Latency Optimization in Large-Scale Cloud-Sensor Systems

With the advent of the Internet of Things and smart city applications, massive cyber-physical interactions between the applications hosted in the cloud and a huge number of external physical sensors and devices is an inevitable situation. This raises two main challenges: cloud cost affordability as the smart city grows (referred to as economical cloud scalability) and the energy-efficient operation of sensor hardware. We have developed Cloud-Edge-Beneath (CEB), a multi-tier architecture for large-scale IoT deployments, embodying distributed optimizations, which address these two major challenges. In this article, we summarize our prior work on CEB to set context for presenting a third major challenge for cloud sensor-systems, which is latency. Prolonged latency can potentially arise in servicing requests from cloud applications, especially given our primary focus on optimizing energy and cloud scalability. Latency, however, is an important factor to optimize for real-time and cyber-physical applications with limited tolerance to delays. Also, improving the responsiveness of any IoT application is bound to improve the user experience and hence the acceptability and adoption of smart city solutions by the city citizens. In this article, we aim to give a formal definition and formulation for the latency optimization problem under CEB. We propose a Prioritized Application Fragment Caching Algorithm (PAFCA) to selectively cache application fragments from the cloud to lower layers of CEB, as a key measure to optimize latency. The algorithm itself is an extension of one of the existing optimization algorithms of CEB (AFCA-1). As will be shown, PAFCA takes into account the expectations of cloud applications on real-timeliness of responses. Through experiments, we measure and validate the effect of PAFCA on latency and cloud scalability. We also introduce and discuss the trade-off between latency and sensor energy in this given context

Service-Relationship Programming Framework for the Social IoT

We argue that for a true realization of innovative programming opportunities for smart spaces, the developers should be equipped with informative tools that assist them in building domain-related applications. Such tools should utilize the services offered by the space's smart things and consider the different relationships that may tie these services opportunistically to build applications. In this paper, we utilize our Inter-thing relationships programming framework to present a distributed programming ecosystem. The framework broadens the restricted set of thing-level relationships of the evolving social IoT paradigm with a set of service-level relationships. Such relationships provide guidance into how services belonging to different things can be combined to build meaningful applications. We also present a uniform way of describing the thing services and the service-level relationships along with new capabilities for the things to dynamically generate their own services, formulate the corresponding programmable interfaces (APIs) and create an ad-hoc network of socially related smart things at runtime. We then present the semantic rules that guide the establishment of IoT applications and finally demonstrate the features of the framework through a proof-of-concept application

Interoperable communication framework for bridging RESTful and topic-based communication in IoT

The promise of the Internet of Things (IoT) and the many visions of unprecedented and transforming IoT applications are challenged by the realities of a highly fragmented ecosystem of devices, standards and industries. Systems research in IoT is shifting priorities to explore explicit “thing architectures” that promote and enable the friction-free interactions of things despite such fragmentations. In this paper, we focus on overcoming light-weight communication protocol fragmentation. We introduce the Atlas IoT communication framework which enables interactions among things that speak similar or different communication protocols. The framework tools up Atlas things with protocol translator “attachments” that could be either hosted on board the Atlas thing platform, or in the cloud. The translator enables the seamless communication between heterogeneous things through a set of well-defined interfaces. The proposed framework supports seamless communication among the widely adopted Constrained Application Protocol (CoAP), Representational State Transfer (REST) over Hypertext Transfer protocol HTTP, and the Message Queue Telemetry Transport protocol (MQTT). Our framework is carefully designed to facilitate interoperability among heterogeneously communicating things without taxing the performance of things that are homogenously communicating. The framework itself utilizes the topic concept and uses a meta-topic hierarchy to map out and guide the translations. We present the details of the Atlas IoT communication framework and give a detailed benchmarking study to measure the energy consumption and code footprint characteristics of the different aspects of the framework on real hardware platforms. In addition to basic characterizations, we compare our framework to the Eclipse Ponte framework and show how our framework is advantageous in energy consumption and how it is unique in that it does not tangibly penalize the homogeneous communication case

What Happens in Peer-Support, Stays in Peer-Support: Software Architecture for Peer-Sourcing in Mental Health

Digital health technology utilizing wearables, IoT and mobile devices has been successfully applied in the monitoring of numerous diseases and conditions. However, intervention, in response to monitored data, is yet to benefit from technological support and continues to follow a traditional point-of-care delivery model by providers and health professionals. Mental health is an example of a critical health area in dire need for technology solutions to enable timely, effective and scalable interventions. This is especially the case with an increasing prevalence of mental health conditions and a declining capacity of the healthcare professional workforce. Numerous studies reveal the potential for peer support groups as an effective, scalable, cost-effective, first-line of response in mental health interventions. Peer support helps participants, at low and moderate risk, better understand their diseases or conditions and empowers them to take control of their own health. Peer support interactions also seems to inform health professionals with insights and intricate knowledge, making it effectively a learning health system. This paper proposes a software architecture to better enable "peer-sourcing". We present related work and show how the proposed architecture might draw similarity to and differences from crowd-sourcing architectures. We also present a study in which we interacted with service users (mental health patients) and mental healthcare professionals to better understand and elicit the key requirements for the software architecture

iPOJO flow:a declarative service workflow architecture for ubiquitous cloud applications

The growth of innovative services backed up by various sensors and devices provides an unprecedented potential for ubiquitous computing applications and systems. However, in order to benefit from the recent developments, the current service middleware technology needs a catch-up of being able to fully support interactions among the services. OSGi is considered as a viable service framework solution due to its ability to deal with the dynamism inherent with ubiquitous cloud environments. iPOJO has also emerged as a service component model that simplifies the development of OSGi applications. However, the technology runs short of providing adequate support to foster declarative service compositions of realistic interaction topologies. Noticing this deficiency, we propose an iPOJO component-based service workflow architecture, named iPOJO Flow, where component services can easily be composed together to form realistic, complicated applications. Along with the architectural design, the paper also introduces a new DSL to specify service workflow topologies in a declarative way. The effectiveness of our proposed approach is validated through a prototype demonstration, comparative design analysis, and performance experiments

The Importance of Being Thing:Or the Trivial Role of Powering Serious IoT Scenarios

In this article, we call for a "Walk Before You Run" adjustment in the Internet-of-Things (IoT) research and development exercise. Without first settling the quest for what thing is or could be or do, we run the risk of presumptuous visions, or hypes, that can only fail the realities and limits of what is actually possible, leading to customers and consumers confusion as well as market hesitations. Specifically, without a carefully-designed Thing architecture in place, it will be very difficult to find the “magic” we are so addicted and accustomed to – programming! Programming the IoT, as we once programmed the mainframe, the workstation, the PC and the mobile devices, is the natural way to realize a fancy IoT scenario or an application. Without Thing architectures and their enablement of new programming models for IoT – we will continue to only envision fancy scenarios but unable to unleash the IoT full potential. This article raises these concerns and provides a view into the future by first looking back into our short history of pervasive computing. The article focuses on the domain of “Personal” IoT and will address key new requirements for such Thing architecture. Also, practicing what we preach, we present our ongoing efforts on the Atlas Thing Architecture showing how it supports a variety of thing notions, and how it enables novel models for programmability

GP Benchmark: Engineering a Crowd-Sourcing Platform for Real-Time Understanding of Personality and Cognitive Biases in Clinical Error

Errors in medicine are a significant problem, highlighted as a global safety priority. General Practice is one clinical arena where error is more likely due to clinical decisions being made on a background of clinical complexity, undifferentiated symptoms and diseases, and multiple other factors as yet unquantified. Interventions designed to reduce error are either underutilised, untested, fail to produce lasting results, are designed on inadequate knowledge, or have failed to appreciate the interaction of multiple factors, both cognitive and systemic. We present a potential solution, in the form of GP Benchmark. GP Benchmark is an online simulation environment and tool designed to test clinical decision making in a group of practicing General Practitioners. Its aim is to address two pressing requirements: 1) the need to capture clinical decision making in real-time, in the context of personality, cognitive bias and environmental factors, and 2) the need to provide a validated platform that models the clinical environment so future intervention decisions may be tested without risking patient safety. We highlight the requirements satisfied for implementing GP Benchmark, the plans for validation, and discuss how GP Benchmark will be used to identify further requirements necessary to develop the environment into a tool for testing clinical decision support systems and error prevention strategies