The growing popularity of wearable camera glasses raises pressing concerns about bystanders being recorded without their consent. Most existing privacy-enhancing technologies (PETs) rely on opt-out models that place the burden of privacy protection on bystanders. We conducted a qualitative study on wearers’ and bystanders’ perceptions of opt-in, privacy-by-default approaches for camera glasses. To enable this study, we designed and evaluated an opt-in privacy-by-default protocol. We then conducted semi-structured interviews with camera glass wearers and bystanders (N = 18) to examine their perceptions of the protocol. Our findings show that bystanders viewed the opt-in protocol as essential and advocated for even stronger anonymization. Wearers appreciated the protocol’s safeguards but found it visually limiting, expressing desire for a context-dependent version that can be enabled in relevant scenarios. Our findings highlight the need for context-aware PETs that provide effective mechanisms for consent negotiation.

Port scanning is a fundamental technique used by attackers to identify open ports, services, and vulnerabilities in target systems. Advanced evasion methods such as distributed scanning, slow scanning, and decoy scanning enable them to bypass traditional detection systems that are often resource-intensive and limited in scope. We introduce PortScout, a novel lightweight detection approach designed to identify port scanning evasion attacks efficiently. Unlike existing methods, PortScout leverages a unique flow aggregation and anomaly scoring mechanism that analyzes communication flows using only three key packet attributes: source IP, destination IP, and destination port. Despite the minimal data requirements, our method maintains high detection accuracy. Evaluated on real-time benign traffic and a diverse set of port scanning attacks, our approach achieves an average attack detection rate (AADR) of 89.5 % and a low false positive rate (AFPR) of 0.34 %. Additionally, it operates with low computational overhead, making it suitable for real-time deployment in high-speed networks. Compared to state-of-the-art methods, our solution offers a robust balance of efficiency and effectiveness, addressing the limitations of existing systems in detecting sophisticated port scanning evasion attacks.

Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs for context-aware code approximations. CheckMate integrates validation of LLM-generated approximations to ensure correct execution and employs Bayesian optimization to fine-tune approximation parameters autonomously, eliminating the need for developer input. Tested across six IoT applications, it reduces power cycles by up to 60% with an accuracy loss of just 8%, outperforming semi-automated tools like ACCEPT in speedup and accuracy. CheckMate's results establish it as a robust, user-friendly tool and a foundational step toward automated approximation frameworks for intermittent computing.

With the continuous and fast-changing land-scape in communication networks and artificial intelligence (AI), the researchers are interested in expedited standardization and realization of 6G networks. Federated learning (FL) is one of the paradigms that allows the 6G networks to support a diverse range of devices. Very few studies address the problem of flexibility and heterogeneity for AI network architectures in FL paradigm, that could be a potential key changer for standardization and realization of 6G networks. However, they either consider width-only or depth-only to provide flexibility support. Furthermore, the existing studies do not address the problem of weight scale variation while performing the global model aggregation at the server side. In this regard, we propose flexible federated learning (FLEXFL) for the support of heterogeneous AI network architectures in 6G communication systems. The proposed network not only considers the width but also the depth of the network architecture to make it compliant with the global model aggregation. We also address weight scale variation (WSV) while updating the global model with weight normalization, which is one of the problems associated with existing studies. We perform experimental analysis on two publicly available datasets and a few network architectures to show the efficacy of the proposed approach. The results reveal that the FLEXFL outperforms existing state-of-the-art works in both the IID and non-IID settings, accordingly.

Batteryless IoT devices, powered by energy harvesting, face significant challenges in maintaining operational efficiency and reliability due to intermittent power availability. Traditional checkpointing mechanisms, while essential for preserving computational state, introduce considerable energy and time overheads. This paper introduces Approxify, an automated framework that significantly enhances the sustainability and performance of batteryless IoT networks by reducing energy consumption by approximately 40% through intelligent approximation techniques. Approxify balances energy efficiency with computational accuracy, ensuring reliable operation without compromising essential functionalities. Our evaluation of applications, SUSAN and Link Quality Indicator (LQI), demonstrates significant reductions in checkpoint frequency and energy usage while maintaining acceptable error bounds.

Fault diagnosis plays a major role in railway condition monitoring, as early diagnosis of the emerging faults can save valuable time, reduce maintenance costs and, most significantly, help save people's lives. However, the conventional data-driven methods used to diagnose track faults, especially in underdeveloped countries, use push trolley/train-based track recording vehicles (TRV) that rely heavily on manual extraction of track data. It is a very demanding process and significantly affects the final results due to its reliance on human judgment in assessing track conditions and its suboptimal performance. In contrast, with the advent of IoT-based smart inertial measurement units, the data-driven fault diagnosis became a core component in the smart industrial automation safety system. We proposed, Muhafiz, a prototype that is an automated and portable TRV with a novel design based on axle-based acceleration methodology for rail track fault diagnosis. Our contribution concluded, based on site-specific experimentation, that Muhafiz is 87% more efficient than the traditional push trolley-based TRV mechanism.

We present the design and evaluation of a 3.5-year embedded sensing deployment at the Mithræum of Circus Maximus, a UNESCO-protected underground archaeological site in Rome (Italy). Unique to our work is the use of energy harvesting through thermal and kinetic energy sources. The extreme scarcity and erratic availability of energy, however, pose great challenges in system software, embedded hardware, and energy management. We tackle them by testing, for the first time in a multi-year deployment, existing solutions in intermittent computing, low-power hardware, and energy harvesting. Through three major design iterations, we find that these solutions operate as isolated silos and lack integration into a complete system, performing suboptimally. In contrast, we demonstrate the efficient performance of a hardware/software co-design featuring accurate energy management and capturing the coupling between energy sources and sensed quantities. Installing a battery-operated system alongside also allows us to perform a comparative study of energy harvesting in a demanding setting. Albeit the latter reduces energy availability and thus lowers the data yield to about 22% of that provided by batteries, our system provides a comparable level of insight into environmental conditions and structural health of the site. Further, unlike existing energy-harvesting deployments that are limited to a few months of operation in the best cases, our system runs with zero maintenance since almost 2 years, including 3 months of site inaccessibility due to a COVID19 lockdown.

Intermittently powered embedded devices ensure forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to the execution times. To minimize this overhead, we present DICE, a system that renders differential checkpointing profitable on these devices. DICE is unique because it is a software-only technique and efficient because it only operates in volatile main memory to evaluate the differential. DICE may be integrated with reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems, and arbitrary code can be enabled with DICE using automatic code-instrumentation requiring no additional programmer effort. By reducing the cost of checkpoints, DICE cuts the peak energy demand of these devices, allowing operation with energy buffers that are one-eighth of the size originally required, thus leading to benefits such as smaller device footprints and faster recharging to operational voltage level. The impact on final performance is striking: with DICE, Hibernus requires one order of magnitude fewer checkpoints and one order of magnitude shorter time to complete a workload in real-world settings.

Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption . Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations. We rather demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.

Embedded devices running on ambient energy perform computations intermittently, depending upon energy availability. System support ensures forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to execution times. To reduce this overhead, we present DICE, a system design that efficiently achieves differential checkpointing in intermittent computing. Distinctive traits of DICE are its software-only nature and its ability to only operate in volatile main memory to determine differentials. DICE works with arbitrary programs using automatic code instrumentation, thus requiring no programmer intervention, and can be integrated with both reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems. By reducing the cost of checkpoints, performance markedly improves. For example, using DICE, Hibernus requires one order of magnitude shorter time to complete a fixed workload in real-world settings.

Transiently-powered computers (TPCs) lay the basis for a battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption, which determine energy efficiency. We demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and develop EPIC, a compile-time energy analysis tool. We use EPIC to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, programmers avoid unnecessary program changes that hurt energy efficiency.

We propose a set of differential techniques to allow transientlypowered embedded devices reduce the amount of data written on non-volatile memory during checkpoints used to cross times of energy unavailability. These techniques track modifications in the application state to isolate data from the slice of the previous checkpoint that remains unaltered. At the following checkpoint, our approach may thus only update the parts it detects as modified. This allows us to shift part of the energy budget from checkpointing overhead to useful computations, yielding better overall energy efficiency.

We present code instrumentation strategies to allow transiently-powered embedded sensing devices efficiently checkpoint the system's state before energy is exhausted. Our solution, called HarvOS, operates at compile-time with limited developer intervention based on the control-flow graph of a program, while adapting to varying levels of remaining energy and possible program executions at run-time. In addition, the underlying design rationale allows the system to spare the energy-intensive probing of the energy buffer whenever possible. Compared to existing approaches, our evaluation indicates that HarvOS allows transiently-powered devices to complete a given workload with 68% fewer checkpoints, on average. Moreover, our performance in the number of required checkpoints rests only 19% far from that of an "oracle" that represents an ideal solution, yet unfeasible in practice, that knows exactly the last point in time when to checkpoint.

We aim at developing software techniques which enables 32-bit transiently powered embedded systems to make progress across periods of energy unavailability without resorting to hardware modifications. Recently, we have seen a huge surge in wearable and smartbuilding centric sensing applications. However, these devices are integrated with batteries for charging which not only increases size and mass but also cost of the system. Powering the system directly from the energy harvesting source can mitigate this problem, but requires system software support to handle computations across power cycles, a paradigm known as transiently powered computing. We have designed portable system techniques to enable checkpointing of the program state on stable storage, along with its later recovery, with minimal latency and energy consumption. Right now, we are investigating how to determine where and when to perform checkpointing, and what support do we need to offer to developers to manage developing applications that may be interrupted for a non-negligible amount of time and later resume.

Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems’ lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite , yet intermittent , energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about.

Infected machines pose threats to not only their users, but also their network owners (ISPs and enterprises). To neutralize the effect of these infected machines, common solutions span two ends of an architectural spectrum; either fully distributed solutions that are host-based, or completely centralized appliances at the network core. We present NetworkRadar, inspired by an SDN-enabled ISP framework, that operates in between these extremes and contains the benefits of both these approaches. We perform data-plane intensive event monitoring at aggregation points close to customers, and maintain a centralized control plane for correlating and high-granularity blocking of malicious bot activity. Here we present the architecture of our solution and evaluate a prototype deployment over an isolated slice of an ISP network, showing its viability due to a negligible (<;1%) impact on customer throughput and its control plane scaling linearly to the customer base.