A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks

To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node is in active mode only if it is required to. In this pa-per, we propose an efficient scheduling method based on learning automata and we called it LAML, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the pro-posed scheduling method can better prolong the lifetime of the network in comparison to similar existing method

Insurance for Improving User Satisfaction Level

Service-level agreement (SLA) violations may lead to losses and user dissatisfaction. Despite the fact that a service guarantee can increase the satisfaction level of users, indemnities may not be commensurate with the importance of a service to a user. While predefined penalties may be insufficient to compensate for the losses of one user, another user may not suffer loss from the SLA violation. With an insurance plan, an insurer can reach an agreement with users on the premium and loss coverage volume; insurance can therefore be considered a solution for providing indemnity which is appropriate to the importance of service. An insurer cannot protect users against these losses, which are caused by a single root event, in the same way as it protects them against the losses caused by independent events. In this paper, a novel approach is proposed for providing insurance coverage for such root events by limiting insurance provisions to the users with the highest priority. A criterion is presented for priority assignment to users, and an algorithm is then proposed for providing insurance according to this priority. A game-theoretic analysis is also provided to assess acceptability of the outcome of the proposed algorithm to rational users and insurers. The results of numerical experiments demonstrate the usefulness of the proposed approach for improving the utility of the Service

Learning automata and its application to priority assignment in a queuing system with unknown characteristics /

Conditions for (epsilon)-optimality of a general class of absorbing barrier and strongly absolutely expedient learning algorithms are derived. As a consequence, a new class of learning algorithms having identical behavior under the occurrence of success and failure are obtained. An application of learning automata to the priority assignment in a queuing system with unknown characteristics is given

Improving Learning Automata based Particle Swarm: An optimization algorithm

Optimization (PSO) algorithms have been recently developed, with the best aim of escaping from local minima. One of these recent variations is PSO-LA model which employs a Learning Automata (LA) that controls the velocity of the particle. Another variation of PSO enables particles to dynamically search through global and local space. This paper presents a Dynamic Global and Local Combined Particle Swarm Optimization based on a 3-action Learning Automata (DPSOLA). The embedded learning automaton accumulates the information from individuals, local best and global best particles then combines them to navigate the particle through the problem space. The proposed algorithm has been tested on eight benchmark functions with different dimensions. The work is unique from its test bed; evaluations contain large population size (150) and high dimension (150). The results show that, fitness and convergence pace is better than traditional PSO, DGLCPSO and previous PSO based LA algorithms. I

Nik Defense: An Artificial Intelligence Based Defense Mechanism against Selfish Mining in Bitcoin

The Bitcoin cryptocurrency has received much attention recently. In the network of Bitcoin, transactions are recorded in a ledger. In this network, the process of recording transactions depends on some nodes called miners that execute a protocol known as mining protocol. One of the significant aspects of mining protocol is incentive compatibility. However, literature has shown that Bitcoin mining's protocol is not incentive-compatible. Some nodes with high computational power can obtain more revenue than their fair share by adopting a type of attack called the selfish mining attack. In this paper, we propose an artificial intelligence-based defense against selfish mining attacks by applying the theory of learning automata. The proposed defense mechanism ignores private blocks by assigning weight based on block discovery time and changes current Bitcoin's fork resolving policy by evaluating branches' height difference in a self-adaptive manner utilizing learning automata. To the best of our knowledge, the proposed protocol is the literature's first learning-based defense mechanism. Simulation results have shown the superiority of the proposed mechanism against tie-breaking mechanism, which is a well-known defense. The simulation results have shown that the suggested defense mechanism increases the profit threshold up to 40\% and decreases the revenue of selfish attackers.Comment: Paper is submitted to Journal of IEEE Transactions on Dependable and Secure Computin

An efficient scheduling method for grid systems based on a hierarchical stochastic petri net

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min