A new labour safety in construction management based on artificial intelligence

Construction management is considered a very important field in civil engineering. A lot of tracks had in civil engineering that achieve construction management, such as risk, time, material and labour. One of the best tracks that achieve construction is risk management in modern civil applications. Moreover, this vital track is connected directly with labour life for this it is a very important issue. Hence, the main cause of death and injuries of labour during working is decline safety measures and sometimes building control management has been utilising traditional risk monitoring. It is considered a major concern of civil engineers. In this paper, a new labour safety system in construction management is proposed to provide a safe environment. However, risk management is designed that based on the fall detection approach of labour during working or walking at high-rise buildings. In other words, online monitoring risk is proposed to support and enhance construction management in civil engineering. The proposed system will play an important role by notifying the console panel on time that helps to reduce the death rate. However, the detection system is heavily based on real pictures received from the cameras. These cameras will be distributed randomly in the building area. These pictures will be analysed at the control panel in construction management for civil engineering. In other words, the decision-maker is strongly dependent on received pictures from these cameras. According to the experimental results, outstanding results is achieved from risk monitoring in construction management

A hybrid approach for scheduling applications in cloud computing environment

Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list

An intelligent intrusion detection system for external communications in autonomous vehicles

Advancements in computing, electronics and mechanical systems have resulted in the creation of a new class of vehicles called autonomous vehicles. These vehicles function using sensory input with an on-board computation system. Self-driving vehicles use an ad hoc vehicular network called VANET. The network has ad hoc infrastructure with mobile vehicles that communicate through open wireless channels. This thesis studies the design and implementation of a novel intelligent intrusion detection system which secures the external communication of self-driving vehicles. This thesis makes the following four contributions: It proposes a hybrid intrusion detection system to protect the external communication in self-driving vehicles from potential attacks. This has been achieved using fuzzification and artificial intelligence. The second contribution is the incorporation of the Integrated Circuit Metrics (ICMetrics) for improved security and privacy. By using the ICMetrics, specific device features have been used to create a unique identity for vehicles. Our work is based on using the bias in on board sensory systems to create ICMetrics for self-driving vehicles. The incorporation of fuzzy petri net in autonomous vehicles is the third contribution of the thesis. Simulation results show that the scheme can successfully detect denial-of-service attacks. The design of a clustering based hierarchical detection system has also been presented to detect worm hole and Sybil attacks. The final contribution of this research is an integrated intrusion detection system which detects various attacks by using a central database in BusNet. The proposed schemes have been simulated using the data extracted from trace files. Simulation results have been compared and studied for high levels of detection capability and performance. Analysis shows that the proposed schemes provide high detection rate with a low rate of false alarm. The system can detect various attacks in an optimised way owing to a reduction in the number of features, fuzzification

An intelligent security system for autonomous cars based on infrared sensors

Safety and non-safety applications in the external communication systems of self-driving vehicles require authentication of control data, cooperative awareness messages and notification messages. Traditional security systems can prevent attackers from hacking or breaking important system functionality in autonomous vehicles. This paper presents a novel security system designed to protect vehicular ad hoc networks in self-driving and semi-autonomous vehicles that is based on Integrated Circuit Metric technology (ICMetrics). ICMetrics has the ability to secure communication systems in autonomous vehicles using features of the autonomous vehicle system itself. This security system is based on unique extracted features from vehicles behaviour and its sensors. Specifically, features have been extracted from bias values of infrared sensors which are used alongside semantically extracted information from a trace file of a simulated vehicular ad hoc network. The practical experimental implementation and evaluation of this system demonstrates the efficiency in identifying of abnormal/malicious behaviour typical for an attack

An enhanced AODV protocol for external communication in self-driving vehicles

The increasing number of autonomous and semi-autonomous vehicles on the road leads to an increasing need for external vehicle communication, in particular through emerging vehicular ad hoc networks also known as VANETs. This technology has the ability to facilitate intelligent transportation applications, comfort and other required services for self-driving vehicles. However, suitable routing protocols need to be utilised in order to provide stable routing and enable high performance for this external communication in autonomous vehicles. Ad hoc on Demand Distance Vector routing (AODV) is to date rarely used in mobile ad hoc network but offers great potential as a reactive routing protocol. However, the AODV protocol is affected by poor performance, when directly employed in VANETs. In this paper, two improvements are presented to the route selection and route discovery of AODV to improve its performance in forms of packet delivery rate and communication link stability for VANETs. Thus, we obtain new vehicle V-AODV that suits the specific requirements of autonomous vehicles communications. Simulation results demonstrate that V-AODV can enhance the route stability, reduce overhead and improve communication performance between vehicles

Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS) to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS) and black hole attacks on vehicular ad hoc networks (VANETs). The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection

A feature extraction method for Arabic Offline Handwritten Recognition System using Naïve Bayes classifier

Handwriting recognition in the Arabic language is considered one of the most challenging problems and the accuracies in recognizing still need more enhancements due to the Arabic character’s nature, cursive writing, style, and size of writing in contrast to working with other languages. In this paper, we propose a system for Arabic Offline Handwritten Character Recognition based on Naïve Bayes classifier (NB). Extraction features preceded by divided the image of character into three horizontal and vertical zones and 3x3 zones in one and two dimensions respectively, then classified by Naïve Bayes. The performance of the system proposes evaluated by using the benchmark CENPARMI database reached up to 97.05% accuracy rate. Experimental results confirm a high enhancement inaccuracy rate in comparison with other Arabic Optical Character Recognition systems

Increasing the rate of intrusion detection based on a hybrid technique

This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data

Spreading Code Identification of Legal Drones in IoT Environment

The widespread use of drones has become very common today with large-scale civil and military applications. In the next few coming years, the outlook is expected that the number of drones will reach millions. So, these need to be well organised and managed in order to achieve the benefits of IoT with this accelerated environment. Drones or Unmanned Aerial Vehicles (UAVs) must achieved a level of communications to authenticate a legal working. The proposed approach concentrated on preparing each drone with identification key based on the combination of its international sim number with the date of the first action and the local country code. This approach is called Drone IDentification (DID) that generate a unique code for each drone via spreading technique. In this case any drone not apply this regulation is considered as unauthenticated drone and does not allowed to fly. This approach is very important to establish drone regulation via IoT

Hybrid intrusion detection in connected self-driving vehicles

Emerging self-driving vehicles are vulnerable to different attacks due to the principle and the type of communication systems that are used in these vehicles. These vehicles are increasingly relying on external communication via vehicular ad hoc networks (VANETs). VANETs add new threats to self-driving vehicles that contribute to substantial challenges in autonomous systems. These communication systems render self-driving vehicles vulnerable to many types of malicious attacks, such as Sybil attacks, Denial of Service (DoS), black hole, grey hole and wormhole attacks. In this paper, we propose an intelligent security system designed to secure external communications for self-driving and semi self-driving cars. The proposed scheme is based on Proportional Overlapping Score (POS) to decrease the number of features found in the Kyoto benchmark dataset. The hybrid detection system relies on the Back Propagation neural networks (BP), to detect a common type of attack in VANETs: Denial-of-Service (DoS). The experimental results show that the proposed BP-IDS is capable of identifying malicious vehicles in self-driving and semi self-driving vehicles