CoAP congestion control for the Internet of Things

“© © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” August Betzler, Javier Isern, Carles Gomez, Ilker Demirkol, Josep Paradells, "Experimental evaluation of congestion control for CoAP communications without end-to-end reliability", Ad Hoc Networks, pp. , 2016, ISSN 15708705. DOI: 10.1109/MCOM.2016.7509394CoAP is a lightweight RESTful application layer protocol devised for the IoT. Operating on top of UDP, CoAP must handle congestion control by itself. The core CoAP specification defines a basic congestion control mechanism, but it is not capable of adapting to network conditions. However, IoT scenarios exhibit significant resource constraints, which pose new challenges on the design of congestion control mechanisms. In this article we present CoCoA, an advanced congestion control mechanism for CoAP being standardized by the Internet Engineering Task Force CoRE working group. CoCoA introduces a novel round-trip time estimation technique, together with a variable backoff factor and aging mechanisms in order to provide dynamic and controlled retransmission timeout adaptation suitable for the peculiarities of IoT communications. We conduct a comparative performance analysis of CoCoA and a variety of alternative algorithms including state-of-the-art mechanisms developed for TCP. The study is based on experiments carried out in real testbeds. Results show that, in contrast to the alternative methods considered, CoCoA consistently outperforms the default CoAP congestion control mechanism in all evaluated scenarios.Peer ReviewedPostprint (author's final draft

Self-Adaptive Role-Based Access Control for Business Processes

© 2017 IEEE. We present an approach for dynamically reconfiguring the role-based access control (RBAC) of information systems running business processes, to protect them against insider threats. The new approach uses business process execution traces and stochastic model checking to establish confidence intervals for key measurable attributes of user behaviour, and thus to identify and adaptively demote users who misuse their access permissions maliciously or accidentally. We implemented and evaluated the approach and its policy specification formalism for a real IT support business process, showing their ability to express and apply a broad range of self-adaptive RBAC policies

Dynamic Scale Genetic Algorithm: An Enhanced Genetic Search for Discrete Optimization

The minimization of operations and support resources of reusable launch vehicles is a complex task, involving discrete optimization and the simulation domain. Genetic algorithms, offering a robust search strategy suitable for integer variables and the simulation domain, can be applied to minimize these resources. This research developed an enhanced genetic algorithm for problems with a linear objective function, the most common class of discrete optimization problems. The dynamic scale genetic algorithm developed here incorporates concepts of implicit enumeration to enhance search. This is achieved by utilizing problem specific information to refine the solution space over successive generations. The utility of the proposed algorithm was demonstrated by comparing its performance, in terms of quality of solutions produced, to that of the simple genetic algorithm. For all test problems, the dynamic scale genetic algorithm consistently produced better solutions in fewer generations. The proposed algorithm was successfully applied to optimize the operation and support resources of reusable launch vehicles, through a discrete event simulation model. The least cost solution so obtained represents an improvement over both the simple genetic algorithm, and the previous manual approach of minimizing operation and support resources