No abstract

BATS codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding.Due to both the non-convex objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems can not be directed applied to solve our NUM problem. We introduce a two-step algorithm for solving the NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm. I. INTRODUCTIONMultihop wireless networks will play a crucial role in the future of Internet of Things, where the communication from a source node to a destination node may go through multiple This paper was presented in part at 2020 IEEE International Conference on Communications.

The current assessment of the security of energy transaction data has vague evaluation dimensions, resulting in large errors in the assessment results. To this end, we propose a method for assessing the security of energy transaction data based on big data and the Pagerank algorithm. Determine assessment requirements based on big data and obtain assessment requirements based on six dimensions. Construct a security assessment model and calculate the indicator weights for this model. The assessment results are obtained dynamically based on the Pagerank algorithm. Experiments show that the evaluation results of the method have a small error, with an average error of only 0.22%, which has a high application value.