Dynamic Topology Changes Research Articles

Adaptive Dynamic Shortest Path Search Algorithm in Networks-on-Chip Based on Circulant Topologies

A new pair routing algorithm for transmitting messages in multiprocessor systems and networks-on-chip based on circulant networks of arbitrary dimension is proposed. It allows using all reserve shortest paths in the presence of destructive factors (deadlocks, livelocks, starvation, failures) at the nodes and channels of the communication network. A distinctive feature of the proposed algorithm is the absence of using the routing tables with fixed shortest paths when message is transmitted. It becomes possible to determine the set of the shortest paths for routing due to the relative addresses of destination nodes based on a parametric description of the network. Estimates of the number of reserve shortest paths are obtained, and an effective algorithm for using these paths to prevent dynamic topology changes and network congestions is proposed. To reduce the required memory in networks-on-chip with a circulant topology, we proposed a version of the routing algorithm for two-dimensional optimal circulants. We experimentally found the minimum number of reference nodes (nodes containing mapping tables) for them and estimates of memory for mapping tables, as well as the average path length for the routing algorithm using the reference nodes.

Survey on Various DTN Routing Algorithms for Vehicular ADHOC Network (VANET)

Vehicle Adhoc Network (VANET) permits self-organized infrastructure. Link or Connectivity breakage or long delay (intermittent connection) is an important issue in the VANET which arises due to the dynamic topological changes. Mainly two algorithms Vehicle assisted Data Delivery (VADD) and Opportunistic routing algorithm supports for handling delay/disruption tolerant network (DTN). Intention of DTN is to create dynamic link between the sender and receiver vehicle in the case of link disruption or absence of path between two nodes and forwards the data using store carry and forward. VADD carry and forward the data as much as possible in disconnected network using intersection mode, straightway mode or destination mode. Opportunistic routing can deal the unreliable data and intermittent connectivity in VANET by considering the multiple nodes as a next hop forwarder instead of considering pre-selected single node to be the best forwarder. This paper gives the study about various opportunistic routing algorithms for handling delay/disruption tolerant network.

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

Low-Energy Adaptive Clustering Hierarchy (LEACH) is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. LEACH on Wireless Sensor Network (WSN) has been widely studied in the recent decade as one key technique for the Internet of Things (IoT). The main aims of the autonomous things, and one of advanced of IoT, is that it creates a flexible environment that enables movement and communication between objects anytime, anywhere, by saving computing power and utilizing efficient wireless communication capability. However, the existing LEACH method is only based on the model with a static topology, but a case for a disposable sensor is included in an autonomous thing’s environment. With the increase of interest in disposable sensors which constantly change their locations during the operation, dynamic topology changes should be considered in LEACH. This study suggests the probing model for randomly moving nodes, implementing a change in the position of a node depending on the environment, such as strong winds. In addition, as a method to quickly adapt to the change in node location and construct a new topology, we propose Q-learning LEACH based on Q-table reinforcement learning and Fuzzy-LEACH based on Fuzzifier method. Then, we compared the results of the dynamic and static topology model with existing LEACH on the aspects of energy loss, number of alive nodes, and throughput. By comparison, all types of LEACH showed sensitivity results on the dynamic location of each node, while Q-LEACH shows best performance of all.

Multi-Path Selection and Congestion Control for NDN: An Online Learning Approach

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path tag</i> to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

HDDS: Hierarchical Data Dissemination Strategy for energy optimization in dynamic wireless sensor network under harsh environments

The primary issue in Wireless Sensor Network (WSN) based monitoring applications is the preservation of the sensing coverage under the harsh physical environment. WSN forms the monitoring infrastructure, where a large set of energy-constrained sensor nodes are deployed to collect the physical parameters of the environment. The sensor nodes communicate their data to the nearby edge server (ES) where data is processed locally before it is forwarded to the distant cloud platform. Most of WSN’s existing routing algorithms aim to develop an energy-efficient data transmission route without recognizing the effect of harsh external environment, rendering it non-adaptive to environmental changes. In this paper, we have focused to develop an energy-efficient hierarchical data dissemination protocol for the sensor to ES communication. Along with energy optimization, the proposed protocol aims to preserve the network coverage and supports the dynamic changes in network topology like node failure, node addition, etc., due to harsh environmental interface. As the sensor nodes are required to operate autonomously, the proposed protocols are designed by considering only the local information of a sensor node. Simulation and experimental results show the efficiency of the proposed protocol over many state-of-the-art protocols with respect to several parameters such as energy consumption, lifetime of the network, coverage ratio, packet delivery ratio, latency, etc.

Gauss–Bonnet–Chern approach to the averaged Universe

The standard model of cosmology with postulated dark energy and dark matter sources may be considered as a fairly successful fitting model to observational data. However, this model leaves the question of the physical origin of these dark components open. Fully relativistic contributions that act like dark energy on large scales and like dark matter on smaller scales can be found through generalization of the standard model by spatially averaging the inhomogeneous Universe within general relativity. The spatially averaged 3 + 1 Einstein equations are effective balance equations that need a closure condition. Heading for closure we here explore topological constraints. Results are straightforwardly obtained for averaged 2 + 1 model universes. For the relevant 3 + 1 case, we employ a method based on the Gauss–Bonnet–Chern theorem generalized to Lorentzian spacetimes and implement a sandwich approach to obtain spatial average properties. The 3 + 1 topological approach supplies us with a new equation linking evolution of scalar invariants of the expansion tensor to the norm of the Weyl tensor. From this we derive general evolution equations for averaged scalar curvature and kinematical backreaction, and we discuss related evolution equations on this level of the hierarchy of averaged equations. We also discuss the relation between topological properties of cosmological manifolds and dynamical topology change, e.g. as resulting from the formation of black holes.

Genetic Algorithm Based Resource Minimization in Network Code Based Peer-to-Peer Network

Block scheduling is difficult to implement in P2P network since there is no central coordinator. This problem can be solved by employing network coding technique which allows intermediate nodes to perform the coding operation instead of store and forward the received data. There is a general assumption in this area of research so far that a target download rate is always attainable at every peer as long as coding operation is performed at all the nodes in the network. An interesting study is made that a maximum download rate can be attained by performing the coding operation at relatively small portion of the network. The problem of finding the minimal set of node to perform the coding operation and links to carry the coded data is called as a network code minimization problem (NCMP). It is proved to be an NP hard problem. It can be solved using genetic algorithm (GA) because GA can be used to solve the diverse NP hard problem. A new NCMP model which considers both minimize the resources needed to perform coding operation and dynamic change in network topology due to disconnection is proposed. Based on this new NCMP model, an effective and novel GA is proposed by implementing problem specific GA operators into the evolutionary process. There is an attempt to implement the different compositions and several options of GA elements which worked well in many other problems and pick the one that works best for this resource minimization problem. Our simulation results prove that the proposed system outperforms the random selection and coding at all possible node mechanisms in terms of both download time and system throughput.

A Vehicular Service-Oriented Dynamical Routing Searching Algorithm in Software-Defined BusNet

With the development of the vehicular network, BusNet-based service is of great interest to both bus companies and public users. How to provide service and ensure service quality is considered as key points for the promotion of such kinds of applications. In this paper, a system architecture, which is designed based on multiaccess edge computing (MEC) architecture and software-defined network (SDN), is proposed. Moreover, considering the problem of the dynamicity of the network, a new routing path optimization algorithm, SPSO (serial particle swarm optimization), is proposed, while a dynamic weight matrix is used to improve routing performance. The comprehensive simulation results demonstrate that the proposed method can well adapt to the dynamic change of network topology and improve communication performance significantly.

Sybil Attack Detection in Vehicular Ad-hoc Networks using Direct Trust Calculation

Vehicular Ad-hoc Networks (VANETs) are gaining rapid momentum with the increasing number of vehicles on the road. VANETs are ad-hoc networks where vehicles exchange information about the traffic, road conditions to each other or to the road-side infrastructures. VANETs are characterized by high mobility and dynamic topology changes due to the high-speed vehicles in the network. These characteristics pose security challenges as vehicles can be conceded. It is critical to address security for the sake of protecting private data of vehicle and to avoid flooding of false data which defeats the purpose of VANETs. Sybil attack is one of the attacks where a vehicle fakes multiple vehicle identity to compromise the whole network. In this work, a direct trust manager is introduced which derives the trust value of each of its neighbor nodes at a regular interval of time. If the trust value is deviated, it confirms sybil attack. The proposed system is compared with the existing system to prove improved sybil attack detection ratio, thus providing better security. NS2 environment is used to prove the simulation results. The experimental results show that the attack detection ratio of SAD-V-DTC is 5 times better than that of the existing system. The packet delivery ratio shows an improvement of 27.27% while the false positive shows a good increase of 65.80% than the existing system.

Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes

In recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more accurately and quickly to maximize the reward. However, the rewards calculated by the system and via human intervention (that make up the learning environment) differ and must be used accordingly. In this paper, we propose a framework for learning the problems of competitive network topologies, wherein the environment dynamically changes agent, by computing the rewards via the system and via human evaluation. The proposed method is adaptively updated with the rewards calculated via human evaluation, making it more stable and reducing the penalty incurred while learning. It also ensures learning accuracy, including rewards generated from complex network topology consisting of multiple agents. The proposed framework contributes to fast training process using multi-agent cooperation. By implementing these methods as software programs, this study performs numerical analysis to demonstrate the effectiveness of the adaptive evaluation framework applied to the competitive network problem depicting the dynamic environmental topology changes proposed herein. As per the numerical experiments, the greater is the human intervention, the better is the learning performance with the proposed framework.

Beam biasing based interference mitigation scheme in millimeter wave satellite cluster with multi-beam receiving

The directional transmission of millimeter wave (mmWave) beam is applied in the satellite cluster, which can efficiently avoid the interference problem caused by the satellites working in the same frequency band. However, due to the dynamic change of the satellite cluster topology and the randomness of the message transmitted by satellite, there will still be interference. In this paper, in terms of beam domain, beam biasing is proposed as an interference mitigation scheme to maximize the achievable rate of the network. Specifically, in the view of resource management, enhanced genetic algorithms is applied to search for the optimal beam bias angle of every beam in mmWave with multi-beam receiving senior. Simulation results show that the proposed schemes can significantly improve the achievable rate compared with the conventional multi-beam receiving scheme for mmWave communication.

Enhancing the Resilience of Low Earth Orbit Remote Sensing Satellite Networks

The Low Earth orbit Remote Sensing (LRS) satellite network is envisioned as an essential component of bolstering space information applications, thus extending conventional ground information applications to the on-board space information applications. However, it inevitably faces grave challenges imposed by the dynamics of the satellite network's topology, by the intermittence of Inter-Satellite communication Links (ISLs) as well as by the mobility-induced satellite-access switching of mobile terminals. Hence, this is a very different networking landscape from that of the terrestrial Internet. Against these challenging problems, we propose a resilient networking architecture for LRS Satellite Networks (LRS-SNs), with special emphasis on their dynamic routing, resilient transmission and their intrinsic mobility. Specifically, path-quality aided and lifetime-aware dynamic routing is proposed for enhancing the routing against dynamic topology changes. Hop-by-hop data transmission is relied upon for providing transmission resilience against ISL intermittence. Furthermore, data caching is invoked for providing resilience against intermittent communications imposed by dynamic satellite access switching. We employ a semi-physical simulation platform for evaluating the achievable performance of the proposed resilient network architecture.

Non-line-of-sight error mitigating algorithms for transmitter localization based on hybrid TOA/RSSI measurements

Rendezvous control of multiple robots without losing network connectivity has important implications in multi-robot system including formation control, coordinated task assignments, and cooperative robotic missions. This paper introduces a new coordinate-free, bearing-based algorithm to enable rendezvous of distributed mobile robots at any designated leader robot node using hierarchical tracking of wireless network topology. An assumption is made that the robot can only sense and communicate with their neighbors (i.e., local sensing). The proposed approach preserves connectivity during the rendezvous task, adapts to dynamic changes in the network topology (e.g., losing or re-gaining a communication link), and is tolerant of mobility faults in the robots. We theoretically analyze the proposed algorithm and experimentally demonstrate the approach through simulations and extensive field experiments. The results indicate that the method is effective in a variety of realistic scenarios in which the robots are distributed in a cluttered environment.

An Efficient Buffer Overflow Control Scheme to Mitigate Packet Loss in Manets

MANET (Mobile Ad hoc Network) is self-configuring, self-organizing, infrastructure-less network of mobile devices connected without wires. Because of the dynamic changes in the network topology, there is no centralized control in ad hoc networks. So, mobile nodes can communicate with each other via intermediate nodes. The buffer size of intermediate nodes plays a significant role to hold packets in the buffer before dropping the newly arrived packets. In MANET, buffer overflow (congestion) occurs in any intermediary nodes when data packets pass through source to destination and incurs packet loss, which causes the performance degradations of a network. Congestion can be reduced by using several Active Queue Management [AQM] techniques. Queue Management Node or “QMN” is an active queue management strategy to control packet drops by assigning space of a buffer node to all its neighbouring nodes dynamically depending upon the total number of packets received from its neighbours. In this scheme, authors did not take into account the impact of collaboration between neighbors of a central node. They only consider the characteristics of nodes, while ignoring the message properties in MANET. And also a new node has to be waiting until it will get enough equal space from the QMN along with other neighbor nodes. During this waiting period, high packet loss can be occurred. So, an efficient buffer overflow control scheme is required to make sure every node has adequate storage space to hold the data once the buffer of the node is near to congestion. In this paper, we proposed a scheme that integrates QMN technique with combinational buffer management (CBM) strategy, which includes both the features of messages and nodes, and transfer the duplicated messages to neighbour node for total utility optimization, rather than deleting them. By using this scheme packet loss can be mitigated in MANETs efficiently. Our simulation results here reveal that, the proposed scheme achieves better performance than other existing schemes in terms of packet delivery ratio, throughput, overhead ratio and end to end delay .

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks.

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks.

With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover scheme is proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introduction of SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance is not influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order to confirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when a handover happens.

Chemically Triggered Synthesis, Remodeling, and Degradation of Soft Materials.

Polymer topology dictates dynamic and mechanical properties of materials. For most polymers, topology is a static characteristic. In this article, we present a strategy to chemically trigger dynamic topology changes in polymers in response to a specific chemical stimulus. Starting with a dimerized PEG and hydrophobic linear materials, a lightly cross-linked polymer, and a cross-linked hydrogel, transformations into an amphiphilic linear polymer, lightly cross-linked and linear random copolymers, a cross-linked polymer, and three different hydrogel matrices were achieved via two controllable cross-linking reactions: reversible conjugate additions and thiol-disulfide exchange. Significantly, all the polymers, before or after topological changes, can be triggered to degrade into thiol- or amine-terminated small molecules. The controllable transformations of polymeric morphologies and their degradation herald a new generation of smart materials.

Reliability refinement in VANET with hybrid jamming attacks using novel index based voting algorithm

Vehicular Adhoc Network (VANET) constitutes a basis for the Intelligent Transportation Systems. VANET can be extensively used in safety and non-safety applications. DSRC Safety purposes are essential usages in VANET, have the basic objective of improving and enhancing safety through notifying drivers in hazards or impending accidents. The dynamic changes in topology are frequent in VANET, resulting in several jamming attacks. Hybrid jamming attacks involve the combination of the features of constant, deceptive and random jamming attacks. In this research work a novel Index Based Voting Approach (IBVA) has been proposed to avoid hybrid jamming as an integral component of devoted short range communication safety applications in VANET. The aim of this work focuses on hybrid jamming detection strategy in terms of node centric misbehaviour detection. The authenticities of vehicular nodes were determined by a new Recursive Candidate Elimination Algorithm (RCEA). Experimental results show that the time required to make decisions 0.3 s, which is reduced decision time compared to previous algorithm reported in the literature. The proposed IBVA algorithm in VANET improves the reliability in terms of malicious nodes detection and reducing the decision which in turn improves the network performance.

Performance Analysis of MANET Proactive and Reactive Routing Protocols Using Network Simulator 2

The development of Mobile ad hoc networks (MANET) has the nature and character of a fast, costeffective. The development of Mobile ad hoc networks (MANET) has the nature and character of a fast, cost-effective deployment and able to manage the topology independently. This has caused in recent years, MANET to be an interesting and popular thing to study. MANET can be applied even in difficult areas, forest fire detection, military operations and even health monitoring. Changes in the topology dynamically on MANET, limited energy consumption, and built without existing infrastructure are a problem in MANET. Problems especially dynamic topology changes make routing very important in MANET design. This article aims to analyze AODV, DSDV and DSR routing protocols based on quality of service (QoS). The test scenario is based on the number of nodes, packet size, area of simulation, simulation time, simulation speed, mobility model, and propagation model. The simulation that has been done produces 4 graphs, each of which explains the packet delivery ratio, throughput, network routing load, and delay. Simulation results are obtained using NS2 network simulators through *. Tcl files and generate *. Tr (trace) and *.nam (animator) files. Simulation results show that the DSR routing protocol has better performance than AODV and DSDV in terms of pdr, throughput, network routing load, and delay. The average value of the DSR routing protocol is 657,498 on throughput and 0.244 milliseconds on delay.

Routing using reinforcement learning in vehicular ad hoc networks

AbstractIn vehicular ad hoc networks (VANETs), the frequent change in vehicle mobility creates dynamic changes in communication link and topology of the network. Hence, the key challenge is to address and resolve longer transmission delays and reduced transmission stability. During the establishment of routing path, the focus of entire research is on traffic detection and road selection with high traffic density for increased packet transmission. This reduces the transmission delays and avoids carry‐and‐forward scenarios; however, these techniques fail in obtaining accurate traffic density in real‐time scenario due to rapid change in traffic density. Thus, it is necessary to create a model that efficiently monitors the traffic density and assist VANETs in route selection in an automated way with increased accuracy. In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintains the traffic information on roads using DRL. The DRL predicts the movement of the vehicle and makes a suitable routing path for transmitting the packets with improved transmission capacity. It further uses predicted transmission delays and the destination location to choose the forwarding directions between two road safety units (RSUs). The application of DRL over VANETs yields increased network performance, which provides on‐demand routing information. The simulation results show that the DRL‐based routing is effective in routing the data packets between the source and destination vehicles than other existing method.