Showing papers by "Wayne Read published in 2018"
TL;DR: An intelligent agent is presented, which provides automatic semantic-based registration/configuration in a large-scale sensor observation system and makes a 'smart' decision on which system function to use by integrating a semantic representation of sensor network data including middleware and web service specifications and applying logical rules to the knowledge-base.
Abstract: Wireless sensor networks (WSNs) employ middleware solutions to coordinate their sensors and provide web services for managing data. Adding/configuring a new or existing sensor requires modification to the middleware and web service. To integrate hundreds of sensors with different capabilities, a system needs to support all the encodings, models and services for registering, tasking and querying sensors. We present an intelligent agent, which provides automatic semantic-based registration/configuration in a large-scale sensor observation system. The agent can react to any changes internally/externally made (i.e., adding a new sensor or sending a task request to sensors/support systems). The agent makes a 'smart' decision on which system function to use by integrating a semantic representation of sensor network data including middleware and web service specifications and applying logical rules to the knowledge-base. The agent's operation is demonstrated using two real-world systems represented in RDF using a domain ontology that extends the W3C SSN-XG ontology.
4 citations
Proceedings Article•
21 Aug 2018TL;DR: This paper presents several market clearing algorithms that focus solely on allocating quantity among matching buy and sell bids, and presents three algorithms that outperform the ASX’s strategy by increasing the number of bids matched, the amount of quantity matched, and theNumber of bids fully matched.
Abstract: Market clearing is the process of matching buy and sell bids in securities markets. The allocative efficiency of such algorithms is important, as the Auctioneer is typically paid a commission on the number of bids matched and the volume of quantity traded. Previous algorithms have concentrated on price issues. This paper presents several market clearing algorithms that focus solely on allocating quantity among matching buy and sell bids. The goal is to maximise the number of bids matched, while at the same time minimise the amount of unmatched quantity. The algorithms attempt to avoid situations resulting in unmarketable quantities (i.e., quantities too small to sell). Algorithmic performance is tested using simulated data designed to emulate the Australian Stock Exchange (ASX) and other world stock markets. Our results show that it is difficult to avoid partial matchings as the complexity of doing so is NP-complete. The optimal offline algorithm for partial quantity matching is used as a benchmark to compare online matching strategies. We present three algorithms that outperform the ASX’s strategy by increasing the number of bids matched, the amount of quantity matched, and the number of bids fully matched.
2 citations
Posted Content•
TL;DR: An algorithm is proposed that provides 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 is occurring in the auctions, each participating bidder's Shill Score is adjusted appropriately to remove any advantages from seller collusion.
Abstract: Shill bidding occurs when fake bids are introduced into an auction on the seller's behalf in order to artificially inflate the final price. This is typically achieved by the seller having friends bid in her auctions, or the seller controls multiple fake bidder accounts that are used for the sole purpose of shill bidding. We previously proposed a reputation system referred to as the Shill Score that indicates how likely a bidder is to be engaging in price inflating behaviour with regard to 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, she 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 is examining, therefore 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 an algorithm that provides 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 is occurring in the auctions, each participating bidder's Shill Score is adjusted appropriately to remove any advantages from seller collusion. Performance has been tested using simulated auction data and experimental results are presented.
1 citations