Algorithm for multicast opportunistic routing in wireless mesh networks

An optimized distributed secure routing protocol using dynamic rate aware classified key for improving network security in wireless sensor network

Abstract: To transmit information over the industrial network today, you need data in a secure way to make it a high security root. Routing security issues in the wireless web network have been well studied. The problem of security in routing in wireless mesh networks (WMN) has been well studied. There exist numerous techniques to resolve this issue but differ and suffer to achieve higher security performance in WMN. To resolve this issue, a dynamic rate aware classified key distributional secure routing (DRCKDS) is proposed. In this approach, the sensor nodes maintain various factors related to the neighbor like energy, transmission involvement, rate of success and so on. According to this, available routes are identified to reach the destination from the source. For each route identified, the method computes the secure route measure (SRM). According to the SRM value, an optimal route is selected for the transmission. Similarly, the method generates different secret keys and distributes them through the transmission route selected. The same key has been used to encrypt the data and forward the packet through the route selected. The method improves the security performance and improves the quality of service of WMN.

Routing in multi-radio, multi-hop wireless mesh networks

Abstract: We present a new metric for routing in multi-radio, multi-hop wireless networks. We focus on wireless networks with stationary nodes, such as community wireless networks.The goal of the metric is to choose a high-throughput path between a source and a destination. Our metric assigns weights to individual links based on the Expected Transmission Time (ETT) of a packet over the link. The ETT is a function of the loss rate and the bandwidth of the link. The individual link weights are combined into a path metric called Weighted Cumulative ETT (WCETT) that explicitly accounts for the interference among links that use the same channel. The WCETT metric is incorporated into a routing protocol that we call Multi-Radio Link-Quality Source Routing.We studied the performance of our metric by implementing it in a wireless testbed consisting of 23 nodes, each equipped with two 802.11 wireless cards. We find that in a multi-radio environment, our metric significantly outperforms previously-proposed routing metrics by making judicious use of the second radio.

ExOR: opportunistic multi-hop routing for wireless networks

Abstract: This paper describes ExOR,an integrated routing and MAC protocol that increases the throughput of large unicast transfers in multi-hop wireless networks. ExOR chooses each hop of a packet's route after the transmission for that hop, so that the choice can reflect which intermediate nodes actually received the transmission. This deferred choice gives each transmission multiple opportunities to make progress. As a result ExOR can use long radio links with high loss rates, which would be avoided by traditional routing. ExOR increases a connection's throughput while using no more network capacity than traditional routine.ExOR's design faces the following challenges. The nodes that receive each packet must agree on their identities and choose one forwarder.The agreement protocol must have low overhead, but must also be robust enough that it rarely forwards a packet zero times or more than once. Finally, ExOR must choose the forwarder with the lowest remaining cost to the ultimate destination.Measurements of an implementation on a 38-node 802.11b test-bed show that ExOR increases throughput for most node pairs when compared with traditional routing. For pairs between which traditional routing uses one or two hops, ExOR's robust acknowledgments prevent unnecessary retransmissions,increasing throughput by nearly 35%. For more distant pairs, ExOR takes advantage of the choice of forwarders to provide throughput gains of a factor of two to four.

Opportunistic routing in multi-hop wireless networks

Abstract: This paper describes Extremely Opportunistic Routing (ExOR), a new unicast routing technique for multi-hop wireless networks. ExOR forwards each packet through a sequence of nodes, deferring the choice of each node in the sequence until after the previous node has transmitted the packet on its radio. ExOR then determines which node, of all the nodes that successfully received that transmission, is the node closest to the destination. That closest node transmits the packet. The result is that each hop moves the packet farther (or average) than the hops of the best possible pre-determined route.The ExOR design addresses the challenge of choosing a forwarding node after transmission using a distributed algorithm. First, when a node transmits a packet, it includes in the packet a simple schedule describing the priority order in which the potential receivers should forward the packet. The node computes the schedule based on shared measurements of inter-node delivery rates. ExOR then uses a distributed slotted MAC protocol for acknowledgements to ensure that the receivers agree who the highest priority receiver was.The efficacy of ExOR depends mainly on the rate at which the reception probability falls off with distance. Simulations based on measured radio characteristics [6] suggest that ExOR reduces the total number of transmissions by nearly a factor of two over the best possible pre-determined route.

Varieties of learning automata: an overview

Abstract: Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper by Narendra and Thathachar (1974). Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA, has been modified in several directions to suit different applications. Concepts such as parameterized learning automata (PLA), generalized learning,automata (GLA), and continuous action-set learning automata (CALA) have been proposed, analyzed, and applied to solve many significant learning problems. Furthermore, groups of LA forming teams and feedforward networks have been shown to converge to desired solutions under appropriate learning algorithms. Modules of LA have been used for parallel operation with consequent increase in speed of convergence. All of these concepts and results are relatively new and are scattered in technical literature. An attempt has been made in this paper to bring together the main ideas involved in a unified framework and provide pointers to relevant references.

SOAR: Simple Opportunistic Adaptive Routing Protocol for Wireless Mesh Networks

Abstract: Multihop wireless mesh networks are becoming a new attractive communication paradigm owing to their low cost and ease of deployment. Routing protocols are critical to the performance and reliability of wireless mesh networks. Traditional routing protocols send traffic along predetermined paths and face difficulties in coping with unreliable and unpredictable wireless medium. In this paper, we propose a simple opportunistic adaptive routing protocol (SOAR) to explicitly support multiple simultaneous flows in wireless mesh networks. SOAR incorporates the following four major components to achieve high throughput and fairness: 1) adaptive forwarding path selection to leverage path diversity while minimizing duplicate transmissions, 2) priority timer-based forwarding to let only the best forwarding node forward the packet, 3) local loss recovery to efficiently detect and retransmit lost packets, and 4) adaptive rate control to determine an appropriate sending rate according to the current network conditions. We implement SOAR in both NS-2 simulation and an 18-node wireless mesh testbed. Our extensive evaluation shows that SOAR significantly outperforms traditional routing and a seminal opportunistic routing protocol, ExOR, under a wide range of scenarios.