Water surface platform for internet-based environmental monitoring system

Currently, environmental monitoring plays such an important role in human life. This research work was carried out to monitor the environment of air and water quality that displayed the data through the mobile phone or computer. This is due to several challenges while monitoring the environment such as accessibility to a site and the safety of the workers. This research work consists of several sensors that were attached to the water surface platform (WSP); carbon monoxide sensor, temperature and humidity sensor, pH sensor, and depth sensor that acts as an input. Besides that, a GPS module also attached to the platform to track down the position of the platform in terms of latitude and longitude. Through this input, the WSP also collects the data of the carbon monoxide released to determine the quality of air, while the data on the pH value and the value of temperature and humidity were collected to determine the quality of water based on Class IV- irrigation. This research work also detects the water level for flood warning and sends a warning to the authorities through Short Message Service (SMS). As a result, all the data from the sensors were successfully sent to the ThingSpeak IoT platform to be monitored by the authorities. The graph for each sensor was generated in the ThingSpeak channel to easier the authorities. The SMS of the parameters’ value also sent to the mobile phone. The power load of the WSP is 10.84W with the total time consumed of one hour and 36 seconds by using Li-ion battery. There is a slightly difference in transferring the data to the ThingSpeak channel and sending SMS due to some delays in coding part. Based on the results obtained when the WSP was deployed at FKAAS Lake in UTHM, the lake can be classified as class IV type. The long-term goal of this research is to ensure that the authorities can monitor the changes that happened on the website without the needs to be at the site

BATTERY-POWERED DEVICE FOR MONITORING PHYSICAL DISTANCING THROUGH WIRELESS TECHNOLOGY

One method for preventing the spread of the coronavirus and other contagious diseases is through social distancing. Therefore, creating a tool to measure and quickly discover the precise distance is necessary. In order to prevent physical contact between individuals, this study aimed to detects individuals’ physical distance, through an inaugurated battery-powered device that monitors physical distance through wireless technology. Specifically, in public or crowded areas, to lessen the spread of the virus. This study focuses on detecting people’s physical distance in the region of interest utilizing an Ultrasonic and VL53L0X sensors and determining the significant difference between the two sensors in monitoring physical distance. This study employs an applied experimental research design. The efficiency of both sensors in measuring physical distance was evaluated through analysis. The Ultrasonic and VL53L0X sensors underwent 15 tests. Furthermore, the researchers effectuated a t-test to determine the significant difference between the two sensors. Data gathered revealed that the sensors’ combined mean in terms of measuring physical distance indicates that the ultrasonic sensor performed better than VL53L0X, measuring 134.7 cm, notwithstanding 99.8 cm for the VL53L0X sensor. Complementary to this, the results of the t-test show that the VL53L0X sensor and Ultrasonic sensor have a significant difference in terms of distance precision

Bayesian inference application to burglary detection

Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic activities evident in various surveillance footages [61]. Surveillance videos can contain high level plans in the form of complex activities [61]. These complex activities are usually a combination of various articulated activities like breaking windshield, digging, and non-articulated activities like walking, running. We have developed a Bayesian framework for recognizing complex activities like burglary. This framework (belief network) is based on an expectation propagation algorithm [8] for approximate Bayesian inference. We provide experimental results showing the application of our framework for automatically detecting burglary from surveillance videos in real time

Adaptive Boltzmann Medical Dataset Machine Learning

The RBM is a stochastic energy-based model of an unsupervised neural network (RBM). RBM is a key pre-training for Deep Learning. Structure of RBM includes weights and coefficients for neurons. Better network structure allows us to examine data more thoroughly, which is good. We looked at the variance of parameters in learning on demand to fix the problem. To determine why RBM's energy function fluctuates, we'll look at its parameter variance. A neuron generation and annihilation algorithm is smeared with an adaptive RBM learning method to determine the optimal number of hidden neurons for attribute imputation during training. When the energy function isn't converged and parameter variance is high, a hidden neuron is generated. If the neuron doesn't disrupt learning, it'll destroy the hidden neuron. In this study, some yardstick PIMA data sets were tested

Data Collection and Aggregation in Mobile Sensing

Nowadays, smartphones have become ubiquitous and are playing a critical role in key aspects of people\u27s daily life such as communication, entertainment and social activities. Most smartphones are equipped with multiple embedded sensors such as GPS (Global Positioning System), accelerometer, camera, etc, and have diverse sensing capacity. Moreover, the emergence of wearable devices also enhances the sensing capabilities of smartphones since most wearable devices can exchange sensory data with smartphones via network interfaces. Therefore, mobile sensing have led to numerous innovative applications in various fields including environmental monitoring, transportation, healthcare, safety and so on. While all these applications are based on two critical techniques in mobile sensing, which are data collection and data aggregation, respectively. Data collection is to collect all the sensory data in the network while data aggregation is any process in which information is gathered and expressed in a summary form such as SUM or AVERAGE. Obviously, the above two problems can be solved by simply collect all the sensory data in the whole network. But that will lead to huge communication cost. This dissertation is to reduce the huge communication cost in data collection and data aggregation in mobile sensing where the following two technical routes are applied. The first technical route is to use sampling techniques such as uniform sampling or Bernoulli sampling. In this way, an aggregation result with acceptable error can be can be calculate while only a small part of mobile phones need to submit their sensory data. The second technical rout is location-based sensing in which every mobile phone submits its geographical position and the mobile sensing platform will use the submitted positions to filter useless sensory data. The experiment results indicate the proposed methods have high performance

A comprehensive review of vehicle detection using computer vision

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection

Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications

Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges

International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community

Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks

An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the same security concerns of legacy ad hoc networks. Unlike exiting works, we consider, in this paper, detection a common attack where nodes modify safety message or drop them. Unfortunately, detecting such a type of intrusion is a challenging problem since some packets may be lost or dropped in normal VANET due to congestion without malicious action. To mitigate these concerns, this paper presents a novel scheme for minimizing the invalidity ratio of VANET packets transmissions. In order to detect unusual traffic, the proposed scheme combines evidences from current as well as past behaviour to evaluate the trustworthiness of both data and nodes. A new intrusion detection scheme is accomplished through a four phases, namely, rule-based security filter, Dempster–Shafer adder, node’s history database, and Bayesian learner. The suspicion level of each incoming data is determined based on the extent of its deviation from data reported from trustworthy nodes. Dempster–Shafer’s theory is used to combine multiple evidences and Bayesian learner is adopted to classify each event in VANET into well-behaved or misbehaving event. The proposed solution is validated through extensive simulations. The results confirm that the fusion of different evidences has a significant positive impact on the performance of the security scheme compared to other counterparts