Learning Automata Based Face-Aware Mobicast

Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protocol, called “mobicast”. In this protocol, it is assumed that the area around the moving target, called the delivery zone, is known at any given time during the operation of the network. The aim of the protocol is to awake sensor nodes, which will be within the delivery zone in the near future, to be prepared for tracking the approaching moving target. In this paper, we propose a novel mobicast algorithm, aiming at reducing the number of awakened sensor nodes. To this end, we equipped every sensor node with a learning automaton, which helps the node in determining the sensor nodes it must awaken. To evaluate the performance of the proposed algorithm, several experiments have been conducted. The results have shown that the proposed algorithm can significantly outperform other existing algorithms such as forward-zone constrained and FAR in terms of energy consumption, number of active nodes, number of exchanged packets and slack time.     

Multi-wall carbon nanotubes supported molybdenyl acetylacetonate: Efficient and highly reusable catalysts for epoxidation of alkenes with tert-butyl hydroperoxide

Efficient epoxidation of olefins catalyzed by MoO₂(acac)₂ supported on amines functionalized MWCNTs is reported. The MWCNTs bearing carboxylic acid groups were modified with 2-aminophenol and 2-aminothiophenol. These amine–MWCNTs act as bidentate ligand for attachment of Mo catalyst. These catalysts were characterized by elemental analysis, scanning electron microscopy, FT-IR and diffuse reflectance UV–Vis spectroscopic methods. The prepared catalysts were used for efficient epoxidation of different alkenes such as cyclic and linear ones with tert-butyl hydroperoxide in refluxing 1,2-dichloroethane. These heterogeneous catalysts can be reused several times without significant loss of their catalytic activity.     

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.     

A Model-Based Reinforcement Learning Protocol for Routing in Vehicular Ad hoc Network

Todays by equipping vehicles with wireless technologies, Vehicular Ad Hoc Network (VANET) has been emerged. This type of network can be utilized in many fields such as emergency, safety or entertainment. It is also considered as a main component of intelligent transportation system. However, due to the nodes velocity (vehicles velocity), varying density, obstacles and lack of fixed infrastructure, finding and maintaining a route between nodes are always challenging in VANET. Any routing protocol can be effective only if the nodes can learn and adapt themselves with such a dynamic environment. One way to achieve this adaptation is using machine learning techniques. In this paper we try to reach this goal by applying Multi-Agent Reinforcement Learning (MARL) that enables agents to solve routing optimization problems in a distributed way. Although model-free Reinforcement Learning (RL) schemes are introduced for this purpose, such techniques learn using a trial and error scheme in a real environment so they cannot reach an optimal policy in a short time. To deal with such a problem, we have proposed a mode-based RL based routing scheme. We have also developed a Fuzzy Logic (FL) system to evaluate the quality of links between neighbor nodes based on parameters such as velocity and connection quality. Outputs of this fuzzy system have been used to form the state transition model, needed in MARL. Results of evaluations have shown that our approach can improve some routing metrics like delivery ratio, end-to-end delay and traffic overhead.

     

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.     

A Novel Model of Sybil Attack in Cluster-Based Wireless Sensor Networks and Propose a Distributed Algorithm to Defend It

Wireless Personal Communications - Today, Wireless Sensor Networks are widely employed in various applications including military, environment, medical and urban applications. Thus, security...     

Data aggregation in sensor networks using learning automata

One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many nodes may have almost the same information which can be aggregated in intermediate nodes, and hence reduce the number of transmitted packets. Aggregation ratio is maximized if data packets of all nodes having almost the same information are aggregated together. For this to occur, each node should forward its packets along a path on which maximum number of nodes with almost the same information as the information of the sending node exist. In many real scenarios, such a path has not been remained the same for the overall network lifetime and is changed from time to time. These changes may result from changes occurred in the environment in which the sensor network resides and usually cannot be predicted beforehand. In this paper, a learning automata-based data aggregation method in sensor networks when the environment’s changes cannot be predicted beforehand will be proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method computer simulations have been conducted and the results are compared with the results of three existing methods. The results have shown that the proposed method outperforms all these methods, especially when the environment is highly dynamic.     

A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks

The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as few sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.