Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.

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Abstract-One of the top challenging problems in data mining domain is the distributed data mining (DDM) and mining multiagent data. In distributed environment, classical techniques require that the distributed data be first collected in a data warehouse which is usually either ineffective or infeasible. Hence, mining over decentralized data sources can overcome such issues. Rule-based classifiers involve sharp cutoffs for continuous attributes. Fuzzy Logic System (FLS) has features that make it an adequate tool for addressing this shortcoming effectively and efficiently. In this paper, a framework for a Parallel FuzzyGenetic Algorithm (PFGA) has been developed for classification and prediction over decentralized data sources. The model parameters are evolved using two nested genetic algorithms (GAs). The outer GA evolves the fuzzy sets whereas the inner GA evolves the fuzzy rules. During optimization, best rules are only distributed among agents to construct the overall optimized model. Several experiments have been conducted over many benchmark datasets. The experiment results show that the developed model has good accuracy and more efficient in performance and comprehensibility of linguistic rules compared to some models implemented in KEEL software tool.

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This chapter introduces two different algorithms to detect intrusions in mission critical communication systems to guarantee their security. The first algorithm is a classification algorithm which applies the concept of supervised learning. The second algorithm is a clustering algorithm which applies the concept of unsupervised learning. The algorithms detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are described in details and their results on well-known dataset are introduced. An enhancement for the J48algorithm is also introduced, where the decision tree for the algorithm is changed to a binary tree. The change enhances the complexity to reach a decision. The chapter includes a brief introduction about the security in Mission critical systems and the reason behind securing such systems. It introduces different methodologies that were introduced to detect intrusions in wireless communications.

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Abstract-The Traveling Salesman Problem (TSP) is the problem of finding the shortest path passing through all given cities while only passing by each city once and finishing at the same starting city. This problem has NP-hard complexity making it extremely impractical to get the most optimal path even for problems as small as 20 cities since the number of permutations becomes too high. Many heuristic methods have been devised to reach "good" solutions in reasonable time. In this paper, we present the idea of utilizing a spatial "geographical" Divide and Conquer technique in conjunction with heuristic TSP algorithms specifically the Nearest Neighbor 2-opt algorithm. We have found that the proposed algorithm has lower complexity than algorithms published in the literature. This comes at a lower accuracy expense of around 9%. It is our belief that the presented approach will be welcomed to the community especially for large problems where a reasonable solution could be reached in a fraction of the time.