Furthermore, this novel routing can also guarantee the minimum delivery latency from each source to the sink. Performance improvements of up to fold and fold are observed in terms of routing traffic load reduction and energy efficiency, respectively, as compared to existing schemes. Operating systems for wireless sensor network nodes are typically less complex than general-purpose operating systems. They more strongly resemble embedded systems , 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.
However, such operating systems are often designed with real-time properties. TinyOS is perhaps the first  operating system specifically designed for wireless sensor networks. TinyOS is based on an event-driven programming model instead of 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. 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 Wikisensing platform. Such platforms simplify online collaboration between users over diverse data sets ranging from energy and environment data to that collected from transport services. 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 retrieval of large volumes of data.
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 modelling was originally based on social simulation. Infrastructure-less architecture i.
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Therefore, security 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 computer networks 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.
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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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. 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. 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.
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This is a form of in-network processing where sensor nodes 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 this scenario.
Providing security to aggregate data in wireless sensor networks is known as secure data aggregation in WSN.
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Two main security challenges in secure data aggregation are confidentiality and integrity of data. While encryption 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 adversary. Similarly an aggregator can inject false data into the aggregate and make the base station accept false data.
Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks - Semantic Scholar
Thus, while data aggregation improves energy efficiency of a network, it complicates the existing security challenges. From Wikipedia, the free encyclopedia. For other uses, see WSN disambiguation. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Main article: Data logging. Main article: Structural health monitoring.
Main article: sensor node. Fundamentals of wireless sensor networks: theory and practice. John Wiley and Sons.
Wireless sensor networks: technology, protocols, and applications. SPIE Newsroom. O'Connor Pervasive Computing Technologies for Healthcare, Archived PDF from the original on Bibcode : arXivB. Hart and K. Martinez, "Environmental Sensor Networks: A revolution in the earth system science? Saleem; N. Al-Muhtadi Bibcode : ISenJ.. This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources e.
These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative. It seems that you're in Germany.
We have a dedicated site for Germany. Authors: Abdelgawad , Ahmed, Bayoumi , Magdy. This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources e. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative.