Expense and the logistical difficulties with deploying scientific monitoring equipment are the biggest limitations to undertaking large scale monitoring of aquatic environments. The Smart ...Environmental Monitoring and Assessment Technologies (SEMAT) project is aimed at addressing this problem by creating an open standard for low-cost, near real-time, remote aquatic environmental monitoring systems. This paper presents the latest refinement of the SEMAT system in-line with the evolution of existing technologies, inexpensive sensors and environmental monitoring expectations. We provide a systems analysis and design of the SEMAT remote monitoring units and the back-end data management system. The system's value is augmented through a unique e-waste recycling and repurposing model which engages/educates the community in the production of the SEMAT units using social enterprise. SEMAT serves as an open standard for the community to innovate around to further the state of play with low-cost environmental monitoring. The latest SEMAT units have been trialled in a peri-urban lake setting and the results demonstrate the system's capabilities to provide ongoing data in near real-time to validate an environmental model of the study site.
Turbidity is a key environmental parameter that is used in the determination of water quality. The turbidity of a water body gives an indication of how much suspended sediment is present, which ...directly impacts the clarity of the water (i.e., whether it is cloudy or clear). Various commercial nephelometric and optical approaches and products exist for electronically measuring turbidity. However, most of these approaches are unsuitable or not viable for collecting data remotely. This paper investigates ways for incorporating a turbidity sensor into an existing remote aquatic environmental monitoring platform that delivers data in near real-time (i.e., 15-min intervals). First, we examine whether an off-the-shelf turbidity sensor can be modified to provide remote and accurate turbidity measurements. Next, we present an inexpensive design for a practical light attenuation turbidity sensor. We outline the sensor's design rationale and how various technical and physical constraints were overcome. The turbidity sensor is calibrated against a commercial turbidimeter using a Formazin standard. Results indicate that the sensor readings are indicative of actual changes in turbidity, and a calibration curve for the sensor could be attained. The turbidity sensor was trialled in different types of water bodies over nine months to determine the system's robustness and responsiveness to the environment.
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This paper details the physical and hardware design of a flexible open-source IoT (Internet of Things) platform for environmental sensing. The application is a remote water quality ...monitoring buoy that can be deployed in calm, shallow near-shore aquatic environments with fresh or brackish water. The system’s development has been informed by experience through conducting multiple actual water quality studies over a prolonged period. The system runs an Arduino Mega 2560 microcontroller using off-the-shelf Adafruit lux and temperature sensors. A light attenuation turbidity sensor is adapted and integrated into the design. A TinySine 3G GSM module transmits data to a server that is displayed via a ThingsBoard IoT dashboard. The system is stable over time, provides reliable remote sensor readings, has low energy consumption, and is powered by renewable energy (up-cycled batteries). The hardware aspires to be general-purpose so that future environmental monitoring applications can repurpose the electronics by adding new compatible sensors and modifying the physical design to match the requirements.
There is an increasing need for environmental measurement systems to further science and thereby lead to improved policies for sustainable management. Marine environments are particularly hostile and ...extremely difficult for deploying sensitive measurement systems. As a consequence the need for data is greatest in marine environments, particularly in the developing economies/regions. Expense is typically the most significant limiting factor in the number of measurement systems that can be deployed, although technical complexity and the consequent high level of technical skill required for deployment and servicing runs a close second. This paper describes the Smart Environmental Monitoring and Analysis Technologies (SEMAT) project and the present development of the SEMAT technology. SEMAT is a "smart" wireless sensor network that uses a commodity-based approach for selecting technologies most appropriate to the scientifically driven marine research and monitoring domain/field. This approach allows for significantly cheaper environmental observation systems that cover a larger geographical area and can therefore collect more representative data. We describe SEMAT's goals, which include: (1) The ability to adapt and evolve; (2) Underwater wireless communications; (3) Short-range wireless power transmission; (4) Plug and play components; (5) Minimal deployment expertise; (6) Near real-time analysis tools; and (7) Intelligent sensors. This paper illustrates how the capacity of the system has been improved over three iterations towards realising these goals. The result is an inexpensive and flexible system that is ideal for short-term deployments in shallow coastal and other aquatic environments.
•This is the first paper to examine the scenario of multiple colluding sellers engaging in shill bidding.•We outline the optimal strategy to engage in to mask shill bidding behaviour.•We propose the ...first algorithm to provide evidence of whether groups of sellers are colluding.•Performance is demonstrated using simulated and commercial auction data.
Shill bidding occurs when fake bids are introduced into an auction on the seller’s behalf in to artificially inflate the final price. The seller either has associates bid in the seller’s auctions, or the seller controls multiple fake bidder accounts that are used for shill bidding. We proposed a reputation system referred to as the Shill Score that indicates how likely a bidder is to be engaging in price inflating behaviour in a specific seller’s auctions. A potential bidder can observe the other bidders’ Shill Scores, and if they are high, the bidder can elect not to participate as there is some evidence that shill bidding occurs in the seller’s auctions. However, if a seller is in collusion with other sellers, or controls multiple seller accounts, the seller can spread the risk between the various sellers and can reduce suspicion on the shill bidder. Collusive seller behaviour impacts one of the characteristics of shill bidding the Shill Score examines; consequently, collusive behaviour can reduce a bidder’s Shill Score. This paper extends the Shill Score to detect shill bidding where multiple sellers are working in collusion with each other. We propose the first algorithm to provide evidence of whether groups of sellers are colluding. Based on how tight the association is between the sellers and the level of apparent shill bidding that is occurring in the auctions, a suspect bidder’s Shill Score is adjusted appropriately to remove any advantage from seller collusion. Performance is demonstrated using simulated and commercial auction data and extensive experimental results are presented.
Environmental monitoring systems have been evolving dynamically to embrace modern Internet of Things (IoT) technology in the last decade. Despite this progress there are, however, continuing ...limitations and issues with some IoT designs. Thus, past research has identified areas of concern in areas such as communications, interoperability, reliability, and scalability. As enabling technologies evolve in an accelerated manner, there will no doubt be a plethora of environmental monitoring solutions under development. Such solutions need to be well evaluated so that potential users are knowledgeable in relation to the best solution for their specific applications. Along these lines, this paper puts forward a framework to evaluate proposed IT designs relevant to smart environmental monitoring systems. This framework is based on model standardized software engineering requirements found in ISO 25010, which it uses as an aid to develop business-driven ‘smart’ environmental monitoring systems.
•Shill bidding is a strategy employed by a seller who submits fake bids into an online auction to inflate an item’s final price, thereby cheating legitimate bidders.•Shill bidding detection becomes ...more difficult when a seller involves two or more bidders to commit shill bidding collaboratively in his/her auction.•The reason is colluding shill bidders can distribute the work evenly among each other to collectively reduce their chances of being detected.•This paper presents a collusive shill bidding detection algorithm based on Markov Random Field for identifying colluding shill bidders.•We implemented the proposed algorithm and applied it on simulated and commercial auction datasets.•Experimental results on the simulated auction datasets show that the algorithm can potentially detect colluding shill bidders with about 99% detection accuracy.•Two existing published approaches applied on the simulated auction datasets achieve a detection accuracy of 85% and 88% approximately.
Shill bidding is where spurious bids are introduced into an auction to drive up the final price for the seller. This causes legitimate bidders to pay more for the item in order to win the auction. Shill bidding detection becomes more difficult when a seller involves two or more bidders and forms a collusive group to commit price-inflating behaviour. Colluding shill bidders can distribute the work evenly among each other to collectively reduce their chances of being detected. This paper presents a Collusive Shill Bidding Detection algorithm to identify the presence of colluding shill bidders. The algorithm calculates an anomaly score for each bidder and then verifies the anomaly scores to improve the detection accuracy. We use a Local Outlier Factor for calculating the anomaly score for each bidder. We then model the auction network in a Markov Random Field and apply Loopy Belief Propagation for identifying the colluding shill bidders. We implemented the proposed algorithm and applied it on both simulated and commercial auction datasets. Experimental results show that the algorithm is able to potentially detect colluding shill bidders. Comparative analysis on simulated auction datasets shows that the proposed algorithm performs better than two existing published approaches.
Two significant problems faced by universities are to ensure sustainability and to produce quality graduates. Four aspects of these problems are to improve engagement, to foster interaction, develop ...required skills and to effectively gauge the level of attention and comprehension within lectures and large tutorials. Process-Oriented Guided Inquiry Learning (POGIL) is a technique used to teach in large lectures and tutorials. It invokes interaction, team building, learning and interest through highly structured group work. This paper describes a new approach to teaching Information Technology (IT) using POGIL. Two IT subjects were chosen for the implementation of the POGIL technique to explore its potential to resolve the aforementioned issues. Preliminary evidence from perspectives of the institution, students and lecturer suggest that POGIL is better able to maximise engagement, foster interaction and effectively gauge the level of attention and comprehension in teaching process-oriented IT concepts than a traditional didactic approach. Author abstract
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