19,892 research outputs found
A HYBRID HOPFIELD NEURAL NETWORK AND TABU SEARCH ALGORITHM TO SOLVE ROUTING PROBLEM IN COMMUNICATION NETWORK
The development of hybrid algorithms for solving complex optimization problems focuses on enhancing the strengths and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of these approaches to find superior solutions to solve optimization problems. Optimal routing in communication network is considering a complex optimization problem. In this paper we propose a hybrid Hopfield Neural Network (HNN) and Tabu Search (TS) algorithm, this algorithm called hybrid HNN-TS algorithm. The paradigm of this hybridization is embedded. We embed the short-term memory and tabu restriction features from TS algorithm in the HNN model. The short-term memory and tabu restriction control the neuron selection process in the HNN model in order to get around the local minima problem and find an optimal solution using the HNN model to solve complex optimization problem. The proposed algorithm is intended to find the optimal path for packet transmission in the network which is fills in the field of routing problem. The optimal path that will be selected is depending on 4-tuples (delay, cost, reliability and capacity). Test results show that the propose algorithm can find path with optimal cost and a reasonable number of iterations. It also shows that the complexity of the network model won’t be a problem since the neuron selection is done heuristically
Adaptive Routing Forwarding Strategy Based on Neural Network Algorithm
With the profound changes in global digital media, the focus of Internet users has gradually shifted to how to quickly obtain information without paying attention to where the information is stored. However, the current TCP/IP network protocol architecture cannot adapt to the rapid development of today#39s content applications. In order to adapt to the changes in the Internet, information-centric networking (ICN)has received extensive attention. Besides, the optimization of the user service request scheduling problem is the core issue affecting the performance of the ICN , and it is one of the hot research topics in the ICN network. To solve this problem, this paper proposes an adaptive routing forwarding strategy based on neural network algorithm. Through the modeling of the classic architecture named data networking (NDN) network delay model of ICN network, a neural network algorithm is used to delay prediction, and a forwarding strategy mechanism based on predict delay is designed to innovate in the NDN. The interface information Stat is added to the forwarding information base (FIB) of the network component to implement the dynamic selection of the forwarding routing. In addition, routing dynamic self-adaptation adjustment mechanism and fault rerouting function are designed in consideration of the situation of route congestion and interruption. Simulation results show that this strategy effectively reduces network delay and improves network performance
Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation
State-of-the-art emergency navigation approaches are designed to evacuate
civilians during a disaster based on real-time decisions using a pre-defined
algorithm and live sensory data. Hence, casualties caused by the poor decisions
and guidance are only apparent at the end of the evacuation process and cannot
then be remedied. Previous research shows that the performance of routing
algorithms for evacuation purposes are sensitive to the initial distribution of
evacuees, the occupancy levels, the type of disaster and its as well its
locations. Thus an algorithm that performs well in one scenario may achieve bad
results in another scenario. This problem is especially serious in
heuristic-based routing algorithms for evacuees where results are affected by
the choice of certain parameters. Therefore, this paper proposes a
simulation-based evacuee routing algorithm that optimises evacuation by making
use of the high computational power of cloud servers. Rather than guiding
evacuees with a predetermined routing algorithm, a robust Cognitive Packet
Network based algorithm is first evaluated via a cloud-based simulator in a
faster-than-real-time manner, and any "simulated casualties" are then re-routed
using a variant of Dijkstra's algorithm to obtain new safe paths for them to
exits. This approach can be iterated as long as corrective action is still
possible.Comment: Submitted to PerNEM'15 for revie
Giving Neurons to Sensors: An Approach to QoS Management Through Artificial Intelligence in Wireless Networks
For the latest ten years, many authors have focused their investigations
in wireless sensor networks. Different researching issues have
been extensively developed: power consumption, MAC protocols, selforganizing
network algorithms, data-aggregation schemes, routing protocols,
QoS management, etc. Due to the constraints on data processing
and power consumption, the use of artificial intelligence has been historically
discarded. However, in some special scenarios the features of
neural networks are appropriate to develop complex tasks such as path
discovery. In this paper, we explore the performance of two very well
known routing paradigms, directed diffusion and Energy-Aware Routing,
and our routing algorithm, named SIR, which has the novelty of being
based on the introduction of neural networks in every sensor node. Extensive
simulations over our wireless sensor network simulator, OLIMPO,
have been carried out to study the efficiency of the introduction of neural
networks. A comparison of the results obtained with every routing protocol
is analyzed. This paper attempts to encourage the use of artificial
intelligence techniques in wireless sensor nodes
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
Dynamic scheduling in a multi-product manufacturing system
To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation
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