International Journal Information and Communication Technology Research
Volume:10 Issue: 3, Summer 2018

This paper considers the power-efficient resource allocation problem in a cloud radio access network (C-RAN). The C-RAN architecture consists of a set of base-band units (BBUs) which are connected to a set of radio remote heads (RRHs) equipped with massive multiple input multiple output (MIMO), via fronthaul links with limited capacity. We formulate the power-efficient optimization problem in C-RANs as a joint resource allocation problem in order to jointly allocate the RRH and transmit power to each user, and fronthaul link and BBU assign to active RRHs while satisfying the minimum required rate of each user. To solve this non-convex optimization problem we suggest iterative algorithm with two-step based on the complementary geometric programming (CGP) and the successive convex approximation (SCA). The simulation results indicate that our proposed scheme can significantly reduce the total transmission power by switching off the under-utilized RRHs.

Resource owners in mobile grids, with financial motivations in mind, provide their clients with the resources they possess. The clients can, on the other hand, obtain what they seek through the payments they make. Mobile grid users may also demand cost- and time-optimizing strategies as part of their service quality. As a result, the scheduling system, through making use of a proper algorithm, should allocate resources to users’ independent tasks in such a way that would minimize time and cost parameters. Hence, in this paper, two scheduling algorithms are introduced with regard to user needs based upon Particle Swarm Optimization (PSO), which, through mutation and population expanding, are able to find the best scheduling scheme for user tasks, while preventing quick algorithm convergence.

Internet of Things (IOT) has prepared for a range of small sensors to popular laptops. Wireless communication in IOT systems assumes the nodes as a terminal as well as a router which can transmit the data packets. However, individual nodes may refuse to cooperate with others sometimes, leading to a selfish node behavior. The existence of selfish nodes degrades the network performance. This paper proposes to detect selfish nodes in IOT (DISOT) in three phases: Setup and Clustering phase which identifies and then clusters all the nodes in the network. The global phase which indicates whether a selfish node(s) exists in the clusters or not using the main cluster head and the cluster heads in each cluster must identify the selfish node(s) within the local phase. The proposed scheme is simulated by 2500 IOT nodes in the network and the results show that DISOT reduces end-to-end delay up to 41% and when the percentage of selfish nodes in the network does not exceed 35%, DISOT increases detection accuracy up to 10% and false positive rate decreases down to 5%.

Security is a critical and vital task in wireless sensor networks (WSNs), therefore different key management systems have been proposed, many of which are based on symmetric primitives. Such systems are very energy efficient, but they lack some other desirable characteristics. On the other hand, systems based on public key cryptography (PKC) have those desirable characteristics, but they consume more energy. Recently based on authenticated messages from base station (BS) a new PKC–based key agreement protocol was proposed. We show this method is susceptible to a form of denial of service (DOS) attack where resources of network can be exhausted with bogus messages. Then, we propose two different improvements to solve this vulnerability. Simulation results show that these new protocols retain desirable characteristics of the basic method but solve its deficiencies.

Human activity recognition is essential for providing services in the Internet of Things. Thanks to their ubiquity, sensing capability, and processing power, modern smartphones have become attractive devices for activity recognition. However, their limited battery capacity places a hurdle to exploit such sensing and processing power. While power is consumed in both the sensing and computation layers of the recognition process, power optimization in the latter layer has not been studied extensively enough. This paper strives towards energy-efficient activity recognition by focusing on the cost of feature extraction. To this end, the energy cost of extracting various features is examined and test-cost sensitive prediction models are employed to recognize activities from features. Experimental results reveal a considerable opportunity to conserve energy by awareness of the cost of feature extraction. With only a small sacrifice in prediction accuracy, the energy cost of computations can be reduced by a factor of three.

The use of cloud computing to implement business processes is becoming increasingly important because users can benefit from the economic and technical advantages of this technology. The concept of the Business Process as a Service (BPaaS) is a new solution in the use of specific business processes as a medium for aligning information technology and business. However, managing and deploying business processes on heterogeneous Cloud providers is still a challenge for organizations due to interoperability concerns. This paper suggests an algorithm for optimizing the resource allocation of the business process in Extended Federated BPaaS model in accordance with the requirements of the user’s policy. The developed model has been compared with other popular models supporting the service/business process policy and shows that the proposed model can effectively execute business processes with regards to infrastructure and data transfer costs. To evaluate the model, we implemented this algorithm on WorkflowSim tool, taking into account the challenges of data transfer in the cloud federation environment. The results of the experiments highlight the algorithm’s efficiency compared with initial deployment in a way that the proposed optimization mechanism reduces the cost of transferring data between clouds and can improve performance of workflow execution in cloud federation in terms of makespan time and cost

Nowadays in the business world, all levels of all organizations need a concise planning. Regarding high-speed growth in globalization and competition trend, moving the organizations to the creation of integrated software systems with using latest technologies is a necessity. One of the most significant among the above-mentioned software systems is ERP. Both SOA and cloud deals with delivering services to business with improved agility increased speed and reduced cost that can lead to greater innovation and effective returns on investment. In this paper, after study about functional and nonfunctional requirements of ERP systems, by a framework, It will be study and research about achievement in ERP systems after engaging SOA and cloud computing and their impacts on ERP. Such that these impacts will resolve so many of shortages and needs of conventional ERP as well as reduction of risks and costs of implementation of ERP in organizations. Finally, we used ATAM for evaluating the framework. The idea of the experts in both business and ICT gathered by questionnaire and the data analyzed via TOPSIS method. Similar research for using strength of SOA and cloud computing in traditional ERP in shipping industry is a novelty.