Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement the first-of-its-kind convolution that uses over-the-air computation and demonstrate it for inference tasks in a convolutional neural network (CNN). We engineer the ambient wireless propagation environment through reconfigurable intelligent surfaces (RIS) to design such an architecture, which we call ’AirNN’. AirNN leverages the physics of wave reflection to represent a digital convolution, an essential part of a CNN architecture, in the analog domain. In contrast to classical communication, where the receiver must react to the channel-induced transformation, generally represented as finite impulse response (FIR) filter, AirNN proactively creates the signal reflections to emulate specific FIR filters through RIS. AirNN involves two steps: first, the weights of the neurons in the CNN are drawn from a finite set of channel impulse responses (CIR) that correspond to realizable FIR filters. Second, each CIR is engineered through RIS, and reflected signals combine at the receiver to determine the output of the convolution. This paper presents a proof-of-concept of AirNN by experimentally demonstrating convolutions with over-the-air computation. We then validate the entire resulting CNN model accuracy via simulations for an example task of modulation classification.

Object sensing and tracking using electric and magnetic fields allow intelligent interaction, automation, and adaptation in cyber-physical systems. Our approach, called iSense, uses a software-defined collaborative sensing technique for the detection of the type of object when placed on a large surface and tracking its mobility. iSense is cost effective, low power, and scalable, which allows its use over large surfaces. First, we introduce a dual-coil magnetic resonant sensing architecture based on nested coils, i.e., passive (outer) and active (inner) coils, for low-power contactless sensing. Second, a data-driven support vector machine-based approach helps to classify different types of objects using the voltage readings obtained at the passive coil. iSense combines sensed voltage information from multiple different coils spread over the surface with a group-based interference mitigation mechanism between coils for collaborative sensing. We validate our system with real-time prototype and experimental evaluations. We demonstrate the detection of seven different types of objects over three different materials, and real-time detection and tracking of mobile objects including a robot car. Experimental results show that each sensing coil only consumes few milliwatts, i.e., 18× less than inductive sensing and 15× less than classical magnetic resonance sensing, extend sensing depth to 3 cm, and enable tracking on the large surface sensing with more than 90% accuracy for velocity estimation.

Wireless RF energy transfer for indoor sensors is an emerging paradigm ensuring continuous operation without battery limitations. However, high power radiation within ISM band interferes with packet reception for existing WiFi devices. The paper proposes the first effort in merging RF energy transfer within a standards compliant 802.11 protocol, realizing practical and WiFi-friendly Energy Delivery with Mobile Transmitters (WiFED Mobile). WiFED Mobile architecture is composed of a centralized controller coordinating the actions of multiple energy transmitters (ETs), and deployed sensors that periodically requires charging. The paper first describes 802.11 supported protocol features that can be exploited by sensors to request energy and for ETs to participate in energy transfer. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of ETs to sensors for efficient energy delivery. Thirdly, it detects outlier sensors (OS), which have limited power reception from static ETs and utilizes mobile ETs (METs) to satisfy their charging cycles. The proposed in-band and protocol supported coexistence in WiFED Mobile is validated via simulations and partly in a software defined radio testbed, showing that METs reduce latency by 42% and improve throughput by 83% in scenarios where using only static ETs fails to satisfy charging cycles of OS.

Object sensing and tracking using electric and magnetic fields allow intelligent interaction, automation, and adaptation in cyber-physical systems. Our approach, called Softsense, uses a software-defined collaborative sensing technique for detection of the type of object and where it is placed on a large surface. Unlike RF based sensing approaches that are generally dependent on channel conditions, capacitive sensing that detects only low conductive objects, and Qi-based inductive sensing that is effective at few mm in range, Softsense is designed to operate without the above limitations. Softsense is cost effective, low power and scalable, which allows extension over large surfaces. First, we introduce a dual-coil inductive sensing architecture based on nested coils, i.e., passive (outer) and active (inner) coils, for low-power contact-less sensing. A data driven support vector machine-based approach helps to classify different materials using the voltage readings obtained at the passive coil. SoftSense combines sensed voltage information from multiple different coils spread over the surface for collaborative sensing. We validate our design on a real sensing prototype with customized coils, fabricated sensing circuit, and a network software controller. Experimental results show that each sensing coil only consumes few milliwatts, i.e., 18x less than the inductive sensing and 15x less than classical magnetic resonance sensing, extends sensing depth to 3 cm, and enables coverage of large surface sensing.

Wireless RF energy transfer for indoor sensors is an emerging paradigm that ensures continuous operation without battery limitations. However, high power radiation within the ISM band interferes with the packet reception for existing WiFi devices. The paper proposes the first effort in merging the RF energy transfer functions within a standards compliant 802.11 protocol to realize practical and WiFi-friendly Energy Delivery (WiFED). The WiFED architecture is composed of a centralized controller that coordinates the actions of multiple distributed energy transmitters (ETs), and a number of deployed sensors that periodically request energy from the ETs. The paper first describes the specific 802.11 supported protocol features that can be exploited by sensors to request energy and for the ETs to participate in the energy delivery process. Second, it devises a controller-driven bipartite matching-based algorithmic solution that assigns the appropriate number of ETs to energy requesting sensors for an efficient energy transfer process. The proposed in-band and protocol supported coexistence in WiFED is validated via simulations and partly in a software defined radio testbed, showing 15% improvement in network lifetime and 31% reduction in the charging delay compared to the classical nearest distance-based charging schemes that do not anticipate future energy needs of the sensors and are not designed to co-exist with WiFi systems.

This paper presents FreeIoT, a control plane paradigm that allows fine grained signaling for city-scale IoT deployments without installing any additional infrastructure. FreeIoT overlays control/wake-up information for sensors over existing standards compliant LTE through the following contributions: First, we develop a novel encoding scheme that changes the spatial positioning of Almost Blank Subframes (ABS) within a standard LTE frame to convey control information. ABS was originally defined in the standard to allow coexistence between the macro-cell eNB and nearby small cells, which FreeIoT leverages as a side channel for IoT signaling. Our approach works with any number of ABS settings chosen by the LTE eNB, and accordingly adjusts the encoding of control messages at maximum possible transmission rates. Second, a session management protocol is introduced to maintain contextual information of the control signaling. This allows FreeIoT to handle situations where the control message may span multiple frames, or when the LTE operator temporarily reduces the number of ABS. FreeIoT also incorporates an error detection and correction mechanism to counter channel and fading errors. Finally, we implement a proof of concept testbed to validate the operation of FreeIoT using a software defined LTE eNB and custom-designed RF energy harvesting circuit interfaced with off-the-shelf sensors.

Emerging network architectures in 60GHz millimeter wave bands will likely use dense deployment of access points (APs) given the high attenuation and frequent line-of-sight related outages. We show in this paper that naively connecting to any available AP, or even multiple APs, may not fully realize the promise of efficiently utilizing the extremely large bandwidths available in this band. This paper holistically addresses these problems through the Multi-AP Association Protocol (MAP) that: (i) indicates ideal durations for beam-searching at the physical/link layers of the protocol stack that will result in minimal interruptions to user-traffic, (ii) devises a Multi-AP association framework based on multipath TCP, which increases the network robustness by allowing immediate redirection of traffic to alternative APs whenever an existing connection is interrupted, and (iii) designs an exploration-exploitation aware flow scheduling algorithm that dynamically activates the sub-flows to optimize transport layer performance. MAP is a lightweight, client side system that needs zero modifications to existing protocol stack, though it interacts with the latter to opportunistically trigger standards-defined functions. The paper also presents convergence analysis of throughput, experimental validation on a 60GHz network testbed, and trace-driven simulation studies that shows MAP achieving 5-7x reduction in re-buffering rate in HD video streaming, rapid convergence to the best possible AP, and fair allocation of resources among clients.

Wireless RF power transmission from dedicated Energy Transmitters (ETs) is emerging as a promising approach to enable battery-less wireless networked sensor systems. However, when data communication and RF energy recharging occur in-band, sharing the RF medium and devoting separate access times for both operations raises architectural and protocol level challenges. This paper proposes a novel method of concurrent transmission of data and energy to solve this problem, allowing ETs to transmit energy and sensors to transmit data in the same band synchronously. Our key idea concerns devising a physical layer modulation scheme that allows the data transmitting node to introduce variations in the envelope of the energy signal at the intended recipient. We implemented a proof-of-concept receiver, modeled and validated through extensive experimentation. We then propose a new physical layer mechanism for guaranteed successful delivery of information in a point-to-point link. Quantitative results demonstrate the feasibility of joint energy-data transfer, along with its associated benefits and tradeoffs.

Simultaneous Wireless Information and Power Transmission (SWIPT) has been proposed as a feasible solution to enable joint power and data transfer for the nodes of a battery-less wireless networked sensor system. Different from existing approaches, where the incident energy is split between decoding and harvesting blocks at the receiver chain, this paper describes the design and implementation of an all-digital receiver circuit. We leverage the internal control signals of the circuit, targeting ultra-low power consumption, low bit-rate applications in SWIPT. A proof-of-concept receiver is modeled, implemented using off-the-shelf hardware, and validated through extensive experiments. Quantitative results demonstrate the benefits of this joint energy-data reception approach through a single receiver chain, offering bit-rates of 400 bps.

Smart homes will compose of multiple sensors that will sense, compute and transmit information to a central cloud, all of which are energy consuming tasks. We propose to demonstrate a software-defined solution for wirelessly charging these sensors using RF energy, thereby extending their lifetimes. In our demo, the actions of more than one energy transmitter (ET) are synchronized in phase and frequency in real time using periodic feedback from the target sensor, but without any common clock reference. The controller selects the optimal subset of ETs to satisfy the energy request from a given sensor, which cooperatively beamform RF energy towards that sensor. Our software-defined framework, implemented in Python, allows the central controller to automatically discover the installed sensors, obtain energy needs, and schedule charging tasks in an asynchronous and non-blocking manner that allows the network to scale. The demonstration includes advancements in design and fabrication of RF energy harvesting circuits that interface with the TI EZ430 sensors, implementation of a software-defined control and data plane, as well as a real-time distributed beamforming algorithm on USRP radios that results in a battery-free network of sensors.

Full duplex communication promises a paradigm shift in wireless networks by allowing simultaneous packet transmission and reception within the same channel. While recent prototypes indicate the feasibility of this concept, there is a lack of rigorous theoretical development on how full duplex impacts medium access control (MAC) protocols in practical wireless networks. In this paper, we formulate the first analytical model of a CSMA/CA based full duplex MAC protocol for a wireless LAN network composed of an access point serving mobile clients. There are two major contributions of our work: First, our Markov chain-based approach results in closed form expressions of throughput for both the access point and the clients for this new class of networks. Second, our study provides quantitative insights on how much of the classical hidden terminal problem can be mitigated through full duplex. We specifically demonstrate that the improvement in the network throughput is up to 35-40 percent over the half duplex case. Our analytical models are verified through packet level simulations in ns-2. Our results also reveal the benefit of full duplex under varying network configuration parameters, such as number of hidden terminals, client density, and contention window size.

Wireless RF power transmission promises battery-less, resilient, and perpetual wireless sensor networks. Through the action of controllable Energy Transmitters (ETs) that operate at-a- distance, the sensors can be re-charged by harvesting the radiated RF energy. However, both the charging rate and effective charging range of the ETs are limited, and thus multiple ETs are required to cover large areas. While this action increases the amount of wireless energy injected into the network, there are certain areas where the RF energy combines destructively. To address this problem, we propose a duty-cycled random- phase multiple access (DRAMA). Non-intuitively, our approach relies on deliberately generating random interferences, both destructive and constructive, at the destination nodes. We demonstrate that DRAMA optimizes the power conversion efficiency, and the total amount of energy harvested. Through real-testbed experiments, we prove that our proposed scheme provides significant advantages over the current state of the art in our considered scenario, as it requires up to 70% less input RF power to recharge the energy buffer of the sensor in the same time.

This paper formulates the location-dependent power harvesting rates in generalized 2D and 3D placement of multiple Radio Frequency (RF) Energy Transmitters (ETs) for recharging the nodes of a wireless sensor network (WSN). In particular, we study the distributions of total available and harvested power over the entire WSN. We provide closed matrix forms of harvestable power at any given point in space due to the action of concurrent energy transfer from multiple ETs, explicitly considering constructive and destructive interference of the transmitted energy signals. We also analyze the performance of energy transfer in the WSN through power outage probability, interference, and harvested voltage as a function of the wireless power received from the ETs. Our results reveal that the network wide received power and interference power from concurrent energy transfers exhibit Log-Normal distributions, and the harvested voltage over the network follows a Rayleigh distribution.

Wireless transfer of energy through directed radio frequency waves has the potential to realize perennially operating sensor nodes by replenishing the energy contained in the limited on-board battery. However, the high power energy transfer from energy transmitters (ETs) interferes with data communication, limiting the coexistence of these functions. This paper provides the first experimental study to quantify the rate of charging, packet loss due to interference, and suitable ranges for charging and data communication of the ETs. It also explores how the placement and relative distances of multiple ETs affect the charging process, demonstrating constructive and destructive energy aggregation at the sensor nodes. Finally, we investigate the impact of the separation in frequency between data and energy transmissions, as well as among multiple concurrent energy transmissions. Our results aim at providing insights on radio frequency-based energy harvesting wireless sensor networks for enhanced protocol design and network planning.

Cognitive radio (CR) networks will enable dynamic spectrum re-use and thereby accelerate the adoption of high bandwidth services in available licensed frequencies with better channel characteristics. However, the possibility of the licensed user reclaiming the channel raises additional concerns on how best to reserve resources for secondary users (SUs) that are likely to have different qualities of service (QoSs) depending on their application requirements. This paper addresses the problem of spectrum resource management for co-located SUs with both streaming and intermittent data by efficiently identifying the number of backup channels that will ensure seamless end to end service. The contributions of this paper are threefold: First, a comprehensive analytical framework based on queueing theory is devised to calculate the theoretical delay in accessing the spectrum depending on the required QoS, with guidelines on how to optimize the set of back-up channels for possible future use; second, a method of spectrum allocation for SUs with these different QoS demands is formulated, especially as they co-exist and affect the performance of each other; third, a case study of applying these techniques in a novel application area of wireless medical telemetry is presented. Results reveal that the simulated spectral efficiency of the channel allocation using our approach matches closely with our theoretical predictions, within a 5% bound.

Wireless charging through directed radio frequency (RF) waves is an emerging technology that can be used to replenish the battery of a sensor node, albeit at the cost of data communication in the network. This tradeoff between energy transfer and communication functions requires a fresh perspective on medium access control (MAC) protocol design for appropriately sharing the channel. Through an experimental study, we demonstrate how the placement, the chosen frequency, and number of the RF energy transmitters impact the sensor charging time. These studies are then used to design a MAC protocol called RF-MAC that optimizes energy delivery to sensor nodes, while minimizing disruption to data communication. In the course of the protocol design, we describe mechanisms for (i) setting the maximum energy charging threshold, (ii) selecting specific transmitters based on the collective impact on charging time, (iii) requesting and granting energy transfer requests, and (iv) evaluating the respective priorities of data communication and energy transfer. To the best of our knowledge, this is the first distributed MAC protocol for RF energy harvesting sensors, and through a combination of experimentation and simulation studies, we observe 300% maximum network throughput improvement over the classical modified unslotted CSMA MAC protocol.

Wireless transfer of energy will help realize perennially operating sensors, where dedicated transmitters replenish the residual node battery level through directed radio frequency (RF) waves. However, as this radiative transfer is in-band, it directly impacts data communication in the network, requiring a fresh perspective on medium access control (MAC) protocol design for appropriately sharing the channel for these two critical functions. Through an experimental study, we first demonstrate how the placement, the chosen frequency, and number of the RF energy transmitters affect the sensor charging time. These studies are then used to design a MAC protocol called RFMAC that optimizes energy delivery to desirous sensor nodes on request. To the best of our knowledge, this is the first distributed MAC protocol for RF energy harvesting sensors, and through a combination of experimentation and simulation studies, we demonstrate 112% average network throughput improvement over the modified unslotted CSMA MAC protocol.

This paper presents SAVE, for Stochastic Analysis and aVailability of Energy, an analytical framework providing closed form expressions for residual energy and lifetime prediction of wireless sensor nodes. SAVE models a wide umbrella of input factors, including channel characteristics, different energy sources and harvesting policies, link layer parameters (e.g., error control and duty cycling) and various data traffic generation models. Our framework uses stochastic semi-Markov models to derive the residual energy distribution for each harvesting node accounting for practically observed temporal variations. We validate the analytical expressions derived by SAVE by means of simulations, and show that SAVE predictions provide a remarkably close match to the simulation results.

Error control is an important mechanism for providing robust multimedia communication in wireless sensor networks. Although there have been several research works in analysis of error control mechanisms in wireless multimedia networks and wireless sensor networks, but none of them are directly applicable to the wireless multimedia sensor networks (WMSNs) which has resource and performance constraints of WSNs as well as QoS requirements of multimedia communications. In this paper, we comprehensively evaluate the performance of several error control mechanisms in WMSNs. The results of our analysis provide an extensive comparison between automatic repeat request (ARQ), forward error correction (FEC), and hybrid FEC/ARQ error control mechanisms in terms of frame loss rate, frame peak signal-to-noise ratio (PSNR), and energy efficiency.