentation of EELRP and conventional strategies, you will see that EELRP offers upgrades in network life expectancy, power utilization, bundle conveyance rate, and number of ways. than race static