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      • Open Access Article

        1 - A Unicast Tree-Based Data Gathering Protocol for Delay Tolerant Mobile Sensor Networks
        Zeynab Mottaginia Ali Ghaffari
        The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be acknowledged that traditional end-to-end routing proto Full Text
        The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be acknowledged that traditional end-to-end routing protocols cannot be applied usefully in such challenging network conditions because of intermittent connections and/or long delays. Hence, this research is intended to propose a Unicast Tree-based Data Gathering protocol (UTDG) to resolve this problem. A UTDG includes 3 phases: tree formation phase, data collection and data transmission phase, and finally the updating phase. The proposed protocol constructs a tree in each community on the basis of transmission ranking, contact probability and the link expiration time. The selection of the next-hop node is based on the tree structure rather than forwarding the message to the neighbor node directly. Each node unicasts the data to its parent in the related community, and the root of the tree successively sends the data to the sink node. The authors contend, based on the simulation results of the study, that the proposed protocol can gain significantly higher message delivery rates with lower transmission overhead and also lower delay in data delivery than the other existing DTMSNs routing protocols in some applications. Manuscript Document
      • Open Access Article

        2 - Coverage Improving with Energy Efficient in Wireless Sensor Networks
        Amir Pakmehr Ali Ghaffari
        Wireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service qua Full Text
        Wireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service quality in WSNs. In most of the applications, the sensor nodes are scattered in the environment randomly that causes the density of the nodes to be high in some regions and low in some other regions. In this case, some regions are not covered with any nodes of the network that are called covering holes. Moreover, creating some regions with high density causes extra overlapping and consequently the consumption of energy increases in the network and life of the network decreases. The proposed approach causes an increase in life of the network and an increase in it through careful selection of the most appropriate approach as cluster head node and form clusters with a maximum length of two steps and selecting some nodes as redundancy nodes in order to cover the created holes in the network. The proposed scheme is simulated using MATLAB software. The function of the suggested approach will be compared with Learning Automata based Energy Efficient Coverage protocol (LAEEC) approach either. Simulation results shows that the function of the suggested approach is better than LAEEC considering the parameters such as average of the active nodes, average remaining energy in nodes, percent of network coverage and number of control packets. Manuscript Document
      • Open Access Article

        3 - Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms
        Amin Kamalinia Ali Ghaffari
        Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cl Full Text
        Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The results of simulation and statistical analysis of proposed scheme indicate that the proposed algorithm, when compared with three other heuristic and a memetic algorithms, has optimized the makespan required for executing tasks. Manuscript Document
      • Open Access Article

        4 - Embedding Virtual Machines in Cloud Computing Based on Big Bang–Big Crunch Algorithm
        Ali Ghaffari Afshin Mahdavi
        Cloud computing is becoming an important and adoptable technology for many of the organization which requires a large amount of physical tools. In this technology, services are provided and presented according to users’ requests. Due to the presence of a large number of Full Text
        Cloud computing is becoming an important and adoptable technology for many of the organization which requires a large amount of physical tools. In this technology, services are provided and presented according to users’ requests. Due to the presence of a large number of data centers in cloud computing, power consumption has recently become an important issue. However, data centers hosting Cloud applications consume huge amounts of electrical energy and contributing to high operational costs to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. Live migration of virtual machines and their scheduling and embedding lead to enhanced efficiency of dynamic resources. The guarantee of service quality and service reliability is an indispensable and irrevocable requirement with respect to service level agreement. Hence, providing a method for reducing costs of power consumption, data transmission, bandwidth and, also, for enhancing quality of service (QoS) in cloud computing is critical. In this paper, a Big Bang–Big Crunch (BB-BC) based algorithm for embedding virtual machines in cloud computing was proposed. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. Simulation results indicate that the proposed method not only enhances service quality, thanks to the reduction of agreement violation, but also reduces power consumption. Manuscript Document
      • Open Access Article

        5 - Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata
        Ali Ghaffari Seyyed Keyvan  Mousavi
        Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the d Full Text
        Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption. Manuscript Document