A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems

The ongoing deployment of 5G cellular systems is continuously exposing the inherent limitations of this system, compared to its original premise as an enabler for Internet of Everything applications. These 5G drawbacks are currently spurring worldwide activities focused on defining the next-generation 6G wireless system that can truly integrate far-reaching applications ranging from autonomous systems to extended reality and haptics. Despite recent 6G initiatives1, the fundamental architectural and performance components of the system remain largely undefined. In this paper, we present a holistic, forward-looking vision that defines the tenets of a 6G system. We opine that 6G will not be a mere exploration of more spectrum at high-frequency bands, but it will rather be a convergence of upcoming technological trends driven by exciting, underlying services. In this regard, we first identify the primary drivers of 6G systems, in terms of applications and accompanying technological trends. Then, we propose a new set of service classes and expose their target 6G performance requirements. We then identify the enabling technologies for the introduced 6G services and outline a comprehensive research agenda that leverages those technologies. We conclude by providing concrete recommendations for the roadmap toward 6G. Ultimately, the intent of this article is to serve as a basis for stimulating more out-of-the-box research around 6G.Comment: This paper has been accepted by IEEE Networ

Joint Data Compression and MAC Protocol Design for Smartgrids with Renewable Energy

In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a data concentrator. Our design exploits the correlation of the reported data in both time and space to efficiently design the data compression using the compressed sensing (CS) technique and theMAC protocol so that the reported data can be recovered reliably within minimum reporting time. Specifically, we perform the following design tasks: i) we employ the two-dimensional (2D) CS technique to compress the reported data in the distributed manner; ii) we propose to adapt the 802.15.4 MAC protocol frame structure to enable efficient data transmission and reliable data reconstruction; and iii) we develop an analytical model based on which we can obtain efficient MAC parameter configuration to minimize the reporting delay. Finally, numerical results are presented to demonstrate the effectiveness of our proposed framework compared to existing solutions.Comment: https://arxiv.org/admin/q/1589135, Wireless Communications and Mobile Computing, 2016. arXiv admin note: substantial text overlap with arXiv:1506.0831

Towards Big data processing in IoT: network management for online edge data processing

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, the huge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed. The raw data generated by distributed network terminals are collected and processed by edge servers. The edge servers split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of limited edge computation ability, part of the raw data in huge data sources may be directly transmitted to the cloud. To manage limited resources online, we propose an algorithm based on Lyapunov optimization to jointly optimize the policy of edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay and saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT

Optimal Virtual Network Function Placement and Resource Allocation in Multi-Cloud Service Function Chaining Architecture

Service Function Chaining (SFC) is the problem of deploying various network service instances over geographically distributed data centers and providing inter-connectivity among them. The goal is to enable the network traffic to flow smoothly through the underlying network, resulting in an optimal quality of experience to the end-users. Proper chaining of network functions leads to optimal utilization of distributed resources. This has been a de-facto model in the telecom industry with network functions deployed over underlying hardware. Though this model has served the telecom industry well so far, it has been adapted mostly to suit the static behavior of network services and service demands due to the deployment of the services directly over physical resources. This results in network ossification with larger delays to the end-users, especially with the data-centric model in which the computational resources are moving closer to end users. A novel networking paradigm, Network Function Virtualization (NFV), meets the user demands dynamically and reduces operational expenses (OpEx) and capital expenditures (CapEx), by implementing network functions in the software layer known as virtual network functions (VNFs). VNFs are then interconnected to form a complete end-to-end service, also known as service function chains (SFCs). In this work, we study the problem of deploying service function chains over network function virtualized architecture. Specifically, we study virtual network function placement problem for the optimal SFC formation across geographically distributed clouds. We set up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs). We consider link delays and computational delays in our model.Comment: E-preprin

Energy-Performance Trade-offs in Mobile Data Transfers

By year 2020, the number of smartphone users globally will reach 3 Billion and the mobile data traffic (cellular + WiFi) will exceed PC internet traffic the first time. As the number of smartphone users and the amount of data transferred per smartphone grow exponentially, limited battery power is becoming an increasingly critical problem for mobile devices which increasingly depend on network I/O. Despite the growing body of research in power management techniques for the mobile devices at the hardware layer as well as the lower layers of the networking stack, there has been little work focusing on saving energy at the application layer for the mobile systems during network I/O. In this paper, to the best of our knowledge, we are first to provide an in depth analysis of the effects of application layer data transfer protocol parameters on the energy consumption of mobile phones. We show that significant energy savings can be achieved with application layer solutions at the mobile systems during data transfer with no or minimal performance penalty. In many cases, performance increase and energy savings can be achieved simultaneously

5G Mobile Cellular Networks: Enabling Distributed State Estimation for Smart Grids

With transition towards 5G, mobile cellular networks are evolving into a powerful platform for ubiquitous large-scale information acquisition, communication, storage and processing. 5G will provide suitable services for mission-critical and real-time applications such as the ones envisioned in future Smart Grids. In this work, we show how emerging 5G mobile cellular network, with its evolution of Machine-Type Communications and the concept of Mobile Edge Computing, provides an adequate environment for distributed monitoring and control tasks in Smart Grids. In particular, we present in detail how Smart Grids could benefit from advanced distributed State Estimation methods placed within 5G environment. We present an overview of emerging distributed State Estimation solutions, focusing on those based on distributed optimization and probabilistic graphical models, and investigate their integration as part of the future 5G Smart Grid services.Comment: 8 pages, 6 figures, version of the magazine paper submitted for publicatio

Getting Virtualized Wireless Sensor Networks IaaS Ready for PaaS

With the recent advances in sensor hardware and software, architectures for virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node- and network-level virtualization, vWSNs can be offered as Infrastructure-as-a-Service (IaaS) which can aid in realizing the true potential of Internet-of-Things (IoT). Cloud computing offers elastic provisioning of large-scale infrastructures to multiple concurrent users where Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host and execute applications over these infrastructures. Amalgamating IoT with cloud computing potentially allows rapid application and service provisioning in an efficient, scalable and robust manner. However, interactions between vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is ready for interactions with PaaS. The proposed architecture is based on our previous works and is rooted in the fundamental differences between traditional IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit and made early performance measurements.Comment: This paper has been accepted in IEEE DCOSS 2015, IoTIP-15 Workshop to be held on 12th June 2015 in Brazi

Communication vs Distributed Computation: an alternative trade-off curve

In this paper, we revisit the communication vs. distributed computing trade-off, studied within the framework of MapReduce in [1]. An implicit assumption in the aforementioned work is that each server performs all possible computations on all the files stored in its memory. Our starting observation is that, if servers can compute only the intermediate values they need, then storage constraints do not directly imply computation constraints. We examine how this affects the communication-computation trade-off and suggest that the trade-off be studied with a predetermined storage constraint. We then proceed to examine the case where servers need to perform computationally intensive tasks, and may not have sufficient time to perform all computations required by the scheme in [1]. Given a threshold that limits the computational load, we derive a lower bound on the associated communication load, and propose a heuristic scheme that achieves in some cases the lower bound

The three primary colors of mobile systems

In this paper, we present the notion of "mobile 3C systems in which the "Communications", "Computing", and "Caching" (i.e., 3C) make up the three primary resources/funcationalties, akin to the three primary colors, for a mobile system. We argue that in future mobile networks, the roles of computing and caching are as intrinsic and essential as communications, and only the collective usage of these three primary resources can support the sustainable growth of mobile systems. By defining the 3C resources in their canonical forms, we reveal the important fact that "caching" affects the mobile system performance by introducing non-causality into the system, whereas "computing" achieves capacity gains by performing logical operations across mobile system entities. Many existing capacity-enhancing techniques such as coded multicast, collaborative transmissions, and proactive content pushing can be cast into the native 3C framework for analytical tractability. We further illustrate the mobile 3C concepts with practical examples, including a system on broadcast-unicast convergence for massive media content delivery. The mobile 3C design paradigm opens up new possibilities as well as key research problems bearing academic and practice significance.Comment: submitted to IEEE Communications Magazine -- Feature Topic: Mobile 3C Network

Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing

In this paper we study inexact dumped Newton method implemented in a distributed environment. We start with an original DiSCO algorithm [Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss, Yuchen Zhang and Lin Xiao, 2015]. We will show that this algorithm may not scale well and propose an algorithmic modifications which will lead to less communications, better load-balancing and more efficient computation. We perform numerical experiments with an regularized empirical loss minimization instance described by a 273GB dataset