In the recent past, platooning evolved into an attractive cooperative driving technology, broadly discussed in research and practice. Vehicles in platoons use cooperative adaptive cruise control to drive at close distances to each other. Platooning (i) increases the capacity of the street by a factor of 2; (ii) reduces the fuel consumption and emissions by up to 20%; and (iii) has social implications as it increases driver comfort and safety. As platooning research progresses, platooning coordination becomes a major research focus. The coordination of platoons, including the assignment of vehicles to platoons, the management of inter- and intra-platoon interactions, and the coordination of interactions with other vehicles is an important step towards an effective usage of platooning in practice. Based on a literature review of 1,600 papers, this survey provides an overview of state of the art in platooning coordination research for both cars and trucks. In this paper, we present a novel taxonomy for platooning coordination and classify existing approaches. We use the results of the literature review to discuss challenges and outline avenues for future work such as multi-objectiveness and individualisation.

End users can mutually share their computing resources in ad-hoc computing environments with code offloading. This augments the computational power of resource-constrained mobile devices and enables interactive user-facing applications that would otherwise exceed single device capabilities. However, ad-hoc computing comes along with new challenges such as heterogeneity and unreliability of devices. Resource consumers have to make task scheduling decisions without relying on a centralized scheduler to facilitate sub-second response times in environments with communication latencies that are in the order of the task execution times. In this paper, we present a decentralized low-latency task scheduling approach that minimizes job execution times in heterogeneous ad-hoc environments. We propose two decentralized task scheduling algorithms that select powerful computing resources for parallel task execution while avoiding delays that arise from congested devices. We provide an analytical model of the performance of these algorithms before conducting an extensive evaluation based on real-world applications and a realistic computing infrastructure. Our results show that decentralized scheduling can dynamically adapt to varying system load and outperform a central scheduler in both task and job execution times, which enables low-latency task offloading in ad-hoc environments.

Code offloading enables resource-constrained devices to leverage idle computing power of remote resources. In addition to performance gains, offloading helps to reduce energy consumption of mobile devices, which is a key challenge in pervasive computing research and industry. In today's distributed computing systems, the decision whether to execute a task locally or remotely for minimal energy usage is non-trivial. Uncertainty about the task complexity and the result data size require a careful offloading decision. In this paper, we present Voltaire- a novel scheduler for sophisticated energy-aware code offloading decisions. Voltaire applies machine learning methods on crowd-sourced data about past executions to accurately predict the complexity and the result data size of an upcoming task. Combining these predictions with device-specific energy profiles and context knowledge allows Voltaire to estimate the energy consumption on the mobile device. Thus, Voltaire makes well-informed offloading decisions and carefully selects local or remote execution based on the expected energy consumption. We integrate Voltaire into the Tasklet distributed computing system and perform extensive experiments in a real-world testbed. Our results with three real-world applications show that Voltaire reduces the energy usage of task executions by 12.5% compared to a baseline scheduler.

Trends such as the Internet of Things lead to a growing number of networked devices and to a variety of communication systems. Adding self-adaptive capabilities to these communication systems is one approach to reducing administrative effort and coping with changing execution contexts. Existing frameworks can help reducing development effort but are neither tailored toward the use in communication systems nor easily usable without knowledge in self-adaptive systems development. Accordingly, in previous work, we proposed REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. REACT addresses heterogeneity and distribution aspects of such systems and reduces development effort. In this article, we propose REACT-ION—an extension of REACT for situation awareness. REACT-ION offers a context management module that is able to acquire, store, disseminate, and reason on context data. The context management module is the basis for (i) proactive adaptation with REACT-ION and (ii) self-improvement of the underlying feedback loop. REACT-ION can be used to optimize adaptation decisions at runtime based on the current situation. Therefore, it can cope with uncertainty and situations that were not foreseeable at design time. We show and evaluate in two case studies how REACT-ION’s situation awareness enables proactive adaptation and self-improvement.

Computationally intensive tasks of IoT applications can be offloaded to powerful devices in the edge. Code offloading reduces energy consumption and increases performance. However, applications that use face recognition, machine learning, or image rendering, rely on large amounts of data. The transfer of this data leads to latencies which contradicts the responsiveness required by many pervasive applications. As a solution, decoupling the data from the tasks allows to apply new scheduling strategies that place data on remote devices before the actual task execution. Grid computing approaches use this technique effectively, however, edge computing introduces further challenges such as device fluctuation and heterogeneity.In this paper, we propose a data management approach for edge computing environments that decouples data placement from task scheduling. We present a multi-level scheduler, which places data on resource providers in the system considering multiple context dimensions. The scheduler allocates tasks according to the current context and observes the state during runtime. If required, the system adjusts the number of data copies to optimize the trade-off between execution latencies and data management overhead. The paper has three contributions: (1) a context-aware multi-level scheduler, (2) the integration of four data placement, three task scheduling, and three runtime adaptation algorithms, (3) an evaluation in a real-world testbed.

Today's modern computing landscape consists of a huge amount of heterogeneous devices, including powerful, stable desktop computers as well as lightweight, unreliable mobile edge devices. This heterogeneity in terms of computation power and reliability increases the complexity for fault tolerance in distributed computing systems. When tasks are offloaded, slow resource providers easily become the bottleneck of a parallel computation. Further, unstable edge devices can leave the system spontaneously, discontinue remote tasks executions, and therefore lose the computation progress. These two effects increase the response time for remote task executions. In this paper, we introduce two mechanisms to avoid delayed or lost task executions caused by edge devices. This paper has five contributions. First, we define a failure model and identify the parameters that determine the magnitude of delays caused by faults and performance bottlenecks. Second, we present reactive and proactive task migration to handle system leaves. Third, we show how computational bottlenecks can be avoided by two-dimensional context-aware task partitioning. Fourth, we integrate these two solutions into an existing heterogeneous distributed computing system. Fifth, we run an evaluation on a real- world testbed to show the benefits of the solutions in practice. The evaluation shows, that we can improve systems with device fluctuation and heterogeneity by up to 39% and 53% respectively.

Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. In this work, we propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Therefore, we combine sensor data of four publicly available datasets, covering about 100 subjects, 5 devices, and 3 sensor placements. In a comprehensive evaluation, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor's position at runtime.