A control-theoretic approach to distributed discrete-valued decision-making in networks of sensing agents

We address the problem of global sensor fusion for the purpose of distributed decision-making, from a control-theoretic perspective. In particular, we introduce a quasi-linear stochastic distributed protocol, using which a network of sensing agents can reach agreement in order to take a collective action. Using control-theoretic methods, we design the parameters of our protocol - which include weights in the local update rules used by the agents and a finite stopping time - to achieve agreement in a fair and rapid manner. We show analytically that the developed protocol achieves fair agreement with certainty in the noise-free case and achieves fair agreement with high probability even in the presence of communication noise and assuming very little information storage capability for the agents. Our development is illustrated throughout with a canonical example motivated by autonomous vehicle control.     

A quadratic optimization method for connectivity and coverage control in backbone-based wireless networks

The use of directional wireless communications to form flexible mesh backbone networks, which provide broadband connectivity to capacity-limited wireless networks or hosts, promises to circumvent the scalability limitations of traditional homogeneous wireless networks. The main challenge in the design of directional wireless backbone (DWB) networks is to assure backbone network requirements such as coverage and connectivity in a dynamic wireless environment. This paper considers the use of mobility control, as the dynamic reposition of backbone nodes, to provide assured coverage-connectivity in dynamic environments. This paper presents a novel approach to the joint coverage-connectivity optimization problem by formulating it as a quadratic minimization problem. Quadratic cost functions for network coverage and backbone connectivity are defined in terms of the square distance between neighbor nodes, which are related to the actual energy usage of the network system. Our formulation allows the design of self-organized network systems which autonomously achieve energy minimizing configurations driven by local forces exerted on network nodes. The net force on a backbone node is defined as the negative energy gradient at the location of the backbone node. A completely distributed algorithm is presented that allows backbone nodes to adjust their positions based on information about neighbors’ position only. We present initial simulation results that show the effectiveness of our force-based mobility control algorithm to provide network configurations that optimize both network coverage and backbone connectivity in different scenarios. Our algorithm is shown to be adaptive, scalable and self-organized.     

Energy and load balancing routing protocol for IoT

Due to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.     

High speed,low power VLSI CMOS vision sensor for geometric centre object tracking

In this article an object tracking CMOS sensor is presented. The architecture incorporates photo detection devices and pixel level processing elements for capturing and processing the image data and extracting the object's coordinates. The edges of the image scene are extracted by in-pixel edge detectors and the region (object) of interest, selected by the user, is segmented using a switch network. Coordinates of the desired region are obtained by extracting the geometric centre of the region. Tracking of the selected object is then performed by automatic reselection of the region using the updated coordinates. The proposed design presents less sensitivity to threshold adjustments than binarisation techniques. The sensor has been designed as a 64 × 64 pixel VLSI CMOS chip in the 0.35 μm standard CMOS technology. The proposed structure is analysed with regard to its operation in the presence of mismatches and noise. Features of the sensor are reported and compared with some previous object tracking designs. Because the power dissipation is small, the chip is ideal for low-power applications.     

Measurements on a 215-GHz subharmonically pumped waveguide mixerusing planar back-to-back air-bridge Schottky diodes

This paper presents design and performance data for a 215-GHz subharmonically pumped waveguide mixer using an antiparallel-pair of planar air-bridge-type GaAs Schottky-barrier diodes. The waveguide design is a prototype for a 640-GHz system and uses split-block rectangular waveguide with a 2:1 width-to-height ratio throughout. The measured mixer noise and conversion loss are below that of the best reported whisker contacted or planar-diode mixers using the subharmonic-pump configuration at this frequency. In addition, the required local oscillator power is as low as 3 mW for the unbiased diode pair, and greater than 34 dB of LO noise suppression is observed. Separate sideband calibration, using a Fabry-Perot filter, indicates that the mixer can be tuned for true double sideband response at an intermediate frequency of 1.5 GHz. Microwave scale model measurements of the waveguide mount impedances are combined with a mixer nonlinear analysis computer program to predict the mixer performance as a function of anode diameter, anode finger inductance, and pad-to-pad fringing capacitance. The computed results are in qualitative agreement with measurements, and indicate that careful optimization of all three diode parameters is necessary to significantly improve the mixer performance     

Enhanced oil recovery from carbonate reservoirs by spontaneous imbibition of low salinity water

An experimental study was performed to investigate the impact of low salinity water on wettability alteration in carbonate core samples from southern Iranian reservoirs by spontaneous imbibition. In this paper, the effect of temperature, salinity, permeability and connate water were investigated by comparing the produced hydrocarbon curves. Contact angle measurements were taken to confirm the alteration of surface wettability of porous media. Oil recovery was enhanced by increasing the dilution ratio of sea water, and there existed an optimum dilution ratio at which the highest oil recovery was achieved. In addition, temperature had a very significant impact on oil recovery from carbonate rocks. Furthermore, oil recovery from a spontaneous imbibition process was directly proportional to the permeability of the core samples. The presence of connate water saturation inside the porous media facilitated oil production significantly. Also, the oil recovery from porous media was highly dependent on ion repulsion/attraction activity of the rock surface which directly impacts on the wettability conditions. Finally, the highest ion attraction percentage was measured for sodium while there was no significant change in pH for all experiments.     

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

The growth of the networks has difficult network management. Recently, a concept called software‐defined network (SDN) has been proposed to address this issue, which makes network management more adaptable. Control and forwarding planes are separated in SDN. The control plane is a centralized logical controller that controls the network. The forwarding plane that consists of transfer devices is responsible for transmitting packets. Because the network resources are limited, optimizing the use of resources in the networks is an important issue. Load balancing improves the balanced distribution of loads across multiple resources in order to maximize the reliability and network resources efficiency. SDN controllers can create an optimal load balancing compared to traditional networks because they have a network global view. The load‐balancing problem can be solved using many different nature‐inspired meta‐heuristic techniques because it has the NP‐complete nature. Hence, for solving load balancing problem in SDN, nature‐inspired meta‐heuristic techniques are important methods. However, to the best of our knowledge, there is not a survey or systematic review on studying these matters. Accordingly, in the area of the load balancing in the SDN, this paper reviews systematically the nature‐inspired meta‐heuristic techniques. Also, this study demonstrates advantages and disadvantages regarded of the chosen nature‐inspired meta‐heuristic techniques and considers their algorithms metrics. Moreover, to apply better load balancing techniques in the future, the important challenges of these techniques have been investigated.     

A rapid smart approach for reliability and failure mode analysis of memories and large systems

This paper presents a novel system simulation methodology based on the known Monte Carlo technique, used for reliability and failure mode analysis of complex and large systems. The presented approach, called “state-merging and assorted random-testing” (SMART), is particularly applicable to systems involving different types of clusters of identical components, and is ideally suited for simulation of huge memories and similar systems. Simulators based on this approach are insensitive to the number of system components, system reliability or the number of associated spares or standby units, and thus they afford an extremely small simulation time compared to the accelerated Monte Carlo simulation time.     

Two-stage thermocatalytic upgrading of fuel oil to olefins and fuels over a nanoporous hierarchical acidic catalyst

A two-stage thermocatalytic upgrading process using a novel catalyst was investigated to produce light olefins and liquid fuels from fuel oil. The upgraded oil from the first thermal stage demonstrated lower viscosity and higher crackability compared to the virgin feedstock. In the next step, the vapor-phase catalytic cracking of the upgraded fraction was implemented over a novel nanoporous composite catalyst, characterized by the XRD, FTIR, NH₃- TPD, and N₂ physisorption techniques. In total, more than 55?wt% of light olefins, particularly propylene (25.5?wt%) together with 25.4?wt% and 32.5?wt% of gasoline and diesel fuel were obtained in this process.