Wireless sensor network

Typical Multihop Wireless Sensor Network Architecture

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants.[1][2] The development of wireless sensor networks was motivated by military applications such as battlefield surveillance. They are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring[3], environment and habitat monitoring, healthcare applications, home automation, and traffic control.[1][4]

In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust,[1] although functioning "motes" of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few pennies, depending on the size of the sensor network and the complexity required of individual sensor nodes.[1] Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.[1]

A sensor network normally constitutes a wireless ad-hoc network, meaning that each sensor supports a multi-hop routing algorithm (several nodes may forward data packets to the base station).

In computer science and telecommunications, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year.

Contents

Applications

The applications for WSNs are varied, typically involving some kind of monitoring, tracking, or controlling. Specific applications include habitat monitoring, object tracking, nuclear reactor control, fire detection, land slide detection and traffic monitoring. In a typical application, a WSN is scattered in a region where it is meant to collect data through its sensor nodes.

Area monitoring

Area monitoring is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. For example, a large quantity of sensor nodes could be deployed over a battlefield to detect enemy intrusion instead of using landmines.[5] When the sensors detect the event being monitored (heat, pressure, sound, light, electro-magnetic field, vibration, etc.), the event needs to be reported to one of the base stations, which can take appropriate action (e.g., send a message on the internet or to a satellite). Depending on the exact application, different objective functions will require different data-propagation strategies, depending on things such as need for real-time response, redundancy of the data (which can be tackled via data aggregation and information fusion[6] techniques), need for security, etc.

Environmental monitoring

A number of WSNs have been deployed for environmental monitoring.[7] Many of these have been short lived, often due to the prototype nature of the projects. Examples of longer-lived deployments are monitoring the state of permafrost in the Swiss Alps: The PermaSense Project, PermaSense Online Data Viewer and glacier monitoring.

Machine Health Monitoring or Condition based maintenance

Wireless sensor networks have been developed for machinery condition-based maintenance (CBM)[3] as they offer significant cost savings and enable new functionalities. In wired systems, the installation of enough sensors is often limited by the cost of wiring, which runs between $10–$1000 per foot.Previously inaccessible locations, rotating machinery, hazardous or restricted areas, and mobile assets can now be reached with wireless sensors. Often, companies use manual techniques to calibrate, measure, and maintain equipment. This labor-intensive method not only increases the cost of maintenance but also makes the system prone to human errors. Especially in US Navy shipboard systems, reduced manning levels make it imperative to install automated maintenance monitoring systems. Wireless sensor networks play an important role in providing this capability [3]

Industrial Monitoring

Water/Wastewater Monitoring

There are many opportunities for using wireless sensor networks within the water/wastewater industries. Facilities not wired for power or data transmission can be monitored using industrial wireless I/O devices and sensors powered using solar panels or battery packs. As part of the American Recovery and Reinvestment Act (ARRA), funding is available for some water and wastewater projects in most states.

Landfill Ground Well Level Monitoring and Pump Counter

Wireless sensor networks can be used to measure and monitor the water levels within all ground wells in the landfill site and monitor leachate accumulation and removal. A wireless device and submersible pressure transmitter monitors the leachate level. The sensor information is wirelessly transmitted to a central data logging system to store the level data, perform calculations, or notify personnel when a service vehicle is needed at a specific well.

It is typical for leachate removal pumps to be installed with a totalizing counter mounted at the top of the well to monitor the pump cycles and to calculate the total volume of leachate removed from the well. For most current installations, this counter is read manually. Instead of manually collecting the pump count data, wireless devices can send data from the pumps back to a central control location to save time and eliminate errors. The control system uses this count information to determine when the pump is in operation, to calculate leachate extraction volume, and to schedule maintenance on the pump.[8]

Flare Stack Monitoring

Landfill managers need to accurately monitor methane gas production, removal, venting, and burning. Knowledge of both methane flow and temperature at the flare stack can define when methane is released into the environment instead of combusted. To accurately determine methane production levels and flow, a pressure transducer can detect both pressure and vacuum present within the methane production system.

Thermocouples connected to wireless I/O devices create the wireless sensor network that detects the heat of an active flame, verifying that methane is burning. Logically, if the meter is indicating a methane flow and the temperature at the flare stack is high, then the methane is burning correctly. If the meter indicates methane flow and the temperature is low, methane is releasing into the environment.[8]

Water Tower Level Monitoring

Water towers are used to add water and create water pressure to small communities or neighborhoods during peak use times to ensure water pressure is available to all users. Maintaining the water levels in these towers is important and requires constant monitoring and control. A wireless sensor network that includes submersible pressure sensors and float switches monitors the water levels in the tower and wirelessly transmits this data back to a control location. When tower water levels fall, pumps to move more water from the reservoir to the tower are turned on.[8]

Vehicle Detection

Wireless sensor networks can use a range of sensors to detect the presence of vehicles ranging from motorcycles to train cars.

Fleet monitoring (outdoor and indoor location)

It is possible to put a mote with a GPS module on board of each vehicle of a fleet. The mote reports its coordinates so that the location is tracked with real time information. The motes can be equipped with temperature sensors to control any disruption of the cold chain. That helps to ensure the safety in the food and pharmaceutical industries and also some chemical shipments. Using the GSM cells helps to get the position in scenarios where there is not GPS coverage, like inside buildings, garages and tunnels. This alternative method consists in taking the information sent by the Mobile Phones Cells and look for their location in a previously saved Data Base[9].

Agriculture

Using wireless sensor networks within the agricultural industry is increasingly common. Gravity fed water systems can be monitored using pressure transmitters to monitor water tank levels, pumps can be controlled using wireless I/O devices, and water use can be measured and wirelessly transmitted back to a central control center for billing. Irrigation automation enables more efficient water use and reduces waste.

Windrow Composting

Composting is the aerobic decomposition of biodegradable organic matter to produce compost, a nutrient-rich mulch of organic soil produced using food, wood, manure, and/or other organic material. One of the primary methods of composting involves using windrows.

To ensure efficient and effective composting, the temperatures of the windrows must be measured and logged constantly. With accurate temperature measurements, facility managers can determine the optimum time to turn the windrows for quicker compost production. Manually collecting data is time consuming, cannot be done continually, and may expose the person collecting the data to harmful pathogens. Automatically collecting the data and wirelessly transmitting the data back to a centralized location allows composting temperatures to be continually recorded and logged, improving efficiency, reducing the time needed to complete a composting cycle, and minimizing human exposure and potential risk.

An industrial wireless I/O device mounted on a stake with two thermocouples, each at different depths, can automatically monitor the temperature at two depths within a compost windrow or stack. Temperature sensor readings are wirelessly transmitted back to the gateway or host system for data collection, analysis, and logging. Because the temperatures are measured and recorded continuously, the composting rows can be turned as soon as the temperature reaches the ideal point. Continuously monitoring the temperature may also provide an early warning to potential fire hazards by notifying personnel when temperatures exceed recommended ranges.[8]

Greenhouse Monitoring

Wireless sensor networks are also used to control the temperature and humidity levels inside commercial greenhouses. When the temperature and humidity drops below specific levels, the greenhouse manager must be notified via e-mail or cell phone text message, or host systems can trigger misting systems, open vents, turn on fans, or control a wide variety of system responses. Because some wireless sensor networks are easy to install, they are also easy to move as the needs of the application change.[8]

Landslide detection

A landslide detection system, make use of a wireless sensor network to detect the slight movements of soil that may occur during a landslide. And through the data gathered it is possible to know the occurrence of landslides long before it actually happens.

Characteristics

Unique characteristics of a WSN include:

Sensor nodes can be imagined as small computers, extremely basic in terms of their interfaces and their components. They usually consist of a processing unit with limited computational power and limited memory, sensors (including specific conditioning circuitry), a communication device (usually radio transceivers or alternatively optical), and a power source usually in the form of a battery. Other possible inclusions are energy harvesting modules, secondary ASICs, and possibly secondary communication devices (e.g. RS-232 or USB).

The base stations are one or more distinguished components of the WSN with much more computational, energy and communication resources. They act as a gateway between sensor nodes and the end user.

Platforms

Standards and specifications

Several standards are currently either ratified or under development for wireless sensor networks. There are a number of standardization bodies in the field of WSNs. The IEEE focuses on the physical and MAC layers; the Internet Engineering Task Force (IETF) works on layers 3 and above. In addition to these, bodies such as the International Society for Automation (ISA) and the HART foundation provide vertical solutions, covering all protocol layer. Finally, there are also several non-standard, proprietary mechanisms and specifications.

Note that RPL, 6LoWPAN, ISA100, WirelessHART, and ZigBee are all based on the same underlying radio standard: IEEE 802.15.4-2006.

ISA100

ISA100 is a new standard under development that makes use of 6lowpan and provides additional agreements for industrial control applications. ISA100 is scheduled for completion in 2009.

WirelessHART

The WirelessHART standard is an extension of the HART Protocol and is specifically designed for Industrial applications like Process Monitoring and Control. WirelessHART was added to the overall HART protocol suite as part of the HART 7 Specification, which was approved by the HART Communication Foundation in June 2007[10]. In 2010, WirelessHart was approved by ANSI and unanimously approved by the IEC as an international standard, IEC 62591.

IETF RPL

ROLL is the group within the IETF which defines RPL, the IPv6 Routing Protocol for Low power and Lossy Networks.

IETF 6LoWPAN

6LoWPAN [11] is a working group within the IETF that has produced a standards track specification for the transmission of IPv6 packets over IEEE 802.15.4.

IEC 62591

The International Electrotechnical Commission (IEC) approved the WirelessHART specification as a full international standard (IEC 62591Ed. 1.0) in April 2010.[12]

IEEE 1451

Also relevant to sensor networks is the emerging IEEE 1451 which attempts to create standards for the smart sensor market. The main point of smart sensors is to move the processing intelligence closer to the sensing device.[13]

ZigBee

ZigBee networking specification for transmission of packets over IEEE 802.15.4 is intended for uses such as embedded sensing, medical data collection, consumer devices like television remote controls, and home automation. Zigbee is promoted by a large consortium of industry players. ZigBee sets extra communication features such as authentication, encryption, association and application services in the upper layer[14].

EnOcean

EnOcean is a system for wireless communication in the building automation world. It is not standardized with any of the generally approved standardization bodies.

EnviroNet

EnviroNet is a wireless sensor network generally geared toward industrial control and environmental automation applications. EnviroNet has non-standardized and standardized versions that comply with ISA100.

Hardware

The main challenge is to produce low cost and tiny sensor nodes. With respect to these objectives, current sensor nodes are mainly prototypes. Miniaturization and low cost are understood to follow from recent and future progress in the fields of MEMS and NEMS. Some of the existing sensor nodes are given below. Some of the nodes are still in research stage.

Also inherent to sensor network adoption is the availability of a very low power method for acquiring sensor data wirelessly.

An overview of commonly used sensor network platforms, components, technology and related topics is available in the SNM - Sensor Network Museumtm.

Software

Energy is the scarcest resource of WSN nodes, and it determines the lifetime of WSNs. WSNs are meant to be deployed in large numbers in various environments, including remote and hostile regions, with ad-hoc communications as key. For this reason, algorithms and protocols need to address the following issues:

Some of the "hot" topics in WSN software research are:

Operating systems

Operating systems for wireless sensor network nodes are typically less complex than general-purpose operating systems both because of the special requirements of sensor network applications and because of the resource constraints in sensor network hardware platforms. For example, sensor network applications are usually not interactive in the same way as applications for PCs. Because of this, the operating system does not need to include support for user interfaces. Furthermore, the resource constraints in terms of memory and memory mapping hardware support make mechanisms such as virtual memory either unnecessary or impossible to implement.

Wireless sensor network hardware is not different from traditional embedded systems and it is therefore possible to use embedded operating systems such as eCos or uC/OS for sensor networks. However, such operating systems are often designed with real-time properties. Unlike traditional embedded operating systems, however, operating systems specifically targeting sensor networks often do not have real-time support.

TinyOS[15] is perhaps the first][16] operating system specifically designed for wireless sensor networks. Unlike most other operating systems, TinyOS is based on an event-driven programming model instead of multithreading. TinyOS programs are composed into event handlers and tasks with run to completion-semantics. When an external event occurs, such as an incoming data packet or a sensor reading, TinyOS calls the appropriate event handler to handle the event. Event handlers can post tasks that are scheduled by the TinyOS kernel some time later. Both the TinyOS system and programs written for TinyOS are written in a special programming language called nesC which is an extension to the C programming language. NesC is designed to detect race conditions between tasks and event handlers.

There are also operating systems that allow programming in C.

Examples of such operating systems include Contiki,[17] MANTIS,[18] BTnut,[19] and Nano-RK[20].

Contiki is designed to support loading modules over the network and supports run-time loading of standard ELF files.[21] The Contiki kernel is event-driven, like TinyOS, but the system supports multithreading on a per-application basis.[22] Furthermore, Contiki includes protothreads that provide a thread-like programming abstraction but with a very small memory overhead.[23]

Unlike the event-driven Contiki kernel, the MANTIS and Nano-RK kernels are based on preemptive multithreading.[24][25] With preemptive multithreading, applications do not need to explicitly yield the microprocessor to other processes. Instead, the kernel divides the time between the active processes and decides which process that currently can be run which makes application programming easier. Nano-RK is a real-time resource kernel that allows fine grained control of the way tasks get access to CPU time, networking and sensors.

Like TinyOS and Contiki, SOS is an event-driven operating system.[26] The prime feature of SOS is its support for loadable modules. A complete system is built from smaller modules, possibly at run-time. To support the inherent dynamism in its module interface, SOS also focuses on support for dynamic memory management.[27] BTnut[19] is based on cooperative multi-threading and plain C code, and is packaged with a developer kit and tutorial[28]

LiteOS is a newly developed OS for wireless sensor networks, which provides UNIX like abstraction and support for C programming language.[29]

ERIKA Enterprise [30] is one of the newcomers as operating systems for sensor networks. Being an open-source real-time kernel, ERIKA Enterprise provides an operating system API similar to the OSEK/VDX API used in automotive, together with the uWireless wireless software stack providing a 802.15.4 with Guaranteed Time Slot (GTS) support, which is very important when there is need for real-time traffic guarantees on wireless sensor networks [31]

Implementations

Sometimes coupled with a specific Operating System are implementations of Wireless Sensor Network protocols.

Algorithms

WSNs are composed of a large number of sensor nodes, therefore, an algorithm for a WSN is implicitly a distributed algorithm. In WSNs the scarcest resource is energy, and one of the most energy-expensive operations are data transmission and idle listening. For this reason, algorithmic research in WSN mostly focuses on the study and design of energy aware algorithms for saving energy by reducing the amount of data being transmitted - using techniques like data aggregation -, changing the transmission power of the sensor nodes or turning nodes off while preserving connectivity and coverage - applying Topology control algorithms -.

Another characteristic to take into account is that due to the constrained radio transmission range and the polynomial growth in the energy-cost of radio transmission with respect to the transmission distance, it is very unlikely that every node will reach the base station, so data transmission is usually multi-hop (from node to node, towards the base stations).

The algorithmic approach to modelling, simulating and analysing WSN differentiates itself from the protocol approach by the fact that the idealised mathematical models used are more abstract, more general, and easier to analyze. However, they are sometimes less realistic than the models used for protocol design, since an algorithmic approach often neglects timing issues, protocol overhead, the routing initiation phase and sometimes distributed implemenentation of the algorithms.

Simulators

Flooding Technique in Wireless Sensor Network by Qasim Siddique

There are network simulator platforms specifically designed to model and simulate Wireless Sensor Networks, like TOSSIM, which is a part of TinyOS and COOJA which is a part of Contiki. Traditional network simulators like ns-2 have also been used. A platform independent component based simulator with wireless sensor network framework, J-Sim ([2]) can also be used. In addition, there is a simulator focused on the evaluation of topology control protocols in WSNs called Atarraya. An extensive list of simulation tools for Wireless Sensor Networks can be found at the CRUISE WSN Simulation Tool Knowledgebase.

Based on the OMNeT++ network simulator architecture, Mobility Framework and Castalia can be used for simulation of wireless sensor networks.

Based on Matlab, the Prowler (Probabilistic Wireless Network Simulator) toolbox is available. JProwler is a version of Prowler written in Java.

QualNet Network Simulator can be used to simulate Wireless Sensor Network

Developed using C# under Visual Studio 2008, WSNSim is available for download from WSNPedia. While WSNSim comes equipped with the behaviour of the classic LEACH and HEED clustering protocols in WSN, it is mainly a simulation framework that can be adopted to try other clustering and/or routing protocols in WSN, on a common testbench. It also introduces the t-SNIPER algorithm that utilizes a socio-economic model of trust based routing scheme.

Unlike traditional simulation tools, Agent-based modeling and simulation are used to model and simulate wireless sensors as a Complex System. Agent-Based Modeling is an exciting tool for development of simulation models for sensor and actuator networks as well as ad-hoc networks because it allows the designer to focus on solving the actual problem of application design rather than worry about the transport of messages and basic working of the communication paradigm. Before their use in the domain of Sensor Networks, they have been used to model and simulate different types of Complex Adaptive Systems. Some recent work demonstrating the use of Agent-based models as a simulation tool for WSANs is [33] and [34]. It would be interesting to note that this is in contrast to use of Agent-Based Computing in WSANs. So, where traditional simulation models are developed using NS2, NS3, OPNET, OMNET++ etc., agent-based simulation models or agent-based models are developed using tools such as NetLogo, Mason, StarLogo, Repast and so on. Another way of examining the difference is by looking at the perspective of the simulation. In traditional agent-based models, the focus is on large-scale and numerous interacting entities where the simple interactions at the node/entity level gives rise to global phenomena such as Emergence, which actually makes this paradigm suitable for use in the simulation of large-scale Wireless Sensor and Actuator Networks.

Data visualization

The data gathered from wireless sensor networks is usually saved in the form of numerical data in a central base station. Additionally, the Open Geospatial Consortium (OGC) is specifying standards for interoperability interfaces and metadata encodings that enable real time integration of heterogeneous sensor webs into the Internet, allowing any individual to monitor or control Wireless Sensor Networks through a Web Browser. There are several techniques to retrieve data from the nodes, some of the protocols rely on flooding mechanisms, other map the data to nodes by applying the concept of DHT[35] [36].

Information Fusion

In wireless sensor networks, information fusion, also called data fusion, has been developed for processing sensor data by filtering, aggregating, and making inferences about the gathered data. Information fusion deals with the combination of multiple sources to obtain improved information: cheaper, greater quality or greater relevance[6]. Within the wireless sensor networks domain, simple aggregation techniques such as maximum, minimum, and average, have been developed for reducing the overall data traffic to save energy.[37]

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 Römer, Kay; Friedemann Mattern (December 2004), "The Design Space of Wireless Sensor Networks", IEEE Wireless Communications 11 (6): 54–61, doi:10.1109/MWC.2004.1368897, http://www.vs.inf.ethz.ch/publ/papers/wsn-designspace.pdf 
  2. ↑ Thomas Haenselmann (2006-04-05). Sensornetworks. GFDL Wireless Sensor Network textbook. http://pi4.informatik.uni-mannheim.de/~haensel/sn_book. Retrieved 2006-08-29. 
  3. ↑ 3.0 3.1 3.2 Tiwari, Ankit et. al, Energy-efficient wireless sensor network design and implementation for condition-based maintenance, ACM Transactions on Sensor Networks (TOSN), http://portal.acm.org/citation.cfm?id=1210670 
  4. ↑ Hadim, Salem; Nader Mohamed (2006), "Middleware Challenges and Approaches for Wireless Sensor Networks", IEEE Distributed Systems Online 7 (3): 1, doi:10.1109/MDSO.2006.19, http://doi.ieeecomputersociety.org/10.1109/MDSO.2006.19  art. no. 0603-o3001.
  5. ↑ Sample, Ian (April 2001), Alternatives to landmines, New Scientist, http://www.scienceblog.com/community/older/2001/C/200113355.html, retrieved 2009-01-15 
  6. ↑ 6.0 6.1 Eduardo F. Nakamura, Antonio A. F. Loureiro, Alejandro C. Frery. Information fusion for wireless sensor networks: Methods, models, and classifications, ACM Computing Surveys, Volume 39, Issue 3, Article 9, September 2007.
  7. ↑ J.K. Hart, K. Martinez, Environmental Sensor Networks:A revolution in the earth system science?, Earth-Science Reviews, 78 . pp. 177-191.2006
  8. ↑ 8.0 8.1 8.2 8.3 8.4 Banner Engineering (March 2009), Application Notes, http://www.bannerengineering.com/en-US/wireless/surecross_web_appnotes 
  9. ↑ http://www.sensor-networks.org/index.php?page=1011111101
  10. ↑ WirelessHART Standard Approved and Released
  11. ↑ RFC 4944 - Transmission of IPv6 Packets over IEEE 802.15.4-2006 Networks
  12. ↑ WirelessHART Approved by IEC as First International Standard for Wireless Communication in Process Automation
  13. ↑ [1]
  14. ↑ http://www.sensor-networks.org/index.php?page=0823123150
  15. ↑ TinyOS, an open-source OS for the networked sensors
  16. ↑ TinyOS Programming, Philip Levis, Cambridge University Press, 2009
  17. ↑ The Contiki Operating System
  18. ↑ MANTIS
  19. ↑ 19.0 19.1 BTnodes - A Distributed Environment for Prototyping Ad Hoc Networks
  20. ↑ Nano-RK: A Wireless Sensor Networking Real-Time Operating System
  21. ↑ Adam Dunkels, Niclas Finne, Joakim Eriksson, and Thiemo Voigt. Run-Time Dynamic Linking for Reprogramming Wireless Sensor Networks. In Proceedings of the Fourth ACM Conference on Embedded Networked Sensor Systems (SenSys 2006), Boulder, Colorado, USA, November 2006.
  22. ↑ Adam Dunkels, Björn Grönvall, and Thiemo Voigt. Contiki - a Lightweight and Flexible Operating System for Tiny Networked Sensors. In Proceedings of the First IEEE Workshop on Embedded Networked Sensors 2004 (IEEE EmNetS-I), Tampa, Florida, USA, November 2004.
  23. ↑ Adam Dunkels, Oliver Schmidt, Thiemo Voigt, and Muneeb Ali. Protothreads: Simplifying Event-Driven Programming of Memory-Constrained Embedded Systems. In Proceedings of the Fourth ACM Conference on Embedded Networked Sensor Systems (SenSys 2006), Boulder, Colorado, USA, November 2006.
  24. ↑ S. Bhatti, J. Carlson, H. Dai, J. Deng, J. Rose, A. Sheth, B. Shucker, C. Gruenwald, A. Torgerson, R. Han, MANTIS OS: An Embedded Multithreaded Operating System for Wireless Micro Sensor Platforms, ACM/Kluwer Mobile Networks & Applications (MONET), Special Issue on Wireless Sensor Networks, vol. 10, no. 4, August 2005.
  25. ↑ A. Eswaran, A. Rowe and R. Rajkumar, Nano-RK: An Energy-Aware Resource-Centric Operating System for Sensor Networks, IEEE Real-Time Systems Symposium, December 2005.
  26. ↑ Chih-Chieh Han, Ram Kumar Rengaswamy, Roy Shea, Eddie Kohler and Mani Srivastava. SOS: A dynamic operating system for sensor networks, Proceedings of the Third International Conference on Mobile Systems, Applications, And Services (Mobisys), 2005.
  27. ↑ [Han, C., Kumar, R., Shea, R., Kohler, E., and Srivastava, M. 2005. A dynamic operating system for sensor nodes. In Proceedings of the 3rd international Conference on Mobile Systems, Applications, and Services (Seattle, Washington, June 6–8, 2005). MobiSys '05. ACM Press, New York, NY, 163-176.]
  28. ↑ BTnode Programming - An Introduction to BTnut Applications
  29. ↑ LiteOS.net
  30. ↑ ERIKA Enterprise web site
  31. ↑ Wireless communication application demo with ERIKA Enterprise
  32. ↑ UC Berkeley's OpenWSN project
  33. ↑ Muaz Niazi, Amir Hussain, Agent based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks, Feature Issue, IEEE Communications Magazine, Vol.47 No.3, March 2009, pp 163 - 173
  34. ↑ Muaz Niazi and Amir Hussain, A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments, IEEE Sensors Journal (Accepted)
  35. ↑ Awad, Abdalkarim and Sommer, Christoph and German, Reinhard and Dressler, Falko. [Virtual Cord Protocol (VCP): A Flexible DHT-like Routing Service for Sensor Networks]. 5th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2008), Atlanta, Georgia, USA, September 2008.
  36. ↑ Ratnasamy, Sylvia and Karp, Brad and Shenker, Scott and Estrin, Deborah and Govindan, Ramesh and Yin, Li and Yu, Fang [Data-Centric Storage in Sensornets with GHT, a Geographic Hash Table]. ACM/Springer Mobile Networks and Applications (MONET), Special Issue on Wireless Sensor Networks, August 2003.
  37. ↑ Eduardo F. Nakamura, Heitor S. Ramos, Leandro A. Villas, Horacio A.B.F. de Oliveira, Andre L.L. de Aquino, Antonio A.F. Loureiro. A reactive role assignment for data routing in event-based wireless sensor networks, Computer Networks Volume 53, Issue 12, pp 1980-1996, August 2009.