While many digital wayfinding systems offer turn-by-turn navigation, this level of support often falls short for blind and visually impaired (BVI) users who need to build a deeper understanding of regularly visited indoor spaces.In these contexts, effective navigation involves more than reaching a destination-it requires recognizing key landmarks, grasping spatial relationships, and constructing a mental map of the environment.This paper introduces two exploration modes-unassisted exploration and assisted exploration-integrated into a smartphone-based indoor navigation platform designed to support spatial learning.The unassisted mode passively announces nearby landmarks and directional cues as users move through the space unaided.In contrast, the assisted mode provides structured, speech-based guidance, leading users through all accessible areas while describing the circulation paths, intersections, and prominent landmarks encountered along the way.By shifting from a purely navigational tool to a platform for spatial learning, our approach offers a promising direction for indoor wayfinding systems-empowering BVI users to independently explore and learn complex environments with greater autonomy. CCS Concepts• Human-centered computing → Accessibility systems and tools; • Social and professional topics → People with disabilities.

Navigating unfamiliar indoor spaces is particularly challenging for blind or visually impaired (BVI) individuals due to complex layouts and inadequate signage.To address this, we introduce Ac-cessQuotient-a metric that quantifies navigational difficulty in built environments for BVI users.Based on a simplified mathematical model and validated through a small-scale user study, Ac-cessQuotient predicts both the likelihood of mistake-free traversal and expected navigation errors.This exploratory model aims to establish a foundation for further research rather than provide definitive assessments.By quantifying spatial complexity, AccessQuotient supports designers and planners in evaluating and improving indoor accessibility.

Navigating indoor environments is challenging due to the absence of satellite signals. Existing solutions often rely on auxiliary sensors such as Bluetooth or LiDAR to estimate user location and provide turn-by-turn navigation cues. However, these approaches typically require the deployment of beacons or transponders throughout a building, increasing installation costs and limiting scalability. In contrast, computer vision-based methods operate independently using only a camera on the user’s device, capturing richer visual information than traditional sensors. This paper focuses on benchmarking absolute pose regression (APR) techniques, which predict a camera’s absolute pose (position and orientation) from a single image in an end-to-end manner. Unlike conventional vision-based approaches, APR does not rely on matching to a 3D model or performing explicit geometric computations such as PnP. While prior work has largely evaluated APR in small-scale environments, we introduce a large-scale image dataset collected in a multi-floor research facility and conduct a comprehensive evaluation of state-of-the-art APR methods. Our results show that APR can achieve robust sub-meter localization accuracy in complex indoor settings, supporting its use as a practical backend for vision-based indoor navigation systems.

GPS accuracy is poor in indoor environments and around buildings. Thus, reading and following signs still remains the most common mechanism for providing and receiving wayfinding information in such spaces. This puts individuals who are blind or visually impaired (BVI) at a great disadvantage. This work designs, implements, and evaluates a wayfinding system and smartphone application called CityGuide that can be used by BVI individuals to navigate their surroundings beyond what is possible with just a GPS-based system. CityGuide enables an individual to query and get turn-by-turn shortest route directions from an indoor location to an outdoor location. CityGuide leverages recently developed indoor wayfinding solutions in conjunction with GPS signals to provide a seamless indoor-outdoor navigation and wayfinding system that guides a BVI individual to their desired destination through the shortest route. Evaluations of CityGuide with BVI human subjects navigating between an indoor starting point to an outdoor destination within an unfamiliar university campus scenario showed it to be effective in reducing end-to-end navigation times and distances of almost all participants.

Deploying auxiliary location-based services to complement GPS-based services has been a recent phenomenon to enable greater independence in navigation and wayfinding for persons with disabilities in unfamiliar environments. All work in this domain has been technical in nature with little known about the perceptions of city planners and non-profit agencies about the long-term sustainability and impact of such technologies on their communities. This work presents results and insights from a study on the perceptions of both city planners and non-profit agency personnel from a medium-sized city in the U.S.A about the importance of auxiliary location-based services and their potential impact.

The inclusion of various intelligent electronic devices such as smart meters for AMI is expected to result in intermittent or frequent communications network congestion if additional network infrastructure investments are not made. One approach to deal with such a data volume challenge in smart grids without additional investments to increase network capacity is to aggregate data streams within the network whenever network congestion happens, but this needs to be done carefully so as not to significantly impact the applications that need the data. This paper proposes a novel approach to manage AMI data traffic volume in multi-level data collection trees through data aggregation that estimates the expected network delays messages would suffer and dynamically determines an aggregation policy to be applied at forwarding nodes of the tree to reduce the network delays with which the electric utility can get the necessary information. The proposed algorithm is evaluated for different network congestion scenarios using the NS-3 simulator. The results illustrate that the algorithm is immensely effective in controlling the increase in network latencies as congestion levels increase in AMI networks. In addition, it does well in satisfying quality-of-service (QoS) requirements in terms of the data granularity required by smart grid applications.

There are currently few options for navigational aids for the blind and visually impaired (BVI) in large indoor spaces. Such indoor spaces can be difficult to navigate even for the general sighted population if they are disoriented due to unfamiliarity or other reasons. This paper presents an indoor wayfinding system called GuideBeacon for the blind, visually impaired, and disoriented (BVID) that assists people in navigating between any two points within indoor environments. The GuideBeacon system allows users equipped with smartphones to interact with low cost Bluetooth-based beacons deployed strategically within the indoor space of interest to navigate their surroundings. This paper describes the technical challenges faced in designing such a system, the design decisions made in building the current version of the GuideBeacon system, the solutions developed to meet the technical challenges, and results from the evaluation of the system. Results presented in this paper obtained from field testing GuideBeacon with BVI and sighted participants suggests that it can be used by the BVID for navigation in large indoor spaces independently and effectively.

We propose “Seer Grid”, a novel two-level energy consumption prediction framework for smart grids, aimed to decrease the trade-off between privacy requirements (of the customer) and data utility requirements (of the energy company (EC)). The first-level prediction at the household level is performed by each smart meter (SM), and the predicted energy consumption pattern (instead of the actual energy usage data) is reported to a cluster head (CH). Then, a second-level prediction at the neighborhood level is done by the CH which predicts the energy spikes in the neighborhood or cluster and shares it with the EC. Our two-level prediction mechanism is designed such that it preserves the correlation between the predicted and actual energy consumption patterns at the cluster level and removes this correlation in the predicted data communicated by each SM to the CH. This maintains the usefulness of the cluster-level energy consumption data communicated to the EC, while preserving the privacy of the household-level energy consumption data against the CH (and thus the EC). Our evaluation results show that Seer Grid is successful in hiding private consumption patterns at the household-level while still being able to accurately predict energy consumption at the neighborhood-level.

Security and privacy concerns in the future power grid have recently received tremendous focus from security advocates. Most existing security mechanisms utilize cryptographic techniques that are computationally expensive and bandwidth intensive. However, aggregating the large outputs of these cryptographic algorithms has not been considered thoroughly. Smart Grid Networks (SGN) generally have limitations on bandwidth, network capacity and energy. Hence, utilizing data aggregation algorithms, the limited bandwidth can be efficiently utilized. Most of the aggregation algorithms use statistical functions such as minimum, maximum, and average. before transmitting data over the network. Existing aggregation algorithms, in SGNs, are generally expensive in terms of communication overhead, processing load and delay. However, our proposed CDMA-based data aggregation method provides access to all the data of all the smart meters in the root node, which in this case is the Utility Center, while keeping the smart metering data secure. The efficiency of the proposed method is confirmed by mathematical analysis.

Location discovery is an essential service in modern wireless consumer devices such as smartphones and mobile PCs. Existing anchor or base station-based positioning systems work well in non-hostile scenarios, but their accuracy suffers in the presence of cheating anchors. Securing location discovery in these positioning systems is an important, and still open, research problem. Earlier research efforts in this direction have mainly focused on either efficient detection and elimination of cheating anchors or on localization in the presence of cheating anchors. Proposals on localization in the presence of cheating anchors fail to perform well in the presence of a large number of cheating anchors, whereas, the issue of elimination of cheating anchors (once detected) has not been clearly addressed in techniques that focus on detection and elimination of cheating anchors. In this paper, we present a novel and deterministic strategy for securing anchor-based location discovery. Our technique employs a novel CDMA-based jamming strategy to eliminate (the effect of) cheating anchors during localization. We validate the performance of our proposal under various adversarial strengths and operating scenarios by means of extensive simulations.

The increasing popularity of online social networks (OSNs) is spawning new security and privacy concerns. Currently, a majority of OSNs offer very naive access control mechanisms that are primarily based on static access control lists (ACL) or policies. But as the number of social connections grow, static ACL based approaches become ineffective and unappealing to OSN users. There is an increased need in social-networking and data-sharing applications to control access to data based on the associated context (e.g., event, location, and users involved), rather than solely on data ownership and social connections. Surveillance is another critical concern for OSN users, as the service provider may further scrutinize data posted or shared by users for personal gains (e.g., targeted advertisements), for use by corporate partners or to comply with legal orders. In this paper, we introduce a novel paradigm of context-based access control in OSNs, where users are able to access the shared data only if they have knowledge of the context associated with it. We propose two constructions for context-based access control in OSNs: the first is based on a novel application of Shamir's secret sharing scheme, whereas the second makes use of an attribute-based encryption scheme. For both constructions, we analyze their security properties, implement proof-of-concept applications for Facebook and empirically evaluate their functionality and performance. Our empirical measurements show that the proposed constructions execute efficiently on standard computing hardware, as well as, on portable mobile devices.

The cloud computing paradigm enables the work anywhere anytime paradigm by allowing application execution and data storage on remote servers. This is especially useful for mobile computing and communication devices that are constrained in terms of computation power and storage. It is however not clear how preferable cloud-based applications would be for mobile device users. For users of such battery life constrained devices, the most important criteria might be the energy consumed by the applications they run. The goal of this work is to characterize under what scenarios cloud-based applications would be relatively more energy-efficient for users of mobile devices. This work first empirically studies the energy consumption for various types of applications and for multiple classes of devices to make this determination. Subsequently, it presents an analytical model that helps characterize energy consumption of mobile devices under both the cloud and non-cloud application scenarios. Finally, an algorithm GreenSpot is presented that considers application features and energy-performance tradeoffs to determine whether cloud or local execution will be more preferable.

The thin client model relies on offloading computations to a remote server. This results in reduced hardware complexity on the thin client which will cut costs and possibly increase lifespan of the thin client. Increased lifespan in turn has benefits in terms of reducing electronic waste. Realizing the benefits of a thin-client paradigm for emerging portable devices however requires consideration of battery life, a critical resource. The optimum design for an energy-efficient thin client-server model for portable devices is yet to be found and is still under investigation. In this work an end-to-end analytical model for power consumption in wireless based portable thin clients is proposed. This model is then evaluated by comparing to a scenario where applications are executed locally on the device to gain insight on the energy-efficiency of the thin-client paradigm for portable devices.

We address the issue of misbehavior detection and reaction in IEEE 802.11 based Ad Hoc networks. Selfish misbehavior involves disobeying standard protocol mechanisms to gain unfair access to the channel at the expense of other users. We outline conditions on genuine (non-misbehaving) node's throughput to guarantee the presence of misbehavior, and propose non-adaptive and strong reaction mechanism for such aggressive misbehaviors. For selfish misbehaviors which do not result in severe throughput degradation for genuine users, we design an adaptive reaction mechanism. Both mechanisms are distributed in nature, rely only upon local information available at genuine nodes, and are thus easily implementable in practice. Proposed reaction mechanisms provide the necessary disincentive towards selfish misbehavior, and are aimed at preventing misbehavior.

Current portable devices e.g. cell phones, PDA's and laptops are mainly Wi-Fi capable. Users can make efficient use of these devices by running various applications. Among these, the Voice over Internet Protocol (VoIP) application is very popular. As new models of these devices emerge, they are increasingly power hungry in terms of processing and communication. As a result, battery lifetime is one of the most critical performance parameters. This paper presents experimental studies on the energy depletion characteristics of VoIP applications when using various codecs in an IEEE 802.11 Wireless LAN setting. The effect of high, medium and low bandwidth codecs on battery life is studied and an adaptive algorithm is proposed that switches codecs in the middle of an active call based on remaining battery life.

Energy consumption of portable RFID readers is becoming an important issue as applications of RFID systems pervade many aspects of our lives. Surprisingly, however, these systems are not energy-aware with the focus till date being on reducing the time to read all tags by the reader. In this work, we consider the problem of tag arbitration in RFID systems with the aim of designing energy-aware anticollision protocols. We explore the effectiveness of using multiple time slots per node of a binary search tree through three anticollision protocols. We further develop an analytical framework to predict the performance of our protocols and enable protocol parameter selection. We demonstrate that all three protocols provide significant energy savings both at the reader and tags (if they are active tags) compared to the existing Query Tree protocol, while sharing the deterministic property of the latter. Further, we show that our protocols provide similar benefits even with correlated tag IDs.

Emerging dual-mode phones incorporate a wireless LAN (WLAN) interface along with the traditional cellular interface. The additional benefits of the WLAN interface are, however, likely to be outweighed by its greater rate of energy consumption. This is especially of concern when real-time applications, that result in continuous traffic, are involved. WLAN radios typically conserve energy by staying in sleep mode. With real-time applications like voice over Internet Protocol (VoIP), this can be challenging since packets delayed above a threshold are lost. Moreover, the continuous nature of traffic makes it difficult for the radio to stay in the lower power sleep mode enough to reduce energy consumption significantly. In this work, we propose the GreenCall algorithm to derive sleep/wake-up schedules for the WLAN radio to save energy during VoIP calls while ensuring that application quality is preserved within acceptable levels of users. We evaluate GreenCall on commodity hardware and study its performance over diverse network paths and describe our experiences in the process. We further extensively investigate the effect of different application parameters on possible energy savings through trace-based simulations. We show that, in spite of the interactive, real-time nature of voice, energy consumption during calls can be reduced by close to 80 percent in most instances.

Cognitive radios have been proposed in recent years to make more efficient use of the wireless spectrum and alleviate congestion on widely used frequency bands. A key aspect of these radios is the `cognition' gained through a spectrum scanning process. The benefits of this cognition is apparent and well studied in terms of better performance of communication. The benefits in terms of reduced energy consumption, however, due to easier channel access and less contention have not been quantified in prior work. On the other hand, spectrum scanning to gain cognition is a power-intensive process and the costs incurred in terms of lost energy need to be accounted for. Thus, it is not clear whether a cognitive radio would be more energy efficient than a conventional radio, and if so, under what circumstances. This focus on energy consumption is particularly important when considering portable communication devices that are energy constrained. This work takes a first step in this direction by exploring whether a cognitive radio can save energy over a conventional radio in the ad hoc wireless LAN scenario. The interplay between different important parameters to this problem is analyzed and their impact on energy consumption studied.

Collection of rare but delay-critical messages from a group of sensor nodes is a key process in many wireless sensor network applications. This is particularly important for security related applications like intrusion detection and fire alarm systems. An event sensed by multiple sensor nodes in the network can trigger many messages to be sent simultaneously. We present Alert, a MAC protocol for collecting event-triggered urgent messages from a group of sensor nodes with minimum latency and without requiring any cooperation or pre-scheduling among the senders or between senders and receiver during protocol execution. Alert is designed to handle multiple simultaneous messages efficiently and reliably minimizing the overall delay to collect all messages along with the delay to get the first message. Moreover, the ability to handle a large number of simultaneous messages does not come at the cost of excessive delays when only a few messages need to be handled. We analyze Alert and evaluate its feasibility and performance with an implementation on commodity hardware. We further compare Alert with existing approaches through simulations and show the performance improvement possible through Alert.

Wireless LAN (WLAN) radios conserve energy by staying in sleep mode. With real-time applications like VoIP, it is not clear how much energy can be saved by this approach since packets delayed above a threshold are lost. In this work we propose the GreenCall algorithm to derive sleep/wakeup schedules for the WLAN radio to save energy during VoIP calls. The schedules provided by GreenCall consider the energy versus loss-rate tradeoff to ensure application quality is preserved. We implement GreenCall on commodity hardware and study its performance and point out possible limitations. We further extensively evaluate the effect of diverse network paths and different application parameters on possible energy savings through trace-based simulations. We show that, in spite of the interactive, real-time nature of voice, a significant amount of energy can be conserved through GreenCall.

In this paper we examine if any energy savings are possi-ble by letting the Wireless LAN interface transition to sleep mode during a VoIP call considering the real time nature of voice. Using our proposed energy saving algorithm Green-Call, we show that it is possible, and quantify the possible savings through both empirical and trace based simulation experiments. 1.

Energy consumption of mobile readers is becoming an important issue as applications of RFID systems pervade different aspects of our lives. Surprisingly, however, these systems are not energy-aware with the focus till date being on reducing the time to read all tags by the reader. The problem of tag arbitration in RFID systems is considered with the aim of trading off time for energy savings at the reader. The approach of using multiple time slots per node of a binary search tree is explored through three anti-collision protocols that aim to reduce the number of colliding responses from tags. This results in fewer reader queries and tag responses and, hence, energy savings at both the reader and tags (if they are active tags). An analytical framework is developed to predict the performance of our protocols, with the numerical evaluation of this framework validated through simulation. It is shown that all three protocols provide significant energy savings when compared to the existing query tree protocol while sharing the deterministic and memoryless properties of the latter

We consider the problem of power-efficient topology control with switched beam directional antennas taking into account their non-uniform radiation pattern within the beamwidth. Previous work have all assumed uniform gain for these antennas. We construct optimal algorithms that take into account a model of non-uniform gain. Moreover, we argue that with such a model of gain, antenna orientation is a significant parameter to be considered while constructing power-efficient topologies. We present heuristic algorithms that construct power-efficient topologies taking antenna orientation into consideration and demonstrate significant reductions in the power required to keep the network connected.