Narrow Results

The Routing Information Protocol (RIP) and Enhanced Interior Gateway Routing Protocol (EIGRP) algorithms are widely used in the field of telecommunications for transmission of data over computer networks. Traffic Cellular Automata (TCA) is a technique that has proved to be very efficient in simulating large-scale road traffic networks. In this paper a multicell TCA model that includes anticipation and probability randomization has been hybridized with the RIP and EIGRP algorithms. Simulation was performed on a small network and the performance of these two algorithms compared and analyzed. The results show that the EIGRP algorithm, through an adaptive routing of vehicles, achieves reduced travel times, more space-headway and lower traffic densities.

Mobile ad hoc networks (MANETs) are ad hoc networks in which the nodes co-operatively route the traffic to the destination nodes which are beyond the wireless range of source nodes. The nodes in the network act as both end devices and routers. The routing mechanism in MANETs differentiates it from other wireless networks. Developing a routing protocol which is light on resources and efficient is a challenging task. Several routing protocols have been developed but the reactive routing protocols have found favor in most applications since these obtain the route when a node has data to send. This results in lower routing load and better conservation of meagre resources of the nodes. The two prominent reactive routing protocols for mobile ad hoc networks-Dynamic Source Routing (DSR) and Ad-hoc On-demand Distance Vector (AODV) routing. Both the protocols have similar on-demand behavior, but the differences in the protocol mechanism can lead to significant performance differentials. The performance differentials are analyzed using varying network load and mobility.

Wireless systems, and mobile ad hoc networks in particular, are more likely to experience transmission and routing errors than their wired counterpart. Factors like the lack of infrastructure, node mobility, and random radio link quality can contribute to significantly higher error rates in these networks. In addition, errors have a more serious impact on the network's resources, due to limitations in bandwidth and battery power inherent to the wireless ad hoc environment. This further complicates the task of designing scalable routing protocols, since larger networks are likely to experience even more errors, which may lead to slower convergence, longer end-to-end delay and unnacceptably high number of retransmissions. In this paper, we focus on the impact of error prevention and recovery on the scaling properties of on-demand protocols for ad hoc networks. Our analytical study, based on the evaluation of the Witness Aided Routing (WAR) and the Dynamic Source Routing (DSR) protocols, shows that the lack of localized intervention in handling errors translates eventually into lack of scalability, both in terms of performance and resource consumption. As route length increases, the performance of DSR degrades dramatically, especially in the presence of fluctuating wireless link quality. Even for small routes, DSR's lack of an error handling mechanism leads to very low probability of success when there is a non-zero probability that links are not bidirectional. On the other hand, WAR remains relatively insensitive both to the length of the route and to variations in mobility and call rates, and has a higher tolerance to radio link instability. This indicates that lacalized error correction can increase route effectiveness and alleviate the effects of short-lived radio link problems to an extent that allows the protocol to scale with the network size.

Water has covered a wide part of the earth’s surface. Oceans and other water bodies contain significant natural and environmental resources as well as aquatic life. Due to humans’ hazardous and unsuitable underwater (UW) settings, these are generally undiscovered and unknown. As a result of its widespread utility in fields as diverse as oceanography, ecology, seismology, and oceanography, underwater wireless sensor networks (UWSNs) have emerged as a cutting-edge area of study. Despite their usefulness, the performance of the network is hampered by factors including excessive propagation delay, a changing network architecture, a lack of bandwidth, and a battery life that is too short on sensor nodes. Developing effective routing protocols is the best way to overcome these challenges. An effective routing protocol can relay data from the network’s root node to its final destination. Therefore, the state of the art in underwater wireless acoustic sensor network (UWASN) routing protocols is assessed with an eye toward their potential for development. In real-world applications, sensor node positions are frequently used to locate relevant information. As a result, it is crucial to conduct research on routing protocols. Reinforcement learning (RL) algorithms have the ability to enhance routing under a variety of conditions because they are experience-based learning algorithms. Underwater routing methods for UWSN are reviewed in detail, including those that rely on machine learning (ML), energy, clustering and evolutionary approaches. Tables are incorporated for the suggested protocols by including the benefits, drawbacks, and performance assessments, which make the information easier to digest. Also, several applications of UWSN are discussed with security considerations. In addition to this, the analysis of node deployment and residual energy is discussed in this review. Furthermore, the domain review emphasizes UW routing protocol research difficulties and future directions, which can help researchers create more efficient routing protocols based on ML in the future.

Mobile ad-hoc wireless networks are established with the mutual cooperation of a number of nodes generally having meagre computing and communication resources. These nodes execute special routing protocols, which enable them to perform multi-hop communication in a dynamic topology. Each node virtually acts as a mobile router and routes packets for other nodes in the network. For accurate functioning of the network it is imperative that all nodes execute these routing protocols in a benevolent manner. However, as ad-hoc networks are usually established in a physically insecure wireless environment, the network memberships are every now and then violated, and so malicious nodes are able to participate in the network. These nodes can launch an array of attacks against different network services including the routing process. In this paper, we evaluate the performance of three well known reactive routing protocols, in a network with varying number of malicious nodes. With the help of exhaustive simulations, we demonstrate that the performance of the three protocols greatly varies with traffic load and mobility conditions even under similar attack conditions.

This chapter presents an overview of how CPS components and their users communicate by following an established set of rules referred to as protocols. First, we argue the need to network CPS entities and describe the modern network protocols. We divide the protocols supporting CPSs into three categories: machine-to-machine (M2M) connectivity protocols for connecting devices stationary or mobile, routing protocols to find traffic routes between sources and destinations wired or wireless, and M2M communication protocols for specific CPS/IoT applications. Finally, we close the chapter with a CPS example for driverless cars.

One of the most important issues in wireless sensor networks (WSNs) is the design of optimized routing protocols that cannot only route the packets in an efficient way and with minimum overheads but also consider severe power restriction of those networks. Correct design of routing protocols can prolong the lifetime of these networks and therefore much research has been carried out to develop new and customized routing protocols for the WSNs. In this chapter, we will first look at different topologies of the WSNs, say flat and hierarchical networks and compare the two topologies from the routing efficiency point of view. We will then review the most important routing protocols designed for the WSNs for flat and hierarchical topologies. We will further look at some other routing protocols in those networks that aim at providing quality of service in sensor networks and can possibly control the power consumption and therefore the lifetime of the networks. Finally we will review data-centric and geographic routing approaches to complete our survey on routing protocols in WSNs.

This chapter presents IEEE 802.11s, an emerging standard for wireless mesh networks (WMNs). IEEE 802.11s proposes multi-hop forwarding at the MAC level, which is a new approach for building WMNs. Traditional solutions for WMNs use network-level routing protocols to allow multi-hop forwarding among wireless mesh nodes. IEEE 802.11s specifies multi-hop MAC functions for mesh nodes using a mandatory path selection mechanism named HWMP (Hybrid Wireless Mesh Protocol) and also provides a path selection framework for alternative mechanisms and future extensions. This chapter discusses the emerging standard details and compares this new solution for WMNs to traditional ones.

Wireless sensor network (WSN) is a network of small embedded systems called nodes. These nodes gather data using various sensors to monitor an event; and wirelessly pass the data to a base station for analysis. Low Energy Adaptive Clustering Hierarchy (LEACH) is a hierarchical cluster-based routing algorithm for WSNs. LEACH groups the nodes into clusters, each having a randomly selected cluster-head that acts as the gateway for its cluster-members. Since the cluster-head selection is random, the selected clusterhead may not have enough energy compared to the other nodes or might be located poorly on the network. These instances can reduce the system lifetime of the WSN. This paper introduces ClusterNet, a modification of the LEACH routing algorithm that considers the residual energy and location of the node as a metric for choosing the cluster-heads to increase the system lifetime of the WSN.