Editing Wireless sensor network (section)

==Platforms==
===Hardware===
{{Main|sensor node}}
One major challenge in a WSN is to produce ''low cost'' and ''tiny'' sensor nodes. There are an increasing number of small companies producing WSN hardware and the commercial situation can be compared to home computing in the 1970s. Many of the nodes are still in the research and development stage, particularly their software. Also inherent to sensor network adoption is the use of very low power methods for radio communication and data acquisition.

In many applications, a WSN communicates with a [[local area network]] or [[wide area network]] through a gateway. The Gateway acts as a bridge between the WSN and the other network. This enables data to be stored and processed by devices with more resources, for example, in a remotely located [[Server (computing)|server]]. A wireless wide area network used primarily for low-power devices is known as a Low-Power Wide-Area Network ([[LPWAN]]).

===Wireless===
There are several wireless standards and solutions for sensor node connectivity. [[Thread (network protocol)|Thread]] and [[Zigbee]] can connect sensors operating at 2.4&nbsp;GHz with a data rate of 250&nbsp;kbit/s. Many use a lower frequency to increase radio range (typically 1&nbsp;km), for example [[Z-wave]] operates at 915&nbsp;MHz and in the EU 868&nbsp;MHz has been widely used but these have a lower data rate (typically 50&nbsp;kbit/s). The IEEE 802.15.4 working group provides a standard for low power device connectivity and commonly sensors and smart meters use one of these standards for connectivity. With the emergence of [[Internet of Things]], many other proposals have been made to provide sensor connectivity. [[LoRa]]<ref>{{cite web|url=https://www.lora-alliance.org/technology|title=LoRa Alliance|url-status=live|archive-url=https://web.archive.org/web/20171109080940/https://www.lora-alliance.org/technology|archive-date=2017-11-09}}</ref> is a form of [[LPWAN]] which provides long range low power wireless connectivity for devices, which has been used in smart meters and other long range sensor applications. Wi-SUN<ref>{{cite web|url=https://www.wi-sun.org/|title=Wi-Sun Alliance|url-status=live|archive-url=https://web.archive.org/web/20171109134725/https://www.wi-sun.org/|archive-date=2017-11-09|date=2018-08-15}}</ref> connects devices at home. [[NarrowBand IOT]]<ref>{{cite web|url=https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox|title=NB-IOT vs. LoRa vs. Sigfox, LINKLabs, Jan 2017.|url-status=live|archive-url=https://web.archive.org/web/20171110171333/https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox|archive-date=2017-11-10}}</ref> and LTE-M<ref>{{cite web|url=https://www.link-labs.com/blog/what-is-lte-m|title=What is LTE-M?|url-status=live|archive-url=https://web.archive.org/web/20171109081312/https://www.link-labs.com/blog/what-is-lte-m|archive-date=2017-11-09}}</ref> can connect up to millions of sensors and devices using cellular technology.

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

* Increased lifespan<ref>{{Cite book |last1=Janakiram |first1=Kottnana |last2=Reginald |first2=P. Joshua |title=2023 7th International Conference on Computing Methodologies and Communication (ICCMC) |chapter=Extending the Lifespan of Wireless Sensor Networks using Graph Theory Approaches |date=2023-02-23 |chapter-url=https://ieeexplore.ieee.org/document/10084135 |location=Erode, India |publisher=IEEE |pages=993–997 |doi=10.1109/ICCMC56507.2023.10084135 |isbn=978-1-6654-6408-6|s2cid=257959382 }}</ref>
* Robustness and fault tolerance<ref>{{Cite journal |last1=Lyakhov |first1=P. A. |last2=Kalita |first2=D. I. |date=2023-05-03 |title=Reliable Kalman Filtering with Conditionally Local Calculations in Wireless Sensor Networks |url=https://link.springer.com/10.3103/S0146411623020062 |journal=Automatic Control and Computer Sciences |language=en |volume=57 |issue=2 |pages=154–166 |doi=10.3103/S0146411623020062 |s2cid=258465232 |issn=0146-4116}}</ref>
* Self-configuration<ref>{{Cite book |last1=Shi |first1=Junyang |last2=Sha |first2=Mo |title=IEEE INFOCOM 2019 - IEEE Conference on Computer Communications |chapter=Parameter Self-Configuration and Self-Adaptation in Industrial Wireless Sensor-Actuator Networks |date=2019-06-17 |chapter-url=https://ieeexplore.ieee.org/document/8737467 |location=Paris, France |publisher=IEEE |pages=658–666 |doi=10.1109/INFOCOM.2019.8737467 |isbn=978-1-7281-0515-4|s2cid=86721016 }}</ref>

Lifetime maximization: Energy/Power Consumption of the sensing device should be minimized and sensor nodes should be energy efficient since their limited energy resource determines their lifetime. To conserve power, wireless sensor nodes normally power off both the radio transmitter and the radio receiver when not in use.<ref name=Zander/>

====Routing protocols====
Wireless sensor networks are composed of low-energy, small-size, and low-range unattended sensor nodes. Recently, it has been observed that by periodically turning on and off the sensing and communication capabilities of sensor nodes, we can significantly reduce the active time and thus prolong network lifetime.<ref>{{cite book|first1=A.|last1=Xenakis|first2=F.|last2=Foukalas|first3=G.|last3=Stamoulis|title=Proceedings of the 19th Panhellenic Conference on Informatics |chapter=Minimum weighted clustering algorithm for wireless sensor networks |doi=10.1145/2801948.2801999|date=October 2015|pages=255–260|isbn=978-1-4503-3551-5|s2cid=9188571}}</ref><ref>{{cite book|first1=T. A. H.|last1=Hassan|first2=G.|last2=Selim|first3=R.|last3=Sadek |publisher=IEEE |title=2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) |chapter=A novel energy efficient vice Cluster Head routing protocol in Wireless Sensor Networks |location=Cairo|year=2015|pages=313–320|doi=10.1109/IntelCIS.2015.7397240|isbn=978-1-5090-1949-6|s2cid=10688614}}</ref> However, this duty cycling may result in high network latency, routing overhead, and neighbor discovery delays due to asynchronous sleep and wake-up scheduling. These limitations call for a countermeasure for duty-cycled wireless sensor networks which should minimize routing information, routing traffic load, and energy consumption. Researchers from Sungkyunkwan University have proposed a lightweight non-increasing delivery-latency interval routing referred as LNDIR. This scheme can discover minimum latency routes at each non-increasing delivery-latency interval instead of each time slot.{{clarify|date=September 2020}} Simulation experiments demonstrated the validity of this novel approach in minimizing routing information stored at each sensor. Furthermore, this novel routing can also guarantee the minimum delivery latency from each source to the sink. Performance improvements of up to 12-fold and 11-fold are observed in terms of routing traffic load reduction and energy efficiency, respectively, as compared to existing schemes.<ref name="LNDIR">{{cite journal |last1=K Shahzad |first1=Muhammad |last2=Nguyen |first2=Dang Tu |last3=Zalyubovskiy |first3=Vyacheslav |last4=Choo |first4=Hyunseung |date=2018 |title=LNDIR: A lightweight non-increasing delivery-latency interval-based routing for duty-cycled sensor networks |journal= International Journal of Distributed Sensor Networks |volume=14 |issue=4 |page=1550147718767605 |doi=10.1177/1550147718767605|doi-access=free }} [[File:CC-BY icon.svg|50px]] Material was copied from this source, which is available under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].</ref>

====Operating systems====
[[Operating system]]s for wireless sensor network nodes are typically less complex than general-purpose operating systems. They more strongly resemble [[embedded system]]s, for two reasons. First, wireless sensor networks are typically deployed with a particular application in mind, rather than as a general platform. Second, a need for low costs and low power leads most wireless sensor nodes to have low-power microcontrollers ensuring that mechanisms such as virtual memory are either unnecessary or too expensive to implement.

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.

[[TinyOS]], developed by [[David Culler]], is perhaps the first operating system specifically designed for wireless sensor networks. TinyOS is based on an [[event-driven programming]] model instead of [[Thread (computer science)|multithreading]]. TinyOS programs are composed of ''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 signals the appropriate event handler to handle the event. Event handlers can post tasks that are scheduled by the TinyOS kernel some time later.

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

[[Contiki]], developed by [[Adam Dunkels]], is an OS which uses a simpler programming style in C while providing advances such as [[6LoWPAN]] and [[Protothreads]].

[[RIOT (operating system)]] is a more recent real-time OS including similar functionality to Contiki.

PreonVM<ref>{{cite web|url=https://www.virtenio.com/en/preonvm-virtual-maschine.html|title=PreonVM – Virtual maschine for wireless sensor devices|archive-url=https://web.archive.org/web/20171111094628/https://www.virtenio.com/en/preonvm-virtual-maschine.html|archive-date=2017-11-11 }}</ref> is an OS for wireless sensor networks, which provides [[6LoWPAN]] based on [[Contiki]] and support for the [[Java (programming language)|Java]] programming language.

===Online collaborative sensor data management platforms===
Online collaborative sensor data management platforms are on-line database services that allow sensor owners to register and connect their devices to feed data into an online database for storage and also allow developers to connect to the database and build their own applications based on that data. Examples include [[Xively]] and the [http://wikisensing.org Wikisensing platform] {{Webarchive|url=https://web.archive.org/web/20210609165209/http://wikisensing.org/ |date=2021-06-09 }}. Such platforms simplify online collaboration between users over diverse data sets ranging from energy and environment data to that collected from transport services. Other services include allowing developers to embed real-time graphs & widgets in websites; analyse and process historical data pulled from the data feeds; send real-time alerts from any datastream to control scripts, devices and environments.

The architecture of the Wikisensing system<ref>{{Cite journal | last1 = Silva | first1 = D. | last2 = Ghanem | first2 = M. | last3 = Guo | first3 = Y. | doi = 10.3390/s121013295 | title = WikiSensing: An Online Collaborative Approach for Sensor Data Management | journal = Sensors | volume = 12 | issue = 10 | pages = 13295–332 | year = 2012 | pmid = 23201997| pmc = 3545568| bibcode = 2012Senso..1213295S | doi-access = free }}</ref> describes the key components of such systems to include APIs and interfaces for online collaborators, a middleware containing the business logic needed for the sensor data management and processing and a storage model suitable for the efficient storage and retrieval of large volumes of data.