Wireless Communications in the Era of Big Data

The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all the aspects of the wireless system design, such as spectrum efficiency, computing capabilities and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace such a "bigdata" era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing the bigdata traffic in wireless networks. On the other hand, instead of viewing mobile bigdata as a unwanted burden, we introduce methods to capitalize from the vast data traffic, for building a bigdata-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications Magazin

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Simulation study of routing protocols in wireless sensor networks

Wireless sensor networks, a distributed network of sensor nodes perform critical tasks in many application areas such as target tracking in military applications, detection of catastrophic events, environment monitoring, health applications etc. The routing protocols developed for these distributed sensor networks need to be energy efficient and scalable. To create a better understanding of the performance of various routing protocols proposed it is very important to perform a detailed analysis of them. Network simulators enable us to study the performance and behavior of these protocols on various network topologies. Many Sensor Network frameworks were developed to explore both the networking issues and the distributed computing aspects of wireless sensor networks. The current work of simulation study of routing protocols is done on SensorSimulator, a discrete event simulation framework developed at Sensor Networks Research Laboratory, LSU and on a popular event driven network simulator ns2 developed at UC Berkeley. SensorSimulator is a discrete event simulation framework for sensor networks built over OMNeT++ (Objective Modular Network Test-bed in C++). This framework allows the user to debug and test software for distributed sensor networks. SensorSimulator allows developers and researchers in the area of Sensor Networks to investigate topological, phenomenological, networking, robustness and scaling issues, to explore arbitrary algorithms for distributed sensors, and to defeat those algorithms through simulated failure. The framework has modules for all the layers of a Sensor Network Protocol stack. This thesis is focused on the simulation and performance evaluation of various routing protocols on SensorSimulator and ns2. The performance of the simulator is validated with a comparative study of Directed Diffusion Routing Protocol on both ns2 and SensorSimulator. Then the simulations are done to evaluate the performance of Optimized Broadcast Protocols for Sensor Networks, Efficient Coordination Protocol for Wireless Sensor Networks on SensorSimulator. Also a performance study of Random Asynchronous Wakeup protocol for Sensor Networks is done on ns2