Fuzzy-logic framework for future dynamic cellular systems

There is a growing need to develop more robust and energy-efficient network architectures to cope with ever increasing traffic and energy demands. The aim is also to achieve energy-efficient adaptive cellular system architecture capable of delivering a high quality of service (QoS) whilst optimising energy consumption. To gain significant energy savings, new dynamic architectures will allow future systems to achieve energy saving whilst maintaining QoS at different levels of traffic demand. We consider a heterogeneous cellular system where the elements of it can adapt and change their architecture depending on the network demand. We demonstrate substantial savings of energy, especially in low-traffic periods where most mobile systems are over engineered. Energy savings are also achieved in high-traffic periods by capitalising on traffic variations in the spatial domain. We adopt a fuzzy-logic algorithm for the multi-objective decisions we face in the system, where it provides stability and the ability to handle imprecise data

Self organising cloud cells: a resource efficient network densification strategy

Network densification is envisioned as the key enabler for 2020 vision that requires cellular systems to grow in capacity by hundreds of times to cope with unprecedented traffic growth trends being witnessed since advent of broadband on the move. However, increased energy consumption and complex mobility management associated with network densifications remain as the two main challenges to be addressed before further network densification can be exploited on a wide scale. In the wake of these challenges, this paper proposes and evaluates a novel dense network deployment strategy for increasing the capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance. Our deployment architecture consists of smart small cells, called cloud nodes, which provide data coverage to individual users on a demand bases while taking into account the spatial and temporal dynamics of user mobility and traffic. The decision to activate the cloud nodes, such that certain performance objectives at system level are targeted, is carried out by the overlaying macrocell based on a fuzzy-logic framework. We also compare the proposed architecture with conventional macrocell only deployment and pure microcell-based dense deployment in terms of blocking probability, handover probability and energy efficiency and discuss and quantify the trade-offs therein

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Motorized cart

Motorized cart is known as an effective tool and timeless that help people carry heavy loads. For farmers, it has an especially vital tool for moving goods. Oil palm farmers typically uses the wheelbarrow to move the oil palm fruit (Figure 10.1). However, there is a lack of equipment that should be further enhanced in capabilities. Motorized carts that seek to add automation to wheelbarrow as it is to help people save manpower while using it. At present, oil palm plantation industry is among the largest in Malaysia. However, in an effort to increase the prestige of the industry to a higher level there are challenges to be faced. Shortage of workers willing to work the farm for harvesting oil palm has given pain to manage oil palm plantations. Many have complained about the difficulty of hiring foreign workers and a high cost. Although there are tools that can be used to collect or transfer the proceeds of oil palm fruits such as carts available. However, these tools still have the disadvantage that requires high manpower to operate. Moreover, it is not suitable for all land surfaces and limited cargo space. Workload and manpower dependence has an impact on farmers' income

IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)