With mobility and flexibility in non-terrestrial environments, unmanned aerial vehicles (UAVs) have shown the potential for emerging applications, such as remote surveillance and mobile base stations. This work raises the emerging need to chain UAV relays in support ofon-demand connectivityto faraway users. However, the ideal isotropic antennas are too simplified to ensure connectivity among UAVs. Since antennas used in realistic communication systems including UAVs are non-isotropic, and the angle of radiations (AoR) between a pair of transmitter and receiver significantly influences received signal strengths (RSSs). Therefore, this work takes the AoR into account to deploy UAV relays in a 3D environment. Not only positions but also the headings of UAVs are modeled in the AoR-based deployment problem to minimize the number of UAVs in the relay chain, while the end-to-end RSSs are guaranteed. We prove the NP-hardness of the AoR-based deployment problem. Then, theAngle-of- Radiation-based Deployment (AIM) algorithmis proposed to solve the problem. The extensive simulation results show that the AIM algorithm reduces the number of UAVs used in the relay chain by 59.6% compared to the baselines when the heading of each UAV is well-adjusted. Also, the proposed AIM algorithm outperforms the baselines by 61.2% in terms of the average number of UAVs and demonstrates adaptability to various terrains.

Detecting conversational groups in proximal spaces is vital for social sensing but remains challenging due to acoustic degradation and privacy concerns. We propose a dual-mode speech-based framework using contextual and temporal cues to identify conversational clusters within Hall’s proxemic zones. The contextual mode fuses word-level embeddings with a novel BERT-based Reply-to Ratio, producing robust semantic affinities. The temporal mode offers a privacy-preserving fallback by estimating turn-taking overlaps from speaking-time distributions and timestamps, avoiding raw audio or text. These affinities form a weighted social graph, with Louvain community detection extracting group structures. Experiments on smartphone-recorded dialogues show a 14% accuracy gain over a hybrid baseline (up to 30% at long distances) for the contextual mode. The temporal mode averages 73% accuracy with an 11% improvement, showing strong performance under distance and privacy constraints.

Recently, unmanned aerial vehicles (UAVs) have shown potential for a variety of emerging applications with their high mobility and versatility. To conduct the tasks and receive control and command signals, the seamless connectivity for the UAVs is crucial. While the aerial connection can be provided by the existing ground cellular network, the down-tilted antennas of the ground base stations (GBSs) and the beam lobes generated by the antenna pattern lead to inevitable null areas in the airspace, making it challenging to ensure connectivity and safety in the sky. In this paper, we leverage an unmanned aerial relay (UAR) to extend the signal coverage of existing GBSs, enabling connectivity for aerial user equipments (AUEs) in aerial null areas. We design an efficient algorithm, Connectivity-Aware UAR (CARE-U), optimizing the UAR's trajectory to serve AUEs with pre-scheduled tasks. By simulations in both synthetic and realworld settings, the connected time ratio of the AUEs improves up to 66.3 % compared to that without the UAR. Besides, CARE-U outperforms the approach that modifies trajectories of AUEs, showing the great compatibility of CARE-U.

This paper proposes a novel method for extracting conversation groups by fusing wireless and audio data. We first use wireless signals to identify groups of people that walk together. Then we propose a novel approach for detecting conversation groups using audio data. Our approach examines non-contextual and contextual analyses to align with user permission levels concerning data utilization. We also incorporate turn-taking likelihood, frequent words, topic similarity, and Named Entity Recognition (NER) to identify conversation groups effectively. To further mitigate privacy issues, we propose to use vector feature representations of speech patterns, a subset of relevant keywords, and a vector space representation of the discussed topic. We first convert the audio files into text and analyze the content by identifying frequent words and topics and extracting NER information on the user's phone. Only the featured vectors are received at the third-party server to compute different similarities among conversation participants. By integrating these similarity values, we obtain the non-contextual and contextual-level Similarity between two users, which allows us to determine if they belong to the same conversation group. Experimental results demonstrate that turn-taking likelihood can identify groups by 90%. However, contextual information can achieve high accuracy rates of up to 96.57% and an average F1-score of 94.77%. These findings indicate that our system is highly effective in analyzing data in audio streams for conversation grouping, with potential applications in various social scenarios.

With the rise of smartphones and wearables, research into human movement, particularly group interactions, has gained significant attention. These devices serve as invaluable sources of diverse data streams, encompassing movement patterns, social interactions, and historical wireless signals crucial for group identification. However, the accuracy of wireless signal recognition could be better in close proximity scenarios. Our paper presents a pioneering methodology to overcome this limitation that integrates wireless signal data with environmental light sensor features. This novel fusion enhances mobility group recognition accuracy significantly. We conducted rigorous experiments in a building. Our method consistently demonstrated robust performance in identifying mobility groups accurately. This empirical validation underscores the effectiveness and adaptability of our proposed approach.

As robots are widely used in assisting manual tasks, an interesting challenge is: Can mobile robots help create a labeled knowledge dataset that can be used for efficiently creating deep learning models for other sensors? This paper proposes an Unsupervised Person Labeling and Identification (UPLIFT) framework to automatically enlarge the labeled knowledge dataset. Typically, manual data labeling is very costly, especially when the user population is large and dynamic. To reduce the cost, we use a mobile robot to serve as a knowledge seed and to provide the pseudo-ground-truth for the system so that unlabeled images from other fixed surveillance cameras can be paired with the pseudo-ground-truth. Ultimately, the knowledge dataset can be generated via a system-to-system knowledge transfer process from the former to the latter and gradually expanded as the system operates longer. Experimental results in two environments indicate that UPLIFT achieves an accuracy of 94.1% on average to detect pedestrians' IDs every 10 seconds.

People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. However, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%.

Multi-modal sensors on mobile devices (e.g., smart watches and smartphones) have been widely used to ubiquitously perceive human mobility and body motions for understanding social interactions between people. This work investigates the correlations between the multi-modal data observed by mobile devices and social closeness among people along their trajectories. To close the gap between cyber-world data distances and physical-world social closeness, this work quantifies the cyber distances between multi-modal data. The human mobility traces and body motions are modeled as cyber signatures based on ambient Wi-Fi access points and accelerometer data observed by mobile devices that explicitly indicate the mobility similarity and movement similarity between people. To verify the merits of modeled cyber distances, we design the localization-free CybeR-physIcal Social dIStancing (CRISIS) system that detects if two persons are physically non-separate (i.e., not social distancing) due to close social interactions (e.g., taking similar mobility traces simultaneously or having a handshake with physical contact). Extensive experiments are conducted in two small-scale environments and a large-scale environment with different densities of Wi-Fi networks and diverse mobility and movement scenarios. The experimental results indicate that our approach is not affected by uncertain environmental conditions and human mobility with an overall detection accuracy of 98.41% in complex mobility scenarios. Furthermore, extensive statistical analysis based on 2-dimensional (2D) and 3-dimensional (3D) mobility datasets indicates that the proposed cyber distances are robust and well-synchronized with physical proximity levels.

Many indoor localization techniques have been widely investigated. As the proportion of people with multiple mobile devices is increasing, an interesting research challenge arising from this growth is how to integrate data from multiple devices carried by a mobile user for localizing her/him. This work designs amulti-device joint localizationsystem, calledJoLo, to realize the idea using a group of off-the-shelf mobile devices carried by a user. The proposed system collects wireless fingerprints at the predefined training locations in the environment. Then, the system fuses the real-time wireless measurements observed by the multiple devices of a user into a summary list for localization reference. Finally, three fusion-based positioning algorithms are proposed to determine the user's location based on the fused summary list. The three proposed algorithms reduce mean distance errors in localization to less than one meter in a lab environment. One of the fusion-based algorithms, namely cluster-based DF algorithm, can even improve the performance by 50% on average compared to the existing single-device localization techniques.

In this paper, we propose the ProTrack system to detect proximity levels and extract movement patterns to infer social relationships, which are key factors in many urban computing applications. In ProTrack, the system passively collects wireless broadcasting packets from the WiFi or Bluetooth-enabled mobile devices by multiple wireless scanners deployed in the region of interest. The received signal strength extracted from these packets is used to infer the devices’ spatial and movement features. The proposed ProTrack mobility framework investigates the trajectory level, namely companion, follow, independent, and the proximity level, namely immediate, near, far for a pair of users. Based on the output results, the system can generate a graph-based proximity map and trajectory map to infer social distance and social relationship without depending on complicated infrastructure and localization.

Time-series sensor data are used to create prediction models, called city profiles, for understanding city dynamics in the smart-city sector. These city profiles are typically created and updated by the Cloud using reported raw sensor data. However, continuously reporting raw sensor data is not energy efficient for boundary computing resources of a network. Thus, this work considers edge servers that are deployed on the boundary computing resources of a network to collaborate with the Cloud for adaptively mitigate city profiling tasks (i.e., creating city profiles) across an edge server and the Cloud. By maintaining the local city profiles on the edge or the global city profiles on the Cloud, either an edge or the Cloud can dynamically respond to user queries. However, there is a trade-off between the energy consumption of an edge and the response accuracy of the city profiles. This work designs an adaptive city profiling and synchronization approach for edges to decide when, where (i.e., an edge or the Cloud), and how to update and synchronize local and global city profiles such that the energy consumption of the edge is reduced while the accuracy of a city profile can be guaranteed. Extensive simulations are conducted using a real-world temperature dataset to evaluate the performance of the proposed approach. The simulation results indicate an average energy saving of 60% of edges compared with a typical Cloud-based approach while the required accuracy is fulfilled.

As mobility is an important key to many applications, this work proposes a location-less model to represent mobility that is used to quantify correlations between mobility traces collected by built-in sensors on smartphones. We analyze the mobility correlations from two aspects: co-direction relationship and co-movement relationship . The former is to quantify the similarity of macroscopic moving directions between mobility traces, whereas the latter is to quantify the similarity of their microscopic vibrations. To verify the merits of the two proposed metrics, an exemplary use case, termed co-mobility detection , is considered to determine if two mobile devices share the same journey on the same mobile entity (e.g., carried by the same person). Comprehensive experiments with diverse combinations of mobility traces are conducted in three different environments with different density of Wi-Fi networks. The experimental results indicate that the proposed metrics can effectively evaluate both the coarse-grained similarity of moving directions and the fine-grained similarity of movement variations along mobility traces. The accuracy of the co-mobility detection algorithm can achieve 90% on average for mobility traces with a duration of 70 s.

The increasing availability of heterogeneous ambient sensing systems challenges the according information processing systems to analyse and compare a variety of different systems in a single scenario. For instance, localization of objects can be performed by image processing systems as well as by radio based localization. If such systems are utilized to localize the same objects, synergy of the outputs is important to enable comparable and meaningful analysis. This demo showcases the practical deployment and challenges of such an example system.

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.

Location-based services (LBSs) typically crowdsource geo-tagged data from mobile users. Collecting more data will generally improve the utility for LBS providers; however, it also leads to more privacy exposure of users’ mobility patterns. Although the tension between data utility and user privacy has been recognized, there lacks a solution that determines how much data to collect—in both spatial and temporal domains—is the “best” for both mobile users and the service provider. This article proposes a strategy toward making an optimal tradeoff such that a user submits data only if her mobility privacy will not be compromised and the data utility of the LBS provider will be sufficiently improved. To this end, we first define and formulate a concept called privacy exposure , which incorporates both the spatial distribution and the temporal transition of a user’s activity points . Second, we define and quantify data utility in terms of spatial repetitions and temporal closeness among data based on an economic principle. Then, we propose a PRivacy-preserving and UTility-Enhancing Crowdsourcing (PRUTEC) algorithm to determine, on behalf of each mobile user, whether a newly sensed piece of data should be submitted to the LBS provider. Our simulation demonstrates that PRUTEC improves the data utility of the service provider with a much less amount of data to collect and reduces privacy exposure for mobile users while collecting useful data continuously.

Crowd mobility has been paid attention for the Internet-of-things (IoT) applications. This paper addresses the crowd estimation problem and builds an IoT service to share the crowd estimation results across different systems. The crowd estimation problem is to approximate the crowd size in a targeted area using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi probe request packets ("Wi-Fi probes" for short) broadcasted by mobile devices to solve this problem. However, using only Wi-Fi probes to estimate the crowd size may result in inaccurate results due to various environmental uncertainties which may lead to crowd overestimation or underestimation. Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals is time-varying and invisible. This paper introduces auxiliary sensors, stereoscopic cameras, to collect the near ground-truth at a specified calibration choke point. Two calibration algorithms are proposed to solve the crowd estimation problem. The key idea is to calibrate the Wi-Fi-only crowd estimation based on the correlations between the two types of data modalities. Then, to share the calibrated results across systems required by different stakeholders, our system is integrated with the FIWARE-based IoT platform. To verify the proposed system, we have launched an indoor pilot study in the Wellington Railway Station and an outdoor pilot study in the Christchurch Re:START Mall in New Zealand. The large-scale pilot studies show that stereoscopic cameras can reach minimum accuracy of 85% and high precision detection for providing the near ground-truth. The proposed calibration algorithms reduce estimation errors by 43.68% on average compared to the Wi-Fi-only approach.

This paper proposes a novel approach for detecting groups of people that walk “together” (group mobility) as well as the people who walk “alone” (individual movements) using wireless signals. We exploit multiple wireless sniffers to pervasively collect human mobility data from people with mobile devices and identify similarities and the group mobility based on the wireless fingerprints. We propose a method which initially converts the wireless packets collected by the sniffers into people's wireless fingerprints. The method then determines group mobility by finding the statuses of people at certain times (dynamic/static) and the space correlation of dynamic people. To evaluate the feasibility of our approach, we conduct real world experiments by collecting data from 10 participants carrying Bluetooth Low Energy (BLE) beacons in an office environment for a two-week period. The proposed approach captures space correlation with 95% and group mobility with 79% accuracies on average. With the proposed approach we successfully 1) detect the groups and individual movements and 2) generate social networks based on the group mobility characteristics.

Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals' activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solution to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers. The goal of this work is to determine if a device is in the queue based on only RSSIs. The key idea is to extract features not only from individual device's data but also mobility similarity between data from multiple devices and mobility correlation observed by multiple sniffers. Thus, we propose single-device feature extraction, cross-device feature extraction, and cross-sniffer feature extraction for model training and classification. We systematically conduct experiments with simulated queue movements to study the detection accuracy. Finally, we compare our signal-based approach against camera-based face detection approach in a real-world social event with a real human queue. The experimental results indicate that our approach can reach minimum accuracy of 77% and it significantly outperforms the camera-based face detection because people block each other's visibility whereas wireless signals can be detected without blocking.

Mobile location-based services (LBSs) empowered by mobile crowdsourcing provide users with context- aware intelligent services based on user locations. As smartphones are capable of collecting and disseminating massive user location-embedded sensing information, privacy preservation for mobile users has become a crucial issue. This paper proposes a metric called privacy exposure to quantify the notion of privacy, which is subjective and qualitative in nature, in order to support mobile LBSs to evaluate the effectiveness of privacy-preserving solutions. This metric incorporates activity coverage and activity uniformity to address two primary privacy threats, namely activity hotspot disclosure and activity transition disclosure. In addition, we propose an algorithm to minimize privacy exposure for mobile LBSs. We evaluate the proposed metric and the privacy-preserving sensing algorithm via extensive simulations. Moreover, we have also implemented the algorithm in an Android-based mobile system and conducted real-world experiments. Both our simulations and experimental results demonstrate that (1) the proposed metric can properly quantify the privacy exposure level of human activities in the spatial domain and (2) the proposed algorithm can effectively cloak users' activity hotspots and transitions at both high and low user-mobility levels.

Two-tiered wireless sensor and actor networks (WSANs) have been proposed to enhance network capabilities, where a set of resource-rich mobile nodes (termed actors ) form a connected backbone to relay sensing data from static sensors to the sink and sometimes are requested by sensors to perform a particular action. Such a two-tiered WSAN facilitates scalability and can efficiently reduce the energy consumption incurred by conventional hop-by-hop relaying via only sensors. However, relocating actors to achieve both connectivity and load balance is a challenge, especially when there is no location information of the nodes. Connectivity ensures that the actors are connected, whereas load balance ensures that actors collect and originate a similar amount of sensory data from the sensors. In this article, we formulate the Connected and Balanced Mobile Actor Relocation (CBMAR) optimization problem to address both connectivity and load balance and prove that the problem is NP-hard. We thus propose a dual-mode distributed actor relocation protocol that does not rely on any location information of nodes to relocate actors. The idea is to locally form virtual Voronoi cells of actors (termed covering cells ) based on the lower-tiered topology, where each actor locally recruits its own sensor members to form its own covering cell. By maintaining the covering cell, each actor locally relocates itself toward a sensor along the lower-tiered topology. Extensive simulation results show that the protocol can achieve both objectives of connectivity and load balance with low moving and communication overheads.

As mobile crowdsourcing techniques are steering many smart-city and Internet-of-Things applications, a new challenge of signal source localization problem arises, which is to infer the locations of signal sources based on crowdsourced data. It will benefit real-world applications such as WiFi advisory systems by locating WiFi access points and urban noise monitoring systems by locating noise sources. However, crowdsourced data collected from diverse mobile devices are often sparse, fluctuating, and inconsistent. In this paper, we propose a source localization scheme to solve this problem, without the need of prior localization infrastructure or reference (anchor) nodes. We also implement a crowdsourcing WiFi advisory system and conduct real-world experiments to evaluate the performance of the proposed scheme. The results show that our scheme can locate the WiFi access points within a small error of 1 ~ 16 meters, and improve the accuracy of a conventional method by up to 50%.

As mobile crowd sensing techniques are steering many smart-city applications, an incentive scheme that motivates the crowd to actively participate becomes a key to the success of such city-scale applications. This paper presents a crowd sensing WiFi advisory system called WiFiScout, which helps smartphone users to find good quality WiFi hotspots. The quality information is defined in terms of user experience and hence the system requires users to contribute information of their experience with WiFi hotspots. To motivate people to contribute such information, we design and implement a gamification-based incentive scheme in WiFiScout. It allows a user to "conquer WiFi territories" by becoming the top contributor for WiFi hotspots at different locations. The contribution is based on the diversity and amount of data a user submits, for which he will be rewarded accordingly. WiFiScout has been implemented on Android and it facilitates the collection of city-wide WiFi advisory information provided by real users according to their actual experience.

This paper exploits smartphones to design a transportation activity survey system that investigates when, where and how people travel in an urban area. In such a system, the essential requirement is collecting and processing big data which will raise two critical issues, energy-conservation and scalability. To address the former issue, the GPS sleeping interval of a smart-phone is controlled by the back-end servers adaptively based on the real-time moving speed and transportation modes. To address the latter issue, we consider MapReduce to design the back-end Cloud, where intelligent learning and classification algorithms are implemented to detect the stops and transportation modes and provide smartphones with an appropriate GPS sleeping interval based on the GPS statistics on the back-end Cloud. The unique feature of our system is to integrate participatory sensing and Cloud-enabled processing system closely which incorporates knowledge extracted from the Cloud (i.e., transportation modes) into sensing control of smartphones. In this way, sensing control could be optimized through the knowledge behind crowdsourced data. Our system has been deployed in Singapore to support the Land Transport Authority's transportation activity survey over 1 year. Extensive experimental results indicate that our system can reduce the energy consumption of smartphones efficiently and process concurrent data arrival from a huge number of users.

The UrbanMobSense is a mobility sensing system designed to collect the travel patterns of people for transportation planning purposes. This system makes use of smartphones with various built-in sensors to capture human mobility automatically in a non-intrusive manner. To improve the user experience of smartphone users, the system is designed to be user-centric to address energy conservation and privacy preservation issues. The UrbanMobSense has been deployed in Singapore to support the Land Transport Authority (LTA)'s household travel survey.

ABSTRACT This paper considers a spatially separated wireless sensor network, which consists of a number of isolated subnetworks that could be far away from each other in distance. We address the issue of using mobile mules to collect data from these sensor nodes. In such an environment, both data‐collection latency and network lifetime are critical issues. We model this problem as a bi‐objective problem, called energy‐constrained mule traveling salesman problem (EM‐TSP), which aims at minimizing the traversal paths of mobile mules such that at least one node in each subnetwork is visited by a mule and the maximum energy consumption among all sensor nodes does not exceed a pre‐defined threshold. Interestingly, the traversal problem turns out to be a generalization of the classical traveling salesman problem (TSP), an NP‐complete problem. With some geometrical properties of the network, we propose some efficient heuristics for EM‐TSP. We then extend our heuristics to multiple mobile mules. Extensive simulation results have been conducted, which show that our proposed solutions usually give much better solutions than most TSP‐like approximations. Copyright © 2011 John Wiley & Sons, Ltd.

This paper considers an ad hoc network, where a set of energy-rich mobile nodes, termed mobile relays, are used to facilitate relaying packets so as to mitigate the energy consumption of static nodes. Existing work has focused on link-level relaying behaviors. In this work, we show how to achieve route-level relaying by overhearing route control and data packets. This allows mobile relays to redirect the traffic of static nodes and thus reduce their energy consumption. As the relocation of mobile relays needs to dynamically adjust the current traffic condition, a dynamic relocation scheme of mobile relays needs to be designed. In this paper, we refer to the mobile relay deployment (MRD) problem and design a distributed protocol for mobile relays. In our protocol, mobile relays do not necessarily participate in the routing discovery process, so static nodes can quickly switch back to their original routing paths without reconstruction efforts once mobile relays leave their communication ranges. Simulations by QualNet are presented to evaluate the performance of our protocol as compared to link-level relaying.

This paper briefly discusses cyper-physical systems that include human beings and vehicles in a built environment such as a building or a city, together with Sensor Networks, Communications and Decision Support Systems, with the purpose of optimising the human outcome in the case of an emergency.

Emergency management systems (EMS) are important and complex examples of Cyber-Physical-Human systems that are deployed so as to optimise the outcome of an emergency from a human perspective. They use sensor networks, networked decision nodes and communications with evacuees and first responders to optimise the overall Quality of Service to benefit primarily human beings in terms of survival, health and safety, and the the protection of nature, property and valuable infrastructures. The use of technology for emergency management also has side effects in terms of failures and malicious attacks of the ICT system, so that the outcome will be affected by how well the ICT system operates under stress. Thus this paper surveys research on wireless sensor- assisted EMS, including networking, distributed control, and knowledge discovery. An evaluation of increased effectiveness and liabilities that wireless communications introduce is conducted when adversaries exacerbate the emergency by malicious attacks through the wireless system.

Abstract Wireless sensor networks (WSNs) offer a convenient way to monitor physical environments. In the past, WSNs are all considered static to continuously collect information from the environment. Today, by introducing intentional mobility to WSNs, we can further improve the network capability on many aspects, such as automatic node deployment, flexible topology adjustment, and rapid event reaction. In this paper, we survey recent progress in mobile WSNs and compare works in this field in terms of their models and mobility management methodologies. The discussion includes three aspects. Firstly, we discuss mobility management of mobile sensors for the purposes of forming a better WSN, enhancing network coverage and connectivity, and relocating some sensors. Secondly, we introduce path‐planning methods for data ferries to relay data between isolated sensors and to extend a WSN's lifetime. Finally, we review some existing platforms and discuss several interesting applications of mobile WSNs. Copyright © 2010 John Wiley & Sons, Ltd.

While sensor-enabled devices have greatly enriched human interactions in our daily life, discovering the essential knowledge behind sensing data is a critical issue to connect the cyber world and the physical world. This motivates us to design an innovative sensor-aided social network system, termed cyber-physical handshake. It allows two users to naturally exchange personal information with each other after detecting and authenticating the handshaking patterns between them. This work describes our design of detection and authentication mechanisms to achieve this purpose and our prototype system to facilitate handshake social behavior.

This paper considers storage management in an isolated WSN, under the constraint that the storage space per node is limited. We formulate the memory spaces of these sensor nodes as a distributed storage system. Assuming that there is a sink in the WSN that will be visited by mobile mules intentionally (e.g., pre-arranged buses) or occasionally (e.g., non-pre-arranged taxis), we address three issues: (1) how to buffer sensory data to reduce data loss due to shortage of storage spaces, (2) if dropping of data is inevitable, how to avoid higher priority data from being dropped, and (3) how to keep higher priority data closer to the sink, such that the mobile mules can download more important data first when the downloading time is limited. We propose a Distributed Storage Management Strategy (DSMS) based on a novel shuffling mechanism similar to heap sort. It allows nodes to exchange sensory data with neighbors based on only local information. To the best of our knowledge, this is the first work addressing distributed and prioritized storing strategies for isolated WSNs.

While social networks on Web platforms have attracted a lot of interests, including more natural and physical inputs into such systems to enhance human interaction is desirable. Using body-area sensor networks (BSNs) to capture human motions opens up an opportunity toward this goal. These motivate us to design a novel virtual-physical social network platform1 with typical social network functions and capable of receiving various inputs from remote BSNs. Through our platform, users can share conventional messages and images as well as sensory data in several interesting ways. We demonstrate a Tai-Chi exercise social network and some testing results.

Following the trend of miniature intelligent sensing, wearing small, integrated wireless sensor nodes, such as one with accelerometers and compasses, to capture human body motions may have many applications in medical care and computer animation. In this paper, we demonstrate the use of intelligent sensors to capture human motions for home rehabilitation. We design a game to help a patient to conduct his/her rehabilitation program. For each exercise, the patient is instructed to wear sensors on specified movable body parts. The system will then estimate the quality of the movements and give scores as if it is advised by a therapist. In this way, patients will no longer feel painful and boring as that in traditional rehabilitation, which is typically done in hospitals.

While wireless sensor networks (WSNs) are typically targeted at large-scale deployment, due to many practical or inevitable reasons, a WSN may not always remain connected. In this paper, we consider the possibility that a WSN may be spatially separated into multiple subnetworks. Data gathering, which is a fundamental mission of WSN, thus may rely on a mobile mule (ldquomulerdquo for short) to conduct data gathering by visiting each subnetwork. This leads to the problem of minimizing the path length traversed by the mobile mule. We show that minimizing the path length, which may reflect the data gathering latency and the energy consumption of the mule is a generalization of the traveling salesman problem and is NP-complete. Some heuristics based on geometrical properties of node deployment are proposed. Our simulation results show that these heuristics perform very close to optimal solutions in most practical cases.

The dynamics of mobile ad-hoc NETworks (MANET), as a consequence of mobility of mobile hosts, pose the problem in finding stable multi-hop routes for effective communication between any pair of source and destination. In this paper, a novel stability-based ad hoc routing protocol is proposed, which considers both radio link affinity and route stability in MANETs. In the proposed protocol, which is named as ad-hoc on-demand stability vector (AOSV) routing protocol, a link/route stability estimation method and a novel path finding algorithm are developed to find out and maintain stable routes for dynamically required communications services in MANETs. A stochastic mobile-to-mobile radio propagation model is constructed for the estimation of the radio link stability as well as the stability of multi-hop route. With the awareness of stabilities of radio links and eligible routes in a MANET, the path finding algorithm is designed to explore the stable route with largest route stability for a given source and destination pair. The performance of AOSV is compared with the well-known ad-hoc on-demand distance vector (AODV) routing protocol. Simulation results indicate that the AOSV routing protocol leads to significant throughput increases by 5% to 68% improvement comparing to AODV.

The deployment of sensor networks have attracted a lot of attention recently. In essence this issue is concerned with how well a sensing field is monitored by sensors to achieve a particular coverage. In this paper, we propose a signal-strength-based approach to evaluate how well a sensing field is covered/monitored. We first formulate object tracking by a single sensor as a Gaussian-error model. Then we establish an error model on location estimation given that the location of an object is known. This leads to a model to evaluate a sensor network with given locations of sensors. We then apply the result to several applications, such as adding more sensor nodes for error reduction and scheduling power modes (awake or sleep) of sensors, and demonstrate our simulation results.