Joy Kuri

Aniket Mukherjee, Joy Kuri, Chandramani Singh

Modern sensing systems generate heterogeneous updates ranging from small status packets to large data objects. We study a single-hop wireless uplink network where sensors generate updates at will, each consisting of a sensor dependent number of packets. Under a strict medium-access constraint and non-preemptive (no-switching) transmissions, decision stages become action-dependent and stochastic. We formulate the problem as a restless multi-armed bandit (RMAB) with semi-Markov decision process (SMDP) dynamics and develop a Lagrange index based heuristic for minimizing weighted average AoI cost. For the weighted AoI setting, we utilize the structural properties of the heuristic to enable efficient index computation. Numerical results demonstrate consistent performance gains over existing non-preemptive scheduling policies, providing a practical solution for heterogeneous freshness-aware systems.

Ravi Raj Saxena, T. V. Prabhakar, Joy Kuri et al.

Vertical Take-Off and Landing (VTOL) vehicles are gaining traction in both the delivery drone market and passenger transportation, driving the development of Urban Air Mobility (UAM) systems. UAM seeks to alleviate road congestion in dense urban areas by leveraging urban airspace. To handle UAM traffic, vertiport terminals (vertiminals) play a critical role in supporting VTOL vehicle operations such as take-offs, landings, taxiing, passenger boarding, refueling or charging, and maintenance. Efficient scheduling algorithms are essential to manage these operations and optimize vertiminal throughput while ensuring safety protocols. Unlike fixed-wing aircraft, which rely on runways for take-off and climbing in fixed directions, VTOL vehicles can utilize multiple surface directions for climbing and approach. This flexibility necessitates specialized scheduling methods. We propose a Mixed Integer Linear Program (MILP) formulation to holistically optimize vertiminal operations, including taxiing, climbing (or approach) using multiple directions, and turnaround at gates. The proposed MILP reduces delays by up to 50%. Additionally, we derive equations to compute upper bounds of the throughput capacity of vertiminals, considering its core elements: the TLOF pad system, taxiway system, and gate system. Our results demonstrate that the MILP achieves throughput levels consistent with the theoretical maximum derived from these equations. We also validate our framework through a case study using a well-established vertiminal topology from the literature. Our MILP can be used to find the optimal configuration of vertiminal. This dual approach, MILP and throughput analysis, allows for comprehensive capacity analysis without requiring simulations while enabling efficient scheduling through the MILP formulation.

Arjun Sekar, Sameer G. Kulkarni, Joy Kuri

In this work, we propose a two-phased approach for real-time detection and deterrence of ransomware. To achieve this, we leverage the capabilities of eBPF (Extended Berkeley Packet Filter) and artificial intelligence to develop both proactive and reactive methods. In the first phase, we utilize signature based detection, where we employ custom eBPF programs to trace the execution of new processes and perform hash-based analysis against a known ransomware dataset. In the second, we employ a behavior-based technique that focuses on monitoring the process activities using a custom eBPF program and the creation of ransom notes, a prominent indicator of ransomware activity through the use of Natural Language Processing (NLP). By leveraging low-level tracing capabilities of eBPF and integrating NLP based machine learning algorithms, our solution achieves an impressive 99.76% accuracy in identifying ransomware incidents within a few seconds on the onset of zero-day attacks.

Ryan Shah, Michael McIntee, Shishir Nagaraja et al.

Secure sensor calibration constitutes a foundational step that underpins operational safety in the Industrial Internet of Things. While much attention has been given to IoT security such as the use of TLS to secure sensed data, little thought has been given to securing the calibration infrastructure itself. Currently traceability is achieved via manual verification using paper-based datasheets which is both time consuming and insecure. For instance, when the calibration status of parent devices is revoked as mistakes or mischance is detected, calibrated devices are not updated until the next calibration cycle, leaving much of the calibration parameters invalid. Aside from error, any party within the calibration infrastructure can maliciously introduce errors since the current paper based system lacks authentication as well as non-repudiation. In this paper, we propose a novel resilient architecture for calibration infrastructure, where the calibration status of sensor elements can be verified on-the-fly to the root of trust preserving the properties of authentication and non-repudiation. We propose an implementation based on smart contracts on the Ethereum network. Our evaluation shows that Ethereum is likely to address the protection requirements of traceable measurements.

Siddhartha Sarma, Samar Agnihotri, Joy Kuri

The problem of secure communication in Amplify-and-Forward (AF) relay networks with multiple eavesdroppers is considered. Assuming that a receiver (destination or eavesdropper) can decode a message only if the received SNR is above a predefined threshold, we introduce SNR based optimization formulations to calculate optimal scaling factors for relay nodes in two scenarios. In the first scenario, we maximize the achievable rate at the legitimate destination, subject to the condition that the received SNR at each eavesdropper is below the target threshold. Due to the non-convex nature of the objective function and eavesdroppers' constraints, we transform variables and obtain a Quadratically Constrained Quadratic Program (QCQP) with convex constraints, which can be solved efficiently. When the constraints are not convex, we consider a Semi-definite relaxation (SDR). In the second scenario, we minimize the total power consumed by all relay nodes, subject to the condition that the received SNR at the legitimate destination is above the threshold and at every eavesdropper, it is below the corresponding threshold. We propose a semi-definite relaxation of the problem in this scenario and also provide an analytical lower bound.

Siddhartha Sarma, Samar Agnihotri, Joy Kuri

We have evaluated the optimal secrecy rate for Amplify-and-Forward (AF) relay networks with multiple eavesdroppers. Assuming i.i.d. Gaussian noise at the destination and the eavesdroppers, we have devised technique to calculate optimal scaling factor for relay nodes to obtain optimal secrecy rate under both sum power constraint and individual power constraint. Initially, we have considered special channel conditions for both destination and eavesdroppers, which led us to analytical solution of the problem. Contrarily, the general scenario being a non-convex optimization problem, not only lacks an analytical solution, but also is hard to solve. Therefore, we have proposed an efficiently solvable quadratic program (QP) which provides a sub-optimal solution to the original problem. Then, we have devised an iterative scheme for calculating optimal scaling factor efficiently for both the sum power and individual power constraint scenario. Necessary figures are provided in result section to affirm the validity of our proposed solution.