Path Selection Algorithm Research Articles

A Novel Fault-Tolerant Approach for Dynamic Redundant Path Selection Service Migration in Vehicular Edge Computing

Vehicular Edge Computing (VEC) provides users with low-latency and highly responsive services by deploying Edge Servers (ESs) close to applications. In practice, vehicles are usually moving rapidly. To ensure the continuity of services, edge service migration technology is in high need, by which an application, infrastructure or any edge-hosted applications or services are not locked into a single vendor and allowed to shift between different edge resource vendors. Nevertheless, due to their complex and dynamic nature, real edge computing environments are error and fault prone and thus the reliability of edge service migrations can be easily compromised if the proactive measures are not taken to counter failures at different levels. In this paper, we propose a novel fault-tolerant approach for Dynamic Redundant Path Selection service migration (DRPS). The DRPS approach consists of path selection algorithm and service migration algorithm. The path selection algorithm is capable of evaluating time-varying failure rates of ESs by leveraging a sliding window-based model and identifying a set of service migration paths. The service migration algorithm incorporates resubmission and replication mechanisms as well and decides edge service migration schemes by choosing multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions by 17.45%, 13.17%, and 7.22% in terms of ACT, TCR, and AFC, respectively.

Adaptive detection for resolvable multipath signal under heterogeneous clutter

In the urban environment, the radar is easily affected by multipath (MP) returns. The problem of detecting point-like targets in the urban environment with the existence of resolvable MP returns under the background of heterogeneous clutter is addressed. Two adaptive detectors have been devised with a combination of prior information on the environment and echoes of target. The detectors can use the MP echoes in all MP prediction range cells for detection or in some available paths for signal detection by the available path selection algorithm that eliminates the weak signal propagation paths. Remarkably, the proposed decision schemes exhibit the desirable constant false alarm rate property with respect to the unknown parameters of the heterogeneous interference. The performance assessment, conducted on simulated data in terms of detection probability also in comparison with conventional solutions, highlights the effectiveness of the MP exploitation, while the MP signal is strong and shows the detection performance enhancement potential.

Ob-EID: Obstacle aware event information dissemination for SDN enabled vehicular network

With rapid increase in traffic density in urban areas, the accident rate is also becoming a serious problem which results in traffic congestion, inconvenience and sometimes life loss. The rate of accidents can be minimized by sharing real-time event information among vehicles on the road through safety application. To realize the safety applications, it is paramount to disseminate event information in real-time through a reliable path which is extremely challenging due to unique vehicular characteristics. To ensure reliable path, there are majorly two critical issues: (i). It is very difficult to form the stable connections due to varying vehicular density and dynamic topology. (ii) the dynamic obstacles affect the signal intensity and hinder the communication between vehicles. Indeed, majority of dissemination schemes are based on distance and dissemination time only, while neglecting the effect of dynamic obstacles. Also, the localized traffic view is considered in such schemes lack in providing the real time vehicular dynamics and network status information. To overcome these issues, a Software Defined Network (SDN) based vehicular network model has been presented to ensure real time vehicular dynamics awareness in urban environment to share event information. For reliable event information dissemination, a heuristic based reliable path selection (HRPS) algorithm is proposed to disseminate event messages between a source and destination vehicle in real-time that will minimize the hindrance due to dynamic obstacles. The proposed algorithm selects a multi-modal path reliability function that depends on multiple routing metrics (connectivity, link reliability and obstacle impact probability) and selects the reliable path by optimizing the path reliability function. The proposed algorithm is simulated for varying urban traffic conditions to analyze its performance. The proposed algorithm shows packet delivery ratio between 90%–95% for varying vehicular density. The simulation results show the supremacy of proposed algorithm as compared to existing compared routing scheme. The proposed algorithm ensures real time event information dissemination for delay sensitive event information that minimizes the impact of dynamic obstacles.

Fuzzy based hybrid BAT and firefly algorithm for optimal path selection and security in wireless sensor network

Nodes are deployed randomly in the network area of the WSN. data transmission from source to destination via intermediate nodes should be done in a secure fashion. Due to the large size of packet loss and energy consumption of sensor nodes, a secure and energy-efficient path must be required. The main objective of this research is to provide secure data transmission among node-to-node for efficient delivery of data packets to the destination. The system uses a novel hybrid firefly and BAT algorithm for path selection, an innovative trust value generation, and optimal neighborhood selection using fuzzy logic. The research employs Elliptic Curve Cryptography (ECC) combined with Diffie-Hellman exchange for key generation and key exchange. Path selection is done by fuzzy logic and optimization of selection has been carried out by hybrid BAT and Firefly algorithms. Key generation includes a time-based randomness factor that increases the complexity of cryptanalysis, thereby providing the most security. The performance of the simulation is analyzed and depicted in terms of delay, throughput, energy, and processing time. The research has been carried out using a network simulator with nodes deployed randomly in the network area with mobility as the primary concern that requires dynamic path selection.

A Game-Theoretic Approach for Cost-Effective Multicast Routing in the Internet of Things

Internet of Things (IoT) devices have enabled communications in resource-limited computing environments. Sensor nodes from the multiple IoT devices collectively work for many applications, such as disaster management, border security management, smart farming, smart cities, etc. In such applications, the data from a single source node is often destined for multiple nodes. Multicast communication is preferred over unicast or broadcast communication for such applications, as multicast uses fewer resources. Efficient construction of the multicast tree leads to cost-effective multicast transmission. This article introduces a path selection game (PSGame), a game-theoretic approach that formulates the construction problem of the least-cost multicast tree as a potential game. Our proposed path selection algorithm (PSA) quickly converges to the pure Nash equilibrium (PNE), bringing the least cost multicast tree. Our findings show that the overhead incurred in terms of energy consumption and delay is minimal in the proposed algorithm compared to other mechanisms. The theoretical analysis proves that the proposed algorithm quickly converges to PNE in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n.r_{\max })$ </tex-math></inline-formula> steps. It also proves that the cost ratio between the proposed solution and the centralized optimum will be bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log (n)$ </tex-math></inline-formula> . The numerical analysis substantiates the theoretical analysis.

Refined Network Topology for Improved Reliability and Enhanced Dijkstra Algorithm for Optimal Path Selection during Link Failures in Cluster Microgrids

Cluster microgrids are a group of interoperable smart microgrids, connected in a local network to exchange their energy resources and collectively meet their load. A microgrid can import/export energy to the neighboring microgrid in the network based on energy deficit/availability. However, in executing such an operation, a well-established communication network is essential. This network must provide a reliable communication path between microgrids. In addition, the network must provide an optimal path between any two microgrids in the network to optimize immediate energy generation, import requirements, and export possibilities. To meet these requirements, different conventional research approaches have been used to provide reliable communication, such as backup/alternative/Hot Standby Router Protocol (HSRP)-based redundant path concepts, in addition to traditional/renowned Dijkstra algorithms, in order to find the shortest path between microgrids. The HSRP-based mechanism provides an additional path between microgrids, but may not completely solve the reliability issue, especially during multiple link failures and simultaneous failures of the actual path and redundant path. Similarly, Dijkstra algorithms discussed in the literature do not work for finding the shortest path during link failures. Thus, to enhance reliability, this paper proposes a refined network topology that provides more communication paths between microgrids, while retaining the same number of total links needed, as in conventional HSRP-based networks. In addition, this paper proposes an enhanced Dijkstra algorithm to find the optimum path during link failures. Simulations are executed using NetSimTM by implementing test cases such as single-link and multiple-link failures. The results prove that the proposed topology and method are superior to conventional approaches.

Research on automatic parking based on A* search algorithm

Automatic parking is one of the most landing scenarios in autonomous driving technology. Automatic parking refers to the automatic parking process of cars in parking lots. In this paper, we consider three parking models under vertical, horizontal and bending conditions. We first establish physical models according to different conditions, introducing A* search algorithm is d as the optimal path selection algorithm for practical cases. We also set up a constrained optimization problem for the situation of car occupancy in the garage. By solving this constraint, we can efficiently calculate the optimal parking path to avoid the situation of car occupancy.

Game Theory and Coverage Optimization Based Multihop Routing Protocol for Network Lifetime in Wireless Sensor Networks

Wireless sensor networks (WSNs) are self-organizing monitoring networks with a large number of randomly deployed micro sensor nodes to collect various physical information so as to realize tasks such as intelligent perception, efficient control and decision-making. However, WSNs nodes are powered by batteries, so they will run out of energy after a certain time. This energy limitation will greatly constrain the network performance like network lifetime and energy efficiency. In this study, for the purpose of prolonging network lifetime, we proposed a multi-hop routing protocol based on game theory and coverage optimization (MRP-GTCO). Briefly, in the stage of setup, two innovational strategies including clustering game with penalty function and cluster head coverage set were designed to realize the uniformity of cluster head distribution and improve the rationality of cluster head election. In the data transmission stage, we firstly derived the applicable conditions theorem of inter-cluster multi-hop routing. Based on this, a novel multi-hop path selection algorithm related to residual energy and node degree was proposed to provide an energy-efficient data transmission path. The simulation results showed that MRP-GTCO protocol can effectively reduce the network energy consumption and extend the network lifetime 159.22%, 50.76% and 16.46% compared with LGCA, RLEACH and ECAGT protocols.

A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT

In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> . First, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust, and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this article is the first to integrate trust inference algorithm into the trust-based recommendation systems. The extensive experiments are conducted on three real-world data sets, and the results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.

A Traffic Splitting Algorithm for Load Balancing in Tor.

As the most popular anonymous communication system, Tor provides anonymous protection for users by sending their messages through a series of relays. Due to the use of the bandwidth-weighted path selection algorithm, many more users choose routers with high bandwidth as relays. This will cause the utilization of high bandwidth routers to be much higher than that of low bandwidth routers, which will bring congestion risk. The Quality of Service (QoS) is difficult to guarantee for users who need delay-sensitive services such as web browsing and instant messaging. To reduce the average load of routers and improve the network throughput, we propose a circuit construction method with multiple parallel middle relays and conduct a dynamic load allocation method. The experiment demonstrates that our proposed method can provide better load balancing. Compared with other multipath anonymous communication networks, our proposed method can provide better anonymity.

Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor

With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.

An improved Aquila optimization with fuzzy model based energy efficient cluster routing protocol for wireless sensor networks

Due to the latest advances in microelectronics, wireless sensor networking (WSN) has been introduced in many applications. The flow of event data in WSN applications requires timely and reliable distribution so that immediate response and appropriate action can be taken. However, the limited power supply to the sensor terminal causes a transmission between the delay on the way to the base station and the power consumption. Clustering techniques are essential in developing the WSN routing algorithm that improves network operating time and power efficiency. However, due to the unbalanced power consumption between the terminals, the WSN is the optimal, energy efficient routing. In this work, we propose an energy efficient cluster based routing protocol (EEC-HO) for WSN using hybrid optimization algorithm. We introduce an improved Aquila optimization with fuzzy (IAO-Fuzzy) model for optimal and efficient cluster formation and cluster head (CH) computation. The main objective of proposed IAO-Fuzzy model used to compute the trust degree of each node, the highest trust node is considered as CH. After that, the hybrid beetle search induced decision making (BSDM) algorithm for optimal path selection to transfer data transfer between two nodes. Finally, the simulation results of proposed EEC-HO routing protocol is compared with the existing routing protocols. For the impact of sensor node density case, we observed that the effectiveness of proposed EEC-HO routing protocol is 45.887%, 30.666%, 56.666%, 17.629% and 41.666% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and dead nodes respectively. For the impact of simulation rounds case, we observed that the effectiveness of proposed EEC-HO routing protocol is 21.216%, 35.417%, 41.667%, 18.568% and 40.000% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and number of dead nodes respectively.

Optimal path selection algorithm for UAV formation guided by a UV virtual fence.

This paper studies the optimal flight path selection problem of unmanned aerial vehicle (UAV) formations in multi-obstacle areas. According to the characteristics of wireless ultraviolet (UV) light communication conforming to the probability coverage model, a wireless UV light self-organizing network coverage model is proposed; the UV light of different wavelengths is divided into a detection area, communication area, and early warning area. A consensus algorithm with leaders and followers is designed, and an artificial potential field method is introduced to ensure that the UAV formation successfully avoids obstacles. Simulation results show that the UAV formation can choose a reasonable flight route under the improved algorithm, and the average obstacle avoidance time is reduced by 6.75 s compared with the original algorithm. Comparing the flight paths of formations in barrier-free and multi-obstacle environments, the obstacle avoidance range of UAVs is not more than 1 m. The UV light self-organizing coverage model can be applied to military communications and disaster relief.

Intelligent Traffic Engineering (TE) system for rural broadband

Providing adequate and cost-effective access to the Internet for rural communities remains a challenge. In this paper, we explore the use of a traffic engineering framework based on a Multi-Objective Evolutionary Algorithm (MOEA) for both path and transmission power selection across a network of Free-Space Optical relay nodes. The configuration takes into account the conflicting requirements of End-to-End speed and the battery state of the relay nodes, as well as the current weather conditions. As such, the system is able to maintain near-optimal transport of data in a changing environment. Furthermore, the use of an MOEA yields a Pareto Front enabling the operator to select from a set of (near) optimal configurations based on the particular balance of requirements that are desired.

Stochastic Arrow-Hurwicz Algorithm for Path Selection and Rate Allocation in Self-Backhauled mmWave Networks

Stochastic Arrow-Hurwicz Algorithm for Path Selection and Rate Allocation in Self-Backhauled mmWave Networks

Enabling Application-Aware Traffic Engineering in IPv6 Networks

The Internet hosts numerous applications with different requirements for network delay, bandwidth, jitter, packet loss, and so on. However, in the TCP/IP network architecture, the network and application layers are decoupled, which means that the network does not have a fine-grained understanding of the application requirements. Therefore, it is not easy to provide truly fine-grained traffic operations for applications and guarantee their corresponding service level agreement requirements. In this article, we propose ATE6, which is an application-aware traffic engineering (TE) framework. For the control plane, we define a request language that can be used by applications to express their requirements, which allows networks to associate IPv6 addresses with the communication requirements of applications. We also devise an efficient path selection algorithm that allows the network operator to deploy an optimal TE path for an application. For the data plane, ATE6 uses segment routing over the IPv6 data plane to enforce and control network paths based on network policies. We implemented a prototype, and evaluation results show that ATE6 is a flexible, privacy-preserving, lightweight, and cost-efficient application-aware TE framework.

Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

Aiming at the problems of low shortest path selection accuracy, longer response time, and poor selection effect in current cold chain logistics transportation methods, a cold chain logistics transportation shortest path selection algorithm based on improved artificial bee colony is proposed. The improved algorithm is used to initialize the food source, reevaluate the fitness value of the food source, generate a new food source, optimize the objective function and food source evaluation strategy, and get an improved artificial bee colony algorithm. Based on the improved artificial bee colony algorithm, the group adaptive mechanism of particle swarm algorithm is introduced to initialize the position and velocity of each particle randomly. Dynamic detection factor and octree algorithm are adopted to dynamically update the path of modeling environment information. According to the information sharing mechanism between individual particles, the group adaptive behavior control is performed. After the maximum number of cycles, the path planning is completed, the shortest path is output, and the shortest path selection of cold chain logistics transportation is realized. The experimental results show that the shortest path selection effect of the cold chain logistics transportation of the proposed algorithm is better, which can effectively improve the shortest path selection accuracy and reduce the shortest path selection time.

An Intelligent Path Selection Algorithm Based on Deep Reinforcement Learning

Path selection is the most important algorithm in intelligent devices such as robots. At present, the traditional path-planning algorithm has achieved some results, but it lacks the ability of environmental perception and continuous learning. In order to solve the above problems, this paper proposes an intelligent path selection algorithm based on deep reinforcement learning, which uses the learning ability of deep learning and the decision-making ability of reinforcement learning to realize the autonomous path planning of robots and other equipment. Simulation results show that the proposed algorithm has faster convergence, efficiency and accuracy.

Reliable path identifying protocol in VANET

Objective: The objective of this work is to enhance Adhoc On-demand Distance Vector (AODV) routing protocol for better communication through reliable routing scheme. Methods: In this paper, a reliable path selection algorithm is proposed to find the Most Reliable Path (MRP) between the source and destination vehicles. Reliable Path Ad-hoc On demand Distance Vector (RPAODV) is proposed for efficient route discovery in Vehicular Ad-hoc Network (VANET). The algorithm divide network into manageable group and it maintains Cluster Member (CM) updates handling, Cluster Head (CH) re-selection, Cluster merging. The efficient routing decisions would lead to better communication in VANET. It is made by assigning dynamic and static information appropriately to the variables in process. The dataset used is both static and dynamic which is efficient in when compared with existing protocols. Findings: The finding of most reliable path in the network of VANET is carried out by the proposed reliable path selection algorithm. The algorithm divides the network as various parts that comprise the information of clustering. The clustering is the mechanism of grouping of nodes into clusters. Clusters possess members and head. The cluster head would be the point of initiation of communication and any node can be re-selected as cluster head. The members of clusters would be updated as per changes in clustering mechanism. The uniqueness of the proposed algorithm revealed through easy identification of vehicles to form a cluster, cluster members maintenance, and fast data communication over the network. The proposed RPAODV adds the extra fields for RouteREQuest (RREQ) and RouteREPly (RREP) messages (CH and CM information). It also works out for static and dynamic topology in good manner by the implementation of modified AODV algorithm. The scalability is also taken care by enhancing topology variant. The performance of the algorithm is measured and analyzed with the existing protocols and posted in graphs. Novelty: The simulation results show that the proposed work outperforms 60% than the existing schemes works Cluster-based VANET-oriented Evolving Graph( CVoEG), Leapfrog-Anti-Colony Optimization(LP-ACO) in terms of route reliability (0.11s high), packet delivery ratio (20% high), end to end delay (0.5 saverage), and throughput (10 kbps high). Keywords: VANET; Routing Protocol; RPAODV; Clustering; Reliable Routing; and Cluster message

Load Balancing in DCN Servers through SDN Machine Learning Algorithm

Development in Internet technologies increases Internet users exponentially. Increase in users leads to more data center network (DCN) and heavy data traffic in servers. Data traffic in servers is managed through software-defined networking (SDN). SDN improves utilisation of large-scale network resource and performance of network applications. In SDN, load balancing technique optimises the data flow during transmission through server load deviation after evaluating the network status dynamically. However, load deviation in network needs optimum server selection and routing path with respect to less time and complexity. In this paper, we proposed a multiple regression-based searching (MRBS) algorithm for optimum server selection and routing path in DCN to improve performance even under heavy load conditions such as message spikes, different message frequencies, and unpredictable traffic patterns. MRBS selects the server based on regression analysis, which predicts types of traffic and response time based on the server data parameters such as load, response time, and bandwidth and server utilisation. MRBS combines heuristic algorithm and regression model for efficient server and path selection. The proposed algorithm reduces the delay and time more than 45% and shows better sever utilisation of 83% when compared with traditional algorithms due to stochastic gradient decent weights estimation.