Wireless sensor network - Knowledge (XXG)

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network lifetime. 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. 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.

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can measure averages or directionality for example of readings from other nodes. For example, in sensing and monitoring applications, it is generally the case that neighboring sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires techniques for in-network data aggregation and mining. Aggregation reduces the amount of network traffic which helps to reduce energy consumption on sensor nodes. Recently, it has been found that network gateways also play an important role in improving energy efficiency of sensor nodes by scheduling more resources for the nodes with more critical energy efficiency need and advanced energy efficient scheduling algorithms need to be implemented at network gateways for the improvement of the overall network energy efficiency.

378:, since more sensors both improves the detection rate and reduces false alarms. WATS sensors could be deployed in permanent positions or mounted in vehicles for mobile protection of specific locations. One barrier to the implementation of WATS is the size, weight, energy requirements and cost of currently available wireless sensors. The development of improved sensors is a major component of current research at the Nonproliferation, Arms Control, and International Security (NAI) Directorate at LLNL. 231:). However, sensors for gases and particulate matter suffer from high unit-to-unit variability, cross-sensitivities, and (concept) drift. Moreover, the quality of data is currently insufficient for trustworthy decision-making, as field calibration leads to unreliable measurement results, and frequent recalibration might be required. A possible solution could be blind calibration or the usage of mobile references. 792:. 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. 892:" which was used for detection of sophisticated attacks, like blackhole or wormhole, which degrade the throughput of large networks to close-to-zero. This primitive has since been used by many researchers and commercial wireless packet sniffers. This was subsequently refined for more sophisticated attacks such as with collusion, mobility, and multi-antenna, multi-channel devices. 187:, or other similar devices. Body-area networks can collect information about an individual's health, fitness, and energy expenditure. In health care applications the privacy and authenticity of user data has prime importance. Especially due to the integration of sensor networks, with IoT, the user authentication becomes more challenging; however, a solution is presented in recent work. 63:. Modern networks are bi-directional, both collecting data and enabling control of sensor activity. The development of these networks was motivated by military applications such as battlefield surveillance. Such networks are used in industrial and consumer applications, such as industrial process monitoring and control and machine health monitoring and agriculture. 3608: 685:, 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. 179:

Environment-embedded systems employ sensors contained in the environment. Possible applications include body position measurement, location of persons, overall monitoring of ill patients in hospitals and at home. Devices embedded in the environment track the physical state of a person for continuous health diagnosis, using as input the data from a network of

2735: 371:. WATS would not use a centralized computer for analysis because researchers found that factors such as latency and available bandwidth tended to create significant bottlenecks. Data processed in the field by the network itself (by transferring small amounts of data between neighboring sensors) is faster and makes the network more scalable. 958:

are assumed to be unsecured with limited available energy, while the base station is assumed to be secure with unlimited available energy. Aggregation complicates the already existing security challenges for wireless sensor networks and requires new security techniques tailored specifically for these

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Reprogramming is the process of updating the code on the sensor nodes. The most feasible form of reprogramming is remote reprogramming whereby the code is disseminated wirelessly while the nodes are deployed. Different reprogramming protocols exist that provide different levels of speed of operation,

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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.

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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

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There are studies that show that using sensors for incident monitoring improve in a great way the response of firefighters and police to an unexpected situation. For an early detection of incidents we can use acoustic sensors to detect a spike in the noise of the city because of a possible accident,

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To reduce communication costs some algorithms remove or reduce nodes' redundant sensor information and avoid forwarding data that is of no use. This technique has been used, for instance, for distributed anomaly detection or distributed optimization. As nodes can inspect the data they forward, they

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As for the distributed filtering over distributed sensor network. the general setup is to observe the underlying process through a group of sensors organized according to a given network topology, which renders the individual observer estimates the system state based not only on its own measurement

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The architecture of the Wikisensing system 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

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has started. The nodes can be equipped with sensors to measure temperature, humidity and gases which are produced by fire in the trees or vegetation. The early detection is crucial for a successful action of the firefighters; thanks to Wireless Sensor Networks, the fire brigade will be able to know

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detection system makes use of a wireless sensor network to detect the slight movements of soil and changes in various parameters that may occur before or during a landslide. Through the data gathered it may be possible to know the impending occurrence of landslides long before it actually happens.

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first motivated and proposed a radio connectivity based system for localization of wireless sensor networks. Subsequently, such localization systems have been referred to as range free localization systems, and many localization systems for wireless sensor networks have been subsequently proposed

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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

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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

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might vary in size from a shoebox to (theoretically) a grain of dust, although microscopic dimensions have yet to be realized. Sensor node cost is similarly variable, ranging from a few to hundreds of dollars, depending on node sophistication. Size and cost constraints constrain resources such as

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Wireless sensor networks also are used for the collection of data for monitoring of environmental information. This can be as simple as monitoring the temperature in a fridge or the level of water in overflow tanks in nuclear power plants. The statistical information can then be used to show how

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If a centralized architecture is used in a sensor network and the central node fails, then the entire network will collapse, however the reliability of the sensor network can be increased by using a distributed control architecture. Distributed control is used in WSNs for the following reasons:

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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

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is traditionally used to provide end to end confidentiality in wireless sensor network, the aggregators in a secure data aggregation scenario need to decrypt the encrypted data to perform aggregation. This exposes the plaintext at the aggregators, making the data vulnerable to attacks from an

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Macro-programming is a term coined by Matt Welsh. It refers to programming the entire sensor network as an ensemble, rather than individual sensor nodes. Another way to macro-program a network is to view the sensor network as a database, which was popularized by the TinyDB system developed by

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Sensors and devices used in wireless sensor networks are state-of-the-art technology with the lowest possible price. The sensor measurements we get from these devices are therefore often noisy, incomplete and inaccurate. Researchers studying wireless sensor networks hypothesize that much more

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monitoring involves analyzing water properties in dams, rivers, lakes and oceans, as well as underground water reserves. The use of many wireless distributed sensors enables the creation of a more accurate map of the water status, and allows the permanent deployment of monitoring stations in

44:) refer to networks of spatially dispersed and dedicated sensors that monitor and record the physical conditions of the environment and forward the collected data to a central location. WSNs can measure environmental conditions such as temperature, sound, pollution levels, humidity and wind. 178:

There are several types of sensor networks for medical applications: implanted, wearable, and environment-embedded. Implantable medical devices are those that are inserted inside the human body. Wearable devices are used on the body surface of a human or just at close proximity of the user.

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Network localization refers to the problem of estimating the location of wireless sensor nodes during deployments and in dynamic settings. For ultra-low power sensors, size, cost and environment precludes the use of Global Positioning System receivers on sensors. In 2000, Nirupama Bulusu,

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operates at 915 MHz and in the EU 868 MHz has been widely used but these have a lower data rate (typically 50 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

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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.

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The base stations are one or more components of the WSN with much more computational, energy and communication resources. They act as a gateway between sensor nodes and the end user as they typically forward data from the WSN on to a server. Other special components in

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At present, agent-based modeling and simulation is the only paradigm which allows the simulation of complex behavior in the environments of wireless sensors (such as flocking). Agent-based simulation of wireless sensor and ad hoc networks is a relatively new paradigm.

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There have been important innovations in securing wireless sensor networks. Most wireless embedded networks use omni-directional antennas and therefore neighbors can overhear communication in and out of nodes. This was used this to develop a primitive called

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Monitoring the quality and level of water includes many activities such as checking the quality of underground or surface water and ensuring a country's water infrastructure for the benefit of both human and animal. It may be used to protect the wastage of water.

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reliability, energy expenditure, requirement of code resident on the nodes, suitability to different wireless environments, resistance to DoS, etc. Popular reprogramming protocols are Deluge (2004), Trickle (2004), MNP (2005), Synapse (2008), and Zephyr (2009).

937:(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. 971:

adversary. Similarly an aggregator can inject false data into the aggregate and make the base station accept false data. Thus, while data aggregation improves energy efficiency of a network, it complicates the existing security challenges.

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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. A military example is the use of sensors to detect enemy intrusion; a civilian example is the

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Traditional layered approach cannot share different information among different layers, which leads to each layer not having complete information. The traditional layered approach cannot guarantee the optimization of the entire

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Wireless sensor networks can be used to monitor the condition of civil infrastructure and related geo-physical processes close to real time, and over long periods through data logging, using appropriately interfaced sensors.

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Because of the interference between the different users, access conflicts, fading, and the change of environment in the wireless sensor networks, traditional layered approach for wired networks is not applicable to wireless

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Infrastructure-less architecture (i.e. no gateways are included, etc.) and inherent requirements (i.e. unattended working environment, etc.) of WSNs might pose several weak points that attract adversaries. Therefore,

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Saleem, Kashif; Fisal, Norsheila; Hafizah, Sharifah; Kamilah, Sharifah; Rashid, Rozeha; Baguda, Yakubu (2009). "Cross layer based biological inspired self-organized routing protocol for wireless sensor network".

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would be useless (or less effective) for WSNs. Hence, lack of security mechanisms would cause intrusions towards those networks. These intrusions need to be detected and mitigation methods should be applied.

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information can be extracted from hundreds of unreliable measurements spread across a field of interest than from a smaller number of high-quality, high-reliability instruments with the same total cost.

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Military Research and Development Subcommittee on October 1, 1997, during a hearing on nuclear terrorism and countermeasures. On August 4, 1998, in a subsequent meeting of that subcommittee, Chairman

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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

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Wireless sensor networks have been used to monitor various species and habitats, beginning with the Great Duck Island Deployment, including marmots, cane toads in Australia and zebras in Kenya.

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Kiran Maraiya, Kamal Kant, Nitin Gupta "Wireless Sensor Network: A Review on Data Aggregation" International Journal of Scientific & Engineering Research Volume 2 Issue 4, April 2011.

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Aghdam, Shahin Mahdizadeh; Khansari, Mohammad; Rabiee, Hamid R; Salehi, Mostafa (2014). "WCCP: A congestion control protocol for wireless multimedia communication in sensor networks".

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in the sense that they rely on wireless connectivity and spontaneous formation of networks so that sensor data can be transported wirelessly. WSNs monitor physical conditions, such as

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There are many applications in monitoring environmental parameters, examples of which are given below. They share the extra challenges of harsh environments and reduced power supply.

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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 connects devices at home.

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The Wide Area Tracking System (WATS) is a prototype network for detecting a ground-based nuclear device such as a nuclear "briefcase bomb." WATS is being developed at the

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is a big concern when WSNs are deployed for special applications such as military and healthcare. Owing to their unique characteristics, traditional security methods of

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can connect sensors operating at 2.4 GHz with a data rate of 250 kbit/s. Many use a lower frequency to increase radio range (typically 1 km), for example

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Bosman, H. H. W. J.; Iacca, G; Tejada, A.; Wörtche, H. J.; Liotta, A. (2015-12-01). "Ensembles of incremental learners to detect anomalies in ad hoc sensor networks".

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Cam, H; Ozdemir, S Nair, P Muthuavinashiappan, D (October 2003). "ESPDA: Energy-efficient and Secure Pattern-based Data Aggregation for wireless sensor networks".

509:(QoS), etc. Sensor nodes can be imagined as small computers which are extremely basic in terms of their interfaces and their components. They usually consist of a 294:

Wireless sensor networks have been developed for machinery condition-based maintenance (CBM) as they offer significant cost savings and enable new functionality.

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A WSN is built of "nodes" – from a few to hundreds or thousands, where each node is connected to other sensors. Each such node typically has several parts: a

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Cross-layer is becoming an important studying area for wireless communications. In addition, the traditional layered approach presents three main problems:

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Magno, M.; Boyle, D.; Brunelli, D.; O'Flynn, B.; Popovici, E.; Benini, L. (2014). "Extended Wireless Monitoring Through Intelligent Hybrid Energy Supply".

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Apte, J.S.; Messier, K.P.; Gani, S.; Brauer, M.; Kirchstetter, T.W.; Lunden, M.M.; Marshall, J.D.; Portier, C.J.; Vermeulen, R.C.H.; Hamburg, S.P. (2017).

1393: 356:(LLNL). WATS would be made up of wireless gamma and neutron sensors connected through a communications network. Data picked up by the sensors undergoes 3518:

Kumar, Vimal; Sanjay K. Madria (August 2012). "Secure Hierarchical Data Aggregation in Wireless Sensor Networks: Performance Evaluation and Analysis".

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Wireless sensors can be placed in locations difficult or impossible to reach with a wired system, such as rotating machinery and untethered vehicles.

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Bosman, H. H. W. J.; Liotta, A.; Iacca, G.; Wörtche, H. J. (October 2013). "Anomaly Detection in Sensor Systems Using Lightweight Machine Learning".

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Bosman, H. H. W. J.; Iacca, G.; Wörtche, H. J.; Liotta, A. (December 2014). "Online Fusion of Incremental Learning for Wireless Sensor Networks".

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Feinberg, S.; Williams, R.; Hagler, G.S.W.; Rickard, J.; Garver, D.; Harshfield, G.; Stauffer, P.; Mattson, E.; Judge, R.; Garvey, S. (2018).

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The data gathered from wireless sensor networks is usually saved in the form of numerical data in a central base station. Additionally, the

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Hassan, T. A. H.; Selim, G.; Sadek, R. (2015). "A novel energy efficient vice Cluster Head routing protocol in Wireless Sensor Networks".

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Tiwari, Ankit; et al. (2007). "Energy-efficient wireless sensor network design and implementation for condition-based maintenance".

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

281:, like floods. Wireless nodes have been deployed successfully in rivers, where changes in water levels must be monitored in real time. 3154:

Bosman, H. H. W. J.; Liotta, A.; Iacca, G.; Wörtche, H. J. (December 2013). "Online Extreme Learning on Fixed-Point Sensor Networks".

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K. Saleem; N. Fisal & J. Al-Muhtadi (2014). "Empirical studies of bio-inspired self-organized secure autonomousRouting protocol".

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FrancescoMario, Di; K, DasSajal; AnastasiGiuseppe (2011-08-01). "Data Collection in Wireless Sensor Networks with Mobile Elements".

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for wireless sensor network nodes are typically less complex than general-purpose operating systems. They more strongly resemble

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Xenakis, A.; Foukalas, F.; Stamoulis, G. (October 2015). "Minimum weighted clustering algorithm for wireless sensor networks".

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Sauce, O.; Hasenfratz, D.; Thiele, L. (2015). "Reducing multi-hop calibration errors in large-scale mobile sensor networks".

1005: 1000: 518: 360:, which converts the information into easily interpreted forms; this data fusion is the most important aspect of the system. 2476: 1928: 1479: 980: 330: 2422: 765: 367:

the sensor network rather than at a centralized computer and is performed by a specially developed algorithm based on

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systems have been working. The advantage of WSNs over conventional loggers is the "live" data feed that is possible.

2301: 934: 706:, is perhaps the first operating system specifically designed for wireless sensor networks. TinyOS is based on an 3958: 3806: 1958: 1858: 985: 711: 609: 963:. were the first few works discussing techniques for secure data aggregation in wireless sensor networks. 3849: 3781: 3477:

Przydatek, Bartosz; Dawn Song; Adrian Perrig (2003). "SIA: Secure information aggregation in sensor networks".

2053:"Psychoacoustic Annoyance Implementation With Wireless Acoustic Sensor Networks for Monitoring in Smart Cities" 1030: 707: 596:. A wireless wide area network used primarily for low-power devices is known as a Low-Power Wide-Area Network ( 497:

So the cross-layer can be used to make the optimal modulation to improve the transmission performance, such as

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energy, memory, computational speed and communications bandwidth. The topology of a WSN can vary from a simple

2711:"LNDIR: A lightweight non-increasing delivery-latency interval-based routing for duty-cycled sensor networks" 1376: 3710: 3574: 1040: 750: 128:, wireless sensor networks are an active research area supporting many workshops and conferences, including 48: 1375:

Tony O'Donovan; John O'Donoghue; Cormac Sreenan; David Sammon; Philip O'Reilly; Kieran A. O'Connor (2009).

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Two main security challenges in secure data aggregation are confidentiality and integrity of data. While

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in cities can vary a lot. Therefore, it is of interest to have higher temporal and spatial resolution of

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Network simulators like Opnet, Tetcos NetSim and NS can be used to simulate a wireless sensor network.

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Iacca, G. (2018). "Distributed optimization in wireless sensor networks: an island-model framework".

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2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

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for sensor networks. However, such operating systems are often designed with real-time properties.

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Balzano, L.; Nowak, R. (2008). "Blind Calibration of Networks of Sensors: Theory and Algorithms".

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There is also no centralised body to allocate the resources and they have to be self organized.

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The traditional layered approach does not have the ability to adapt to the environmental change.

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Wireless sensor networks are used to monitor wine production, both in the field and the cellar.

3864: 3586: 3531: 3490: 3384: 3340: 3281: 3218: 3167: 3126: 3095: 3050: 3009: 2960: 2945:"LITEWORP: A Lightweight Countermeasure for the Wormhole Attack in Multihop Wireless Networks" 2811: 2681: 2640: 2599: 2556: 2507: 2305: 2256: 2131: 1838: 1682: 1649: 1618: 1534: 1461: 1332: 1301: 1276: 1211: 1193: 1109: 1091: 876: 806: 589: 553:

based networks are routers, designed to compute, calculate and distribute the routing tables.

530: 437: 103: 90: 2537:"Reliable Kalman Filtering with Conditionally Local Calculations in Wireless Sensor Networks" 1671:

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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Peiris, V. (2013). "Highly integrated wireless sensing for body area network applications".

1322: 1201: 1183: 1136: 1099: 1081: 880: 678: 278: 121: 3072:. Special Issue on Big Data Inspired Data Sensing, Processing and Networking Technologies. 2092:"A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification" 3801: 2454: 1389: 1020: 789: 682: 86: 78: 74: 1505:"High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data" 393:

administration to a subsistence level and that the program had been poorly re-organized.

81:, an electronic circuit for interfacing with the sensors and an energy source, usually a 3269: 2859: 2787: 2709:

K Shahzad, Muhammad; Nguyen, Dang Tu; Zalyubovskiy, Vyacheslav; Choo, Hyunseung (2018).

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scenarios. Providing security to aggregate data in wireless sensor networks is known as

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and LTE-M can connect up to millions of sensors and devices using cellular technology.

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Hu, Lingxuan; David Evans (January 2003). "Secure aggregation for wireless networks".

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2023 7th International Conference on Computing Methodologies and Communication (ICCMC)

2404: 529:), and a power source usually in the form of a battery. Other possible inclusions are 4041: 3750: 3745: 3307:

Bosman, H. H. W. J.; Iacca, G.; Tejada, A.; Wörtche, H. J.; Liotta, A. (2017-01-01).

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Proceedings of the 1st international conference on Embedded networked sensor systems

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Li, Wangyan; Wang, Zidong; Wei, Guoliang; Ma, Lifeng; Hu, Jun; Ding, Derui (2015).

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is the use of very low power methods for radio communication and data acquisition.

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There are several wireless standards and solutions for sensor node connectivity.

3628: 3380: 3081: 2491: 2389:"Cross-layer energy-aware topology control through Simulated Annealing for WSNs" 2338: 2252: 2019: 1645: 955: 567: 463: 386: 277:

Wireless sensor networks can be effective in preventing adverse consequences of

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Spie (2013). "Vassili Karanassios: Energy scavenging to power remote sensors".

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Parametric Analog Signal Amplification Applied to Nanoscale CMOS Technologies

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Chalermek Intanagonwiwat, Deborah Estrin, Ramesh Govindan, John Heidemann, "

3029:"A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks" 2636: 1756: 1678: 1529: 1504: 1140: 411: 253: 167: 2815: 2418: 2135: 1834: 1622: 1538: 1465: 1215: 1113: 3633: 3607: 3486: 3204: 3163: 3045: 3028: 2772:"WikiSensing: An Online Collaborative Approach for Sensor Data Management" 2213:"THE WORLD LEADER IN VIBRATION HARVESTER POWERED WIRELESS SENSING SYSTEMS" 753:

is a more recent real-time OS including similar functionality to Contiki.

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locations of difficult access, without the need of manual data retrieval.

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2005 International Conference on Dependable Systems and Networks (DSN'05)

2159:"Israeli Tech Firm Rolls Out Tracking Devices the Size of Postage Stamps" 1859:"A national strategy against terrorism using weapons of mass destruction" 1719: 1164:"Wireless Sensor/Actuator Network Design for Mobile Control Applications" 498: 60: 3575:

Impact of Network Density on Data Aggregation in Wireless SensorNetworks

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THE WORLD LEADER IN VIBRATION HARVESTER POWERED WIRELESS SENSING SYSTEMS

1823:"Monitoring High-Quality Wine Production using Wireless Sensor Networks" 1062:"Advances in Smart Environment Monitoring Systems Using IoT and Sensors" 239:

A network of Sensor Nodes can be installed in a forest to detect when a

3872: 3854: 3791: 3740: 3720: 3705: 3700: 3682: 3122: 2796: 2734: 2028: 1010: 761: 757: 740: 732: 550: 110: 3428:"Hybrid energy aware clustered protocol for IoT heterogeneous network" 2921: 2116: 1866: 1446: 1086: 625:, many other proposals have been made to provide sensor connectivity. 4020:

International Conference on Information Processing in Sensor Networks

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To provide nodes with backup in case of failure of the central node.

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2013 IEEE International Conference on Systems, Man, and Cybernetics

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It is therefore possible to use embedded operating systems such as

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2012 IEEE 13th International Conference on Mobile Data Management

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Material was copied from this source, which is available under a

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Wireless sensor networks: technology, protocols, and applications

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2013 IEEE 13th International Conference on Data Mining Workshops

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Xia, Feng; Tian, Yu-Chu; Li, Yanjun; Sun, Youxian (2007-10-09).

689: 626: 451: 3637: 2906:"GPS-less low cost outdoor localization for very small devices" 3624:

Secure Data Aggregation in Wireless Sensor Networks: A *Survey

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Chain-based Gateway nodes routing for energy efficiency in WSN

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IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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PreonVM is an OS for wireless sensor networks, which provides

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Proceedings of the 19th Panhellenic Conference on Informatics

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Anastasi, G.; Farruggia, O.; Lo Re, G.; Ortolani, M. (2009).

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Fundamentals of wireless sensor networks: theory and practice

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International Workshop on Embedded Networked Sensors (EmNetS)

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2009 42nd Hawaii International Conference on System Sciences

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Bulusu, Nirupama; Heidemann, John; Estrin, Deborah (2000).

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Power consumption constraints for nodes using batteries or

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Amir Hozhabri; Mohammadreza Eslaminejad; Mitra Mahrouyan,

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2014 IEEE International Conference on Data Mining Workshop

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stated that research funding for WATS had been cut by the

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Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498)

2212: 1384:. Pervasive Computing Technologies for Healthcare, 2009. 782: 2916:(5). IEEE Personal Communications, October 2000: 28–34. 2753:"PreonVM – Virtual maschine for wireless sensor devices" 2490:

Janakiram, Kottnana; Reginald, P. Joshua (2023-02-23).

1412:"An Authentication Protocol for Future Sensor Networks" 1230:"Wireless sensor networks for battlefield surveillance" 537:, and possibly secondary communication interface (e.g. 440:. Examples of suppliers are ReVibe Energy and Perpetuum 2740:

Creative Commons Attribution 4.0 International License

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Workshop on Security and Assurance in Ad Hoc Networks

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Online collaborative sensor data management platforms

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with limited computational power and limited memory,

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International Journal of Distributed Sensor Networks

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Some mobility of nodes (for highly mobile nodes see

3999: 3972: 3929: 3896: 3863: 3838: 3729: 3671: 2943:Khalil, Issa; Bagchi Saurabh; Shroff, N.B. (2005). 1986:"INRISCO: INcident monitoRing in Smart COmmunities" 1898:. Federation of American Scientists. Archived from 469:

Ability to withstand harsh environmental conditions

1275:. John Wiley and Sons. pp. 168–183, 191–192. 1060:Ullo, Silvia Liberata; Sinha, G. R. (2020-05-31). 27:Group of spatially dispersed and dedicated sensors 2387:Xenakis, A.; Foukalas, F.; Stamoulis, G. (2016). 1935:. Science & Technology Review. Archived from 1865:. Science & Technology Review. Archived from 402:or use termic sensors to detect a possible fire. 211:Experiments have shown that personal exposure to 2455:"NB-IOT vs. LoRa vs. Sigfox, LINKLabs, Jan 2017" 2296:; Jens Zander; Ki Won Sung; Ben Slimane (2016). 1378:A Context Aware Wireless Body Area Network (BAN) 584:In many applications, a WSN communicates with a 420:, WSN:s can be cost-efficiently applied also in 244:when a fire is started and how it is spreading. 4026:Conference on Embedded Networked Sensor Systems 4014:European Conference on Wireless Sensor Networks 3583:Wireless Sensor Networks: A Systems Perspective 521:(including specific conditioning circuitry), a 3629:A list of secure aggregation proposals for WSN 2988:Mitchell, Robert; Chen, Ing-Ray (2014-04-01). 954:This is a form of in-network processing where 3649: 2183:"ReVibe Energy – Powering The Industrial IoT" 525:(usually radio transceivers or alternatively 8: 3411:: CS1 maint: multiple names: authors list ( 2535:Lyakhov, P. A.; Kalita, D. I. (2023-05-03). 2245:TENCON 2009 - 2009 IEEE Region 10 Conference 3356: 3354: 2354:IEEE Transactions on Industrial Electronics 1296:Sohraby, K.; Minoli, D.; Znati, T. (2007). 839:Sensor data calibration and fault tolerance 809:was originally based on social simulation. 572:One major challenge in a WSN is to produce 374:An important factor in WATS development is 3656: 3642: 3634: 3457: 3455: 1959:"U.S./Russian National Security Interests" 432:The main characteristics of a WSN include 30:"WSN" redirects here. For other uses, see 3443: 3370: 3334: 3324: 3259: 3212: 3089: 3044: 2849: 2805: 2795: 2726: 2590:. Paris, France: IEEE. pp. 658–666. 2288: 2286: 2284: 2282: 2280: 2125: 2115: 2027: 2001: 1746: 1638:Networked Sensing Information and Control 1612: 1602: 1528: 1455: 1445: 1427: 1300:. John Wiley and Sons. pp. 203–209. 1205: 1187: 1103: 1085: 3984:Ad hoc On-Demand Distance Vector Routing 3432:Future Computing and Informatics Journal 2498:. Erode, India: IEEE. pp. 993–997. 77:or connection to an external antenna, a 3619:IEEE 802.15.4 Standardization Committee 3033:Discrete Dynamics in Nature and Society 2770:Silva, D.; Ghanem, M.; Guo, Y. (2012). 2541:Automatic Control and Computer Sciences 1892:"Striving for a Safer World Since 1945" 1052: 3581:Bulusu, Nirupama; Jha, Sanjay (2005). 3404: 2393:Computers & Electrical Engineering 2193:from the original on 22 September 2017 1922: 1920: 1918: 1916: 1886: 1884: 354:Lawrence Livermore National Laboratory 1410:Bilal, Muhammad; et al. (2017). 1390:10.4108/ICST.PERVASIVEHEALTH2009.5987 1329:Springer Science & Business Media 7: 2582:Shi, Junyang; Sha, Mo (2019-06-17). 2298:Fundamentals of Mobile Data Networks 1509:Environmental Science and Technology 1271:Dargie, W.; Poellabauer, C. (2010). 835:including AHLoS, APS, and Stardust. 796:retrieval of large volumes of data. 621:connectivity. With the emergence of 443:Ability to cope with node failures ( 2829:Niazi, Muaz; Hussain, Amir (2011). 1735:ACM Transactions on Sensor Networks 1321:Oliveira, Joao; Goes, João (2012). 1129:ACM Transactions on Sensor Networks 991:Center for Embedded Network Sensing 2223:from the original on 13 April 2018 1583:Atmospheric Measurement Techniques 905:Sensor nodes are prone to failure, 714:. TinyOS programs are composed of 25: 3365:. Vol. 2. pp. 732–736. 2672:. Cairo: IEEE. pp. 313–320. 2405:10.1016/j.compeleceng.2016.02.015 3606: 2733: 2504:10.1109/ICCMC56507.2023.10084135 2479:from the original on 2017-11-09. 2461:from the original on 2017-11-10. 2443:from the original on 2017-11-09. 2425:from the original on 2017-11-09. 1399:from the original on 2016-10-09. 996:List of ad hoc routing protocols 152:million remote units worldwide. 2057:IEEE Internet of Things Journal 1016:Optical wireless communications 1006:Mobile wireless sensor networks 923:Data integration and sensor web 363:The data fusion process occurs 3949:Sensor network query processor 3426:Rowayda, A. Sadek (May 2018). 1001:Meteorological instrumentation 961:secure data aggregation in WSN 908:For better collection of data, 653:Robustness and fault tolerance 383:U.S. House of Representatives' 1: 2678:10.1109/IntelCIS.2015.7397240 1965:. US House of Representatives 3326:10.1016/j.inffus.2016.04.007 3006:10.1016/j.comcom.2014.01.012 2910:IEEE Personal Communications 2596:10.1109/INFOCOM.2019.8737467 919:but also on its neighbors'. 466:to large scale of deployment 331:Structural health monitoring 325:Structural health monitoring 316:Water/waste water monitoring 225:dangerous gases for citizens 3381:10.1109/icsens.2003.1279038 3082:10.1016/j.adhoc.2015.07.013 2339:10.1016/j.adhoc.2013.10.006 2253:10.1109/TENCON.2009.5395945 2020:10.1109/ACCESS.2020.2987483 1646:10.1007/978-0-387-68845-9_1 273:Natural disaster prevention 199:Environmental/Earth sensing 4064: 3445:10.1016/j.fcij.2018.02.003 2302:Cambridge University Press 935:Open Geospatial Consortium 926: 896:Distributed sensor network 661:receiver when not in use. 565: 409: 328: 304: 29: 3959:Wireless powerline sensor 3278:10.1007/s00500-013-1091-x 2868:10.1109/jsen.2010.2068044 2553:10.3103/S0146411623020062 2069:10.1109/JIOT.2019.2946971 1800:10.1109/JSEN.2014.2308725 986:Bluetooth mesh networking 381:WATS was profiled to the 290:Machine health monitoring 170:of gas or oil pipelines. 109:. Propagation can employ 3782:Near-field communication 3613:Wireless sensor networks 2728:10.1177/1550147718767605 2366:10.1109/TIE.2013.2267694 2157:Liz Young (2022-06-08). 1984:Aguilar, Mónica (2020). 1604:10.5194/amt-11-4605-2018 1362:10.1117/2.1201312.005120 1041:Wireless ad hoc networks 1031:Unattended ground sensor 708:event-driven programming 476:Cross-layer optimization 261:Water quality monitoring 49:wireless ad hoc networks 38:Wireless sensor networks 4048:Wireless sensor network 3665:Wireless sensor network 2994:Computer Communications 2721:(4): 1550147718767605. 2637:10.1145/2801948.2801999 2051:Pastor, Adolfo (2020). 1757:10.1145/1210669.1210670 1679:10.1145/2737095.2737113 1530:10.1021/acs.est.7b00891 1141:10.1145/1993042.1993049 950:Secure data aggregation 751:RIOT (operating system) 424:in various industries. 3990:Dynamic Source Routing 2090:Lloret, Jaime (2009). 1835:10.1109/HICSS.2009.313 1036:Virtual sensor network 768:programming language. 457:Heterogeneity of nodes 207:Air quality monitoring 174:Health care monitoring 3487:10.1145/958491.958521 3205:10.1109/ICDMW.2014.79 3164:10.1109/ICDMW.2013.74 1492:on November 23, 2015. 941:In-network processing 807:Agent-based modelling 418:low-power electronics 285:Industrial monitoring 235:Forest fire detection 107:wireless mesh network 47:These are similar to 3761:Bluetooth Low Energy 3615:at Wikimedia Commons 3577:," November 4, 2001. 3522:. pp. 196–201. 3481:. pp. 255–265. 3199:. pp. 525–532. 3158:. pp. 319–326. 2951:. pp. 612–621. 2838:IEEE Sensors Journal 2631:. pp. 255–260. 1929:"Sensing for Danger" 1780:IEEE Sensors Journal 1720:10.1117/2.3201305.05 1673:. pp. 274–285. 783:Wikisensing platform 764:and support for the 523:communication device 460:Homogeneity of nodes 32:WSN (disambiguation) 3944:Location estimation 3528:10.1109/MDM.2012.49 3270:2018arXiv181002679I 3046:10.1155/2015/683701 2957:10.1109/DSN.2005.58 2860:2011ISenJ..11..404N 2788:2012Senso..1213295S 2163:Wall Street Journal 2108:2009Senso...9.8722L 2012:2020IEEEA...872435I 1792:2014ISenJ..14.2232S 1595:2018AMT....11.4605F 1521:2017EnST...51.6999A 1438:2017Senso..17..979B 1180:2007Senso...7.2157X 1078:2020Senso..20.3113U 533:modules, secondary 397:Incident monitoring 369:Bayesian statistics 248:Landslide detection 3313:Information Fusion 3123:10.1109/SMC.2013.9 2797:10.3390/s121013295 1963:commdocs.house.gov 788:2021-06-09 at the 656:Self-configuration 650:Increased lifespan 646:following issues: 623:Internet of Things 586:local area network 507:quality of service 376:ease of deployment 191:Habitat monitoring 126:telecommunications 4035: 4034: 3611:Media related to 3592:978-1-58053-867-1 3537:978-1-4673-1796-2 3496:978-1-58113-707-1 3390:978-0-7803-8133-9 3254:(12): 2257–2277. 3224:978-1-4799-4274-9 3173:978-1-4799-3142-2 3132:978-1-4799-0652-9 3117:. pp. 7–13. 2922:10.1109/98.878533 2782:(10): 13295–332. 2687:978-1-5090-1949-6 2646:978-1-4503-3551-5 2605:978-1-7281-0515-4 2513:978-1-6654-6408-6 2437:"Wi-Sun Alliance" 2311:978-1-107-14321-0 2262:978-1-4244-4546-2 2117:10.3390/s91108722 2102:(11): 8722–8747. 1844:978-0-7695-3450-3 1688:978-1-4503-3475-4 1655:978-0-387-68843-5 1640:. pp. 9–37. 1515:(12): 6999–7008. 1447:10.3390/s17050979 1338:978-1-4614-1670-8 1307:978-0-471-74300-2 1282:978-0-470-99765-9 1246:Global, I. G. I. 1174:(10): 2157–2173. 1087:10.3390/s20113113 981:Autonomous system 881:computer networks 777:Examples include 710:model instead of 679:Operating systems 674:Operating systems 665:Routing protocols 590:wide area network 531:energy harvesting 503:energy efficiency 438:energy harvesting 279:natural disasters 91:energy harvesting 73:with an internal 16:(Redirected from 4055: 3939:Key distribution 3688:ERIKA Enterprise 3658: 3651: 3644: 3635: 3610: 3596: 3585:. Artech House. 3567: 3550: 3549: 3515: 3509: 3508: 3474: 3468: 3467: 3459: 3450: 3449: 3447: 3423: 3417: 3416: 3410: 3402: 3374: 3358: 3349: 3348: 3338: 3328: 3304: 3298: 3297: 3263: 3243: 3237: 3236: 3216: 3192: 3186: 3185: 3151: 3145: 3144: 3110: 3104: 3103: 3093: 3065: 3059: 3058: 3048: 3024: 3018: 3017: 2985: 2979: 2978: 2940: 2934: 2933: 2901: 2895: 2894: 2892: 2886:. Archived from 2853: 2835: 2826: 2820: 2819: 2809: 2799: 2767: 2761: 2760: 2755:. 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