Sensing data acquired with crowdsensing are generally stored at central cloud servers, since massive data are involved and sensing devices do not have enough space to store them. Although each sensing device only has limited storage capacity, the total size of storage across thousands of devices can be considerable. In view of this, this paper addresses decentralized storage problem in mobile crowdsensing system, providing an alternative to cloud-based data storage. By investigating a virtual sensor model, the movement of a participant in the target sensing area is formulated as a random sampling over the data field related to this area. With a particular encoding algorithm, the data field is compressed into only one measurement along with a random sampling process. Each participant stores its own measurements as if various compressed snapshots of the data field are separately stored by different participants. We further investigate a recovery algorithm, reconstructing the original data field by carefully decoding enough measurements. Extensive experiments validate the proposed storage scheme under various crowdsensing scenarios, and our scheme achieves excellent performance in terms of recruitment overhead, decoding time, and decoding accuracy.

In the recent past, human activity recognition research has focused on using WiFi channel state information (CSI) as a viable alternative to legacy systems like video and sensor-based activity recognition having limitations such as privacy invasion, obtrusiveness, and the inconvenience of wearing sensory devices. While the performance of CSI-based activity recognition models is impressive, many of the models are built using offline processed data from regulated settings which hinders their application in real-time. However, real-life human activity recognition requires models to be responsive to identifying activities in real-time. To address the shortcoming of CSI-based activity recognition models, we propose a deep learning object detection framework and instance segmentation for multiple human activity recognition using WiFi signals. The real-time CSI data from the signal is captured on a sliding window and converted into time-frequency domain images of the activity stream using continuous wavelet transform (CWT). Since it is impossible to pre-segment activities within a stream in real-time, the power profile from the transformed images is exploited to provide insights for deep learning instance segmentation to identify each unique human activity. The evaluation is carried out using real-time CSI data with single and multiple human activities. The results show that real-time model classification accuracy is 93.80% on average and instance segmentation accuracy of 90.73%.

A number of image compressive sensing (CS) algorithms were proposed in the past two decades, aiming at yielding recovered images with the best possible visual effect. However, it is quite difficult to further improve the image quality for human eyes. For example, in the low-rate sampling scenarios, CS algorithms always suffer degraded performance and can only recover less visually appealing images. We notice that what human beings concern with is the visual quality of an image, while machine users care much more about its latent metrics, such as recognition accuracy, rather than the subjective visual effect. Inspired by this point, we develop a machine recognition-oriented image CS with an adversarial learning strategy. Some adversarial models are investigated to make the recognition accuracy as an additional optimization goal of the CS reconstruction network. Through end-to-end training, CS reconstruction network automatically learns an image recognition pattern, and produce recovered images owning extra recognition metric, which makes them become more suited for machine users. Experimental results indicate that the images recovered with the proposed adversarial learning strategy can be recognized with significantly higher accuracy compared to that with the existing CS algorithms.

Gesture recognition has been the subject of intensive research in recent years owing to its wide applications. Unlike traditional systems, which usually require wearable sensors, many recent works have achieved the desirable gesture recognition performance using wireless channel state information from commercially available WiFi devices. However, existing works generally require training new models for different locations due to the location-dependent nature of channel state information. This paper proposes a location-independent system that can recognize gestures performed in a new location without training a new model. Our approach uses disentanglement that extricates location and other extraneous information from those needed for gesture recognition. The implementation is based on an unsupervised invariance induction framework consisting of feature extraction, a multi-output latent space, gesture recognition, and decoder modules. The key idea in designing this system is to separate gesture-dependent features from location-dependent features. Specifically, the feature extraction module consisting of a long short-term memory network is employed to select representative features; it essentially serves as an encoder to generate the latent space. During the training process, the network learns to cluster features representation for the gesture recognition and decoder by minimizing the total loss of the gesture recognition and decoder modules. We test our system with a dataset collected from various subjects performing four different gestures in multiple locations in seven rooms with different layouts. The results show that our location-independent gesture recognition system can achieve 88.69% accuracy for new locations.

Privacy concerns over face recognition systems have attracted extensive attention in various fields. For gender privacy-preserving work, there are two key challenges: 1)privacy, i.e., confusing gender classifiers and 2)utility, i.e., maintaining its face verification performance. To address both issues, this article develops a novel gender-adversarial network, referred to as Gender-AN, to impart gender privacy to face images. Gender-AN employs an attribute-independent encoder–decoder GAN-based network to perturb the input face image, training with the assistance of the proper facial attributes. The perturbed image is then able to obfuscate gender classifiers while maintaining identity discriminability. To optimize the generator, a multitask-based loss function is utilized, which includes attribute manipulation loss, face matcher loss, adversarial loss, and reconstruction loss functions. This optimization facilitates our model to achieve the generalization, verification preserve, and natural appearance, simultaneously. Extensive experiments confirm the effectiveness of the proposed model in enhancing gender privacy and preserving face verification utility.

Taxi demand prediction plays a significant role in assisting the pre-allocation of taxi resources to avoid mismatches between demand and service, particularly in the era of the sharing economy and autonomous driving. However, most studies have only tried to figure out the complex spatial-temporal pattern of taxi demand from historical taxi demand series, neglecting the intrinsic influences of regional functions, and failing to effectively capture the dynamic long-term periodicity. In this paper, we make two important observations: (1) taxi demand pattern varies significantly between different functional regions; and (2) taxi demand follows a dynamic daily and weekly pattern. To address these two issues, we adopt Points of Interest (POIs) to identify regional functions, and propose a novel BERT-based Deep Spatial-Temporal Network (BDSTN) to model the complex spatial-temporal relations from heterogeneous local and global features. In BDSTN, a Spatiotemporal Pattern Matching module is introduced to capture the complex spatiotemporal pattern of taxi demand while considering its dynamic temporal periodicity, and a Functional Similarity Embedding module is adopted to learn the functional similarity among all regions via POIs. To the best of our knowledge, this is the first work to use BERT-based architecture to learn taxi demand patterns, and is also the first to take functional similarity represented by POIs into consideration. Our experimental results on real-world traffic datasets in New York City demonstrate that the effectiveness of the proposed method outperforms the state-of-the-art methods, and that the efficiency of our proposed model is higher than other deep learning methods.

Mobile crowdsensing systems typically operate centralized cloud storage management, and the environment data sensed by the participants are usually uploaded to certain central cloud servers. Instead, this article addresses the decentralized data storage problem in scenarios where cloud servers or network infrastructures do not work as expected and the sensing data have to be temporarily stored on the mobile devices carried by the participants. Considering that the sensing data are generally correlated, this article investigates a compressive distributed storage scheme for mobile crowdsensing. We notice a key observation: when a participant has a random walk in the target sensing area, his walking/sensing process can be considered as a random sampling for the entire area, although the activity of the participant may only have a local scope. We then propose an encoding algorithm based on compressive sensing theory. Each participant encodes the sensing data in their local trajectory, but the encoded CS measurement is capable of roughly reflecting the entire information of the whole area. While a participant stores a blurred global image of the target sensing area, the entire data can then be collaboratively stored by a certain number of participants. We further present a period-based data recovery algorithm to exploit the inter-period correlations, improving the recovery accuracy. Experimental results using real environmental data demonstrate the performance of the proposed compressive storage scheme. The test datasets and our source codes are available at https://github.com/siwangzhou/MCS-Storage .

Past years have witnessed the rapid increasing number of smart devices and objects deployed in the urban environment. Leveraging helpful data generated by hundreds of millions of smart objects, a large number of services in the Internet of Things (IoT) are devised and developed to improve our urban life quality. However, uploading the unprecedented volume of sensing data from IoT sensors to the cloud directly can lead to huge unnecessary consumption and hurt the quality of IoT services. This work leverages the fog architecture to devise a task-driven data offloading (TDO) algorithm in urban IoT services. Specifically, a three-layer urban IoT service architecture is proposed, and the TDO process is formulated as a combination optimization problem taking task deadlines and abilities of fog devices into consideration. Then, we prove the TDO problem is NP-hard, and the G-TDO algorithm is devised to solve it with a careful designed utility function. Also, we propose RG-TDO algorithm to improve the G-TDO algorithm considering the overlaps of tasks. Finally, we demonstrate the significant performance of the proposed algorithms with extensive evaluations based on real-world data set.

In recent years, erasure codes have become the de facto standard for data protection in large scale distributed cloud storage systems at the cost of an affordable storage overhead. However, traditional erasure coding schemes, such as Reed-Solomon codes, suffer from high reconstruction cost and I/Os. The recent past has seen a plethora of efforts to optimize the tradeoff between the reconstruction cost, I/Os and storage overhead. Quiet different from all prior studies, in this paper, our erasure coding technique makes the first attempt to take advantage of the unequal failure rates across the disks/nodes to optimize the system reliability and reconstruction performance. Specifically, our proposed technique, the Unequal Failure Protection based Local Reconstruction Code (UFP-LRC) divides the data blocks into several unequal-sized groups with local parities, assigning the data blocks stored on more failure-prone disks/nodes into the smaller-sized group, so as to provide unequal failure protection for each group. In this way, by exploiting the nonuniform local parity degrees, the proposed UFP-LRC enables the data blocks that are stored on more failure-prone disks/nodes to tolerate a greater number of failures while suffering from less repair cost than others, leading to a substantial improvement of the overall reliability and repair performance for cloud storage systems. We perform numerical analysis and build a prototype storage system to verify our approach. The analytical results show that the UFP-LRC technique gradually outperforms LRC along the increase of failure rate ratio. Also, extensive experiments show that, when compared to LRC, UFP-LRC is able to achieve a 10 to 15 percent improvement in throughput, and an 8 to 12 percent reduction in decoding latency, while retaining a comparable overall reliability.

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network-based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full-image removal of blocking artifacts. Specifically, with our multi-channel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block-based CS recovery algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics and subjective visual image quality. Our source codes are available athttps://github.com/siwangzhou/DeepBCS.

Low-cost access points have proliferated wireless local area networks (WLAN) providing main wireless access in many unmanaged networks. The majority of these APs rely on received signal strength indication (RSSI), known to be unreliable, as a measure of the wireless link quality. However, an accurate link measurement is a precursor to channel selection which in turn allows more efficient use of the wireless resources, especially in a crowded and dense wireless environment. In this paper, we present CSI-EWCA, an entropy-based WLAN channel allocation model using channel state information to combat the unreliability in RSSI. To develop a self-reliant system that is independent of CSI data for devices with low computational power, we develop a machine learning model to predict channel spectral entropy from physical layer network information extracted from the Linux kernel. Our experimental results show that CSI-EWCA can consistently select a channel with high throughput and low jitters and fewer retries.

Existing works on human activity recognition predominantly consider single-person scenarios, which deviates significantly from real world where multiple people exist simultaneously. In this work, we leverage transfer learning, a deep learning technique, to present a framework (TL-HAR) that accurately detects multiple human activities; exploiting CSI of WiFi extracted from 802.11n. Specifically, for the first time we employ packet-level classification and image transformation together with transfer learning to classify complex scenario of multiple human activities. We design an algorithm that extracts activity based CSI using the variance of MIMO subcarriers. Subsequently, TL-HAR transforms CSI to images to capture correlation among subcarriers and use a deep Convolutional Neural Network (d-CNN) to extract representative features for the classification. We further reduce training complexity through transfer learning, that infers knowledge from a pre-trained model. Experimental results confirm the significance of our approach. We show that using transfer learning TL-HAR improves recognition accuracy to 96.7% and 99.1 % for single and multiple MIMO links.

Channel state information-based activity recognition has gathered immense attention over recent years. Many existing works achieved desirable performance in various applications, including healthcare, security, and Internet of Things, with different machine learning algorithms. However, they usually fail to consider the availability of enough samples to be trained. Besides, many applications only focus on the scenario where only single subject presents. To address these challenges, in this paper, we propose a three-phase system Wi-multi that targets at recognizing multiple human activities in a wireless environment. Different system phases are applied according to the size of available collected samples. Specifically, distance-based classification using dynamic time warping is applied when there are few samples in the profile. Then, support vector machine is employed when representative features can be extracted from training samples. Lastly, recurrent neural networks is exploited when a large number of samples are available. Extensive experiments results show that Wi-multi achieves an accuracy of 96.1% on average. It is also able to achieve a desirable tradeoff between accuracy and efficiency in different phases.

Existing work on distributed networked storage, although extensive, has generally focused on the recovery of global data field covering the entire network. This, while demanded by a broad range of applications, has ignored cases where only a subset of the data are needed, for example, from a local region of the network. Based on this observation and the fact that the sensor readings are correlated, this paper proposes a compressive networked storage solution. Specifically, by employing the compressive sensing (CS) theory, we present a lazy-encoding algorithm with local dissemination and a region-based reconstruction algorithm. Utilizing our local dissemination strategy, sensor readings only have to be disseminated and stored in their respective regions, which makes the dissemination cost decrease significantly. With the lazy-encoding algorithm, the readings in specified local regions are capable of being encoded individually, dramatically reducing the decoding ratio. The region-based reconstruction algorithm is introduced to explore the inter-region correlation, aiming at offering improved data accuracy. We further provide the mathematical foundation that our reconstruction algorithm could ensure efficient CS recovery. Experimental results using real sensor readings show that the proposed scheme is especially beneficial to the recovery of local data. At the same time, our scheme can recover the global data field as well without increasing reconstruction error.

We investigate the problem of distributed networked storage with compressive sensing in wireless sensor networks, and a compressive storage scheme for local data query is proposed. Specifically, we propose a simple but efficient one-step data dissemination strategy, and the dissemination cost is reduced dramatically. We further present a lazy-encoding algorithm, using which the local data are capable of being reconstructed without recovering the global data field if not necessary. Thus the decoding ratio decreases significantly. Experiments using real sensor data show that the proposed scheme achieves far better local data recovery performance compared to the existing ones.

We present the first WiFi based driver state recognition system: SafeDrive-Fi. Our proposed framework extracts fine-grain Channel State Information (CSI) of WiFi signal to accurately predict driver states through gestures and body movements. Different from vision based techniques, SafeDrive-Fi provides a simple, cost-effective and ubiquitous solution to prevent accidents and loss of lives due to reckless driving. We incorporate a unique DETECT algorithm to differentiate between normal and dangerous driving in a challenging and noisy in-vehicle conditions. Using only commercially available products, SafeDrive-Fi is compatible with 802.11n/ac and can assist drivers and law enforcement in discovering dangerous driving states. To the best of our knowledge, this is the first system that aggregates information from all the channel subcarriers and use multidomain CSI features to classify dangerous driving conditions. SafeDrive-Fi achieves an overall 98.04% recognition accuracy and 19.8% improvement over similar Received Signal Strength (RSS) based solution using an already deployed infrastructure.

Channel State Information of WiFi signal has attracted tremendous interests in recent years for activity identification. Although existing work can achieve desirable performance using different algorithms, similar system modules are often shared. In this paper, we first summarize and compare various techniques employed in different modules such as preprocessing, activity extraction, feature dimension reduction, and classification. Specifically, different feature reduction methods are applied in order to address the challenge of classifying various length signals and extracting representative abstractions, including manually selecting features and Dynamic Time Warping based classification with Principal Component Analysis. By targeting at multiple human activities, we then compare the performance of two common system structures from difference aspects. Experimental results show that it can be subjective and environment dependent by manually selecting particular features, while DTW based classification can be time consuming especially with larger dataset. In order to address these challenges, we propose a novel framework based on Deep Learning Network. Long Short Term Memory model, a type of Recurrent Neutral Network, is employed for time-series sequence classification. Extensive results show that it can achieve higher efficiency and accuracy.

Geomagnetic field signal has gained increasing wide investigated for indoor positioning problems. Because of the variation of magnetic signals and the sensor observation drift, the recent positioning technology development based on magnetic field or pedestrian dead reckoning (PDR) has been restricted. In addition, the accumulative error and cold-start problem can also cause huge positioning error. In this paper, we present a novel indoor localization approach, GeoLoc, for combining magnetic fingerprint matching and PDR by Kalman Filter. First, magnetic field intensity of every positions is gathered and a fingerprint map is built for matching. A candidate set of positions is introduced to include uncertainty and increase robustness for our estimation. With the squeezing of candidate sets, uncertainties of orientation and position estimation have been eliminated. Realistic experiment results show that GeoLoc successfully addresses accumulative error and cold-start problems by sensor data fusion. GeoLoc achieves a good estimation for both short (less than 17.5m) and long walking distance, and it can work in both offline and online real-time positioning. GeoLoc is able to achieve an online positioning accuracy of less than 1.2m, and an offline positioning accuracy of 0.3m only with a smart phone. GeoLoc only uses the built-in sensors of mobile phones, thus users can get their position only by using their phone.

The latest research progress of the theory of compressed sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data in WSNs. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of gathered data. Simulation results show that the proposed scheme improves the reconstruction quality compared to the existing ones.

Collaborative representation classification (CRC) has attracted increasing attention in face recognition (FR) tasks. The two-phase sparse representation (TPSR) methods are the improved schemes. However, most TPSR methods decrease training samples in the first step, resulting in less similarities or discrimination for representation, even unstable classification. In this paper, we propose a new two-phase representation based FR approach with random-filtering virtual samples, called Random-Filtering based Sparse Representation (RFSR) scheme. To increase the similarity in the same class and the discrimination between different classes, RFSR first uses original training samples and their corresponding random-filtering virtual samples to constructs a new training set. Then it exploits the new training set to perform CRC. The experiment results indicate that our method outperforms the two-phase test sample sparse representation (TPTSSR) method and the simple and fast representation-based (SFRB) scheme.

An extensive set of research efforts have explored Channel State Information for human activity detection. By extracting CSI from a sequence of packets, one can statistically analyze the temporal variations embedded therein and recognize corresponding human activities. In this paper, we present Wi-Chase, a sensorless system based on CSI from ubiquitous WiFi packets for human activity detection. Different from existing schemes utilizing only CSI of one or a small subset of subcarriers, Wi-Chase fully utilizes all available subcarriers of the WiFi signal and incorporates variations in both their phases and magnitudes. As each subcarrier carries integral information that will improve the recognition accuracy because of detailed correlated information content in different subcarriers, we can achieve much higher detection accuracy. To the best of our knowledge, this is the first system that gathers information from all the subcarriers to identify and classify multiple activities. Our experimental results show that Wi-Chase is robust and achieves an average classification accuracy greater than 97% for multiple communication links.

In recent years, erasure codes have become the de facto standard for data protection of large scale distributed cloud storage systems at the cost of an affordable storage overhead. While traditional erasure coding schemes, such as Reed-Solomon codes, suffer from high reconstruction cost and I/Os. The recent past has seen a plethora of efforts to optimize the tradeoff between the reconstruction cost, I/Os and storage overhead. Quietly different from all prior studies, in this paper, our erasure coding technology makes the first attempt to take advantage of the unequal failure rates across the disks/nodes to optimize the reconstruction performance and system reliability. Specifically, our proposed technology, the Unequal Failure Protection based Local Reconstruction Code (UFP-LRC) divides the data blocks into several unequal-sized groups with local parities, assigning the data blocks stored on more failure-prone disks/nodes into the smaller-sized group, so as to provide unequal failure protection for each group. In this way, by exploiting the nonuniform local parity degrees, the proposed UFP-LRC enables the data blocks that are stored on more failure-prone disks/nodes to tolerate a greater number of failures while suffer from less repair cost than others, leading to a substantial improvement of overall repair performance and reliability for cloud storage system. We perform numerical analysis and build a prototype storage system to verify our approach. The analytical results show that the UFPLRC technique gradually outperforms LRC along the increase of failure rate ratio. Also, extensive experiments show that, when compared to LRC, UFP-LRC is able to achieve a 10% to 13% improvement in throughput, and a 8% to 12% reduction in decoding latency, while retaining a comparable overall reliability.

In the big data era, with the rapid development of location-based applications, GPS enabled devices and big data institutions, location correlation privacy raises more and more people's concern. Because adversaries may combine location correlations with their background knowledge to guess users' privacy, such correlation should be protected to preserve users' privacy. In order to deal with the location disclosure problem, location perturbation and generalization have been proposed. However, most proposed approaches depend on syntactic privacy models without rigorous privacy guarantee. Furthermore, many approaches only consider perturbing the locations of one user without considering multi-user location correlations, so these techniques cannot prevent various inference attacks well. Currently, differential privacy has been regarded as a standard for privacy protection, but there are new challenges for applying differential privacy in the location correlations protection. The privacy protection not only should meet the needs of users who request location-based services, but also should protect location correlation among multiple users. In this paper, we propose a systematic solution to protect location correlations privacy among multiple users with rigorous privacy guarantee. First of all, we propose a novel definition, private candidate sets which are obtained by hidden Markov models. Then, we quantify the location correlation between two users by using the similarity of hidden Markov models. Finally, we present a private trajectory releasing mechanism which can preserve the location correlations among users who move under hidden Markov models in a period of time. Experiments on real-world datasets also show that multi-user location correlation protection is efficient.

With the ever expanding mobile device ecosystem, mobile users face a vast and constantly growing application pool. At the same time, in our daily life, waiting occurs regularly at different places such as shopping centers, where mobile applications become the de facto means to consume the time periods. In this paper, we propose a novel application recommendation system that utilizes human activity information at different places, to better match the applications with the characteristics of the users current contexts. Specifically, we design a place/application matching model and present two application list recommending algorithms with bounded approximation ratio. We also implement the recommendation system on real mobile phones and conduct field studies to show its feasibility. Our experimental and simulation results show that the proposed schemes can achieve satisfactory results.

Vehicular ad hoc networks, as a special case of delay tolerant networks, have become increasingly attractive to academia and industry. Different from most of the work in this field, which has focused on short periods of transient opportunistic contacts, in our previous work, we have analyzed the position data of a large set of urban private vehicles in Changsha, China and proposed a Location based Urban Vehicular network (LUV) utilizing the stable connections among vehicles. Place serves as a central message exchange and routing component in LUV that is critical in providing relatively reliable network connections. In this paper, we present a simple threshold based approach for identifying the places or vehicle aggregation areas, in an urban environment. We perform experimental study over a real set of data gathered over three months for 8900 vehicles and show the method is effective.

In absence of fixed infrastructure in Opportunistic Networks (OppNet), connectivity between OppNet nodes (usually characterized by human-portable devices), is one of the most challenging issues. The traditional assumption considers every proximity triggered human contact to be an effective OppNet connection. However, the high dynamicity of human mobility impairs the interchangeable notion of human contact and effective oppnet connection, thus necessitating the consideration of other critical contact properties like contact volume, defined as the maximum amount of data transferable during a contact. Recently a few works were proposed to predict the contact volume, using the instantaneous movement direction and velocity of the users. However none of those considered previous mobility history of the users which has a significant role on the future estimations. In this paper, we propose a novel scheme called HiPCV, which uses a distributed learning approach to capture preferential movements of the individuals, with spatial contexts and directional information and paves the way for mobility history assisted contact volume prediction. Experimenting on real world human mobility traces, HiPCV first learns and structures human walk patterns, along her frequently chosen trails. By creating a Mobility Markov Chain (MMC) out of this pattern and infusing it into HiPCV algorithm, we then devise a decision model for data transmissions during opportunistic contacts. Experimental results show the robustness of HiPCV in terms mobility prediction, reliable opportunistic data transfers and bandwidth saving, at places where people show regularity in their movements.

Geocasting aims to deliver information to all nodes within a geographic area rather than an arbitrary group of nodes. Supporting geocasting in the context of opportunistic networks where nodes are not well-connected is still an open problem. We present a routing algorithm to provide geocasting service for opportunistic networks, termed Geoopp. Geoopp combines unicasting and flooding by first forwarding a message to the specified geographic region and then flooding the message to all nodes inside the region. To forward a message toward a region, Geoopp adapts geographic greedy routing for opportunistic networks. Nodes choose neighbors that can take the message closer to destination. A progress within radius metric (PWRM) is introduced to measure the geographic progress a node can make carrying the message toward its destination region given one of its future visited regions. To determine the future mobility, the regularity embedded in human movement is exploited, since human movements often exhibit a high degree of repetition including regular visits to certain places and regular contacts during daily activities. A node's mobility is characterized by inter-visiting time and contact availability per visiting to capture the regular visits and contacts in a specific region. Chebyshev's inequality is employed to compute the probabilities that a node visiting a region and having contact inside. Our simulation results show that Geoopp can attain 80% of the maximum achievable delivery rate at a cost of 20% of the maximum consumable relays.

Opportunistic networks exploits opportunistic contacts among mobile devices to facilitate data forwarding. In this paper, we focus on visited places of the mobiles and resulting opportunistic yet relative stable contacts within the places. We focus on investigating the capacity of this place based opportunistic network. We reveal that the capacity of the network is mainly determined by two factors, the message exchanges at the places and the people flow among the places. We propose a two-layer model to analyze this problem. In the first layer, a mixed form queueing network model is constructed to compute the population in each place and also the visitor arrival rate as well as visiting probability to each place. After this, in the second layer we transform the results from first layer into node capacity and link capacity which in turn will be used to calculate the network capacity. We perform simulation experiments to study the theoretical results.

It is our great pleasure to welcome you to the Fourteenth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2013.

As a key enabling technology for pervasive computing, opportunistic networks have attracted intensive research efforts recently. In this paper, we present a new routing scheme for opportunistic networks that aims at forwarding messages to a destination location/area, instead of forwarding to specific nodes. Our routing scheme, termed LOOP for LOcation based routing for OPportunistic networks, exploits the regularity embedded in human moving pattern. As human movements often exhibit a high degree of repetition including regular visits to certain places and regular contacts during daily activities, we can predict a mobile node's future locations based on its mobility trace with high confidence. We formulate the movement pattern mining as a multi-label classification problem and construct a Bayes' predictive model to explore the mobility history and learn the movement pattern. This movement pattern will then be used to predict the node's future movement. Based on the prediction, the ability of the node to deliver a message to the destination is quantified through defined metrics. These metrics will be the determining factor for choosing proper relaying nodes in several proposed strategies. Our scheme can preserve privacy as no information, including location information, needs to be exchanged among nodes. At the same time, our scheme achieves total distributed control as each node can choose its individual forwarding strategy without involving network wide changes. Our analytical and simulation results show that LOOP can achieve significant performance gains over well known existing strategies for routing in opportunistic networks.

Opportunistic networks are characterized by intermittent connectivity among mobile devices that occurs during their opportunistic contacts. With the increasing number of capable wireless devices and hence increasing potential formation of opportunistic networks, enabling applications over opportunistic networks has become critical. In this paper, we design and analyze a mobile file sharing system over opportunistic networks using Bluetooth technology. Our goal is to enable a mobile device to download desired files from available devices in its neighborhood and also allow its files to be disseminated to the network with the facilitation of opportunistic contacts. The key challenges we strive to address are system automation and file sharing efficiency. The mobile file sharing system is implemented on HP iPAQ 910 smart phones. Extensive experiments are conducted and the results indicate that our mobile file sharing system works efficiently and the data rate for file transferring can be up to 417 KB per second.

Localization is one of the key enabling technologies for wireless services now penetrating into everyday life. Unfortunately, existing localization schemes are often energy inefficient (such as GPS), or inaccurate (based on signal-distance conversion), or labor intensive (requiring periodic fingerprinting). In this paper we propose a novel localization scheme termed COAL, for COntext Aware Localization, to target at both energy efficiency and accuracy. Our key idea is to leverage users' context information such as an ongoing event to facilitate the localization scheme. By employing these context information, we can significantly reduce localization frequency to conserve energy while maintaining high degree of accuracy. COAL is complementary to existing location schemes using any wireless signal and can be implemented readily as their enhancement. We implement our scheme in iPAQ smart phones using WiFi signals and perform extensive tests in real life and on synthetic data. The results show that the scheme can significantly reduce localization frequency while maintaining high accuracy.

Existing code update protocols for reprogramming nodes in a sensor network are either unsuitable or inefficient when used in a mobile environment. The prohibitive factor of uncertainty about a node's location due to their continuous movement coupled with the obvious constraint of a node's limited resources, pose daunting challenges to the design of an effective code dissemination protocol for mobile sensor networks. In this paper, we propose ReMo, an energy-efficient, multihop reprogramming protocol for mobile sensor networks. Without making any assumptions on the location of nodes, ReMo uses the LQI and RSSI measurements of received packets to estimate link qualities and relative distances with neighbors in order to select the best node for code exchange. The protocol is based on a probabilistic broadcast paradigm with the mobile nodes smoothly modifying their advertisement transmission rates based on the dynamic changes in network density, thereby saving valuable energy. Contrary to previous protocols, ReMo downloads pages regardless of their order, thus, exploiting the mobility of the nodes and facilitating a fast transfer of the code. Our simulation results show significant improvement in reprogramming time and number of message transmissions over other existing protocols under different settings of network mobility. Our implementation results of ReMo on a testbed of SunSPOTs also showcase its better performance than existing reprogramming protocols in terms of transfer time and number of message transmissions.

A low sampling rate leads to reduced congestion and hence energy consumption in the resource-constrained wireless sensor networks. In this article, we propose asynchronous sampling that shifts the sampling time instances of sensor nodes from each other. For lossy data gathering scenarios, the proposed approach provides more information about the physical phenomena in terms of increased entropy at a low sampling rate. For lossless data gathering scenarios, on the other hand, the sampling rate is lowered without sacrificing critical knowledge required for signal reconstruction. As lower sampling rates lead to smaller energy consumption for processing and transmitting the collected sensory data, the proposed asynchronous sampling strategies are capable of achieving a better trade-off between the lifetime of the network and the quality of collected information. In addition to mathematical analysis, simulation results based on real data also verify the benefits of our asynchronous sampling.

While multi-hop broadcast protocols, such as Trickle, Deluge and MNP, have gained tremendous popularity as a means for fast and convenient propagation of data/code in large scale wireless sensor networks, they can, unfortunately, serve as potential platforms for virus spreading if the security is breached. To understand the vulnerability of such protocols and design defense mechanisms against piggy-backed virus attacks, it is critical to investigate the propagation process of these protocols in terms of their speed and reachability. In this paper, we propose a general framework based on the principles of epidemic theory, for vulnerability analysis of current broadcast protocols in wireless sensor networks. In particular, we develop a common mathematical model for the propagation that incorporates important parameters derived from the communication patterns of the protocol under test. Based on this model, we analyze the propagation rate and the extent of spread of a malware over typical broadcast protocols proposed in the literature. The overall result is an approximate but convenient tool to characterize a broadcast protocol in terms of its vulnerability to malware propagation. We have also performed extensive simulations which have validated our model.

In sensor networks, en route aggregation decision regarding where and when aggregation shall be performed along the routes has been explicitly or implicitly studied extensively. However, existing solutions have omitted one key dimension in the optimization space, namely, the aggregation cost. In this paper, focusing on optimizing over both transmission and aggregation costs, we develop an online algorithm capable of dynamically adjusting the route structure when sensor nodes join or leave the network. Furthermore, by only performing such reconstructions locally and maximally preserving existing routing structure, we show that the online algorithm can be readily implemented in real networks in a distributed manner requiring only localized information. Analytically and experimentally, we show that the online algorithm promises extremely small performance deviation from the offline version, which has already been shown to outperform other routing schemes with static aggregation decision.

Motivated by recently surfacing viruses that can spread over the air interfaces, in this article, we investigate the potentially disastrous threat of node compromise spreading in wireless sensor networks. We assume such a compromise originating from a single infected node, can propagate to other sensor nodes via communication and pre-established mutual trust. We focus on the possible epidemic breakout of such propagations where the whole network may fall victim to the attack. Using epidemic theory, we model and analyze this spreading process and identify key factors determining potential outbreaks. In particular, we perform our study on random graphs precisely constructed according to the parameters of the network, such as distance, key sharing constrained communication and node recovery, thereby reflecting the true characteristics therein. Moreover, a comparative study of the epidemic propagation is performed based on the effects of two types of sensor deployment strategies, viz., uniform random and group-based deployment. The analytical results provide deep insights in designing potential defense strategies against this threat. Furthermore, through extensive simulations, we validate the model and perform investigations on the system dynamics. Our analysis and simulation results indicate that the uniform random deployment is more vulnerable to an epidemic outbreak than the group based deployment strategy.

Intensive research has focused on redundance reduction in wireless sensor networks among sensory data due to the spatial and temporal correlation embedded therein. We propose a novel approach termed asynchronous sampling that complements existing study. The key idea of asynchronous sampling is to spread the sampling times of the sensor nodes over the time line instead of performing them in a synchronous manner. Compared with existing strategies, asynchronous sampling introduces another dimension for optimization, without additional computation or communication overhead on sensor nodes. Theoretically, we show that asynchronous sampling benefits sensor networks through increased entropy of the sensory data or reduced reconstruction distortion. Furthermore, we formulate the optimal asynchronous sampling problem for determining the time shifts among the nodes. A heuristic solution, termed O-ASYN, is presented that uses local optimum search to approximate the global optimal solution. Simulation results based on simulated data and real experimental data both demonstrate the entropy increases.

Existing code update protocols for reprogramming nodes in a sensor network are either unsuitable or inefficient when used in a mobile environment. The prohibitive factor of uncertainty about a node's location due to their continuous movement coupled with the obvious constraint of a node's limited resources, pose daunting challenges to the design of an effective code dissemination protocol for mobile sensor networks. In this paper, we propose ReMo, an energy efficient, multihop reprogramming protocol for mobile sensor networks. Without making any assumptions on the location of nodes, ReMo uses the LQI and RSSI measurements of received packets to estimate link qualities and relative distances with neighbors in order to select the best node for code exchange. The protocol is based on a probabilistic broadcast paradigm with the mobile nodes smoothly modifying their advertisement transmission rates based on the dynamic changes in network density, thereby saving valuable energy. Contrary to previous protocols, ReMo downloads pages regardless of their order, thus, exploiting the mobility of the nodes and facilitating a fast transfer of the code. Our simulation results show significant improvement in reprogramming time and number of message transmissions over other existing protocols under different settings of network mobility.

The method of using in-network aggregation for energy efficient data collection often results in processing delay and additional time for gathering related data at an intermediate node. This unfortunately may make the scheme undesirable for networks deployed for emergent event monitoring. In this paper, we propose ReCoDa -- Reliable forwarding of Correlated Data with low latency, an energy-efficient and reliable delivery scheme benefiting from data correlation among sensor nodes but without performing in-network aggregation. Our idea is to allow each node to determine the data transmission reliability it shall obtain based on the preceding transmissions from other nodes, subjecting to the overall information reliability requirement. This in turn is achieved through controllable multi-path forwarding. Simulation shows that ReCoDa can successfully reduce redundancy up to 2.9 times compared with schemes not exploiting data redundancy and achieves energy efficiency up to 4.79 times higher.

While multi-hop broadcast protocols, such as Trickle, Deluge and MNP, have gained tremendous popularity as a means for fast and convenient propagation of data/code in large scale wireless sensor networks, they can, unfortunately, serve as potential platforms for virus propagation if the security is breached. To understand the vulnerability of such protocols and design defense mechanisms against piggy-backed virus attacks, it is critical to investigate the propagation process of these protocols in terms of their speed and reachability. In this paper, we propose a general framework based on the principles of epidemic theory, for vulnerability analysis of current broadcast protocols in wireless sensor networks. In particular, we develop a common mathematical model for the propagation that incorporates important parameters derived from the communication patterns of the protocol under test. Based on this model, we analyze the propagation rate and the extent of spread of a malware over typical broadcast protocols proposed in the literature. The overall result is an approximate but convenient tool to characterize a broadcast protocol in terms of its vulnerability to malware propagation. We have also performed extensive simulations which have validated our model.

In this paper, the authors explore a novel method based on asynchronous sampling in order to reduce the data redundancy among spatially correlated nodes in wireless sensor networks. The authors show that when the sensor nodes sample the interested field at different time points, the correlation among the sensory data can be effectively reduced and hence the sampling rate can be decreased while maintaining desired reconstruction precision. The method is different from the approaches working in a synchronized fashion and implementing compression algorithm for redundancy reduction. Our key idea is to separate the signal into a two parts. One is the common part among the sensor nodes. The other is the innovation part, which represents the distinctions among the sensor nodes. The correlation resulted from the common part can be effectively reduced through asynchronous sampling. Furthermore, the authors design reconstruction method to recover the signal from the asynchronous samples, even with imperfect clock synchronization and irregular samples. In this sampling based approach, a sensor node is not required to perform any additional function such as compression or information exchange. Rather, the extensive work of reconstruction is performed at the sink, which is usually with abundant resources. This asymmetric operation is in particular suitable for resource-constraint wireless sensor networks.

In this paper, we propose a pairwise key distribution scheme based on a novel interconnection graph termed Hierarchical Hypercube. Motivated by the fact that sensor nodes are often deployed in groups (for example, dropped from an airplane at different locations) and hence the whole network is composed of multiple such groups, we design Hierarchical Hypercube as a two layer topology, where each group is modeled by an inner hypercube and connections between the groups are modeled using an outer hypercube. we propose a topology termed Hierarchy Hypercube. While retaining the desirable properties already shown by existing pairwise schemes, by using Hierarchical Hypercube, direct communication from a node to any other nodes is not required, either within a group or among groups, and hence this topology can effectively and realistically reflect the connectivity of the physical sensor network deployed in groups. Furthermore, we propose key pre-distribution scheme based on this novel topology and show that the new scheme possesses high probability of direct key establishment, low memory overhead, and resilience in the presence of broken communication links and compromised nodes, and thus still retain the desirable properties even when the ideal logical connection are distorted in the real deployment.

In-network processing presents a critical challenge for data authentication in wireless sensor networks (WSNs). Current schemes relying on message authentication code (MAC) cannot provide natural support for this operation since even a slight modification to the data invalidates the MAC. In this paper, based on digital watermarking, we propose an end-to-end approach for data authentication in WSNs that provides inherent support for in-network processing. In this scheme, authentication information is modulated as watermark and superposed to the sensory data at the sensor nodes. The watermarked data can be aggregated by the intermediate nodes without incurring any en-route checking. Upon reception of the sensory data, possibly distorted by the operations along the route, the data sink is able to authenticate the data by validating the watermark, detecting whether the data has been altered and where it has occurred. In this way, the aggregation-survivable authentication information is only added at the sources and checked by the data sink, without any involvement of intermediate nodes. Furthermore, the simple operation of watermark embedding and complex operation of watermark detection provide a natural solution of function partitioning between the resource limited sensor nodes and resource abundant data sink. The simulation results show that the proposed scheme can successfully authenticate the sensory data with high confidence.

In unattended and hostile environments, node compromise can become a disastrous threat to wireless sensor networks and introduce uncertainty in the aggregation results. A compromised node often tends to completely reveal its secrets to the adversary which in turn renders purely cryptography-based approaches vulnerable. How to secure the information aggregation process against compromised-node attacks and quantify the uncertainty existing in the aggregation results has become an important research issue. In this paper, we address this problem by proposing a trust based framework, which is rooted in sound statistics and some other distinct and yet closely coupled techniques. The trustworthiness (reputation) of each individual sensor node is evaluated by using an information theoretic concept, Kullback-Leibler (KL) distance, to identify the compromised nodes through an unsupervised learning algorithm. Upon aggregating, an opinion, a metric of the degree of belief, is generated to represent the uncertainty in the aggregation result. As the result is being disseminated and assembled through the routes to the sink, this opinion will be propagated and regulated by Josang's belief model. Following this model, the uncertainty within the data and aggregation results can be effectively quantified throughout the network. Simulation results demonstrate that our trust based framework provides a powerful mechanism for detecting compromised nodes and reasoning about the uncertainty in the network. It further can purge false data to accomplish robust aggregation in the presence of multiple compromised nodes

Motivated by recent surfacing viruses that can spread over the air interfaces, in this paper; we investigate the potential disastrous threat of node compromise spreading in wireless sensor networks. Originating from a single infected node, we assume such a compromise can propagate to other sensor nodes via communication and pre-established mutual trust. We focus on the possible epidemic breakout of such propagations where the whole network may fall victim to the attack. Based on epidemic theory, we model and analyze this spreading process and identify key factors determining potential outbreaks. In particular, we perform our study on random graphs precisely constructed according to the parameters of the network, such as distance, key sharing constrained communication and node recovery, thereby reflecting the true characteristics therein. The analytical results provide deep insights in designing potential defense strategies against this threat. Furthermore, through extensive simulations, we validate our model and perform investigations on the system dynamics

While in-network data fusion can reduce data redundancy and, hence, curtail network load, the fusion process itself may introduce significant energy consumption for emerging wireless sensor networks with vectorial data and/or security requirements. Therefore, fusion-driven routing protocols for sensor networks cannot optimize over communication cost only—fusion cost must also be accounted for. In our prior work [2], while a randomized algorithm termed MFST is devised toward this end, it assumes that fusion shall be performed at any intersection node whenever data streams encounter. In this paper, we design a novel routing algorithm, called Adaptive Fusion Steiner Tree (AFST), for energy efficient data gathering. Not only does AFST jointly optimize over the costs for both data transmission and fusion, but also AFST evaluates the benefit and cost of data fusion along information routes and adaptively adjusts whether fusion shall be performed at a particular node. Analytically and experimentally, we show that AFST achieves better performance than existing algorithms, including SLT, SPT, and MFST.

In this paper, we propose a routing algorithm called Minimum Fusion Steiner Tree (MFST) for energy efficient data gathering with aggregation (fusion) in wireless sensor networks. Different from existing schemes, MFST not only optimizes over the data transmission cost, but also incorporates the cost for data fusion, which can be significant for emerging sensor networks with vectorial data and/or security requirements. By employing a randomized algorithm that allows fusion points to be chosen according to the nodes' data amounts, MFST achieves an approximation ratio of {\frac{5}{4}}\log(k+1), where k denotes the number of source nodes, to the optimal solution for extremely general system setups, provided that fusion cost and data aggregation are nondecreasing against the total input data. Consequently, in contrast to algorithms that only excel in full or nonaggregation scenarios without considering fusion cost, MFST can thrive in a wide range of applications.

While extensive efforts have been devoted to providing optimization based, distributed congestion control schemes for efficient bandwidth utilization and fair allocation in both wireline and wireless networks, a common assumption therein is fixed link capacities. This unfortunately will limit the application scope in multi-hop wireless networks where channels are ever changing. In this paper, we explicitly model link capacities to be time varying and investigate congestion control problems in multi-hop wireless networks. In particular we propose a primal-dual congestion control algorithm which is proved to be trajectory stable in the absence of feedback delay. Different from system stability around a single equilibrium point, trajectory stability Guarantees the system is stable around a time varying reference trajectory. Moreover, we obtain sufficient conditions for the scheme to be locally stable in the presence of delay. Our key technique is to model time variations of capacities as perturbations to a constant link. Furthermore, to study the robustness of the algorithm against capacity variations, we investigate the sensitivity of the control scheme and through simulations to study the tradeoff between stability and sensitivity.

Wireless sensor networks have been recognized as one of the most important technologies in the networking world. Security of sensor networks is one of the major concerns today. To this end, a whole suite of protocols have been designed to provide various security features which includes key management.\nThis thesis covers the issue of group key management in wireless sensor networks. Traditional cryptographic techniques can be used to provide communication privacy and integrity, but do not provide scalable solutions to group key management. A group key management scheme for sensor networks has been discussed that targets at fast response to changes in security conditions. Motivated by the fact that a compromised sensor is most likely to be detected first by its fellow neighboring nodes, the concept of local collaboration during the process of group key distribution is introduced. In the proposed scheme, a sensor node is not able to obtain the secret key solely based on the broadcast message and its pre-deployed secret share. Rather, it has to seek for collaboration from its fellow sensor nodes. Only by jointly exploiting the secret shares disclosed by the broadcast message, its own pre-distributed secret, as well as secrets revealed by other nodes, can a node reconstruct the group key. By empowering the sensor nodes themselves to be able to exclude a compromised node, the scheme promises fast reaction to the ever changing network conditions. Furthermore, a set of enhancements to the basic scheme including self-evolving design for significant reduction in communication and memory overhead are developed.

The Resilient Packet Ring (RPR) IEEE 802.17 standard is a new technology for high-speed backbone metropolitan area networks. A key performance objective of RPR is to simultaneously achieve high utilization, spatial reuse, and fairness, an objective not achieved by current technologies such as SONET and Gigabit Ethernet nor by legacy ring technologies such as FDDI. The core technical challenge for RPR is the design of a bandwidth allocation algorithm that dynamically achieves these three properties. The difficulty is in the distributed nature of the problem, that upstream ring nodes must inject traffic at a rate according to congestion and fairness criteria downstream. Unfortunately, we show that under unbalanced and constant-rate traffic inputs, the RPR fairness algorithm suffers from severe and permanent oscillations spanning nearly the entire range of the link capacity. Such oscillations hinder spatial reuse, decrease throughput, and increase delay jitter. In this paper, we introduce a new dynamic bandwidth allocation algorithm called Distributed Virtual-time Scheduling in Rings (DVSR). The key idea is for nodes to compute a simple lower bound of temporally and spatially aggregated virtual time using per-ingress counters of packet (byte) arrivals. We show that with this information propagated along the ring, each node can remotely approximate the ideal fair rate for its own traffic at each downstream link. Hence, DVSR flows rapidly converge to their ring-wide fair rates while maximizing spatial reuse. To evaluate DVSR, we develop an idealized fairness reference model and bound the deviation in service between DVSR and the reference model, thereby bounding the unfairness. With simulations, we find that compared to current techniques, DVSR's convergence times are an order of magnitude faster (e.g., 2 versus 50 ms), oscillations are mitigated (e.g., ranges of 0.1% versus up to 100%), and nearly complete spatial reuse is achieved (e.g., 0.1% throughput loss versus 33%). Finally, we provide a proof-of-concept implementation of DVSR on a 1 Gb/s network processor testbed and report the results of testbed measurements.

Emerging spread spectrum high-speed data networks utilize multiple channels via orthogonal codes or frequency-hopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic channel model, any subset of users can be selected for data transmission at any time, albeit with different throughputs and system resource requirements. We first transform selection of the best users and rates from a complex general optimization problem into a decoupled and tractable formulation: a multiuser scheduling problem that maximizes total system throughput and a control-update problem that ensures long-term deterministic or probabilistic fairness constraints. We then design and evaluate practical schedulers that approximate these objectives.

We present wireless credit-based fair queuing (WCFQ), a new scheduler for wireless packet networks with provable statistical short- and long-term fairness guarantees. WCFQ exploits the fact that users contending for the wireless medium will have different "costs" of transmission depending on their current channel condition. For example, in systems with variable coding, a user with a high-quality channel can exploit its low-cost channel and transmit at a higher data rate. Similarly, a user in a code-division multiple access system with a high-quality channel can use a lower transmission power. Thus, WCFQ provides a mechanism to exploit inherent variations in channel conditions and select low-cost users in order to increase the system's overall performance (e.g., total throughput). However, opportunistic selection of the best user must be balanced with fairness considerations. In WCFQ, we use a credit abstraction and a general "cost function" to address these conflicting objectives. This provides system operators with the flexibility to achieve a range of performance behaviors between perfect fairness of temporal access independent of channel conditions and purely opportunistic scheduling of the best user without consideration of fairness. To quantify the system's fairness characteristics within this range, we develop an analytical model that provides a statistical fairness bound in terms of the cost function and the statistical properties of the channel. An extensive set of simulations indicate that the scheme is able to achieve significant throughput gains while balancing temporal fairness constraints.