6 research outputs found
Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks
The battery-less Internet of Things (IoT) devices are a key element in the
sustainable green initiative for the next-generation wireless networks. These
battery-free devices use the ambient energy, harvested from the environment.
The energy harvesting environment is dynamic and causes intermittent task
execution. The harvested energy is stored in small capacitors and it is
challenging to assure the application task execution. The main goal is to
provide a mechanism to aggregate the sensor data and provide a sustainable
application support in the distributed battery-less IoT network. We model the
distributed IoT network system consisting of many battery-free IoT sensor
hardware modules and heterogeneous IoT applications that are being supported in
the device-edge-cloud continuum. The applications require sensor data from a
distributed set of battery-less hardware modules and there is provision of
joint control over the module actuators. We propose an application-aware task
and energy manager (ATEM) for the IoT devices and a vector-synchronization
based data aggregator (VSDA). The ATEM is supported by device-level federated
energy harvesting and system-level energy-aware heterogeneous application
management. In our proposed framework the data aggregator forecasts the
available power from the ambient energy harvester using long-short-term-memory
(LSTM) model and sets the device profile as well as the application task rates
accordingly. Our proposed scheme meets the heterogeneous application
requirements with negligible overhead; reduces the data loss and packet delay;
increases the hardware component availability; and makes the components
available sooner as compared to the state-of-the-art.Comment: 10 pages, 11 figure
Panda: Neighbor Discovery on a Power Harvesting Budget
Object tracking applications are gaining popularity and will soon utilize
Energy Harvesting (EH) low-power nodes that will consume power mostly for
Neighbor Discovery (ND) (i.e., identifying nodes within communication range).
Although ND protocols were developed for sensor networks, the challenges posed
by emerging EH low-power transceivers were not addressed. Therefore, we design
an ND protocol tailored for the characteristics of a representative EH
prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND
accounting for unique prototype characteristics (i.e., energy costs for
transmission/reception, and transceiver state switching times/costs). Then, we
present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in
which nodes transition between the sleep, receive, and transmit states. We
analyze \name and select its parameters to maximize the ND rate subject to a
homogeneous power budget. We also present Panda-D, designed for non-homogeneous
EH nodes. We perform extensive testbed evaluations using the prototypes and
study various design tradeoffs. We demonstrate a small difference (less then
2%) between experimental and analytical results, thereby confirming the
modeling assumptions. Moreover, we show that Panda improves the ND rate by up
to 3x compared to related protocols. Finally, we show that Panda-D operates
well under non-homogeneous power harvesting
Sophisticated Batteryless Sensing
Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly
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Algorithms and Experimentation for Future Wireless Networks: From Internet-of-Things to Full-Duplex
Future and next-generation wireless networks are driven by the rapidly growing wireless traffic stemming from diverse services and applications, such as the Internet-of-Things (IoT), virtual reality, autonomous vehicles, and smart intersections. Many of these applications require massive connectivity between IoT devices as well as wireless access links with ultra-high bandwidth (Gbps or above) and ultra-low latency (10ms or less). Therefore, realizing the vision of future wireless networks requires significant research efforts across all layers of the network stack. In this thesis, we use a cross-layer approach and focus on several critical components of future wireless networks including IoT systems and full-duplex (FD) wireless, and on experimentation with advanced wireless technologies in the NSF PAWR COSMOS testbed.
First, we study tracking and monitoring applications in the IoT and focus on ultra-low-power energy harvesting networks. Based on realistic hardware characteristics, we design and optimize Panda, a centralized probabilistic protocol for maximizing the neighbor discovery rate between energy harvesting nodes under a power budget. Via testbed evaluation using commercial off-the-shelf energy harvesting nodes, we show that Panda outperforms existing protocols by up to 3x in terms of the neighbor discovery rate. We further explore this problem and consider a general throughput maximization problem among a set of heterogeneous energy-constrained ultra-low-power nodes. We analytically identify the theoretical fundamental limits of the rate at which data can be exchanged between these nodes, and design the distributed probabilistic protocol, EconCast, which approaches the maximum throughput in the limiting sense. Performance evaluations of EconCast using both simulations and real-world experiments show that it achieves up to an order of magnitude higher throughput than Panda and other known protocols.
We then study FD wireless - simultaneous transmission and reception at the same frequency - a key technology that can significantly improve the data rate and reduce communication latency by employing self-interference cancellation (SIC). In particular, we focus on enabling FD on small-form-factor devices leveraging the technique of frequency-domain equalization (FDE). We design, model, and optimize the FDE-based RF canceller, which can achieve >50dB RF SIC across 20MHz bandwidth, and experimentally show that our prototyped FD radios can achieve a link-level throughput gain of 1.85-1.91x. We also focus on combining FD with phased arrays, employing optimized transmit and receive beamforming, where the spatial degrees of freedom in multi-antenna systems are repurposed to achieve wideband RF SIC. Moving up in the network stack, we study heterogeneous networks with half-duplex and FD users, and develop the novel Hybrid-Greedy Maximum Scheduling (H-GMS) algorithm, which achieves throughput optimality in a distributed manner. Analytical and simulation results show that H-GMS achieves 5-10x better delay performance and improved fairness compared with state-of-the-art approaches.
Finally, we described experimentation and measurements in the city-scale COSMOS testbed being deployed in West Harlem, New York City. COSMOS' key building blocks include software-defined radios, millimeter-wave radios, a programmable optical network, and edge cloud, and their convergence will enable researchers to remotely explore emerging technologies in a real world environment. We provide a brief overview of the testbed and focus on experimentation with advanced technologies, including the integrating of open-access FD radios in the testbed and a pilot study on converged optical-wireless x-haul networking for cloud radio access networks (C-RANs). We also present an extensive 28GHz channel measurements in the testbed area, which is a representative dense urban canyon environment, and study the corresponding signal-to-noise ratio (SNR) coverage and achievable data rates. The results of this part helped drive and validate the design of the COSMOS testbed, and can inform further deployment and experimentation in the testbed.
In this thesis, we make several theoretical and experimental contributions to ultra-low-power energy harvesting networks and the IoT, and FD wireless. We also contribute to the experimentation and measurements in the COSMOS advanced wireless testbed. We believe that these contributions are essential to connect fundamental theory to practical systems, and ultimately to real-world applications, in future wireless networks