*The Research Roundup is a semi-regular list of outside research we have found interesting and think is worth sharing. The views and conclusions of the papers’ authors do not necessarily reflect the opinions of anyone affiliated with TPI. The information below includes edited author abstracts*
This month’s Research Roundup includes new research on the integration of emerging technologies into daily life. “Scheduling last-mile deliveries with truck-based autonomous robots” by Nils Boysen, Stefan Schwerdfeger, and Felix Weidinger discusses the challenges and potential benefits of combining human-driven and autonomous vehicles in delivery systems on crowded urban streets. Their proposed approach is similar, in some ways, to municipal bike share programs that have cropped up in major cities recently – adding a measure of flexibility for delivery companies with far-flung customers. Click through for more information on this and other recent research.
Click through for more detailed descriptions and links.
Scheduling Last-Mile Deliveries with Truck-Based Autonomous Robots
In Digital We Trust: Bitcoin Discourse, Digital Currencies, and Decentralized Network Fetishism
IoT Big Data Analytics for Smart Homes with Fog and Cloud Computing
Privacy Preserving Governmental Data Publishing: A Fog-Computing-Based Differential Privacy Approach
Is There a Need for Platform Neutrality Regulation in the EU? Approach
Descriptions of papers below are edited abstracts from authors
Scheduling Last-Mile Deliveries with Truck-Based Autonomous Robots
Nils Boysen, Stefan Schwerdfeger, and Felix Weidinger
The authors consider a novel delivery concept relying on autonomous robots, specifically launching them from trucks for last-mile delivery in hopes of reducing the negative impact of excessive traffic in large urban areas. They derive a novel scheduling problem for launching robots from trucks and develop an efficient solution procedure. They model their proposal using scenarios in which a truck moves freight destined for a set of to the city center. Small autonomous robots on board can then be loaded with the freight dedicated to a single customer and launched from the truck. Then, the autonomous robots move to their dedicated customers and, after delivery, autonomously return to some robot depot in the city center. The truck can replenish robots at these decentralized depots to launch further of them until all its customers are supplied. This paper develops scheduling procedures which determine the truck route along robot depots and drop-off points where robots are launched, such that the weighted number of late customer deliveries is minimized. The authors formulate the resulting scheduling problem, investigate computational complexity, and develop suited solution methods.
A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments
Edward Curry, Wassim Derguech, Souleiman Hasan, Christos Kouroupetroglou, and Umair Ul Hassan
As smart environments move from a research vision to concrete manifestations in real-world enabled by the Internet of Things, they are encountering a number of very practical challenges in data management in terms of the flexibility needed to bring together contextual and real-time data, the interface between new digital infrastructures and existing information systems, and how to easily share data between stakeholders in the environment. Therefore, data management approaches for smart environments need to support flexibility, dynamicity, incremental change, while keeping costs to a minimum. A Dataspace is an emerging approach to data management that has proved fruitful for personal information and scientific data management. However, their use within smart environments and for real-time data remains largely unexplored. This paper introduces a Real-time Linked Dataspace (RLD) as an enabling platform for data management within smart environments. This paper identifies common data management requirements for smart energy and water environments, details the RLD architecture and the key support services and their tiered support levels, and a principled approach to “Pay-As-You-Go” data management. The paper presents a dataspace query service for real-time data streams and entities to enable unified entity-centric queries across live and historical stream data.
In Digital We Trust: Bitcoin Discourse, Digital Currencies, and Decentralized Network Fetishism
Jon Baldwin
This paper outlines how the digital currency and network technology of bitcoin functions and explores the context from which it emerged. Bitcoin was conceived in 2008 as an attempt to alleviate trust in government and banks which was at a low during this period of financial crisis. However, with bitcoin trust does not dissipate; rather, it shifts. Trust moves from trust in banks or states to trust in algorithms and encryption software. There is a move from conventional trust in the gold standard—“In Gold We Trust”—to the trust announced on U.S. currency—“In God We Trust”—to trust in software and networks—“In Digital We Trust”. The hyperbole of bitcoin discourse is deemed to be an expression of the Californian Ideology, which itself often conceals a right-wing agenda. The paper analyses the hype behind the celebration of decentralized digital networks. It proposes that a form of network fetishism operates here. The failure of bitcoin as a currency (rather than as a hoarded commodity in an emergent bubble) and as an idea might be attributed to the failure to see how ultra-modern digital networks conceal very traditional consolidation of power and capital. The rise and fall of bitcoin, in terms of its original ambition, serves as a cautionary tale in the digital age—it reveals how ingenious innovations that might challenge power and the consolidation of capital become co-opted and colonized by capital. Finally, the paper offers a discussion of the possible progressive uses of the digital technology bitcoin has facilitated.
IoT Big Data Analytics for Smart Homes with Fog and Cloud Computing
Abdulsalam Yassine, Shailendra Singh, Shamim M. Hossain, and Ghulam Muhammad
Internet of Things (IoT) analytics is an essential mean to derive knowledge and support applications for smart homes. Connected appliances and devices inside the smart home produce a significant amount of data about consumers and how they go about their daily activities. IoT analytics can aid in personalizing applications that benefit both homeowners and the ever-growing industries that need to tap into consumers profiles. This article presents a new platform that enables innovative analytics on IoT captured data from smart homes. The authors propose the use of fog nodes and cloud system to allow data-driven services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. They discuss in this paper the requirements and the design components of the system. To validate the platform and present meaningful results, they present a case study using a dataset acquired from real smart home in Vancouver, Canada. The results of the experiments show clearly the benefit and practicality of the proposed platform
Privacy Preserving Governmental Data Publishing: A Fog-Computing-Based Differential Privacy Approach
Chunhui Piao, Yajuan Shi, Jiaqi Yan, Changyou Zhang, and Liping Liu
With the growing availability of public open data, the protection of citizens’ privacy has become a vital issue for governmental data publishing. However, there are a large number of operational risks in the current government cloud platforms. When the cloud platform is attacked, most existing privacy protection models for data publishing cannot resist the attacks if the attacker has prior background knowledge. Potential attackers may gain access to the published statistical data, and identify specific individual’s background information, which may cause the disclosure of citizens’ private information. To address this problem, the authors propose a fog-computing-based differential privacy approach for privacy-preserving data publishing in this paper. They discuss the risk of citizens’ privacy disclosure related to governmental data publishing and present a differential privacy framework for publishing governmental statistical data based on fog computing.
Is There a Need for Platform Neutrality Regulation in the EU?
Jan Krämer & Daniel Schnurr
Motivated by the policy discussion in the EU whether to impose non-discrimination obligations for dominant online platforms, the authors analyze whether such regulation is warranted from an economic point of view. Their contribution is threefold. First, across several platform contexts, they identify (i) (paid) prominence of some third parties over others and (ii) the favoring of a platform’s integrated services over independent entities as common discriminatory conducts of online platforms. Second, within this scope, they review the economic literature and find that discrimination in the form of paid prominence may often be in the interest of consumers. However, smaller or low-quality content providers are likely to be worse off, which gives rise to concerns regarding dynamic efficiency and long-term variety in those markets. Additional problems may arise if platform operators are vertically integrated with content providers. Third, based on these theoretical insights, they recommend that EU policy makers should not adopt a neutrality regulation for platforms prematurely. Instead, they recommend imposing new proportionate transparency rules for dominant platforms in order to facilitate the identification of actual misconduct and legal enforcement.
