مجله رایانش نرم و فناوری اطلاعات
سال هشتم شماره 1 (بهار 1398)

Considering the facts that existing biosensors in Wireless Body Area Networks (WBANs) - that are in charge of collecting vital sign data of patients, performing preprocessing and sending them to a data fusion center - have limitations in energy consumption and data processing capability of biosensors, therefore, sampling rate has a direct impact on network energy consumption and its useful lifetime. Regarding the above issues, this paper presents an adaptive approach to determining optimal sampling rate for data management. In this regard, by using National Early Warning Score (NEWS) system, the biosensors collect relevant data and recognize the emergency information locally. To optimize nodes' activity, information about the patient's activity is extracted so that when the patient's activity is normal some of the nodes are transferred from active mode to sleep mode. Also, to determine the exact sampling rate, a statistical test was first developed to evaluate the variance associated with the patient's vital signs. Then, by using an appropriate interpolation function, the optimal sampling rate is determined. The interpolation function uses two main factors to determine the sampling rate: the patient's risk information and the values measured by the pivot node. Simulation results show about 66 percent optimization in number of communicated data and increasing more than 2.5 times of network lifetime.

The use of web services has been increased by IoT technology development and increasing interoperability between objects. IoT web services Access control is a challenging problem in IoT environment. Many standards such as SAML has been proposed for authorization and access control in common web services, but it is not possible to use these standards in IoT web services, because IoT resources has constraints in network, memory and procces.In comparision of SAML standard, optimized SAML is more appropriate fot IoT Web services because of low memory usage and processing time and, consequently, the reduction of the time for providing access in IoT environments.

The proposed fuzzy expert system helps high school students to choose their appropriate academic course in every time and place with low cost. The needed knowledge of the system is collected through interviews with several academic advisers (human experts) in Yazd province. These data are combined together in different ways. Distinct fuzzy expert systems are designed to identify the points of each academic course for students. These points are fed to another system called results modifier system in order to specify the priority of each field of study as the final system output. Real data, collected from schools in Yazd province, are preprocessed in Excel software and the fuzzy systems are implemented in MATLAB software. The experimental results show that the proposed system is 80% consistent with the opinion of the expert academic advisers and it can well replace the academic counselor and offer an appropriate course considering the student's circumstances.

Today, it is important to reduce the original huge data set to a manageable volume. Also, unbalanced data distribution between different classes is a serious challenge in data mining. In the proposed method, the instance reduction problem is considered as a multi-objective problem, which can perform well by considering the two contradict criteria, classification accuracy and reduction rate of instances. The multi-objective problem is solved using the chaotic particle swarm optimization algorithm. The distance-based decision classifier has the task of distinguishing the maintenance or deletion of test instances. Creating and maintaining balances for different types of data distribution is the main goal of the proposed method. The results of the experiments have been compared with the state-of-the-art methods, which show superiority of the proposed method in terms of classification accuracy and reduction percentage.The results of the experiments have been compared with the state-of-the-art methods, which show superiority of the proposed method in terms of classification accuracy and reduction percentage.

Recently, a new technique called network coding has been introduced where it has numerous advantages on the computer networks. Network coding changes the traditional view of data transfer in store and forward paradigm and allows the nodes to store packets in their memory to combine with other packets, then, send to next nodes. In this paper, an optimization problem is proposed which balance memory utilization in network-coding-based-multicast routing of Wireless Sensor Networks (WSNs). In the large WSNs, solving this optimization problem in a centralized manner is impractical and almost impossible, therefore, by using sub-gradient and decomposition techniques, a distributed algorithm is presented in which the nodes make a decision based on local information. The performance evaluation of proposed mechanisms are carried by OMNETT++ simulator and the results show that the proposed model averagely decreases the end to end delay and buffer overflow by 17% and 95%, respectively. However, it increases the energy consumption and consequently decreases lifetime by 14%. The decentralized algorithm decreases buffer overflow 69% and has more delay by 11% than the optimization model, however, it can be utilized in large WSNs due to its distributed computations.

In this paper a selective harmonic mitigation-PWM control technique has been applied to a five-level cascaded H-bridge inverter to satisfy the grid standards. The objective of this technique is to minimize the output voltage odd, non-triplen harmonic contents from the 5th up to the 41th. But due to the only four degrees of freedom, reducing all harmonic contents under their permitted range is impossible. However, the objective function and the constraints are defined in such a way that except the twenty third and twenty fifth harmonics, all harmonics will be mitigated to their acceptable levels. The first priority in solving the optimization problem is fitting the first component which is then followed by mitigating each individual harmonic and the total harmonic distortion. The proposed objective function is optimized using EPSO algorithm and the simulation results show the superiority of the SHM-PWM technique from the harmonic mitigation and THD points of view.

The energy and time constrained task scheduling on multi-processing environments with different frequency levels is considered as an important optimization issue in a cloud computing. In addition, a processor executed with a reduced frequency will dynamically increase transient faults, which will weaken the reliability of the applications. Reliability is an important figure of merit of the system and it must be satisfied in safety-critical applications. In this paper, the parallel task scheduling problem in multi-processors has been explored to reduce energy and to increase reliability in the scheduling. Multi-processing and Dynamic Voltage and Frequency Scaling (DVFS) techniques can decrease the processor’s frequency level as much as possible; therefore, the voltage consumption of the processor will be reduced. Here, a two-phase algorithm is proposed to minimize energy consumption with reliability goal on multiple processors. The first phase is for initial assignment and the second phase is for either satisfying the reliability goal and improving energy efficiency. Specifically, when the application’s reliability goal cannot be achieved via initial assignment, based on our defined current reliability ratio, an enhanced algorithm is designed to satisfy application’s reliability goal. The proposed algorithm compared with existing algorithms. Experimental results demonstrate that the proposed algorithm consume less energy while satisfying the application’s reliability goal.