Abstract-In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well-known for their remarkable generality and versatility, and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.

Quality-of-service (QoS) requirements have always posed a challenge from scheduling perspective and it becomes more complicated with the emergence of new standards and applications. Classical techniques like maximum throughput, proportional fair, and exponential rule have been used in common network scenarios but these techniques fail to address diverse service requirements for QoS provisioning in long-term evolution (LTE). These QoS requirements in LTE are implemented in the form of delay budgets, scheduling priorities, and packet loss rates. Scheduler design for LTE networks therefore requires handling service class attributes but preciously proposed scheduling methods ignored service class-based design and focused more on single network prospect. To address service class requirements in LTE, we propose a modified radio resource management-based scheduler with minimum guarantee in the downlink following network capacity and service class attributes defined in LTE standard. The scheduler takes advantage of best available channel conditions while maintaining data rates corresponding to minimum resources guaranteed for all major classes including the best effort class. A method is proposed to determine the scheduling resource capacity of active users in LTE networks with an admission control to limit the number of users according to available resources. In addition to closely matched theoretical and simulated active users that can be accommodated in the system, promising results are provided for system delay, throughput, and user mobility.

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

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