The evolution of packet capture technology has made substantial advances in packet capture whereby both software and hardware solutions have been designed to optimize packet capture performance. Packet capture is the process of capturing data packets across a computer network. Packet capture has become more challenging with the advent of fast speed networks. The complexity of capturing in high packet rates and high bandwidth creates the need for more sophisticated packet capture mechanism in real-time network traffic. Existing solutions are able to capture packets; however at certain rates in high speed network, packets will be lost. Thus, this paper aims to classify and compare the existing solutions and to address their inherent issues.
An energy-efficient transmission and computation resource allocation problem for federated learning (FL) on wireless communication networks is investigated. Based on the considered model, each user exploits limited local computing resources to train a local FL model with its collected data. The local FL model is then transmitted to a base station (BS), which aggregates the local FL model and broadcasts it back to all users. Based on the learning accuracy level, computation and communication latency are determined by the exchange of learning models between users and BS. During the FL process, both the local computation energy and the transmission energy must be considered. Due to wireless users’ limited energy consumption, the communication problem is formulated as an optimization problem whose objective is to minimize the overall energy consumption of the system with a latency limitation. To solve this problem, we resort to an iterative algorithm with a solution of bandwidth, power, computational and other factors. Numerical results show that the proposed algorithms can reduce energy consumption compared to the conventional FL method.
This paper decomposes the routing process of industrial robot network using the application of analytic hierarchy process in decision-making. The influence of four factors, such as path length, data integrity, energy consumption, and receiving delay, on routing effect is analyzed. Simultaneous interpreting routes are selected to achieve the purpose of routing. Simulation results show that this method can more comprehensively consider the factors affecting routing and is superior to the existing methods in terms of energy consumption, data integrity, and transmission delay.
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