Traditional Routing Algorithms Research Articles

An interference‐aware energy‐efficient routing algorithm with quality of service requirements for software‐defined WSNs

To address the energy-efficient (EE) routing problem in software-defined wireless sensor networks (SDWSNs), in this study, a centralised routing algorithm, namely, the interference-aware EE routing algorithm (IA-EERA), is proposed to extend the network lifetime (NL) in SDWSNs. Both the link quality of service requirements and the balance between the link energy loads are considered in the proposed IA-EERA when selecting the EE relays. Concretely, the IA-EERA comprises the EE relay selection (RS) and the centralised relay scheduling schemes, which are responsible for generating a valid link set with RS priorities and scheduling the eligible relay nodes with expected link rates from the valid link set, respectively. For supporting the network compatibility and scalability, we propose a hierarchical SDWSN based network architecture, upon which the IA-EERA can be devoted to solving the EE routing problem in the relay layer of SDWSN. Simulation results show that for one data source without interference, the proposed IA-EERA can significantly improve the NL compared with the traditional routing algorithm utilising the energy efficiency maximisation. For multiple data sources incurring interference at nodes, the IA-EERA is able to reduce the NL-dropping rate by adjusting the interference-aware parameter that affects the RS priorities

MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks

Recently, with the development of big data and 5G networks, the number of intelligent mobile devices has increased dramatically, therefore the data that needs to be transmitted and processed in the networks has grown exponentially. It is difficult for the end-to-end communication mechanism proposed by traditional routing algorithms to implement the massive data transmission between mobile devices. Consequently, opportunistic social networks propose that the effective data transmission process could be implemented by selecting appropriate relay nodes. At present, most existing routing algorithms find suitable next-hop nodes by comparing the similarity degree between nodes. However, when evaluating the similarity between two mobile nodes, these routing algorithms either consider the mobility similarity between nodes, or only consider the social similarity between nodes. To improve the data dissemination environment, this paper proposes an effective data transmission strategy (MSSN) utilizing mobile and social similarities in opportunistic social networks. In our proposed strategy, we first calculate the mobile similarity between neighbor nodes and destination, set a mobile similarity threshold, and compute the social similarity between the nodes whose mobile similarity is greater than the threshold. The nodes with high mobile similarity degree to the destination node are the reliable relay nodes. After simulation experiments and comparison with other existing opportunistic social networks algorithms, the results show that the delivery ratio in the proposed algorithm is 0.80 on average, the average end-to-end delay is 23.1% lower than the FCNS algorithm (A fuzzy routing-forwarding algorithm exploiting comprehensive node similarity in opportunistic social networks), and the overhead on average is 14.9% lower than the Effective Information Transmission Based on Socialization Nodes (EIMST) algorithm.

Energy Optimization for WSN in Ubiquitous Power Internet of Things

This paper attempts to solve the problems of uneven energy consumption and premature death of nodes in the traditional routing algorithm of rechargeable wireless sensor network in the ubiquitous power Internet of things. Under the application environment of the UPIoT, a multipath routing algorithm and an opportunistic routing algorithm were put forward to optimize the network energy and ensure the success of information transmission. Inspired by the electromagnetic propagation theory, the author constructed a charging model for a single node in the wireless sensor network (WSN). On this basis, the network energy optimization problem was transformed into the network lifecycle problem, considering the energy consumption of wireless sensor nodes. Meanwhile, the traffic of each link was computed through linear programming to guide the distribution of data traffic in the network. Finally, an energy optimization algorithm was proposed based on opportunistic routing, in a more realistic low power mode. The experimental results show that the two proposed algorithms achieved better energy efficiency, network lifecycle and network reliability than the shortest path routing (SPR) and the expected duty-cycled wakeups minimal routing (EDC). The research findings provide a reference for the data transmission of UPIoT nodes.

A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment.

In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR’s improvement in performance.

Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network.

A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.

An effective positive transmission routing algorithm based on social relationships in opportunistic social networks

Opportunistic social network enables users to form an instant network for information sharing, which is a type of multi-hop wireless network in nature, thus relies on cooperation between nodes to complete message transmission. At present, most traditional routing algorithms require relay nodes to unconditionally forward received data. However, due to the limited of network resources, not all nodes are willing to forward data for other nodes, which is manifested as refusing to accept or even discarding messages, leading to deterioration of network performance. In this regard, this paper proposes an effective positive transmission routing algorithm (EPTR) based on social relationships in opportunistic social networks. This technology optimizes the method of community division by modularity, which not only considers the tightness of networks, but also discusses the social attributes of nodes. In this scheme, nodes with high relationship strength are selected to participate in data forwarding process, which makes packets are transmitted in the direction of increasing forwarding capability of nodes to the destination community. Simulation results show that the proposed algorithm can discover most abnormal nodes including malicious nodes and selfish nodes. Besides, compared with EIMCT algorithm, ITPCM algorithm and Spray and Wait algorithm, the proposed scheme has the highest transmission success rate and the lowest network load.

Temporal Netgrid Model-Based Dynamic Routing in Large-Scale Small Satellite Networks

In recent years, Small Satellite Networks (SSNs) are attracting increasing attention due to its economical prospects and advantages in high bandwidth and low latency. More and more companies and organizations are planning to construct large-scale SSNs to provide global services. In this way, the traffic pattern is more complicated because of random transmission requirements and stochastic packet generations/arrivals, and routing faces more challenges due to network scale and limited resource in small satellites. Traditional satellite routing algorithms, which attempt to exploit the predictable satellite's trajectory with time-discrete graph models, cannot handle these challenges. In this paper, we propose a novel Temporal Netgrid Model (TNM) to portray the time-varying topology of large-scale SSNs. In TNM, the whole space is divided into small cubes (i.e., netgrids) and then, satellites can be located by netgrids instead of coordinates. By doing so, we can construct a network topology for random traffic routing. Furthermore, an efficient Netgrid-based Shortest Path Routing (NSR) algorithm is proposed based on TNM; NSR attempts to find the optimal path from source netgrid to any other reachable netgrids. In this way, the routing complexity is significantly reduced. We also develop a Large-scale Satellite Network Simulator (LSNS) to validate our study. The results show that NSR achieves a significant reduction in computational complexity as well as near-optimal routing performance in terms of end-to-end delay and packet drop rate in scenarios of large-scale SSNs compared with existing satellite routing algorithms.

Quantitative social relations based on trust routing algorithm in opportunistic social network

The trust model is widely used in the opportunistic social network to solve the problem of malicious nodes and information flooding. The previous method judges whether the node is a cooperative node through the identity authentication, forwarding capability, or common social attribute of the destination node. In real applications, this information does not have integrity and does not take into account the characteristics and dynamic adaptability of nodes, network structures, and the transitivity of social relationships between nodes. Therefore, it may not be effective in solving node non-cooperation problems and improving transmission success rate. To address this problem, the proposed node social features relationship evaluation algorithm (NSFRE) establishes a fuzzy similarity matrix based on various features of nodes. Each node continuously and iteratively deletes the filtered feature attributes to form a multidimensional similarity matrix according to the confidence level and determines the weights under different feature attributes. Then, the social relations of nodes are further quantified. The experimental results show that, compared with the traditional routing algorithm, NSFRE algorithm can effectively improve the transmission success rate, reduce transmission delay, ensure the safe and reliable transmission of information in the network, and require low buffer space and computing capacity.

IoT Enabled UAV: Network Architecture and Routing Algorithm

Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.

A probabilistic home-based routing scheme for delay tolerant networks

In traditional Mobile Adhoc Networks routing algorithms, the existence of a sustainable path between the source and destination is a crucial issue. These algorithms are shown to be unsuccessful in Delay Tolerant Networks (DTNs) since the network is almost disrupted, and then such a path is not guaranteed. Although Store-and-Forward routing approach has provided an adequate solution for these networks, it suffers from the large end-to-end delay and the additional traffic and communication costs incurring the network. This study presents an efficient single-copy home-based routing scheme for DTNs. Two different scenarios, referred as blind and semi-blind, are considered based on the amount of available knowledge about the destination. Simulation results presented in this paper demonstrate that the performance of the proposed scheme is closely approaching the unrestricted flooding algorithm, the Epidemic. Moreover, the proposed scheme provides a low communication cost, an outstanding end-to-end delay and a high successful delivery ratio as compared to state-of-art algorithms.

Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks

Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility characteristics of the nodes. According to the node prediction information, the metric value of the candidate node is calculated, and the node with the smaller metric value is selected as the data forwarding node, which can effectively reduce the packet loss rate and avoids excessive consumption. Simulation results show that compared with other algorithms, the proposed algorithm has higher data delivery ratio, and end-to-end data delay and routing overhead are significantly reduced.

Dynamic Routing Algorithm with Q-learning for Internet of things with Delayed Estimator

With the popularity of the Internet of things (IoT), tremendous objects are connected to the network, making the network topology complex. Mechanical engineering achieves intelligent identification, positioning, tracking, monitoring and management of engineering machinery through the IoT, where information exchange and communication require novel intelligent routing algorithms as traditional routing algorithms are unfit for current network environment. Q-routing implemented a dynamic adjustment which was based on the network environment by combining the Q-learning algorithm. However, Q-routing is a highly random network environment and leads to a decline in performance because of overestimation of values. To solve the problem, we propose an algorithm called Delayed Q-routing (DQ-routing), which uses two sets of value functions to carry out random delayed updates so as to reduce the overestimation of the value function and improve the rate of convergence. The experiments indicate that DQ-routing algorithm gets well performance in several problems.

A Node-Protection-Based ZBR Routing Algorithm

The Zigbee network is a new type of wireless short-range communication network with powerful self-organizing capabilities. Routing algorithms are key to Zigbee network research. However, the segmentation of the Zigbee network is commonly observed, as nodes closer to the coordinator consume a larger amount of energy and are thereby vulnerable to early stage death. Thus, we propose a node-protection-based Zigbee routing (NPZBR) algorithm to address this problem. First, Zigbee network allocation mechanisms and shortages of typical routing protocols i.e., the cluster-tree and ad-hoc on-demand distance vector junior (AODVjr) algorithms, are analyzed. Second, the NPZBR algorithm, based on a combination of the cluster-tree and AODVjr algorithms, is proposed, and an energy threshold model for node protection is developed. A node energy flag bit is introduced to determine whether the node should be protected. Finally, the simulation of NPZBR parameters for the selection of the node threshold model, node residual energy, node survival rate, node mortality rate, and end-to-end delay, is performed. Simulations demonstrate that compared to the traditional Zigbee routing (ZBR) algorithm, the proposed algorithm consumes 10% less energy and improves the node survival rate by 4%. Moreover, it reduces the node mortality rate and the end-to-end delay, effectively protecting network nodes and enhancing network integrity.

Tidal-Traffic-Aware Routing and Spectrum Allocation in Elastic Optical Networks

With the growing popularity of 5G mobile communications, cloud and fog computing, 4K video streaming, etc., population distribution and migration have increasing influence on distribution in metro elastic optical networks (EONs). Traffic distribution is further diversified according to people's tendency to use network services in different places at different times. We use the term traffic to represent distribution with strong disequilibrium in the time and space dimensions. Note that tidal can potentially result in low bandwidth utilization and weak service capability, as network resources are not allocated properly. To address this problem, in this study, we first analyze characteristics of access networks and metro networks, and mathematically formulate an onion tidal model (OTTM) in EONs. Second, based on the traditional routing and spectrum allocation (RSA) algorithm, which provides end-to-end connection by allocating frequency slot resources in EONs, we propose two algorithms to enhance bandwidth efficiency based on the OTTM model. We call them pre-detour RSA (PD-RSA) and pre-detour k-shortest paths RSA (PDK-RSA). Next, we analyze the shortcomings of a benchmark algorithm named min-hop k-shortest paths RSA (MHK-RSA) under tidal traffic, and compare PD-RSA and PDK-RSA with MHK-RSA via simulation. Simulation results show that PD-RSA and PDK-RSA can effectively reduce at least 26% and 18% of blocking probability, respectively, compared to MHK-RSA with the same time complexity.

Location-assistant content distribution scheme for emergency rescue

In order to optimize the timeliness and pertinence of message delivery in emergency rescue scenarios and improve the service performance of emergency communications network, we propose a location-assistant content distribution scheme based on delay tolerant network. First of all, considering that the movement patterns of rescue teams tend to follow a predetermined course of action, we design a location-based group mobility model. Due to the intermittent network connectivity and variety of emergency service, a content-classification-based publish/subscribe architecture and a GenericSpray routing algorithm based on the prediction of overlap opportunity in spatio-temporal positions are proposed. Furthermore, we also give a cache management strategy based on the content significance. Since location-assistant content distribution scheme can predict the overlap of activity between rescue teams through the course of action, not only can the number of copy forwarding and message delivery delays be significantly reduced but also the priority delivery of important messages can be ensured by message classification. Simulation experiments show that compared with the traditional delay tolerant network routing algorithm and the classic first-in-first-out caching strategy, location-assistant content distribution scheme improves the performance of message delivery rate, transmission delay, and control overhead significantly.

Limitation of Erlang B Traffic Model in Elastic Optical Network for Blocking Probability Estimation

Abstract In this paper, an algorithm for multipath connection provisioning in elastic optical network (EON) has been proposed. Initially, the algorithm prefers the single-path routing for service provisioning. But when single-path routing is not adequate to serve a dynamic connection, the algorithm switches to the connection request fragmentation. Its computation is based on the parameters such as capacity_constant and capacity_allowed to fragment the connection request on disjoint paths. Simulation results clearly state that the proposed algorithm performs well in service provisioning as compared to the traditional single-path routing algorithms and improves the average network throughput. Thereafter, we have investigated the limitation of Erlang B traffic model in EON for calculation of link blocking probability using routing and spectrum assignment (RSA) algorithm. It is verified by the following two ways: (i) effect on the blocking probability in case of constant load and (ii) effect of slot width on the blocking probability. Our simulation results indicate that in EON due to dynamic RSA, blocking probability is not constant in case of proportionate varying of call arrival and service rate giving constant load and blocking probability depends on the number of slots per link, but in Erlang B traffic model blocking probability is always constant in case of constant load and it considers wavelength per link instead of slots per link. This is attributed to the fact that Erlang B traffic model fails to calculate blocking probability accurately in EON. We have computed the carried traffic on 14 nodes, 21-link National Science Foundation Network (NSFNET) topology.

An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes

The mobility of nodes leads to dynamic changes in topology structure, which makes the traditional routing algorithms of a wireless network difficult to apply to the opportunistic network. In view of the problems existing in the process of information forwarding, this paper proposed a routing algorithm based on the cosine similarity of data packets between nodes (cosSim). The cosine distance, an algorithm for calculating the similarity between text data, is used to calculate the cosine similarity of data packets between nodes. The data packet set of nodes are expressed in the form of vectors, thereby facilitating the calculation of the similarity between the nodes. Through the definition of the upper and lower thresholds, the similarity between the nodes is filtered according to certain rules, and finally obtains a plurality of relatively reliable transmission paths. Simulation experiments show that compared with the traditional opportunistic network routing algorithm, such as the Spray and Wait (S&W) algorithm and Epidemic algorithm, the cosSim algorithm has a better transmission effect, which can not only improve the delivery ratio, but also reduce the network transmission delay and decline the routing overhead.

Crosstalk Free Routing in Optical Multistage Interconnection Networks using Fast Window Method

A major problem in parallel computing systems which are based on optical interconnection networks is optical crosstalk that degrades the performance of network, and limits the network size. This paper discusses the implementation of fast scheduling methods (fast window methods) to the traditional zero routing algorithm and in term of running time and the ability of enlarging the network. The proposed routing algorithm reduces the running time and gives the ability to increase the network size.

A reliable routing protocol against hotspots and burst for UASN-based fog systems

In Underwater Acoustic Sensor Network (UASN) based fog systems, the monitored messages are transmitted from the sensors deployed under the water to the surface sinks, which act as the fog nodes, for further processing. To ensure the reliability of the data transmission process, effective routing algorithms are necessary to reduce the packet loss rate as well as the transmission delay caused by potential traffic burst and hotspots. For this reason, a reliable routing protocol against hotspots and burst (RRAHB) is proposed in this paper. The fuzzy decision algorithm (FDA) for nodes path selecting is designed firstly. When the node chooses the next hop, the candidate nodes are evaluated by FDA. It eliminates the subjectivity of expert scoring and ensure fairness and effectiveness of the evaluated scores. But, FDA cannot solve the problems of hotspots and the load unbalance effectively, which are two important factors that cause data packets loss. Based on FDA, the random selection and hotspots avoidance mechanism are presented. The probability of the hotspots can be reduced by the random selection, and the hotspots effect is eliminated by the hotspots avoidance mechanism. In addition, in order to guarantee the network load balance, a priority-based traffic scheduling mechanism (PTSM) is proposed. It solves the traffic surge problem caused by emergency events and reduces the possibility of packets loss. The simulation results verify the effectiveness of RRAHB in the network with dynamic topology. Compared with the traditional routing algorithm, it has great advantages in improving network reliability and enhancing network performance.

Temporal Centrality-Balanced Traffic Management for Space Satellite Networks

Structural centrality metrics of the networks are important features to identify central nodes or links, to assess network vulnerability against removal of nodes or links, and to manage or control the network traffic to reduce the network congestion. Classic graph-based centrality metrics are not effective in dynamic space satellite networks due to the changing connectivity and they cannot effectively capture important temporal properties. In this paper, we employ a new technique called time-varying graphs (TVGs) to express temporal concepts and definitions of space satellite networks and propose several temporal centrality metrics in order to account for the temporal features of the networks. To calculate these temporal centrality metrics, we adopt a modified matrix multiplication algorithm to find the shortest journeys between nodes in the TVG. A temporal centrality-balanced traffic management scheme is further developed to enhance the network performance, which is integrated into a temporal centrality-balanced routing algorithm. To show the performance of the proposed centrality metrics and centrality balanced routing algorithm, we conduct some experiments performed on a scenario of the LEO satellite network. The simulation results show that the proposed centrality metrics are more powerful to analyze the evolution of time-varying centrality and vulnerability of space satellite networks than topological adaptation of vanilla centrality methods. Besides, the proposed centrality-balanced routing algorithm reaches the superior quality values of the network performance (in terms of several factors) in comparison with those attained by the traditional shortest journeys routing algorithm and proves to be more powerful to support the high traffic load.