In recent years, digital services have experienced significant growth, exemplified by platforms like Netflix achieving unprecedented revenue levels. Some of these services employ subscription models, with certain content requiring additional payments or offering third-party products. To ensure the widespread availability of diverse digital services anytime and anywhere, providers must have control over content accessibility. To address the multifaceted challenges in this domain, one promising solution is the adoption of attribute-based encryption (ABE). Over the years, various approaches have been proposed in the literature, offering a wide range of features. In a prior study [18], we assessed the security of one of these proposed approaches and identified one that did not meet its promised security standards. In this research we focuses on conducting a security analysis for another ABE scheme to pinpoint its shortcomings and emphasize the critical importance of evaluating the safety and effectiveness of newly proposed schemes. Specifically, we uncover an attack vector within this ABE scheme, which enables malicious users to decrypt content without the required permissions or attributes. Furthermore, we propose a solution to rectify this identified vulnerability.

Edge computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness and mobility support are the major contributors to edge-enabled smart systems. Such systems require handling new situations and change on the fly and ensuring the quality of service while only having access to constrained computation and communication resources and operating in mobile, dynamic and ever-changing environments. Hence, adaptation and self-organisation are crucial for such systems to maintain their performance, and operability while accommodating new changes in their environment. This article reviews the current literature in the field of adaptive edge computing systems. We use a widely accepted taxonomy, which describes the important aspects of adaptive behaviour implementation in computing systems. This taxonomy discusses aspects such as adaptation reasons, the various levels an adaptation strategy can be implemented, the time of reaction to a change, categories of adaptation technique and control of the adaptive behaviour. In this article, we discuss how these aspects are addressed in the literature and identify the open research challenges and future direction in adaptive edge computing systems. The results of our analysis show that most of the identified approaches target adaptation at the application level, and only a few focus on middleware, communication infrastructure and context. Adaptations that are required to address the changes in the context, changes caused by users or in the system itself are also less explored. Furthermore, most of the literature has opted for reactive adaptation, although proactive adaptation is essential to maintain the edge computing systems’ performance and interoperability by anticipating the required adaptations on the fly. Additionally, most approaches apply a centralised adaptation control, which does not perfectly fit the mostly decentralised/distributed edge computing settings.

Abstract In warehouses, order picking is known to be the most labor-intensive and costly task in which the employees account for a large part of the warehouse performance. Hence, many approaches exist, that optimize the order picking process based on diverse economic criteria. However, most of these approaches focus on a single economic objective at once and disregard ergonomic criteria in their optimization. Further, the influence of the placement of the items to be picked is underestimated and accordingly, too little attention is paid to the interdependence of these two problems. In this work, we aim at optimizing the storage assignment and the order picking problem within mezzanine warehouse with regards to their reciprocal influence. We propose a customized version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the storage assignment problem as well as an Ant Colony Optimization (ACO) algorithm for optimizing the order picking problem. Both algorithms incorporate multiple economic and ergonomic constraints simultaneously. Furthermore, the algorithms incorporate knowledge about the interdependence between both problems, aiming to improve the overall warehouse performance. Our evaluation results show that our proposed algorithms return better storage assignments and order pick routes compared to commonly used techniques for the following quality indicators for comparing Pareto fronts: Coverage, Generational Distance, Euclidian Distance, Pareto Front Size, and Inverted Generational Distance. Additionally, the evaluation regarding the interaction of both algorithms shows a better performance when combining both proposed algorithms.

In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. To operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this article, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.

Abstract In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

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.

In the next few years, both the number of IoT devices and the performance of quantum computers will increase. Both technologies pose a challenge to our current crypto-strategies. Therefore, post-quantum n-to-n communication encryption is a crucial field of research. Here, the development of new schemes and the analysis, and comparison of existing schemes is necessary. However, current work only investigates the performance of post-quantum schemes only for 1-to-1 communication. Therefore, in this paper, we analyze existing post-quantum schemes concerning n-to-n communication and compare them with pre-quantum schemes. Our results show that the pre-quantum schemes perform better regarding computation times than the post-quantum schemes, but the differences are sometimes only marginal. However, these marginal differences in computation times lead to the lower energy efficiency of the post-quantum schemes. In terms of features, there is no difference between both scheme classes. We show that the post-quantum schemes require unicast, whereas some pre-quantum schemes also support broadcast. Deciding whether to use pre- or post-quantum schemes for n-to-n encryption in IoT use cases depends on (i) whether energy efficiency is essential – e.g., in case of limited power supply – and (ii) whether unicast or broadcast is available.

Quantum Computing threatens security of today’s systems. Confidence in today’s security technologies largely relies on Public Key Cryptography (PKC), which depends on computational difficulty of mathematical problems that cannot be solved efficiently using any technology available today. This will, however, change once a sufficiently capable quantum computer will become available. Similarly, security of symmetric crypto algorithms will also be substantially weakened. Current progress in research proves that Quantum Computing is no longer science fiction. Hence, research and development of post-quantum cryptographic algorithms that can withstand attacks in Quantum Computing era are of paramount importance. This paper complements existing research in this domain with a performance analysis of a post-quantum cryptosystem capable of encrypting and decrypting messages either bit-wise or string-wise. Specifically, we describe a workflow for implementing the scheme, design a reproducible hardware performance evaluation testbed for an IoT and an online shopping scenario, define performance metrics, and perform performance evaluation case studies. Our performance analysis shows that bit-wise encryption and decryption and the corresponding key generation fits resource-constrained IoT microcontrollers as well as on average laptops. The encryption and decryption of a bit each take less than 30 ms and the key generation less than 300 ms.

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.

The interconnectivity of devices on the Internet of Things (IoT) provides many new and smart applications. However, the integration of many devices - especially by inexperienced users - might introduce several security threats. Further, several often used communication protocols in the IoT domain are not out-of-the-boxsecured. On the other hand, security inherently introduces overhead, resulting in a decrease in performance. The Message QueuingTelemetry Transport (MQTT) protocol is a popular communication protocol for IoT applications - for example, in Industry 4.0, railways, automotive, or smart homes. This paper analyzes the influence on performance when using MQTT with TLS in terms of throughput, connection build-up times, and energy efficiency using a reproducible testbed based on a standard off-the-shelf microcontroller. The results indicate that the impact of TLS on performance across all QoS levels depends on (i) the network situation and (ii) the connection reestablishment frequency. Thus, a negative influence of TLS on the performance is noticeable only in deteriorated networksituations or at a high connection reestablishment frequency.

The number of devices connected to the Internet of Things (IoT) is continuously increasing to several billion nowadays. As those devices often share sensitive data, encryption of those data is an important issue. The pure volume of data and the complexity of communication patterns increases, and, accordingly, the importanceof group encryption is recently gaining more importance. Still, the choice of the best-suited group encryption scheme for a specific application is complicated. Benchmarks can support this choice. However, while literature distinguishes three categories for theschemes (central, decentral, and hybrid), a one-fits-all benchmark seems challenging to achieve. In this paper, we go the first step towards a structured benchmark for group encryption schemes by presenting a benchmark for centralized group encryption schemes in an IoT scenario. To this end, our benchmark includes a descriptionof workloads, a baseline scheme, a measurement setup, and metrics while also considering the requirements and features of centralized group encryption schemes.

The technical maturity of autonomous driving enables the discussion of beneficial use cases to leverage its full potential. In this paper, we target one such use case: Platooning is the efficient convoying of vehicles by making use of self-driving capabilities and inter-vehicle communication. Many advantages arise from grouping vehicles in platoons with a small inter-vehicle distance, such as energy savings, congestion reduction, and safety improvements. However, due to the diversity of involved stakeholders, numerous objectives have to be balanced to leverage the full potential of platooning. Furthermore, these objectives also depend on various factors that influence their optimization. The vast majority of existent literature only targets a subset of related objectives and underlying factors. This paper provides an overview which categorizes objectives and influencing factors. Additionally, metrics for the evaluation of objective attainment are proposed.

New emerging technologies, such as autonomous driving, intelligent buildings, and smart cities, are promising to revolutionize user experience and offer new services. The world has to undergo large scale deployment of billions of things - cost-efficient intelligent sensors that will be interconnected into extensive networks and will collect and supply data to intelligent algorithms - to make it happen. To date, however, it is challenging to secure such an infrastructure for many-fold reasons, such as resource constraints of things, large scale deployment, many-to-many communication patterns, and dynamically changing communication groups. All these factors rule out most of the state-of-the-art encryption and key-management techniques. Group encryption algorithms are well-suitable for many-to-many communication patterns typical for IoT networks, and many of them can deal with dynamic groups. There are, however, very few constructions that could potentially fulfill the computational and storage constraints of IoT devices while providing sufficient scalability for large networks. The promising candidates, such as construction by Nishat et al. [1], were not evaluated using IoT platforms and under constraints typical for IoT networks. In this paper, we aim to fill this gap and present the evaluation of a state-of-the-art group-oriented encryption scheme by Nishat et al. to identify its applicability to IoT systems. In detail, we provide a measurement workflow, a revised version of the approach, and describe a reproducible hardware testbed. Using this evaluation environment, we analyze the performance of the encryption scheme in a typical IoT scenario from a group member perspective. The results show that all calculation times can be assumed to be constant and are always below 2 seconds. The memory requirement for permanent parameters can also be considered to be constant and are below 8.5 kbit in each case. However, the information that has to be stored temporarily for group updates has turned out to be the bottleneck of the scheme, since their memory requirements increase linearly with the group size.

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.

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.

Scheduling in distributed computing systems is the process of allocating resources to a computational task. The complexity of this allocation process increases with the amount of criteria that are considered for the scheduling decision. Pervasive computing systems show a high degree of heterogeneity and dynamism. The constant joining and leaving of devices makes the system error-prone and less predictable. The involved devices differ in various properties that we subsume as their context. We argue, that these context dimensions can be used to implement fault-avoidant scheduling strategies. In this paper, we introduce the concept of context-aware scheduling for pervasive computing systems. The schedulers in these systems consider multiple context dimensions to avoid failing resource providers. We discuss relevant context dimensions, develop context-aware scheduling strategies and implement them into an existing distributed computing system. We show how to monitor the context dimensions and evaluate the fault-avoidant scheduling strategies in a large-scale simulation.

In today's world, smart devices - such as laptops, smartphones, or smart watches - may substitute the need for physical presence of people. By offering a virtual representation, these devices support social interactions, e.g., telecommuting, remote studying through e-learning, or long-distance relationships. With audio and video streaming platforms like Skype, Apple FaceTime, or Google Hangouts it is possible to stay in contact with friends and colleagues. However, these platforms only allow communication between two peers or at most in very small groups. Additionally, they are very inflexible regarding content sharing and support only limited, defined use cases. In this paper, we present an extension to our pervasive display service for a remote virtual environment. This allows an immersive participation at remote activities in larger groups, while retaining the flexibility regarding joining devices of a pervasive middleware. Our contributions are threefold. First, we present a reusable design for integrating an interactive display service into virtual environments. Second, we implement our approach using the BASE middleware and the Unity 3D engine. Third, we evaluate our approach in a smart meeting room scenario.

Applications in intelligent environments are context-aware and context-altering in order to support users in their everyday tasks. In multi-user environments with shared context, interferences are likely to occur. An interference is an application-induced context that forces other applications to react. In our COMITY project, we developed interference detection and resolution algorithms. However, especially the constraint satisfaction-based resolution algorithm is computationally expensive. It requires a full-fledged machine in order to achieve runtimes suitable for interactive systems. In spontaneously formed smart peer groups, such infrastructure is not given. In this paper, we present an approach for detecting and resolving interferences in smart peer groups. The approach is inspired by two coordination mechanisms from nature. We map the problems of interference detection and resolution to the rules of the flocking mechanism, show how to implement a flocking-based interference management, and present a local inhibition-based leader election for smart peer groups in order to elect the coordinating entity. Finally, we show the feasibility of our approach by evaluating our prototype.

Pervasive applications are designed to support users in their daily lives. In order to provide their services, these applications interact with the environment, i.e. their context. They either adapt themselves as a reaction to context changes, or adapt the context via actuators according to their needs. If multiple applications are executed in the same context, interferences are likely to occur. In this paper, we present COMITY-a framework for interference management in multi-platform pervasive systems. Based on contracts specifying an application's interaction with the context, the framework automatically detects interferences and resolves them through a coordinated application adaptation. We analyze the problem of interference resolution, discuss respective algorithms and extensively evaluate our prototype.