基于改进蚁群算法的铁路乘务交路计划的编制

针对提高铁路乘务交路计划编制质量和效率的问题,将乘务交路计划编制问题抽象为单基地、均衡行驶路程的多旅行商问题（MTSP）,引入均衡因子,建立了以乘务交路用时少和子乘务交路间任务均衡为目标的数学模型。针对该模型提出了一种双重策略蚁群优化算法,该算法首先构建满足时空约束的解空间,分别对乘务区段节点和接续路径设置信息素浓度,然后采用双重策略状态的转移概率,使蚂蚁遍历所有乘务区段,最终找到符合乘务约束规则的子乘务交路。最后运用广深线城际铁路数据对设计的模型及算法进行检验,经与遗传算法的实验结果对比分析表明：在相同的模型条件下,运用双重策略蚁群优化算法编制的乘务交路计划乘务交路个数减少了约21.74%、乘务交路总时长降低了约5.76%、交路超劳率为0。运用所设计的模型和算法编制乘务交路计划能够减少乘务计划交路时长,均衡工作量,避免产生超劳交路。     

ABC algorithm‐based trustworthy energy‐efficient MIMO routing protocol

This paper introduces artificial bee algorithm (ABC)‐based energy‐efficient protocol with security for the enhancement of lifetime of wireless sensor network. A trustworthy energy‐efficient routing MIMO (TEEM) technique is used by implementing MIMO technique in TEER protocol to isolate malicious nodes and to reduce the fading effects and interference in the network. The performance of ABC‐based trustworthy energy‐efficient MIMO routing (ABC‐TEEM) protocol is analysed. Alive node performance, residual energy, throughput analysis, and pocket loss rate of ABC‐TEEM protocol for WSN are computed and compared with the performance of existing TEEM protocol for various diversity orders.     

A comparative study of name resolution and routing mechanisms in information-centric networks

Information-Centric Networking (ICN) is an innovative paradigm for the future internet architecture, which addresses IP network limitations in supporting content distribution and information access by decoupling content from hosts and providing the ability to retrieve a content object by its name (identifier), rather than its storage location (IP address). Name resolution and routing is critical for content retrieval in ICN networks. In this research, we perform a comparative study of two widely used classes of ICN name resolution and routing schemes, namely flooding and Distributed Hash Table (DHT). We consider the flooding-based routing in Content-Centric Networks due to its wide acceptance. For the DHT scheme, we design a multi-level DHT that takes into account the underlying network topology and uses name aggregation to further reduce control overhead and improve network efficiency. Then, we compare the characteristics and performance of these two classes of name resolution and routing through extensive simulations. The evaluation results show that the performances of these two approaches are reliant on several factors, including network size, content location dynamics, and content popularity. Our study reveals insights into the design tradeoffs and offers guidelines for design strategies.     

基于地理信息的航空自组网安全路由算法

地理信息路由是提高航空自组网性能的有效途径,针对地理信息路由安全性无法保证的问题,提出一种适用于高动态网络的安全地理信息路由算法。基于身份的签名和加密实现了节点认证、消息认证及加密传输,保证了地理信息的可靠性和安全性;在下一跳选择时,综合评估节点的位速因子和信任因子,选择最优下一跳。通过进行安全性和计算开销分析,表明该算法有效可行;实验仿真结果表明算法能够显著提高网络性能和安全性。     

带多软时间窗VRP及其禁忌搜索算法

分析了带多软时间窗VRP实际应用背景和特点,以使用的车辆数、行驶费用和偏离时间窗的惩罚费用为优化目标,结合车辆载重、最大路长等限制,建立该问题的数学模型,并设计求解该问题的自适应禁忌搜索算法。为增强算法的全局寻优能力,设计了多邻域结构并在算法中嵌入一种有限地接受不可行解的自适应机制。分别用文献中的算例和以Solomon标准算例为基础构建的新算例测试该算法,并将结果与其他方法进行对比分析。对比结果表明,所提出的算法性能较好,能在可接受的时间内求出运输成本更少、满意度更高的解。     

LLN中基于能量均衡的高效低时延路由算法

针对有损低功耗网络(Low-power and Lossy Network,LLN)中由于无线链路的有损特性以及节点部署不均匀无法有效实现网络能量均衡,提出一种基于能量均衡的高效低时延路由算法(Efficient Low Latency Routing Algorithm Based on Energy Balance in Low-Power and Lossy Networks,ELLEB-RPL)。该算法采用“ELLEB组网”策略,使得节点能够有效避免在DODAG(Destination Oriented Directed Acyclic Graph)间来回切换;通过采用“最优父节点选择”策略,使得节点选择的父节点达到最优;通过采用“流量自适应分配”策略,达到网络能量均衡的目的。仿真结果表明,ELLEB-RPL在Sink节点平均吞吐量、网络平均寿命以及端到端时延上均有提升。     

E‐TDGO: An encrypted trust‐based dolphin glowworm optimization for secure routing in mobile ad hoc network

Mobile ad hoc networks (MANETs) are independent networks, where mobile nodes communicate with other nodes through wireless links by multihop transmission. Security is still an issue to be fixed in MANETs. Hence, a routing protocol named encrypted trust‐based dolphin glowworm optimization (DGO) (E‐TDGO) is designed using Advanced Encryption Standard‐128 (AES‐128) and trust‐based optimization model for secure routing in MANET. The proposed E‐TDGO protocol includes three phases, namely, k‐path discovery, optimal path selection, and communication. At first, k paths are discovered based on the distance and the trust level of the nodes. From the k paths discovered, the optimal path is selected using a novel algorithm, DGO, which is developed by combining glowworm swarm optimization (GSO) algorithm and dolphin echolocation algorithm (DEA). Once the optimal path is selected, communication begins in the network such that E‐TDGO protocol ensures security. The routing messages are encrypted using AES‐128 with shared code and key to offer security. The experimental results show that the proposed E‐TDGO could attain throughput of 0.11, delay of 0.01 second, packet drop of 0.44, and detection rate of 0.99, at the maximum number of rounds considered in the network of 75 nodes with attack consideration.     

Link efficiency and quality of experience aware routing protocol to improve video streaming in urban VANETs

In a vehicular ad‐hoc network (VANET), vehicles can play an essential role in monitoring areas of a smart city by transmitting data or multimedia content of environmental circumstances like disasters or road conditions. Multimedia content communication with quality of experience (QoE) guarantees is a challenging undertaking in an environment such as that of a VANET. Indeed, a VANET is characterized by numerous varying conditions, significantly impacting its topology, quality of communication channels, and paths with respect to bandwidth, loss, and delay. This paper introduces a link efficiency and quality of experience aware routing protocol (LEQRV) to improve video streaming provisioning in urban vehicular ad‐hoc networks. LEQRV uses an enhanced greedy forwarding‐based approach to create and maintain stable high quality routes for video streaming delivery. It improves the performance of the quality of experience by increasing the achieved QoE scores and reducing the forwarding end‐to‐end delay and frame loss.     

Energy‐efficient void avoidance geographic routing protocol for underwater sensor networks

Underwater wireless sensor networks (UWSNs) consist of a group of sensors that send the information to the sonobuoys at the surface level. Void area, however, is one of the challenges faced by UWSNs. When a sensor falls in a void area of communication, it causes problems such as high latency, power consumption, or packet loss. In this paper, an energy‐efficient void avoidance geographic routing protocol (EVAGR) has been proposed to handle the void area with low amount of energy consumption. In this protocol, a suitable set of forwarding nodes is selected using a weight function, and the data packets are forwarded to the nodes inside the set. The weight function includes the consumed energy and the depth of the candidate neighboring nodes, and candidate neighboring node selection is based on the packet advancement of the neighboring nodes toward the sonobuoys. Extensive simulation experiments were performed to evaluate the efficiency of the proposed protocol. Simulation results revealed that the proposed protocol can effectively achieve better performance in terms of energy consumption, packet drop, and routing overhead compared with the similar routing protocol.     

ORuML: Optimized Routing in wireless networks using Machine Learning

Routing is a process of selecting a path in a network for delivering a packet from source node to destination node. Successful delivery of a message is a challenge, and therefore, this paper proposes an algorithm for a wireless network called Optimized Routing in wireless networks using Machine Learning (ORuML), which uses machine learning algorithm namely, K‐nearest neighbor (KNN), Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR), to predict the network type of the source and destination nodes. The ML model is trained by using characteristic features of a node collected in real time such as battery power utilization, available internal storage, IP address, and range of a node. Intuitively, the MLR should outperform KNN and SVM in terms of accuracy and Area under ROC Curve (AUC). The proposed algorithm determines whether the source and destination nodes are co‐located and also, determines the best neighboring hop for efficient routing.