Downtime is a key performance index for industrial automation systems. An industrial automation system achieves maximum productivity when its downtime is reduced to the minimum. One approach to minimize downtime is to predict system faults and recover from them automatically. A cloud-based decision support system is proposed for rapid problem identifications and to assist the self-management processes. By running multiple parallel simulations of control software with real-time inputs ahead of system time, faults could be detected and corrected automatically using autonomous industrial software agents. Fault trees, as well as control algorithms, are modeled using IEC 61499 function blocks that can be directly executed on both physical controllers and cloud services. A case study of water heating process is used to demonstrate the self-healing process supported by the cloud-based decision support system.

The next generation of computing peripherals will be low-power ubiquitous computing devices such as door locks, smart watches, and heart rate monitors. Bluetooth Low Energy is a primary protocol for connecting such peripherals to mobile and gateway devices. Current operating system support for Bluetooth Low Energy forces peripherals into vertical application silos. As a result, simple, intuitive applications such as opening a door with a smart watch or simultaneously logging and viewing heart rate data are impossible. We present Beetle, a new hardware interface that virtualizes peripherals at the application layer, allowing safe access by multiple programs without requiring the operating system to understand hardware functionality, fine-grained access control to peripheral device resources, and transparent access to peripherals connected over the network. We describe a series of novel applications that are impossible with existing abstractions but simple to implement with Beetle.

We identify and address two primary challenges for computing on encrypted data in Internet of Things applications: synchronizing encrypted data across devices and selecting an appropriate encryption scheme. We propose a caching mechanism that operates across the three devices, enabling interactive order-preserving encryption schemes on resource-constrained devices. Additionally, the system can use a high-level description of an IoT application to select automatically appropriate encryption for the data on corresponding tiers and their mathematical operations. This assists in fine-tuning and choosing the core parameters for underlying data structures.

We describe Tethys, an energy-harvesting wireless water flow sensor that can monitor water use at a per-fixture level with the intention of associating water use with specific individuals. Tethys was motivated by recent efforts at Stanford University to reduce water use due to the California drought. Understanding how the university population uses water at per-person level can greatly influence policies and conservation approaches. Tethys uses Bluetooth Smart to both identify individuals as well as asynchronously upload data to the cloud for later analysis. We describe two challenges encountered in deploying Tethys: energy harvesting design and the mechanical considerations for high-pressure water at high temperatures.

The interoperability between various automation systems is considered as one of the major character of future automation systems. Service-oriented Architecture is a possible interoperability enabler between legacy and future automation systems. In order to prove the interoperability between those systems, a verification framework is essential. This paper proposes a configurable cloud-based validation environment for interoperability tests between various distributed automation systems. The testing framework is implemented in a multi-layer structure which provides automated closed-loop testing from the protocol level to the system level. The testing infrastructure is also capable for simulating automation systems as well as wireless sensor networks in the cloud. Test cases could be automatically generated and executed by the framework.

Ravel is a software framework for developing sensor network applications that follow the eMbedded-Gateway-Cloud architecture. Developers describe a Ravel application as a data processing pipeline in terms of two abstractions: models and transforms between models. This pipeline generates code for controllers that can compile to and run on any element of the architecture, from embedded devices to cloud servers. Developers also specify views, that represent the data set on a particular device. Therefore, each device type is a space where data flows via transform. The framework automatically handles moving data between spaces using appropriate network protocols. Compile-time tools verify that the code, once modified by the developer, still follows application specification as defined by the data pipeline.

The concept of “Internet-of-Things” will undoubtedly emerge as the technology of the future. Educating specialists ready to bring the concept to the reality remains challenging in the scope of traditional university courses. The main challenge is how to enable students to think outside the boundaries of the particular discipline and therefore to enable the innovative thinking. This article describes an experiment with teaching Internet-of-Things as a common red thread across three courses which ran in parallel during fall semester 2012 at Luleå University of Technology in Sweden. We discuss the teaching methodology, the technology blocks which laid the ground for our teaching philosophy as well as the experiences and lessons learned.

Early 2012 Luleå University of Technology started a project on adopting cloud technology for implementation in the university's IT-infrastructure. This work-in-progress article describes the results of its pre-study phase aiming at understanding the feasibility of integrating and/or migrating main IT-infrastructure components into an IaaS system and opening ways for making university's resources more accessible to a wider public. Numbers of logistical, technical and education related challenges make such transition far from being trivial. The article focuses on the educational aspect of the pre-study. Specifically, work flows in education process of several courses in different disciplines in natural and engineering sciences were analyzed from the student and teacher perspectives. In the article a schematic of a sustainable IT infrastructure adjusted to the needs of higher education will be drafted. Further, technical readiness and challenges of using cloud technology for university scale IT-infrastructure are discussed.

In this article, we report on a mathematical model for the throughput and delay of LTE's Random Access Channel (RACH). The model is an improvement of a cell-level Markov model of Multichannel slotted ALOHA proposed previously in the literature. The improvements concern accounting for a possibility of contention resolution in the case when terminals select same preambles and distinguishing initially-transmitting nodes from retransmitting nodes. The improved model is verified and agrees with measurements obtained from a discrete-time event-based simulator of an LTE cell.