Disruption-tolerant networks (DTNs) have a wide range of applications, including emergencies where traditional communication infrastructure has been destroyed, remote rural deployments where communication infrastructure does not exist, and environmental monitoring in which animals are equipped with sensors and transmit data whenever they come into contact with a base station. Using the de-facto DTN protocol standard, Bundle Protocol version 7, nodes transmit data using Convergence Layer Protocols, which serve as abstractions for the underlying communication technology. In this article, we introduce QUICL , a novel convergence layer for disruption-tolerant networks. QUICL is built on the QUIC transport protocol, which offers advantages over TCP in a disruption-tolerant setting. In particular, it improves congestion control, supports multiplexing, ensures reliable transmission, effectively manages unstable networks, and encrypts traffic by default. Our implementation, already merged upstream, is based on the free and open-source DTN7-go -protocol suite and the QUIC-go -library. Our experimental evaluation shows that even in challenging situations such as bundle transmission over 63 hops with a packet loss of 30% on each hop, QUICL still delivers data where most other DTN software/convergence layer combinations fail to transmit any data.

Wireless sensor networks (WSNs) can automate data sensing tasks. To ensure redundancy and manage network connectivity issues, a sensing node stores a copy of the gathered data. Since this data may contain sensitive personal or business information, protecting privacy and preventing unauthorized access is crucial. We introduce the Write-only File System (WoFS), a novel encryption system for WSNs that secures data without user interaction, even if a sensor node is stolen. WoFS utilizes either symmetric encryption with volatile keys via a ratchet mechanism or asymmetric encryption. Asymmetric encryption, while slower, allows operation post-reboot, unlike the ratchet-based method. Our experiments show that WoFS achieves write speeds of 200 MB/s or higher, making it suitable for WSN applications. All developed software and artifacts are available under a permissive open-source license.

Fires are among the most dangerous and life-threatening emergency situations that might occur in residential settings. Therefore, smoke detectors are required in many rooms of a house in many European countries, such as the Netherlands, the UK, Germany, and Austria. We present a novel smart smoke detector that can be used for communication in emergency situations. In addition to playing a sound when it detects smoke, it provides (i) an acoustic message interface to call for help or leave a message, (ii) a mesh-based wireless communication interface to allow communication even if infrastructure is damaged, and (iii) an accelerometer to detect earthquakes.

Recent advances in low-cost microcontrollers have enabled innovative smart home applications. However, existing systems typically consist of single-purpose devices that only report sensed data to a controller. Given the potential for residential emergencies, we propose to integrate emergency detection systems into smart home environments. We present an ad-hoc distributed sensor network (DSN) designed to detect five common residential emergencies: fires, gas and water leakages, earthquakes, and intrusions. Our novel approach combines diverse sensors with a voting-based consensus algorithm among multiple nodes, improving accuracy and reliability over traditional alert systems. The consensus algorithm employs a majority rule with weighted votes, allowing adjustments for various scenarios. An experimental evaluation confirms our approach’s effectiveness in accurately detecting emergencies while demonstrating reliability in mitigating node failures, ensuring system longevity, and maintaining robust communication. Additionally, our approach significantly reduces power consumption compared to alternatives.

We present a novel multi-hop data dissemination protocol for wireless networks that minimizes the total energy consumption across an entire network by minimizing the transmission power at each hop. It is based on a game-theoretic model, constructs a spanning tree topology in a decentralized manner, and is usable in practice. We evaluate the protocol via simulation and a pratical implementation on a testbed of 75 Raspberry Pis, demonstrating that a total energy reduction of up to 90% can be achieved compared to a simple broadcast protocol.

Disruption-tolerant networks (DTNs) have a wide range of applications, such as emergencies where traditional communication infrastructure has been destroyed, remote rural deployments where communication infrastructure has never existed, and environmental monitoring where animals are equipped with sensors and transmit data whenever they come into contact with a base station. Using the de-facto DTN protocol standard, i.e., Bundle Protocol version 7 (BPv7), nodes transmit data via so-called Convergence Layer Protocols (CLPs) that act as general abstractions for the underlying communication technologies. BPv7 specifies MTCP and TCPCL as the two current CLPs for DTNs. However, both of them have different but equally undesirable shortcomings in terms of functionality, complexity, performance, and reliability. In this paper, we present QUICL, a novel CLP for DTNs. QUICL is based on the QUIC transport protocol and fully leverages QUIC's advantages over TCP-based transport protocols in a DTN environment. In particular, QUICL provides improved congestion control, allows multiplexing, ensures reliable transmission, effectively manages unreliable links, and uses encryption by default. Our prototypical implementation, already merged upstream, is based on the free and open-source DTN7-go protocol suite and the QUIC-go implementation. Our experimental evaluation shows that even with 30% packet loss, QUICL can still deliver data with minimal CPU overhead in scenarios where most other DTN/CLP combinations fail to transmit any data successfully.

A network for environmental monitoring typically requires a large number of sensors. If a longer service life is intended, it is essential that the deployed sensor systems can be upgraded without modifying hardware. Often, these networks rely on proprietary hardware/software components tailored to the desired functionality, but these could technically also be used for other applications. We present a demo of mechanism interception, a novel approach to unobtrusively add or modify the functionality of an existing networked system, in our case a TreeTalker, without touching any proprietary components. We demonstrate how a cloud infrastructure can be unobtrusively replaced by an edge infrastructure in a wireless sensor network. Our results indicate that mechanism interception is a compelling approach for our scenario to provide previously unavailable functionality without modifying existing components.

Networked systems and applications are often based on proprietary hardware/software components that manufacturers might not be willing to adapt or update if new requirements arise. We present mechanism interception, a novel approach to unobtrusively add or modify functionality to/of an existing networked system or application without touching any proprietary components. Behavioral changes are achieved by functionality-enhancing yet unobtrusive interceptors, i.e., components introduced between systems and their environments adding or updating mechanisms. We illustrate our approach by unobtrusively adding a vertical handover mechanism between Wi-Fi and LTE to a mobile end device without disconnecting TCP sessions. Our results indicate that mechanism interception is a compelling approach to achieve improved service quality and provide previously unavailable functionality.

Mobile edge computing based on cloudlets is an emerging paradigm to improve service quality by bringing computation and storage facilities closer to end users and reducing operating cost for infrastructure providers (IPs) and service providers (SPs). To maximize their individual benefits, IP and SP have to reach an agreement about placing and executing services on particular cloudlets. We show that a Nash Bargaining Solution (NBS) yields the optimal solution with respect to social cost and fairness if IP and SP have complete information about the parameters of their mutual cost functions. However, IP and SP might not be willing or able to share all information due to business secrets or technical limitations. Therefore, we present a novel iterative bargaining approach without complete mutual information to achieve substantial cost reductions for both IP and SP. Furthermore, we investigate how different degrees of information sharing impact social cost and fairness of the different approaches. Our evaluation based on the mobile augmented reality game Ingress shows that our approach achieves up to about 82% of the cost reduction that the NBS achieves and a cost reduction of up to 147% compared to traditional Take-it-or-Leave-it approaches, despite incomplete information.

We present a novel data-driven approach to perform smooth Wi-Fi/cellular handovers on smartphones. Our approach relies on data provided by multiple smartphone sensors (e.g., Wi-Fi RSSI, acceleration, compass, step counter, air pressure) to predict Wi-Fi connection loss and uses Multipath TCP to dynamically switch between different connectivity modes. We train a random forest classifier and an artificial neural network on real-world sensor data collected by five smartphone users over a period of three months. The trained models are executed on smartphones to reliably predict Wi-Fi connection loss 15 seconds ahead of time, with a precision of up to 0.97 and a recall of up to 0.98. Furthermore, we present results for four DASH video streaming experiments that run on a Nexus 5 smartphone using available Wi-Fi/cellular networks. The neural network predictions for Wi-Fi connection loss are used to establish MPTCP subflows on the cellular link. The experiments show that our approach provides seamless wireless connectivity, improves quality of experience of DASH video streaming, and requires less cellular data compared to handover approaches without Wi-Fi connection loss predictions.

Computation offloading is often used in mobile cloud, edge, and/or fog computing to cope with resource limitations of mobile devices in terms of computational power, storage, and energy. Computation offloading is particularly challenging in situations where network connectivity is intermittent or error-prone. In this paper, we present OPPLOAD, a novel framework for offloading computational workflows in opportunistic networks. The individual tasks forming a workflow can be assigned to particular remote execution platforms (workers) either preselected ahead of time or decided just in time where a matching worker will automatically be assigned for the next task. Tasks are only assigned to capable workers that announce their capabilities. Furthermore, tasks of a workflow can be executed on multiple workers that are automatically selected to balance the load. Our Python implementation of OPPLOAD is publicly available as open source software. The results of our experimental evaluation demonstrate the feasibility of our approach.

Augmented Reality (AR) is an approach to enrich the real world with additional information. It allows users to interact with virtual objects that are linked to locations in the real world. In the area of mobile computer games, AR is quite demanding for managing resources in communication networks, since in-game points of interest typically lead to high network loads, whereas network utilization is otherwise below the average. Predicting the number of in-game actions of mobile AR games can help to scale the game back-end reasonably without over- or under-provisioning of resources. Context information like weather or available Wi-Fi access points can play a key role in estimating or predicting the number of in-game actions. In this paper, we analyze a comprehensive dataset that contains players' actions of one of the most popular mobile AR games, Ingress. The dataset entails more than 23.9 million player actions over a period of 17 months, as well as additional contextual information. Our analysis shows a highly significant relationship between context factors and in-game actions, which explains large parts of users' behavior in terms of when, where and how often they play the game. By combining four different context factors (i.e., time, user, physical and computing context), our analysis can explain up to 84.44% of the variation in the number of in-game actions.