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The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.
Fawad Ahmad; Ayaz Ahmad; Irshad Hussain; Peerapong Uthansakul; Suleman Khan. Cooperation Based Proactive Caching in Multi-Tier Cellular Networks. Applied Sciences 2020, 10, 6145 .
AMA StyleFawad Ahmad, Ayaz Ahmad, Irshad Hussain, Peerapong Uthansakul, Suleman Khan. Cooperation Based Proactive Caching in Multi-Tier Cellular Networks. Applied Sciences. 2020; 10 (18):6145.
Chicago/Turabian StyleFawad Ahmad; Ayaz Ahmad; Irshad Hussain; Peerapong Uthansakul; Suleman Khan. 2020. "Cooperation Based Proactive Caching in Multi-Tier Cellular Networks." Applied Sciences 10, no. 18: 6145.
Faults and failures are familiar case studies in centralized and decentralized tracking systems. The processing of sensor data becomes more severe in the presence of faults/failures and/or noise. Effective schemes have been presented for decentralized systems, in the presence of faults only. In some practical scenarios of systems, there are certain interruptions in addition to these faults. These interruptions may occur in the form of noise. However it is expected that the decision about the sensor data is difficult in the presence of noise. This is because the noise adversely affects the communication amongst sensors and the processing unit. More complexity is expected when there are faults and noise simultaneously. To deal with this problem, in addition to existing fault detection and isolation schemes, the Kalman filter is employed. Here, a generic discussion is provided, which is equally applicable to other situations. This work addresses various faults in the presence of noise for decentralized tracking systems. Local single faults and multiple faults in the presence of noise are the core issues addressed in this paper. The proposed work is comprised of a general scenario for a decentralized tracking system followed by a case study of a target tracking scenario with and without noise. The presented schemes are also tested for different types of faults. The proposed work presents effective tracking in the presence of noise and faults. The results obtained demonstrate the acceptable performance of the scheme of this work.
Wasi Ullah; Irshad Hussain; Iram Shehzadi; Zahid Rahman; Peerapong Uthansakul. Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment. Sensors 2020, 20, 2127 .
AMA StyleWasi Ullah, Irshad Hussain, Iram Shehzadi, Zahid Rahman, Peerapong Uthansakul. Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment. Sensors. 2020; 20 (7):2127.
Chicago/Turabian StyleWasi Ullah; Irshad Hussain; Iram Shehzadi; Zahid Rahman; Peerapong Uthansakul. 2020. "Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment." Sensors 20, no. 7: 2127.
Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.
Ibrar Ullah; Irshad Hussain; Peerapong Uthansakul; M. Riaz; M. Naeem Khan; Jaime Lloret. Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities. Applied Sciences 2020, 10, 2095 .
AMA StyleIbrar Ullah, Irshad Hussain, Peerapong Uthansakul, M. Riaz, M. Naeem Khan, Jaime Lloret. Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities. Applied Sciences. 2020; 10 (6):2095.
Chicago/Turabian StyleIbrar Ullah; Irshad Hussain; Peerapong Uthansakul; M. Riaz; M. Naeem Khan; Jaime Lloret. 2020. "Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities." Applied Sciences 10, no. 6: 2095.
Due to the exponential increase in the human population of this bio-sphere, energy resources are becoming scarce. Because of the traditional methods, most of the generated energy is wasted every year in the distribution network and demand side. Therefore, researchers all over the world have taken a keen interest in this issue and finally introduced the concept of the smart grid. Smart grid is an ultimate solution to all of the energy related problems of today’s modern world. In this paper, we have proposed a meta-heuristic optimization technique called the dragonfly algorithm (DA). The proposed algorithm is to a real-world problem of single and multiple smart homes. In our system model, two classes of appliances are considered; Shiftable appliances and Non-shiftable appliances. Shiftable appliances play a significant role in demand side load management because they can be scheduled according to real time pricing (RTP) signal from utility, while non-shiftable appliances are not much important in load management, as these appliances are fixed and cannot be scheduled according to RTP. On behalf of our simulation results, it can be concluded that our proposed algorithm DA has achieved minimum electricity cost with a tolerable waiting time. There is a trade-off between electricity cost and waiting time because, with a decrease in electricity cost, waiting time increases and vice versa. This trade-off is also obtained by our proposed algorithm DA. The stability of the grid is also maintained by our proposed algorithm DA because stability of the grid depends on peak-to-average ratio (PAR), while PAR is reduced by DA in comparison with an unscheduled case.
Irshad Hussain; Majid Ullah; Ibrar Ullah; Asima Bibi; Muhammad Naeem; Madhusudan Singh; Dhananjay Singh. Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm. Electronics 2020, 9, 406 .
AMA StyleIrshad Hussain, Majid Ullah, Ibrar Ullah, Asima Bibi, Muhammad Naeem, Madhusudan Singh, Dhananjay Singh. Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm. Electronics. 2020; 9 (3):406.
Chicago/Turabian StyleIrshad Hussain; Majid Ullah; Ibrar Ullah; Asima Bibi; Muhammad Naeem; Madhusudan Singh; Dhananjay Singh. 2020. "Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm." Electronics 9, no. 3: 406.
Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS.
Ibrar Ullah; Irshad Hussain; Madhusudan Singh. Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries. Electronics 2020, 9, 105 .
AMA StyleIbrar Ullah, Irshad Hussain, Madhusudan Singh. Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries. Electronics. 2020; 9 (1):105.
Chicago/Turabian StyleIbrar Ullah; Irshad Hussain; Madhusudan Singh. 2020. "Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries." Electronics 9, no. 1: 105.
An adaptive splitting algorithm was implemented for numerical evaluation of Fourier-type highly oscillatory integrals involving stationary point. Accordingly, a modified Levin collocation method was coupled with multi-resolution quadratures in order to tackle the stationary point and irregular oscillations of the integrand caused by ω . Some test problems are included to verify the accuracy of the proposed methods.
Sakhi Zaman; Irshad Hussain; Dhananjay Singh. Fast Computation of Integrals with Fourier-Type Oscillator Involving Stationary Point. Mathematics 2019, 7, 1160 .
AMA StyleSakhi Zaman, Irshad Hussain, Dhananjay Singh. Fast Computation of Integrals with Fourier-Type Oscillator Involving Stationary Point. Mathematics. 2019; 7 (12):1160.
Chicago/Turabian StyleSakhi Zaman; Irshad Hussain; Dhananjay Singh. 2019. "Fast Computation of Integrals with Fourier-Type Oscillator Involving Stationary Point." Mathematics 7, no. 12: 1160.