Sensor Network Applications Wireless Sensor Networks for Habitat

Sensor Network Applications

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Introduction – Habitat and environmental monitoring represent essential class of sensor network applications by placing numerous networked micro-sensors in an environment where long-term data collection can be achieved – The sensor nodes perform filtering and triggering functions as well as application-specific or sensor-specific data compression algorithms thru the integration of local processing and storage – The ability to communicate allows nodes to cooperate in performing tasks such as statistical sampling, data aggregation, and system health and status monitoring – Increased power efficiency assists in resolving fundamental design tradeoffs, e. g. , between sampling rates and battery lifetimes

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Introduction – The sensor nodes can be reprogrammed or retasked after deployment in the field by the networking and computing capabilities provided – Nodes can adapt their operation over time in response to changes in the environment – The application context helps to differentiate problems with simple and concrete solutions from open research areas – An effective sensor network architecture and general solutions should be developed for the domain – The impact of sensor networks for habitat and environmental monitoring is measured by their ability to enable new applications and produce new results

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Introduction – This paper develops a specific habitat monitoring application, but yet a representative of the domain – It presents a collection of requirements, constraints and guidelines that serve as a basis for general sensor network architecture – It describes the core components of the sensor network for this domain– hardware and sensor platforms, the distinct networks involved, their interconnection, and the data management facilities – The design and implementation of the essential network services – power management, communications, re-tasking, and node management can be evaluated in this context

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Habitat Monitoring – Researchers in the Life Sciences are concerned about the impacts of human presence in monitoring plants and animals in the field conditions – It is possible that chronic human disturbance may adversely effect results by changing behavioral patterns or distributions – Disturbance effects are of concern in small island situations where it may be physically impossible for researchers to avoid some impact on an entire population – Seabird colonies are extreme sensitive to human disturbance – Research in Maine [Anderson 1995], suggests that a 15 minute visit to a cormorant colony can result in up to 20% mortality among eggs and chicks in a given breeding year. Repeated disturbance can lead to the end of the colony

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Habitat Monitoring – On Kent Island, Nova Scotia, research learned that Leach’s Storm Petrels are likely to desert their nesting burrows in case of disturbance during the first two weeks of incubation – Sensor networks advances the monitoring methods over the traditional invasive ones – Sensors can be deployed prior to the breeding season or other sensitive period or while plants are dormant or the ground is frozen on small islets where it would be unsafe or unwise to repeatedly attempt field studies – Sensor network deployment may be more economical method for conducting long-term studies than traditional personnel-rich methods – A “deploy ‘em and leave ‘em” strategy of wireless sensor usage would decrease the logistical needs to initial placement and occasional servicing

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Great Duck Island – The College of Atlantic (COA) is field testing in-situ sensor networks for habitat monitoring – Great Duck Island (GDI) is a 237 acre island located 15 km south of Mount Desert Island, Maine – At GDI, three major questions in monitoring the Leach’s Storm Petrel [Anderson 1995]: 1. What is the usage pattern of nesting burrows over the 24 -72 hour cycle when one or both members of a breeding pair may alternate incubation duties with feeding at sea? 2. What changes can be observed in the burrow and surface environmental parameters during the course of the approximately 7 month breeding season (April-October)?

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Great Duck Island 3. What are the differences in the micro-environments with and without large numbers of nesting petrels? – Presence/absence data is obtained through occupancy detection and temperature differentials between burrows with adult birds and burrows that contain eggs, chicks, or are empty – Petrels will most likely enter or leave during the daytime; however, 5 -10 minutes during late evening and early morning measurements are needed to capture the entry and exit timings – More general environmental differentials between burrow and surface conditions can be captured by records every 2 -4 hours during the extended breeding season; whereas, the differences between “popular” and “unpopular” sites benefit from hourly sampling

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Great Duck Island Requirements 1. Internet Access – The sensor networks at GDI must be accessible via the Internet since the ability to support remote interactions with in-situ networks is essential 2. Hierarchical Network – Habitats of interest are located up to several kilometers away. A second tier of wireless networking provides connectivity to multiple patches of sensor networks deployed at each of the areas. 3. Sensor Network Longevity – Sensor networks that runs for several month from non-rechargeable power sources would be desirable since studies at GDI can span multiple field seasons

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Great Duck Island Requirements 4. Operating off-the grid – Every level of the network must operate with bounded energy supplies – Renewable energy such as solar power may be available some locations, disconnected operation is a possibility – GDI has enough solar power that run the application 24 x 7 with small probabilities of service interruptions due to power loss 5. Management at-a-distance – Remoteness of the field sites requires the ability to monitor and manage sensor networks over the Internet. The goal is no on-site presence for maintenance and administration during the field season, except for installation and removal of nodes

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Great Duck Island Requirements 6. Inconspicuous operation – It should not disrupt the natural processes or behaviors under study – Removing human presence from the study areas would eliminate a source of error and variation in data collection and source of disturbance 7. System behavior – Sensor networks should present stable, predictable, and repeatable behavior at all times since unpredictable system is difficult to debug and maintain – Predictability is essential in developing trust in these new technologies for life scientists

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Great Duck Island Requirements 8. In-situ interactions – Local interactions are required during initial development, maintenance and on-site visits – PDAs can be useful in accomplishing these tasks – they may directly query a sensor, adjust operational parameters and so on 9. Sensors and sampling – The ability to sense light, temperature, infrared, relative humidity, and barometric pressure are essential set of measurements – Additional measurements may include acceleration/vibration, weight, chemical vapors, gas concentrations, p. H, and noise levels

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Great Duck Island Requirements 10. Data archiving – Sensor readings must be achieved for off-line data mining and analysis – The reliable offloading of sensor logs to databases in the wired, powered infrastructure is essential – It is desirable to interactively “drill-down” and explore sensors in near real -time complement log-based studies. In this mode of operation, the timely delivery of sensor data is the key – Nodal data summaries and periodic health-and-status monitoring also requires timely delivery of the data

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture – A tiered architecture is developed – The lowest level consists of the sensor nodes that perform general purpose computing and networking as well as application-specific sensing – The sensor nodes may be deployed in dense patches and transmit their data through the sensor network to the sensor network gateway – Gateway is responsible for transmitting sensor data from the sensor patch through a local transit network to the remote base station that provides WAN connectivity and data logging – The base station connects to database replicas across the internet – At last, the data is displayed to researchers through a user interface

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture Figure 1: System architecture for habitat monitoring

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture – The autonomous sensor nodes are placed in the areas of interest where each sensor node collects environmental data about its immediate surroundings – Since these sensors are placed close to the area of interest, they can be built using small and inexpensive individual sensors – high spatial resolution can be achieved through dense deployment of sensor nodes – This architecture offers higher robustness compared to traditional approaches which use a few high quality sensors with complex signal processing – The computational module is a programmable unit that provides computation, storage and bidirectional communication with other nodes

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture – The computational module interfaces with the analog and digital sensors on the sensor module, performs basic signal processing and dispatches the data according to the needs of the application – Compared to traditional data logging systems, networked sensors offer two main advantages: they can be re-tasked in the field and they can communicate with the rest of the system – In-situ re-tasking gives researchers the ability to refocus their observations based on the analysis of the initial results – initially, absolute temperature readings are desired, after a while, only significant temperature changes exceeding a threshold may become more useful

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture – Individual sensor nodes communicate and coordinate with one another – These nodes form a multi-hop network by forwarding each other’s messages and if needed, the network can perform in-network aggregation (e. g. , relaying the average temperature across the region) – Eventually, data from each sensor needs to be propagated to the Internet – The propagated data may be raw, filtered or processed data – Since direct wide area connectivity cannot be brought to each sensor path due to several reasons (e. g. , cost of equipment, power, disturbance created by the installation of the equipment in the environment), wide are connectivity is brought to a base station instead

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture – The base station may communicate with the sensor patch using a wireless LAN where each sensor patch is equipped with a gateway that can communicate with the sensor network and provides connectivity to the transit network – The transit network may consist of a single hop link or series of networked wireless nodes and each transit network design has different characteristics with respect to expected robustness, bandwidth, energy efficiency, cost and manageability – To provide data to remote end-users, the base station includes WAN connectivity and persistent data storage for the collection of sensor patches

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture – It is expected that WAN connection will be wireless – The architecture needs to address the disconnection possibilities – Each layer (sensor nodes, gateways, base stations) has some persistent storage to protect against data loss due to power outage as well as data management services – At the sensor level, these will be primitive, taking the form of data logging – The base station may provide relational database service while the data management at the gateways falls somewhere in between – When it comes to data collection, long-latency is preferable to data loss – Users interact with the sensor network in two ways

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] System Architecture – Remote users access the replica of the base station database – This approach assists on integration with data analysis and mining tools while masking the potential wide area disconnections with the base stations – On-site users may require direct interaction with the network and this can be accomplished with a small, PDA-sized device, referred to as gizmo – Gizmo allows the user to interactively control the network parameters by adjusting the sampling rates, power management parameters and other network parameters – The connectivity between any sensor node and gizmo may or may not rely on functioning on multi-hop sensor network routing

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Sensor Network Node – UC Berkeley motes are used as the sensor nodes – Mica uses a single channel, 916 MHz radio from RF Monolithics to provide bi-directional communication at 40 Kbps, an Atmel Atmega 103 microcontroller running at 4 MHz and 512 KB nonvolatile storage – A pair of conventional AA batteries and a DC boost converter provide the power source; however, other renewable energy sources can be used

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Sensor Board – The Mica Weather Board provides sensors that monitor changing environmental conditions with the same functionality as a traditional weather station – The Mica Weather Board includes temperature, photoresistor, barometric pressure, humidity, and passive infrared (thermopile) sensors Table 1: Mica Weather Board

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Sensor Board Figure 2: Mica Hardware Platform: The Mica sensor node (left) with the Mica Weather Board developed for environmental monitoring applications

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Energy Budget – Typical habitat monitoring applications need to run for nine months – The application chooses how to allocate the energy budget between sleep modes, sensing, local calculations and communications – Since different nodes have different functions, they also have different power requirements, for instance, the nodes near the gateway may need to forward all messages from a patch while a node in a nest may only need to report its own readings – When a set of power limited nodes exhaust their power supplies, the network can become disconnected and inoperable – There is a need to budget the power with respect to the energy bottlenecks of the network

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Energy Budget – The baseline life time of the node is determined by the current draw in the sleep state – Minimizing power in sleep mode means turning off the sensors, the radio and putting the processor into a deep sleep mode Table 2: Power required by various Mica operations

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Sensor Deployment – A wireless sensor network using Mica motes with Mica Weather Board has been deployed in July 2002 – Environmental protective packaging has been designed which minimally obstruct sensing functionality Figure 3: Acrylic enclosure used for deploying the Mica mote

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Patch Gateways – Usage of different gateway nodes directly affects the underlying available transit network – Two designs implemented: an 802. 11 b single hop with an embedded Linux system and a single hop mote-to-mote network – Initially, Cerf. Cube [Cerfcube] which is a small Strong. ARM-based embedded system to act as a sensor patch gateway, is chosen – Each gateway is equipped with a Compact. Flash 802. 11 b adapter – Gateway use permanent storage of up to 1 GB – The mote-to-mote solution consisted of a mote connected to the base station and a mote in the sensor patch

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Patch Gateways – The differences between the mote and Cerf. Cube include different o communication frequency o power requirements o software components – The mote’s MAC layer does not require bi-directional link like 802. 11 b – In addition, the mote sends raw data with a small packet header (4 bytes) directly over the radio as opposed to overheads imposed by 802. 11 b and TCP/IP connections

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Base-station installation – For achieve remote access, collection of sensor patches is connected to the Internet through a wide-area link – On GDI, Internet connectivity is accomplished through a two-way satellite connection provided by Hughes and similar to Direc. TV system – The satellite system is connected to a laptop which coordinates the sensor patches and provides a relational database service Database Management System – The base station uses Postgres SQL database which stores timestamped readings from the sensors, health status of the individual sensors, and metadata (e. g. , sensor locations)

Wireless Sensor Networks for Habitat Monitoring [Mainwaring+ 2002] Implementation Strategies Database Management System – The GDI database is replicated every fifteen minutes over the wide-area satellite link to Postgres database in Berkeley User Interfaces – Many user interfaces can be implemented on top of the sensor database – GIS systems provide a widely used standard for analyzing geographical data and most statistics and data analysis packages implement interfaces to relational databases – Number of web interfaces can be implemented to provide the ubiquitous interfaces to the habitat data

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Introduction – Focus is on issues related to dynamic sensor networks with mobile nodes and wireless communication between them – In this system, the sensor nodes collars carried by the animals under study; wireless ad hoc networking techniques are used to swap and store data in a peer-to-peer manner and to pass it towards a mobile base station that sporadically traverses the area to upload data – Biology and biocomplexity research has been focused on gathering data and observations on a range of species to understand their interactions and influences on each other – For example, how human development into wilderness areas affects indigenous species there; understand the migration patterns of wild animals and how they may be affected by changes in weather patterns or plant life, by introduction of non-native species, and by other influences

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Introduction – Finding and learning these details require long-term position logs and other biometric data such as heart rate, body temperature, and frequency feeding – Current wildlife tracking studies rely on simple technology, for example, many studies rely on collaring a sample subset of animals with simple VHF transmitters – Researchers periodically drive through and/or fly over an area with a receiver antenna, and listen for pings from previously collared animals – Once animal is found, its behavior can be observed and its observed position can be logged; however, there are limits to such studies – First, data collection is infrequent and can miss many “interesting events”

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Introduction – Second, data collection is mostly limited to daylight hours, but animal behavior and movements in night hours can be different – Third, data collection is impossible or very limited for secluded species that avoid human contact – The most elegant trackers commercially available use GPS to track position and use satellite uploads to transfer data to a base station – These systems also suffer from several limitations – First, at most a log of 3000 position samples can be logged and no biometric data – Second, since satellite uploads are slow and uses high power consumption, they are done infrequently – this limits how often position samples can be gathered without overflowing 3000 -entry log storage

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Introduction – Third, downloads of data from the satellite to the researchers are both slow and expensive, therefore, constraining the amount of data collected – Finally, these systems operate on batteries without recharge – when power is drained, the system become unusable unless it is retrieved, recharged and re-deployed – Zebra. Net project is building tracking nodes that include a low-power miniature GPS system with user-programmable CPU, non-volatile storage for data logs, and radio transceivers for communicating either with other nodes or with a base station

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Introduction – One of the key principles of Zebra. Net is that the system should work in arbitrary wilderness locations; no assumptions are made about the presence of of fixed antenna towers or cellular phone service – The system uses peer-to-peer data swaps to move the data around; periodic researcher drives bys and/or fly-overs can collect logged data from several animals despite encountering relatively few within range – Even though ad hoc sensor networks have been heavily studied, not much has been published about the characteristics of mobile sensor networks with mobile base stations and very few studies focus on building real systems

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Introduction – This paper has the following unique contributions: o To the best knowledge of authors, this is the first study of mobile sensor networks protocols in which the base station is also mobile. It is presumed that researchers will upload data while driving or flying by the region o Zebra-tracking is a domain in which the node mobility models are unknown which makes it a research goal. Understanding how, when and why zebras undertake long-term migrations is the most essential biological question of this work. o Zebra. Net’s data collection has communication patterns in which data can be cooperatively passed towards a base station o Energy tradeoffs are examined in detail using real system energy measurements for Zebra. Net prototype hardware in operation

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Introduction – – Some of the interesting research questions to be explored are: o How to make the communications protocol both effective and powerefficient? o To what extent can we rely on ad hoc, peer-to-peer transfers in a sparsely-connected spatially-huge sensor network? o How can we provide comprehensive tracking of a collection of animals, even if some of the animals are reclusive and rarely are close enough to humans to have their data logs updated directly? This research work gives quantitative explorations of the design decisions behind some of these questions

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Zebra. Net Design Goals – The Zebra. Net project is a direct and ongoing collaboration between researchers in experimental computer systems and in wildlife biology – The wildlife biologists have determined the tracker’s overall design goals: o GPS position samples are taken every three minutes o Detailed activity logs taken for three minutes every hour o One year of operation without direct human intervention – that is, not counting on tranquilizing and re-collaring an animal more than once per year o No fixed base stations, antennas, or cellular service o A high success rate for eventually delivering all logged data is essential while latency is not as critical o For a zebra collar, a weight limit of 3 -5 lbs is recommended

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Zebra. Net Design Goals – Ultimately, this detailed information may include several position estimates, temperature information, weather data, environmental data, and body movements that will serve as signatures of behavior; however, in this initial system, the focus is only on position data – Overall, the key goal is to deliver to researchers a very high fraction of the data collected over the months or years that the system is in operation – Therefore, Zebra. Net must be power-efficient, designed with appropriate data log storage, and must be rugged to ensure reliability under tough environmental conditions

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Zebra. Net Problem Statement – The biologists design goals need to be translated into the engineering task at hand – Success rate at delivering position data to the researchers –data homing rate– should approach 100% – Weight limits on each node translate almost directly to computational energy limits since weight of the battery and solar panel takes bulk of the total weight of a Zebra. Net node; therefore, collar and protocol design decisions must manage the number and size of data transmissions required – System design choices must be made that limit the range of transmissions since the required transmitter energy increases dramatically with the distance transmitted

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Zebra. Net Problem Statement – The amount of storage needed to hold position logs must be limited – if many redundant copies are stored and swapped, the storage requirements can scale as O(n 2) – Although the energy cost of storage is small compared to that of transmissions, it is still necessary to develop storage-efficient design – Due to limited transceiver, coverage and a base station only sporadically available, Zebra. Net must forward data through other nodes in peer-topeer manner and store redundant copies of position logs in other tracking nodes – Some of the key challenges in Zebra. Net come from the spatial and temporal scale of the system

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Zebra. Net Problem Statement – In terms of temporal scale, keeping a system running autonomously months at a time is challenging; it requires tremendous design-time attention to both hardware and software reliability – In terms of spatial scale, Zebra. Net is also aggressive; it is the specific intent of the system to operate over an area of hundreds or thousands of square kilometers – Due to the large distances involved and sparse sensor coverage, energy/connectivity tradeoffs become key

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net [Juang+ 2002] Zebra. Net Problem Statement – These challenges mentioned here tackles several open problems: – Zebra. Net protocol promises good communication behavior on mobile sensors forwarding data towards a mobile base station – Zebra. Net explores design issues for sensors that are more coarsegrained than many prior sensor proposals. Larger the weight limits and storage budgets allow researchers to consider different protocols with improved leverage for sparsely-connected, physicallywidespread sensors

References [Anderson 1995] J. G. T. Anderson, Pilot survey of mid-coast Maine seabird colonies: an evaluation of techniques, Bangor, ME, 1995. Report to the State of Maine Dept. of Inland Fisheries and Wildlife. [Cerfcube] Cerfcube embedded Strong. ARM system, http: //www. intrinsys. com/products/cerfcube [Juang+ 2002] P. Juang, H. Oki, Y. Wang, M. Martonosi, L-S Peh, and D. Rubenstein, Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebra. Net, ACM SIGARCH Computer Architecture News, vol. 30, no. 5, December 2002. [Mainwaring+ 2002] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, Wireless Sensor Networks for Habitat Monitoring, 1 st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA 2002), Atlanta, Georgia, September 28, 2002.