Poster Abstract: Opportunistic RPL

Sensor nodes constituting Wireless Sensor Networks (WSN) are often battery- operated and have limited resources. To save energy, nodes sleep most of the time, and wake up periodically to handle communication. Such radio duty cycling poses a basic trade-off between energy and latency. In previous work, we have shown that opportunistic routing is an efficient way to achieve low-latency yet energy efficient data collection in WSN (ORW [3]). In this paper, we extend this approach to the context of low-power IP networks, where nodes need to be addressed individually and where traffic patterns are irregular. We present ORPL, an opportunistic extension of RPL, the stan- dard, state-of-the-art routing protocol for low-power IP networks. We discuss our preliminary results obtained with Contiki in a 137-node testbed

Let the Tree Bloom: Scalable Opportunistic Routing with ORPL

Routing in battery-operated wireless networks is challenging, posing a tradeoff between energy and latency. Previous work has shown that opportunistic routing can achieve low-latency data collection in duty-cycled networks. However, applications are now considered where nodes are not only periodic data sources, but rather addressable end points generating traffic with arbitrary patterns. We present ORPL, an opportunistic routing protocol that supports any-to-any, on-demand traffic. ORPL builds upon RPL, the standard protocol for low-power IPv6 networks. By combining RPL's tree-like topology with opportunistic routing, ORPL forwards data to any destination based on the mere knowledge of the nodes' sub-tree. We use bitmaps and Bloom filters to represent and propagate this information in a space-efficient way, making ORPL scale to large networks of addressable nodes. Our results in a 135-node testbed show that ORPL outperforms a number of state-of-the-art solutions including RPL and CTP, conciliating a sub-second latency and a sub-percent duty cycle. ORPL also increases robustness and scalability, addressing the whole network reliably through a 64-byte Bloom filter, where RPL needs kilobytes of routing tables for the same task

Towards Energy Efficient, High-speed Communication in WSNs

Traditionally, protocols in wireless sensor networks focus on low-power operation with low data-rates. In addition, a small set of protocols provides high throughput communication. With sensor networks developing into general propose networks, we argue that protocols need to provide both: low data-rates at high energy-efficiency and, additionally, a high throughput mode. This is essential, for example, to quickly collect large amounts of raw-data from a sensor. This paper presents a set of practical extensions to the low-power, low delay routing protocol ORW. We introduce the capability to handle multiple, concurrent bulk-transfers in dynamic application scenarios. Overall, our extensions allow ORW to reach an almost 500% increase in the throughput with less than a 25% increase of the power consumption during a bulk transfer. Thus, we show that instead of developing a new protocol from scratch, we can carefully enhance an existing, energy-efficient protocol with high-throughput extensions. Both the energy-efficient low data-rate mode and the high throughput extensions transparently coexist inside a single protocol

Opportunistic Routing and Synchronous Transmissions Meet TSCH

Low-power wireless networking commonly uses either Time-Slotted Channel Hopping (TSCH), synchronous transmissions, or opportunistic routing. All three of these different, orthogonal approaches strive for efficient and reliable communication but follow different trajectories. With this paper, we combine these concepts into one protocol: AUTOBAHN.AUTOBAHN merges TSCH scheduling with opportunistically routed, synchronous transmissions. This opens the possibility to create long-term stable schedules overcoming local interference. We prove the stability of schedules over several days in our experimental evaluation. Moreover, AUTOBAHN outperforms the autonomous scheduler Orchestra under interference in terms of reliability by 13.9 percentage points and in terms of latency by a factor of 9 under a minor duty cycle increase of 2.1 percentage points

Whisper: Fast Flooding for Low-Power Wireless Networks

This paper presents Whisper, a fast and reliable protocol to flood small amounts of data into a multi-hop network. Whisper relies on three main cornerstones. First, it embeds the message to be flooded into a signaling packet that is composed of multiple packlets. A packlet is a portion of the message payload that mimics the structure of an actual packet. A node must intercept only one of the packlets to know that there is an ongoing transmission. Second, Whisper exploits the structure of the signaling packet to reduce idle listening and, thus, to reduce the radio-on time of the nodes. Third, it relies on synchronous transmissions to quickly flood the signaling packet through the network. Our evaluation on the Flocklab testbed shows that Whisper achieves comparable reliability but significantly lower radio-on time than Glossy -- a state-of-the-art flooding algorithm. Specifically, Whisper can disseminate data in FlockLab twice as fast as Glossy with no loss in reliability. Further, Whisper spends 30% less time in channel sampling compared to Glossy when no data traffic must be disseminated

Low Power, Low Delay: Opportunistic Routing meets Duty Cycling

Traditionally, routing in wireless sensor networks consists of two steps: First, the routing protocol selects a next hop, and, second, the MAC protocol waits for the intended destination to wake up and receive the data. This design makes it difficult to adapt to link dynamics and introduces delays while waiting for the next hop to wake up. In this paper we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. In a dutycycled setting, packets are addressed to sets of potential receivers and forwarded by the neighbor that wakes up first and successfully receives the packet. This reduces delay and energy consumption by utilizing all neighbors as potential forwarders. Furthermore, this increases resilience to wireless link dynamics by exploiting spatial diversity. Our results show that ORW reduces radio duty-cycles on average by 50% (up to 90% on individual nodes) and delays by 30% to 90% when compared to the state of the art

Dimmer: Self-Adaptive Network-Wide Flooding with Reinforcement Learning

The last decade saw an emergence of Synchronous Transmissions (ST) as an effective communication paradigm in low-power wireless networks. Numerous ST protocols provide high reliability and energy efficiency in normal wireless conditions, for a large variety of traffic requirements. Recently, with the EWSN dependability competitions, the community pushed ST to harsher and highly-interfered environments, improving upon classical ST protocols through the use of custom rules, hand-tailored parameters, and additional retransmissions. The results are sophisticated protocols, that require prior expert knowledge and extensive testing, often tuned for a specific deployment and envisioned scenario. In this paper, we explore how ST protocols can benefit from self-adaptivity; a self-adaptive ST protocol selects itself its best parameters to (1) tackle external environment dynamics and (2) adapt to its topology over time. We introduce Dimmer as a self-adaptive ST protocol. Dimmer builds on LWB and uses Reinforcement Learning to tune its parameters and match the current properties of the wireless medium. By learning how to behave from an unlabeled dataset, Dimmer adapts to different interference types and patterns, and is able to tackle previously unseen interference. With Dimmer, we explore how to efficiently design AI-based systems for constrained devices, and outline the benefits and downfalls of AI-based low-power networking. We evaluate our protocol on two deployments of resource-constrained nodes achieving 95.8% reliability against strong, unknown WiFi interference. Our results outperform baselines such as non-adaptive ST protocols (27%) and PID controllers, and show a performance close to hand-crafted and more sophisticated solutions, such as Crystal (99%)

Poster: Trace yourself-it could be easy

Contact tracing helps to predict and prevent the spread of viruses. This work proposes Tracey for decentralized, privacy-preserving tracing. Unlike automated tracing solutions that operate in the background, such as the widespread governmental Corona Tracing Apps, our system builds on manual contact exchanges to ensure reliable contact tracing even for groups and venues. The devices share secrets that allow anonymous notifications using the health authorities’ trusted database. This work illustrates the concept, provides initial security analysis, first results, and gives an outlook on possible extensions

eAFH: Informed Exploration for Adaptive Frequency Hopping in Bluetooth Low Energy

With more than 4 billion devices produced in 2020, Bluetooth and Bluetooth Low Energy (BLE) have become the dominant solutions for short-range wireless communication in IoT. BLE mitigates interference via Adaptive Frequency Hopping (AFH), spreading communication over the entire spectrum. However, the ever-growing number of BLE devices and WiFi traffic in the already crowded 2.4 GHz band lead to situations where the quality of BLE connections dynamically changes with nearby wireless traffic, location, and time of day. These dynamic environments demand new approaches for channel management in AFH, by both dynamically excluding frequencies suffering from localized interference and adaptively re-including channels, thus providing sufficient channel diversity to survive the rise of new interference.We introduce eAFH, a new channel-management approach in BLE with a strong focus on efficient channel re-inclusion. eAFH introduces informed exploration as a driver for inclusion: using only past measurements, eAFH assesses which frequencies we are most likely to benefit from re-inclusion into the hopping sequence. As a result, eAFH adapts in dynamic scenarios where interference varies over time. We show that eAFH achieves 98-99.5% link layer reliability in the presence of dynamic WiFi interference with 1% control overhead and 40% higher channel diversity than state-of-the-art approaches

Competition: Centrally Scheduled Low-Power Wireless Networking for Dependable Data Collection

For low-power wireless networks, it is important to survive interference to be usable for Industrial Internet-of-Things (IIoT) applications. Distributed flooding protocols like Glossy or Chaos have shown that they can meet the expectations of surviving interference and node failures. However, non-distributed, centralized schedulers are favorable for IIoT but are not used yet in these environments. In this paper, we explore the use of centralized schedulers for low-power wireless networks to achieve robustness in data collection applications