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Mobile ad hoc networks communicate in a self-organized way without depending on any fixed infrastructure. The issue of selfish nodes, which may refuse to cooperate, is a great challenge in such networks and may cause network throughput to drastically reduce. Energy-based selfishness is a category of selfishness in which a selfish node shows nondeterministic and probabilistic selfishness behaviors based on level of its energy. In this paper, we propose a mechanism for coping with this kind of selfishness. This mechanism called CEMDEEM not only detects and isolates energy-based and traditional selfish nodes, but also malicious behaviors like spoofing. We also evaluate performance of Dynamic Source Routing (DSR) protocol fortified by CEMDEEM in the presence of different percentages of selfish nodes. Results show that CEMDEEM noticeably improves network performance with a reasonable additional packet overhead and network delay especially when percentage of selfish nodes is not more than 40.

Data center has recently emerged as a common infrastructure that includes numerous servers and host diversity of applications and cloud‐based services. In this paper, a dynamic method is proposed for traffic engineering in software‐defined data center networks helping to design an optimal demand‐path mapping with a tolerable computational complexity. To achieve the aim of this study, we used a linear programming to define the problem statement and then modeled the problem as a Path‐based multicommodity flow. To reduce time complexity, first, a decomposition technique is used to divide the problem model into smaller subproblems, which are then solved simultaneously by applying parallelizing techniques (ie, multiple‐core computing and OpenMP). Next, a suitable iterative algorithm is utilized to quickly converge subproblem solutions leading to an answer to the master problem. Simulation results show that solving time is considerably improved compared with a number of reported schemes devoted to this topic. In addition, implementing the proposed method serially yields an optimum solution, which takes significantly shorter time to reach than that of the generic (the LP model that is not decomposed) approach. In this paper, a dynamic method is proposed for traffic engineering in software‐defined data center networks helping to design an optimal demand‐path mapping with a tolerable computational complexity. To achieve the aim of this study, we used a linear programming to define the problem. To reduce time complexity, a decomposition technique is used to divide the problem model into smaller subproblems, which are then solved simultaneously by applying parallelizing techniques.