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Abstract:Recent advances in information and communication technology (ICT) have enabled interaction and cooperation between components of the transportation system, and cooperative eco-driving systems that apply ICT to eco-driving systems are receiving significant attention. A cooperative eco-driving system is a complex system that requires consideration of the electronic control unit (ECU) and vehicle-to-everything (V2X) communication. To evaluate these complex systems, it is needed to integrate simulators with expertise. Therefore, this study presents an integrated driving hardware-in-the-loop (IDHIL) simulator for the testing and evaluation of cooperative eco-driving systems. The IDHIL simulator is implemented by integrating the driving hardware-in-the-loop simulator and a vehicular ad hoc network simulator to develop and evaluate a hybrid control unit and cooperative eco-driving application for the connected hybrid electric vehicle (CHEV). A cooperative eco-driving speed guidance application is utilized to demonstrate the use of our simulator. The results of the evaluation show the improved fuel efficiency of the CHEV through a calculation of the optimal speed profile and the optimal distribution of power based on V2X communication. Finally, this paper concludes with a description of future directions for the testing and evaluation of cooperative eco-driving systems.Keywords: hardware-in-the-loop (HIL); VANET simulator; driving simulator; cooperative eco-driving; integrated simulator; connected hybrid electric vehicle
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The planet Earth is the most water-rich place because oceans cover more than 75% of its land area. Because of the extraordinary activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while also monitoring and recording the physical and environmental parameters of their surroundings. An underwater wireless sensor network (UWSN) is the name given to the network created by the collection of these underwater wireless sensors. This particular technology is the most efficient way to analyse performance parameters. A network path is chosen to send traffic by using the routing method, a process that is also known as a protocol. The routing protocols ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR), dynamic manet on demand routing protocol (DYMO), location-aided routing 1 (LAR 1), optimized link state routing (OLSR), source-tree adaptive routing optimum routing approach (STAR-ORA), zone routing protocol (ZRP), and STAR-least overhead routing approach (STAR-LORA) are a few models of routing techniques. By changing the number of nodes in the model and the maximum speed of each node, performance parameters such as average transmission delay, average jitter, percentage of utilisation, and power used in transmit and receive modes are explored. The results obtained using QualNet 7.1 simulator suggest the suitability of routing protocols in the UWSN.
An optimal, collaborative, and resource-saving strategy is being examined [23]. In order to create a UWSN that consumes less energy, a relay node is chosen. The indices of dead and end-to-end delays, dead packet delivery ratio, and energy usage are investigated. The nodes are organized in a two-dimensional environment with a regular distribution. In the case of the UWSN, both interference and noise are considered. Khan et al. examines interference-free localization routing for ultrawideband sensor networks (UWSNs), with the goal of minimizing the energy hole [23]. It specifies the total number of dropped packets, the total number of dead nodes, a packet received at the sink, the total amount of energy used, and the total number of dropped packets.
In multihop wireless networks, reliable data transfer is one of the most difficult tasks. When transmission control protocol (TCP) operates in multihop wireless networks, the performance of TCP reduces drastically. TCP retransmission timeouts (RTOs) related to non-congestion events such as spurious and random packet losses have been reported as one of the main problems in the performance degradation of TCP in multihop wireless networks. The RTOs triggered by random packet losses due to transmission errors lead to unnecessary reduction of TCP congestion window size, and the spurious RTOs due to sudden delay of packets on the network paths often cause unnecessary retransmissions as well as reduction of congestion window size. Existing solutions for detecting non-congestion RTOs have no mechanism to differentiate the spurious RTOs from RTOs caused by random packet loss. In this paper, we introduce an efficient algorithm called non-congestion retransmission timeouts (TCP NRT) which is capable of recovering packets after RTOs by reducing unnecessary retransmissions and needless reduction of congestion window size in order to improve the performance of TCP in multihop wireless networks. TCP NRT consists of three key components: NRT-detection, NRT-differentiation, and NRT-reaction. We implemented the algorithm in Qualnet network simulator and compared its performance to existing TCP versions. Results from the experiments show that our algorithm achieves significant performance improvement in terms of throughput and accuracy. Also, the results showed that our algorithm, TCP NRT, maintains a fair and friendly behavior compared to the most widely deployed TCP, NewReno.
With the help of these components, TCP NRT can improve the performance of TCP in MWNs. We implemented TCP NRT in a Qualnet network simulator and compared its performance using important metrics such as throughput, accuracy, fairness, and friendliness with the existing TCP versions such as Eifel, F-RTO, DSACK, EQRTO, and NewReno. The results demonstrate that TCP NRT achieves significant improvement in throughput and accuracy compared to other TCP versions, especially when RTOs occur in non-congested environments. Moreover, the simulation results show that when RTOs occurred in a congestion-free network with packet loss rate ranging from 1% to 9%, the TCP NRT performance achieves 29% higher throughput than EQRTO and more than 40% throughput improvement over Eifel, DSACK, and F-RTO especially at 9% packet loss rate due to transmission errors. In addition, the experiment on multiple TCP flows clearly shows that TCP NRT maintains a fair and friendly behavior with respect to other TCP flows. The remainder of this paper is organized as follows: Section 2 describes the problem of TCP RTOs. In Section 3, we briefly summarize the existing research related to the work done in this paper. We introduce TCP NRT in Section 4, where the main features of each component are discussed in detail. Section 5 describes the performance evaluation of TCP NRT against existing protocols. Finally, Section 6 concludes our work by pointing out our major achievements.
Figure 2a shows the throughput degradation of TCP according to the number of hops in a congestion-free network with random packet loss RTOs and spurious RTOs when the bandwidth of the wireless channel is 9Mbps. When the number of hop increases to seven, the TCP cannot achieve more than 1.5 Mbps of throughput. The main reason for this degradation is shown in Figure 2b. From this figure, it is clear that the number of retransmissions due to RTOs is higher than that of fast retransmissions. Among that, in our experiment, more than 40% of the TCP retransmissions happen needlessly due to spurious RTOs. As a result, it is very important to differentiate random loss RTOs from spurious RTOs to reduce unnecessary retransmissions and reduction of the cwnd size.
To evaluate the effectiveness of the TCP NRT algorithm, we implement our algorithm in the network simulator Qualnet version 5 [22] and test it in various conditions. As the main focus of our algorithm is to reduce unnecessary retransmissions and needless reduction of cwnd due to spurious RTOs and RTOs caused by random packet losses, we adopt the methodology used in [2] for tracing RTOs. We use two types of multihop topologies, as shown in Figure 9. One is a chain topology consisting of ten hops or 11 nodes, and another one is the grid topology consisting of 18 nodes. All wireless nodes are considered as static, which are using IEEE 802.11a with a basic data rate of 9 Mbps in our experiments, unless stated otherwise. The distance between two neighboring nodes is given as 200 m so that the nodes can communicate with each other. The maximum size of cwnd is set to 32 packets. For routing, we use DSR protocol and FTP-generic used for application. We designed about 300 scenarios by setting different parameters in terms of the number of hops, packet loss rate, bandwidths, number of RTOs, number of unnecessary retransmissions, and number of unnecessary reduction of cwnd. Through experiments, we aimed to evaluate how much our algorithm improves the performance of TCP in MWNs.
The trend towards adoption of Wireless Sensor Networks is increasing in recent years because of its diverse use in various fields. Wireless Sensor Network is formed via interconnection of large number of sensor nodes. Each and every sensor node deployed in network monitors various parameters like Temperature, Humidity, Ambient Light, Gas etc. and send the data to the master node. Despite of several applications and diverse uses, sensor networks face various shortcomings like energy, localization, security, self-organization, fault tolerance and many more. So, the area of Wireless Sensor Network is under rigorous research and development by various researchers across the globe to develop new algorithms, protocols and techniques to make WSN network more efficient and reliable. Before live implementation, testing of the developed technique requires rigorous testing. But it is not always possible to have live sensor network environment. So, in that case, Simulation is the only way to test the research before moving towards live implementation. Large numbers of simulation tools are available for WSN network till date, out of which some are dedicated towards wireless sensor networks and some for both wireless and wired networks. The main objective behind this research paper is to do a comprehensive review of various simulation tools of Wireless Sensor Networks to enable researcher to select the most competent tool for simulating wireless sensor networks and testing the research proposed.A comprehensive review of 31 WSN simulators is being presented along with their respective features comparison to assist researchers in advanced WSN based research.