Current Issue : October - December Volume : 2016 Issue Number : 4 Articles : 5 Articles
Energy harvesting is currently a hot research topic, mainly as a consequence of the\nincreasing attractiveness of computing and sensing solutions based on small, low-power distributed\nembedded systems. Harvesting may enable systems to operate in a deploy-and-forget mode,\nparticularly when power grid is absent and the use of rechargeable batteries is unattractive due to\ntheir limited lifetime and maintenance requirements. This paper focuses on wind flow as an energy\nsource feasible to meet the energy needs of a small autonomous embedded system. In particular the\ncontribution is on the electrical converter and system integration. We characterize the micro-wind\nturbine, we define a detailed model of its behaviour, and then we focused on a highly efficient circuit\nto convert wind energy into electrical energy. The optimized design features an overall volume\nsmaller than 64 cm3. The core of the harvester is a high efficiency buck-boost converter which\nperforms an optimal power point tracking. Experimental results show that the wind generator boosts\nefficiency over a wide range of operating conditions....
In this paper, we propose two adaptive frame size Aloha algorithms, namely adaptive frame size Aloha 1 (AFSA1) and\nadaptive frame size Aloha 2 (AFSA2), for solving radio frequency identification (RFID) multiple-tag anti-collision\nproblem. In AFSA1 and AFSA2, the frame size in the next frame is adaptively changed according to the real-time\ncollision rate measured in the current frame. It is shown that AFSA1 and AFSA2 can significantly improve the\ntransmission efficiency of RFID systems compared to the static Aloha, and AFSA2 produces transmission efficiency\nsimilar to that of the electronic product code (EPC) Q-selection algorithm (Variant II), while the mean identification\ndelay of AFSA2 is much smaller than that of EPC Q-selection algorithm (Variant II). It is also shown that the transmission\nefficiency of AFSA2 and EPC Variant II is very close to its upper bound which is obtained by assuming that the reader\nknows the number of unidentified tags. It is worth noting that when the threshold of the collision rate is chosen to be\n0.5 or 0.6, AFSA2 can maintain the transmission efficiency well above 0.65 for the case of a typical EPC code length of\n96 bits and for the investigated range of tag population, i.e., from 2 to 1000, while keeping the mean identification\ndelay below ten transmit contentions. Very light computational burden at the reader is needed: the reader needs only\nto measure the collision rate in the current frame and then to double or halve the frame size accordingly. No\nadditional computational burden is required at the tag side....
Dynamic voltage and frequency scaling (DVFS) is a means to adjust the computing capacity and power consumption\nof computing systems to the application demands. DVFS is generally useful to provide a compromise between\ncomputing demands and power consumption, especially in the areas of resource-constrained computing systems.\nMany modern processors support some form of DVFS.\nIn this article, we focus on the development of an execution framework that provides lightweight DVFS support for\nreactive stream processing systems (RSPS). RSPs are a common form of embedded control systems, operating in\ndirect response to inputs from their environment. At the execution framework, we focus on support for many-core\nscheduling for parallel execution of concurrent programs. We provide a DVFS strategy for RSPs that is simple and\nlightweight, to be used for dynamic adaptation of the power consumption at runtime. The simplicity of the DVFS\nstrategy became possible by the sole focus on the application domain of RSPs. The presented DVFS strategy does not\nrequire specific assumptions about the message arrival rate or the underlying scheduling method.\nWhile DVFS is a very active field, in contrast to most existing research, our approach works also for platforms like\nmany-core processors, where the power settings typically cannot be controlled individually for each computational\nunit. We also support dynamic scheduling with variable workload. While many research results are provided with\nsimulators, in our approach, we present a parallel execution framework with experiments conducted on real\nhardware, using the single-chip cloud computer many-core processor. The results of our experimental evaluation\nconfirm that our simple DVFS strategy provides potential for significant energy saving on RSPs....
In order to improve the intelligent degree and robustness optimization of power grid management system, the\nopportunistic embedded architecture was proposed for power network measurement with mobile service aware\nscheme. First, the mobile crowd sensing network for power grid management was proposed to realize the\nintelligent power grid management. Then, we designed the mobile service aware opportunistic embedded system\nbased on the requirements of intelligent power grid management and deployment of mobile crowd sensing\nnetwork. Thirdly, the grid of embedded systems was demonstrated for intelligent management. The experimental\nresults show that the proposed scheme has obvious advantages in system complexity, execution efficiency,\nintelligent power grid management level, etc....
Nowadays, many enterprises provide cloud services based on their own Hadoop clusters. Because the resources of\na Hadoop cluster are limited, the Hadoop cluster must select some specific tasks to allocate limited resources in\norder to get the maximal profit. In this paper, we study the maximal profit problem for a given candidate task set.\nWe describe the candidate task set with a valid sequence and propose a sequence-based scheduling strategy. In\norder to improve the efficiency of finding a valid sequence, we design some pruning strategies and give the\ncorresponding scheduling algorithm. Finally, we propose a timeout handling algorithm when some task runs\ntimeout. Experiments show that the total profit of the proposed algorithm is very close to the ideal maxima and is\nobviously bigger than related scheduling algorithms under different experimental settings....
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