A micro-artificial bee colony based multicast routing in vehicular ad hoc networks

Shen

2019

Algorithms

Quality of service multicast routing is an important research topic in networks. Research has sought to obtain a multicast routing tree at the lowest cost that satisfies bandwidth, delay and delay jitter constraints. Due to its non-deterministic polynomial complete problem, many meta-heuristic algorithms have been adopted to solve this kind of problem. The paper presents a new hybrid algorithm, namely ACO&CM, to solve the problem. The primary innovative point is to combine the solution generation process of ant colony optimization (ACO) algorithm with the Cloud model (CM). Moreover, within the framework structure of the ACO, we embed the cloud model in the ACO algorithm to enhance the performance of the ACO algorithm by adjusting the pheromone trail on the edges. Although a high pheromone trail intensity on some edges may trap into local optimum, the pheromone updating strategy based on the CM is used to search for high-quality areas. In order to avoid the possibility of loop formation, we devise a memory detection search (MDS) strategy, and integrate it into the path construction process. Finally, computational results demonstrate that the hybrid algorithm has advantages of an efficient and excellent performance for the solution quality.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem

Shen

2019

Algorithms

“…From a theoretical study perspective, several studies on MDD or multicast routing for IoV scenario and VANETs were undertaken recently, including Internet-based multicasting, 13,14 trajectory-based multicast, 26 spatiotemporal multicast, 27 QoS-constrained multicast routing, 28 and cognitive multicast routing. 29 Jemaa et al 13,14 proposed an extended membership management and mobility-aware tree-based multicast message dissemination protocol, to enable Internetbased multicast services on top of VANETs.…”

Section: Related Workmentioning

confidence: 99%

“…Chen et al 27 presented a spatiotemporal multicast protocol to support the spatiotemporal coordination in VANETs. In the context of QoS-constrained multicast routing, Zhang et al 28 modeled multicast routing as the continuous optimization problem to maximize network lifetime and minimize communication cost. A micro-artificial bee colony algorithm was further proposed to deal with this continuous optimization problem.…”

Section: Related Workmentioning

confidence: 99%

MAC²: Enabling multicasting and congestion control with multichannel transmission for intelligent vehicle terminal in Internet of Vehicles

Cao

International Journal of Distributed Sensor Networks

et al. 2018

Internet of Vehicles has become a promising way to realize the evolution from vehicular ad hoc networks and nextgeneration intelligent transportation system into future autonomous driving scenarios, clean-energy intelligent vehicles, and Smart Cities. However, multicasting service messages on available service channels and periodic exchanges of beacon messages on control channel cause the problem of efficiently scheduling those messages via multichannel transmission for intelligent vehicle terminal, to support real-world applications in Internet of Vehicles scenario. In this article, we investigate the intelligent vehicle terminal architecture and, particularly, design the wireless communication board by incorporating multicasting and congestion control modules. Especially, we present a multicast data delivery scheme with random-delay lowest-cost constraint to transfer service messages on service channels. Furthermore, a priority-aware congestion control scheme is also proposed by considering differentiated priorities of beacon messages on control channel, to cope with the congestion problem at bottleneck vehicle node. Based on the proposed schemes, we build up the RanLow (Random-delay Lowest-cost) module and the priority-aware congestion control (PARCEL) module by enabling multicasting and congestion control together in wireless communication board of the intelligent vehicle terminal architecture. Finally, the experimental results and comparison show that our devised RanLow module and PARCEL module are feasible and more efficient than existing schemes.

“…The previous work directly applies compressive sensing method to the route tree. Since clustering method has many advantages over the routing tree [36][37][38][39][40], compressive sensing method on clustering network is applied. Compared with routing tree data aggregating methods, clustering algorithm generally has a better communication load balance [41,42].…”

Section: Introductionmentioning

confidence: 99%

A kind of effective data aggregating method based on compressive sensing for wireless sensor network

Zhang³

et al. 2018

J Wireless Com Network

132

Wireless sensor network (WSN) in the Internet of Things consists of a large number of nodes. The proposal of compressive sensing technology provides a novel way for data aggregation in WSN. Based on the clustering structure of WSN, a kind of effective data aggregating method based on compressive sensing is proposed in this paper. The aggregating process is divided into two parts: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector and collect data through compressive sensing technology. Among clusters, clusters forward measurement values to the sink node along multi-hop routing tree. Performance analysis and comparison with the relative methods show that our method is effective and superior to other methods regardless of intra-cluster or inter-cluster on the total energy consumption of network.