This paper introduces D-SYNC, a cutting-edge framework for enhancing the security and reliability of vehicle localization systems. D-SYNC offers an innovative solution to utilize dashcam footage for vehicle localization in the scenario of the primary localization system, GPS, under security attacks or simply failing to work. D-SYNC synchronizes the visual data from dashcam recordings with geotagged satellite imagery, achieving reliable vehicle positioning and trajectory mapping without precise dashcam-to-satellite alignment. D-SYNC introduces novel designs to address critical challenges, such as the limited field of vision in dashcam videos during real-world driving and how to correlate the significantly distinct spatial and temporal patterns between dashcam sequences and satellite images. Moreover, a novel geo-assisted loss function is introduced in D-SYNC that further elevates the performance of the localization process. D-SYNC surpasses existing methods and significantly increases vehicle localization accuracy. It achieves a 91% top-1 retrieval accuracy in urban environments and a 34% improvement in sub-10 meter localization error compared to benchmarks, relying solely on dashcam and satellite imagery.

The robustness, security, and safety of artificial intelligence (AI) systems have become growing concerns, particularly as deep learning models are increasingly deployed in critical applications. Among emerging threats, backdoor attacks pose a serious risk by embedding hidden malicious behaviors into otherwise well-performing models. Although recent advances in detection techniques have improved defenses for computer vision systems, our findings demonstrate that even simple but carefully designed poisoning strategies can successfully evade these defenses. In this paper, we introduce GhostBackdoor, a novel backdoored model trained using a custom loss function and targeted data augmentation. The proposed loss function aligns neuron activations between clean and poisoned inputs, effectively masking activation anomalies, while the augmentation enforces strict location- and pattern-specific triggers that activate the backdoor only under specific conditions. After training, the model maintains behavior indistinguishable from a clean model unless exposed to the designated trigger with the specific pattern and at the designed locations. We evaluate GhostBackdoor against a broad range of leading defense mechanisms, most of which fail to detect the implanted backdoor. Our results highlight how the vast hypothesis space of deep learning models can be exploited to conceal malicious activations, underscoring the need for more robust security strategies in AI-driven systems, including those used in Internet of Things (IoT) applications.

Artificial intelligence (AI) increasingly powers sensitive applications in domains such as healthcare and finance, relying on both extit{linear operations} (e.g., matrix multiplications in large language models) and extit{non-linear operations} (e.g., sorting in retrieval-augmented generation). Fully homomorphic encryption (FHE) has emerged as a promising tool for privacy-preserving computation, but it remains unclear whether existing methods can support the full spectrum of AI workloads that combine these operations. In this SoK, we ask: extit{Can FHE support general AI computation?} We provide both a functional analysis and a cost analysis. First, we categorize ten distinct FHE approaches and evaluate their ability to support general computation. We then identify three promising candidates and benchmark workloads that mix linear and non-linear operations across different bit lengths and SIMD parallelization settings. Finally, we evaluate five real-world, privacy-sensitive AI applications that instantiate these workloads. Our results quantify the costs of achieving general computation in FHE and offer practical guidance on selecting FHE methods that best fit specific AI application requirements. Our codes are available at https://github.com/UCF-ML-Research/FHE-AI-Generality.

In recent years, we have witnessed a remarkable proliferation of Internet of Things (IoT) devices, which are quickly penetrating into almost every industry and making tremendous impacts on the national economy and the entire society. However, security and privacy remain a fundamental hurdle in the collection, transmission, and processing of IoT data. This work focuses on privacy-preserved data access that is critical for implementing and exploiting the full potential of future IoT. This work represents the first endeavor to develop practical Compact Inner Product Encryption (C-IPE) aiming to achieve privacy-preserved data access in IoTs. We propose a practical scheme that provides effective, fine-grained, and privacy-preserved access to IoT data while at the same time, is computationally efficient for practical deployment on resource-constrained IoT devices to preserve the designers of energy-efficient embedded systems and applications a balance between performance, power, security, and cost-effectiveness. We also analyze the efficiency of C-IPE. Compared with the original IPE, the key size is reduced from n + 1 to a small constant; the ciphertext size is reduced by half, i.e., from 2n + 2 to n + 1; and the decryption effectively avoids the high cost of cryptographic pairing. These salient properties result in high efficiency in computation and storage, making C-IPE well-suited for IoT applications. To demonstrate the practicality of our scheme, we implement C-IPE in three representative privacy-preserving applications: privacy-preserved attribute matching, distance matching, and linear regression in IoT settings. We carry out extensive experiments on four platforms, i.e., Dell workstation, Raspberry Pi 3 with an ARM Cortex processor, Samsung Galaxy 7, and ultra-low-power Arduino Nano 33 micro-controller using 32-bit ARM Cortex-M0 CPU with 256KB Flash and 16KB RAM. The experimental results demonstrate significant improvements over existing IPE schemes, supported by detailed numerical evidence and comparative figures across practical application settings.

Privacy-preserving Machine Learning as a Service (MLaaS) enables the powerful cloud server to run its well-trained neural model upon the input from resource-limited client, with both of server's model parameters and client's input data protected. While computation efficiency is critical for the practical implementation of privacy-preserving MLaaS and it is inspiring to witness recent advances towards efficiency improvement, there still exists a significant performance gap to real-world applications. In general, state-of-the-art frameworks perform function-wise efficiency optimization based on specific cryptographic primitives. Although it is logical, such independent optimization for each function makes noticeable amount of expensive operations unremovable and misses the opportunity to further accelerate the performance by jointly considering privacy-preserving computation among adjacent functions. As such, we propose COIN: Conjunctive Optimization with Interleaved Nexus, which remodels mainstream computation for each function to conjunctive counterpart for composite function, with a series of united optimization strategies. Specifically, COIN jointly computes a pair of consecutive nonlinear-linear functions in the neural model by reconstructing the intermediates throughout the whole procedure, which not only eliminates the most expensive crypto operations without invoking extra encryption enabler, but also makes the online crypto complexity independent of filter size. Experimentally, COIN demonstrates 11.2x to 29.6x speedup over various function dimensions from modern networks, and 6.4x to 12x speedup on the total computation time when applied in networks with model input from small-scale CIFAR10 to large-scale ImageNet.

This paper proposes SEER, a novel backdoor detection algorithm for vision-language models, addressing the gap in the literature on multi-modal backdoor detection. While backdoor detection in single-modal models has been well studied, the investigation of such defenses in multi-modal models remains limited. Existing backdoor defense mechanisms cannot be directly applied to multi-modal settings due to their increased complexity and search space explosion. In this paper, we propose to detect backdoors in vision-language models by jointly searching image triggers and malicious target texts in feature space shared by vision and language modalities. Our extensive experiments demonstrate that SEER can achieve over 92% detection rate on backdoor detection in vision-language models in various settings without accessing training data or knowledge of downstream tasks.

Machine Learning as a Service (MLaaS) is an innovative framework that enables a broad range of users to capitalize on the powerful Artificial Intelligence (AI) technologies. Nevertheless, MLaaS raises a privacy concern for both the client data and server model. To address this issue, several Secure Inference (SI) frameworks for MLaaS have been proposed in the literature that take advantage of Homomorphic Encryption (HE) operations. However, the computation cost of these frameworks is still high, especially for real-time applications. In this paper, we propose a novel system called input structure optimization for neural networks (TILE) to accelerate SI. The goal of TILE is to reduce both linear and non-linear computation costs, as well as non-linear communication costs in MLaaS, while maintaining the model accuracy. TILE defines two novel HE-friendly input structures: Internal Tile and External Tile Structures, aimed at reducing the HE operations for SI. We also develop a search mechanism to identify optimal application locations for these input structures. We apply TILE to widely used models such as VGG and ResNet, and datasets including Cifar10 and Tiny-ImageNet. The experimental results demonstrate that TILE effectively reduces the computation time, with up to 51.57% reduction for a state-of-the-art SI framework. Furthermore, TILE can also be applied to models that have already been pruned to significantly reduce the computation time, to further reduce the overall computation time by 25.90%.

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic approach to remarkably compress DL models. However, for practical deep neural networks, a problem called pruning structure inflation significantly limits the pruning efficiency, as it can seriously hurt the model accuracy. In this paper, we propose MOSAIC, a highly flexible pruning framework, to address this critical challenge. By first pruning the network with the carefully selected basic pruning units, then assembling the pruned units into suitable HE Pruning Structures through smart channel transformations, MOSAIC achieves a high pruning ratio while avoiding accuracy reduction, eliminating the problem plagued by the pruning structure inflation. We apply MOSAIC to popular DL models such as VGG and ResNet series on classic datasets such as CIFAR-10 and Tiny ImageNet. Experimental results demonstrate that MOSAIC effectively and flexibly conducts pruning on those models, significantly reducing the Perm, Mult, and Add operations to achieve the global cost reduction without any loss in accuracy. For instance, in VGG-16 on Tiny ImageNet, the total cost is reduced to 21.14% and 29.49% under the MLaaS frameworks GAZELLE and CrypTFlow2, respectively.

Machine Learning as a Service (MLaaS) has paved the way for numerous applications for resource-limited clients, such as IoT/mobile users. However, it raises a great challenge for privacy, including both the data privacy of clients and model privacy of the server. While there have been extensive studies on privacy-preserving MLaaS, a direct adoption of current frameworks leads to intractable efficiency bottleneck for MLaaS with resource constrained clients. In this paper, we focus on MLaaS with resource constrained clients and propose a novel privacy-preserving framework called SPOT to address a unique challenge, the memory constraint of such clients, such as IoT /mobile devices, which results in significant computation stalls at the server in privacy-preserving MLaaS. We develop 1) a novel structure patching scheme to enable independent computations for sequential inputs at the server to eliminate the computation stall, and 2) a patch overlap tweaking scheme to minimize overlapped data between adjacent patches and thus enable more efficient computation with flexible cryptographic parameters. SPOT demonstrates significant improvement on computation efficiency for MLaaS with IoT /mobile clients. Compared with the state-of-the-art framework for privacy-preserving MLaaS, SPOT achieves up to 2 × memory utilization boost and a speedup up to 3 × on computation time for modern neural networks such as ResNet and VGG.

Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.

Privacy-preserving Machine Learning as a Service (MLaaS) enables the resource-limited client to cost-efficiently obtain inference output of a well-trained neural model that is possessed by the cloud server, with both client’s input and server’s model parameters protected. While efficiency plays a core role for practical implementation of privacy-preserving MLaaS and it is encouraging to witness recent advances towards efficiency improvement, there still exists a significant performance gap to real-world applications. The basic logic in state-of-the-art frameworks involves an individual computation for each function of the neural model, based on specific cryptographic primitives. While it is definitely logical, we look back to the necessity of this function-wise methodology and initiate the comprehensive exploration towards allied optimization for efficient privacy-preserving MLaaS. Under such fresh perspective, we remodel the computation process that is always from input to output of the same function in mainstream works, to the allied counterpart that is from one function’s input associated with the start of expensive overhead to another function’s output enabling effective circumvention of unnecessary cost within the procedure. As such we propose FIT (Function Input Tuning) which features by a computation module for composite function with a series of joint optimization strategies. Theoretically, FIT not only eliminates the most expensive crypto operations without invoking extra encryption enabler, but also makes the running-time crypto complexity independent of filter size. Experimentally, FIT demonstrates tens of times speedup over various function dimensions from modern models, and 4.5× to 35.5× speedup on the total computation time when plugged in neural networks with data from small-scale MNIST to large-scale ImageNet.

Federated learning relies on distributed training on mobile device. The previous research mainly focuses on addressing the heterogeneity from computation and data distributions. As battery life remains to be the performance bottleneck on mobile devices, energy consumption from the persistent training tasks poses great challenges. In this paper, we propose an online scheduler to optimize energy usage by leveraging application co-execution and asynchronous gradient updates. Motivated by a series of preliminary experiments, we find that placing the training process in the background while co-running a foreground application gives the system a large energy discount. Based on these findings, we first study an offline baseline assuming all the application occurrences are known in advance, and propose a dynamic programming solution. Then we propose an online scheduler using the Lyapunov framework to exploit the energy-staleness/slowdown trade-offs and prove the convergence at the rate of$1/\sqrt{K}$. We conduct extensive experiments on a mobile testbed with devices from different vendors. The results indicate 10-30% energy saving and much faster convergence compared to FedAvg and FedProx with 3-4% higher testing accuracy under the non-IID data setting. The design is also validated in terms of resource utilization, memory bandwidth and Frame-Per-Second rates.

Implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis due to the absence of connectives.Most existing methods utilize one-hot labels as the sole optimization target, ignoring the internal association among connectives.Besides, these approaches spend lots of effort on template construction, negatively affecting the generalization capability.To address these problems, we propose a novel Connective Prediction via Knowledge Distillation (CP-KD) approach to instruct large-scale pre-trained language models (PLMs) mining the latent correlations between connectives and discourse relations, which is meaningful for IDRR.Experimental results on the PDTB 2.0/3.0 and CoNLL 2016 datasets show that our method significantly outperforms the state-of-the-art models on coarse-grained and fine-grained discourse relations.Moreover, our approach can be transferred to explicit discourse relation recognition (EDRR) and achieve acceptable performance.Our code is released in https://github.com/cubenlp/CP_KD-for-IDRR.

This paper introduces the Chinese Essay Discourse Coherence Corpus (CEDCC), a multi-task dataset for assessing discourse coherence.Existing research tends to focus on isolated dimensions of discourse coherence, a gap which the CEDCC addresses by integrating coherence grading, topical continuity, and discourse relations.This approach, alongside detailed annotations, captures the subtleties of real-world texts and stimulates progress in Chinese discourse coherence analysis.Our contributions include the development of the CEDCC, the establishment of baselines for further research, and the demonstration of the impact of coherence on discourse relation recognition and automated essay scoring.The dataset and related codes is available at https: //github.com/cubenlp/CEDCC_corpus.

Recent advances in Blockchain-based Federated Learning (FL) aim to address the inherent limitations of traditional FL, such as single node failure and the lack of an appropriate incentive mechanism. This approach replaces the central parameter server in FL with a blockchain that stores and disseminates updated models. However, its decentralized nature introduces significant communication and storage over-head, which considerably constrains its practical application. Additionally, as it allows participants to contribute to the shared model by training locally using private data, it is especially prone to privacy leaks and poisoning attacks. This study introduces a novel framework, BlockFed, designed to substantially reduce overhead and mitigate vulnerabilities in blockchain-based FL systems.

This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks. We have used two commercial-grade mmWave testbeds with operational frequencies on the 27 Ghz and 71 GHz, respectively, for beam profiling. The obtained datasets were used to train the machine learning model to estimate the received downlink signal power, and data rate at the receivers (user equipment with different geographical locations in the range of a transmitter (base station). The results have showed high prediction accuracy with low mean square error (loss), indicating the model's ability to estimate the received signal power or data rate at each individual receiver covered by a beam. The dataset and the machine learning based beamforming model can assist researchers in optimizing various network design problems for mmWave networks.

Federated Learning (FL) has been adopted in practical network applications and plays a critical role. As FL allows participants to contribute to the global model by training locally with private data, it is known particularly vulnerable to neural backdoor attacks. This paper proposes a new defense, ScanFed, against neural backdoor attacks to FL systems. It leverages the synchronous nature of FL to effectively single out malicious neuron candidates and further validate if they indeed hijack the model's behaviors. Compared to existing neural backdoor defenses, ScanFed has the following distinct properties. First, it is extremely computation-friendly that is six orders of magnitude faster than state-of-the-art behavior-based backdoor defenses, rendering it highly suitable for large-scale FL systems. Second, it inherits the precise nature of behavior-based backdoor detection, making it significantly more effective than similarity-based defenses against advanced attacks. Third, it is robust to biased models uploaded by clients with non-IID (Independent and Identically Distributed) data, which is very common in practical FL systems. In addition, it is a plug-n-play scheme that can be seamlessly integrated into existing FL systems. To the best of our knowledge, this is the first behavior-based defense that enables scalable, efficient and accurate neural backdoor detection of FL systems in non-IID scenarios. This work delivers a ScanFed prototype and fully tests it in various settings of datasets, neural architectures, and backdoor attacks. The experiments demonstrate ScanFed achieves competitive accuracy and minimal detection time.

GPS spoofing is a great threat to the safety of transportation systems as well as other systems that rely on GPS for navigation. This paper proposes a novel computer vision based approach for GPS spoofing detection, termed SEquential dashcam-based vEhicle localization frameworK (SEEK). SEEK utilizes vehicle dashcam images to identify a vehicle's true location and detects possible GPS spoofing attacks through verifying if the reported GPS locations of the vehicle are correct. However, it is nontrivial to use dashcam images for vehicle localization due to multiple challenges caused by real-world driving, including the complicated lighting/weather conditions, season/timing variations of the images, large blockage ratio in the images, and varying driving speeds. SEEK features a unique design with novel schemes to address complicated lighting/weather conditions, transform images to align with season changes, reduce blockage, and adopt a sequential image matching scheme. The performance evaluation shows that SEEK significantly outperforms the previous GPS spoofing detection scheme, and achieves a detection accuracy of up to 94%.

Blockchain and distributed ledger technology started as a decentralized infrastructure to enable and manage digital currency like Bitcoin without relying on a central authority. One of the attractive features provided by blockchain technology is its append-only “immutability” feature, which means the stored data cannot be modified or manipulated by any means once it is validated in the blockchain ledger. Such immutability helps traceability, auditing, and non-repudiation, which builds decentralized trust among un-trusted parties. Despite that, immutability if misused could lead to the permanent existence of sensitive information and misinformation in the blockchain. Incidents like broadcasting illegal content have already taken their place in blockchain systems. Such incidents call for prompt solutions for mitigation. One emerging research theme, “redactable distributed ledgers” such as redactable blockchain provides approaches for modifying ledgers with certain controllability. This article aims to survey the current research landscape about redactable distributed ledgers. We will first describe the motivations behind redactable distributed ledgers. Compared to other relevant surveys, we comprehensively summarized and briefly explained the underlying technologies for supporting redactable distributed ledgers. We mainly focused on chameleon hash-based redactable blockchain structure and classifications, with detailed comparisons and illustrations. Further, we tackled new distributed ledger structures, including the new state-of-the-art block matrix structure. Furthermore, new applications that can be enabled by redactable distributed ledgers and future research directions are discussed in detail. This article emphasizes the motivation of utilizing the redactable distributed ledgers in several critical applications to mitigate misuse of immutability features threatening the original known design of distributed ledgers.

Digital identity management (DIM) systems have become challenging, especially, given the current progression in ubiquitous environments enclosing cooperative Internet of Things (IoT) devices, individuals, and organizations. Self-sovereign identity (SSI) has recently surfaced to manifest the notion of decentralized DIMs enlivened by users’ autonomy. This article presents a proactive, trustworthy solution for managing digital users’ data and identity information without risking its integrity, security, privacy, and confidentiality. Accordingly, we propose a decentralized SSI-enabled cloud storage model that enables secure access control and frequent data integrity checking (IC) mechanism. The presented model ensures the resiliency of SSI-enabled operations, including users’ registration, data identification, external information distribution, IC operations, identity information authentication and verification, and decentralized external and shareable operations.

The pandemics, such as COVID-19 are worldwide health risks and result in catastrophic impacts on the global economy. To prevent the spread of pandemics, it is critical to trace the contacts between people to identify the infection chain. Nevertheless, the privacy concern is a great challenge to contact tracing. Moreover, existing contact tracing apps cannot obtain the macro-level infection risk information, e.g., the hotspots where the infection occurs, which, however, is critical to optimize healthcare planning to better control and prevent the outbreak of pandemics. In this article, we develop a novel privacy-preserved pandemic tracing system, privacy-preserved pandemic infection risk computation (PRISC), to compute the infection risk through cellular-enabled IoT devices. In the PRISC system, there are three parties: 1) a mobile network operator (MNO); 2) a social network provider; and 3) the department of health. The physical contact records between users are obtained by the MNO from the users’ cellular-enabled IoT devices. The social contacts are obtained by the social network provider, while the health department has the records of pandemic patients. The three parties work together to compute a heatmap of pandemic infection risk in a region, while fully protecting the data privacy of each other. The heatmap provides both macro and micro-level infection risk information to help control pandemics. The experiment results indicate that PRISC can compute an infection risk score within a couple of seconds and a few mega-bytes (MBs) communication cost, for data sets with 100000 users.

We report a new neural backdoor attack, named Hibernated Backdoor, which is stealthy, aggressive and devastating. The backdoor is planted in a hibernated mode to avoid being detected. Once deployed and fine-tuned on end-devices, the hibernated backdoor turns into the active state that can be exploited by the attacker. To the best of our knowledge, this is the first hibernated neural backdoor attack. It is achieved by maximizing the mutual information (MI) between the gradients of regular and malicious data on the model. We introduce a practical algorithm to achieve MI maximization to effectively plant the hibernated backdoor. To evade adaptive defenses, we further develop a targeted hibernated backdoor, which can only be activated by specific data samples and thus achieves a higher degree of stealthiness. We show the hibernated backdoor is robust and cannot be removed by existing backdoor removal schemes. It has been fully tested on four datasets with two neural network architectures, compared to five existing backdoor attacks, and evaluated using seven backdoor detection schemes. The experiments demonstrate the effectiveness of the hibernated backdoor attack under various settings.

In order to protect user privacy in Machine Learning as a Service (MLaaS), a series of ingeniously designed privacy-preserving frameworks have been proposed. The state-of-the-art approaches adopt Homomorphic Encryption (HE) for linear function and Garbled Circuits (GC)/Oblivious Transfer (OT) for nonlinear operation to improve computation efficiency. Despite the encouraging progress, the computation cost is still too high for practical applications. This work represents the first step to effectively prune privacy-preserving deep learning models to reduce computation complexity. Although model pruning has been discussed extensively in the machine learning community, directly applying the plaintext model pruning schemes offers little help to reduce the computation in privacy-preserving models. In this paper we propose Hunter, a structured pruning method that identifies three novel HE-friendly structures, i.e., internal structure, external structure, and weight diagonal to guide the pruning process. Hunter outputs a pruned model that, without any loss in model accuracy, achieves a significant reduction in HE operations (and thus the overall computation cost) in the privacy-preserving MLaaS. We apply Hunter in various deep learning models, e.g., AlexNet, VGG and ResNet over classic datasets including MNIST, CIFAR-10 and ImageNet. The experimental results demonstrate that, without accuracy loss, Hunter efficiently prunes the original networks to reduce the HE Perm, Mult, and Add operations. For example, in the state-of-the-art VGG-16 on ImageNet with 10 chosen classes, the total number of Perm is reduced to as low as 2% of the original network, and at the same time, Mult and Add are reduced to only 14%, enabling a significantly more computation-efficient privacy-preserving MLaaS.

Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from the machine learning algorithms, the impact on the mobile system still remains unclear. In this paper, we design and implement an online optimization framework by connecting asynchronous execution of federated training with application co-running to minimize energy consumption on battery-powered mobile devices. From a series of experiments, we find that co-running the training process in the background with foreground applications gives the system a deep energy discount with negligible performance slowdown. Based on these results, we first study an offline problem assuming all the future occurrences of applications are available, and propose a dynamic programming-based algorithm. Then we propose an online algorithm using the Lyapunov framework to explore the solution space via the energy-staleness trade-off. The extensive experiments demonstrate that the online optimization framework can save over 60% energy with 3 times faster convergence speed compared to the previous schemes.

While deep learning (DL)-based network traffic classification has demonstrated its success in a range of practical applications, such as network management and security control to just name a few, it is vulnerable to adversarial attacks. This paper reports TrojanFlow, a new and practical neural backdoor attack to DL-based network traffic classifiers. In contrast to traditional neural backdoor attacks where a designated and sample-agnostic trigger is used to plant backdoor, TrojanFlow poisons a model using dynamic and sample-specific triggers that are optimized to efficiently hijack the model. It features a unique design to jointly optimize the trigger generator with the target classifier during training. The trigger generator can thus craft optimized triggers based on the input sample to efficiently manipulate the model’s prediction. A well-engineered prototype is developed using Pytorch to demonstrate TrojanFlow attacking multiple practical DL-based network traffic classifiers. Thorough analysis is conducted to gain insights into the effectiveness of TrojanFlow, revealing the fundamentals of why it is effective and what it does to efficiently hijack the model. Extensive experiments are carried out on the well-known ISCXVPN2016 dataset with three widely adopted DL network traffic classifier architectures. TrojanFlow is compared with two other backdoor attacks under five state-of-the-art backdoor defenses. The results show that the TrojanFlow attack is stealthy, efficient, and highly robust against existing neural backdoor mitigation schemes.

Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in practice. To fill this gap, we propose a novel attacking paradigm, named Generative Adversarial Fake Node Camouflaging (GAFNC), with its crux lying in crafting a set of fake nodes in a generative-adversarial regime. These nodes carry camouflaged malicious features and can poison the victim GNN by passing their malicious messages to the original graph via learned topological structures, such that they 1) maximize the devastation of classification accuracy (i.e., global attack) or 2) enforce the victim GNN to misclassify a targeted node set into prescribed classes (i.e., target attack). We benchmark our experiments on four real-world graph datasets, and the results substantiate the viability, effectiveness, and stealthiness of our proposed poisoning attack approach. Code is released in github.com/chao92/GAFNC.

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. A deep convolutional neural network is constructed to analyze signal level information extracted from Channel State Information (CSI) between the communication peers to provide additional authentication protection at the physical layer. A significant merit of the proposed detection system is that this system can distinguish two different devices even at the same location, which was not well addressed by the existing approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located.

Abstract Convolutional neural networks (CNN) are becoming increasingly popular in modern remote sensing image classification tasks and have exhibited excellent results. For the existing CNN‐based hyperspectral image (HSI) classification methods, most of which extract spatial or spectral features separately by convolution. But nearly all of these methods ignore the fact that the weighted summation of convolution may lead to appear new features in another dimension. To address this issue, a novel multiscale spectral‐spatial cross‐extraction network (MSSCEN) is proposed for HSI classification. Specifically, the proposed MSSCEN introduces spectral‐spatial features cross extraction module (SSCEM), which fed extracted features from previous layer into spatial and spectral extraction branches separately again, so that the changes that occurred in the other domain after each convolution can be fully utilized. In addition, a new independent data augmentation module based on U‐Net is designed to mitigate the problem of limited labelled samples. The paper conducts experiments on three classic hyperspectral datasets and the results demonstrate that the proposed method achieves the best classification accuracy than other state‐of‐the‐art methods.

In a ubiquitous environment enclosing cooperative Internet-of-Things (IoT) devices, individuals, and entities, digital identity management (DIM) becomes critical and challenging. DIM pertains to device identities authentication and verification to enable trustworthy service exchange, data collection, and decision making. DIM is the supporting pillar for all online services and the foundation for security and authentication mechanisms. Due to the extreme heterogeneity, scale, and configuration complexity of such environments, enabling trustworthy DIM is crucial and seriously challenging. In an IoT context, devices use local digital identities stored within a tamper-proof unit and verified by a centralized authority for authentication. The recent attacks on IoT systems showed how vulnerable such a design is. It is also an inherent problem that influences humans. From that, self-sovereign identity (SSI) has emerged as a decentralized DIM approach embracing the concept of portable self-possession identity. SSI was presented to couple the digital identity from the owner to enable large-scale cooperation. However, digital identity storage and verification still occur on the device and in a centralized manner. Utilizing a local single-point-of-failure storage memory for verifiable credentials is one of the considerable drawbacks in contemporary SSI. In this regard, this article introduces decentralized trustworthy-self-sovereign identity management (DT-SSIM), a novel decentralized trustworthy SSI management framework. DT-SSIM integrates the secret share scheme with the blockchain-based smart contracts technologies to provide transparent and trustworthy SSI-based DIM services for IoT. Storing IoT identity credentials outside the devices’ local storage preserves the identity credentials from being tampered with or misused. Evaluations and discussions show the resiliency assessment of the system and the cost and estimated running times for verification processes in DT-SSIM.

In most of the existing mobile applications for data collection and data analytics, either the privacy issue is frequently neglected or the privacy options are not configurable by the participants. This paper proposes configurable privacy level by potential crowdsourcing participants who are able to choose their desirable privacy level and get paid based on the quality of data they provide to the originator in Device to Device network (D2D). Combining with the encryption technique, not only the user’s privacy is protected, but also the encryption complexity is reduced. We first formulate the participant recruitment process into an optimization problem from the participants’ perspective by considering the competition and collaboration among existing and candidate participants in order to achieve the best utility. Then we design a distributed approximate scheme that relies on participants’ local knowledge to complete the overall recruitment task. We implement the approximate approach in Dell Streak tablets and carry out a campus-scale experiment for 21 days, plus run simulations for more extensive and detailed evaluation under various task settings. The results demonstrate the efficiency of the proposed approaches and disclose valuable insights for practical considerations in D2D based crowdsourcing.

The data poisoning attack has raised serious security concerns on the safety of deep neural networks, since it can lead to neural backdoor that misclassifies certain inputs crafted by an attacker. In particular, the sample-targeted backdoor attack is a new challenge. It targets at one or a few specific samples, called target samples, to misclassify them to a target class. Without a trigger planted in the backdoor model, the existing backdoor detection schemes fail to detect the sample-targeted backdoor as they depend on reverse-engineering the trigger or strong features of the trigger. In this paper, we propose a novel scheme to detect and mitigate sample-targeted backdoor attacks. We discover and demonstrate a unique property of the sample-targeted backdoor, which forces a boundary change such that small "pockets" are formed around the target sample. Based on this observation, we propose a novel defense mechanism to pinpoint a malicious pocket by "wrapping" them into a tight convex hull in the feature space. We design an effective algorithm to search for such a convex hull and remove the backdoor by fine-tuning the model using the identified malicious samples with the corrected label according to the convex hull. The experiments show that the proposed approach is highly efficient for detecting and mitigating a wide range of sample-targeted backdoor attacks.

This paper reports a new clean-label data poisoning backdoor attack, named Invisible Poison, which stealthily and aggressively plants a backdoor in neural networks. It converts a regular trigger to a noised trigger that can be easily concealed inside images for training NN, with the objective to plant a backdoor that can be later activated by the trigger. Compared with existing data poisoning backdoor attacks, this newfound attack has the following distinct properties. First, it is a blackbox attack, requiring zero-knowledge of the target model. Second, this attack utilizes "invisible poison" to achieve stealthiness where the trigger is disguised as `noise', and thus can easily evade human inspection. On the other hand, this noised trigger remains effective in the feature space to poison training data. Third, the attack is practical and aggressive. A backdoor can be effectively planted with a small amount of poisoned data and is robust to most data augmentation methods during training. The attack is fully tested on multiple benchmark datasets including MNIST, Cifar10, and ImageNet10, as well as application specific data sets such as Yahoo Adblocker and GTSRB. Two countermeasures, namely Supervised and Unsupervised Poison Sample Detection, are introduced to defend the attack.

Deep learning (DL) has demonstrated superior success in various of applications, such as image classification, speech recognition, and anomalous detection. The unprecedented performance gain of DL largely depends on tremendous training data, high-performance computation resources, and well-designed model structures. However, privacy concerns raise from such necessities. First, as the training data are usually distributed among multiple parties, directly exposing and collecting such large amount of data could violate the laws especially for private information, such as personal identities, medical records, and financial profiles. Second, locally deploying advantageous computation resources is costly for individual party having partial data. Third, direct release of well-trained model parameters threatens the information about training data or the intellectual property of model owners. Therefore, individual party prefers outsourcing computation (data) in a secure way to powerful cloud servers such as Microsoft Azure, and how to enable the cloud servers to perform DL algorithms without revealing data owners’ private information and model owners’ valuable parameters is emerging as an urgent task, which is termed as privacy-preserving (outsourcing) DL. In this article, we review the state-of-the-art researches in privacy-preserving DL based on multiparty secure computation with data encryption and summarize these techniques in both training phase and inference phase. Specifically, we categorize the techniques with respect to the linear and nonlinear computations, which are the two basic building blocks in DL. Following a comprehensive overview of each research scheme, we present primary technical hurdles needed to be addressed and discuss several promising directions for future research.

This paper considers a fundamental problem of designing routing scheme resilient to node or link failures for wireless sensor networks deployed on a surface of a complex-connected three-dimensional (3D) setting. Instead of heuristically detouring around the failed path, we borrow homotopy, an important topological concept, to effectively create and evaluate the diversity of alternative paths. We propose a tessellation-free and GPS-free method to compute paths with different homotopy types on surface networks. A source node greedily forwards a packet to its destination based on the computed nodes’ virtual planar coordinates. When the current path fails, the source node can flexibly choose another greedy path from a different homotopy type to deliver the packet. The proposed algorithms are distributed and scalable to both the size and genus number of a surface network. We evaluate the performance of the proposed routing scheme under three different failure models. Simulation results show that our method achieves the best performance under geographically correlated failure models compared with other resilient routing schemes. We also compare our routing scheme with existing state-of-the-art ones specifically designed for surface networks when a network is failure free. Our method achieves the lowest stretch factor.

The millimeter wave (mmWave) band offers vast bandwidth and plays a key role for next generation wireless networks. However, the mmWave network raises a great challenge for user association and scheduling, due to the limited power budget and beamformers, diverse user traffic loads, user quality of service requirement, etc. In this paper, we propose a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink UE assOciation, Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time, subject to the base station power budget, number of beamformers, user traffic loads, and the quality of service requirement at users. We compare BOOST with three state-of-the-art user scheduling schemes. On average, BOOST reduces the transmission time by 37, 30, and 26 percent, and achieves a sum rate gain of 56, 43, and 34 percent, respectively.

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks ( WRNs ). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of the vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. We formalize the effective charging problem as a charging reward maximization problem ( CRMP ), where the amount of reward obtained by charging a device is inversely proportional to the residual lifetime of the device. Then, we prove that CRMP is NP-hard. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning ( RL ) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks (WRNs). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. Then, we formalize the effective charging problem as a charging reward maximization problem (CRMP), where the amount of reward obtained by charging a de-vice is inversely proportional to the residual lifetime of the device. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning (RL) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

This work proposes GangSweep, a new backdoor detection framework that leverages the super reconstructive power of Generative Adversarial Networks (GAN) to detect and ''sweep out'' neural backdoors. It is motivated by a series of intriguing empirical investigations, revealing that the perturbation masks generated by GAN are persistent and exhibit interesting statistical properties with low shifting variance and large shifting distance in feature space. Compared with the previous solutions, the proposed approach eliminates the reliance on the access to training data, and shows a high degree of robustness and efficiency for detecting and mitigating a wide range of backdoored models with various settings. Moreover, this is the first work that successfully leverages generative networks to defend against advanced neural backdoors with multiple triggers and their polymorphic forms.

The millimeter wave (mmWave) frequency band is a promising candidate for next generation cellular and wireless networks. To compensate the significantly higher path loss due to the higher frequency, the mmWave band usually uses the beamforming technology. However, this makes the network topology control a great challenge. In this paper, we propose a novel framework for network topology control in mmWave networks, termed Beamforming Oriented tOpology coNtrol (BOON). The objective is to reduce total transmit power of base stations and interference between beams. BOON smartly groups nearby user equipment into clusters, constructs sets from user equipment clusters, and associates user equipment to base stations and beams. We compare BOON with three existing topology control schemes in terms of transmit power, network sum rate, signal to interference and noise ratio, and computation complexity. The results indicate that overall BOON significantly outperforms them. In particular, on average BOON uses only 10, 32, and 25 percent transmit power of other three schemes, respectively, to achieve the same network sum rate.

As a candidate trust management scheme, trust models based on the cloud theory are always taken into account when detecting malicious attacks in Underwater Acoustic Sensor Networks (UASNs). To evaluate the trust values of nodes accurately, the evidence of trust ought to be collected frequently. As a result, continual trust update results in excessive energy consumption or premature death of some sensor nodes that are close to the trust cloud node. To address the above issues, in this paper, we propose an Energy-balanced Trust Cloud Migration scheme (ETCM) for UASNs, which consists of Destination Node Determination (DND), trust cloud migration and trust cloud update. Particularly, DND is performed hierarchically to obtain the destination node for trust cloud migration by selecting the candidate destination clusters, determining the destination cluster and the destination node, respectively. First, the candidate destination clusters are selected based on the distribution for the overall residual energy in UASNs using the simulated annealing algorithm. Then, an indicator of Cluster Ability (CA) is proposed to seek the destination cluster. Particularly, to calculate CA, the improved standardized Euclidean distance formula is employed to evaluate the connectivity between clusters and their neighboring clusters. Finally, on the basis of the defined node density reachability and the residual energy, the Node Ability (NA) is presented to indicate the capacity of nodes for trust cloud storage, calculation and update. The destination node in the destination cluster can be determined as the new trust cloud node using the NA. Simulation results demonstrate that the proposed ETCM scheme can balance energy consumption, increase node survival ratio and prolong lifetime effectively.

This work proposes an innovative approach, named CapJack, to detect in-browser malicious cryptocurrency mining activities by using the latest CapsNet technology. To the best of our knowledge, this is the first work to introduce CapsNet to the field of malware detection through system behavioral analysis. It is particularly effective to detect malicious miners under multitasking environments where multiple applications run simultaneously. Experimental data show appealing performance of CapJack, with a detection rate of as high as 87% instantly and 99% within a window of 11 seconds.

Identity-based attacks such as MAC spoofing are common in wireless networks. The recently developed virtualization technologies bring a new type of MAC spoofing attack, virtual MAC spoofing. This makes it even more challenging to detect such attacks, especially in a tight environment with spatial similarities. In this paper, we design, implement and evaluate a system to effectively detect virtual MAC spoofing attacks via deep learning. A deep convolutional neural network is constructed to extract physical features from CSI obtained from packet transmissions, to detect virtual MAC spoofing attacks. An important merit of the proposed detection system is that this system can distinguish two devices even at the same location, which was not well addressed by previous approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located.

Privacy is a fundamental challenge for a variety of smart applications that depend on data aggregation and collaborative learning across different entities. In this paper, we propose a novel privacy-preserved architecture where clients can collaboratively train a deep model while preserving the privacy of each client’s data. Our main strategy is to carefully partition a deep neural network to two non-colluding parties. One party performs linear computations on encrypted data utilizing a less complex homomorphic cryptosystem, while the other executes non-polynomial computations in plaintext but in a privacy-preserved manner. We analyze security and compare the communication and computation complexity with the existing approaches. Our extensive experiments on different datasets demonstrate not only stable training without accuracy loss, but also 14 to 35 times speedup compared to the state-of-the-art system.

While the Internet of Things (IoT) is embraced as important tools for efficiency and productivity, it is becoming an increasingly attractive target for cybercriminals. This work represents the first endeavor to develop practical Puncturable Attribute Based Encryption schemes that are light-weight and applicable in IoTs. In the proposed scheme, the attribute-based encryption is adopted for fine grained access control. The secret keys are puncturable to revoke the decryption capability for selected messages, recipients, or time periods, thus protecting selected important messages even if the current key is compromised. In contrast to conventional forward encryption, a distinguishing merit of the proposed approach is that the recipients can update their keys by themselves without key re-issuing from the key distributor. It does not require frequent communications between IoT devices and the key distribution center, neither does it need deleting components to expunge existing keys to produce a new key. Moreover, we devise a novel approach which efficiently integrates attribute-based key and punctured keys such that the key size is roughly the same as that of the original attribute-based encryption. We prove the correctness of the proposed scheme and its security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. We also implement the proposed scheme on Raspberry Pi and observe that the computation efficiency of the proposed approach is comparable to the original attribute-based encryption. Both encryption and decryption can be completed within tens of milliseconds.

In this paper, we report a newfound vulnerability on smartphones due to the malicious use of unsupervised sensor data. We demonstrate that an attacker can train deep Convolutional Neural Networks (CNN) by using magnetometer or orientation data to effectively infer the Apps and their usage information on a smartphone with an accuracy of over 80%. Furthermore, we show that such attacks can become even worse if sophisticated attackers exploit motion sensors to cluster the magnetometer or orientation data, improving the accuracy to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only 15% and at the same time has negligible effect on benign Apps.

Device-to-device (D2D) is a new paradigm in cellular networks that enhances network performance by introducing increased spectral efficiency and reduced communication delay. Efficient data dissemination is indispensable for supporting many D2D applications such as content distribution and location-aware advertisement. In this work, we investigate a new and interesting data dissemination problem where the receivers are not explicitly known and data must be disseminated to the receivers within a probabilistic delay budget. We propose to exploit data depositories, which can temporarily house data and deliver them to interested receivers upon requests. We formally formulate the delay-constrained profit maximization problem for data deposition in D2D networks and show its NP-hardness. Under the unique mobile opportunistic network setting, a practical solution must be distributed, localized, and online. To this end, we introduce three algorithms for Direct Online Selection of 1-Depository, Direct Online Selection of L-Depositories, and Mixed Online Selection of L-Depositories. To demonstrate and evaluate the system, we implement a prototype using Google Nexus handsets and conduct experiments for five weeks. We further carry out simulations based on real-world mobility traces for evaluation of large-scale networks and various network settings that are impractical to experiment.

In mobile opportunistic networks, a common assumption is that nodes are able to complete as much data exchange as needed during a communication opportunity, which is, however, not the case in practice due to limited wireless link bandwidth. Beyond that, the storage capacity at a node is also limited, further impacting the communication efficiency. In this paper, we explore delay sensitive and network resource constrained traffic allocation for mobile device-to-device crowdsourcing. With the requirement of restricted node storage and link bandwidth, we first formulate a non-linear traffic allocation optimization problem that would be at least as hard as NP-hard. In order to practically solve it, based on the submodular property, we propose an approximation algorithm and a distributed heuristic in the same design principle. We implement the latter on Dell Streak tablets and deploy an experiment with 21 nodes for a period of three weeks. Moreover, we extract the implementation codes from the prototype and run simulations using the Haggle trace to study its performance trend. The experiment and simulation outcomes verify that the proposed mechanisms achieve the close-to-optimal performance with affordable computation complexity, which can be easily implanted in practical network environment.

With the remarkable proliferation of intelligent mobile devices and fast growing broadband wireless technology, social networking is undergoing explosive growth in recent years as more and more users access social networks via mobile platforms. It is often expensive or even impossible to deploy a large online social network (OSN) on a single server. A cost-effective approach is horizontal scaling, where the OSN is partitioned and deployed on a set of low-cost servers. In this research, we study the problem of minimum-cost balanced partitioning of OSNs. Our goal is to achieve the best partitioning by minimizing the total inter-server traffic cost and at the same time balancing the load among servers. Given its NP-hardness, we propose new techniques and explore efficient heuristics to address the problem, especially for extremely large OSNs with an enormous volume of social nodes, social connections, and social data. Our key contributions include a localized approach with O(δ2) complexity to explicitly calculate the projected gain in inter-server traffic cost (named Server Change Benefit (SCB)). Built upon this technique, we devise two algorithms that offer online and offline solutions to achieving minimum-cost balanced partitioning of OSNs. The online algorithm is fast and highly efficient to process newly arrival individual nodes. The offline algorithm uses the current online result as a starting point. It further reduces inter-server traffic cost by applying relocation and swapping. It employs a merging process to group the nodes according to the social structure and swap the groups with similar size to further reduce the total inter-server traffic cost. We implement both algorithms and evaluate them based on a variety of real-world OSN datasets from Facebook, Arxiv, Gnutella, Amazon, and Twitter. The simulations demonstrate that the proposed algorithms can significantly reduce the execution time by an average of three folds and at the same time yield supreme performance (i.e., inter-server traffic cost) in comparison with existing solutions.

(First paragraph) In the first edition of the special issue titled “Wireless Sensor Networks for Smart Communications”, a total of 22 manuscripts were received and 6 of these were accepted. This issue demonstrated that network congestion, user mobility, and adjacent spectrum interference are the main reasons for the degradation ofcommunication quality inWireless Sensor Networks (WSNs).

With the remarkable proliferation of intelligent wireless devices and active mobile users, we have witnessed a rapid increasing trend of mobile crowdsourcing applications. While crowdsourcing does not depend on any specific underlying network, the device-to-device (D2D)-based crowdsourcing is highly desired when the originator of a crowdsourcing task cannot directly reach out to the participants or the conventional approaches for data transportation are costly. The marriage of crowdsourcing and D2D creates new, interesting research problems, mainly due to the unique non-deterministic setting in D2D. In this work, we focus on the problem of how to efficiently distribute a crowdsourcing task and recruit participants based on D2D communications. We formally formulate the minimum-cost crowdsourcing (MCC) problem in D2D networks, which explores a multi-dimensional design space to seek an optimal solution that minimizes the total crowdsourcing cost while satisfying the coverage probability over the field of interest. We introduce an approximation algorithm based on a reduced solution space and formally prove that its cost is bounded by a desired approximation ratio in comparison with the optimal solution. We further propose a lightweight online heuristic that inherits the same design philosophy but implements it in a distributed manner. We prototype the proposed online scheme in Android and carry out experiments in a campus environment, involving 21 Dell Streak tablets carried by students for a period of 15 days. We also extract the algorithm codes from our prototype and perform extensive simulations based on Haggle trace. The results demonstrate the efficiency of the proposed heuristics and reveal empirical insights into the design tradeoffs and practical considerations in D2D-based crowdsourcing.

In wireless rechargeable sensor networks (WRSNs), prior studies mainly focus on the optimization of power transfer efficiency. In this work, we consider the cost for building and operating WRSNs. In the network, sensor nodes can be charged by mobile chargers, that have limited energy which is used for charging and moving. We introduce a novel concept called “shuttling” and introduce an optimal charging algorithm, which is proven to achieve the minimum number of chargers in theory. We also point out the limitations of the optimal algorithm, which motivates the development of solutions named Push-Shuttle-Back (PSB). We formally prove that PSB achieves the minimum number of chargers and the optimal shuttling distance in a 1D scenario with negligible energy loss. When the loss in wireless charging is non-negligible, we propose to exploit detachable battery pack (DBP) and propose a DBP-PSB algorithm to avoid energy loss. We further extend the solution to 2D scenarios and introduce a new circle-based “shortcutting” scheme that improves charging efficiency and reduces the number of chargers needed to serve the sensor network. We carry out extensive simulations to demonstrate the performance of the proposed algorithms, and the results show the proposed algorithms achieve a low overall cost.

The authors of "Low-Cost Collaborative Mobile Charging for Large-Scale Wireless Sensor Networks" which appeared in August issue of this journal [ibid., vol. 16, no. 8, pp. 2213–2227, Aug. 2017] would like to correct a typo that occurred in Fig. 1. The numbers above the X axis were wrong. The corrected Fig. 1 is provided

This work focuses on the incorporation of incentive stimulations into data dissemination in autonomous mobile social networks with selfish nodes. The key challenge of enabling incentives is to effectively track the value of a message under such a unique network setting with intermittent connectivity and multiple interest data types. We propose two data dissemination models: the data pulling model where mobile users pull data from data providers, and the data pushing model where data providers generate personalized data and push them to the intended users. For data pulling, we present effective mechanisms to estimate the expected credit reward of a message that helps intermediate nodes to evaluate the potential reward of it. Nodal message communication is formulated as a two-person cooperative game, whose solution is found by a heuristic approach which achieves Pareto optimality. Under the data pushing model, “virtual checks” are introduced to eliminate the needs of accurate knowledge about whom and how many credits data providers should pay. The check buying process is formulated as an online auction model to further accelerate the circulation of credits. Extensive simulations carried out based on real-world traces show the proposed schemes achieve better performance than fully cooperative scheme, but significantly reduce communication cost.

Reliability is a critical feature of chip integration and unreliability can lead to performance, cost, and time-to-market penalties. Moreover, upcoming Many-Core System-on-Chips (MCSoCs), notably future generations of mobile devices, will suffer from high power densities due to the dark silicon problem. Thus, in this paper, a novel NoC-based MCSoC architecture, called Shift Sprinting, is introduced in order to reliably utilize dark silicon under the power budget constraint. By employing the concept of distributional sprinting, our proposed architecture provides Quality of Service (QoS) to efficiently run real-time streaming applications in mobile devices. Simulation results show meaningful gain in performance and reliability of the system compared to state-of-the-art works.

To fully exploit the massive parallelism of many cores, this work tackles the problem of mapping large-scale applications onto heterogeneous on-chip networks (NoCs) to minimize the peak workload for energy hotspot avoidance. A task-resource co-optimization framework is proposed which configures the on-chip communication infrastructure and maps the applications simultaneously and coherently, aiming to minimize the peak load under the constraints of computation power and communication capacity and a total cost budget of on-chip resources. The problem is first formulated into a linear programming model to search for optimal solution. A heuristic algorithm is further developed for fast design space exploration in extremely large-scale many-core NoCs. Extensive simulations are carried out under real-world benchmarks and randomly generated task graphs to demonstrate the effectiveness and efficiency of the proposed schemes.

The recent advances in sensors, actuators, robots, and mobile wireless communication technologies have acceleratedinterest in autonomous networked robots (ANRs), where theindividual robots coordinate among themselves to complete atask, e.g., to explore or monitor a Field of Interest (FoI). Byteamwork, which is especially important in complex tasks, ANRsystem expresses much more capacity than traditional staticsensor networks. Existing work focuses on improving the coverageperformance of a group of ANRs within a single FoI. In thisresearch, we consider a group of ANRs that are instructed toexplore a number of FoIs. After they complete a task at currentFoI, they move to the next one, which may be far away fromcurrent one and the shape can also vary dramatically. Ourresearch focuses on how to efficiently enable such transition. TheANRs must be able to redeploy themselves to desired positionsin the new FoI based on distributed algorithms. Besides, to avoidunexpected event breaks network's integrity, the ANRs shouldpreserve their local connectivities as much as they can andorganize themselves as a whole network without any isolatednodes during the transition. Furthermore, considering energyconsumption, such relocation algorithm should work at the costof reasonable total moving distance. We study this problemand call it optimal marching of autonomous networked robots. The proposed algorithms guarantee global connectivity, andpreserve local connectivities as much as possible at negligiblecost of moving distance. Additionally, ANRs can automaticallyadjust their deployment density in the new FoI based on therequirements of various tasks or regions.

In this work, we propose a time-sensitive incentive-aware mechanism for mobile video offloading by using the idea of crowdsourcing, where video packet holder cooperates with mobile users to deliver video packets to destination. The objective is to maximize video provider and mobile relay users' payoffs. We formulate the interaction among video packet provider and mobile relay users as a two-person cooperative game, where the video packets are treated as commodities. We apply the Nash bargain solution to obtain the optimal cooperation decision and payment. We carry out extensive simulation based on the real-world traces to validate the superiority of our proposed scheme.

It is our pleasure to introduce the technical program of WoWMoM 2016.

Device-to-Device (D2D) networks impose a significant challenge on delay-sensitive crowdsourcing due to the highly nondeterministic and intermittent network connectivity. Under this setting, the paper investigates a participant recruitment problem in which an initial set of recruited nodes, which we call seeds, need to make an optimal decision on what other nodes to recruit to perform the crowdsourcing task. These seeds face the dilemma that recruiting more nodes increases their own payment but on the other hand also increases the risk of being excluded from the crowdsourcing task. As a first attack to this problem, we propose a dynamic programming algorithm. However, it is a centralized solution and hence the practicality is compromised. Therefore, we introduce two distributed alternatives. One is based on the divide-and-conquer paradigm by first partitioning a network into a set of opportunistic Voronoi cells and then running an optimization algorithm in each cell. The other is a task-splitting scheme which recursively delegates the recruitment task to newly joined nodes. We implemented our proposed solutions on an Android-based prototype and built a testbed using 25 Dell Streak tablets. Our experiments which lasted for 24 days demonstrate that the distributed schemes approximate the theoretical optimum with affordable complexity. Moreover, we conducted simulations with a much larger scale and more diverse settings. The simulation results corroborate the experimental data and confirm that our proposed distributed solutions closely approach the performance of the centralized solution while satisfying the optimization goal under different network configurations.

Incentive mechanisms for crowdsourcing have been extensively studied under the framework of all-pay auctions. Along a distinct line, this paper proposes to use Tullock contests as an alternative tool to design incentive mechanisms for crowdsourcing. We are inspired by the conduciveness of Tullock contests to attracting user entry (yet not necessarily a higher revenue) in other domains. In this paper, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize - which is fixed in conventional Tullock contests - with a prize function that is dependent on the (unknown) winner's contribution, in order to maximize the crowdsourcer's utility. We show that this approach leads to attractive practical advantages: (a) it is well-suited for rapid prototyping in fully distributed web agents and smartphone apps; (b) it overcomes the disincentive to participate caused by players' antagonism to an increasing number of rivals. Furthermore, we optimize conventional, fixed-prize Tullock contests to construct the most superior benchmark to compare against our mechanism. Through extensive evaluations, we show that our mechanism significantly outperforms the optimal benchmark, by over three folds on the crowdsourcer's utility cum profit and up to nine folds on the players' social welfare.

This special issue presents the recent advances in cloud computing and software-defined network, which \were selected out of the significantly extended versions of accepted papers in the Fifth IEEE International Conference on Big Data and Cloud Computing (BDCloud 2015) 1, the Ninth International Conference on Frontier of Computer Science and Technology 2, and a large number of open submissions. The selection has been very rigorous, and only the best papers were selected. Wei et al. 3 observe that most existing message forwarding algorithms in delay-tolerant networks prefer to deliver messages to the nodes with a higher popularity or centrality. This forwarding scheme can achieve high delivery ratio and low end-to-end delay but is prone to cause unfair load distribution and further lead to network congestion. To tackle this, they first track the evolution of communities through a novel distributed community detection approach. The second one is to develop a congestion avoidance mechanism to divert load away from congested areas and further present a congestion-aware message forwarding algorithm where messages can avoid being transmitted to the congested nodes Since the rapid growth of large-scale online services, massive amounts of the generated traffic have been seen in the data center network. Li et al. 4 study the emerging congestion problem in the software-defined data center network. Note that existing approaches are either hard to be implemented in hardware or unable to obtain the optimal solutions. The authors first propose a heuristic algorithm for efficiently compute a timeslot allocation for the coming packets. Further, they model the path selection as a bin-packing problem. By seamlessly combining the timeslot allocation and path selection, each data packet will not suffer queuing and waiting in the data center network. Anomaly detection is an effective approach to enhance availability and reliability of cloud infrastructures. Hong et al. 5 study the anomaly detection problem in cloud computing systems without the need for prior knowledge about normal or anomalous behaviors. They propose an unsupervised online anomaly detection scheme based on hidden Markov model. In order to achieve high scalability, their proposed algorithm runs in a distribution manner among multiple computing machines in the cloud. They also perform extensive experiments based on real data sets to validate the high detection accuracy for their proposed algorithm. Because of the benefits of reducing the communication overhead, distributed data-centric storage in wireless sensor networks have received considerable attention. Xu et al. 6 focus on the big data storage problem in wireless sensor network with the nonuniform node distribution. Note that most existing distribution methods can significantly consume more energy and are unable to deal with the case of nonuniform sensor nodes distribution. To address this issue, they propose an efficient storage retrieval algorithm to estimate the real distribution of the sensor nodes and the real addresses of these nodes. Based on this algorithm, they further take the data redundancy among sensor nodes into account and exploit an efficient routing mechanism. Incorporating cloud computing into vehicular networks is a promising solution to the collection, storage, and analysis of big traffic-related data but can lead to new challenges to the allocation and management for cloud resources in road-side cloudlet. Yao et al. 7 study a VM migration problem with the goal of minimizing the total network cost, by making the decisions on which VM should be migrated and where the VM shall be migrated. They further formulate an optimization for the static off-line VM placement problem and then propose a heuristic algorithm with polynomial time to solve the optimization. NoSQL systems, replicating and partitioning data over many servers for improving the performance, are widely used for storing big data. Conventional radon virtual nodes and manual configuration methods for consistent hashing can significantly lead to imbalanced data partition. Huang et al. 8 study the performance degradation problem caused by the imbalanced data partition. They first propose a novel imbalance coefficient of data distribution. They further propose a dynamic programming algorithm to compute the position of the new coming node in the consistent ring. Finally, they conduct comprehensive simulations based on a benchmark Yahoo Cloud Serving Benchmark (YCSB) to show the benefit of their proposed algorithm. Data centers are increasingly deploying the NUMA architecture. Zhu et al. 9 focus on the performance degradation problem when running multi-threaded programs on such NUMA systems. Note that the existing works mainly use the single-threaded multi-programming workloads to study the performance of NUMA on the resource contention and data locality. To solve the performance lagging problem, they propose a novel scheduler—symmetric scheduler, which can balance the number of costly remote shared data accesses for threads on NUMA systems. Finally, they perform extensive simulations on the PARSEC benchmark, and their proposed schedulers can significantly outperform Linux kernel scheduling mechanism. As the number of functionally equivalent services in the cloud grows, collaborative service QoS prediction has recently garnered increasing attention. Tang et al. 10 propose a collaborative QoS prediction method with location-based data smoothing, for addressing the data sparsity issue and improving the QoS prediction accuracy. Note that existing solutions, simply exploring the historical QoS information generated by interactions between users and services, however, can significantly suffer from the data sparsity issue. To address this issue, the authors firstly compute neighborhoods of users and services based on their locations, which provide a basis for data smoothing. They further combine user-based and service-based collaborative filtering techniques to make QoS predictions. Finally, they conduct comprehensive experiments on real service invocation dataset to validate the performance of their proposed QoS prediction method. We hope that you will enjoy reading these papers in this special issue. We would like to thank the authors for contributing their papers to this issue and thank all the reviewers for their time and constructive reviews. Finally, we would like to thank the editors of Concurrency and Computation: Practice and Experience for providing this opportunity to publish this special issue.

Distributed in-network data-centric processing aims to reduce energy consumed for communication and establish a self-contained data storage, retrieval, aggregation, and query sensor system that focuses more on the data itself rather than the identities of the individual network nodes. Double-ruling-based schemes support efficient in-network data-centric information storage and retrieval, especially for aggregated data, since all data with different types generated in a network can be conveniently retrieved along any single retrieval curve. Previous double-ruling-based research focuses on two-dimensional (2-D) wireless sensor networks where a 2-D planar setting is assumed. With increasing interests in deploying wireless sensors in three-dimensional (3-D) space for various applications, it is urgent yet fundamentally challenging to design double-ruling-based approach in general 3-D sensor networks because double-ruling-based schemes in general have much harder geometric constraints than other distributed in-network data-centric processing schemes. In this research, we propose a geographic location-free double-ruling-based approach for general 3-D sensor networks with possibly complicated topology and geometric shapes. Without the knowledge of the geographic location and the distance bound, a query simply travels along a simple curve with the guaranteed success to retrieve aggregated data through time and space with one or different types across the network. Extensive simulations and comparisons show the proposed scheme with low cost and a balanced traffic load.

This research focuses on distributed and localized algorithms for precise boundary detection in 3-D wireless networks. Our objectives are twofold. First, we aim to identify the nodes on the boundaries of a 3-D network, which serve as a key attribute that characterizes the network, especially in such geographic exploration tasks as terrain and underwater reconnaissance. Second, we construct locally planarized 2-manifold surfaces for inner and outer boundaries in order to enable available graph theory tools to be applied on 3-D surfaces, such as embedding, localization, partition, and greedy routing among many others. To achieve the first objective, we propose a Unit Ball Fitting (UBF) algorithm that discovers a majority of boundary nodes, followed by a refinement algorithm, named Isolated Fragment Filtering (IFF), to remove isolated nodes that are misinterpreted as boundary nodes. Based on the identified boundary nodes, we develop an algorithm that constructs a locally planarized triangular mesh surface for each 3-D boundary. Our proposed scheme is localized, requiring information within 1-hop neighborhood only. We further extend the schemes for online boundary detection in mobile sensor networks aiming to achieve low overhead. Our simulation and experimental results demonstrate that the proposed algorithms can effectively identify boundary nodes and surfaces, even under high measurement errors.

This work addresses the problem of how to enable efficient data query in a Mobile Ad-hoc SOcial Network (MASON), formed by mobile users who share similar interests and connect with one another by exploiting Bluetooth and/or WiFi connections. The data query in MASONs faces several unique challenges including opportunistic link connectivity, autonomous computing and storage, and unknown or inaccurate data providers. Our goal is to determine an optimal transmission strategy that supports the desired query rate within a delay budget and at the same time minimizes the total communication cost. To this end, we propose a centralized optimization model that offers useful theoretic insights and develop a distributed data query protocol for practical applications. To demonstrate the feasibility and efficiency of the proposed scheme and to gain useful empirical insights, we carry out a testbed experiment by using 25 off-the-shelf Dell Streak tablets for a period of 15 days. Moreover, extensive simulations are carried out to learn the performance trend under various network settings, which are not practical to build and evaluate in laboratories.

The majority of current research on sensor network localization focuses on wireless sensor networks deployed on two dimensional (2D) plane or in three dimensional (3D) space, very few on 3D surface. However, many real world applications require large-scale sensor networks deployed on the surface of a complex 3D terrain. Compared with planar and 3D network localizations, surface network localization generates unique and fundamental hardness. In this research, we explore 3D surface network localization with terrain model. A digital terrain model (DTM), available to public with a variable resolution up to one meter, is a 3D representation of a terrain's surface. It is commonly built using remote sensing technology or from land surveying and can be easily converted to a triangular mesh. Given a sensor network deployed on the surface of a 3D terrain with one-hop distance information available, we can extract a triangular mesh from the connectivity graph of the network. The constraint that the sensors must be on the known 3D terrain's surface ensures that the triangular meshes of the network and the DTM of the terrain's surface approximate the same geometric shape and overlap. We propose a fully distributed algorithm to construct a well-aligned mapping between the two triangular meshes. Based on this mapping, each sensor node of the network can easily locate reference grid points from the DTM to calculate its own geographic location. We carry out extensive simulations under various scenarios to evaluate the overall performance of the proposed localization algorithm. We also discuss the possibility of 3D surface network localization with mere connectivity and the results are promising.

We propose a distributed and deterministic routing algorithm with constant storage, communication and computation overhead, dubbed trace-routing, for strong-connected 3D wireless sensor networks. Its basic idea is to construct a virtual cutting plane that intersects boundary surface to yield a trace, along which a routing path with guaranteed delivery can be established. We prove the correctness of trace-routing under both continuous and discrete settings. We implement the trace-routing algorithm on Crossbow sensors and carry out extensive simulations to evaluate its routing efficiency.

In this work we study the problem of delay-constrained data transmission in mobile opportunistic networks. In contrast to the single-copy single-path and multi-copy multi-path routing schemes that have been discussed in the literature, we aim to determine an optimal single-copy multi-path transmission strategy that satisfies delay requirement and at the same time minimizes communication cost. We first propose a centralized optimal formulation, and then develop a distributed routing algorithm under practical network settings. We implement the proposed algorithm on Dell Streak tablets and carry out an experiment with 25 nodes for a period of two weeks. Moreover, we extract the algorithm codes from our prototype and run simulations based on the Haggle trace to study its performance trends under various network settings.

This paper focuses on greedy routing in wireless networks deployed on 2-D and 3-D surfaces. It introduces a distributed embedding scheme based on the conformal map theory. The proposed scheme identifies the convex hull of each boundary and employs Yamabe flow to compute flat metric under convex hull boundary condition to establish virtual coordinates. Such virtual coordinates are then used for greedy routing. Since the proposed embedding algorithm maps the outer boundary to a convex shape and an interior concave void to a circle-like convex polygon, it effectively eliminates local minimum and attains guaranteed delivery. At the same time, it introduces a small distortion only and consequently achieves a low stretch factor. Our simulations show that its stretch factor is lower than any existing greedy embedding algorithms. Moreover, the proposed scheme is merely based on local connectivity and consumes a small constant storage, thus scaling to arbitrarily large networks.

In this paper, we propose a Self-Interest-Driven (SID) incentive scheme to stimulate cooperation among selfish nodes for ad dissemination in autonomous mobile social networks. As a key innovation of SID, we introduce “virtual checks” to eliminate the needs of accurate knowledge about whom and how many credits ad provider should pay. A virtual check is included in each ad packet. When an intended receiver receives the packet for the first time from an intermediate node, the former authorizes the latter a digitally signed check, which serves as a proof of successful ad delivery. Multiple copies of a virtual check can be created and signed by different receivers. When a node that owns a signed check meets the ad provider, it requests the provider to cash the check. Both ad packets and signed checks can be traded among mobile nodes. We propose the effective mechanisms to define virtual rewards for ad packets and virtual checks, and formulate the nodal interaction as a two-player cooperative game, whose solution is obtained by the Nash Bargaining Theorem. Extensive simulations are carried out to compare SID with other existing incentive algorithms under real world mobility traces.

The medial axis of a shape provides a compact abstraction of its global topology and a proximity of its geometry. The construction of medial axis in two-dimensional (2D) sensor networks has been discussed in the literature, in support of several applications including routing and navigation. In this work, we first reveal the challenges of constructing medial axis in a three-dimensional (3D) sensor network. With more complicated geometric features and complex topology shapes, previous methods proposed for 2D settings cannot be extended easily to 3D networks. Then we propose a distributed algorithm with linear time complexity and communication cost to build a well-structured medial axis of a 3D sensor network without knowing its global shape or global position information. Furthermore we apply the computed medial axis for safe navigation and distributed information storage and retrieval in 3D sensor networks. Simulations are carried out to demonstrate the efficiency of the proposed medial axis-based applications in various 3D sensor networks.

We address the problem of in-network information processing, storage, and retrieval in three-dimensional (3D) sensor networks in this research. We propose a geographic location free double-ruling-based scheme for large-scale 3D sensor networks. The proposed approach does not require a 3D sensor network with a regular cube shape or uniform node distribution. Without the knowledge of the geographic location and the distance bound, a data query simply travels along a simple curve with the guaranteed success to retrieve aggregated data through time and space with one or different types across the network. Simulations and comparisons show the proposed approach with low cost and a balanced traffic load.

Location awareness is imperative for a variety of sensing applications and network operations. Although a diversity of GPS-less and GPS-free solutions have been developed recently for autonomous localization in wireless sensor networks, they primarily target at 2D planar or 3D volumetric settings. There exists unique and fundamental hardness to extend them to 3D surface. The contributions of this work are twofold. First, it proposes a theoretically-proven algorithm for the 3D surface localization problem. Seeing the challenges to localize general 3D surface networks and the solvability of the localization problem on single-value (SV) surface, this work proposes the {\em cut-and-sew} algorithm that takes a divide-and-conquer approach by partitioning a general 3D surface network into SV patches, which are localized individually and then merged into a unified coordinates system. The algorithm is optimized by discovering the minimum SV partition, an optimal partition that creates a minimum set of SV patches. Second, it develops practically-viable solutions for real-world sensor network settings where the inputs are often noisy. The proposed algorithm is implemented and evaluated via simulations and experiments in an indoor testbed. The results demonstrate that the proposed cut-and-sew algorithm achieves perfect 100% localization rate and the desired robustness against measurement errors.

A wireless sensor network can be represented by a graph. While the network graph is extremely useful, it often exhibits undesired irregularity. Therefore, special treatment of the graph is required by a variety of network algorithms and protocols. In particular, many geometry-oriented algorithms depend on a type of subgraph called Delaunay triangulation. However, when location information is unavailable, it is nontrivial to achieve Delaunay triangulation by using connectivity information only. The only connectivity-based algorithm available for Delaunay triangulation is built upon the property that the dual graph for a Voronoi diagram is a Delaunay triangulation. This approach, however, often fails in practical wireless sensor networks because the boundaries of Voronoi cells can be arbitrarily short in discrete sensor network settings. In a sensor network with connectivity information only, it is fundamentally unattainable to correctly judge neighboring cells when a Voronoi cell boundary is less than one hop. Consequently, the Voronoi diagram-based Delaunay triangulation fails. The proposed algorithm employs a distributed approach to perform centroidal Voronoi tessellation, and constructs its dual graph to yield Delaunay triangulation. It exhibits several distinctive properties. First, it eliminates the problem due to short cell boundaries and thus effectively avoids crossing edges. Second, the proposed algorithm is proven to converge and succeed in constructing a Delaunay triangulation, if the CVT cell size is greater than a constant threshold. Third, the established Delaunay triangulation consists of close-to-equilateral triangles, benefiting a range of applications such as geometric routing, localization, coverage, segmentation, and data storage and processing. Extensive simulations are carried out under various 2D network models to evaluate the effectiveness and efficiency of the proposed CVT-based triangulation algorithm.

This paper pursues RFID support for localization, aiming to pinpoint an object in 3D space. Given a set of RFID tags and/or readers deployed as reference points at known locations in a hexahedron (like shipping container or storage room), a passive and an active localization schemes are considered in this paper. Being the very first range-free 3D localization, our schemes depend solely on RFID tags and readers without other devices or sensors, and it avoids the need of distance estimation according to received wireless signal strength or phase difference. Our passive scheme locates an RFID tag attached to the target object, with both tags and readers as reference points. The active scheme locates an RFID reader, by iteratively determining a 3D sphere best covering the activated reference tags, referred to as the decision boundary optimization scheme (DeB). Results by simulations and testbed experiments using Alien RFID kits have been obtained, and they reveal that DeB outperforms its passive counterpart and achieves the localization error of 0.07 ft. Additionally, DeB yields better location accuracy and yet is much faster than a previous counterpart. With enhanced DeB (EDeB), accuracy of an object located near a hexahedron side or corner is improved considerably.

This paper focuses on the problem of rostering in intermittently connected passive RFID networks. It aims to report a list of tagged mobile nodes that appear in given interested area(s) and time interval(s). Such rostering faces several unique challenges. First, the network consists of two dramatically different types of nodes: powerful static readers and extremely resource-constrained mobile tags. Communication can be established from a reader to a tag only, but not tags to tags or readers to readers. Therefore, the connectivity is very low and intermittent. Besides connectivity, the tag's computation power is also intermittent. It is available only for a short interval when the tag is powered up by a nearby reader, rendering any continuous functions impossible. Moreover, the capacity of tags is so limited that it becomes the critical network resource and communication bottleneck. To address the above challenges, we propose a rostering algorithm that employs a dynamic space-efficient coding scheme to construct hypothetic packet candidates, appraises their values according to information redundancy and tag mobility, and establishes a 0-1 Knapsack model to choose the best set of packets, which together maximize their total (redundancy-excluded) value, but do not exceed the capacity of a tag. We carry out experiments that involve 38 volunteers for nine days and perform large-scale simulations to evaluate the proposed rostering scheme.

This paper focuses on distributed data query in intermittently connected passive RFID networks, which are characterized by extraordinarily limited communication capacity and asynchronous and opportunistic communication links. To address such unique challenges, we propose a distributed data query framework that clusters RFID readers and establishes a 0-1 Knapsack model based on dynamic packet appraisal to enable highly efficient data transmission. We implement a prototype by using Alien RFID gears and carry out experiments that involve 52 volunteers for 14 days to evaluate the proposed data query framework.

Sensor deployment is a fundamental issue in a wireless sensor network, which often dictates the overall network performance. Previous studies on sensor deployment mainly focused on sensor networks on 2D plane or in 3D volume. In this paper, we tackle the problem of optimal sensor deployment on 3D surfaces, aiming to achieve the highest overall sensing quality. In general, the reading of a sensor node exhibits unreliability, which often depends on the distance between the sensor and the target to be sensed, as observed in a wide range of applications. Therefore, with a given set of sensors, a sensor network offers different accuracy in data acquisition when the sensors are deployed in different ways in the Field of Interest (FoI). We formulate this optimal surface deployment problem in terms of sensing quality by introducing a general function to measure the unreliability of monitored data in the entire sensor network. We present its optimal solution and propose a series of algorithms for practical implementation. Extensive simulations are conducted on various 3D mountain surface models to demonstrate the effectiveness of the proposed algorithms.

This work aims to address the problem of localization in 3D surface wireless sensor networks. First, it reveals the unique hardness in localization on 3D surface in comparison with the well-studied localization problems in 2D and 3D space, and offers useful insight into the necessary conditions to achieve desired localizability. Second, it formulates the localization problem under a practical setting with estimated link distances (between nearby nodes) and nodal height measurements, and introduces a layered approach to promote the localizability of such 3D surface sensor networks. Crossbow sensor-based experiments and large-scale simulations are carried out to evaluate the performance of the proposed localization algorithm. The numeric results show that it can effectively improve localizable rate and achieve low location errors and computational overhead, with the desired tolerability to measurement errors and high scalability to large-size wireless sensor networks.

In this work we develop a distributed boundary detection algorithm, dubbed Coconut, for 3D wireless sensor networks. It first constructs a tetrahedral structure to delineate the approximate geometry of the 3D sensor network, producing a set of “sealed” triangular boundary surfaces for separating non-boundary nodes and boundary node candidates. The former are hollowed out immediately while the latter are further refined to yield the final boundary nodes and fine-grained boundary surfaces. The proposed Coconut algorithm offers several salient features. First, it requires connectivity information only, with no need for localization or distance measurement. Second, it does not rely on particular communication models, but only assumes a constant maximum transmission range, which is generally known in practical wireless sensor networks. Third, it is robust to sensor distribution, effectively identifying boundaries in both uniformly and non-uniformly distributed sensor networks. We prove the correctness of the algorithm and quantitatively demonstrate its effectiveness via simulations under various network models.

Compared with its 2D counterpart, the scalability problem is greatly exacerbated in a 3D wireless sensor network. In this paper, we propose a scalable routing algorithm, dubbed Bubble Routing. It preprocesses global knowledge via a distributed algorithm, such that a node only needs to store a small constant information to make correct and efficient local routing decisions and achieve guaranteed delivery at the same time. More specifically, the proposed bubble routing algorithm first decompose a 3D network into a set of hollow spherical cells (HSCs). A continuous and one-to-one mapping is applied and a virtual tree structure is established inside each HSC to enable greedy routing. On the other hand, routing across HSCs is guided by a small routing table whose size is bounded by the number of interior holes. Our simulation results show that bubble routing can achieve guaranteed data delivery, low stretch factor, and well balanced traffic load.

Bidirectional reflectance factors (BRFs) play a key role in land surface studies. Snow has a significant influence on vegetative surface BRF. To evaluate the surface reflectance behaviors of snow-covered regions, a surface BRF database has been constructed from Multi-angle Imaging SpectroRadiometer BRF products for five biomes in the mid-high latitude regions of the U.S. (evergreen needleleaf forests, shrublands, grasslands, croplands, and urban areas). Using corresponding surface snow depth data from 26 meteorological stations, BRF signatures with snow cover are derived from the database to show the effect of snow on the BRF of vegetation. Five bidirectional reflectance distribution function models' abilities of capturing vegetation-snow mixed BRF shape are evaluated by fitting all the BRF data with snow. The results show that the Rahman model, Ross-Li model, and Walthall model perform well in fitting forest, grassland, and cropland BRFs when the surface is covered by snow. The Rahman model, Ross-Li model, and Roujean model fit visible reflectance well for mixed surfaces. The Rahman model best captures the BRF shapes, followed by the Ross-Li model.

Surface albedo is one of the key radiation parameters required for modeling of the Earth's energy budget. The future Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager (ABI) will provide the observations in several shortwave spectral bands together with both high spatial and temporal resolutions which will carry much angular information for estimating instantaneous surface albedo and bi-directional reflectance. According to these advanced sensor characteristics, we propose an improved algorithm that retrieves surface albedo and aerosol optical depth (AOD) simultaneously. To prototype this algorithm, satellite observations with the similar spectral bands and spatial resolution acquired by MODIS are used. Results show a good agreement between retrieved albedo values and ground measurements from SURFRAD.

This paper explores snow bidirectional reflectance distribution function (BRDF) properties over some snow covered regions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) surface reflectance products. In the visible and near infrared (NIR) region, MODIS and MISR surface bidirectional reflectance factors (BRFs) over snow are accumulated to extract snow BRDF properties. Five surface BRDF models are concerned to simulate snow surface reflectance shape. All the models capture the distribution of snow BRFs with limit of accuracy. The simulated BRFs from several models have similar distribution trend and different details. The BRDF properties discussed can be used as background in the snow BRDF retrieval from spaceborne measurements.

This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and non-uniform nodal distribution. In contrast to current state-of-art connectivity-based localization methods, the proposed algorithm is fully distributed, where each node only needs the information of its neighbors, without cumbersome partitioning and merging process. The algorithm is highly scalable, with limited error propagation and linear computation and communication cost with respect to the size of the network. Moreover, the algorithm is theoretically guaranteed and numerically stable. Extensive simulations and comparison with other methods under various representative network settings are carried out, showing superior performance of the proposed algorithm.

Triangulation serves as the basis for many geometry-based algorithms in wireless sensor networks. In this paper we propose a distributed algorithm that produces a triangulation for an arbitrary sensor network, with no constraints on communication model or granularity of the triangulation. We prove its correctness in 2D, and further extend it to sensor networks deployed on 3D open and closed surfaces. Our simulation results show that the proposed algorithms can tolerate distance measurement errors, and thus work well under practical sensor network settings and effectively promote the performance a range of applications that depend on triangulations.

With both computational complexity and storage space bounded by a small constant, greedy routing is recognized as an appealing approach to support scalable routing in wireless sensor networks. However, significant challenges have been encountered in extending greedy routing from 2D to 3D space. In this research we develop decentralized solutions to achieve greedy routing in 3D sensor networks. Our proposed approach is based on a unit tetrahedron cell (UTC) mesh structure. We propose a distributed algorithm to realize volumetric harmonic mapping of the UTC mesh under spherical boundary condition. It is a one-to-one map that yields virtual coordinates for each node in the network. Since a boundary has been mapped to a sphere, node-based greedy routing is always successful thereon. At the same time, we exploit the UTC mesh to develop a face-based greedy routing algorithm, and prove its success at internal nodes. To deliver a data packet to its destination, face-based and node-based greedy routing algorithms are employed alternately at internal and boundary UTCs, respectively. As far as we know, this is the first work that realizes truly deterministic greedy routing with constant-bounded storage and computation in 3D wireless sensor networks.

This paper focuses on the problem of rostering in intermittently connected passive RFID networks. It aims to report a list of tagged mobile nodes that appear in given interested area(s) and time interval(s). Such rostering faces several unique challenges. First, the network consists of two dramatically different types of nodes: powerful static readers and extremely resource-constrained mobile tags. Communication can be established from a reader to a tag only, but not tags to tags or readers to readers. Therefore the connectivity is very low and intermittent. Besides connectivity, the tag's computation power is also intermittent. It is available only for a short interval when the tag is powered up by a nearby reader, rendering any continuous functions impossible. Moreover, the capacity of tags is so limited that it becomes the critical network resource and communication bottleneck. To address the above challenges, we propose a rostering algorithm that employs a dynamic space-efficient coding scheme to construct hypothetic packet candidates, appraises their values according to information redundancy and tag mobility, and establishes a 0-1 Knapsack model to choose the best set of packets, which together maximize their total (redundancy-excluded) value but do not exceed the capacity of a tag. We carry out experiments that involve 38 volunteers for 9 days and perform large-scale simulations to evaluate the proposed rostering scheme.

This work centers on data dissemination in delay-tolerant mobile networks, where data fall into a range of interest types and each node may have one or multiple interests. The goal is to deliver data messages from sources to nodes with corresponding interests. We consider selfish nodes with rational behavior, and propose a credit-based incentive scheme to promote nodal collaboration. The key challenge is to effectively track the value of a message under such a unique network setting with intermittent connectivity and multiple interest types. Given poor end-to-end connections, credits are rewarded to the final deliverer only. Thus the value of a message for an intermediate node highly depends on its probability to deliver the message. Such probability itself is nontrivial to estimate. Moreover, a message is usually desired by multiple mobile users. Therefore, it can be potentially “sold” multiple times to different receivers. On the other hand, while more than one copies can be created during the transmissions of a message, a particular receiver “pays” for the first received copy only. These characteristics together make the development of incentive mechanism a unique, interesting, and challenging problem. In this paper, we present effective schemes to estimate the expected credit reward, and formulate nodal communication as a two-person cooperative game, whose solution is found by using the Nash Theorem. Extensive simulations are carried out based on real-world traces to evaluate the proposed scheme in terms of data delivery rate, delay and overhead. To our best knowledge, this is the first work that incorporates incentive stimulation into data dissemination in delay-tolerant mobile networks with selfish nodes and multiple interest types.

Segmentation decomposes a network with complex and irregular shape into a set of subnetworks, each under a simple boundary condition without bottlenecks. It has a wide spectrum of applications in routing, coverage, localization, backbone construction and maintenance, and in-network data centric storage and retrieval. To our best knowledge, this is the first work that tackles the segmentation problem in 3D wireless sensor networks. We propose a fully distributed 3D segmentation scheme with mere network connectivity information. Each node on boundary computes its injectivity radius, which reflects the narrowness of the corresponding boundary area and thus is employed to locate the undesired bottlenecks. A cluster of connected boundary nodes with similar smallest injectivity radii form a bottleneck segment. A recursive process is applied to identify a set of such bottlenecks, which together divide the network boundary into segments. An internal non-boundary node simply joins the nearest segment, thus completing the segmentation of the entire 3D sensor network. Our simulations show that the proposed algorithm works efficiently under various sensor network models with different boundary conditions and noise levels, always yielding appropriate segmentation results. We further demonstrate that segmentation can effectively promote the performance of a range of applications in 3D wireless sensor networks.

In this paper, we investigate the use of radio frequency identification (RFID) gear for wireless sensor network construction, aiming to find events of interest and gather aggregate information. In particular, we develop a featherlight information network with delay-endurable RFID support (FINDERS), composed of passive RFID tags that are ultralight, durable, and flexible, without power supply for long-lasting applications. FINDERS faces unprecedented challenges in communication and networking due to its sporadic wireless links, unique asymmetric communication paradigm, intermittent computation capability, and extremely small memory of tags. Several effective techniques are proposed to address these challenges, arriving at an efficient communication protocol for FINDERS. A prototype system is developed, and test-bed experiments are carried out with 38 participants and for 9 days, yielding interesting experimental results that offer valuable insights into RFID-based delay-tolerant networks and provide useful practical guidance for the setup of FINDERS systems.

A self-learning Smart Trend-Traversal (STT) protocol for tag arbitration is proposed in this work, which effectively reduces the collision overhead occurred in large-scale RFID systems. The protocol dynamically issues queries according to the adaptively learned tag density and distribution; and therefore, it significantly reduces delay and energy consumption. The optimality of STT does not rely on any presumed network conditions, which is in sharp contrast to other available schemes and renders it a highly desirable and practical solution.

This research addresses the problem of counting in Delay Tolerant Networks (DTNs). The goal is to estimate the total number of nodes in the network with short delay time, high accuracy and small storage overhead. DTNs are occasionally connected networks that may suffer from frequent partitions. While counting in conventional (well-connected) networks has been extensively studied, it remains challenging in DTNs, due to the intermittent network connectivity and heterogeneous nodal mobility. In this paper, we propose a novel scheme that exploits effective nodal contact probability to guide the counting process. Extensive simulations based on real mobility traces are carried out to evaluate the performance of our proposed scheme. The results demonstrate that it is highly efficient and outperforms all existing solutions.

This research focuses on distributed and localized algorithms for precise boundary detection in 3D wireless networks. Our objectives are in two folds. First, we aim to identify the nodes on the boundaries of a 3D network, which serve as a key attribute that characterizes the network, especially in such geographic exploration tasks as terrain and underwater reconnaissance. Second, we construct locally planarized 2-manifold surfaces for inner and outer boundaries, in order to enable available graph theory tools to be applied on 3D surfaces, such as embedding, localization, partition, and greedy routing among many others. To achieve the first objective, we propose a Unit Ball Fitting (UBF) algorithm that discovers a set of potential boundary nodes, followed by a refinement algorithm, named Isolated Fragment Filtering (IFF), which removes isolated nodes that are misinterpreted as boundary nodes by UBF. Based on the identified boundary nodes, we develop an algorithm that constructs a locally planarized triangular mesh surface for each 3D boundary. Our proposed scheme is localized, requiring information within one-hop neighborhood only. Our simulation results demonstrate that the proposed algorithms can effectively identify boundary nodes and surfaces, even under high measurement errors. As far as we know, this is the first work for discovering boundary nodes and constructing boundary surfaces in 3D wireless networks.

This research investigates distributed clustering scheme and proposes a cluster-based routing protocol for Delay-Tolerant Mobile Networks (DTMNs). The basic idea is to distributively group mobile nodes with similar mobility pattern into a cluster, which can then interchangeably share their resources (such as buffer space) for overhead reduction and load balancing, aiming to achieve efficient and scalable routing in DTMN. Due to the lack of continuous communications among mobile nodes and possible errors in the estimation of nodal contact probability, convergence and stability become major challenges in distributed clustering in DTMN. To this end, an exponentially weighted moving average (EWMA) scheme is employed for on-line updating nodal contact probability, with its mean proven to converge to the true contact probability. Based on nodal contact probabilities, a set of functions including Sync(), Leave(), and Join() are devised for cluster formation and gateway selection. Finally, the gateway nodes exchange network information and perform routing. Extensive simulations are carried out to evaluate the effectiveness and efficiency of the proposed cluster-based routing protocol. The simulation results show that it achieves higher delivery ratio and significantly lower overhead and end-to-end delay compared with its non-clustering counterpart.

We propose a novel tag selection algorithm to improve the efficiency of tag arbitration in RFID systems. Based on the probability that a given tag is located in the sensing range of the reader, our algorithm involves two steps. First, tags with higher probabilities are selected and directly arbitrated by their IDs, in order to eliminate the collisions caused by multiple tag replies and reduce the number of empty tag replies. And then, the remaining unidentified tags, which will be in a small quantity if any, are arbitrated with the Query-Tree or the frame-slotted Aloha protocols specified in the RFID standards. Our proposed Probability-based Tag Selection (PTS) algorithm is compatible with the RFID standards with very minor modification on the readers and no changes on the tags. An analytical model is constructed to find the optimal threshold of tag selection in the first step. The simulation shows the result from our model nicely represents the optimal threshold value. It further demonstrates that PTS significantly reduces the arbitration delay and improves the arbitration rate with a given budget, comparing with the Query-Tree and frame-slotted Aloha protocols.

We propose a Smart Trend-Traversal (STT) protocol for RFID tag arbitration, which effectively reduces the collision overhead occurred in the arbitration process. STT, a query tree-based scheme, dynamically issues queries according to the online learned tag density and distribution; and therefore, it significantly reduces delay and energy consumption comparing with the existing tree-based and aloha-based protocols. Our analytic studies further show that the optimality of STT does not rely on any presumed network conditions, which is in sharp contrast to other available schemes and renders it a highly desirable and practical solution.

This paper focuses on the Participatory Sensing Network (PSN) that consists of selfish participants stimulated by certain reward programs. We propose a bargain-based mechanism to encourage cooperative message trading among the selfish nodes to maximize their rewards. We state the necessary condition for feasible message transactions in a theorem. We model message transaction as a two-person cooperative game, and we apply Nash Theorem to obtain optimal solution which is fair and Pareto optimal. We also present a greedy algorithm to reach the optimal solution. The effectiveness of the bargain-based stimulation mechanism is studied by extensive simulations based on real mobility traces.

This research centers on the Featherlight Information Network with Delay-Endurable RFID Support (FINDERS), composed of passive RFID tags which are ultra light, durable, and flexible, without power supply for long-lasting applications under strict weight constraints and harsh environments. It expands the use of RFID gear for wireless network construction, aiming to find events of interest and gather aggregate information. FINDERS faces unprecedented challenges in communication and networking, due to its sporadic wireless links, unique asymmetric communication paradigm, intermittent computation capability, and extremely small memory of tags. Analytic and simulation data are collected to show the feasibility and efficiency of FINDERS.

The Delay/Fault-TolerantMobile Sensor Network (DFTMSN) has been proposed recently for pervasive information gathering. A DFT-MSN consists of a number of wearable sensor nodes and high-end sink nodes, forming a loosely connected mobile sensor network. In this paper we introduce a generic queuing analytic model for DFT-MSN, where the inputs are the data delivery scheme employed and the nodal mobility pattern, while the outputs are the queuing characteristics of the network. Based on our analysis of the message arrival and service processes, we find that each individual sensor can be modeled as an M/M/1/K queue, and the whole network can be treated as a network of queues. Following Jackson network theory, major queuing characteristics of the network can thus be obtained. We also exemplify the generic analytic model with several representative data delivery schemes (including Direct Transmission, ZebraNet, and Replication-based Data Delivery) and nodal mobility patterns (such as uniform and power-law distributions). To validate our analytic model, we have carried out extensive simulations and observed a good match between analytic and simulation results.

The code division multiple access (CDMA) technology has been recently introduced into mesh wireless networks for improving channel efficiency. The existing approaches, however, do not maximize the capacity of a CDMA system, because the sender transmits to one receiver only. In this paper, we explore novel approaches to allow multiple data frames be transmitted from a sender to multiple receivers simultaneously by using multi-user detection techniques and efficient power control schemes, thus maximizing network capacity and decreasing data delivery delay. More specifically, we propose two CDMA-based medium access control schemes, the PNO (Pseudo Noise Only) scheme and the PPO (PN Plus Orthogonal) scheme, for highly efficient data transmission in mesh wireless networks, especially for those networks serving as communication backbone and thus experiencing high traffic load. The performance of our proposed schemes is evaluated via simulations, and compared with IEEE 802.11 and other CDMA-based schemes under the same channel bandwidth. Our results show that PPO achieves the highest channel efficiency, because of its use of orthogonal codes for channelization. Both PPO and PNO can significantly improve network throughput and reduce packet delivery delay, without increasing signaling overhead noticeably.

The Distributed and Fair Access (DFA) protocol is proposed for multihop wireless networks. The proposed protocol eliminates several problems existed in the original binary countdown (BCD) algorithm, such as lack of fairness, data collision and the inefficiency of channel usage, by introducing hidden station elimination and second chance channel contention that are suitable for multihop networks. Further in this paper, numerical analysis of modeling the behavior of DFA in multihop networks are presented. With low computational complexity, the proposed model estimates the transmission probability and the channel throughput. In our analysis, the data transmission influenced by the remote stations is carefully monitored and analyzed. Our extensive simulation results have verified the proposed model and demonstrated the superior performance of DFA comparing with other existing MAC protocols including the IEEE 802.11 and SYN-MAC. Equipped with many attractive features such as high efficiency, fairness, simplicity and robustness, DFA can be served as a promising alternative MAC protocol for the distributed wireless networks.

Besides its original focus on space communications, the Delay-Tolerant Network (DTN) has been introduced into terrestrial mobile wireless networks. DTN is fundamentally an opportunistic communication system, where communication links exist temporarily, making it impossible to establish end-to-end connections. Therefore, most of conventional communication protocols developed for well connected networks simply fail here. Various new approaches such as SWIM [1], DFT-MSN [3], and ProPhet [2] have been investigated, where routing is largely based on nodal contact probabilities. They can be classified as more or less “flat”, where every node plays a similar role in routing. The flat architecture is simple and effective in small networks, but not scalable to large size DTNs.

In this paper, we propose three algorithms for border landmark selection, namely the convex hull-based (CHB) algorithm, the center node elimination (CNE) algorithm, and the hierarchy-structured (HS) algorithm. CHB works perfectly in theory and provides a deep insight into the landmark selection problem. At the same time, it is noticed that CHB is centralized and sensitive to errors in distance estimation. The CNE algorithm is a distributed approach, devised to gradually exclude the nodes in the ldquocenterrdquo of the network till the desired number of nodes left, which are employed as landmarks. While CNE works effectively in a small network, its high order computation complexity and communication overhead may eventually lead to scalability problem when it is applied in very large networks. To address this problem, we propose the HS algorithm for striking the balance between accuracy and complexity/overhead. In HS, we establish a hierarchical structure with multiple layers, and apply the CNE algorithm in an appropriate layer to identify an initial set of candidate nodes. The outcomes are then rectified through a recursive process, yielding the final landmarks. Three applications, including coordinates establishment, border detection, and landmark-based routing in general networks without location information, are introduced based on the selected landmarks. We carry out extensive simulations to compare the performance of our landmark selection algorithms and demonstrate their effectiveness in all of the applications.

Data transmission in self-organized multi-hop networks heavily depends on the cooperation among nodes. In many applications, however, the autonomous nodes exhibit selfish behaviors, aiming to optimize their own performance without consideration of other nodes in the network. Although a selfish node is interested in transmitting its own data only, part of its resource has to be traded for the cooperation of other nodes in the network, in order to establish a routing path through them to deliver data to its destination. In this paper, we propose a stimulating mechanism to encourage cooperation among the selfish nodes. Specifically, a credit-based Markov chain model is established to analyze the packet dropping probability, with given total bandwidth, bandwidth allocation, buffer space, and the maximum credit of each node. Based on the Markovian model, bandwidth allocation is optimized so that the dropping probability of a node’s own packets is minimum. It is a main contribution of this work to address the bandwidth constraint, which is a key resource in wireless networks but has been ignored in all existing incentive schemes. Extensive simulations are carried out to evaluate the proposed incentive scheme, and the simulation results show that it can effectively enable cooperation among selfish nodes and minimize overall packet dropping probability.

While extensive studies have been carried out in the past several years for many sensor applications, the main approach for sensor networking cannot be applied to the sceonarios with extremely low and intermittent connectivity, dubbed the Delay/Fault-Tolerant Mobile Sensor Network (DFT-MSN). Without end-to-end connections due to sparse network density and sensor node mobility, routing in DFT-MSN becomes localized and ties closely to medium access control, which naturally calls for merging Layer 3 and Layer 2 protocols in order to reduce overhead and improve network efficiency. Due to the unique characteristics of DFT-MSN, the communication links exist only with certain probabilities and become the scarcest resource. At the same time, the sensor nodes in DFT-MSN have very limited battery power like those in other sensor networks. Clearly, there is a tradeoff between link utilization and energy efficiency. In order to address the trade-off, we develop a cross-layer data delivery protocol for DFT-MSN, which includes two phases, i.e., the asynchronous phase and the synchronous phase. In the first phase, the sender contacts its neighbors to identify a set of appropriate receivers. Since no central control exists, the communication in the first phase is contention-based. In the second phase, the sender gains channel control and multicasts its data message to the receivers. Furthermore, several optimization issues in these two phases are identified, with solutions provided to reduce the collision probability and to balance between link utilization. Our results show that the proposed cross-layer data delivery protocol for DFT-MSN achieves a high message delivery ratio with low energy consumption and an acceptable delay.

An enhanced synchronized medium access protocol, named ES-MAC, for wireless ad hoc networks is proposed in this paper. ES-MAC employs a binary-countdown scheme to resolve contentions between wireless stations. Multiple contention periods and hidden station elimination periods are adopted to increase the throughput and channel utilization of the system. Our simulation and analysis show that ES-MAC can achieve a promising performance in terms of throughput, fairness and channel utilization. With channel utilization as high as 96%, ES-MAC can also be employed as an alternative MAC protocol in wireless access network.

Due to the complexity of throughput analysis in wireless networks with multiple collision domains, how to model system performance in multihop wireless networks remains challenging. Based on our previous proposed synchronized medium access control (SYN-MAC) protocol and its performance analysis in single collision domain, we propose an effective analytic model with low computational complexity for evaluating the performance of SYN-MAC in multihop wireless networks. Our simulation results verify the numerical analysis very well, showing the effectiveness of our proposed approach. Furthermore, an enhanced SYN-MAC protocol is presented to optimize its channel efficiency and throughput in multihop wireless networks. Our simulation results show the SYN-MAC protocol significantly outperforms IEEE 802.11 in all scenarios, regardless of the payload sizes, network sizes, or nodal densities.

While extensive studies have been carried out in the past several years for many sensor applications, they cannot be applied to the network with extremely low and intermittent connectivity, dubbed the delay/fault-tolerant mobile sensor network (DFT-MSN). Without end-to-end connections due to sparse network density and sensor node mobility, routing in DFT-MSN becomes localized and ties closely to medium access control, which naturally calls for merging Layer 3 and Layer 2 protocols in order to reduce overhead and improve network efficiency. DFT-MSN is fundamentally an opportunistic network, where the communication links exist only with certain probabilities and become the scarcest resource. At the same time, the sensor nodes in DFT-MSN have very limited battery power like those in other sensor networks. Clearly, there is a tradeoff between link utilization and energy efficiency. To address this tradeoff, we develop a cross-layer data delivery protocol for DFT-MSN, which includes two phases, i.e., the asynchronous phase and the synchronous phase. In the first phase, the sender contacts its neighbors to identify a set of appropriate receivers. Since no central control exists, the communication in the first phase is contention-based. In the second phase, the sender gains channel control and multicasts its data message to the receivers. Furthermore, several optimization issues in these two phases are identified, with solutions provided to reduce the collision probability and to balance between link utilization and energy efficiency. Our results show that the proposed cross-layer data delivery protocol for DFT-MSN achieves a high message delivery ratio with low energy consumption and an acceptable delay.

This article deals with distributed address determination for mobile Grids, realized by ADENS (address determination via neighboring states), where a new mobile host (MH) determines a conflict-free address for itself efficiently according to state information only from neighboring MHs. With low traffic overhead, ADENS achieves higher address space utilization than the best known approach. The optimal design of basic ADENS has been derived analytically for the first time. Enhanced ADENS can be achieved by designating appropriate MHs (instead of permitting all MHs) to respond to address requests of newly arrived MHs, further improving address space utilization markedly while lowering traffic overhead drastically. Our simulation results reveal that enhanced ADENS enables a mobile Grid to operate practically as long as it needs.

This research focuses on RFID-based 3-D positioning schemes, aiming to locate an object in a 3-dimensional space, with reference to a predetermined arbitrary coordinates system, by using RFID tags and readers. More specifically, we consider a hexahedron which may be a shipping container, a storage room, or other hexahedral shape spaces. A number of RFID tags and/or readers with known locations are deployed as reference nodes. We propose two positioning schemes, namely, the active scheme and the passive scheme. The former scheme locates an RFID reader. For example, it may be employed to locate a mobile person who is equipped with an RFID reader or an object that is approached by an RFID reader. The passive scheme locates an RFID tag, which is attached to the target object. Both approaches are based on a Nelder-Mead nonlinear optimization method that minimizes the error objective functions. We have carried out analyses and extensive simulations to evaluate the proposed schemes. Our results show that both schemes can locate the targets with acceptable accuracy. The active scheme usually results in smaller errors and has a lower hardware cost compared to its passive counterpart. On the other hand, the passive scheme is more efficient when locating multiple targets simultaneously. The effectiveness of our proposed approaches is verified experimentally using the IDENTEC RFID kits.

This paper focuses on the delay/fault-tolerant mobile sensor network (DFT-MSN) for pervasive information gathering. We develop simple and efficient data delivery schemes tailored for DFT-MSN, which has several unique characteristics, such as sensor mobility, loose connectivity, fault tolerability, delay tolerability, and buffer limit. We first study two basic approaches, namely, direct transmission and flooding. We analyze their performance by using queuing theory and statistics. Based on the analytic results that show the trade-off between data delivery delay/ratio and transmission overhead, we introduce an optimized flooding scheme that minimizes transmission overhead in flooding. Then, we propose two simple and effective DFT-MSN data delivery schemes, namely, the replication-based efficient data delivery scheme (RED) and the message fault tolerance-based adaptive data delivery scheme (FAD). The RED scheme utilizes the erasure coding technology in order to achieve the desired data delivery ratio with minimum overhead. It consists of two key components for data transmission and message management. The former makes the decision on when and where to transmit data messages according to the delivery probability, which is the likelihood that a sensor can deliver data messages to the sink. The latter decides the optimal erasure coding parameters (including the number of data blocks and the needed redundancy) based on its current delivery probability. The FAD scheme employs the message fault tolerance, which indicates the importance of the messages. The decisions on message transmission and dropping are made based on fault tolerance for minimizing transmission overhead. The system parameters are carefully tuned on the basis of thorough analyses to optimize network performance. Extensive simulations are carried out for performance evaluation. Our results show that both schemes achieve a high message delivery ratio with acceptable delay. The RED scheme results in lower complexity in message and queue management, while the FAD scheme has a lower message transmission overhead.

The delay/fault-tolerant mobile sensor network (DFT-MSN) has been proposed for pervasive information gathering. DFT-MSN distinguishes itself from conventional sensor networks by several unique characteristics such as sensor mobility, loose connectivity, and delay/fault tolerability. This paper focuses on the performance evaluation of DFT-MSN. We first introduce a queuing model by using Jackson network theory. While the queuing model is based on a few simplification assumptions for analytic tractability, it provides insights into the queuing behavior of the mobile sensors in DFT-MSN. Extensive simulations are performed under realistic environment and assumptions. Our simulation results show that the dynamic DFT-MSN data delivery scheme achieves the highest message delivery ratio with acceptable delay and transmission overhead, compared with simple schemes such as flooding and direct transmission or other approaches in the literature such as Zebranet. We have also implemented a DFT-MSN testbed by deploying crossbow motes for noise level monitoring in our university library. Though in a small scale, the testbed demonstrates the feasibility of DFT-MSN and provides guidance for future large scale deployment.

Abstract This paper presents a survey of analytic studies on the Delay‐Tolerant Mobile Sensor Network (DTMSN). We first give an overview of the analytic study on DTMSN, by identifying several key design components, that is, nodal mobility model, data delivery scheme, and queue management scheme. Then, several representative analytic models of DTMSN are exemplified and discussed in detail, with thorough comparison presented and possible open research issues enumerated. We expect that this paper will provide a deep understanding of the characteristics of DTMSN, and at the same time reveal the key design issues in its modeling and performance analysis, leading to useful insights for future theoretic study and protocol design. Copyright © 2007 John Wiley & Sons, Ltd.

With the standardization of IEEE 802.11, there has been an explosive growth of wireless local area networks (WLAN). Recently, this cost effective technology is being deployed aggressively for establishing the network, where a large number of access points (APs) are interconnected by wireless links, forming a metro-scale wireless mesh network in order to support ubiquitous Internet access in the urban area. This paper focuses on the optimization of cellular Wi-Fi. In particular, we aim at finding a set of gateways and their optimal placement so as to minimize the network installation costs while maintaining reliability, flexibility, and an acceptable grade of service. We propose two approaches in this research. The first approach is based on integer linear programming (ILP), where we developed a set of linear inequalities according to various constraints and find the solution of the ILP model by using lp-solve. The second approach is an OPEN/CLOSE heuristic, tailored for cellular Wi-Fi, which arrives at a sub-optimal solution. Extensive simulations are carried out for performance evaluation. Simulation results show that the proposed approaches can effectively identify a set of gateways at optimal locations in a cellular Wi-Fi network, resulting in an overall cost reduction of up to 50%.

Abstract — This paper focuses on the Delay/Fault-Tolerant Mobile Sensor Network (DFT-MSN) for pervasive information gathering. We develop simple and efficient data delivery schemes tailored for DFT-MSN, which has several unique characteristics such as sensor mobility, loose connectivity, fault tolerability, delay tolerability, and buffer limit. We first study two basic approaches, namely, direct transmission and flooding. We analyze their performance by using queuing theory and statistics. Based on the analytic results that show the tradeoff between data delivery delay/ratio and transmission overhead, we introduce an optimized flooding scheme that minimizes transmission overhead in flooding. Then, we propose a simple and effective DFT-MSN data delivery scheme, which consists of two key components for data transmission and queue management, respectively. The former makes decision on when and where to transmit data messages based on the delivery probability, which reflects the likelihood that a sensor can deliver data messages to the sink. The latter decides which messages to transmit or drop based on the fault tolerance, which indicates the importance of the messages. The system parameters are carefully tuned on the basis of thorough analyses to optimize network performance. Extensive simulations are carried out for performance evaluation. Our results show that the proposed DFT-MSN data delivery scheme achieves the highest message delivery ratio with acceptable delay and transmission overhead. I.

In this paper, we use an integrated architecture that takes advantage of the low cost mobile sensor networks and 3G cellular networks to accommodate multimedia medical calls with differentiated Quality-of-Service (QoS) requirements. We propose a low-energy, distributed, and concentric-zone-based data query mechanism that takes advantages of hierarchical ad hoc routing algorithms to enable a medical specialist to collect physiological data from mobile and/or remote patients. The medical specialist uses cellular network to report patients' data to the medical center. Moreover, we propose a transmission scheme among different zones with balance-based energy efficiency, which can extend network lifetime. We evaluate the validity of our proposals through simulations and analyze their performance. Our results clearly indicate the energy efficiency of the proposed sensor network query algorithms and the efficiency of our multiclass medical call admission control scheme in terms of meeting the multimedia telemedicine QoS requirements.

In this paper, we propose a novel medium access control protocol with a separate control channel (MAC-SCC) to increase the channel efficiency and address the unfairness and instability problems of IEEE 802.11 MAC protocol. In MAC-SCC, the available bandwidth is partitioned into two channels: a data channel and a control channel, each associated with a network allocation vector (NAV). To reduce hardware complexity, the station transmits or receives on one channel only at any given time. In the network employing MAC-SCC, the next data frame can be pre-scheduled during the current data transmission via the separate control channel, and thus reducing the frame collision probability and the bandwidth wasted during backoff. Moreover the use of the separate control channel helps to achieve fair medium access and solve the instability problem resulted from frequent link failures. The optimal bandwidth partitioning between the two channels is analyzed via a statistical model, which shows 10% bandwidth for the control channel and 90% bandwidth for the data channel. The performance of MAC-SCC is quantified via extensive simulations in both a stand-alone simulator developed by using PARSEC and a comprehensive network simulator called QualNet with whole protocol stack. Our results show that MAC-SCC can effectively reduce the link failure probability, achieve fair medium access when running multiple TCP sessions, and yield a throughput gain up to 60% under high traffic load

Geographic location information can effectively improve the performance (e.g., in routing or intelligent coordination) of large wireless networks. In this paper, we propose a novel self-configurable positioning technique for multihop wireless networks, based on a Euclidean distance estimation model and a coordinates establishment scheme. A number of nodes serve as the landmarks to establish a coordinates system. Specifically, any pair of landmarks estimate their Euclidean distance according to the shortest path length between them and establish the coordinates system by minimizing an error objective function. Other nodes in the network can accordingly contact the landmarks and determine their own coordinates. The proposed technique is independent of the Global Navigation Satellite Systems (GNSSs), and the established coordinates can be easily tuned to GNSS if at least one node in the network is equipped with GNSS receiver. Our simulation results show that the proposed self-configurable positioning technique is highly fault-tolerable to measurement inaccuracy and can effectively establish the coordinates for multihop wireless networks. More landmarks yield more accurate results. With the rectification of our Euclidean distance estimation model, four to seven landmarks are usually sufficient to meet the accuracy requirement in a network with hundreds of nodes. The computing time for coordinates establishment is in the order of milliseconds for a GHz CPU, acceptable for most applications in the mobile ad hoc networks as well as the sensor networks.

In this research, we study the self-maintenance scheduling problem in next generation wireless networks, with the consideration of resource maintenance constraints and resource conflicting constraints. We propose a linear programming (ILP) model and two heuristic algorithms, and evaluate their effectiveness and time complexity via analysis and simulations. Our results show that all of the proposed approaches can effectively schedule the requests within a reasonable period of time, but with different suitable scenarios. The ILP approach is effective when the number of requests is large and yields close-to-optimal results; the RC-Cliques-RM algorithm is suitable at the presence of many constraints; while the RC-RM-Cliques algorithm can scale to large size networks at the expense of reduced accuracy. It is anticipated that the proposed scheduling algorithms will be generally applicable to various mobile wireless networks where self-maintenance is needed.

In this paper, we propose a grid-based working node (WN) selection approach for wireless sensor networks. Due to coverage redundancy, it is highly desirable to identify a minimum subset of sensors in a wireless sensor network to serve as WNs, while the remaining sensors are deactivated to save power and reduce potential interference. The basic idea of our solution approach is to represent the coverage of the sensors by a number of sample points, i.e., the intersection points of the established grid. A simple approximation algorithm and a linear programming method are employed to select as few sensors as possible to cover all sample points. In order to reduce the computational time, clusters are formed and WN selection is performed within each cluster. The performance of the proposed WN selection schemes is quantified and the tradeoff among accuracy, communication overhead and computational time is evaluated via analyses and simulations.

We have introduced a novel concept called "managed mobility", and addressed the mobility of the relaying devices called mobile ad hoc relaying stations (MARSs) in the integrated cellular and ad hoc relaying (iCAR) system. We anticipate that the idea of managed mobility proposed in this paper for the iCAR system as well as the mobility management strategies may also be applied in other ad hoc networks, such as the self-reconfigurable sensor network, to reduce additional node deployment cost and increase fault tolerance.

As the optical network evolves from static long haul connection provider to an adaptive and "smart" backbone solution, optical burst switching (OBS) becomes an attractive scheme for its flexibility and efficiency. However, how to reduce data loss is a crucial issue in such an asynchronous and one-way reservation system. In this paper, we study one contention resolution strategy in OBS networks: deflection routing. We extend an existing work to provide approximate and accurate models for the data loss analysis of single OBS node with and without wavelength conversion capability. The accuracy of our models is evaluated by simulation results.

The integrated cellular ad hoc relaying (iCAR) system is a representative heterogeneous wireless system, proposed to address the congestion problem in the wireless networks. In this paper, we present an analytic model based on Markov chains for the queuing delay performance of iCAR. Our results show that the new call requests in iCAR have a significantly lower queuing delay than that of the conventional cellular system. The analytic model developed in this paper may serve as the guideline for the delay performance evaluation of the next generation heterogeneous wireless systems.

In this paper, a simplified analysis technique for the integrated cellular and Ad hoc relay (iCAR) systems is presented. First, a simple two-cell system is analyzed using a multi-dimensional Markov-chain. The performance metric employed is the call blocking probability of each cell in the system. To this end, first a closed-form expression for the call blocking probability in the two-cell system is provided. Then, it is shown that these closed-form expressions could be used to analyze more practical systems. The accuracy of the developed simple analytical expressions is checked and verified by comparing the results predicted by these analytical expressions with simulation results. It is shown that there is an excellent match between analytical and simulation results.

Due to limited functionalities and potentially large number of sensors, conventional routing strategies proposed for distributed control applications (such as mobile ad hoc networks) are not directly applicable in wireless sensor networks. In this paper, we propose a novel mesh multipath routing (M-MPR) with selective forwarding of packets. Our evaluation shows that M-MPR achieves much improved throughput performance over conventional disjoint multipath routing, with comparable power consumption and receiver complexity. We also show that for comparable throughput, M-MPR achieves better load distribution and requires lesser route maintenance overhead with respect to packet forwarding along a preferred route.

One of the common problems faced by the wireless service providers worldwide is coping with congestion or hot spots. To handle this hot spot problem, methods that combine the existing cellular networks with ad hoc networks have been proposed. Integrated Cellular and Ad Hoc Relay (iCAR) system employs ad hoc relay stations (ARSs) within the cellular network to balance traffic loads efficiently and to share channels between cells via primary and secondary relaying. These ARSs operate in the ISM band, and therefore, do not cause interference to the cellular band. When analyzing the performance of WAR systems, there are several factors that should be taken into account These factors include the coverage area of the ARSs, the number of ARS channels, the placement of ARSs, etc. In this paper, the impact of the number of ARS channels on the performance of WAR systems is studied. To this end, a multi-dimensional Markov-chain analysis is performed for a simplified two-cell system model. Results show that, with a proper amount of ARS coverage within each cell the call blocking probabilities can be decreased significantly with a small number of channels. Results also suggest that by increasing the number of ARS channels perfect load balancing can be achieved.

iCAR is a new wireless architecture based on the integration of cellular and modern ad-hoc relaying technologies. We analyze its performance and compare it with conventional cellular system. In particular, we prove that due to the ability of ad-hoc relay stations (ARS) to relay traffic from one cell to another cell dynamically. iCAR has a lower system-wide call blocking probability than any corresponding cellular system without ARS, even if traffic can be evenly distributed among cells. We also study two typical scenarios and present some numeric results.

Integrated cellular and ad hoc relaying systems (iCAR) is a new wireless system architecture based on the integration of cellular and modern ad hoc relaying technologies. It addresses the congestion problem due to unbalanced traffic in a cellular system and provides interoperability for heterogeneous networks. The iCAR system can efficiently balance traffic loads between cells by using ad hoc relaying stations (ARS) to relay traffic from one cell to another dynamically. This not only increases the system's capacity cost effectively, but also reduces the transmission power for mobile hosts and extends system coverage. We compare the performance of the iCAR system with conventional cellular systems in terms of the call blocking/dropping probability, throughput, and signaling overhead via analysis and simulation. Our results show that with a limited number of ARSs and some increase in the signaling overhead (as well as hardware complexity), the call blocking/dropping probability in a congested cell and the overall system can be reduced.

Ever increasing data traffic and limited capacity are major causes for congestion in current cellular systems. This paper presents a new architecture for the next generation wireless systems based on the integration of the cellular infrastructure and modern ad-hoc relaying technologies. The new architecture can efficiently balance traffic loads between cells by using ad-hoc relay stations (ARS) to relay traffic from one cell to another cell dynamically. This can not only increase a system's capacity cost-effectively, but also reduce transmission power for mobile hosts, and provide services for shadow areas. In this paper, we present the architectural concept including its basic operations and principal benefits. We also propose a seed-growing approach for ARS placement, and discuss the upper bound on the number of seed ARSs needed in the system. We evaluate the performance improvement of the new architecture through analysis and simulations.

A fundamental problem in current cellular systems is limited capacity. Adding to this problem is unbalanced traffic among the cells. Given the explosion of the wireless traffic, especially wireless data traffic for Internet/Web access, and limited spectrum available for licensing, congestion will occur in some cells, resulting in blocked new calls and dropped handoffs due to the lack of available data channels (or DCHs). Since the locations of the congested cells vary from time to time (e.g. downtown on Monday morning, or amusement parks on Sunday afternoon), it's difficult to guarantee a sufficient amount of resources in each cell in a cost-effective way. In this paper, we propose to integrate the cellular infrastructure with modern wireless/mobile relaying technologies to achieve dynamic load balancing among different cells. Our basic idea is to place a number of mobile relay stations (or MRSs) within each cell to divert traffic in one (possibly congested) cell to another (non-congested) cell.