#Data transmit for mac how to
#Data transmit for mac mac

Simulation results confirm that the proposed algorithm can approach the optimal performance with a low complexity.

In the second stage, the joint channel selection and peer discovery problem is solved by the proposed pricing-based matching algorithm based on the established preference lists. In the first stage, M2M-TXs, M2M receivers (M2M-RXs) and RBs are temporally matched together, and then the joint power control and time allocation problem is solved by combining alternating optimization (AO), nonlinear fractional programming, and linear programming to construct the preference lists. We propose a two-stage three-dimensional matching algorithm.

#Data transmit for mac how to

In this paper, we consider how to maximize the energy efficiency of M2M-TXs via the joint optimization of channel selection, peer discovery, power control, and time allocation. However, the complex interference scenarios and the stringent quality of service (QoS) requirements pose new challenges on resource allocation optimization.

#Data transmit for mac mac

The simulation results demonstrated that REE-MAC exhibits superior performance for the harvested energy, average freezing time, and fairness to existing representative WPSN MAC protocols.Įnergy harvesting based cognitive machine-to-machine (EH-CM2M) communication has been proposed to overcome the problem of spectrum scarcity and limited battery capacity by enabling M2M transmitters (M2M-TXs) to harvest energy from ambient radio frequency signals, as well as to reuse the resource blocks (RBs) allocated to cellular users (CUs) in an opportunistic manner. It then determines the devices’ charging priorities, based on which it allocates dedicated power slots (DPSs) within the WET superframe.

At the beginning of each superframe, the coordinator estimates the change in the residual energy of individual sensor devices caused by their energy consumption and energy harvesting during the previous superframe. REE-MAC uses two types of superframes that operate simultaneously in different frequency bands: the wireless energy transfer (WET) superframe and wireless information transfer (WIT) superframe. REE-MAC aims to reduce overhead due to control messages for scheduling the energy harvesting operation of sensor devices and provide fairness for data transmission opportunities to sensor devices. This paper presents a residual energy estimation-based medium access control (REE-MAC) protocol for wireless powered sensor networks (WPSNs) composed of a central coordinator and multiple sensor devices. Finally, the simulation performance of EH protocols HE-MAC, RF-MAC, and DOS are compared, where the results show that HE-MAC provides in a superior performance for the range of interest. Next, based on the steady-state conditions, optimization is conducted to maximize the EH rate, which satisfies the frame generation rate and transfers additional energy to achieve a self-sustained energy consumption profile. In addition, the HE-MAC’s Markov chain model and steady-state probabilities are derived and used in the performance analysis. In order to overcome this problem, this paper extends the IEEE 802.11e enhanced DCF (EDCF) into a Harvest-then-transmit based modified EDCF MAC (HE-MAC) protocol. However, a utilization degradation problem occurs when the distributed coordination function (DCF) MAC protocol of IEEE 802.11 is applied to WPSNs. Therefore, a proper medium access control (MAC) protocol is needed in wireless powered sensor networks (WPSNs). However, there is a tradeoff between the RF based ET and data communication when they operate in the same frequency band. Perhaps this is used for some logging or history, etc.Energy transfer (ET) and energy harvesting (EH) through radio frequency (RF) signals are a promising technology that can reduce the dependency on batteries in wireless sensor networks.

The files are cleaned up more or less immediately after uploading to the destination server (afaict) but the subdirectories seem to stick around for some period of time (such that I now have ~600 files in the Caches/Transmit directory. Latest version (5.1.5) appears to store a file at a time in: /Volumes//Users//Library/Caches/Transmit///path/to/file The name and path of the folder implies to me that the system might clean it up if Transmit doesn't get the chance. Transmit appears to clean up after itself within a second or two of the upload completing. The opensnoop program is remarkably useful, but to answer the actual question in case somebody is in a quick bind, for Transmit 4 the location of the temporary files is: /Volumes//Users//Library/Caches/Cleanup At Startup/Transmit/server-to-server-B18AC7A9-81C6-4AAD-8F2C-6CD70E65D7FD/data-file-being-transferred