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With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks. [ABSTRACT FROM AUTHOR]

Multiwall carbon nanotubes (CNTs) and iron oxide impregnated carbon nanotubes (CNTs-iron oxide) were investigated for the adsorption of hazardous toluene and paraxylene (p-xylene) from aqueous solution. Pure CNTs were impregnated with iron oxides nanoparticles using wet impregnation technique. Various characterization techniques including thermogravimetric analysis, scanning electron microscopy, elemental dispersion spectroscopy, X-ray diffraction, and nitrogen adsorption analysis were used to study the thermal degradation, surface morphology, purity, and surface area of the materials. Batch adsorption experiments show that iron oxide impregnated CNTs have higher degree of removal of p-xylene (i.e., 90%) compared with toluene (i.e., 70%), for soaking time 2 h, with pollutant initial concentration 100 ppm, at pH 6 and shaking speed of 200 rpm at 25°C. Pseudo-second-order model provides better fitting for the toluene and p-xylene adsorption. Langmuir and Freundlich isotherm models demonstrate good fitting for the adsorption data of toluene and p-xylene. [ABSTRACT FROM AUTHOR]

In this paper, carbon nanotubes (CNTs) impregnated with iron oxide nanoparticles were employed for the removal of benzene from water. The adsorbents were characterized using scanning electron microscope, X-ray diffraction, BET surface area, and thermogravimetric analysis. Batch adsorption experiments were carried out to study the adsorptive removal of benzene and the effect of parameters such as pH, contact time, and adsorbent dosage. The maximum removal of benzene was 61% with iron oxide impregnated CNTs at an adsorbent dosage 100 mg, shaking speed 200 rpm, contact time 2 hours, initial concentration 1 ppm, and pH 6. However, raw CNTs showed only 53% removal under same experimental conditions. Pseudo-first-order kinetic model was found well to describe the obtained data on benzene removal from water. Initial concentration was varied from 1 to 200 mg/L for isotherms study. Langmuir isotherm model was observed to best describe the adsorption data. The maximum adsorption capacities were 987.58 mg/g and 517.27 mg/g for iron oxide impregnated CNTs and raw CNTs, respectively. Experimental results revealed that impregnation with iron oxide nanoparticles significantly increased the removal efficiency of CNTs. [ABSTRACT FROM AUTHOR]