Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

Development and evaluation on a wireless multi-gas-sensors system for improving traceability and transparency of table grape cold chain

There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS2) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433 MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS2 in a cold chain scenario and analyses the monitoring data. The results show that the WGS2 is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain.N/

Systems approach to model the conceptual design process of vertical take-off unmanned aerial vehicle

The development and induction in-service of Unmanned Air Vehicles (UAV) systems in a variety of civil, paramilitary and military roles have proven valuable on high-risk missions. These UAVs based on fixed wing configuration concept have demonstrated their operational effectiveness in recent operations. New UAVs based on rotary wing configuration concept have received major attention worldwide, with major resources committed for its research and development. In this thesis, the design process of a rotary-wing aircraft was re-visualised from an unmanned perspective to address the requirements of rotary-wing UAVs – Vertical Take-off UAVs (VTUAV). It investigates the conventional helicopter design methodology for application in UAV design. It further develops a modified design process for VTUAV addressing the requirements of unmanned missions by providing remote command-and-control capabilities. The modified design methodology is automated to address the complex design evaluations and optimisation process. An illustration of the automated design process developed for VTUAVs is provided through a series of inputs of the requirements and specifications, resulting in an output of a proposed VTUAV design configuration for “design decision support”. The VTUAV automated design process has been developed to pioneer an aerospace design tool for further detailed development and application as a – Design Decision Support System

Applications of Prediction Approaches in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) collect data and continuously monitor ambient data such as temperature, humidity and light. The continuous data transmission of energy constrained sensor nodes is a challenge to the lifetime and performance of WSNs. The type of deployment environment is also and the network topology also contributes to the depletion of nodes which threatens the lifetime and the also the performance of the network. To overcome these challenges, a number of approaches have been proposed and implemented. Of these approaches are routing, clustering, prediction, and duty cycling. Prediction approaches may be used to schedule the sleep periods of nodes to improve the lifetime. The chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in scheduling the sleep/wake-up periods of sensor nodes

DESIGN OF EFFICIENT IN-NETWORK DATA PROCESSING AND DISSEMINATION FOR VANETS

By providing vehicle-to-vehicle and vehicle-to-infrastructure wireless communications, vehicular ad hoc networks (VANETs), also known as the “networks on wheels”, can greatly enhance traffic safety, traffic efficiency and driving experience for intelligent transportation system (ITS). However, the unique features of VANETs, such as high mobility and uneven distribution of vehicular nodes, impose critical challenges of high efficiency and reliability for the implementation of VANETs. This dissertation is motivated by the great application potentials of VANETs in the design of efficient in-network data processing and dissemination. Considering the significance of message aggregation, data dissemination and data collection, this dissertation research targets at enhancing the traffic safety and traffic efficiency, as well as developing novel commercial applications, based on VANETs, following four aspects: 1) accurate and efficient message aggregation to detect on-road safety relevant events, 2) reliable data dissemination to reliably notify remote vehicles, 3) efficient and reliable spatial data collection from vehicular sensors, and 4) novel promising applications to exploit the commercial potentials of VANETs. Specifically, to enable cooperative detection of safety relevant events on the roads, the structure-less message aggregation (SLMA) scheme is proposed to improve communication efficiency and message accuracy. The scheme of relative position based message dissemination (RPB-MD) is proposed to reliably and efficiently disseminate messages to all intended vehicles in the zone-of-relevance in varying traffic density. Due to numerous vehicular sensor data available based on VANETs, the scheme of compressive sampling based data collection (CS-DC) is proposed to efficiently collect the spatial relevance data in a large scale, especially in the dense traffic. In addition, with novel and efficient solutions proposed for the application specific issues of data dissemination and data collection, several appealing value-added applications for VANETs are developed to exploit the commercial potentials of VANETs, namely general purpose automatic survey (GPAS), VANET-based ambient ad dissemination (VAAD) and VANET based vehicle performance monitoring and analysis (VehicleView). Thus, by improving the efficiency and reliability in in-network data processing and dissemination, including message aggregation, data dissemination and data collection, together with the development of novel promising applications, this dissertation will help push VANETs further to the stage of massive deployment

Anomalous behaviour detection using heterogeneous data

Anomaly detection is one of the most important methods to process and find abnormal data, as this method can distinguish between normal and abnormal behaviour. Anomaly detection has been applied in many areas such as the medical sector, fraud detection in finance, fault detection in machines, intrusion detection in networks, surveillance systems for security, as well as forensic investigations. Abnormal behaviour can give information or answer questions when an investigator is performing an investigation. Anomaly detection is one way to simplify big data by focusing on data that have been grouped or clustered by the anomaly detection method. Forensic data usually consists of heterogeneous data which have several data forms or types such as qualitative or quantitative, structured or unstructured, and primary or secondary. For example, when a crime takes place, the evidence can be in the form of various types of data. The combination of all the data types can produce rich information insights. Nowadays, data has become ‘big’ because it is generated every second of every day and processing has become time-consuming and tedious. Therefore, in this study, a new method to detect abnormal behaviour is proposed using heterogeneous data and combining the data using data fusion technique. Vast challenge data and image data are applied to demonstrate the heterogeneous data. The first contribution in this study is applying the heterogeneous data to detect an anomaly. The recently introduced anomaly detection technique which is known as Empirical Data Analytics (EDA) is applied to detect the abnormal behaviour based on the data sets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev Inequality) can be applied to any data distribution. Then, the second contribution is applying image data. The image data is processed using pre-trained deep learning network, and classification is done using a support vector machine (SVM). After that, the last contribution is combining anomaly result from heterogeneous data and image recognition using new data fusion technique. There are five types of data with three different modalities and different dimensionalities. The data cannot be simply combined and integrated. Therefore, the new data fusion technique first analyses the abnormality in each data type separately and determines the degree of suspicious between 0 and 1 and sums up all the degrees of suspicion data afterwards. This method is not intended to be a fully automatic system that resolves investigations, which would likely be unacceptable in any case. The aim is rather to simplify the role of the humans so that they can focus on a small number of cases to be looked in more detail. The proposed approach does simplify the processing of such huge amounts of data. Later, this method can assist human experts in their investigations and making final decisions

Icing Effects on Power Lines and Anti-icing and De-icing Methods

Icing on power lines may lead to compromise safety and reliability of electric supply network. Prolong icing can lead to power breakdown and collapse of towers. Since power transmission lines are mostly overhead and could face the direct impact of icing, and it is one of the main challenges faced by power distribution companies in cold regions. When the ice accretion crosses the safety limit then deicing action can be carried out. We can find number of deicing methods that are used in different parts of the world. However, all of these deicing techniques have their own advantages and disadvantages on implementation. It is one of the most difficult as well as dangerous process to perform deicing on power lines. If a fault is detected and that has been occurred due to icing or during routine maintenance, extra care must be taken in order to ensure safety of the personals when performing de-icing of lines. However, as technology evolved, new ways and techniques are adopted with the help of sensors that give quick feedback to control room in the national grid via wireless communication network for real time action. In the thesis we have discussed atmospheric icing impacts on power lines in the cold regions across the world. A literature review has been done for anti-icing and deicing methods that are currently adopted in the power distribution network. Methods that are used against ice buildups have also been analyzed. This work also shows the impacts of icing and deicing techniques presently adopted, and also throws light on their pros and cons during maintenance operations. It provides an overview of the evolving technology trends that are practiced to ensure the availability of existing power transmission system in cold climate regions

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas