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1. Overcoming Energy and Reliability Challenges for IoT and Mobile Devices with Data Analytics

   Pasricha, S. (January 2018, IEEE International Conference on VLSI Design (VLSID))

   A very large amount of data is produced by mobile and Internet-of-Thing (IoT) devices today. Increasing computational abilities and more sophisticated operating systems (OS) on these devices have allowed us to create applications that are able to leverage this data to deliver better services. But today’s mobile and IoT solutions are heavily limited by low battery capacity and limited cooling capabilities. This motivates a search for new ways to optimize for energy-efficiency. Advanced data analytics and machine-learning techniques are becoming increasingly popular to analyze and extract meaning from Big Data. In this paper, we make the case for designing and deploying data analytics and learning mechanisms to improve energy-efficiency in IoT and mobile devices with minimal overheads. We focus on middleware for inserting energy-efficient data analytics-driven solutions and optimizations in a robust manner, without altering the OS or application code. We discuss several case studies of powerful and promising developments in deploying data analytics middleware for energy-efficient and robust execution of a variety of applications on commodity mobile devices. 

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2. Intent to share: enhancing Android inter-component communication for distributed devices

   https://doi.org/10.1145/3197231.3197245  

   Cruz, Breno Dantas; Tilevich, Eli (January 2018, Proceedings of the 5th International Conference on Mobile Software Engineering and Systems)

   Data-intensive applications in diverse domains, including video streaming, gaming, and health monitoring, increasingly require that mobile devices directly share data with each other. However, developing distributed data sharing functionality introduces low-level, brittle, and hard-to-maintain code into the mobile codebase. To reconcile the goals of programming convenience and performance efficiency, we present a novel middleware framework that enhances the Android platform's component model to support seamless and efficient inter-device data sharing. Our framework provides a familiar programming interface that extends the ubiquitous Android Inter-Component Communication (ICC), thus lowering the learning curve. Unlike middleware platforms based on the RPC paradigm, our programming abstractions require that mobile application developers think through and express explicitly data transmission patterns, thus treating latency as a first-class design concern. Our performance evaluation shows that using our framework incurs little performance overhead, comparable to that of custom-built implementations. By providing reusable programming abstractions that preserve component encapsulation, our framework enables Android devices to efficiently share data at the component level, providing powerful building blocks for the development of emerging distributed mobile applications. 

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3. Workload Shaping Energy Optimizations with Predictable Performance for Mobile Sensing

   https://doi.org/10.1109/IoTDI.2018.00026  

   Lai, Farley; Radi, Marjan; Chipara, Octav; Griswold, William G. (April 2018, 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI))

   Energy-efficiency is a key concern in mobile sensing applications, such as those for tracking social interactions or physical activities. An attractive approach to saving energy is to shape the workload of the system by artificially introducing delays so that the workload would require less energy to process. However, adding delays to save energy may have a detrimental impact on user experience. To address this problem, we present Gratis, a novel paradigm for incorporating workload shaping energy optimizations in mobile sensing applications in an automated manner. Gratis adopts stream programs as a high-level abstraction whose execution is coordinated using an explicit power management policy. We present an expressive coordination language that can specify a broad range of workload-shaping optimizations. A unique property of the proposed power management policies is that they have predictable performance, which can be estimated at compile time, in a computationally efficient manner, from a small number of measurements. We have developed a simulator that can predict the energy with a average error of 7% and delay with a average error of 15%, even when applications have variable workloads. The simulator is scalable: hours of real-world traces can be simulated in a few seconds. Building on the simulator's accuracy and scalability, we have developed tools for configuring power management policies automatically. We have evaluated Gratis by developing two mobile applications and optimizing their energy consumption. For example, an application that tracks social interactions using speaker-identification techniques can run for only 7 hours without energy optimizations. However, when Gratis employs batching, scheduled concurrency, and adaptive sensing, the battery lifetime can be extended to 45 hours when the end-to-end deadline is one minute. These results demonstrate the efficacy of our approach to reduce energy consumption in mobile sensing applications. 

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4. Service Placement for Real-Time Applications: Rate-Adaptation and Load-Balancing at the Network Edge

   Kassir, Saadallah; de Veciana, Gustavo; Wang, Nannan; Wang, Xi; Palacharla, Paparao (September 2020, International Conference on Edge Computing)

   Mobile Edge Computing may become a prevalent platform to support applications where mobile devices have limited compute, storage, energy and/or data privacy concerns. In this paper, we study the efficient provisioning and man- agement of compute resources in the Edge-to-Cloud continuum for different types of real-time applications with timeliness requirements depending on application-level update rates and communication/compute delays. We begin by introducing a highly stylized network model allowing us to study the salient features of this problem including its sensitivity to compute vs. communication costs, application requirements, and traffic load variability. We then propose an online decentralized service placement algorithm, based on estimating network delays and adapting application update rates, which achieves high service availability. Our results exhibit how placement can be optimized and how a load-balancing strategy c 

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5. End-to-End IoT Security Middleware for Cloud-Fog Communication

   https://doi.org/10.1109/CSCloud.2017.62  

   Mukherjee, Bidyut; Neupane, Roshan Lal; Calyam, Prasad (June 2017, 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud))

   IoT (Internet of Things) devices such as sensors have been actively used in 'fogs' to provide critical data during e.g., disaster response scenarios or in-home healthcare. Since IoT devices typically operate in resource-constrained computing environments at the network-edge, data transfer performance to the cloud as well as end-to-end security have to be robust and customizable. In this paper, we present the design and implementation of a middleware featuring "intermittent" and "flexible" end-to-end security for cloud-fog communications. Intermittent security copes with unreliable network connections, and flexibility is achieved through security configurations that are tailored to application needs. Our experiment results show how our middleware that leverages static pre-shared keys forms a promising solution for delivering light-weight, fast and resource-aware security for a variety of IoT-based applications. 

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