Sensor node

Another approach to the breach path problem is finding the path which is as far as possible from the sensor nodes as suggested in [26], where the maximum breach path and maximum support path problems are formulated. In the maximum breach path formulation the objective is to find a path from the initial point to the destination point where the smallest distance from the set of sensor nodes is maximized. In the former problem, the longest distance between any point and the set of sensor nodes is minimized. To solve these problems, Kruskal s algorithm is modified to find the maximal spanning tree, and the definition of a breach number tree is introduced as a binary tree whose leaves are the vertices of the Voronoi graph. 

The field to be monitored is usually narrow and long in perimeter security applications. Thus, non-uniform deployment may be necessary. He et al. conclude that the sensor nodes generate false alarms at a non-negligible rate [18], and an exponentially weighted moving average on the sensor node is sufficient to eliminate transient alarms. 

Figure 5. The effect of the number of sensor nodes on the breach probability for yv Uniform(0, M — 1).

While analyzing the required number of sensor nodes for a given breach probability, we consider two cases of random deployment. In the first case, we assume that the sensor nodes are uniformly distributed along both the vertical and horizontal axes. In the second case, the sensor nodes are deployed uniformly along the horizontal axis and normally distributed along the vertical axis with mean M/2 and a standard deviation of 10% of the width of the field. The latter represents cases where the sensor nodes are deployed from an aircraft of a vehicle. In the... 

Analyzing Fig. 6, the above-mentioned saturation is seen more clearly for the normal-distributed y-axis scheme. For this kind of deployment, since the sensor node may fall outside the field, the breach probability decreases slower compared to the uniformly distributed y-axis scheme. 

Wireless sensor networks are prone to failures. Furthermore, the sensor nodes die due to their limited energy resources. Therefore, the failures of sensor nodes must be modeled and incorporated into the breach path calculations in the future. Simulating the reliability of the network throughout the entire life of the wireless sensor network is also required. Lastly, especially for perimeter surveillance applications, obstacles in the environment play a critical role in terms of sensing and must be incorporated into the field model. 

The nodes of a wireless sensor network can be broken down into two types energy unconstrained network stations and energy constrained sensor nodes. While many applications will only need one network station, the demand for sensor nodes may reach thousands [Bha 02],... 

As these networked sensors become more prevalent, clusters of sensors at a given node are also likely to be developed, which will introduce additional functions to the sensor node. For example, in the vehicle dynamics application, both inertial and angular rate sensors will be required. It is likely that these will be clustered into a single module that communicates to the ECU via the sensor network. 

Pister KSJ, Kahn JM, Boser BE et al. (1999) Smart dust Wireless networks of millimeter-scale sensor nodes. Electro. Res. Lab. Res. Sum. 

The evaluation of DQ dimensions, such as completeness, accuracy, timeliness and correctness, among others, allows us to diagnose the quality of the network and to recommend improvements. The evaluation of completeness (degree to which values are present in a data harvest) of the physical parameters, enable us to infer how many sensor nodes are active and functioning properly. The evaluation of accuracy (distance between the correct and the measured value), enable us to ascertain the efficiency of the system. 

There are many applications for WSNs, mostly in the research and development field, due to the complexity and multidisciplinary aspects of WSNs. Generally, these applications may be classified into three loose groups monitoring, tracking and control. In a typical WSN application, sensor nodes are scattered around an area in order to collect some particular data of interest. 

Evaluating a WSN, which may be considered a distributed database and that each sensor node is, in essence, a tuple of a relation, where the completeness of the tuple could be measured, that is, if all the data that could be collected by the sensor node was actually made available. Another set of metrics, for the same dimension, could be for the attribute, in which it would be possible to verify that all the sensor nodes have executed the collection of a special physical quantities, and with this, inferred the quantity of nodes that are active in the relation. Furthermore, a third set of metrics for the same dimension could be executed for the complete relation, in which all the values of the relations would be evaluated by their existence or inexistence. 

By employing the same line of reasoning from the previous stage, in the event it has created an alert informing the need to take corrective action against a potential problem in the system, data requisitions could be executed more frequently, in order to confirm the existence or inexistence of the problem. Another action aimed at the automatic recovery of the system, would be the execution of a specific measurement for each failed node. In the event the existence of a problem is actually confirmed, the system administrator must be advised, and possible physical and localized corrections must be made, such as the changing of batteries (if access to the location is possible) or even the introduction of new sensor nodes. 

Almost all types of sensors can be attached to such a sensor node as long as the power consumption is in relation to the purpose of the sensing system. Low-power sensors are first choice and that is the reason why micro-sensors called MEMS (micro electro-mechanical systems) are very attractive to be combined with such a system. MEMS are small integrated devices combining electrical and mechanical components that could be produced for about 50 each. Because the process elements and internal linkage movements are now small, these MEMS-based transducers consume very little power. The low cost, low power and small size of MEMS-based transducers have revolutionized the way we can measure. This includes also the combination of sensor data and the formation of networks of sensors as well as combination with low power video techniques (VSLI cameras). 

Fig. 15.2. Scheme of a multi-hop sensor network using clustered sensor nodes. 

However, MEMS sensors are not available for all kind of applications regarding structural health monitoring in civil engineering. Therefore, sensor nodes are developed to enable motes to communicate with conventional sensors as well, i.e. in addition to MEMS. These sensing techniques are called hybrid sensor nodes. Although these sensors are low-power sensors, they will partly be replaced by MEMS as soon as they are available. 

The development in integrated radio-frequency identification (RFID)-enabled wireless sensor network infrastructures using UHF and microwave frequencies has been discussed (2). Inkjet printed technology on flexible paper substrates and the integration of sensors, wireless modules, discrete components and power sources have been proposed as a solution for low-cost, lightweight, and environmental-friendly methods for RFID-enabled sensors and wireless sensor nodes. 

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