This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and Sparse Encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σd, which is used to derive error bounds for SE using σd, the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios.

Haptic teleoperation is a promising technology with applications in telemaintenance and disaster management. However, it faces significant challenges when the application is subjected to a high network latency and environments with moving objects. This work aims to extend Model Mediated Teleoperation (MMT) to overcome challenges in supporting dynamic environments. Instead of striving for perfect model alignment, we acknowledge the inevitable mismatch between the remote environment and its model at the operator. We propose a set of design principles and an accompanying framework for designing MMT solutions that prioritize operator intent. Our approach is exemplified through an application where an operator, located 8000 km away (The Netherlands – India) and subjected to an average of 179 ms end-to-end latency, guides a robot arm to draw on a whiteboard whose position is actively altered. We evaluate the effectiveness of our approach through a user study. We show a 3-point improvement on a 7-point Likert scale when users utilize our approach to teleoperate over significant network latency of up to 1 second.

Drones represent a significant technological shift at the convergence of on-demand cyber-physical systems and edge intelligence. However, realizing their full potential necessitates managing the limited energy resources carefully. Prior work looks at factors such as battery characteristics, intelligent edge sensing considerations, planning, and robustness in isolation. But a global view of energy awareness that considers these factors and looks at various tradeoffs is essential. To this end, we present results from our detailed empirical study of battery charge-discharge characteristics and the impact of altitude and lighting on edge inference accuracy. Our energy models, derived from these observations, predict energy usage while performing various manoeuvres with an error of 5.6%, a 2.5X improvement over the state-of-the-art. Furthermore, we propose a holistic energy-aware multi-drone scheduling system that decreases the energy consumed by 21.14% and the mission times by 46.91% over state-of-the-art baselines. To achieve system robustness in the event of link or drone failure, we observe trends in Packet Delivery Ratio to propose a methodology to establish reliable communication between nodes. We release an open-source implementation of our system. Finally, we tie all of these pieces together using a people-counting case study.

Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems. Since WER considers only literal word-level correctness, new evaluation metrics based on semantic similarity such as semantic distance (SD) and BERTScore have been developed. However, we found that these metrics have their own limitations, such as a tendency to overly prioritise keywords. We propose HEVAL, a new hybrid evaluation metric for ASR systems that considers both semantic correctness and error rate and performs significantly well in scenarios where WER and SD perform poorly. Due to lighter computation compared to BERTScore, it offers 49 times reduction in metric computation time. Furthermore, we show that HEVALcorrelates strongly with downstream NLP tasks. Also, to reduce the metric calculation time, we built multiple fast and lightweight models using distillation techniques without significant reduction in performance.

Surveillance and monitoring are highly critical in many application scenarios like wildlife conservation, restricted areas such as nuclear spillover, and border security. Moreover, in these scenarios, intrusions do not happen frequently thus, conventional surveillance is overkill and expensive that also requires extensive human involvement which can be arduous, expensive, and inefficient. To address these issues we propose an end-to-end smart acoustic surveillance solution for intrusion detection using a simple low-cost system called Balls for Walls (B4 W). The objective is to create a network of sensors that could also be remotely launched. The nodes responsible for surveillance employ audio sensors which are packaged within hard balls thus allowing the launch of these sensors from a distance of over 500 m. We use microphones for detecting human activity inferred through sensing the sound of footsteps against background noise. We evaluate the systems across five different terrain types. We propose a novel, low complexity detection algorithm called SEED which leverages signal energy and shape to distinguish humans from ambient noise. B4 W offers a maximum detection rate of 98.3% on dry leaves and a low false alarm rate of 0.9%. The system is energy efficient to last a maximum of 170 days and it is orientation agnostic. The proposed system has been extensively tested across varying terrains and ambient signal scenarios to demonstrate its efficacy.

Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device. Despite the existence of numerous research frameworks for simulating FL algorithms, they do not facilitate comprehensive deployment for automatic speech recognition tasks on heterogeneous edge devices. This is where Ed-Fed, a comprehensive and generic FL framework, comes in as a foundation for future practical FL system research. We also propose a novel resource-aware client selection algorithm to optimise the waiting time in the FL settings. We show that our approach can handle the straggler devices and dynamically set the training time for the selected devices in a round. Our evaluation has shown that the proposed approach significantly optimises waiting time in FL compared to conventional random client selection methods.

The IEEE 802.1CB standard for Frame Replication and Elimination for Reliability (FRER) emphasises improving reliability by introducing link redundancy. In this paper, two approaches towards the elimination of duplicates are implemented on a programmable pipelined switch. The first approach meets the intermittent stream traffic goal of the standard. A sliding window-based algorithm is used for storing and comparing identification numbers of packets. The second approach handles bulk stream traffic by using a hash table along with the sliding window for a faster lookup, making it more scalable. Hash-collision mitigation with the help of stream identification is also incorporated in the hash table approach. The algorithms are implemented in P4 and Micro-C. Latency versus window size obtained for both the approaches and the end-to-end packet loss reduction yielded by FRER in presence of lossy intermediate links are demonstrated.

We evolve a methodology and define a metric to evaluate Tactile Cyber-Physical Systems (TCPS). Towards this goal, we use the step response analysis, a well-known control-theoretic method. The adoption includes replacing the human operator (or master) with a controller with known characteristics and analyzing its response to slave side step disturbances. The resulting step response curves demonstrate that the Quality of Control (QoC) metric is sensitive to control loop instabilities and serves as a good indicator of potential factors that contribute to operator-side cybersickness. Through experiments, we demonstrate how QoC accounts for network overheads such as the link latency and jitter and non-networking overheads such as the testbed settings and robot performances in a TCPS. We show that there is a one-to-one correlation between QoC and end-to-end latency, jitter, and packet drops of a TCPS implementation. We show through experiments how QoC can be used to estimate positional errors in tactile-visual control applications. Since higher positional errors can result in poor task performance, estimating them is useful in developing a better-performing TCPS. We also evaluate a TCPS using Fitts’ test and compare its results with QoC. We show that QoC is useful in distinguishing TCPS with differences in their specifications that are not detectable using Fitts’ test.

Cybersickness and control-loop instabilities are two main concerns in Tactile Cyber-Physical Systems (TCPS). TCPS applications demand stringent bounds on end-to-end latencies to avoid their occurrences. Traditional best-effort networks cannot guarantee packet latencies in the presence of external traffic. However, emerging deterministic networks such as IEEE 802.1 Time-Sensitive Networking (TSN) can isolate time-critical flows from external traffic using IEEE 802.1Qbv Time-Aware Shaper (TAS) to guarantee bounded end-to-end packet latencies. In this work, we develop eDDSCH-TSN, a decentralized dynamic scheduling protocol to configure non-overlapping gate slots in TAS-enabled TSN switches to support TCPS flows. eDDSCH-TSN supports plug-and-play operation of compatible TCPS terminals with guaranteed minimal end-to-end packet latencies. Compared to the state-of-the-art, eDDSCH-TSN provides three orders lower end-to-end packet latencies for TCPS flows in mid-size networks with 10 hops between source and destination terminals. Further, we also present PYTSN, an open-source discrete-event TSN simulator that we use for evaluating eDDSCH-TSN. In particular, we use PYTSN to show the isolation of TCPS flows from external traffic and plug-and-play operation of TCPS terminals.

Tactile Internet based Cyber-Physical Systems (TCPS) are highly sensitive to component and communication latencies and packet drops. Building a high performing TCPS, thus, necessitates experimenting with different hardware, algorithms, access technologies, and communication protocols. To facilitate such experiments, we have developed TCPSbed, a modular testbed for TCPS. TCPSbed facilitates the integration of different components, both real and simulated, to realize different TCPS applications and evaluate their latency and control performances. TCPSbed’s latency analyzer tool employs a novel method to isolate latencies of individual TCPS components such as the latencies contributed by actuation, sensing, algorithms, and by the network, all in an online fashion. TCPSbed’s method of analyzing stability is also novel. It involves the use of the step response analysis method, a classic control-theoretic method used for analyzing the stability of generic control systems. TCPSbed’s support for edge intelligence modules enables prediction of command and feedback signals at the network’s edge allowing TCPS applications to perform well in adverse network conditions. TCPSbed’s source-code, made available through our GitHub pageTactileInternet, allows developers to extend its features and functionalities further. In this paper, we describe the architecture and implementation details of TCPSbed and demonstrate its features through several proof-of-concept experiments.

With the advent of Space-IoTs, the rate of launch of satellites has grown significantly. Alongside, the failure rate of satellites has also surged increased tremendously. Satellites are non-repairable systems in orbit, and the financial loss incurred when the satellites fail before their expected mission time is substantial. If the source of a failure is known while the satellite is in orbit, then there is a possibility to revive it by sending appropriate commands from ground stations. In this work, we present a simple, independent satellite health monitoring system called Chirper. The Chirper is equipped with multiple modules such as IMU, isolated voltage and current measurement probes, and an onboard communication channel. We present a new approach to measure low DC voltages in an isolated way, providing a resolution and accuracy of around 1 V. We evaluated the design and performance of the Chirper through simulation, testing it in space systems test facility, and by mounting it on a helium balloon. With extensive experiments we show that 90% of the time the dc voltage measurement error is within 0.8 V, and the maximum error is 0.9 V. We expect to launch the Chirper soon on a space system.

Summary Perpetual lifetimes and low maintenance are few of the attractive aspects of sensor nodes that harvest ambient energy. However, their operation depends heavily on the energy profiles of their harvesting source(s). In this work, we study the suitability of energy harvesting sensor (EHS) Nodes, powered using indoor lighting and vibrations, for a simple temperature monitoring application. To help these nodes sustain, we have proposed schemes that allow the nodes to sample and transmit data judiciously. Along with an adaptive sampling based on autoregressive (AR) model, we have proposed a regulating function based transmission scheme that regulates the amount of transmitted data based on the energy available at the node and the characteristics of the data so that even with limited transmissions the fidelity of the data is not lost. With the help of thorough evaluations, we can conclude that an EHS node fares quite well. Results show that adaptive sampling and transmissions based on regulating function not only save energy at the sensor nodes, but they also reduce the amount of data generated and accumulated in a network.

As the number of IoT devices start to explode, establishing the irrefutable device identity is at the root of trust. Physically Unclonable Functions (PUFs) are emerging as a significant alternative to the conventional methods to establish the hardware root of trust. The existing research in PUFs has focused on the design of custom circuits for deriving signatures. These signatures are used as security primitive. We design a novel GPIO PUF from GPIOs of COTS microcontrollers. Further, we demonstrate our method with low-cost off-the-shelf Arduino boards and characterize the repeatability and uniqueness of the PUF. Our experimental evaluation proves the robustness of GPIO PUF. We achieved 100% accuracy for the identification of 12 devices on the 12000 measurements collected over the duration of one month. Our evaluation has shown that the GPIO PUF is a promising alternative for generating strong identification which could be used as a primitive for several applications. We further discuss ways to enhance the uniqueness for a larger set of smart devices.

High accuracy and device-free indoor localization is still a holy grail to enable smart environments. With the growing privacy concerns and regulations, it is necessary to develop methods and systems that can be low-power, device-free as well as privacy-aware. While IR-based solutions fit the bill, they require many modules to be installed in the area of interest for higher accuracy, or proper planning during installation, or they may not work if the background has multiple heat-emitting objects, etc. In this paper, we propose a custom-built miniature device called LOCI that uses IR sensing. One unit of LOCI can provide three-dimensional localization at best. LOCI uses only a thermopile and a PIR sensor built within a 5x5x2 cm3module. Since IR-based sensing is used, LOCI consumes around 80 mW. LOCI uses analog waveform from the PIR sensor with the gain of the PIR sensor dynamically controlled through software in real-time to simulate spatial diversity. LOCI proposes low-complexity techniques with sensor fusion to eliminate the noise in the background, which has not been handled in previous works even with sophisticated signal processing techniques. Since LOCI uses raw data from the thermopile, the computations are power-efficient. We present the complete design of LOCI and the proposed methodology to estimate height and location. LOCI achieves accuracies of sub-22 cm with a confidence of 0.5 and sub-35 cm with a confidence of 0.8. The best-case location accuracy is 12.5 cm. The accuracy of height estimation is within 8 cm in majority cases. LOCI can easily be extended to recognize activities.

Global Positioning System is a widely adopted localization technique. With the increasing demand for small satellites, the need for a low-power GPS for satellites is also increasing. To enable many state-of-the-art applications, the exact position of the satellites is necessary. However, building low-power GPS receivers which operate in low earth orbit pose significant challenges. This is mainly due to the high speed (~7.8 km/s) of small satellites. While duty-cycling the receiver is a possible solution, the high relative Doppler shift between the GPS satellites and the small satellite contributes to the increase in Time To First Fix (TTFF), thus increasing the energy consumption. Further, if the GPS receiver is tumbling along with the small satellite on which it is mounted, longer TTFF may lead to no GPS fix due to disorientation of the receiver antenna. In this paper, we elucidate the design of a low-cost, low-power GPS receiver for small satellite applications. We also propose an energy optimization algorithm called F3to improve the TTFF which is the main contributor to the energy consumption during cold start. With simulations and in-orbit evaluation from a launched nanosatellite with our μGPS and high-end GPS simulators, we show that up to 96.16% of energy savings (consuming only ~ 1/25th energy compared to the state of the art) can be achieved using our algorithm without compromising much (~10 m) on the navigation accuracy. The TTFF achieved is at most 33 s.

In this paper, we evolve a methodology and define a metric to evaluate Tactile Cyber-Physical Systems (TCPS). Towards this goal, we adopt the step response analysis, a well-known control theoretic method. The adoption includes replacing the human operator (or master) with a controller with known characteristics and analyzing its response to slave side haptic sensor step changes. The resulting step response curves demonstrate that the Quality of Control (QoC) metric is sensitive to control loop instabilities and serves as a good indicator of cybersickness experienced by human operators. We demonstrate the efficacy of the proposed methodology and metric through experiments on a TCPS testbed. The experiments include assessing the suitability of several access technologies, intercontinental links and testbed configurations.

The constructive interference (CI) phenomenon has been exploited by a number of protocols for providing energy-efficient, low-latency, and reliable data collection and dissemination services in wireless sensor networks. These protocols consider CI to provide highly reliable packet delivery. This has attracted attention to understand the working of CI; however, the existing works present inconsistent views. Furthermore, these works do not study in the real-world settings where the physical conditions of deployment and unreliable wireless channels also impact the performance of CI. Therefore, we study the phenomenon of CI, considering a receiver's viewpoint and analyze the parameters that affect CI. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into the CI phenomenon. With the understanding, we develop the destructive interference-based power adaptation (DIPA), an energy-efficient and distributed algorithm, that adapts transmission power to improve the performance of CI. Since CI-based protocols cannot have an explicit acknowledgment packet, we make use of destructive interference on a designated byte to provide a feedback. We leverage this feedback to adapt transmission powers. We compared CI with and without DIPA in two real-life testbeds. On one testbed, we achieve around 25% lower packet losses while using only half of its transmission power for 64-B packets. On the other testbed, we achieve 25% lower packet losses while consuming only 47% of its transmission power for 128-B packets. Existing CI-based protocols can easily incorporate DIPA into them to achieve lower packet losses and higher energy efficiencies.

Constructive Interference (CI) phenomenon has been exploited by Glossy, a mechanism for low-latency and reliable network flooding and time synchronization for wireless sensor networks. Recently, CI has also been used for other applications such as data collection and multicasting in static and mobile WSNs. These applications base their working on the high reliability promised by Glossy regardless of the physical conditions of deployment, number of nodes in the network, and unreliable wireless channels that may be detrimental for CI. There are several works that study the working of CI, but they present inconsistent views. We study CI from a receiver's viewpoint, list factors that affect CI and also specify how and why they affect. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into CI phenomenon. With this understanding, we improve the performance of CI through an energy-efficient and distributed algorithm. We cause destructive interference on a designated byte to provide negative feedback. We leverage this to adapt transmission powers. Compared to Glossy, we achieve 25% lesser packet losses while using only half of its transmission power.

Pyroelectric Infra-Red (PIR) sensors are used in many applications including security. PIRs detect the presence of humans and animals from the radiation of their body heat. This could be used to trigger events, e.g., opening doors, recording video, etc. PIRs are used widely because of their low power consumption.

We propose energy harvesting technologies and cooperative relaying techniques to power the devices and improve reliability. We propose schemes to (a) maximize the packet reception ratio (PRR) by cooperation and (b) minimize the average packet delay (APD) by cooperation amongst nodes. Our key result and insight from the testbed implementation is about total data transmitted by each relay. A greedy policy that relays more data under a good harvesting condition turns out to be a sub optimal policy. This is because, energy replenishment is a slow process. The optimal scheme offers a low APD and also improves PRR.

Physical layer security is one possible alternative for the exploding Internet of Things in the coming years. We advance the state of art in physical layer security by implementing self-jamming on ultra low power transceivers without the assistance of “friendly” nodes. The implementation supports flexible payload sizes; where the simplest implementation carries a full data payload. We consider a passive eavesdropper with varying attack potentials; constantly attempting to recover the original data from the transmitter. We show that a 3dB shift and transmit power control between transmitter and receiver is good enough to ensure data security. The key insights from this work is that large payloads have a 50% lower throughput compared to smaller payloads. Also smaller payloads have the advantage of a 25% delay performance improvement over large payloads.

We implement two energy models that accurately and comprehensively estimates the system energy cost and communication energy cost for using Bluetooth and Wi-Fi interfaces. The energy models running on a system is used to smartly pick the most energy optimal network interface so that data transfer between two end points is maximized.

In this work, we study the adaptability of well known cryptography algorithms to energy harvesting wireless sensor networks. We are particularly interested in algorithms that have the ability to adapt to varying power in such networks. Our investigations and implementation on hardware platforms indicate that it is optimal to precompute a few key stream bytes, store in memory and later used during the time when the system is low on harvested power level. Our demonstrable setup shows using a precomputed key stream can decrease the energy consumption by 14%. We have implemented the Trivium stream cipher for two different microcontrollers, the MSP430 and the AVR ATmega1281 and show the performance results for these implementations. We have implemented an algorithm based on universal hash functions to provide message authentication with the assistance of stream ciphers. We show that this authentication algorithm has exciting properties for energy harvesting system and more generally for resource constrained devices.

We describe the on-going design and implementation of a sensor network for agricultural management targeted at resource-poor farmers in India. Our focus on semi-arid regions led us to concentrate on water-related issues. Throughout 2004, we carried out a survey on the information needs of the population living in a cluster of villages in our study area. The results highlighted the potential that environment-related information has for the improvement of farming strategies in the face of highly variable conditions, in particular for risk management strategies (choice of crop varieties, sowing and harvest periods, prevention of pests and diseases, efficient use of irrigation water etc.). This leads us to advocate an original use of Information and Communication Technologies (ICT). We believe our demand-driven approach for the design of appropriate ICT tools that are targeted at the resource-poor to be relatively new. In order to go beyond a pure technocratic approach, we adopted an iterative, participatory methodology