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Additionally it would be possible for an attacker (either external or internal) to modify the measured distance by jamming the mutual communication of the nodes and then replaying these signals with higher or lower signal strengths The adversary can attempt to change the underlying propagation physics, say by introducing an absorbing barrier between the transmitter and the receiver This would result in signal attenuation, leading to signi cantly lower received signal strength (RSS) As a result the receiver might conclude that it is further from the transmitter than it actually is Ambient noise introduced by the adversary as well as changes in the environment would also affect the signal strength measurements Given the several easy ways in which an adversary can attack a SS based locationing system, the above approaches have not been used so far in the design of secure locationing systems for ad hoc networks Another novel localization approach that leverages signal strength measurement has been recently proposed [148] This is called the radio interferometric positioning system (RIPS) RIPS proposes to use two transmitters which are transmitting at frequencies that are nearly equal The interference caused by these two signals results in a composite signal that has a low frequency and whose envelope signal can be measured using the RSSI indicator on low precision RF chips RIPS proposes to then use two receivers to calculate the relative phase offset of the composite signal at the two receivers This relative phase offset between the two receivers depends only on the four distances between the two transmitters and two receivers By using relative phase offsets calculated at different frequencies, RIPS provides a method to calculate the relative positions of the transmitters and the receivers The security aspects of RIPS need to be investigated, however 72114 Other Techniques Other techniques can also be designed such as the angle-of-arrival (AoA) techniques or the time-difference-of-arrival (TDoA) techniques AoA systems estimate the location based on measurements of the angle of arrival This requires hardware that is capable of measuring the angle at which a signal arrives APS [149] is a system where the AoA technique is used to measure relative angle between two nodes, which is then used to calculate the distance between hem using triangulation The TDoA technique consists of determining the times taken by the same signal to reach different points This is typically made use of in cellular networks where the signal from the mobile device is received at three different stationary base stations, each of which notes the time at which they observe the signal The time difference in arrival of the signal at the three synchronized base stations is then used to estimate the location of the mobile device In TDoA systems an internal attacker can send the signals at different times to the three base stations while fooling these base stations into assuming that they are the same signal thereby resulting in false location estimation 7212 Range-Independent Localization Techniques The other category of ranging schemes is the range-independent technique, which is a less accurate but more costeffective alternative to the range-dependent approach The characteristic of range-independent localization schemes is that, when using these schemes the system nodes can determine their location without measuring any property related to physical distance such as time, angle, or power Since the nodes do not have to perform any measurements there is typically no need for any special hardware16 We next look at some schemes that belong to this family. software ocr brother control center 4 windows 10: Best Free OCR Software for Windows 10 | TechWiser top ocr software for windows 10FreeOCR is Optical Character Recognition Software for Windows and supports scanning from ... 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However, there is a drawback in this proposed technique: The access point constitutes a single point of failure in each cluster If the access node is a cute node (if its removal leaves the graph disconnected), the system attempts to attach the disconnected nodes to an adjacent cluster Otherwise, the algorithm should send the proper recovery messages to modify T for reconnecting the detaching nodes, which can be very costly for a WSN In the Max Min D-Cluster formation proposed in reference 30, the CHs are selected such that they form a d-hop DSG , where d > 1 distance hops form the vertices in the DSG away from any other vertex in C Since d is an input value to the heuristic, it enables control over the density of cluster heads in the network. 5 data, size, image with visual basicnet barcode sdk 6 we estimated the regression coefficients for the full model These are used to calculate the residuals ei = Yi - (-4229 + 005868x il + 1254x i2 + 03372x i3 ) We then rank the residuals and calculate SRWR full = L:[rank(e) - 255]e i = 27120 For the reduced model without X 2 and X 3 , the estimated regression coefficients are ~o = 3688 and ~l = 005151 These are used to calculate the residuals ei = Yi - (3688 + 005151x i1 ) After ranking the residuals, we calculate SRWRrcduccd = L:[rank(e) - 255]e i = 31000 Next we use the residuals from the full model to obtain f From the 50 residuals we compute the 1275 (= 50(51)/2) pairwise averages. The Topology Adaptive Spatial Clustering (TASC) algorithm presented in reference 31 is a distributed algorithm that partitions the network into a set of locally isotropic, nonoverlapping clusters without prior knowledge of the number of clusters, cluster size, and node coordinates The spatial grouping of nodes with respect to regions of close proximity and similar deployment density does the following: (a) It promotes ef cient data aggregation, (b) it allows the use of different data compression rates in each cluster, (c) it improves the overall compression rate in the whole network, and (d) as a consequence, it also helps reducing the propagation of redundant data inside the network Spatial clustering would also assist transmission power control, since intracluster communication requires less transmission power in dense clusters TASC is useful for proactive networks, where the clusters provide the information needed for the cluster formation process. This includes the residuals themselves, since Aii = (e i + e)/2 = ei' as well as the averages Aij = (e i + e)/2 of distinct residuals for i j Put these pairwise averages in increasing order: -81803 < -67586 < .. < 107648 < 139279 Calculate a = 50(51)/4 = 6375, b = J50(51)(101)/24 = 1036,05 + 6375 (1645)(1036) = 4676, and 05 + 6375 + (1645)(1036) = 8084 So k I = 468 and k 2 = 808 The 468th smallest pairwise average is - 8957 and the 808th smallest pairwise average is 9343 The factor f is VSO/46 = 1043 Now we obtain f = (1043)v'SO[9343 - ( - 8957)11[2(1645)] = 4101 The denominator of F NP in formula (6. The nodes in the sensor eld are assumed to be stationary Each node can measure distances to its 1-hop neighbors and has knowledge of its 2-hop neighborhood, as shown in Figure 79 TASC operates on a combination of node weights and a dynamic density reachability criterion The main objective of TASC is to cluster nonuniform sensor networks In this type of network, the node density variations are globally large but there exist subgroups of nodes such that density variations are locally small The idea of TASC is to partition those networks in a way such that relative node density variation in individual clusters is smaller than relative node density variation in the whole network, obtaining a distribution similar to the one shown in Figure 710 For the cluster formation, two different parameters must be previously speci ed: required minimum cluster size and density reachability parameter The latter. 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