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Energy optimization in Software Creator barcode standards 128 in Software Energy optimization




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10.3 Energy optimization generate, create none none for none projects Barcodes for Mobile Applications (b) With the none for none most powerful coding and diversity techniques, energy costs approach those of optimal codes on the Gaussian channel. As seen from Example 6.6, the transmission energy costs for 1000 bits is 4.

3 mJ. Thus investing in a more sophisticated radio can lead to very large energy saving, and in this case can lead to comparable costs for signal processing and communicating. Indeed, at this level, power consumption is typically dominated, not by the power amplifier, but by other radio components.

In general the energy-optimal strategy for locally aggregating data for fusion and processing is dependent on a broad set of factors.. The infrastr ucture can assist in reducing the communication energy cost in several ways. Elevating base stations reduces propagation losses as roughly the square of their height above the ground (for the near-ground approximation) and in the limit results in the propagation law transitioning to second power losses. Base stations may include directional elements to provide antenna gain.

They may also have access to communication backbones, wired or wireless, to provide long-range communication and so reduce the extent of multihopping, and thus the energy used to convey other nodes traffic.. 10.3 Energy optimization Whatever the long-term trends for hardware, the largest immediate impact on energy consumption of embedded systems comes from the combination of node architecture and management of the resources made available within the node and across the network. Network lifetime can be dramatically increased by focusing resources on the highest-priority functions and by arranging for the most common of these functions to be executed in efficient code, special purpose machines, or where energy resources are most abundant. Energy optimization begins with the selection of appropriate hardware for energy generation and storage, sensing, signal processing, and communication.

In this there is a balance to be achieved between the competing requirements for decision fidelity, time to create a working application, energy consumption and component cost, as will be discussed in detail in 13 on node architecture. Thus, for example, specialized energy-efficient processors may be selected for operations that are expected to be common, with more easily programmed but less efficient processors available for less common or unexpected occurrences. Location of processing For now assume the hardware suite and software tools have been selected.

A prerequisite for energy awareness at the application level is the provision of information about node resources by application program interfaces (APIs). This enables applications to decide between alternative operations based upon the relative priorities of, e.g.

, higher-resolution reporting against the desire to conserve energy and thus permit future operations to occur. Thus, raw data might be communicated or various levels of processing performed prior to transmission to reduce the volume of data and (usually) the overall energy cost across the network. Routes may be chosen to balance the energy reserves of the participating nodes.

. Energy management 1 B 2. C 0.5. 1 D 1. Figure 10.1 Data fusion network. Example 10.8 Where to process Consider the situation depicted in Figure 10.1.

A source is observed by node A, which obtains an SNR of 18 dB, and by node B, which obtains an SNR of 12 dB. The noise is assumed independent and Gaussian. The data rate to describe the observations to a common level of fidelity is linearly proportional to the SNR in decibels.

Here assume 1 bit is needed per 6 dB. The data sink F accepts decisions made elsewhere in the network if the SNR is above 19 dB at the point of fusion. Otherwise it demands the fused data.

Where should data be fused to minimize communication costs, for the costs given on the edges of the communication diagram How does this change if the SNR must be above 20 dB for F to accept a decision Solution Data fusion is by MR combining in this case, wherever the fusion eventually takes place. Without loss of generality, let the noise variance at each sensor be unity, so that with an SNR of 12 dB the signal power is 16 and the amplitude 4; at 18 dB the signal power is 64 and the amplitude 8. Thus the SNR after fusion is (8 8 4 4)2/(82 42) 80, or just slightly above 19 dB.

By fusing at node D, the communication cost is 2 1.5 3 1 6 whereas fusion at C would cost 3 1.5 2 1 6.

5. After a decision is made, the number of bits is generally far less than needed to describe the raw data. If the threshold for F to accept the decision is 20 dB, then there is a non-negligible cost for hopping the fused data to it.

Fusing at D as above, four bits are needed to represent the fused decision, and so the cost will be 6 (to reach D) and 4 2 (to reach F) for a total of 14. Alternatively, fusing at E the cost will be 2 2 3 2 (to reach E) and 4 1 (to reach F), also for a total of 14. Both options are better than sending the raw data all the way to F.

. Communicatio none for none ns duty cycling The simple capability of turning off different system components can lead to significant energy conservation. Components may be powered up either according to a deterministic schedule or dynamically in response to events. Generally, the system may be modeled by a state diagram in which the activity level is identified with the state and the transitions indicate the permitted relations among states, as illustrated in Figure 10.

2 for a simple radio. The states T, R, I, and S correspond to transmission, reception, idle, and sleep modes. Associated with each state and transition from one state to another is a power consumption.

Each transition also takes a specified delay, during which power is consumed. Generally transitions from active to less active states are short, while time to turn on components can be considerably longer. Thus there are both energy and delay costs associated with state transitions that affect what kinds of schedules are optimal.

Large transition costs tend to bias the system towards longer dwell times in particular states..
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