https://www.science.smith.edu/~jcardell/Courses/EGR328/Readings/WSNSurvey2.pdf

Wireless sensor network survey

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

/pdf/energy-conservation-in-wireless-sensor-networks-a-survey-1bl7d2obnm.pdf

Energy conservation in wireless sensor networks: A survey

Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Energy Harvesting Sensor Nodes: Survey and Implications

This paper presents and evaluates two principles for wireless routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle algorithm to routing control traffic reduces route repair latency and sends fewer beacons.We evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol. We use 12 different testbeds ranging in size from 20--310 nodes, comprising seven platforms, and six different link layers, on both interference-free and interference-prone channels. In all cases, CTP Noe delivers > 90% of packets. Many experiments achieve 99.9%. Compared to standard beaconing, CTP Noe sends 73% fewer beacons while reducing topology repair latency by 99.8%. Finally, when using low-power link layers, CTP Noe has duty cycles of 3% while supporting aggregate loads of 30 packets/minute.

/pdf/collection-tree-protocol-selhhlblmq.pdf

Collection tree protocol

Augmenting heavy and power-hungry data collection equipment with lighten smaller wireless sensor network nodes leads to faster, larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible, allowing scientific studies that aren't feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.

Deploying a wireless sensor network on an active volcano

As wireless sensor networks have emerged as a exciting new area of research in computer science, many of the logistical challenges facing those who wish to develop, deploy, and debug applications on realistic large-scale sensor networks have gone unmet. Manually reprogramming nodes, deploying them into the physical environment, and instrumenting them for data gathering is tedious and time-consuming. To address this need we have developed MoteLab, a Web-based sensor network testbed. MoteLab consists of a set of permanently-deployed sensor network nodes connected to a central server which handles re programming and data logging while providing a Web interface for creating and scheduling jobs on the testbed. MoteLab accelerates application deployment by streamlining access to a large, fixed network of real sensor network devices; it accelerates debugging and development by automating data logging, allowing the performance of sensor network software to be evaluated offline Additionally, by providing a Web interface MoteLab allows both local and remote users access to the testbed, and its scheduling and quota system ensure fair sharing. We have developed and deployed MoteLab at Harvard and found ft invaluable for both research and teaching. The MoteLab source is freely available, easy to install, and already in use at several other research institutions. We expect that widespread use of MoteLab will accelerate and improve wireless sensor network research.

/pdf/motelab-a-wireless-sensor-network-testbed-23lx0rwavr.pdf

MoteLab: a wireless sensor network testbed

This paper describes our experiences using a wireless sensor network to monitor volcanic eruptions with low-frequency acoustic sensors. We developed a wireless sensor array and deployed it in July 2004 at Volcan Tingurahua, an active volcano in central Ecuador. The network collected infrasonic (low-frequency acoustic) signals at 102 Hz, transmitting data over a 9 km wireless link to a remote base station. During the deployment, we collected over 54 hours of continuous data which included at least 9 large explosions. Nodes were time-synchronized using a separate GPS receiver, and our data was later correlated with that acquired at a nearby wired sensor array. In addition to continuous sampling, we have developed a distributed event detector that automatically triggers data transmission when a well-correlated signal is received by multiple nodes. We evaluate this approach in terms of reduced energy and bandwidth usage, as well as accuracy of infrasonic signal detection.

/pdf/monitoring-volcanic-eruptions-with-a-wireless-sensor-network-4oo8br715m.pdf

Monitoring volcanic eruptions with a wireless sensor network

Synchronicity is a useful abstraction in many sensor network applications. Communication scheduling, coordinated duty cycling, and time synchronization can make use of a synchronicity primitive that achieves a tight alignment of individual nodes' firing phases. In this paper we present the Reachback Firefly Algorithm (RFA), a decentralized synchronicity algorithm implemented on TinyOS-based motes. Our algorithm is based on a mathematical model that describes how fireflies and neurons spontaneously synchronize. Previous work has assumed idealized nodes and not considered realistic effects of sensor network communication, such as message delays and loss. Our algorithm accounts for these effects by allowing nodes to use delayed information from the past to adjust the future firing phase. We present an evaluation of RFA that proceeds on three fronts. First, we prove the convergence of our algorithm in simple cases and predict the effect of parameter choices. Second, we leverage the TinyOS simulator to investigate the effects of varying parameter choice and network topology. Finally, we present results obtained on an indoor sensor network testbed demonstrating that our algorithm can synchronize sensor network devices to within 100 μsec on a real multi-hop topology with links of varying quality.

/pdf/firefly-inspired-sensor-network-synchronicity-with-realistic-5bp535az64.pdf

Firefly-inspired sensor network synchronicity with realistic radio effects

An emerging class of sensor networks focuses on reliable collection of high-resolution signals from across the network. In these applications, the system is capable of acquiring more data than can be delivered to the base station, due to severe limits on radio bandwidth and energy. Moreover, these systems are unable to take advantage of conventional approaches to in-network data aggregation, given the high data rates and need for raw signals. These systems face an important challenge: how to maximize the overall value of the collected data, subject to resource constraints.In this paper, we describe Lance, a general approach to bandwidth and energy management for reliable data collection in wireless sensor networks. Lance couples the use of optimized, data-driven reliable data collection with a model of energy cost for extracting data from the network. Lance's design decouples resource allocation mechanisms from application-specific policies, enabling flexible customization of the system's optimization metrics.We describe the Lance architecture in detail, demonstrating its use through a range of target applications and resource management policies. We present an extensive study driven by both real and synthetic data distributions, through simulations and runs on a large sensor testbed. We show that Lance maximizes the value of the collected data under a range of resource constraints, achieving near-optimal allocation of radio bandwidth and energy. Finally, we present results from a real sensor network deployment at Tungurahua volcano, Ecuador, in which Lance was used to drive data collection for an eight-node network collecting seismic and acoustic signals from the active volcano.

/pdf/lance-optimizing-high-resolution-signal-collection-in-1z1rcnelyt.pdf

Geoffrey Werner-Allen

Papers

Deploying a wireless sensor network on an active volcano

MoteLab: a wireless sensor network testbed

Monitoring volcanic eruptions with a wireless sensor network

Firefly-inspired sensor network synchronicity with realistic radio effects

Lance: optimizing high-resolution signal collection in wireless sensor networks