An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

New mixed adaptive detection algorithm for moving target with big data

Aiming at the troubles (such as complex background, illumination changes, shadows and others on traditional methods) for detecting of a walking person, we put forward a new adaptive detection algorithm through mixing Gaussian Mixture Model (GMM), edge detection algorithm and continuous frame difference algorithm in this paper. In time domain, the new algorithm uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection algorithm which mixes the edge detection algorithm, continuous frame difference algorithm and GMM to get the initial contour of moving target with big data, and gets the ultimate moving target with big data. This algorithm not only can adapt to the illumination gradients and background disturbance occurred on scene, but also can solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional algorithm. As experimental result showing, this algorithm holds better real-time and robustness. It is not only easily implemented, but also can accurately detect the moving target with big data

Study on the control algorithm for lower limb exoskeleton based on ADAMS/Simulink co-simulation

A sliding mode control algorithm based on proportional switching function was developed to make the lower limb exoskeleton more fit the human walking gait trajectory. It could improve the comfort of the exoskeleton wearer and enhance the reliability of the system. The three-dimensional mechanical model of the exoskeleton built using software SolidWorks was introduced to ADAMS and then the model parameters were set. The model was combined with the software MATLAB so that the human-machine cooperation control algorithm for lower limb exoskeleton based on ADAMS and Simulink co-simulation was developed. The simulation result was compared with the desired trajectory and the trajectory under PID control. The research discovered that the ability of trajectory tracking under the sliding mode control was much better than that under PID control. It provided an important theoretical basis for the research on human-machine cooperation control algorithm

Determining the role of the ERGIC-53 cargo receptor complex in arenavirus propagation

Arenaviruses and hantaviruses are human pathogens that cause significant morbidity and mortality. The current lack of vaccines and treatment options for these viruses is a global concern. Despite producing only 4 proteins, these viruses are able to maintain a persistent and asymptomatic infection in wild rodents while being continuously shed into the environment. In humans, these viruses cause a spectrum of diseases ranging from aseptic meningitis to severe hemorrhagic fever syndromes. Little is known about how arenavirus and hantavirus proteins engage and interact with the human proteome during the complex process of viral biogenesis, or how the interactions with human proteins contribute to viral propagation as well as the onset and progression of disease. This dissertation provides a road map of the protein interactions formed between a prototypic envelope glycoprotein encoded by either an arenavirus or hantavirus, and the human proteome. The viral envelope glycoprotein (GP) decorates the surface of the virion. The primary function of the GP is to mediate attachment of the virus to specific cellular receptors, and after internalization of the virion, fuse the viral membrane with an internal endosomal membrane. In order to carry out these specific tasks, the viral GPs must first co-opt the extensive machinery found within the cellular secretory pathway to coordinate the proper glycosylation, folding, proteolytic maturation, and targeting of the GP during its biosynthesis. We identified a human protein with a conserved interaction amongst these two groups of viral GPs termed the Endoplasmic Reticulum (ER)-Golgi Intermediate Compartment Protein of 53 kiloDaltons (ERGIC-53). ERGIC-53 is an intracellular cargo receptor that normally cycles within the early secretory pathway of cells, where it is responsible for ferrying a small subset of cellular glycoproteins, most notably the coagulation factors FV and FVIII, from the ER to the Golgi apparatus. Herein we describe a novel role for ERGIC-53 in the propagation of not only arenaviruses, but also coronaviruses and filoviruses. Following infection with an arenavirus, ERGIC-53 leaves the early secretory pathway and becomes incorporated into the virus as it pinches off from the cell surface. Newly formed viruses lacking ERGIC-53 are no longer infectious due, in part, to a defect in their ability to attach to host cells. We suggest that ERGIC-53 represents a promising broad-spectrum antiviral target because of its association with the GPs from many families of pathogenic viruses, as well as its ability to exert control over their infectivity; and finally, because ERGIC-53 itself is not required for human health. The discovery of ERGIC-53 outside of its normal location inside of cells suggests that it may have additional unknown functions. Lastly, by revealing the importance of the cellular protein in controlling viral infectivity, we provide insight into the ongoing co-evolution of virus and host