Alts : An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

Abstract: According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches

Message Admission Control along with Buffer Space Advertisement to Control Congestion in Delay Tolerant Networks (DTNs)

Delay and Disruption Tolerant networks (DTN) are subject to intermittent connection and long delay, thus the internet congestion control mechanisms are not suitable for DTNs. Data Delivery Rate and Delivery Delay are the parameters affected badly when network or part of a network is in congestion. In recent years, Congestion control strategies in DTNs are considered an active area for research. In this research a hybrid congestion control mechanism, Message Admission Control with Buffer Space Advertisement (MACBSA) is proposed which is based on MaxProp routing protocol. Preliminary results show the improvement in network performance while utilizing network resources efficiently

Police Culture and Women in Police Leadership

We have in this thesis chosen to write about the organizational culture of the police and female police leadership development. The study has been carried out with the cooperation of eleven different females in leadership positions in the police, placed in five different Norwegian police districts. Our research question is: “How does the police culture influence female police leadership development?” The findings show that the entrance into the police has been decisive for how the police culture is perceived, as there are cultural differences in the different departments and areas. These differences seem to be related to the gender distribution in the different places. Female leaders feel a need for behaving in a masculine manner in order to fit into the culture, but the so-called “old male culture” seems to fade away, as there are only individuals that behave in that way, and not groups. The possible cultural barriers contain of; need for power motivation, need for confidence, assumptions of female leaders, expectations of masculine traits and access to leadership networks

Detection of Primary User Emulation Attack Using the Differential Evolution Algorithm in Cognitive Radio Networks

Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding any interference from SUs. However, the spectrum sensing process in CRN is often disturbed by a security issue known as the Primary User Emulation Attack (PUEA). PUEA is one of the main security issues that disrupt the whole activity of CRN. The attacker transmits false information to interrupt the spectrum sensing process of CRN, which leads to poor usage of the spectrum. The proposed study uses a proficient Time Difference of Arrival (TDOA) based localization method using the Differential Evolution (DE) algorithm to identify the PUEA in CRNs. The DE algorithm is used to solve the objective function of TDOA values. The proposed methodology constructs a CRN and identifies PUEA. The proposed method aims to sense and localize PUEA efficiently. Mean Square Error (MSE) is the performance evaluation parameter that is used to measure the accuracy of the proposed technique. The results are compared with the previously proposed Firefly optimization algorithm (FA). It is clear from the results that DE converges faster than FA

A review on state-of-the-art face recognition approaches

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out

Detection of Primary User Emulation Attack Using the Differential Evolution Algorithm in Cognitive Radio Networks

Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding any interference from SUs. However, the spectrum sensing process in CRN is often disturbed by a security issue known as the Primary User Emulation Attack (PUEA). PUEA is one of the main security issues that disrupt the whole activity of CRN. The attacker transmits false information to interrupt the spectrum sensing process of CRN, which leads to poor usage of the spectrum. The proposed study uses a proficient Time Difference of Arrival (TDOA) based localization method using the Differential Evolution (DE) algorithm to identify the PUEA in CRNs. The DE algorithm is used to solve the objective function of TDOA values. The proposed methodology constructs a CRN and identifies PUEA. The proposed method aims to sense and localize PUEA efficiently. Mean Square Error (MSE) is the performance evaluation parameter that is used to measure the accuracy of the proposed technique. The results are compared with the previously proposed Firefly optimization algorithm (FA). It is clear from the results that DE converges faster than FA

Efficient Clustering Based Routing for Energy Management in Wireless Sensor Network-Assisted Internet of Things

Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR’s performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life