A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.
The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning "motes" of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of dollars, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth. The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.
An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system. Why would be necessary the implementation of artificial neural networks? Although computing these days is truly advanced, there are certain tasks that a program made for a common microprocessor is unable to perform; even so a software implementation of a neural network can be made with their advantages and disadvantages.
Advantages:
* A neural network can perform tasks that a linear program can not. * When an element of the neural network fails, it can continue without any problem by their parallel nature. * A neural network learns and does not need to be reprogrammed. * It can be implemented in any application. * It can be implemented without any problem.
Disadvantages:
* The neural network needs training to operate. * The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated. * Requires high processing time for large neural networks.
Another aspect of the artificial neural networks is that there are different architectures, which consequently requires different types of algorithms, but despite to be an apparently complex system, a neural network is relatively simple.
Artificial neural networks (ANN) are among the newest signal-processing technologies in the engineer's toolbox. The field is highly interdisciplinary, but our approach will restrict the view to the engineering perspective. In engineering, neural networks serve two important functions: as pattern classifiers and as nonlinear adaptive filters. We will provide a brief overview of the theory, learning rules, and applications of the most important neural network models. Definitions and Style of Computation An Artificial Neural Network is an adaptive, most often nonlinear system that learns to perform a function (an input/output map) from data. Adaptive means that the system parameters are changed during operation, normally called the training phase . After the training phase the Artificial Neural Network parameters are fixed and the system is deployed to solve the problem at hand (the testing phase ). The Artificial Neural Network is built with a systematic step-by-step procedure to optimize a performance criterion or to follow some implicit internal constraint, which is commonly referred to as the learning rule . The input/output training data are fundamental in neural network technology, because they convey the necessary information to 'discover' the optimal operating point. The nonlinear nature of the neural network processing elements (PEs) provides the system with lots of flexibility to achieve practically any desired input/output map, i.e., some Artificial Neural Networks are universal mappers . There is a style in neural computation that is worth describing.
An input is presented to the neural network and a corresponding desired or target response set at the output (when this is the case the training is called supervised ). An error is composed from the difference between the desired response and the system output. This error information is fed back to the system and adjusts the system parameters in a systematic fashion (the learning rule). The process is repeated until the performance is acceptable. It is clear from this description that the performance hinges heavily on the data. If one does not have data that cover a significant portion of the operating conditions or if they are noisy, then neural network technology is probably not the right solution. On the other hand, if there is plenty of data and the problem is poorly understood to derive an approximate model, then neural network technology is a good choice. This operating procedure should be contrasted with the traditional engineering design, made of exhaustive subsystem specifications and intercommunication protocols. In artificial neural networks, the designer chooses the network topology, the performance function, the learning rule, and the criterion to stop the training phase, but the system automatically adjusts the parameters. So, it is difficult to bring a priori information into the design, and when the system does not work properly it is also hard to incrementally refine the solution. But ANN-based solutions are extremely efficient in terms of development time and resources, and in many difficult problems artificial neural networks provide performance that is difficult to match with other technologies. Denker 10 years ago said that 'artificial neural networks are the second best way to implement a solution' motivated by the simplicity of their design and because of their universality, only shadowed by the traditional design obtained by studying the physics of the problem. At present, artificial neural networks are emerging as the technology of choice for many applications, such as pattern recognition, prediction, system identification, and control
Network security consists of the provisions made in an underlying computer network infrastructure, policies adopted by the network administrator to protect the network and the network-accessible resources from unauthorized access, and consistent and continuous monitoring and measurement of its effectiveness (or lack) combined together.
Comparison with information security
The terms network security and information security are often used interchangeably, however network security is generally taken as providing protection at the boundaries of an organization, keeping the intruders (e.g. black hat hackers, script kiddies, Trudy, etc.) out. Network security systems today are mostly effective, so the focus has shifted to protecting resources from attack or simple mistakes by people inside the organization, e.g. with Digital Leak Protection (DLP). One response to this insider threat in network security is to compartmentalize large networks, so that an employee would have to cross an internal boundary and be authenticated when they try to access privileged information. Information security is explicitly concerned with all aspects of protecting information resources, including network security and DLP.
Network security concepts Network security starts from authenticating any user, commonly (one factor authentication) with a username and a password (something you know). With two factor authentication something you have is also used (e.g. a security token or 'dongle', an ATM card, or your mobile phone), or with three factor authentication something you are is also used (e.g. a fingerprint or retinal scan). Once authenticated, a stateful firewall enforces access policies such as what services are allowed to be accessed by the network users. Though effective to prevent unauthorized access, this component fails to check potentially harmful content such as computer worms being transmitted over the network. An intrusion prevention system (IPS helps detect and inhibit the action of such malware. An anomaly-based intrusion detection system also monitors network traffic for suspicious content, unexpected traffic and other anomalies to protect the network e.g. from denial of service attacks or an employee accessing files at strange times. Communication between two hosts using the network could be encrypted to maintain privacy. Individual events occurring on the network could be tracked for audit purposes and for a later high level analysis. Honeypots, essentially decoy network-accessible resources, could be deployed in a network as surveillance and early-warning tools. Techniques used by the attackers that attempt to compromise these decoy resources are studied during and after an attack to keep an eye on new exploitation techniques. Such analysis could be used to further tighten security of the actual network being protected by the honeypot.
A network diagram is a general type of diagram, which represents some kind of network. A network in general is an interconnected group or system, or a fabric or structure of fibrous elements attached to each other at regular intervals, or formally: a graph.
A network diagrams is a special kind of cluster diagram, which even more general represents any cluster or small group or bunch of something, structured or not. Both the flow diagram and the tree diagram can be seen as a specifictype of network diagram.
Types of network diagrams
There are different types network diagrams:
Artificial neural network or "neural network" (NN), is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation.
Computer network diagram is a schematic depicting the nodes and connections amongst nodes in a computer network or, more generally, any telecommunications network.
In project management a network diagram is the logical representation of activities, that defines the sequence or the work of a project. It shows the path of a project, lists starting and completion dates , and names the responsibilities for each task. At a glance it explains how the work of the project goes together. A network for a simple project might consist one or two pages, and on a larger project several network diagrams may exist. Specific diagrams here are
Project network: a general flow chart depicting the sequence in which a project's terminal elements are to be completed by showing terminal elements and their dependencies.
PERT network
Neural network diagram: ia a network or circuit of biological neurons or artificial neural networks, which are composed of artificial neurons or nodes.
A semantic network is a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes].
A sociogram is a graphic representation of social links that a person has. It is a sociometric chart that plots the structure of interpersonal relations in a group situation
A network diagram is a general type of diagram, which represents some kind of network. A network in general is an interconnected group or system, or a fabric or structure of fibrous elements attached to each other at regular intervals, or formally: a graph.
A network diagrams is a special kind of cluster diagram, which even more general represents any cluster or small group or bunch of something, structured or not. Both the flow diagram and the tree diagram can be seen as a specific type of network diagram.
Types of network diagrams
There are different types network diagrams:
Artificial neural network or "neural network" (NN), is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation.
Computer network diagram is a schematic depicting the nodes and connections amongst nodes in a computer network or, more generally, any telecommunications network.
In project management a network diagram is the logical representation of activities, that defines the sequence or the work of a project. It shows the path of a project, lists starting and completion dates , and names the responsibilities for each task. At a glance it explains how the work of the project goes together. A network for a simple project might consist one or two pages, and on a larger project several network diagrams may exist. Specific diagrams here are
Project network: a general flow chart depicting the sequence in which a project's terminal elements are to be completed by showing terminal elements and their dependencies.
PERT network
Neural network diagram: ia a network or circuit of biological neurons or artificial neural networks, which are composed of artificial neurons or nodes.
A semantic network is a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes]].
A sociogram is a graphic representation of social links that a person has. It is a sociometric chart that plots the structure of interpersonal relations in a group situation
3G is the third generation of tele standards and technology for mobile networking, superseding 2.5G. It is based on the International Telecommunication Union (ITU) family of standards
3G networks enable network operators to offer users a wider range of more advanced services while achieving greater network capacity through improved spectral efficiency. Services include wide-area wireless voice telephony, video calls, and broadband wireless data, all in a mobile environment. Additional features also include HSPA data transmission capabilities able to deliver speeds up to 14.4 Mbit/s on the downlink and 5.8 Mbit/s on the uplink.
Unlike IEEE 802.11 networks, which are commonly called Wi-Fi or WLAN networks, 3G networks are wide-area cellular telephone networks that evolved to incorporate high-speed Internet access and video telephony. IEEE 802.11 networks are short range, high-bandwidth networks primarily developed for data.
The International Telecommunication Union (ITU) defined the demands for 3G mobile networks with the IMT-2000 standard. An organization called 3rd Generation Partnership Project (3GPP) has continued that work by defining a mobile system that fulfills the IMT-2000 standard. This system is called Universal Mobile Telecommunications System (UMTS).
An artificial neural network (ANN) or simply "neural network" (NN), is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. In more practical terms neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
The network in artificial neural network
The word network in the term 'artificial neural network' arises because the function f(x) is defined as a composition of other functions gi(x), which can further be defined as a composition of other functions। This can be conveniently represented as a network structure, with arrows depicting the dependencies between variables। A widely used type of
composition is the nonlinear weighted sum, where , where K is some predefined function, such as the hyperbolic tangent। It will be convenient for the following to refer to a collection of functions gi as simply a vector
ANN dependency graph
The figure depicts such a decomposition of f, with dependencies between variables indicated by arrows. These can be interpreted in two ways. The first view is the functional view: the input x is transformed into a 3-dimensional vector h, which is then transformed into a 2-dimensional vector g, which is finally transformed into f. This view is most commonly encountered in the context of optimization. The second view is the probabilistic view: the random variable F = f(G) depends upon the random variable G = g(H), which depends upon H = h(X), which depends upon the random variable X. This view is most commonly encountered in the context of graphical models. The two views are largely equivalent. In either case, for this particular network architecture, the components of individual layers are independent of each other (e.g., the components of g are independent of each other given their input h). This naturally enables a degree of parallelism in the implementation.
Recurrent ANN dependency graph
Networks such as the previous one are commonly called feedforward, because their graph is a directed acyclic graph. Networks with cycles are commonly called recurrent. Such networks are commonly depicted in the manner shown at the top of the figure, where f is shown as being dependent upon itself. However, there is an implied temporal dependence which is not shown. What this actually means in practice is that the value of f at some point in time t depends upon the values of f at zero or at one or more other points in time. The graphical model at the bottom of the figure illustrates the case: the value of f at time t only depends upon its last value.
A network diagram is a general type of diagram, which represents some kind of network. A network in general is an interconnected group or system, or a fabric or structure of fibrous elements attached to each other at regular intervals, or formally: a graph.
