Low-power wireless networks must leverage radio duty cycling to reduce energy consumption, but duty cycling drastically increases the risk of radio collisions, resulting in power-expensive retransmissions or data loss. We present Strawman, a contention resolution mechanism designed for low-power duty-cycled networks that experience traffic bursts. Strawman efficiently resolves network contention, mitigates the hidden terminal problem, and has zero overhead unless activated to resolve data collisions. Our testbed experiments show that Strawman instantaneously provides increased network capacity when needed, allocates the available bandwidth evenly among contenders, and increases energy efficiency in multi-hop collection networks compared to the traditionally used random backoff.

Achieving low-power operation in wireless sensor networks with high data load or bursty traffic is challenging. The hidden terminal problem is aggravated with increased amounts of data in which traditional backoff-based contention resolution mechanisms fail or induce high latency and energy costs. We analyze and optimize Strawman, a receiver-initiated contention resolution mechanism that copes with hidden terminals. We propose new techniques to boost the performance of Strawman while keeping the resolution overhead small. We finally validate our improved mechanism via experiments.

As sensor networks move towards general-purpose low-power wireless networks, there is a need to support both traditional low-data rate traffic and high-throughput transfer. To attain high throughput, existing protocols monopolize the network resources and keep the radio on for all nodes involved in the transfer, leading to poor energy efficiency. This becomes progressively problematic in networks with packet loss, which inevitably occur in any real-world deployment. We present burst forwarding, a generic packet forwarding technique that combines low power consumption with high throughput for multi-purpose wireless networks. Burst forwarding uses radio duty cycling to maintain a low power consumption, recovers efficiently from interference, and inherently supports both single streams and cross-traffic. We experimentally evaluate our mechanism under heavy interference and compare it to PIP, a state-of-the-art sensornet bulk transfer protocol. Burst forwarding gracefully adapts radio duty cycle both to the level of interference and to traffic load, keeping a low and nearly constant energy cost per byte when carrying TCP traffic.

We demonstrate low-power IP-based sensor networks by showing a system that interacts with the sensor network using a RESTful web service interface. The sensor data is displayed with overlaid 3D graphics on top of a live camera feed, so-called augmented reality. The augmented reality application is built with off-the-shelf components with no sensor network-specific code. The IP-based sensor network runs the Contiki operating system.

The increased industrial interest in wireless sensor networks demands a shift from optimizing protocols for energy-efficient reporting of sporadic events to developing solutions for high-rate real-time data collection and dissemination. We study time-optimal convergecast under the communication constraints of commodity sensor network platforms, and propose a novel convergecast model in which packet copying between the microcontroller and the radio transceiver is separated from packet transmission, thereby improving channel utilization and system throughput. Based on this model, we establish tight lower bound on the number of time slots for convergecast in networks with tree routing topology, and present both centralized and distributed algorithms for generating time-optimal convergecast schedules. Our scheme is also memory-efficient as each node needs to buffer at most one packet at any time. We evaluate our scheme in simulation and on real hardware, and show that our scheme can achieve a throughput of 203 kbit/s (86.4% of the theoretical upper bound) and up to 86.24% improvement compared with traditional TDMA-based convergecast. With optimal routing tree and maximum MAC layer payload, convergecast in a network with 20 sensor nodes can be completed in only 100 ms.

High-coverage testing of sensornet applications is vital for pre-deployment bug cleansing, but has previously been difficult due to the limited set of available tools. We integrate the KleeNet symbolic execution engine with the COOJA network simulator to allow for straight-forward and intuitive high-coverage testing initiated from a simulation environment. A tight coupling of simulation and testing helps detect, narrow down, and fix complex interaction bugs in an early development phase. We demonstrate the seamless transition between COOJA simulation and KleeNet symbolic execution. Our framework enables future research in how high coverage testing tools could be used in cooperation with simulation tools.

Power consumption is one of the most important factors in wireless sensor network research, but most simulators do not provide support for visualizing the power consumption of an entire sensor network. This makes it hard to develop, debug, and understand mechanisms and protocols based on power-saving mechanisms. We present Cooja TimeLine, an extension to Contiki's Cooja network simulator, that visualizes radio traffic and radio usage of sensor networks. Cooja TimeLine makes is possible to visually see the behavior of low-power protocols and mechanisms thereby increasing the understanding of the behavior of sensor networks. We see this as an important tool for the field moving forward.

Low-power networked devices, such as sensors and actuators, are becoming a vital part of our everyday infrastructure. Being networked, the continued development of these systems needs involvement from the networking community. We present a framework for simulation, experimentation, and evaluation of routing mechanisms for low-power IPv6 networking. The framework provides a detailed simulation environment for low-power routing mechanisms, and allows the system to be directly uploaded to a physical testbed for experimental measurements.

Power consumption is of utmost concern in sensor networks. Researchers have several ways of measuring the power consumption of a complete sensor network, but they are typically either impractical or inaccurate. To meet the need for practical and scalable measurement of power consumption of sensor networks, we have developed a cycle-accurate simulator, called COOJA/MSPsim, that enables live power estimation of systems running on MSP430 processors. This demonstration shows the ease of use and the power measurement accuracy of COOJA/MSPsim. The demo setup consists of a small sensor network and a laptop. Beside gathering software-based power measurements from the motes, the laptop runs COOJA/MSPsim to simulate the same network.We visualize the power consumption of both the simulated and the real sensor network, and show that the simulator produces matching results.

Most of the existing sensor network deployments are convergecast data collection applications that transmit data from multiple sources to a sink. In this paper, we present CoReDac, a self-organizing, collision-free convergecast protocol. In contrast to previous solutions, CoReDac consists not only of a data collection phase but also an efficient and collision-free command phase. This way, the protocol can be used below the application layer for wireless versions of building automation protocols such as BACnet that require both efficient response and command phases. We present experiments that show that CoReDac works as expected and demonstrate its energy-efficiency for low duty cycle command-response applications by comparing it to X-MAC.

Building automation systems (BAS) are used to control and improve indoor building climate at reduced costs. By integrating BAS with wireless sensor networks, the need for cabling can be removed, and both installation and operational costs significantly reduced. Furthermore, temporary BAS installations are made possible. By implementing and evaluating the open BAS standard BACnet on resource-constrained sensor nodes we show that integrating existing standard BAS protocols with wireless sensor networks is feasible.

In this paper we present the design and implemen tation of a small-scale marine sensor network. The network monitors the temperature in the Baltic Sea on different heights from the water surface down to the bottom. Unlike many other wireless sensor networks, this network contains both a wired and a wireless part. One of the major challenges is that the network is hard to access after its deployment and hence both hard- and software must be robust and reliable. We also present the design of an advanced buoy system featuring a diving unit that achieves a better vertical resolution and discuss remaining challenges of sensor networking in aquatic environments.

As sensor networks move towards increasing heterogeneity, the number of link layers, MAC protocols, and underlying transportation mechanisms increases. System developers must adapt their applications and systems to accommodate a wide range of underlying protocols and mechanisms. However, existing communication architectures for sensor networks are not designed for this heterogeneity and therefore the system developer must redevelop their systems for each underlying communication protocol or mechanism. To remedy this situation, we present a communication architecture that adapts to a wide range of underlying communication mechanisms, from the MAC layer to the transport layer, without requiring any changes to applications or protocols. We show that the architecture is expressive enough to accommodate typical sensor network protocols. Measurements show that the increase in execution time over a non-adaptive architecture is small.

Being able to estimate the energy consumption of sensor nodes is essential both for evaluating existing sensor network mechanisms and for constructing new energy-aware mechanisms. We present a software-based mechanism for estimating the energy consumption of sensor node at run-time. Unlike previous energy estimation mechanisms, our mechanism does not require any additional hardware components or add-ons.

Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach