Solving target coverage problem in directional sensor networks with ability to adjust sensing range using continuous learning automata

Directional sensor networks (DSNs) are classified under wireless networks that are largely used to resolve the coverage problem. One of the challenges to DSNs is to provide coverage for all targets in the network and, at the same time, to maximize the lifetime of network. A solution to this problem is the adjustment of the sensors’ sensing ranges. In this approach, each sensor adjusts its own sensing range dynamically to sense the corresponding target(s) and decrease energy consumption as much as possible through forming the best cover sets possible. In the current study, a continuous learning automata-based method is proposed to form such cover sets. To assess the proposed algorithm’s performance, it was compared to the results obtained from a greedy algorithm and a learning automata algorithm. The obtained results demonstrated the superiority of the proposed algorithm regarding the maximization of the network lifetime.

Construction of Differentiated Periodic Freight Train Paths in Dense Mixed Traffic

The article is focused on detailed framework process for hierarchized construction of periodic freight train paths (PFTPs) – allocation of pre-arranged railway capacity to freight rail operators. The framework process considers fluctuations in demand for capacity from freight rail opera-tors, so the quality of a freight train path is related with its construction priority. Introduced framework process aims to offer freight rail operators attractive train paths, with low number of scheduled stops, and this way enhance competitiveness and decrease energy consumption of freight railway as a factor for sustainable development. The proposed generic process is in-tended for all time horizons of capacity allocation. Correctness of the framework process is tested on the example of mainline Prague – Dresden, in the context of prospective (denser) mod-el passenger timetable.

50 Pelleting and Starch Characteristics of Diets Containing Enogen® Feed Corn

This experiment determined the effects of die thickness and conditioning temperature on pelleting and starch characteristics in diets containing conventional or Enogen® feed corn (Syngenta Seeds, LLC). Treatments were arranged as a 2 × 2 × 3 factorial of corn type (conventional [CON] and Enogen® feed corn [EFC]), die thickness (5.6 and 8.0 length:diameter [L:D]), and conditioning temperature (74, 79, and 85°C). Diets were steam conditioned and pelleted (CPM Model 1012-2) with a 4 × 22.2 mm or 4 × 31.8 mm pellet die. Conditioner retention time was set at 30 s and production rate was set at 15 kg/min. All treatments were replicated on 3 separate days. Data were analyzed using the GLIMMIX procedure in SAS (v. 9.4, SAS Institute Inc., Cary, NC). Increasing die L:D improved PDI (P=0.01) and increased (P=0.02) energy consumption. Increasing conditioning temperature from 74 to 85°C increased (linear, P< 0.03) PDI (84.2, 84.9, and 88.2%, respectively) and tended to decrease energy consumption (quadratic, P=0.07). There was a corn × conditioning temperature interaction (P=0.01) for gelatinized starch in conditioned mash. Enogen® feed corn diets steam conditioned at 85°C had the greatest quantity of gelatinized starch. Cooked starch of conditioned mash was greater (P< 0.01) for EFC diets compared to CON diets and increased (linear, P< 0.01) with increasing conditioning temperature. Starch gelatinization was greater (P< 0.01) in pelleted EFC diets (13.4%) compared to CON diets (11.7%) and was increased (linear, P=0.05) by increasing conditioning temperature from 74 to 85°C (12.0, 12.1, and 13.4%, respectively). Pelleted diets containing EFC had increased (P< 0.01) cooked starch compared to CON diets. In conclusion, increasing die L:D and increasing conditioning temperature improved PDI. Starch gelatinization was increased when diets were pelleted at the highest conditioning temperature of 85°C, and EFC diets resulted in greater gelatinized starch.

Biological pretreatment of biomass to decrease energy consumption in mechanical defiberization process

It is critical to develop sustainable, effective, and innovative technologies for society, particularly for processing of biomass, so that the green/ sustainable advantages can be extended to the final products. This review examined two-step biological-mechanical defiberization of lignocellulosic biomass to produce fibers. Two biological pretreatment methods of fungi and enzymes were mainly introduced, with particular focus on the energy consumption. Potential application methods, advantages, disadvantages, process economics, and future prospects of two biological pretreatment methods were considered to derive a complete road map for the proposed process. With the help of biological pretreatment, the mechanical pulping production could not only improve the paper strength, but also decrease energy consumption at about 40%. This process fits well with the green/sustainable strategy to produce lignocellulosic fibers with reasonable quality while having minimal environmental impact.

On the electrical operation of batch electrodialysis for reduced energy consumption

Batch electrodialysis (ED) may be used for the desalination of brackish water and seawater and for the concentration of high-salinity brine. Better system operation can significantly decrease energy consumption and cost, especially for seawater desalination.

Light weight encryption algorithms for wireless body area networks

Wireless Body Area Network (WBAN) emerges as a major network in today’s world. WBAN comprise of sensors used for applications like health monitoring, military, etc. These sensors are embedded within or out of the body. Security is a major factor in WBAN’s and hence it is essential to provide security for patient’s data. So we study about various encryption algorithms in this paper to provide security. The WBAN comprise of body sensors in which the energy resources are limited and since consumption of energy will be more it is essential to go for light weight algorithms to provide security and decrease energy consumption.

Increasing Efficiency of Grain Drying With the Use of Electroactivated Air and Heater Control

This chapter describes how drying is the most power-consuming process of post-harvest processing. Methods are viewed of how to heighten productivity of grain-drying equipment and to decrease energy consumption of the process. Using electrically activated air as a drying agent provides greater efficiency in the process of drying grain with active ventilation. Saturation of the air with ions in this case is conducted cyclically. This allows the use of reactions of the biological object called “caryopsis” to cause external impacts to the material increasing the efficiency of drying. The parameters of the drying agent are changed by controlling the electric heater. The dependency of the rate and energy consumption of drying on the drying agent allows control of the process. The chapter presents experimental obtained dependencies, which allow for the design of algorithms to control the aforementioned type.

A Novel QoS Aware Shortest Path Algorithm for VSDN

This article describes how a rapid increase in usage of internet has emerged from last few years. This high usage of internet has occurred due to increase in popularity of multimedia applications. However, there is no guarantee of Quality of Service to the users. To fulfill the desired requirements, Internet Service Providers (ISPs) establish a service level agreement (SLA) with clients including specific parameters like bandwidth, reliability, cost, power consumption, etc. ISPs make maximum SLAs and decrease energy consumption to raise their profit. As a result, users do not get the desired services for which they pay. Virtual Software Defined Networks are flexible and manageable networks which can be used to achieve these goals. This article presents shortest path algorithm which improves the matrices like energy consumption, bandwidth usage, successful allocation of nodes in the network using VSDN approach. The results show a 40% increase in the performance of proposed algorithms with a respect to existing algorithms.