Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach

The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks

Real-Time IoV Task Offloading through Dynamic Assignment of SDN Controllers: Algorithmic Approaches and Performance Evaluation

Task offloading in Internet of Vehicles (IoV) is very crucial. The widespread use of IoT applications frequently interacts with the cloud, thereby increasing the load on centralized cloud controllers. Centralized network management in cloud infrastructure is not feasible for the latest IoT trends. Decentralized and decoupled network management in Software Defined Networks (SDN) can enhance IoV services. SDN and IoV coupling can better handle task offloading in ubiquitous and dynamic IoV environments. However, appropriate SDN controller assignment and allotment strategies play a prominent role in IoV communication. In this study, we developed algorithms for SDN controller assignment and allotment namely 1) Next Fit Allotment and Assignment of SDN Controller in IoV (NFAAC), 2) Dynamic Bin Packing Allotment and Assignment of SDN Controller in IoV (DBPAAC), and 3) Dynamic Focused and Bidding Allotment and Assignment algorithm of SDN Controller in IoV (DFBAAC). These algorithms were simulated using open-flow switch controllers. The controllers were modeled as Road Side Units (RSU) that can allocate bandwidth and resource requirements to vehicles on the road. Our results show that our proposed algorithm works efficiently for SDN controller assignment and allocation, outperforming the existing work by a significant improvement of 13.5%. The working of the proposed algorithms are verified, tested, and analytically presented in this study

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin