On Optimal Neighbor Discovery

Mobile devices apply neighbor discovery (ND) protocols to wirelessly initiate a first contact within the shortest possible amount of time and with minimal energy consumption. For this purpose, over the last decade, a vast number of ND protocols have been proposed, which have progressively reduced the relation between the time within which discovery is guaranteed and the energy consumption. In spite of the simplicity of the problem statement, even after more than 10 years of research on this specific topic, new solutions are still proposed even today. Despite the large number of known ND protocols, given an energy budget, what is the best achievable latency still remains unclear. This paper addresses this question and for the first time presents safe and tight, duty-cycle-dependent bounds on the worst-case discovery latency that no ND protocol can beat. Surprisingly, several existing protocols are indeed optimal, which has not been known until now. We conclude that there is no further potential to improve the relation between latency and duty-cycle, but future ND protocols can improve their robustness against beacon collisions.Comment: Conference of the ACM Special Interest Group on Data Communication (ACM SIGCOMM), 201

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Large foundation models can exhibit unique capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g. from spreadsheets, to SAT questions). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this model diversity is symbiotic, and can be leveraged to build AI systems with structured Socratic dialogue -- in which new multimodal tasks are formulated as a guided language-based exchange between different pre-existing foundation models, without additional finetuning. In the context of egocentric perception, we present a case study of Socratic Models (SMs) that can provide meaningful results for complex tasks such as generating free-form answers to contextual questions about egocentric video, by formulating video Q&A as short story Q&A, i.e. summarizing the video into a short story, then answering questions about it. Additionally, SMs can generate captions for Internet images, and are competitive with state-of-the-art on zero-shot video-to-text retrieval with 42.8 R@1 on MSR-VTT 1k-A. SMs demonstrate how to compose foundation models zero-shot to capture new multimodal functionalities, without domain-specific data collection. Prototypes are available at socraticmodels.github.io.Comment: https://socraticmodels.github.io

Controlled-mobile Sensor Networks for Dynamic Sensing and Monitoring Applications

Many potential indoor sensing and monitoring applications are characterized by hazardous and constantly-changing operating environments. For example, consider emergency response scenarios such as urban fire rescue. Traditionally, first responders have little access to situational information. In-situ information about the conditions, such as the extent and evolution of the indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Static sensor networks that are pre-deployed or manually deployed have been proposed for such applications, but are less practical due to need for large infrastructure, lack of adaptivity and limited coverage. The main hypothesis of this thesis is that controlled-mobile networked sensing – the capability of nodes to move as per network needs, is a novel, feasible, and beneficial approach to monitoring dynamic and hazardous environments. Controlled-mobility in sensor networks can provide the desired autonomy and adaptability to overcome the limitations of static sensors. The research focuses on four of the major challenges in realizing controlled-mobile sensor networking systems: Understanding the trade-off between cost, weight, and sensing and actuation capabilities in designing a hardware platform for controlled-mobile sensing together with a complementary firmware architecture. Designing simulation environments for controlled-mobile sensing platforms that adequately incorporate both the cyber (network, processing, planning) and physical (motion, environment) components of such systems. Investigating the effects of controlled-mobility on network group discovery and maintenance protocols and designing approaches that meet the mobility, latency and energy constraints. Exploring novel low-overhead infrastructure-less mechanisms for collaborative coverage, deployment and navigation of resource-constrained controlled-mobile nodes in previously unseen environments. The thesis validates and evaluates the presented architecture, tools, and algorithms for controlled-mobile sensing systems through extensive simulations and a real-system test-bed implementation. The results show that controlled-mobility is feasible and can enable new class of sensing and monitoring applications

CoughLoc: Location-Aware Indoor Acoustic Sensing for Non-Intrusive Cough Detection

Pervasive medical monitoring has become an ideal alternative to nursing care for elderly people and patients in hospitals. Existing systems using single body-worn sensors are often intrusive and less reliable. By contrast, ubiquitous acoustic sensing techniques can support non-intrusive and robust medical monitoring. In this paper, we describe CoughLoc, a ubiquitous acoustic sensing system for continuous cough detection using a wireless sensor network. We show how knowledge of sound source locations can be leveraged to improve the detection accuracy of sound events caused by mobile users. Experiments in indoor environments show our system achieves over 90% cough detection performance under quiet backgrounds, and 1.6 times higher performance compared to a baseline approach with no location information.</p

SugarMap: Location-less Coverage for Micro-Aerial Sensing Swarms

Carnegie Mellon Silicon Valle

SensorFly: Controlled-mobile Sensing Platform for Indoor Emergency Response Applications

Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly-changing operating environments with little access to situational information for first responders. In-situ information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Static sensor networks that are pre-deployed or manually deployed have been proposed, but are less practical due to need for large infrastructure, lack of adaptivity and limited coverage. Controlled-mobility in sensor networks, i.e. the capability of nodes to move as per network needs can provide the desired autonomy to overcome these limitations. In this paper, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve 3-D sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy

PANDAA: Physical Arrangement Detection of Networked Devices through Ambient-Sound Awareness

Future ubiquitous home environments can contain 10s or 100s of devices. Ubiquitous services running on these devices (i.e. localizing users, routing, security algorithms) will commonly require an accurate location of each device. In order to obtain these locations, existing techniques require either a manual survey, active sound sources, or estimation using wireless radios. These techniques, however, need additional hardware capabilities and are intrusive to the user. Non-intrusive, automatic localization of ubiquitous computing devices in the home has the potential to greatly facilitate device deployments. This paper presents the PANDAA system, a zero-configuration spatial localization system for networked devices based on ambient sound sensing. After initial placement of the devices, ambient sounds, such as human speech, music, footsteps, finger snaps, hand claps, or coughs and sneezes, are used to autonomously resolve the spatial relative arrangement of devices using trigonometric bounds and successive approximation. Using only time difference of arrival measurements as a bound for successive estimations, PANDAA is able to achieve an average of 0.17 meter accuracy for device location in the meeting room deployment