This paper presents an adversarial jamming attack detection method using energy-detection-based hardware for wireless Internet of Things (IoT) systems. The proposed method utilizes accurate RF signal energy level detection to establish a learning baseline for identifying different types of jamming attacks in wireless networks, which are often characterized by changes in RF energy levels. An adaptive threshold-based binary classification algorithm is implemented to use the detected signal’s energy level as the classification metric. This enables the detection of deceptive/reactive and periodic jamming attacks. Energy levels of RF signal are measured using an ultra-low power (ULP) direct RF-to-digital Received Signal Strength Indicator (RSSI) circuit, developed in a 65-nm CMOS technology, which consumes only 6 nW of power. Further, the RSSI circuit incorporates a band-pass response to filter out ambient signal in the network, providing resilience against continuous RF jammers. Furthermore, a system-level model of the proposed method is presented to demonstrate its detection capabilities. To mitigate the stochastic effects of the wireless channel, a minimum mean-square error (MMSE) channel equalizer is integrated, which improves the detection accuracy by reducing the attack detection error by 21.6%. The RF transmission packet structure is modeled based on the IEEE 802.15.4 protocol. We conducted extensive over-the-air measurements under various attack modalities and scenarios using universal software radio peripheral (USRP) B210 software defined radios (SDRs) to validate the detection accuracy of our proposed hardware-based detection method for energy-constrained IoT systems at 915 MHz. Measurement results demonstrate an accuracy of 96.4% for detecting deceptive jammer and periodic jammer, closely aligning with the simulation results.

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.

Emerging applications like distributed coordinated beamforming (DCB), intelligent reflector arrays, and networked robotic devices will transform wireless applications. However, for systems-centric work on these topics, the research community must first overcome the hurdle of implementing fine-grained, over-the-air timing synchronization, which is critical for any coordinated operation. To address this gap, this paper presents an open-source design and implementation of 'RFClock' that provides timing, frequency and phase synchronization for software defined radios (SDRs). It shows how RFClock can be used for a practical, 5-node DCB application without modifying existing physical/link layer protocols. By utilizing a leader-follower architecture, RFClock-leader allows follower clocks to synchronize with mean offset under 0.107Hz, and then corrects the time/phase alignment to be within a 5ns deviation. RFClock is designed to operate in generalized environments: as standalone unit, it generates a 10MHz/1PPS signal reference suitable for most commercial-off-the-shelf (COTS) SDRs today; it does not require custom protocol-specific headers or messaging; and it is robust to interference through a frequency-agile operation. Using RFClock for DCB, we verify significant increase in channel gain and low BER in a range of [0 -- 10--3] for different modulation schemes. We also demonstrate performance that is similar to a popular wired solution and significant improvement over a GPS-based solution, while delivering this functionality at a fractional price/power point.

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.

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.

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.

We present CapBand, a battery-free hand gesture recognition wearable in the form of a wristband. The key challenges in creating such a system are (1) to sense useful hand gestures at ultra-low power so that the device can be powered by the limited energy harvestable from the surrounding environment and (2) to make the system work reliably without requiring training every time a user puts on the wristband. We present successive capacitance sensing, an ultra-low power sensing technique, to capture small skin deformations due to muscle and tendon movements on the user's wrist, which corresponds to specific groups of wrist muscles representing the gestures being performed. We build a wrist muscles-to-gesture model, based on which we develop a hand gesture classification method using both motion and static features. To eliminate the need for per-usage training, we propose a kernel-based on-wrist localization technique to detect the CapBand's position on the user's wrist. We prototype CapBand with a custom-designed capacitance sensor array on two flexible circuits driven by a custom-built electronic board, a heterogeneous material-made, deformable silicone band, and a custom-built energy harvesting and management module. Evaluations on 20 subjects show 95.0% accuracy of gesture recognition when recognizing 15 different hand gestures and 95.3% accuracy of on-wrist localization.

Ambient radio frequency (RF) energy harvesting (RF-EH) allows powering low-power electronic devices without wires, batteries, and dedicated energy sources. Current RF-EH circuit designs for ambient RF harvesting are optimized and fabricated for a predetermined frequency band. Thus, a single circuit is tuned for a given band with simple extensions to multiple circuits operating individually in distinct bands. Our approach is different in the sense that it designs and implements a common circuit design that can operate on multiple different RF cellular and ISM bands. This paper makes two contributions. First, it presents a study of ambient RF signal strength distribution conducted in Boston, MA, USA, indicating locations and associated RF bands that can point toward the practicality of ambient RF-EH. Second, it demonstrates an adjustable circuit for harvesting from LTE 700-MHz, GSM 850MHz, and ISM 900-MHz bands with one single circuit. Our circuit design is fabricated on printed circuit board with comprehensive evaluations at each associated frequency to test the power conversion efficiency (PCE). In addition, we characterize the charging performance, and feasibility of powering sensors outdoors such as TI eZ430-RF2500. Results reveal more than 45% PCE for our prototype.

Galvanic coupling is the enabler of closed-loop communication between implanted sensors and embedded actuating devices (such as drug injectors) by providing energy-efficient and reliable non-RF transmission through links formed within tissue. For safe deployment, it is critical to verify that the amount of heat generated within tissues during signal propagation stays within permissible bound. In this paper, we analyze the thermal distribution within tissues, for galvanic coupling-based communication for varying transmission power levels, number of collocated transmitters, and blood perfusion conditions using finite element based numerical simulation and skin-phantom based experiments. Our results confirm that tissue heating remains well below safe limit of 1 °C. Using the temperature dissipation profile, we derive the suitable transmission duty cycles, separation distances and number of concurrent sources that may co-exist without raising the tissue temperature. The proposed strategies provide upto four fold increase in bandwidth efficiency through concurrent transmissions, ensuring sufficient bandwidth for implant communications.

Implanted sensors and actuators in the human body promise in-situ health monitoring and rapid advancements in personalized medicine. We propose a new paradigm where such implants may communicate wirelessly through a technique called as galvanic coupling, which uses weak electrical signals and the conduction properties of body tissues. While galvanic coupling overcomes the problem of massive absorption of RF waves in the body, the unique intra-body channel raises several questions on the topology of the implants and the external (i.e., on skin) data collection nodes. This paper makes the first contributions towards (i) building an energy-efficient topology through optimal placement of data collection points/relays using measurement-driven tissue channel models, and (ii) balancing the energy consumption over the entire implant network so that the application needs are met. We achieve this via a two-phase iterative clustering algorithm for the implants and formulate an optimization problem that decides the position of external data-gathering points. Our theoretical results are validated via simulations and experimental studies on real tissues, with demonstrated increase in the network lifetime.

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.

This paper proposes the design and implementation of low-- energy tokens for smart interaction with capacitive touch-- enabled devices by associating the token's identity with its contact, or touch. The proposed token's design features two key novel technical components: (1) a through--touch--sensor low--energy communication method for token identification and (2) a touch--sensor energy harvesting technique. The communication mechanism involves the token transmitting its identity (ID) directly through the touch--sensor by artificially modifying the effective capacitance between the touch-- sensor and token surfaces. This approach consumes significantly lower energy compared to traditional electrical signal modulation approaches. By enabling the token to harvest energy from touch--screen sensors or touch--surfaces the token is rendered battery--free. Through experimental evaluations using a prototype implementation, the proposed design is shown to achieve at least 95% identification accuracy. It is also shown to consume less energy than competitive techniques (NFC P2P and Bluetooth Low--Energy) for communicating a short ID sequence. The adoption of this technology among users is evaluated through a user study on 12 subjects.

A wireless sensor network that employs passive radio wake-up of the sensor nodes can reduce the energy cost for unnecessary idle listening and communication overhead, extending the network lifetime. A passive wake-up radio is powered by the electromagnetic waves transmitted by a wake-up transmitter rather than a battery on the sensor node. However, this method of powering the wake-up radio results in a short wake-up range, which limits the performance of a passive wake-up radio sensor network. In this article, we describe our design of a passive wake-up radio sensor node—REACH 2 -Mote—using a high-efficiency, energy-harvesting module and a very low power wake-up circuit to achieve an extended wake-up range. We implemented REACH 2 -Mote in hardware and performed field tests to characterize its performance. The experimental results show that REACH 2 -Mote can achieve a wake-up range of 44 feet. We also modeled REACH 2 -Mote and evaluated its performance through simulations, comparing its performance to that of another passive wake-up radio approach, an active wake-up radio approach, and a conventional duty cycling approach. The simulation results show that REACH 2 -Mote can significantly extend the network lifetime while achieving high packet delivery rate and low latency.

Use of a passive wake-up radio can drastically increase the network lifetime in a sensor network by reducing or even completely eliminating unnecessary idle listening. A sensor node with a wake-up radio receiver (WuRx) can operate in an extremely low power sleep mode until it receives a trigger signal sent by a wake-up radio transmitter (WuTx). After receiving the trigger signal, the attached WuRx wakes up the sensor node to start the data communication. In this paper, we implement and compare the performance of three passive wake-up radio-based sensor nodes: 1) WISP-Mote, which is a sensor mote that employs an Intel WISP passive RFID tag as the WuRx; 2) EH-WISP-Mote, which combines a novel energy harvester with the WISP-Mote; and 3) REACH-Mote, which uses the energy harvester circuit combined with an ultra-low-power pulse generator to trigger the wake-up of the mote. Experimental results show that the wake-up range and wake-up delay for the EH-WISP-Mote are improved compared with the WISP-Mote, while providing the ability to perform both broadcast-based and ID-based wake-ups. On the other hand, the REACH-Mote, which can only provide broadcast-based wake-up, can achieve a much longer wake-up range than any known passive wake-up radio to date, achieving feasible wake-up at a range of up to 37 ft.