4,314 research outputs found
QoS routing in ad-hoc networks using GA and multi-objective optimization
Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version
Fuzzy based load and energy aware multipath routing for mobile ad hoc networks
Routing is a challenging task in Mobile Ad hoc Networks (MANET) due to their dynamic topology and lack of central administration. As a consequence of un-predictable topology changes of such networks, routing protocols employed need to accurately capture the delay, load, available bandwidth and residual node energy at various locations of the network for effective energy and load balancing. This paper presents a fuzzy logic based scheme that ensures delay, load and energy aware routing to avoid congestion and minimise end-to-end delay in MANETs. In the proposed approach, forwarding delay, average load, available bandwidth and residual battery energy at a mobile node are given as inputs to a fuzzy inference engine to determine the traffic distribution possibility from that node based on the given fuzzy rules. Based on the output from the fuzzy system, traffic is distributed over fail-safe multiple routes to reduce the load at a congested node. Through simulation results, we show that our approach reduces end-to-end delay, packet drop and average energy consumption and increases packet delivery ratio for constant bit rate (CBR) traffic when compared with the popular Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol
Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks
For improving the efficiency and the reliability of the opportunistic routing
algorithm, in this paper, we propose the cross-layer and reliable opportunistic
routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the
improved efficiency fuzzy logic and humoral regulation inspired topology
control into the opportunistic routing algorithm. In CBRT, the inputs of the
fuzzy logic system are the relative variance (rv) of the metrics rather than
the values of the metrics, which reduces the number of fuzzy rules
dramatically. Moreover, the number of fuzzy rules does not increase when the
number of inputs increases. For reducing the control cost, in CBRT, the node
degree in the candidate relays set is a range rather than a constant number.
The nodes are divided into different categories based on their node degree in
the candidate relays set. The nodes adjust their transmission range based on
which categories that they belong to. Additionally, for investigating the
effection of the node mobility on routing performance, we propose a link
lifetime prediction algorithm which takes both the moving speed and moving
direction into account. In CBRT, the source node determines the relaying
priorities of the relaying nodes based on their utilities. The relaying node
which the utility is large will have high priority to relay the data packet. By
these innovations, the network performance in CBRT is much better than that in
ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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