Sensornet protocols periodically broadcast beacons for neighborhood information advertisement, but beacon transmissions are costly when power-saving radio duty cycling mechanisms are used. We show that piggybacking multiple beacons in a single transmission significantly reduces transmission costs and argue that this shows the need for a new layer in the sensornet stack—an announcement layer—that coordinates beacons across upper layer protocols. An announcement layer piggybacks beacons and coordinates their transmission so that the total number of transmissions is reduced. With an announcement layer, new or mobile nodes can quickly gather announcement information from all neighbors and all protocols by issuing an announcement pull operation. Likewise, protocols can quickly disseminate new announcement information to all neighbors by issuing an announcement push operation. We have implemented an announcement layer in the Contiki operating system and three data collection and dissemination protocols on top of the announcement layer. We show that beacon coordination both improves protocol performance and reduces power consumption.

As sensor networks move towards general-purpose lowpower 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.

As sensor networks mature, there will be an increasing need for re-usable, dynamically taskable software systems that support multiple concurrent applications. In this paper, we consider the problem of energy management in such systems, taking Tenet as a case study. Our work considers energy management under three new constraints: dynamic multi-hop routing and tasking, multiple concurrent applications, and reliable end-toend data delivery. We present AEM, an energy management system that satisfies these constraints. AEM statically analyzes and infers the traffic profile for the application and accordingly tunes the duty-cycling protocol to provide the best trade-off in latency and data delivery performance. Furthermore, unlike other duty-cycling protocols with pre-computed or fixed transmission and reception time slots, AEM uses elastic schedules that allows it to adapt to dynamics while enabling bounded latency of event detection. Our experiments show that AEM achieves 1-3 % duty-cycles, while allowing concurrent applications to transmit 100 % of the sensor data in a multi-hop 40-node network testbed.

This paper proposes and assesses analytical tools for largescale monitoring applications with wireless sensor networks powered by energy-harvesting supplies. We introduce the concept of an energy budget, the amount of energy available to a sensor node for a given period of time. The presented tools can be utilized to realize distributed algorithms that determine a schedule to perform the monitoring task and the inherent communication. Scheduling is based on the energy budgets of the nodes or on latency requirements. In this context, we derive theoretical results for the energy consumption of the individual nodes plus the latency of eventreporting. These results are verified by simulations and a real testbed implementation. 1.

Abstract—This paper presents DISSense, an adaptive, ultralow-power communication protocol for wireless sensor networks. DISSense is specifically designed for long-term environmental monitoring applications and it provides for both data collection and data dissemination services. By automatically adapting the length of its active phases, DISSense can guarantee for both a very low duty cycle and reliable data delivery. We tested the performance of DISSense on both a testbed and on the TOSSIM simulation environment. Our experimental results show that a sensor network running DISSense can provide for average data delivery ratios above 98 % and at the same time achieve a lifetime of several years. Our TinyOS 2.1 implementation of DISSense is publicly available. I.

Abstract. This paper presents and evaluates ORiNoCo, a novel datacollection and event-reporting protocol for sensor networks. ORiNoCo is built upon the asynchronous duty-cycle protocol RI-MAC and breaks with the tradition of exchanging extensive neighborhood information, a cornerstone of many competing collection protocols and one of their major source of communication overhead and energy expenditure. The merit of this venture is an opportunistic, energy-efficient, latency-reducing, and self-stabilizing protocol. ORiNoCo comes at virtually no extra costs in terms of memory demand and communication overhead compared to RI-MAC. We derive theoretical boundaries for the improvements in radio efficiency, latency, and energy-consumption. ORiNoCo is verified with these findings via simulation and compared with CTP. ORiNoCo achieves lower energy-consumption while reducing end-to-end delays. 1

Abstract—Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99 % of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime—a remarkable result per se, but a far cry from the aforementioned 99 % data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary. I.

Abstract—Home area networks (HANs) promise to enable sophisticated home automation applications such as smart energy usage and assisted living. However, recent empirical study of HAN reliability in real-world residential environments revealed significant challenges to achieving reliable performance in the face of significant and variable interference from a multitude of coexisting wireless devices. We propose the Adaptive and Robust Channel Hopping (ARCH) protocol: a lightweight receiver-oriented protocol which handles the dynamics of residential environments by reactively channel hopping when channel conditions have degraded. ARCH has several key features. First, ARCH is an adaptive protocol that channelhops based on changes in channel quality observed in real time. Second, ARCH is a distributed protocol that selects channels on a per-link basis, due to the large link-to-link variations in channel quality observed under empirical study. Third, ARCH is designed to be robust and lightweight. ARCH uses a practical handshaking approach to handle channel desynchronization and an efficient sliding-window scheme that does not involve expensive calculations or modeling, and can be reasonably implemented on memory-constrained wireless sensor platforms. Fourth, ARCH introduces minimal communication overhead for applications where packet acknowledgements are already enabled. We evaluate our approach through real deployment in real-life apartments with residents ’ daily activity. Our results demonstrate that ARCH can reduce packet retransmissions by a median of 42.3 % compared to using a single, fixed wireless channel, and can enable up to a 2.2 × improvement in delivery rate on the most unreliable links in our experiment. Under a multi-hop routing scenario, ARCH reduced radio usage by 31.6 % on average, by reducing the ETX of each link by up to 83.6%. Due to ARCH’s lightweight reactive design, most links achieve this improvement in reliability with 10 or fewer channel hops per day. I.

Low-power wireless devices must keep their radio transceivers off as much as possible to reach a low power consumption, but must wake up often enough to be able to receive communication from their neighbors. This report describes the ContikiMAC radio duty cycling mechanism, the default radio duty cycling mechanism in Contiki 2.5, which uses a power efficient wake-up mechanism with a set of timing constraints to allow device to keep their transceivers off. With ContikiMAC, nodes can participate in network communication yet keep their radios turned off for roughly 99 % of the time. This report describes the ContikiMAC mechanism, measures the energy consumption of individual ContikiMAC operations, and evaluates the efficiency of the fast sleep and phase-lock optimizations. 1

We present Koala, a reliable data retrieval system designed to operate at permille (.1%) duty cycles, essential for long term environmental monitoring networks. Koala achieves these low duty cycles by letting the network’s nodes sleep most of the time and reviving them through an efficient wake-up strategy whenever the gateway performs a bulk data download. Unlike other systems which consume energy to maintain consistent network state (e.g. routes, sleep schedules, etc.) across the network’s nodes, Koala maintains no persistent routing state on the motes. Instead, a basestation calculates the network paths using reachability information collected by the motes. The Flexible Control Protocol (FCP), a protocol we developed, is then used to install this routing information on the network’s nodes. This paradigm of operation not only eliminates the overhead of maintaining routing state, but also significantly reduces the complexity of the networking code running on the motes. Results from simulation and an actual implementation on TinyOS 2 indicate that Koala can achieve very low duty cycles under a wide range of download and network sizes. 1.