Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 4 Articles
Energy modeling is an important issue for designing and dimensioning low power wireless sensor networks (WSN). In order\r\nto help the developers to optimize the energy spent by WSN nodes, a pragmatic and precise hybrid energy model is proposed.\r\nThis model considers different scenarios that occur during the communication and evaluates their energy consumption based\r\non software profiling as well as the hardware components power profiles. The proposed model is a combination of analytical\r\nderivations and real time measurements. Firstly, the analytical model provides a global view of various elements of the link and\r\nMAC layers and shows their impact on the energy consumption. Secondly, the real-time measurements provide an accurate\r\nestimate of the power consumption of the software as well as the hardware platform. These experiments are particularly useful\r\nto understand the MAC layer mechanisms, such as wake up or data collisions for the preamble sampling category, and the energy\r\nwasted by collisions is evaluated. The presented model is validated under a specific setup with three different test cases. The results\r\nverify that the relative error is between 1 to 8%....
We present our earlier results (not included in Hars and Petruska due to space and time limitations), as well as\r\nsome updated versions of those, and a few more recent pseudorandom number generator designs. These tell a\r\nsystems designer which computer word lengths are suitable for certain high-quality pseudorandom number\r\ngenerators, and which constructions of a large family of designs provide long cycles, the most important property\r\nof such generators. The employed mathematical tools could help assessing the bit-mixing and mapping properties\r\nof a large class of iterated functions, performing only non-multiplicative computer operations: SHIFT, ROTATE, ADD,\r\nand XOR....
Scheduling recurring task sets that allow some instances of the tasks to be skipped produces holes in the schedule which are\r\nnonuniformly distributed. Similarly, when the recurring tasks are not strictly periodic but are sporadic, there is extra processor\r\nbandwidth arising because of irregular job arrivals. The additional computation capacity that results from skips or sporadic tasks\r\ncan be reclaimed to service aperiodic task requests efficiently and quickly.We present techniques for improving the response times\r\nof aperiodic tasks by identifying nonuniformly distributed spare capacityââ?¬â?because of skips or sporadic tasksââ?¬â?in the schedule and\r\nadding such extra capacity to the capacity queue of a BASH server. These gaps can account for a significant portion of aperiodic\r\ncapacity, and their reclamation results in considerable improvement to aperiodic response times.We present two schemes: NCLBCBS,\r\nwhich performs well in periodic real-time environments with firm tasks, and NCLB-CUS, which can be deployed when the\r\nbasic task set to schedule is sporadic. Evaluation via simulations and implementation suggests that performance improvements for\r\naperiodic tasks can be obtained with limited additional overhead....
Smart homes have been viewed with increasing interest by both home owners and the research community in the past few years.\r\nOne reason for this development is that the use of modern automation technology in the home or building promises considerable\r\nsavings of energy, therefore, simultaneously reducing the operational costs of the building over its whole lifecycle. However, the\r\nfull potential of smart homes still lies fallow, due to the complexity and diversity of the systems, badly engineered and configured\r\ninstallations, as well as the frequent problem of suboptimal control strategies. Summarized, these problems converge to two\r\nundesirable conditions in the ââ?¬Å?not-so-smartââ?¬Â home: energy consumption is still higher than actually necessary and users are\r\nunable to yield full comfort in their automated homes. This work puts its focus on alleviating the current problems by proposing\r\na comprehensive system concept, that shall ensure that smart homes can keep their promise in the future. The system operates\r\non an extensive knowledge base that stores all information needed to fulfill the goals of energy efficiency and user comfort. Its\r\nintelligence is implemented as and within a multiagent system that also caters for the systemââ?¬â?¢s openness to the outside world. As a\r\nfirst evaluation, a profile-based control strategy for thermal comfort is developed and verified by means of simulation....
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