Olaf Landsiedel

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 duty-cycled 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.

Bursty traffic over bursty links

Accurate estimation of link quality is the key to enable efficient routing in wireless sensor networks. Current link estimators focus mainly on identifying long-term stable links for routing. They leave out a potentially large set of intermediate links offering significant routing progress. Fine-grained analysis of link qualities reveals that such intermediate links are bursty, i.e., stable in the short term. In this paper, we use short-term estimation of wireless links to accurately identify short-term stable periods of transmission on bursty links. Our approach allows a routing protocol to forward packets over bursty links if they offer better routing progress than long-term stable links. We integrate a Short Term Link Estimator and its associated routing strategy with a standard routing protocol for sensor networks. Our evaluation reveals an average of 19% and a maximum of 42% reduction in the overall transmissions when routing over long-range bursty links. Our approach is not tied to any specific routing protocol and integrates seamlessly with existing routing protocols and link estimators.

KleeNet: discovering insidious interaction bugs in wireless sensor networks before deployment

Complex interactions and the distributed nature of wireless sensor networks make automated testing and debugging before deployment a necessity. A main challenge is to detect bugs that occur due to non-deterministic events, such as node reboots or packet duplicates. Often, these events have the potential to drive a sensor network and its applications into corner-case situations, exhibiting bugs that are hard to detect using existing testing and debugging techniques. In this paper, we present KleeNet, a debugging environment that effectively discovers such bugs before deployment. KleeNet executes unmodified sensor network applications on symbolic input and automatically injects non-deterministic failures. As a result, KleeNet generates distributed execution paths at high-coverage, including low-probability corner-case situations. As a case study, we integrated KleeNet into the Contiki OS and show its effectiveness by detecting four insidious bugs in the μIP TCP/IP protocol stack. One of these bugs is critical and lead to refusal of further connections.

Orchestra: Robust Mesh Networks Through Autonomously Scheduled TSCH

Time slotted operation is a well-proven approach to achieve highly reliable low-power networking through scheduling and channel hopping. It is, however, difficult to apply time slotting to dynamic networks as envisioned in the Internet of Things. Commonly, these applications do not have pre-defined periodic traffic patterns and nodes can be added or removed dynamically. This paper addresses the challenge of bringing TSCH (Time Slotted Channel Hopping MAC) to such dynamic networks. We focus on low-power IPv6 and RPL networks, and introduce Orchestra. In Orchestra, nodes autonomously compute their own, local schedules. They maintain multiple schedules, each allocated to a particular traffic plane (application, routing, MAC), and updated automatically as the topology evolves. Orchestra (re)computes local schedules without signaling overhead, and does not require any central or distributed scheduler. Instead, it relies on the existing network stack information to maintain the schedules. This scheme allows Orchestra to build non-deterministic networks while exploiting the robustness of TSCH. We demonstrate the practicality of Orchestra and quantify its benefits through extensive evaluation in two testbeds, on two hardware platforms. Orchestra reduces, or even eliminates, network contention. In long running experiments of up to 72~h we show that Orchestra achieves end-to-end delivery ratios of over 99.99%. Compared to RPL in asynchronous low-power listening networks, Orchestra improves reliability by two orders of magnitude, while achieving a similar latency-energy balance.

Chaos: versatile and efficient all-to-all data sharing and in-network processing at scale

An important building block for low-power wireless systems is to efficiently share and process data among all devices in a network. However, current approaches typically split such all-to-all interactions into sequential collection, processing, and dissemination phases, thus handling them inefficiently. We introduce Chaos, the first primitive that natively supports all-to-all data sharing in low-power wireless networks. Different from current approaches, Chaos embeds programmable in-network processing into a communication support based on synchronous transmissions. We show that this design enables a variety of common all-to-all interactions, including network-wide agreement and data aggregation. Results from three testbeds and simulations demonstrate that Chaos scales efficiently to networks consisting of hundreds of nodes, achieving severalfold improvements over LWB and CTP/Drip in radio duty cycle and latency with almost 100 % reliability across all scenarios we tested. For example, Chaos computes simple aggregates, such as the maximum, in a 100-node multi-hop network within less than 90 milliseconds.

Opportunistic Routing in Low Duty-Cycle Wireless Sensor Networks

Opportunistic routing is widely known to have substantially better performance than unicast routing in wireless networks with lossy links. However, wireless sensor networks are usually duty cycled, that is, they frequently enter sleep states to ensure long network lifetime. This renders existing opportunistic routing schemes impractical, as they assume that nodes are always awake and can overhear other transmissions. In this article we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. ORW uses a novel opportunistic routing metric, EDC, that reflects the expected number of duty-cycled wakeups that are required to successfully deliver a packet from source to destination. We devise distributed algorithms that find the EDC-optimal forwarding and demonstrate using analytical performance models and simulations that EDC-based opportunistic routing results in significantly reduced delay and improved energy efficiency compared to traditional unicast routing. In addition, we evaluate the performance of ORW in both simulations and testbed-based experiments. 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.

Towards Scalable Mobility in Distributed Hash Tables

For the use in the Internet domain, distributed hash tables (DHTs) have proven to be an efficient and scalable approach to distributed content storage and access. In this paper, we explore how DHTs and mobile ad-hoc networks (MANETs) fit together. We argue that both share key characteristics in terms of self organization, decentralization, redundancy requirements, and limited infrastructure. However, node mobility and the continually changing physical topology pose a special challenge to scalability and the design of a DHT for mobile ad-hoc networks. In this paper, we show that with some local knowledge we can build a scalable and mobile structured peer-to-peer network, called mobile hash table (MHT). Furthermore, we argue that with little global knowledge, such as a map of the city or whatever area the nodes move in, one can even further improve the scalability and reduce DHT maintenance overhead significantly, allowing MHT to scale up to several ten thousands of nodes

MobiSense: Power-efficient micro-mobility in wireless sensor networks

Emerging applications in industrial automation as well as tracking and monitoring applications of humans, objects or animals share common requirements: micro-mobility, high-throughput, and two-way end-to-end communications. In this paper we present MobiSense, a MAC layer and routing architecture for micro-mobility environments providing reliable and energy-efficient communication and low-latency handoffs. MobiSenses energy-efficiency and reliability comes from a set of carefully chosen design elements: rapid network information gathering, rapid network (re)admission and convergence, distributed network formation, and dynamic scheduling. Testbed evaluations show that a mobile sensor achieves: (i) reliability above 95% even in scenarios with high data rates of 6pps/node; (ii) low latency-handoffs typically below 1 second; (iii) a high aggregate system throughput of more than 95kbps; (iv) two-way communication without the need for flooding; and (v) communication at very low duty-cycles below 4% at 6pps/node.

Dynamic TinyOS: Modular and Transparent Incremental Code-Updates for Sensor Networks

Long-term deployments of sensor networks in physically inaccessible environments make remote re-programmability of sensor nodes a necessity. Ranging from full image replacement to virtual machines, a variety of mechanisms exist today to deploy new software or to fix bugs in deployed systems. However, TinyOS - the current state of the art sensor node operating system - is still limited to full image replacement as nodes execute a statically-linked system-image generated at compilation time. In this paper we introduce Dynamic TinyOS to enable the dynamic exchange of software components and thus incrementally update the operating system and its applications. The core idea is to preserve the modularity of TinyOS, i.e.~its componentization, which is lost during the normal compilation process, and enable runtime composition of TinyOS components on the sensor node. The proposed solution integrates seamlessly into the system architecture of TinyOS: It does not require any changes to the programming model of TinyOS and existing components can be reused transparently. Our evaluation shows that Dynamic TinyOS incurs a low performance overhead while keeping a smaller - upto one third - memory footprint than other comparable solutions.

When Timing Matters: Enabling Time Accurate and Scalable Simulation of Sensor Network Applications

The rising complexity of data processing algorithms in sensor networks combined with their severely limited computing power necessitates an in-depth understanding of their temporal behavior. However, today only cycle accurate emulation and test-beds provide a detailed and accurate insight into the temporal behavior of sensor networks. In this paper we introduce fine grained, automated instrumentation of simulation models with cycle counts derived from sensor nodes and application binaries to provide detailed timing information. The presented approach bridges the gap between scalable but abstracting simulation and cycle accurate emulation for sensor network evaluation. By mapping device-specific code with simulation models, we can derive the time and duration a certain code line takes to get executed on a sensor node. Hence, eliminating the need to use expensive instruction-level emulators with limited speed and restricted scalability. Furthermore, the proposed design is not bound to a specific hardware platform, a major advantage compared to existing emulators. Our evaluation shows that the proposed technique achieves a timing accuracy of 99% compared to emulation while adding only a small overhead. Concluding, it combines essential properties like accuracy, speed and scalability on a single simulation platform.