Forwarding Nodes Research Articles

Zone-based stable and secure clustering technique for VANETs

Vehicular Ad-hoc Network (VANET) is a pivotal element of the Intelligent Transportation System (ITS) and has gained significant attention in both academia as well as in the industry. The driving experience has been improved with the deployment of VANETs in the transportation system. In this scenario, accurate real-time traffic information is required to be forwarded to the correlated vehicles using Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. The V2V system is prone to the issue of communication overhead and security, whereas the V2I-based model for data dissemination faces the issue of scalability. In addition to this, wireless communication among vehicles is affected by potential attackers, that not only threaten the vehicle's security but also the commuter's life. An impersonation attack is considered the most common and dangerous attack in VANETs. In this paper, we propose a hybrid system that utilizes both V2V and V2I communications for secure data dissemination in urban scenarios. We use a zone-based stable and secure clustering mechanism to identify Cluster Heads (CH) as a message forwarding node to and from the Road Side Unit (RSU), which reduces the communication overhead and latency of the network. We utilize the concept of Internet Control Message Protocol (ICMP) and the monitoring mode of RSU for the detection and mitigation of impersonation attacks in VANETs. Finally, we validate our results using a network simulator (NS2).

TBDD: Territory-Bound Data Delivery for Large-Scale Mobile Sink Wireless Sensor Networks

The hierarchical structure-based data dissemination is the most popular technique in mobile sink wireless sensor networks (MS-WSNs). An ingenious virtual structure design combined with a precise routing management strategy is significant to attaining efficient data dissemination in hierarchical approaches. This paper proposes a hierarchical protocol called TBDD that divides the network into multiple partitions called Regions, and spots the location of the mobile sink (MS) according to these partitions. TBDD dynamically assigns a defined role to each division by adopting the mobility of the sink. Thus, the protocol takes advantage of the sink’s movement and the Regions’ flexible role in balancing energy consumption throughout the network. A Region is designated as active if it contains the sink node or passive otherwise. By using the territory of the active region as a temporal location of the MS, the proposed protocol hides the local movements (i.e., moves inside the active region) of the sink from the rest of the network. In such a way, regardless of the exact position of the sink, sensed data flows from different network ends to the sink’s temporal location. Therefore, TBDD reduces the query request and response burden employed to get the position of the sink. Besides, TBDD implements a spanning tree to report the location information of the MS. Last; we applied an opportunistic routing technique that captures multiple network criteria to elect packet forwarder nodes. The proposed protocol is mathematically analyzed and experimentally evaluated and shows outstanding performance in terms of the number of hops, energy consumption, delay, network lifetime, and success ratio.

Exploiting social context awareness for intelligent data forwarding in social Internet of Things

In the social internet of things, community structure exists objectively and affects the transmission of network messages. If the social context such as community is fully utilized, the efficiency of data forwarding will be effectively improved. A community-based routing algorithm (MSAR) is proposed by studying the multiple social relationships. First, we propose four measures of social relationships. They are social closeness degree, in-community activeness, cross-community activeness and community interaction. Then, the design of routing algorithm considers two stages. One is in-community forwarding and the other is cross-community forwarding. The measurement of node forwarding capability depends on closeness degree and in-community activeness in the in-community forwarding stage. In the cross-community stage, the measurement of node forwarding capability depends on closeness degree, cross-community activeness and community interaction. The relay node with higher cross-community forwarding utility will be selected. This prevents messages from being limited to the local community. Therefore, messages can always travel in the direction of the destination node’s community. Finally, a lot of simulation experiments and analyses are carried out. The analysis results show that the proposed algorithm has good performance in the following two aspects, the average latency and the message delivery rate respectively.

An RSU-Assisted Hybrid Emergency Message Broadcasting Protocol for VANETs

In vehicular ad hoc networks (VANETs), emergency messages broadcasting has been considered as one of the important parts for safety related applications. The efficiency and reliability of the emergency message broadcasting are severely affected by the network structure, message redundancy, channel contention, etc. To address this problem, in this paper, we propose an RSUs-assisted hybrid emergency message broadcasting (RA-HEMB) protocol for two-way grid roads in urban VANETs. In doing so, the broadcasting target region is first generated based on the instantaneous traffic status and types of the emergency messages. To balance the deliver latency and the reliability, an adaptive forwarding nodes selection scheme based on position is proposed to dynamically determine the forwarding area according to different vehicle densities and vehicle-to-vehicle (V2V) communication ranges. Then, based on the forwarding nodes selection scheme and roadside unit (RSU) deployment, an RA-HEMB protocol is established by properly utilizing both the wired links via RSUs and the wireless links to broadcast the emergency message in hybrid vehicular networks. Simulation results show that the proposed RA-HEMB can greatly shorten the broadcasting time needed to notice the nodes within the target region performance compared to the benchmark protocols.

Dingo algorithm-based forwarder selection and huffman coding to improve authentication

<span>In wireless sensor network (WSN), the high volume of observe and transmitted data among sensor nodes make it requires to maintain the security. Even though numerous secure data transmission approaches designed over a network, an inadequate resource and the complex environment cause not able to used in WSNs. Moreover, secure data communication is a big challenging problem in WSNs especially for the military application. This paper proposes a dingo algorithm-based forwarder selection and huffman coding (DAHC) to improve authentication in internet of things (IoT) WSN. Initially, it detects the anomalous nodes by applying support vector machine (SVM) algorithm based on sensor node energy, node selfishness, and signal to noise ratio (SNR). Next, we using the dingo algorithm to select the forwarder node. This dingo algorithm computes the fitness function based on node degreee, node distance and node energy. Finally, the huffman coding to provide end to end authentication established on node energy from sender to receiver. During data transmission, the huffman coding to build the binary hop count value, it improves the authentication in the WSN. Performance results specify that this approach enhances the detection ratio and throughput.</span>

An Opportunistic Routing Based on Directional Transmission in the Internet of Underwater Things

The Internet of Underwater Things (IoUT) has attracted a lot of attention because of its promising applications in underwater environmental monitoring; however, the characteristics of acoustic communication, e.g., long propagation delay and high attenuation, pose great challenges for efficient and reliable underwater data transmission. Currently, opportunistic routing is regarded as a key technology to improve the packet delivery ratio, because it can dynamically choose several forwarding nodes and leverage their cooperative forwarding to increase network throughput. However, choosing an excessive number of forwarding nodes may result in energy waste and lengthy communication delays. Therefore, an Opportunistic Routing based on Directional Transmission (ORDT) is studied to improve packet delivery timeliness and reliability and lower energy consumption. ORDT mainly contains three phases: forwarding area division, candidate forwarder selection, and candidate forwarder coordination. The forwarding region is first established based on directional transmission. Only neighbors located in the forwarding region can forward packets. Following that, candidate forwarders in the forwarding region are chosen depending on their forwarding capability. Additionally, the chosen candidate’s time of holding packets is specified so that they might collaborate to reduce redundant data transmission and transmit packets to gateway nodes. Then, an Autonomous Underwater Vehicle (AUV) is employed to gather packets from gateway nodes. Simulation findings demonstrate that ORDT performs better than existing opportunistic routing in terms of packet delivery success rate, transmission latency, and energy usage.

AOR: Adaptive opportunistic routing based on reinforcement learning for planetary surface exploration

Planetary surface exploration mobile ad-hoc networks (PSEMANET) show the characteristics of routing void caused by craters and electromagnetic interference, relatively high dynamics caused by node heterogeneity, and small node capacity caused by load constraints, which is the reason for the increase of communication delay and packet loss. Adaptive opportunistic routing based on reinforcement learning (AOR) is an opportunistic routing protocol that determines the forwarding node based on the current coordinates, queue length, and the number of neighbors. The full forwarding areas are used to avoid routing void, and the dual competition mechanism is designed to avoid the node hidden problem. The Q-value obtained by reinforcement learning is used to design the dynamic delay cost (DDC) so that nodes in the forwarding areas can compete to achieve adaptive path switching to the environment. Compared with representative proactive routing OLSR, reactive routing AODV, geographic routing GPSR and opportunistic routing BLR, AOR and AOR with memory(AOR-M) have high packet delivery ratio (PDR) and low end-to-end delay in planetary surface exploration scenarios implemented by our simulation system. And the AOR-M protocol shows the lowest expected end-to-end delay and highest channel utilization in both high and low speed scenarios. The result shows that the AOR-M provides efficient and robust routing in planetary surface exploration mobile ad-hoc networks with a complex environment, highly dynamic nodes, and performance constraints.

Congestion Detection and Mitigation Technique for Multi-Hop Communication in WSN

The primary function of a network system is to gather information from the observation region and transmit it to the base station. The network life span and congestion are the two major concerns in wireless networks. To enhance the lifespan of the sensor system; multi-hopping has been proved as best in class. Congestion is an important factor to be taken, where multiple nodes forward data to one another in the process of communication. Hence to overcome the issue of congestion in WSN, we proposed a congestion detection and mitigation method along with the multi-hop concept. In this technique, we have considered different routes among communication units that were classified on distance, relative attainment rate (RAR) and node storage occupancy. A utility function (U) has been proposed and calculated using the above illustrated factors for every node that acts as a neighbour to the transmitting node. Neighbour node with highest U-valued will be considered as the packet forwarding node's next hop. In this manner congestion free nodes are selected for data transmission.

A novel routing protocol for underwater wireless sensor networks based on shifted energy efficiency and priority

Underwater Wireless Sensor Networks (UWSNs) are among the most promising research areas these days due to their unique characteristics and diverse underwater applications. Though a number of routing protocols have been designed and implemented for UWSNs over the past few years, the researchers face several challenges, e.g., low speed of propagation, small bandwidth, limited battery power, etc., while designing routing protocols for communication in UWSNs. Acoustic sensor nodes are equipped with batteries with limited power and it is quite costly to replace or recharge them. The network will not survive for the desired period of time if the power of node batteries is not efficiently used. To effectively resolve this issue, this paper proposes a Shifted Energy Efficiency and Priority (SHEEP) routing protocol for UWSNs. The proposed protocol aims to enhance the efficiency of the state-of-the-art Energy Balanced Efficient and Reliable Routing (EBER2) protocol for UWSNs. SHEEP is built upon the depth and energy of the current forwarding node, the depth of the expected next forwarding node, and the average energy difference among the expected forwarders. Simulation results demonstrate that SHEEP improves the energy efficiency and packet delivery ratio in comparison to EBER2 by 7.4% and 13% respectively.

THSI-RP: A two-tier hybrid swarm intelligence based node clustering and multi-hop routing protocol optimization for wireless sensor networks

Wireless sensor networks exploit clustering and routing techniques to improve energy efficiency, but these methods are generally considered as a non-deterministic polynomial (NP-hard) problem. To tackle this problem, we propose a novel two-tier hybrid swarm intelligence-based hierarchical routing protocol (THSI-RP). In the first tier, THIS-RP incorporates a hybrid swarm intelligence algorithm that combines Grey Wolf Optimization (GWO) and Marine Predators Algorithm (MPA) for the clustering algorithm. By incorporating the wolf factors from GWO into MPA as elite predators, we enhance the network search efficiency. This algorithm achieves adaptive optimal clustering with a controllable scale, considering the residual energy of nodes, relative distance between nodes, and node centrality. In the second tier, the routing algorithm uses a hybrid SI algorithm based on GWO and the graph model. First, we integrate distance and energy factors to dynamically select forwarding nodes using GWO. Then, based on the distance and energy balance principle, a weight cost function is established and combined with the minimum spanning tree method to construct a communication routing tree between the forwarding node and the base station to achieve optimized inter-cluster multi-hop routing. Simulation results demonstrate that THSI-RP outperforms several typical routing protocols in various metrics over EEUC, LPSO, LGWO, and LACO. For instance, THSI-RP achieves percentage improvement total packets by 290%, 10.9%, 8.9%, 19.9%, respectively. It also performs better in FND by 88.7%, 10.9%, 8.6%, and 16.3%, in HDN by 86.6%, 8.1%, 6.3%, and 17.1%, and in ADN by 41.7%, 8.2%, 6.4%, 18.3%.

Performance analysis of NOMA enabled multi-user co-operative IoT network with SWIPT protocol

This study investigates the performance of user equipments (UEs) and IoT devices in a multi-user (MU) multiple-input single-output (MISO) configuration based non-orthogonal multiple-access (NOMA) system. The proposed model includes a base station surrounded by many users and a number of dispersed IoT devices. In the considered MU IoT network scenario, power allocation coefficients are set to ensure that both the relaying UE and the targeted IoT device experience increased performance. To assist the intended IoT device with weak channel conditions, an optimal user selection method is developed that picks a UE with comparatively better reception, and thereby the selected UE acts as relay forward node utilizing the simultaneous wireless information and power transfer protocol. The characterization of a key performance metric, i.e., the outage probability is derived by adopting a two-step procedure. First, quadratic forms of signal to interference plus noise ratio (SINR) at the relaying UE and the signal-to-noise ratio (SNR) and SINR at the intended IoT device are constructed. Then, using the indefinite quadratic form approach, we arrive at closed-form expressions for the outage probability (OP) of intended IoT device under linearly dependent beamformers. To validate the analytical expressions provided in this study, simulations are performed. We demonstrate that by utilizing the proposed user selection technique in MU-MISO-NOMA system decreases the OP of the intended IoT device by 40% on average. Furthermore, utilizing multiple transmit antenna elements decreases the OP of the intended IoT device by approximately 10%.

ECKN: An Integrated Approach for Position Estimation, Packet Routing, and Sleep Scheduling in Wireless Sensor Networks.

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead.

Multi-granularity fusion resource allocation algorithm based on dual-attention deep reinforcement learning and lifelong learning architecture in heterogeneous IIoT

Deep reinforcement learning (DRL) is a promising technology to address the resource allocation problem for efficient data transmission in complex network environments. However, most DRL-based resource allocation algorithms suffer from limited feature extraction capabilities and lack scalability and generalization, especially in heterogeneous Industrial Internet of Things (IIoT) environments. In this paper, we develop a lifelong learning architecture that can integrate artificial intelligence (AI) algorithms into the heterogeneous IIoT network for efficient data transmission. Based on this, we propose an intelligent resource allocation algorithm based on dual-attention DRL (DADR) for forwarding node selection and channel access slot allocation in a specific network environment. The proposed DADR algorithm combines the advantages of multi-dimension convolutional attention and multi-head self-attention mechanisms. It can provide local- and global-feature fusion capabilities for distributed nodes while maximizing the performance of data transmission. Furthermore, we present a lifelong federated meta reinforcement learning (LFMRL) that can effectively utilize prior knowledge and enable the DRL agent quickly adapt to a new environment. Specifically, LFMRL adopts a federated meta learning-based knowledge fusion algorithm to fuse the knowledge of learned DADR algorithms and iteratively update the shared model, thereby improving the scalability and generalization of the shared model in heterogeneous IIoT environments. In addition, a simple and efficient knowledge transfer mechanism is enabled to accelerate the DRL model convergence by transferring the knowledge of the shared model to the new environment. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of energy efficiency, data transmission reliability, and network stability. Compared to DADR and FedAvg algorithms, LFMRL algorithm can further reduce the energy consumption, training time, and average forwarding node switching times, while improving packet delivery rate to 99.2%.

Optimal Intelligent Vehicle Control System for Emergency Vehicle Using Visible Light Communication VANET

Abstract: Traffic congestion is one of the significant problems in every metropolitan city. Traffic congestion occurs, when large numbers of vehicles are all together and are not able to move or move slowly, it is also known as a traffic jam. The main aim of the proposed OIVC-VLC VANET system is to improve the data transmission rate to control traffic in high density loads for emergency vehicles. Traffic congestion leads to wasting of time, road accidents, delays of trips, and reduces regional economic health and fuel consumption. Moreover, the most critical concern of traffic congestion is a delay of emergency vehicles like ambulance and police vehicles, and firefighting trucks leading to an increase the human death and loss their essential things. In addition, this paper focuses to reduce the traffic congestion and traveling time of emergency vehicles. To overcome traffic congestion issues, an Optimal Intelligent Vehicle Control System for the emergency vehicle by intelligent traffic clearance (OIVC-VLC-VANET) is proposed. Firstly an improved whale optimization (IWO) algorithm is submitted for grouping the vehicle nodes based on their behaviors. Secondly, the differential search algorithm is used to select the next forwarding node using multiple constraints received from vehicles. Thirdly, the dragonfly algorithm is submitted for avoiding extra time due to the control packets exchange process. The results show that the proposed OIVC-VLC-VANET system can perform very efficiently in terms of quality constraints than existing systems.

Traffic and delay aware routing using optimization algorithm for wireless sensor networks

The Internet of Things (IoT) is a system of machines, computing devices, electronic equipment, and different sensors. It forms a network, where the transmission of device-related data can be accomplished. The devices in the IoT are connected to each other through wireless links and form ad-hoc networks. In IoT based applications, the lifetime of the communicating nodes is a greater concern. The network lifetime can be maximized by introducing energy efficient data transmission in the network. Therefore, a traffic and delay-aware energy-efficient routing (TADEER) protocol for IoT-based networks are proposed in this work. The proposed technique assigns delay for transmitting data based on the criticality level of data and traffic rate at the forwarding nodes. Fixing delays for data transmission helps to avoid unnecessary transmissions. The route selection process is implemented using an optimization algorithm. A Fuzzy logic (FL) based biogeography-based optimization (BBO) algorithm is presented in this work. Thus, the number of data transmission and energy consumption can be reduced. The performance of the proposed method is evaluated by analyzing transmission delay, network lifetime, and energy consumption. By comparing the simulation results to the existing methods TEAR and ETASA, the simulation results are validated.

Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint

Network topology construction plays an important role in the application of large-scale unmanned aerial vehicle (UAV) swarm. Current researches usually perform the topology construction in terms of criteria of nodes energy consumption, transmission delay and network throughput, etc. However, another important criterion, the stability of swarm network topology, which is much critical for dynamic scenarios, has not been fully considered. In this paper, a novel topology construction method for UAV swarm network based on the criterion of topology duration is proposed. Specially, the topology construction of swarm network is formulated as an optimization problem of maximizing the topology duration while satisfying the constraints of certain network throughput, end-to-end delay, and nodes energy consumption. Then, a novel Group Trend Similarity based double-head Clustering method(GTSC) is employed to solve this problem, in which group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy are comprehensively taken into account. The proposed method is effective when used to perform the network topology construction for UAV swarm, which is verified by the simulation results. Furthermore, in comparison with representative algorithms, the proposed GTSC method exhibits better performance on topology duration, network throughput, end-to-end delay and energy consumption balance especially in a large-scale swarm scenarios.

An anti-collusion attack defense method for physical layer key generation scheme based on transmission delay.

Physical layer security (PLS) is considered one of the most promising solutions to solve the security problems of massive Internet of Things (IoTs) devices because of its lightweight and high efficiency. Significantly, the recent physical layer key generation (PLKG) scheme based on transmission delay proposed by Huang et al. (2021) does not have any restrictions on communication methods and can extend the traditional physical layer security based on wireless channels to the whole Internet scene. However, the secret-sharing strategy adopted in this scheme has hidden dangers of collusion attack, which may lead to security problems such as information tampering and privacy disclosure. By establishing a probability model, this article quantitatively analyzes the relationship between the number of malicious collusion nodes and the probability of key exposure, which proves the existence of this security problem. In order to solve the problem of collusion attack in Huang et al.'s scheme, this article proposes an anti-collusion attack defense method, which minimizes the influence of collusion attack on key security by optimizing parameters including the number of the middle forwarding nodes, the random forwarding times, the time delay measurement times and the out-of-control rate of forwarding nodes. Finally, based on the game model, we prove that the defense method proposed in this article can reduce the risk of key leakage to zero under the scenario of the "Careless Defender" and "Cautious Defender" respectively.

Evolutionary trend and network structure characteristics of publicity information dissemination about waste separation by different opinion leaders

Opinion leaders play significant roles in encouraging waste separation behaviors and increasing individual knowledge about environmental protection. To encourage waste reduction at its source, it is critical to determine the dissemination law and the structure characteristics of opinion leaders when they discuss waste separation information. This study explored the dynamic evolution law of information dissemination by official and celebrity opinion leaders. The results showed that the information released by celebrities showed a higher level of dissemination. The key forwarding nodes appeared in the transmission chain of highly influential celebrities, boosting the transmission depth of information. Official opinion leaders can use weak relationships to spread information to different groups, expanding the spread of information. Besides, the audience distribution pattern for official opinion leaders was highly coincident with the Hu Line and that the audience response degree achieved by celebrities showed a decreasing trend from the southeast coast to northwest inland.

Emergency Data Transmission Mechanism in VANETs using Improved Restricted Greedy Forwarding (IRGF) Scheme

One of the most critical tasks in Vehicular Ad-hoc Networks (VANETs) is broadcasting Emergency Messages (EMs) at considerable data delivery rates (DDRs). The enhanced spider-web-like Transmission Mechanism for Emergency Data (TMED) is based on request spiders and authenticated spiders to create the shortest route path between the source vehicle and target vehicles. However, the adjacent allocation is based on the DDR only and it is not clear whether each adjacent vehicle is honest or not. Hence, in this article, the Improved Restricted Greedy Forwarding (IRGF) scheme is proposed for adjacent allocation with the help of trust computation in TMED. The trust and reputation score value of each adjacent vehicle is estimated based on successfully broadcast emergency data. The vehicles’ position, velocity, direction, density, and the reputation score, are fed to a fuzzy logic (FL) scheme, which selects the most trusted adjacent node as the forwarding node for broadcasting the EM to the destination vehicles. Finally, the simulation results illustrate the TMED-IRGF model’s efficiency compared to state-of-the-art models in terms of different network metrics.

Delay-Aware and Link-Quality-Aware Geographical Routing Protocol for UANET via Dueling Deep Q-Network.

In an unmanned aerial vehicles ad hoc network (UANET), UAVs communicate with each other to accomplish intricate tasks collaboratively and cooperatively. However, the high mobility of UAVs, the variable link quality, and heavy traffic loads can lead to difficulties in finding an optimal communication path. We proposed a delay-aware and link-quality-aware geographical routing protocol for a UANET via the dueling deep Q-network (DLGR-2DQ) to address these problems. Firstly, the link quality was not only related to the physical layer metric, the signal-to-noise ratio, which was influenced by path loss and Doppler shifts, but also the expected transmission count of the data link layer. In addition, we also considered the total waiting time of packets in the candidate forwarding node in order to decrease the end-to-end delay. Then, we modeled the packet-forwarding process as a Markov decision process. We crafted an appropriate reward function that utilized the penalty value for each additional hop, total waiting time, and link quality to accelerate the learning of the dueling DQN algorithm. Finally, the simulation results illustrated that our proposed routing protocol outperformed others in terms of the packet delivery ratio and the average end-to-end delay.