Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments

In the Web of Things (WoT) context, an increasing number of stationary and mobile objects provide functions as RESTful services, also called resources, that can be combined with other existing Web resources, to create value-added processes. However, nowadays resource discovery and selection are challenging, due to (1) the growing number of resources providing similar functions, making Quality of Resource (QoR) essential to select appropriate resources, (2) the transient nature of resource availability due to sporadic connectivity, and (3) the location changes of mobile objects in time. In this paper, we first present a location-aware resource discovery that relies on a 3-dimensional indexing schema, which considers object location for resource identification. Then, we present a QoR-driven resource selection approach that uses a Selection Strategy Adaptor (SSA) to form i-compositions (with i ∈N*) offering different implementation alternatives. The defined SSA allows forming resource compositions while considering QoR constraints and Inputs/Outputs matching of related resources, as well as resource availability and users different needs (e.g., optimal and optimistic compositions obtained using a scoring system). Analyses are made to evaluate our service quality model against existing ones, and experiments are conducted in different environments setups to study the performance of our solution.

Notes Fields in Metadata Records Generated by the Children’s and Young Adults’ Cataloguing Program: Exploration of Support for General Specific Needs of School Library Users

Notes fields in metadata records used in school library catalogs provide important value added and facilitate resource discovery for students and teachers. Variety of notes are intended to support general user tasks, as well as specific user tasks of school library users. The study reported in this paper examined levels and patterns of application of summary notes, audience notes, grade level notes, reading interest level notes, study program information notes, table of contents notes etc. in the bibliographic records created by the United States Library of Congress Children’s and Young Adults’ Cataloguing Program for fiction books between 2014 and 2020.

LaMRD: Location-Aware and Decentralized Multi-Layer Resource Discovery for IoT

The resources in the Internet of Things (IoT) network are distributed among different parts of the network. Considering huge number of IoT resources, the task of discovering them is challenging. While registering them in a centralized server such as a cloud data center is one possible solution, but due to billions of IoT resources and their limited computation power, the centralized approach leads to some efficiency and security issues. In this paper we proposed a location aware and decentralized multi layer model of resource discovery (LaMRD) in IoT. It allows a resource to be registered publicly or privately, and to be discovered in a decentralized scheme in the IoT network. LaMRD is based on structured peer-to-peer (p2p) scheme and follows the general system trend of fog computing. Our proposed model utilizes Distributed Hash Table (DHT) technology to create a p2p scheme of communication among fog nodes. The resources are registered in LaMRD based on their locations which results in a low added overhead in the registration and discovery processes. LaMRD generates a single overlay and it can be generated without specific organizing entity or location based devices. LaMRD guarantees some important security properties and it showed a lower latency comparing to the cloud based and decentralized resource discovery.

Pairing automated mark–recapture and social network models to explore the effects of landscape configuration on hummingbird foraging patterns

Landscape changes can alter pollinator movement and foraging patterns which can in turn influence the demographic processes of plant populations. We leveraged social network models and four fixed arrays of five hummingbird feeders equipped with radio frequency identification (RFID) data loggers to study rufous hummingbird ( Selasphorus rufus ) foraging patterns in a heterogeneous landscape. Using a space-for-time approach, we asked whether forest encroachment on alpine meadows could restrict hummingbird foraging movements and impede resource discovery. We fit social network models to data on 2221 movements between feeders made by 29 hummingbirds. Movements were made primarily by females, likely due to male territoriality and early migration dates. Distance was the driving factor in determining the rate of movements among feeders. The posterior mean effects of forest landscape variables (local canopy cover and intervening forest cover) were negative, but with considerable uncertainty. Finally, we found strong reciprocity in hummingbird movements, indicative of frequent out and back movements between resources. Together, these findings suggest that reciprocal movements by female hummingbirds could help maintain bidirectional gene flow among nearby subpopulations of ornithophilous plants; however, if the distance among meadows increases with further forest encroachment, this may limit foraging among progressively isolated meadows.

Attred: Attribute Based Resource Discovery for IoT

While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.