Conventional Routing Research Articles

A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks.

With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN’s monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.

Interference Aware Cooperative Routing for Edge Computing-Enabled 5G Networks

Recently, there has been growing research on developing interference-aware routing (IAR) protocols for supporting multiple concurrent transmission in next-generation wireless communication systems. The existing IAR protocols do not consider node cooperation while establishing the routes because motivating the nodes to cooperate and modeling that cooperation is not a trivial task. In addition, the information about the cooperative behavior of a node is not directly visible to neighboring nodes. Therefore, in this paper, we develop a new routing method in which the nodes’ cooperation information is utilized to improve the performance of edge computing-enabled 5G networks. The proposed metric is a function of created and received interference in the network. The received interference term ensures that the Signal to Interference plus Noise Ratio (SINR) at the route remains above the threshold value, while the created interference term ensures that those nodes are selected to forward the packet that creates low interference for other nodes. The results show that the proposed solution improves ad hoc networks’ performance compared to conventional routing protocols in terms of high network throughput and low outage probability.

A novel slot scheduling technique for duty-cycle based data transmission for wireless sensor network

The duty cycling process involves turning a radio into an active and dormant state for conserving energy. It is a promising approach for designing routing protocols for a resource-constrained Wireless Sensor Networks (WSNs). In the duty cycle-based WSNs, the network lifetime is improved and the network transmission is increased as compared to conventional routing protocols. In this study, the active period of the duty cycle is divided into slots that can minimize the idle listening problem. The slot scheduling technique helps determine the most efficient node that uses the active period. The proposed routing protocol uses the opportunistic concept to minimize the sender waiting problem. Therefore, the forwarder set will be selected according to the node's residual active time and energy. Further, the optimum routing path is selected to achieve the minimum forwarding delay from the source to the destination. Simulation analysis reveals that the proposed routing scheme outperforms existing schemes in terms of the average transmission delay, energy consumption, and network throughput.

Optimal Deployment of SRv6 to Enable Network Interconnection Service

Many organizations nowadays have multiple sites at different geographic locations. Typically, transmitting massive data among these sites relies on the interconnection service offered by ISPs. Segment Routing over IPv6 (SRv6) is a new simple and flexible source routing solution which could be leveraged to enhance interconnection services. Compared to traditional technologies, e.g., physical leased lines and MPLS-VPN, SRv6 can easily enable quick-launched interconnection services and significantly benefit from traffic engineering with SRv6-TE. To parse the SRv6 packet headers, however, hardware support and upgrade are needed for the conventional routers of ISP. In this paper, we study the problem of SRv6 incremental deployment to provide a more balanced interconnection service from a traffic engineering view. We formally formulate the problem as an SRID problem with integer programming. After transforming the SRID problem into a graph model, we propose two greedy methods considering short-term and long-term impacts with reinforcement learning, namely GSI and GLI. The experiment results using a public dataset demonstrate that both GSI and GLI can significantly reduce the maximum link utilization, where GLI achieves a saving of 59.1% against the default method.

Few-photon routing via chiral light-matter couplings

A quantum router is one of the essential elements in the quantum network. Conventional routers only direct a single photon from one quantum channel into another. Here, we propose a few-photon router. The active element of the router is a single qubit chirally coupled to two independent waveguides simultaneously, where each waveguide mode provides a quantum channel. By introducing the operators of the scatter-free space and the controllable space, the output state of the one-photon and two-photon scattering are derived analytically. It is found that the qubit can direct one and two photons from one port of the incident waveguide to an arbitrarily selected port of the other waveguide with unity, respectively. However, two photons cannot be simultaneously routed to the same port due to the anti-bunch effect.

A Novel Energy-Efficient, Static Scenario-Oriented Routing Method of Wireless Sensor Network Based on Edge Computing

In wireless sensor network (WSN), the energy of sensor nodes is limited. Designing efficient routing method for reducing energy consumption and extending the WSN’s lifetime is important. This paper proposes a novel energy-efficient, static scenario-oriented routing method of WSN based on edge computing named the NEER, in which WSN is divided into several areas according to the coverage of gateway (or base station), and each of the areas is regarded as an edge area network (EAN). Each edge area network is abstracted into a weighted undirected graph model combined with the residual energy of the sensor nodes. The base station (or a gateway) calculates the optimal energy consumption path for all sensor nodes within its coverage, and the nodes then perform data transmission through their suggested optimal paths. The proposed method is verified by the simulations, and the results show that the proposed method may consume about 37% less energy compared with the conventional WSN routing protocol and can also effectively extend the lifetime of WSN.

An Optimized Load Balancing Using Firefly Algorithm in Flying Ad-Hoc Network

In flying ad hoc networks (FANETs), load balancing is a vital issue. Numerous conventional routing protocols that have been created are ineffective at load balancing. The different scope of its applications has given it wide applicability, as well as the necessity for location assessment accuracy. Subsequently, implementing traffic congestion control based on the current connection status is difficult. To successfully tackle the above problem, we frame the traffic congestion control algorithm as a network utility optimization problem that takes different parameters of the network into account. For the location calculation of unknown nodes, the suggested approach distributes the computational load among flying nodes. Furthermore, the technique has been optimized in a FANET utilizing the firefly algorithm along with the traffic congestion control algorithm. The unknown nodes are located using the optimized backbone. Because the computational load is divided efficiently among the flying nodes, the simulation results show that our technique considerably enhances the network longevity and balanced traffic.

Access protocol aware controller design for eMBB traffic in SD-CDN

Under a tremendous increase in 5th Generation (5G) traffic, the conventional Domain Name Server(DNS)-based routing in Content Delivery Network (CDN) cannot meet enhanced Mobile BroadBand (eMBB) communication requirements. It routes the traffic flow to the local CDN server which is closest to the end-user geographically using Border Gateway Protocol (BGP). Therefore; the overloading in the local CDN server is inevitable. According to literature, the access types of eMBB content achieve a higher hit-ratio in CDN caching and the anycast-based routing can redirect some of the traffic flows to the original server despite having a geographical disadvantage. Therefore, we consider access protocol types of a user such as TCP-based Hyper-Text Transfer Protocol 2 (HTTP2) and Quick User Datagram Protocol Internet Connections (QUIC) which are commonly used today in iphone Operating System (ioS) and Android-based applications. However, they do not have a central view to select optimal server according to access type in CDN and they are unaware of network dynamics such as protocol-based load and heterogeneity; i.e. the number of total matches packets and the ratio of the packets according to access protocols (HTTP2/QUIC). Therefore, we propose access protocol aware Software Defined Content Delivery Networks (SD-CDN) for eMBB traffic which decouples Data and Control planes and enables easy implementation of anycast routing on this infrastructure. It dynamically takes statistics from OpenFlow switches in the Data plane to calculate a novel load and the heterogeneity metrics per device periodically. It embeds the OpenFlow rules by using a novel access protocol-aware routing algorithm in SD-CDN. According to performance evaluation in a real test-bed environment; the Quality of Server (QoS) of received eMBB content by end-user from either the local or the original CDN servers, the proposed SD-CDN outputs 60% better PSNR than the conventional DNS-based routing with an acceptable rate for 5G requirements.

Enhanced-AODV Routing Protocol to Improve Route Stability of MANETs

Establishing and maintaining link stability in Mobile Ad hoc Networks (MANETs) is one of the key challenging issues. Topology changes in MANET because overhead traffic that leads to consuming extra energy of nodes as well as decreasing the performance of routing protocols. Thus, a comprise approach should be considered during the design of a routing scheme in MANETs to deal with challenges incurred by the mobility of the nodes. In this study, a simple efficient routing scheme called Enhanced_AODV (E-AODV) is proposed, aiming to enhance Ad Hoc On-Demand Distance Vector (AODV) routing protocol performance by constructing the most stable and reliable route from source to the destination node. In this routing scheme, the remaining lifetime of links and hop count are the metrics considered for calculating the Route Stability Factor (RSF) that can be utilized as a cost metric to establish the best route between source and destination node. The simulation results reveal that the proposed E-AODV routing scheme effectively outperforms the conventional AODV routing protocol and Stable and Bandwidth Aware Dynamic Routing Protocol (SBADR) in terms of packet delivery ratio, average network throughput, average end-to-end delay, and normalized routing overhead.

Tackling the Crowdsourced Delivery Problem at Scale Through a Set-Partitioning Formulation and Novel Decomposition Heuristic

This paper presents a set-partitioning formulation and a novel decomposition heuristic (D-H) solution algorithm to solve large-scale instances of the urban crowdsourced shared-trip package delivery problem. The D-H begins by dividing the packages between shared personal vehicles (SPVs) and dedicated vehicles (DVs). For package-assignment to SPVs, this paper enumerates the set of routes each SPV can traverse and constructs a package-SPV route assignment problem. For package-assignment to DVs and routing, the paper first obtains DV routes by solving a conventional vehicle routing problem and then seeks potential solution improvements by switching packages from SPVs to DVs. The switching process is cost driven. The D-H significantly outperforms a commercial solver in terms of computational efficiency, while obtaining near-optimal solutions for small problem instances. This paper presents a city-scale case study to analyze the important service design factors that impact the efficiency of crowdsourced shared-trip delivery. The paper further analyzes the impact of three important service design factors on system performance, namely (i) the number of participating SPVs, (ii) the maximum detour willingness of SPVs, and (iii) the depot locations. The results and findings provide meaningful insights for industry practice, while the algorithms illustrate promise for large real-world systems.

Deep Reinforcement Learning-Based Routing on Software-Defined Networks

With an exponential increase in network traffic demands requiring quality of services, the need for routing optimization has become more prominent. Recently, the advent of software-defined networking (SDN) technology enables centralized management and operation, and the networking resources such as switches become flexibly configurable through programmable interfaces. In this paper, we propose a deep reinforcement learning (DRL)-based routing optimization on an SDN. In the proposed method, the DRL agent learns the interdependency between the traffic load of network switches and the network performance, and decides an optimal set of link weights to make a balance between the end-to-end delay and packet losses of the network. The SDN controller determines the routing paths using the set of link weights and installs the flow-rules on the SDN-enabled switches. To overcome an extensively long learning process of DRL in a case of topology change, we develop an M/M/1/K queue-based network model and perform the learning process of DRL using the network model in an offline manner until it is converged. The simulation results demonstrate the proposed routing method outperforms a conventional hop-count routing and a traffic demand-based RL algorithm in several network topologies.

Ant-based Energy Efficient Routing Algorithm for Mobile Ad hoc Networks

In this paper, an Ant Colony Optimization (ACO) based Energy Efficient Shortest Path Routing (AESR) algorithm is developed for Mobile Ad hoc Network (MANET). The Mobile Ad hoc Network consists of a group of mobile nodes that can communicate with each other without any predefined infrastructure. The routing process is critical for this type of network due to its dynamic topology, limited resources and wireless channel. The technique incorporated in this paper for optimizing the routing in a Mobile ad hoc network is Ant Colony Optimization. The ACO algorithm is used to solve network problems related to routing, security, etc. in wired and wireless networks. This proposed work is used for selecting the energy efficient shortest route between the source and the destination of the network. This routing technique uses important parameters such as residual energy of the nodes, minimum residual energy of the path, the distance between the nodes, trip time and hop count to select the optimal route for communication to extend the lifetime of the network by reducing the energy consumption. The proposed routing protocol is compared with other conventional routing protocols. The experimental results demonstrate that the proposed AESR routing protocol outperforms the existing protocols in terms of performance metrics such as Packet Delivery Ratio, Average Path Length, Network Lifetime, Number of Dead nodes and Average Energy Consumption in high mobility networks.

AI-driven adaptive reliable and sustainable approach for internet of things enabled healthcare system.

Artificial Intelligence (AI) driven adaptive techniques are viable to optimize the resources in the Internet of Things (IoT) enabled wearable healthcare devices. Due to the miniature size and ability of wireless data transfer, Body Sensor Networks (BSNs) have become the center of attention in current medical media technologies. For a long-term and reliable healthcare system, high energy efficiency, transmission reliability, and longer battery lifetime of wearable sensors devices are required. There is a dire need for empowering sensor-based wearable techniques in BSNs from every aspect i.e., data collection, healthcare monitoring, and diagnosis. The consideration of protocol layers, data routing, and energy optimization strategies improves the efficiency of healthcare delivery. Hence, this work presents some key contributions. Firstly, it proposes a novel avant-garde framework to simultaneously optimize the energy efficiency, battery lifetime, and reliability for smart and connected healthcare. Secondly, in this study, an Adaptive Transmission Data Rate (ATDR) mechanism is proposed, which works on the average constant energy consumption by varying the active time of the sensor node to optimize the energy over the dynamic wireless channel. Moreover, a Self-Adaptive Routing Algorithm (SARA) is developed to adopt a dynamic source routing mechanism with an energy-efficient and shortest possible path, unlike the conventional routing methods. Lastly, real-time datasets are adopted for intensive experimental setup for revealing pervasive and cost-effective healthcare through wearable devices. It is observed and analysed that proposed algorithms outperform in terms of high energy efficiency, better reliability, and longer battery lifetime of portable devices.

Feature Correlation-Steered Capsule Network for object detection

Despite Convolutional Neural Networks (CNNs) based approaches have been successful in objects detection, they predominantly focus on positioning discriminative regions while overlooking the internal holistic part-whole associations within objects. This would ultimately lead to the neglect of feature relationships between object and its parts as well as among those parts, both of which are significantly helpful for detecting discriminative parts. In this paper, we propose to “look insider the objects” by digging into part-whole feature correlations and take the attempts to leverage those correlations endowed by the Capsule Network (CapsNet) for robust object detection. Actually, highly correlated capsules across adjacent layers share high familiarity, which will be more likely to be routed together. In light of this, we take such correlations between different capsules of the preceding training samples as an awareness to constrain the subsequent candidate voting scope during the routing procedure, and a Feature Correlation-Steered CapsNet (FCS-CapsNet) with Locally-Constrained Expectation-Maximum (EM) Routing Agreement (LCEMRA) is proposed. Different from conventional EM routing, LCEMRA stipulates that only those relevant low-level capsules (parts) meeting the requirement of quantified intra-object cohesiveness can be clustered to make up high-level capsules (objects). In doing so, part-object associations can be dug by transformation weighting matrixes between capsules layers during such “part backtracking” procedure. LCEMRA enables low-level capsules to selectively gather projections from a non-spatially-fixed set of high-level capsules. Experiments on VOC2007, VOC2012, HKU-IS, DUTS, and COCO show that FCS-CapsNet can achieve promising object detection effects across multiple evaluation metrics, which are on-par with state-of-the-arts.

Photon routing based on non-chiral interaction between atoms and waveguides

The photon router plays an essential role in the optical quantum network. However, conventional routers generally couple photons chirally into waveguides to achieve complete transmission from the input port to the required port. Here, we use non-chiral photon-atom interactions for targeted routing. The system consists of two V-type three-level atoms and two parallel waveguides. In addition, the two atoms are driven by external coherent fields, respectively. With a real-space Hamiltonian, the probability of photon transmitted to four ports can be obtained. The study shows that a single photon input from the left port of the waveguide-a can be deterministically transferred to any of the four ports of the two waveguides by adjusting the detuning of the atom and the driving field on the atom, as well as the distance between the two atoms.

Link and stability-aware adaptive cooperative routing with restricted packets transmission and void-avoidance for underwater acoustic wireless sensor networks

In underwater acoustic wireless sensor networks (UAWSNs), the conventional routing protocols continuously transmit data packets containing the same information when a source node detects an event of interest. This approach, however, leads to high energy consumption, high interference and challenges the stability of the network due to heavy data load on the nodes close to water surface. To overcome these challenges, this article proposes two routing protocols for UAWSNs. They are, SRRPV: stability-aware routing with restricted packets transmission and void-avoidance and LS-ACRPV: link and stability-aware adaptive cooperative routing with restricted packets transmission and void-avoidance. In SRRPV, a source node detects the event of interest, generates and routes packets. Subsequent packets routing is performed only when a change in intensity of the event is detected. Due to restricted packets transmission, three-hop connected paths with suitable neighbors are chosen to overcome the existence of a void (non-availability of a suitable forwarder) and, therefore, control packets loss. Moreover, the defined packet holding time ensures that nodes with the highest available energy transfer the most packets by holding the packets for the shortest interval of time. This strategy, in combination with restricted packets transmission, achieves network stability. Since limited number of packets are routed, the LS-ACRPV protocol is designed that adds reliability to the SRRPV protocol by selecting a routing link with the highest probability of successfully delivering packets to the destination or using adaptive cooperative routing to combat severe link conditions. Unlike the conventional protocols, the proposed protocols do not require the geographical position coordinates of nodes, which is challenging in underwater environment as nodes change their positions with sea tides and currents. Extensive simulation results reveal promising performance of the proposed schemes in terms of energy expenditure, delay and network stability at the expense of low packets delivery due to restricted packets transmission.

An Optimized Framework for WSN Routing in the Context of Industry 4.0.

The advancements in Industry 4.0 have opened up new ways for the structural deployment of Smart Grids (SGs) to face the endlessly rising challenges of the 21st century. SGs for Industry 4.0 can be better managed by optimized routing techniques. In Mobile Ad hoc Networks (MANETs), the topology is not fixed and can be encountered by interference, mobility of nodes, propagation of multi-paths, and path loss. To extenuate these concerns for SGs, in this paper, we have presented a new version of the standard Optimized Link State Routing (OLSR) protocol for SGs to improve the management of control intervals that enhance the efficiency of the standard OLSR protocol without affecting its reliability. The adapted fault tolerant approach makes the proposed protocol more reliable for industrial applications. The process of grouping of nodes supports managing the total network cost by reducing severe flooding and evaluating an optimized head of clusters. The head of the unit is nominated according to the first defined expectation factor. With a sequence of rigorous performance evaluations under simulation parameters, the simulation results show that the proposed version of OLSR has proliferated Quality of Service (QoS) metrics when it is compared against the state-of-the-art-based conventional protocols, namely, standard OLSR, DSDV, AOMDV and hybrid routing technique.

Smart stochastic routing for 6G-enabled massive Internet of Things

Faster and energy-efficient data transmission is desired for massive Internet of Things (IoT) applications in sixth-generation networks. In such high speed networks, providing reliable data delivery with low delay, while maintaining energy-efficiency, is a challenging task. In this paper, a deep learning-based stochastic routing approach, called smart stochastic routing (SSR), is presented to address this challenge. SSR takes into account reliability, delays due to transmission, reception and processing of the neighbors’ information, and energy consumption and remaining energy of IoT devices. Through our proposed mathematical model, a dataset is generated to train a deep neural network, which predicts the best routing path from source to destination and achieves substantial accuracy over the mathematically generated dataset. Through simulations, we show the efficacy of SSR over conventional stochastic routing in terms of reduced energy consumption and expected delivery delay.

Triangular Fuzzified Support Vector Regression Based Multivariate Rocchio Robust Boost Classification for Traffic and Congestion Aware Data Delivery in WSN

Wireless sensor network (WSN) is a huge set of sensor nodes distributed geographically with limited power supply. These nodes are used to sense environmental changes, gather information and send the collected data to a remote destination for data analysis and decision making. During continuous data transmission, the network traffic causes congestion. Congestion in WSN is a major cause for packet loss and it degrades the network performance. In order to improve the congestion-aware routing, a novel technique called as the Triangular Fuzzified Support Vector Regression Based Multivariate Rocchio Robust Boost Classification (TFSVR-MRRBC) technique is introduced. The proposed TFSVR-MRRBC consists of two processes such as route path identification and congestion aware data delivery. Initially, route paths are constructed by estimating node residual energy. After that, Support Vector Regression is applied to analyze the energy using the fuzzy Triangular membership function. Thus, energy-efficient nodes construct the multiple paths between sources and sink node through control message distributions namely route request with route reply is identified. Then the Multivariate Rocchio adaptive robust boost technique is applied for congestion aware data delivery based on the buffer space and bandwidth capacity. The route path with low buffer space and higher bandwidth capacity is selected as the best route among the multiple paths. Finally, Delay and packet loss is reduced by achieving congestion-aware data delivery. The simulation is achieved by various performance factors namely, energy consumption, packet delivery ratio, packet loss rate, and end-to-end delay with versus to amount of data packets and sensor nodes. Experimental results of proposed TFSVR-MRRBC technique improves data packet delivery and energy consumption is minimized, packet loss rate, delay than the conventional routing techniques.

A Multipole Approach Toward On-Chip Metal Routing for Reduced EM Side-Channel Leakage

With the proliferation of smart interconnected devices, the importance of securing computational data has increased manifold. While computational security layers like cryptographic algorithms protect against software-domain vulnerabilities, they cannot prevent leakage of electromagnetic (EM) fields from its parent device, hence compromising physical layer security. Most proposed methods of reducing EM side-channel leakage involve incorporating complex blocks into the design of an integrated circuit (IC). In this letter, we take an entirely novel stance, by borrowing fundamental concepts from multipole EM field decay and adapting the same for ICs by proposing a multipole routing approach. By building proof-of-concept scaled-up routing in PCBs, we demonstrate that unlike conventional dipole-style routing, metal layers incorporating higher-order multipoles result in a much faster-decaying side-channel leakage. Through detailed measurements, we infer guidelines toward optimizing the order of multipole needed to minimize side-channel leakage, thus enabling convenient implementation of physical layer security.