Efficient Routing Protocol with Localization Based Priority & Congestion Control for UWSN

The nodes in the sensor network have a wide range of uses, particularly on under-sea links that are skilled for detecting, handling as well as management. The underwater wireless sensor networks support collecting pollution data, mine survey, oceanographic information collection, aided navigation, strategic surveillance, and collection of ocean samples using detectors that are submerged in water. Localization, congestion routing, and prioritizing the traffic is the major issue in an underwater sensor network. Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource. Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper. We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio, dispatching of packets, power, and distance among the nodes. This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic. The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet, delay of latency, and power utilization than the existing algorithms.     

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP (Non-deterministic Polynomial-time) hard problem, Swarm Intelligence (SI) techniques can be implemented. The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence (QoSCRSI) algorithm. The proposed QoSCRSI technique performs two-level clustering and proficient routing. Initially, the fuzzy is hybridized with Glowworm Swarm Optimization (GSO)-based clustering (HFGSOC) technique for optimal selection of Cluster Heads (CHs). Here, Quantum Salp Swarm optimization Algorithm (QSSA)-based routing technique (QSSAR) is utilized to select the possible routes in the network. In order to evaluate the performance of the proposed QoSCRSI technique, the authors conducted extensive simulation analysis with varying node counts. The experimental outcomes, obtained from the proposed QoSCRSI technique, apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency, network lifetime, overhead, throughput, and delay.     

QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is a Nondeterministic Polynomial time (NP) hard problem which can be solved only with the help of metaheuristic optimization algorithms. With this motivation, the current research paper presents a new Improved Black Widow Optimization with Levy Distribution model (IBWO-LD)-based multicast routing protocol for smart cities. The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive. Besides, a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints. A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects. The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.     

An Energy Based Dynamic AODV Routing Protocol in Wireless Ad Hoc Networks

In recent years, with the rapid development of the Internet and wireless communication technology, wireless Ad hoc networks have received more attention. Due to the limited transmission range and energy of nodes in Ad hoc networks, it is important to establish a reliable and energy-balanced transmission path in Ad hoc networks. This paper proposes an energy-based dynamic routing protocol based on the existing AODV routing protocol, which has the following two aspects of improvement: (1) In the route discovery process, a node selects a suitable route from the minimum energy consumption route and the energy-balanced route designed in this paper according to a “Mark” bit that representing remaining energy of a node. (2) Based on (1), a route interruption update strategy was proposed to restart the route discovery process when node energy was used excessively. Simulation results demonstrate that compared with AODV and other existing routing protocols, proposed algorithm can reduce network energy consumption and balance node energy, thus extending the network lifetime.     

Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing

To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (MEC) has been drawing increased attention from both industry and academia recently. This paper focuses on mobile users’ computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy. Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown, we use the model-free reinforcement learning (RL) framework to formulate and tackle the computation offloading problem. Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function, then it chooses the overhead-aware optimal computation offloading action (local computing or edge computing) based on its state. The state spaces are high-dimensional in our work and value function is unrealistic to estimate. Consequently, we use deep reinforcement learning algorithm, which combines RL method Q-learning with the deep neural network (DNN) to approximate the value functions for complicated control applications, and the optimal policy will be obtained when the value function reaches convergence. Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.     

Energy Efficient Ambience Awake Routing with OpenFlow Approach

A major problem in networking has always been energy consumption. Battery life is one parameter which could help improve Energy Efficiency. Existing research on wireless networking stresses on reducing signaling messages or time required for data transfer for addressing energy consumption issues. Routing or Forwarding packets in a network between the network elements like routers, switches, wireless access points, etc., is complex in conventional networks. With the advent of Software Defined Networking (SDN) for 5G network architectures, the distributed networking has embarked onto centralized networking, wherein the SDN Controller is responsible for decision making. The controller pushes its decision onto the network elements with the help of a control plane protocol termed OpenFlow. Decentralized networks have been largely in use because of their ease in physical and logically setting the administrative hierarchies. The centralized controller deals with the policy funding and the protocols used for routing procedures are designated by the decentralized controller. Ambience Awake is a location centered routing protocol deployed in the 5G network architecture with OpenFlow model. The Ambience Awake mechanism relies on the power consumption of the network elements during the packet transmission for unicast and multicast scenarios. The signalling load and the routing overhead witnessed an improvement of 30% during the routing procedure. The proposed routing mechanism run on the top of the decentralized SDN controller proves to be 19.59% more efficient than the existing routing solutions.     

Traffic Engineering in Dynamic Hybrid Segment Routing Networks

The emergence of Segment Routing (SR) provides a novel routing paradigm that uses a routing technique called source packet routing. In SR architecture, the paths that the packets choose to route on are indicated at the ingress router. Compared with shortest-path-based routing in traditional distributed routing protocols, SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress router. Despite the advantages of SR, it may be difficult to update the existing IP network to a full SR deployed network, for economical and technical reasons. Updating partial of the traditional IP network to the SR network, thus forming a hybrid SR network, is a preferable choice. For the traffic is dynamically changing in a daily time, in this paper, we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR network. WASAR algorithm can be divided into three steps: firstly, representative Traffic Matrices (TMs) and the expected TM are obtained from the historical TMs through ultra-scalable spectral clustering algorithm. Secondly, given the network topology, the initial network weight setting and the expected TM, we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning (DRL) algorithm. Thirdly, we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands, in order to improve the network performance. In the evaluation, we exploit historical TMs to test the performance of the obtained routing configuration in WASAR. The extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization (MLU) under the dynamic traffic.     

基于Ad hoc无线网络的AODV路由协议仿真

利用以Ubuntu为平台的NS2网络仿真器,对AODV路由协议进行仿真.通过分析不同仿真场景的仿真数据,绘制出相应的仿真结果对比图,并对AODV路由协议进行了性能分析.仿真结果表明,在数据流不断增加的情况下,网络平均吞吐量、归一化路由开销以及端到端平均时延等性能指标会相应下降,得出了AODV路由协议适合于中小规模且发包率不高的网络场合的结论.     

Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN

The Internet of Things (IoT) is gaining attention because of its broad applicability, especially by integrating smart devices for massive communication during sensing tasks. IoT-assisted Wireless Sensor Networks (WSN) are suitable for various applications like industrial monitoring, agriculture, and transportation. In this regard, routing is challenging to find an efficient path using smart devices for transmitting the packets towards big data repositories while ensuring efficient energy utilization. This paper presents the Robust Cluster Based Routing Protocol (RCBRP) to identify the routing paths where less energy is consumed to enhances the network lifespan. The scheme is presented in six phases to explore flow and communication. We propose the two algorithms: i) energy-efficient clustering and routing algorithm and ii) distance and energy consumption calculation algorithm. The scheme consumes less energy and balances the load by clustering the smart devices. Our work is validated through extensive simulation using Matlab. Results elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption, the number of packets received at BS and the number of active and dead nodes. In the future, we shall consider edge computing to analyze the performance of robust clustering.     

Dynamic Power-Conscious Routing for MANETs: An Initial Approach

We develop an initial dynamic power-concious routing scheme (MPR) that incorporates physical layer and link layer statistics to conserve power, while compensating for the channel conditions and interference environment at the intended receiver. The aim of MPR is to route a packet on a path that will require the least amount of total power expended and for each node to transmit with just enough power to ensure reliable communication. We evaluate the performance of MPR and present our preliminary results.     

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles (UAVs) in an open environment, they have generated considerable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications. The difficulty stems from features other than mobile ad-hoc network (MANET), namely aerial mobility in three-dimensional space and often changing topology. In the UAV network, a single node serves as a forwarding, transmitting, and receiving node at the same time. Typically, the communication path is multi-hop, and routing significantly affects the network’s performance. A lot of effort should be invested in performance analysis for selecting the optimum routing system. With this motivation, this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication (COAER-UAVC) technique. The presented COAER-UAVC technique establishes effective routes for communication between the UAVs. It is primarily based on the coati characteristics in nature: if attacking and hunting iguanas and escaping from predators. Besides, the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay. A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system. The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.     

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN

Wireless sensor network (WSN) has been widely used due to its vast range of applications. The energy problem is one of the important problems influencing the complete application. Sensor nodes use very small batteries as a power source and replacing them is not an easy task. With this restriction, the sensor nodes must conserve their energy and extend the network lifetime as long as possible. Also, these limits motivate much of the research to suggest solutions in all layers of the protocol stack to save energy. So, energy management efficiency becomes a key requirement in WSN design. The efficiency of these networks is highly dependent on routing protocols directly affecting the network lifetime. Clustering is one of the most popular techniques preferred in routing operations. In this work we propose a novel energy-efficient protocol for WSN based on a bat algorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithm for WSN) to prolong the network lifetime. We use an objective function that generates an optimal number of sensor clusters with cluster heads (CH) to minimize energy consumption. The performance of the proposed approach is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy Efficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interesting in terms of energy-saving and prolongation of the network lifetime.     

基于Q学习的任务调度问题的改进研究

论文针对协同工作中的任务调度问题,建立了相应的马尔可夫决策过程模型,在此基础上提出了一种改进的基于模拟退火的Q学习算法。该算法通过引入模拟退火,并结合贪婪策略,以及在状态空间上的筛选判断,显著地提高了收敛速度,缩短了执行时间。最后与其它文献中相关算法的对比分析,验证了本改进算法的高效性。     

LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy-efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%–10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols.     

基于神经网络的组播路由调度方法

针对在高速包交换计算机网络中端到端时延及时延抖动限制的组播路由问题，提出了基于Ｈｏｐｆｉｅｌｄ神经网络的组播路由优化算法。实验表明，本算法能根据组播应用对时延及时延抖动限制的要求，快速，有效地构造最优组播树，有较强的实时性。     

一种ATM网络中的动态路由算法

针对前期工作中讨论的多阶段虚通道（ＶＰ）控制和ＶＰ拓扑优化问题，以及基于在每一个源目（ＳＤ）节点对之间存在一组备选路由集这一假设的相应的优化算法，提出了一个补充算法，它能够求出任意两节点间的所有可能路径。在此基础上，进一步研究了一种动态虚通路（ＶＣ）路由策略。与其它路由策略不同，它是在更一般的网络环境中加以考虑的。最后，给出了一个动态ＶＰ路由算法，这是动态ＶＣ路由策略中的一个重要组成部分。理论分析和试验结果表明，这些算法是正确的，且有极高的实用价值。     

基于LEACH协议的改进路由算法

针对LEACH协议在簇头选择过程中消耗能量多和节点间能量消耗不均匀的问题,本文提出了一种基于时间的均匀分簇混合路由协议( ECHT)在簇头竞选阶段中,节点广播成为簇头的时间与其剩余能量成反比,越早广播的节点将成为簇头.在数据传输阶段中,采用多跳与单跳相结合的方式将数据传送到基站,并计算数据传送开销来修改节点能量以此确定网络生命周期.仿真结果显示,ECHT协议能有效地均衡网络节点的能量消耗和延长网络生命周期.     

基于模拟退火法的组播路由算法(英文)

本文提出了一种基于模拟退火算法的延时约束最小代价组播路由算法(SADLMA)。首先,本算法使用Dijkstra第K最短路算法建立了从源节点到每个目的节点的候选集。然后生成了相应的邻居结构。当温度下降时,根据接收概率从邻居结构里把新解选择出来,并且代替旧解。仿真试验表明本算法对实际网络是有效的。     

Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks

Efficient aggregation in wireless sensor nodes helps reduce network traffic and reduce energy consumption. The objective of this work Localization Based Evolutionary Routing (LOBER) is to achieve global optimization for aggregation and WMSN lifetime. Improved localization is achieved by a novel Centroid Based Octant Localization (CBOL) technique considering an arbitrary hexagonal region. Geometric principles of hexagon are used to locate the unknown nodes in the centroid positions of partitioned regions. Flower pollination algorithm, a meta heuristic evolutionary algorithm that is extensively applied in solving real life, complex and nonlinear optimization problems in engineering and industry is modified as Enhanced Flower Pollination Algorithm (EFPA) to fit into WMSN and enhance routing mechanism and ensure efficiency in data aggregation. The system is simulated using MATLAB and found to have a considerable improvement in the optimization process.