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For various features of heterogeneous network access, combined with multi-master multiple slave Stackelberg game model, network pricing and resource allocation scheme of a heterogeneous IOT is proposed. A mobile end-user utility function based on income and spending is designed. After the operator's price is determined, the utility function satisfies the concave conditions to ensure the existence of mobile user's non-cooperative Nash equilibrium point, resulting in multiple resource participants in the joint optimal objective.

With the popularity of Android intelligent terminals, malicious applications targeting Android platform are growing rapidly. Therefore, efficient and accurate detection of Android malicious software becomes particularly important. Dynamic API call sequences are widely used in Android malware detection because they can reflect the behaviours of applications accurately. However, the raw dynamic API call sequences are very usually too long to be directly used, and most existing works just use a truncated segment of the sequence or statistical features of the sequence to perform malware detection, which loses the execution order information of applications and consequently results in high false alarm rate. In this work, we propose a method that transforms the dynamic API call sequence into a function call graph, which retains most of the application execution order information with significantly reduced sequence size. To compensate for the missed behaviour information during the transformation, the advanced features of permission requests extracted from the application are utilized. We then propose FGL_Droid, which fusions the transformed function call graph feature and the extracted permission request feature to perform accurate malware detection. Experiments on benchmark dataset show that FGL_Droid achieves a high detection accuracy of 0.975 and a high F-score of 0.978, which are better than the existing methods.

Generally, scheduling problems refer to allocation of available shared resources and the sorting of production tasks, in order to satisfy the specified performance target within a certain time. The fundamental scheduling problem is that all jobs need to be processed on the same route, which is called flow shop scheduling problems (FSSP). The goal of FSSP, proven as an NP-hard problem, is to find a job sequence that minimizes the makespan. In this paper, an improved Q-learning algorithm is proposed for solving the FSSP. Firstly, a problem model based on the basic Q-learning algorithm is constructed. The makespan is used as the feedback signal, and the process of environmental state change is defined as the process of job selection. Q-learning gives the expected utility of taking a given action in a given state. Afterwards, combined with the NEH heuristic, the algorithm efficiency is enhanced by changing the job inserting mode. In order to validate the proposed method, several simulation experiments are carried out on a set of test problems having different scales. The obtained optimization results of the proposed algorithm are compared to the standard Q-learning algorithm and a hybrid algorithm. The discussion and analysis show that the proposed algorithm performs better than the others in solving the permutation FSSP. As a future direction, in order to shorten the running time, further improvements will be studied to increase the performance of the proposed algorithm and make it applicable and efficient for solving multi-objective optimization problems. K E Y W O R D S decision making, flow shop scheduling, learning (artificial intelligence), schedulingThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

RFID technologies have been applied in various applications such as inventory control and supply chain management. In this paper, we investigate the tag searching problem in large RFID systems containing multiple readers. Given a particular set of wanted tags, tag searching refers to finding which of them present in the system. Tag searching is important to many practical applications and it should be done quickly. Prior researches on tag searching did not consider interference among nearby readers, which will greatly affect the time efficiency of tag searching solutions. In this paper, we propose a tag searching protocol that achieves high time efficiency by jointly optimizing the searching precision and reader scheduling. We propose a novel technique to quickly test whether a wanted tag exists in the system or not. The execution time of the protocol is greatly affected by the scheduling of readers. We formulate the minimum time reader scheduling problem and prove its NP-hardness. We then design a heuristic algorithm to find a feasible reader scheduling. Simulation results show the superior performance of the proposed searching protocol: Compared with the best existing solutions, the proposed protocol reduces execution time by more than 40 percent.