Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks

The next generation wireless networks (i.e. 5G and beyond), which would be extremely dynamic and complex due to the ultra-dense deployment of heterogeneous networks (HetNets), poses many critical challenges for network planning, operation, management and troubleshooting. At the same time, generation and consumption of wireless data are becoming increasingly distributed with ongoing paradigm shift from people-centric to machine-oriented communications, making the operation of future wireless networks even more complex. In mitigating the complexity of future network operation, new approaches of intelligently utilizing distributed computational resources with improved context-awareness becomes extremely important. In this regard, the emerging fog (edge) computing architecture aiming to distribute computing, storage, control, communication, and networking functions closer to end users, have a great potential for enabling efficient operation of future wireless networks. These promising architectures make the adoption of artificial intelligence (AI) principles which incorporate learning, reasoning and decision-making mechanism, as natural choices for designing a tightly integrated network. Towards this end, this article provides a comprehensive survey on the utilization of AI integrating machine learning, data analytics and natural language processing (NLP) techniques for enhancing the efficiency of wireless network operation. In particular, we provide comprehensive discussion on the utilization of these techniques for efficient data acquisition, knowledge discovery, network planning, operation and management of the next generation wireless networks. A brief case study utilizing the AI techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on communication networks and services, (To appear

Using Modified Partitioning Around Medoids Clustering Technique in Mobile Network Planning

Every cellular network deployment requires planning and optimization in order to provide adequate coverage, capacity, and quality of service (QoS). Optimization mobile radio network planning is a very complex task, as many aspects must be taken into account. With the rapid development in mobile network we need effective network planning tool to satisfy the need of customers. However, deciding upon the optimum placement for the base stations (BS s) to achieve best services while reducing the cost is a complex task requiring vast computational resource. This paper introduces the spatial clustering to solve the Mobile Networking Planning problem. It addresses antenna placement problem or the cell planning problem, involves locating and configuring infrastructure for mobile networks by modified the original Partitioning Around Medoids PAM algorithm. M-PAM (Modified Partitioning Around Medoids) has been proposed to satisfy the requirements and constraints. PAM needs to specify number of clusters (k) before starting to search for the best locations of base stations. The M-PAM algorithm uses the radio network planning to determine k. We calculate for each cluster its coverage and capacity and determine if they satisfy the mobile requirements, if not we will increase (k) and reapply algorithms depending on two methods for clustering. Implementation of this algorithm to a real case study is presented. Experimental results and analysis indicate that the M-PAM algorithm when applying method two is effective in case of heavy load distribution, and leads to minimum number of base stations, which directly affected onto the cost of planning the network.Comment: 10 pages, 15 figure

Characterization of behavioral patterns exploiting description of geographical areas

The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location's contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification.Comment: 17 pages, 13 figure

Data-driven Co-clustering Model of Internet Usage in Large Mobile Societies

Design and simulation of future mobile networks will center around human interests and behavior. We propose a design paradigm for mobile networks driven by realistic models of users' on-line behavior, based on mining of billions of wireless-LAN records. We introduce a systematic method for large-scale multi-dimensional coclustering of web activity for thousands of mobile users at 79 locations. We find surprisingly that users can be consistently modeled using ten clusters with disjoint profiles. Access patterns from multiple locations show differential user behavior. This is the first study to obtain such detailed results for mobile Internet usage.Comment: 10 pages, 10 figure

Spatio-Temporal Modeling of Wireless Users Internet Access Patterns Using Self-Organizing Maps

User online behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional analysis of online activity for thousands of mobile users across 79 buildings over a variety of web domains. We propose a modeling approach based on self-organizing maps (SOM) for discovering, organizing and visualizing different mobile users' trends from billions of WLAN records. We find surprisingly that users' trends based on domains and locations can be accurately modeled using a self-organizing map with clearly distinct characteristics. We also find many non-trivial correlations between different types of web domains and locations. Based on our analysis, we introduce a mixture model as an initial step towards realistic simulation of wireless network usage

Charging Wireless Sensor Networks with Mobile Charger and Infrastructure Pivot Cluster Heads

Wireless rechargeable sensor networks (WRSNs) consisting of sensor nodes with batteries have been at the forefront of sensing and communication technologies in the last few years. Sensor networks with different missions are being massively rolled out, particularly in the internet-of-things commercial market. To ensure sustainable operation of WRSNs, charging in a timely fashion is very important, since lack of energy of even a single sensor node could result in serious outcomes. With the large number of WRSNs existing and to be existed, energy-efficient charging schemes are becoming indispensable to workplaces that demand a proper level of operating cost. Selection of charging scheme depends on network parameters such as the distribution pattern of sensor nodes, the mobility of the charger, and the availability of the directional antenna. Among current charging techniques, radio frequency (RF) remote charging with a small transmit antenna is gaining interest when non-contact type charging is required for sensor nodes. RF charging is particularly useful when sensor nodes are distributed in the service area. To obtain higher charging efficiency with RF charging, optimal path planning for mobile chargers, and the beamforming technique, implemented by making use of a directional antenna, can be considered. In this article, we present a review of RF charging for WRSNs from the perspectives of charging by mobile charger, harvesting using sensor nodes, and energy trading between sensor nodes. The concept of a pivot cluster head is introduced and a novel RF charging scheme in two stages, consisting of charging pivot cluster heads by a mobile charger with a directional antenna and charging member sensor nodes by pivot cluster heads with directional antennae, is presented.Comment: to be submitted to an SCI journa

An Interference-Aware Virtual Clustering Paradigm for Resource Management in Cognitive Femtocell Networks

Femtocells represent a promising alternative solution for high quality wireless access in indoor scenarios where conventional cellular system coverage can be poor. Femtocell access points (FAP) are normally randomly deployed by the end user, so only post deployment network planning is possible. Furthermore, this uncoordinated deployment creates the potential for severe interference to co-located femtocells, especially in dense deployments. This paper presents a new femtocell network architecture using a generalized virtual cluster femtocell (GVCF) paradigm, which groups together FAP, which are allocated to the same femtocell gateway (FGW), into logical clusters. This guarantees severely interfering and overlapping femtocells are assigned to different clusters, and since each cluster operates on a different band of frequencies, the corresponding virtual cluster controller only has to manage its own FAP members, so the overall system complexity is low. The performance of the GVCF algorithm is analysed from both a resource availability and cluster number perspective, and a novel strategy is proposed for dynamically adapting these to network environment changes, while upholding quality-of-service requirements. Simulation results conclusively corroborate the superior performance of the GVCF model in interference mitigation, particularly in high density FAP scenarios

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions

Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas

Migration Networks: Applications of Network Analysis to Macroscale Migration Patterns

An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places. In this chapter, we first review advances in the study of migration networks and recent work that has employed network analysis to examine such networks at different geographical scales. In our discussion, we focus in particular on global scale migration networks. We then propose ways to leverage network analysis in concert with digital technologies and online geolocated data to examine the structure and dynamics of migration networks. The implementation of such approaches for studying migration networks faces many challenges, including ethical ones, methodological ones, socio-technological ones (e.g., data availability and reuse), and research reproducibility. We detail these challenges, and we then consider possible ways of linking digital geolocated data to administrative and survey data as a way of harnessing new technologies to construct increasingly realistic migration networks (e.g., using multiplex networks). We also briefly discuss new methods (e.g., multilayer network analysis) in network analysis and adjacent fields (e.g., machine learning) that can help advance understanding of macroscale patterns of migration.Comment: key words: migration networks, social networks, spatial networks, network analysis, international migration, global migratio

CUPSMAN: Control User Plane Separation Based Routing in Ad-hoc Networks

Separation of user (data) plane from the control plane in networks helps scale resources independently, increase the quality of service and facilitate autonomy by employing software-defined networking techniques. Clustering introduces hierarchy in ad hoc networks where control functions can be carried out by some designated cluster heads. It is also an effective solution to handle challenges due to lack of centralized controllers and infrastructure in ad-hoc networks. Clustered network topologies gain a significant amount of scalability and reliability in comparison to flat topologies. Different roles that nodes have in a clustered network can be effectively used for routing as well. In this paper, we propose a novel plane-separated routing algorithm, Cluster-based Hybrid Routing Algorithm (CHRA). In CHRA, we take advantage of the hierarchical clustered structure through control and user plane separation (CUPS) in mobile ad-hoc networks. In the cluster neighborhood with a particular size, a link-state routing is used to minimize delay, control overhead, and also utilize energy consumption. For facilitating the communication with distant nodes, we form a routing backbone that is responsible for both control and data messages. The results show that CHRA outperforms its opponents in terms of fair energy consumption and end-to-end delay