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In addition to stochastics indicating a bearish divergence sell signal, euro prices for the following week, ending July 26, had formed a long dark candle and closed lower and below the previous week s close and low. Further, if you had the monthly pivot point price projections, you would have known that the R1 target high for the July time period was 101.40. The actual high was 101.80. Yes, this chart should be used as an example under pivot point analysis, but the stochastics bearish divergence was a powerful influence as well. Look at the euro picture again a few weeks later as shown in Figure 8.9. After the combination of sell signals, the market tumbled back to the 96.00 96.50 level, failing to continue higher and consolidating in a lower trading range. This is a good example of how to use the verification from stochastics to exit long positions or, at the very least, to not enter a long position at the very top. Using candlestick charting may have given you the visual pattern recognition alert to help you identify a top or turning point in the trend. Combined with pivot point monthly analysis, these signals again provided a proven winning technique, verified by the stochastics signal.

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The goal here is that of determining the schedules of the collectors visits to the nodes that minimize transmission energy consumption, data latency, and nodal buffer requirements A rst discussion on how to include controllable mobile relays into the network has been presented in reference 32 In the paper, an implementation of a sensor network with an autonomous mobile relay (a robot) is presented The robot visits the (static) sensors, collects their data (single-hop exchange), and delivers them to the sink, similarly to what happens for the MULEs However, in this case the movements of the robot adapt to the network application priorities, which dictate data collection performance parameters In other words, the robot is part of the system, and it is the system that controls its mobility The testbed-based experimental results concern the evaluation of methods for controlling the speed of the robot for optimizing data collection.

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In Proceedings of the IEEE 75, 1987 40 J C Park and S K Kasera Expected data rate: An accurate high-throughput path metric for multi-hop wireless routing In SECON: Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005 41 A Cerpa, J L Wong, M Potkonjak, and D Estrin Temporal properties of low power wireless links: Modeling and implications on multi-hop routing In MobiHoc 05: Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing, ACM Press, New York, 2005, pp 414 425 42 H Zhang, A Arora, and P Sinha Learn on the y: Data-driven link estimation and routing in sensor network backbones In 25th IEEE International Conference on Computer Communications (INFOCOM), 2006..

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The robot traverses networks with different densities following a straight trail and collects the data The data are then delivered to the sink Methods are de ned for routing the sensed data to nodes that are one hop from the robot route when the robot itself does not pass suf ciently close to some sensors Further development of this work with multiple controlled mobile elements (here called explicitly data MULEs) has been presented in reference 33 This work considers the two cases where nodes are deployed uniformly and randomly in a given geographic area and when, more realistically, they are distributed differently In the rst case, criteria are given for the choice of the number of MULEs and for dealing with nodes that can be served by multiple mules.

43 Y Yang, J Wang, and R Kravets Designing routing metrics for mesh networks In WiMesh: First IEEE Workshop on Wireless Mesh Networks, 2005 44 R Draves, J Padhye, and B Zill Comparison of routing metrics for static multi-hop wireless networks In SIGCOMM, 2004 45 D Eckhardt and P Steenkiste Measurement and analysis of the error characteristics of an in-building wireless network In SIGCOMM 96: Conference Proceedings on Applications, Technologies, Architectures, and Protocols for Computer Communications, ACM Press, New York, 1996, pp 243 254 46 D Kotz, C Newport, and C Elliott The mistaken axioms of wireless-network research Technical Report TR2003-467, Department of Computer Science, Dartmouth College, July 2003 47 S Keshav A control-theoretic approach to ow control In SIGCOMM 91: Proceedings of the Conference on Communications Architecture & Protocols, ACM Press, New York, 1991, pp 3 15 48 M Gastpar and M Vetterli.

In the case of nonuniform nodal distribution, a load balancing algorithm is introduced for distributing the number of sensor nodes to the various MULEs so that each MULE serves approximately the same number of sensors MULEs roam through the network in straight lines and gather information about the nodes they can reach Then the MULEs, which can wirelessly talk to each other directly, elect a leader and send the information they gather to it Based on this information, the leader executes the load balancing algorithm and associate nodes to MULEs Data collection is nally performed by the MULEs that travel through the network (in straight lines) and explicitly poll the assigned sensor nodes for collecting their data.

Euro rally stalled with advanced warning from the monthly pivot point R1 of 1.0140 and the stochastics indicator signaling bearish divergence.

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