Metric Differential Privacy (mDP) extends classical differential privacy (DP) by replacing Hamming adjacency with application-aware distance metrics, which offers utility-preserving protection for structured and continuous data including locations, trajectories, images, and text embeddings. This Systematization of Knowledge (SoK) paper synthesizes a decade of progress (2013-2025), clarifying mDP's foundations and its connections to central and local DP, and surveying three principal mDP mechanism families: homogeneous distance mechanisms, non-homogeneous distance mechanisms, and optimized perturbation mechanisms. We organize applications across geo-location privacy, text and embeddings, image and voice protection, graphs and network telemetry, and federated/edge settings. We also surface open challenges, including robust composition and adversarial modeling, context-adaptive privacy, high-dimensional scalability, and principled geometry-aware trade-off bounds, and distill practical guidance for selecting metrics, mechanisms, and metrics of utility. The goal is a unified reference and roadmap for deploying scalable, utility-preserving metric privacy in real-world systems.

Acquiring high-resolution video is essential in applications such as surveillance, remote sensing, and microscopy. Yet pushing spatial resolution often compromises temporal resolution due to readout and bandwidth limitations. Rather than relying solely on hardware improvements, reference-based video superre-solution (Ref-VSR) provides a promising solution to achieve HR video acquisition with off-the-shelf cameras. However, existing Ref-VSR approaches are predominantly offline, relying on future frames and computationally expensive matching, which fundamentally limits their causal real-time applicability. In addition, the inherent trade-off between reconstruction fidelity and inference speed remains a key obstacle to practical use. To overcome these challenges, we present the first online reference-based video super-resolution framework (OnRef-VSR), which enables causal real-time reconstruction through efficient spatial and temporal feature transfers. The framework incorporates three key modules: Flow-Local Linear Transform-based Feature Enhancement (FLFE) module, Hierarchical Heterogeneous Feature Transfer (HHFT) module, and Detail Refinement with Flow-guided Local nonlinear transform (DRFL) module, which in combination realize effective integration of temporal cues and reference information. Extensive experiments demonstrate the superiority of the proposed approach. Specifically, OnRefVSR achieves a 5.2× faster speed with a 0.29 dB gain over the state-of-the-art real-time method, and its offline variant yields a 3.6× speedup and a 0.84 dB PSNR gain on the standard Vid4 benchmark, confirming both the effectiveness and practicality of OnRef-VSR for real-time high-fidelity video reconstruction.

Metric Differential Privacy (mDP) extends the local differential privacy (LDP) framework to metric spaces, enabling more nuanced privacy protection for data such as geo-locations.However, existing mDP optimization methods, particularly those based on linear programming (LP), face scalability challenges due to the quadratic growth in decision variables.In this paper, we propose Perturbation via Anchor-based Distributed Approximation (PAnDA), a scalable two-phase framework for optimizing metric differential privacy (mDP).To reduce computational overhead, PAnDA allows each user to select a small set of anchor records, enabling the server to solve a compact linear program over a reduced domain.We introduce three anchor selection strategies, exponential decay (PAnDA-e), power-law decay (PAnDA-p), and logistic decay (PAnDA-l), and establish theoretical guarantees under a relaxed privacy notion called probabilistic mDP (PmDP).Experiments on real-world geo-location datasets demonstrate that PAnDA scales to secret domains with up to 5,000 records, two times larger than prior LP-based methods, while providing theoretical guarantees for both privacy and utility.

Accurate radio source localization on resource-constrained hardware presents a primary challenge in wireless networks. We introduce EfficientLocNet, a novel architecture achieving superior accuracy with exceptional computational efficiency, driven by two distinct design choices. Its efficiency stems from lightweight Depthwise Separable Convolutions, while its accuracy is enhanced by two components working in tandem: Atrous Spatial Pyramid Attention to capture long-range spatial features, and a Self-Attention module to refine the latent representation. Evaluated against state-of-the-art (SOTA) methods, EfficientLocNet outperforms the top-performing model, DSLoc, on all key metrics: it reduces the mean localization error by at least 5%, possesses a 40x smaller model size, and requires over 100x fewer computations. This compelling combination of performance and efficiency validates EfficientLocNet as a powerful solution for deployment in edge computing environments.

Accurate traffic forecasting is crucial for Intelligent Transportation Systems (ITS) but is significantly challenged by non-periodic external events that disrupt regular traffic patterns. While Graph Neural Networks (GNNs) excel at modeling periodic traffic, they often falter in predicting event-driven dynamics. Existing event-aware methods either rely on manually engineered features with limited generalization or depend on curated textual event datasets that are costly to maintain and incomplete. The advent of Large Language Models (LLMs) offers new avenues for understanding and integrating event information. However, directly applying LLMs for all spatio-temporal reasoning can be inefficient, and effectively leveraging their event understanding capabilities within structured forecasting workflows remains a challenge. This paper introduces FUSE-Traffic, a framework which synergizes the dynamic event querying and understanding prowess of LLMs with the spatio-temporal modeling capabilities of GNNs. FUSE-Traffic features an on-demand event information extraction module using LLM prompting and a cross-attention based multimodal fusion mechanism to integrate rich event semantics with traffic flow features. This design enables the model to dynamically perceive and adapt to event-triggered traffic pattern changes. Comprehensive experiments on the METR-LA and PEMS datasets demonstrate that FUSE-Traffic significantly outperforms state-of-the-art models, especially under high-impact event conditions, showcasing robust predictive accuracy and resilience where traffic patterns are most disrupted. Code available at https://github.com/GeoAICenter/FUSE-Traffic_Sigspatial2025

Geo-obfuscation serves as a location privacy protection mechanism (LPPM), enabling mobile users to share obfuscated locations with servers, rather than their exact locations. This method can protect users’ location privacy when data breaches occur on the server side since the obfuscation process is irreversible. To reduce the utility loss caused by data obfuscation, linear programming (LP) is widely employed, which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in largescale geo-obfuscation applications. In this paper, we propose a new LPPM, called Locally Relevant Geo-obfuscation (LR-Geo), to optimize geo-obfuscation using LP in a time-efficient manner. This is achieved by confining the geoobfuscation calculation for each user exclusively to the locally relevant (LR) locations to the user’s actual location. Given the potential risk of LR locations disclosing a user’s actual whereabouts, we enable users to compute the LP coefficients locally and upload them only to the server, rather than the LR locations. The server then solves the LP problem based on the received coefficients. Furthermore, we refine the LP framework by incorporating an exponential obfuscation mechanism to guarantee the indistinguishability of obfuscation distribution across multiple users. Based on the constraint structure of the LP formulation, we apply Benders’ decomposition to further enhance computational efficiency. Our theoretical analysis confirms that, despite the geo-obfuscation being calculated independently for each user, it still meets geo-indistinguishability constraints across multiple users with high probability. Finally, the experimental results based on a real-world dataset demonstrate that LR-Geo outperforms existing geo-obfuscation methods in computational time, data utility, and privacy preservation.

Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit, holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-stage framework for adaptively allocating the CS sampling based on previous CS measurements. However, reconstruction is commonly required for analyzing and steering the successive CS sampling, which bottlenecks the ACS speed and impedes the practical application in time-sensitive scenarios. Addressing this challenge, we propose a reconstruction-free cascaded ACS method, which requires NO reconstruction during the adaptive sampling process. A lightweight Score Network (ScoreNet) is proposed to directly determine the ACS allocation with previous CS measurements and a differentiable adaptive sampling module is proposed for end-to-end training. For image reconstruction, we propose a Multi-Grid Spatial-Attention Network (MGSANet) that could facilitate efficient multi-stage training and inferencing. By introducing the reconstruction-fidelity supervision outside the loop of the multi-stage sampling process, ACS can be efficiently optimized and achieve high imaging fidelity. The effectiveness of the proposed method is demonstrated with extensive quantitative and qualitative experiments, compared with the state-of-the-art CS algorithms.

Geo-obfuscation is a Location Privacy Protection Mechanism used in location-based services that allows users to report obfuscated locations instead of exact ones. A formal privacy criterion, geoindistinguishability (Geo-Ind), requires real locations to be hard to distinguish from nearby locations (by attackers) based on their obfuscated representations. However, Geo-Ind often fails to consider context, such as road networks and vehicle traffic conditions, making it less effective in protecting the location privacy of vehicles, of which the mobility are heavily influenced by these factors.

Understanding and predicting Origin-Destination (OD) flows is crucial for urban planning and transportation management. Traditional OD prediction models, while effective within single cities, often face limitations when applied across different cities due to varied traffic conditions, urban layouts, and socio-economic factors.

The allocation of tasks in emergency response services, particularly within the framework of 911 operations, presents a critical challenge especially when ensuring the confidentiality of workers’ whereabouts. This issue arises from the necessity for responders to disclose their whereabouts to a central server for efficient task assignment, raising concerns regarding privacy breaches and potential risks to responders’ safety. In this paper, we address this issue by introducing a novel framework capable of assigning two 911 workers to respond to a patient’s request for help, while effectively safeguarding the workers’ location privacy through a devised location obfuscation strategy. Based on the constrained optimization of the location privacy protection model and task allocation model, we find that it can be approximated as two linear programming problems that can be solved by mature methods. Finally, we conduct a simulation experiments, the results of which illustrate the effectiveness of our approach in task allocation while preserving location privacy.

Scene-dependent adaptive compressive sensing (CS) has been a long pursuing goal that has huge potential to significantly improve the performance of CS. However, with no access to the ground truth, how to design the scene-dependent adaptive strategy is still an open problem. In this paper, a restricted isometry property (RIP) condition-based error-clamping is proposed, which could directly predict the reconstruction error, i.e., the difference between the current-stage reconstructed image and the ground truth image, and adaptively allocate more samples to regions with larger reconstruction error at the next sampling stage. Furthermore, we propose a CS reconstruction network composed of Progressively inverse transform and Alternating Bi-directional Multi-grid Network, named PiABM-Net, that could efficiently utilize the multi-scale information for reconstructing the target image. The effectiveness of the proposed adaptive and cascaded CS method is demonstrated with extensive quantitative and qualitative experiments, compared with the state-of-the-art CS algorithms.

Customizing the location obfuscation functions generated by existing systems can result in weakening the privacy guarantees offered by these functions as they are not robust against such updates. In this demo, we present a new framework called, CORGI, i.e., CustOmizable Robust Geo Indistinguishability. The demonstration platform is a web application which is built on top on a real world dataset (Gowalla). The user-friendly interface of the demo allows participants to easily specify their customization preferences and generate a customizable and robust location obfuscation function. They can also examine the trade-offs among privacy, utility, and customization; visualized on a map for comparison between CORGI and a state of the art baseline.

This work models the costs and benefits of personal information sharing, or self-disclosure, in online social networks as a networked disclosure game. In a networked population where edges represent visibility amongst users, we assume a leader can influence network structure through content promotion, and we seek to optimize social welfare through network design. Our approach considers user interaction non-homogeneously, where pairwise engagement amongst users can involve or not involve sharing personal information. We prove that this problem is NP-hard. As a solution, we develop a Mixed-integer Linear Programming algorithm, which can achieve an exact solution, and also develop a time-efficient heuristic algorithm that can be used at scale. We conduct numerical experiments to demonstrate the properties of the algorithms and map theoretical results to a dataset of posts and comments in 2020 and 2021 in a COVID-related Subreddit community where privacy risks and sharing tradeoffs were particularly pronounced.

In this article, we demonstrate that by using a temporal sequence of applied brake signals collected from a vehicle, attackers can still possibly infer the vehicle’s route over the period, even though brake-signal data does not reveal any specific location information. Our route inference is basically composed of three steps. At first, we categorize brake-signal subsequences into four different driving maneuvers (i.e., stopping from a certain speed, reducing speed to adjust with the traffic flow, and taking left and right turns). Second, we estimate the number of intersections traversed by the vehicle using the applied brake signals and their corresponding maneuvers. Finally, we design a graph-based route-selection algorithm to find a list of (paths) routes from the regional map using the predicted driving maneuvers and the speed profile. We evaluate our approach using over 450 km of transportation data, which has been collected from 25 individuals. The experimental results demonstrate that, by resorting to our solution, 92.04% of the original drivers’ trajectory can be successfully recovered from their brake data regardless of driver and vehicle models.

Catalysis and fidelity of multisubunit RNA polymerases rely on a highly conserved active site domain called the trigger loop (TL), which achieves roles in transcription through conformational changes and interaction with NTP substrates. The mutations of TL residues cause distinct effects on catalysis including hypo- and hyperactivity and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate phenotypes with MD simulated structural alterations. Using fitness values of mutants under various stress conditions, we modeled phenotypes along a spectrum of continual values. We found that ML could predict the phenotypes with 0.68 R2 correlation from amino acid sequences alone. It was more difficult to incorporate MD data to improve predictions from machine learning, presumably because MD data is too noisy and possibly incomplete to directly infer functional phenotypes. However, a variational auto-encoder model based on the MD data allowed the clustering of mutants with different phenotypes based on structural details. Overall, we found that a subset of loss-of-function (LOF) and lethal mutations tended to increase distances of TL residues to the NTP substrate, while another subset of LOF and lethal substitutions tended to confer an increase in distances between TL and bridge helix (BH). In contrast, some of the gain-of-function (GOF) mutants appear to cause disruption of hydrophobic contacts among TL and nearby helices.

One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure.

To enable self-driving without a human driver, an autonomous vehicle needs to perceive its surrounding obstacles using onboard sensors, of which the perception accuracy might be limited by their own sensing range. An effective way to improve vehicles’ perception accuracy is to let nearby vehicles exchange their sensor data so that vehicles can detect obstacles beyond their own sensing ranges, called cooperative perception. The shared sensor data, however, might disclose the sensitive information of vehicles’ passengers, raising privacy and safety concerns (e.g. stalking or sensitive location leakage).In this paper, we propose a new data-sharing policy for the cooperative perception of autonomous vehicles, of which the objective is to minimize vehicles’ information disclosure without compromising their perception accuracy. Considering vehicles usually have different desires for data-sharing under different traffic environments, our policy provides vehicles autonomy to determine what types of sensor data to share based on their own needs. Moreover, given the dynamics of vehicles’ data-sharing decisions, the policy can be adjusted to incentivize vehicles’ decisions to converge to the desired decision field, such that a healthy cooperation environment can be maintained in a long term. To achieve such objectives, we analyze the dynamics of vehicles’ data-sharing decisions by resorting to the game theory model, and optimize the data-sharing ratio in the policy based on the analytic results. Finally, we carry out an extensive trace-driven simulation to test the performance of the proposed data-sharing policy. The experimental results demonstrate that our policy can help incentivize vehicles’ data-sharing decisions to the desired decision fields efficiently and effectively.

In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless power transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We proposeCatChargerto tackle this challenge. By analyzing a metropolitan-scale data set, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks), and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the kernel density estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multiobjective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate thatCatChargerincreases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost.

Nowadays, vehicles have been increasingly adopted in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location to servers to facilitate the service utilities. Traditional location privacy protection mechanisms cannot be applied to vehicle-based SC since they assume workers’ mobility on a 2-dimensional plane without considering the network-constrained mobility features of vehicles. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) over road networks. Our objective is to design a location obfuscation strategy to minimize the quality loss due to obfuscation withgeo-indistinguishabilitysatisfied. Considering the computational complexity of VLP, by resorting to discretization, we first approximate VLP to a linear programming problem that can be solved by well-developed approaches. To further improve the time-efficiency, we conduct constraint reduction for VLP by exploiting key features of geo-indistinguishability in road networks and problem decomposition based on VLP’s constraint structure. Finally, we carry out both trace-driven simulation and real-world experiments, where our experimental results demonstrate the superiority of our approach over a known state-of-the-art location obfuscation strategy in terms of both quality-of-service and privacy.

To maximize its own profit, cloud service brokerage (CSB) aims to distribute tenant demands to reserved servers such that the total reservation cost is minimized with the tenants’ service level agreement (SLA) being satisfied. Thedemand allocationproblem for CSB is non-trivial to solve due to uncertainty of tenants’ behavior. To avoid possible violations among demands, existing schemes allocate additional padding resources on the predicted demands, which leads to under-utilization of reserved resources. Accordingly, we propose aProbabilistic Demand Allocation(PDA) system to address the demand allocation problem for CSB. In PDA, we not only predict tenants’ demands based on their historical records, but also estimate the probability distribution of prediction errors. As over- and under-estimation are equally likely to happen with our prediction method, when allocating demands to a single server, their errors are possibly offset. Hence, it is unnecessary to allocate additional resource to each demand for violation prevention. Given the predication results, we formulate the demand allocation problem by probabilistic optimization, of which the objective is to minimize the overall cost from reserved servers while satisfying tenants’ SLA with high probability. Both simulation and real-world experimental results demonstrate the superiority of PDA in reducing servers’ reservation cost.

As video streaming applications are deployed on the cloud, cloud providers are charged by ISPs for inter-datacenter transfers under the dominant percentile-based charging models. In order to minimize the payment costs, existing works aim to keep the traffic on each link under the charging volume. However, these methods cannot fully utilize each link's available bandwidth capacity. As a solution, we propose an economical and deadline-driven video flow scheduling system, called EcoFlow. Considering that different video flows have different transmission deadlines, EcoFlow transmits videos in the order of their deadline tightness and postpones the deliveries of later-deadline videos to later time slots. The flows that are expected to miss their deadlines are divided into subflows to be rerouted to other under-utilized links. We also propose setting each link's initial charging volume to reduce the scheduling latency at the beginning of the charging period and discuss how to deal with issues such as the prediction errors of link available bandwidth and the lack of charging volume's prior knowledge. Furthermore, we designed implementation strategies for using EcoFlow in both centralized and distributed situations. Experimental results demonstrate that EcoFlow achieves lower bandwidth costs and higher video flow transmission rates when compared to existing methods.

To promote cost-effective task assignment in Spatial Crowdsourcing (SC), workers are required to report their location to servers, which raises serious privacy concerns. As a solution, geo-obfuscation has been widely used to protect the location privacy of SC workers, where workers are allowed to report perturbed location instead of the true location. Yet, most existing geo-obfuscation methods consider workers? mobility on a 2 dimensional (2D) plane, wherein workers can move in arbitrary directions. Unfortunately, 2D-based geo-obfuscation is likely to generate high traveling cost for task assignment over roads, as it cannot accurately estimate the traveling costs distortion caused by location obfuscation. In this paper, we tackle the SC worker location privacy problem over road networks. Considering the network-constrained mobility features of workers, we describe workers? mobility by a weighted directed graph, which considers the dynamic traffic condition and road network topology. Based on the graph model, we design a geo-obfuscation (GO) function for workers to maximize the workers? overall location privacy without compromising the task assignment efficiency. We formulate the problem of deriving the optimal GO function as a linear programming (LP) problem. By using the angular block structure of the LP's constraint matrix, we apply Dantzig-Wolfe decomposition to improve the time-efficiency of the GO function generation. Our experimental results in the real-trace driven simulation and the real-world experiment demonstrate the effectiveness of our approach in terms of both privacy and task assignment efficiency.

Many usage-based automotive insurance programs do not directly use any GPS-based location tracking devices. Instead, CAN-bus data, such as brake signal data, can be collected by these programs to evaluate drivers’ driving habits and vehicle usage policies. In this paper, we demonstrate that by using a temporal sequence of applied brake signals collected from a vehicle, attackers can still possibly infer the vehicle’s route over the period, even though brake signal data does not reveal any specific location information. Our route inference is basically composed of three steps: At first, we categorize brake signal subsequences into four different driving maneuvers (i.e., stopping from a certain speed, reducing speed to adjust with the traffic flow, and taking left and right turns). Second, we estimate the number of intersections traversed by the vehicle using the applied brake signals and their corresponding maneuvers. Particularly, we also estimate the overall speed profile based on the magnitude and interval of different brake signals. From the estimated speed profile, we infer the distances, traveling time, and traffic signs corresponding to the candidate edges. Finally, we design a graph-based route-selection algorithm to find a list of (paths) routes from the regional map using the predicted driving maneuvers and the speed profile. We use a score function based on three factors (i.e., distance, traveling time, and traffic signs) to identify a candidate edge. We evaluate our approach using over 450km of transportation data, which has been collected from 24 individuals. The experimental results demonstrate that, by resorting to our solution, 89% of the original drivers’ trajectory can be successfully recovered from their brake data regardless of driver and vehicle models.

Always keeping a certain distance between vehicles in a platoon system is important for collision avoidance. Centralized platoon systems let the leader vehicle determine and notify the velocities of all the vehicles in the platoon. Unfortunately, such a centralized method generates high packet drop rate and communication delay due to the leader vehicle's limited communication capability. Therefore, we propose a decentralized platoon network, in which each vehicle determines its velocity by only communicating with the vehicles in a short range. However, the multiple simultaneous transmissions between different pairs of vehicles may interfere with each other. By leveraging a typical feature of a platoon, we devise a channel allocation algorithm, called the Fast and Lightweight Autonomous channel selection algorithm (FLA), in which each vehicle determines its channel simply based on its distance to the leader vehicle. We also devise a strategy, in which a succeeding vehicle uses its stored common velocity profile when it is disconnected from its preceding vehicle and then adjusts its velocity once the connection is built. We conduct experiments on NS-3 and Matlab to evaluate the performance of our proposed methods and implement a real-world prototype by equipping vehicles with Android mobile devices. The experimental results demonstrate the superior performance of our decentralized platoon network over the previous centralized platoon networks.

Crowdsourcing leverages the diverse skill sets of large collections of individual contributors to solve problems and execute projects, where contributors may vary significantly in experience, expertise, and interest in completing tasks. Hence, to ensure the satisfaction of its task requesters, most existing crowdsourcing platforms focus primarily on supervising contributors' behavior. This lopsided approach to supervision negatively impacts contributor engagement and platform sustainability.

Nowadays, vehicles have been increasingly adopted as participants in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location information to servers to facilitate the utilities of SC services. Traditional location privacy protection mechanisms cannot be directly applied to vehicle-based SC since they assume workers' location on a 2-dimensional plane, which does not take into account the features of vehicle workers' mobility in vehicle road networks. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) in vehicle road networks. Our objective is to design a location obfuscation strategy to minimize the loss of quality-of-service (QoS) due to task distribution with location obfuscation, while guaranteeing geo-indistinguishability to be satisfied. Considering the computational complexity of the VLP problem, by resorting to discretization, we approximate VLP to a linear programming problem that can be solved by existing well-developed approaches (such as the simplex method). To further improve the time efficiency, we reduce the number of constraints in VLP by exploiting key features of geo-indistinguishability in vechicle road networks (such as transitivity). Finally, our experimental results demonstrate that our approach can achieve a reasonable approximation of the minimum QoS loss with location privacy protected, and also outperforms a known state-of-the-art location obfuscation strategy in terms of both QoS and privacy.

Functioning as an intermediary between tenants and cloud providers, cloud service brokerages (CSBs) can bring about great benefits to the cloud market. As energy costs of cloud computing have been increasing rapidly, there is a need for cloud providers to optimize energy efficiency while maintain high service level performance to tenants, not only for their own benefit but also for social welfares. Thus, for green cloud companies, two questions have arisen: 1) under what pricing policies from the cloud providers to the CSB, a profit-driven CSB is willing to minimize the total energy cost while satisfy tenant demands and 2) how should a CSB distribute tenants demands to achieve this objective? To address question 1), we find a pricing policy for cloud providers such that maximizing CSBs profit is equivalent to minimizing cloud providers energy cost. To address question 2), we first devise a greedy solution, and then propose an approximation algorithm and a decomposition-based solution with a constant approximation ratio. Both simulation and real-world Amazon EC2 experimental results demonstrate the effectiveness of our pricing policy to incentivize CSBs to save energy and the superior performance of our algorithms in energy efficiency and resource utilization over the previous algorithms.

In recent years, many efforts have been devoted to reducing vehicles' energy consumption through optimizing their velocities. However, all previous methods neglect avoiding vehicle collision when calculating vehicles' optimal velocity profiles. In this paper, we formulate a new problem, called the Collision-Aware vehicle Energy consumption Minimization (CAEM) problem that calculates the optimal velocity profiles which avoid vehicle collision. CAEM is more difficult to solve than the traditional velocity optimization problem that is for a single vehicle, since CAEM needs to take into account the mobility of all the vehicles together to avoid collision. This problem is a convex problem that cannot be directly solved by existing methods. Further, it is impractical to get the mobility information of all vehicles at the beginning. Even if it is feasible, the computation efficiency is very low. We propose a novel method that can tackle these challenges. In order to keep each vehicle velocity as stable as possible to reduce the energy consumption, it builds a light schematic map to help identify the green light time interval of each traffic light in the source-destination route of a vehicle, during which the vehicle must drive through the traffic light. Rather than considering the mobility of all vehicles at a time, it calculates each vehicle's velocity profile in sequence based on the starting time to prevent the vehicle from colliding with all the previously scheduled vehicles. Finally, CAEM is transformed to non-convex problem and can be solved by existing optimization methods. Simulation and real-world testbed experimental results demonstrate the superior performance of our method over the previous methods.

Present approaches to achieve k-coverage for Wireless Sensor Networks still rely on centralized techniques. In this paper, we devise a distributed method for this problem, namely Distributed VOronoi based Cooperation scheme (DVOC), where nodes cooperate in hole detection and recovery. In previous Voronoi based schemes, each node only monitors its own critical points. Such methods are inefficient for k-coverage because the critical points are far away from their generating nodes in k-order Voronoi diagram, causing high cost for transmission and computing. As a solution, DVOC enables nodes to monitor others' critical points around themselves by building local Voronoi diagrams (LVDs). Further, DVOC constrains the movement of every node to avoid generating new holes. If a node cannot reach its destination due to the constraint, its hole healing responsibility will fall to other cooperating nodes. The experimental results from the real world testbed demonstrate that DVOC outperforms the previous schemes.

Crowdsourcing sites heavily rely on paid workers to ensure completion of tasks. Yet, designing a pricing strategies able to incentivize users' quality and retention is non trivial. Existing payment strategies either simply set a fixed payment per task without considering changes in workers' behaviors, or rule out poor quality responses and workers based on coarse criteria. Hence, task requesters may be investing significantly in work that is inaccurate or even misleading. In this paper, we design a dynamic contract to incentivize high-quality work. Our proposed approach offers a theoretically proven algorithm to calculate the contract for each worker in a cost-efficient manner. In contrast to existing work, our contract design is not only adaptive to changes in workers' behavior, but also adjusts pricing policy in the presence of malicious behavior. Both theoretical and experimental analysis over real Amazon review traces show that our contract design can achieve a near optimal solution. Furthermore, experimental results demonstrate that our contract design 1) can promote high-quality work and prevent malicious behavior, and 2) outperforms the intuitive strategy of excluding all malicious workers in terms of the requester's utility.

In this paper, we study the link scheduling problem considering the fluctuating fading effect in transmissions. We extend the previous deterministic physical interference model to the Rayleigh-fading model that uses the stochastic propagation to address fading effects. Based on this model, we formulate a problem called Fading-Resistant Link Scheduling (Fading-R-LS) problem, which aims to maximize the throughput of all links in a single time slot. We prove that this problem is NP-hard. Based on the geometric structure of Fading-R-LS, we then propose two centralized schemes with O(g(L)) and O(1) performance guarantee, respectively, where g(L) is the number of magnitudes of transmission link lengths. Our experimental results show that the superior performance of our proposed schemes compared to previous schemes.

The future generation of transportation system will be featured by electrified public transportation. To fulfill metropolitan transit demands, electric vehicles (EVs) must be continuously operable without recharging downtime. Wireless Power Transfer (WPT) techniques for in-motion EV charging is a solution. It however brings up a challenge: how to deploy charging lanes in a metropolitan road network to minimize the deployment cost while enabling EVs' continuous operability. In this paper, we propose CatCharger, which is the first work that handles this challenge. From a metropolitan-scale dataset collected from multiple sources of vehicles, we observe the diversity of vehicle passing speed and daily visit frequency (called traffic attributes) at intersections (i.e., landmarks), which are important factors for charging lane deployment. To select landmarks for deployment, we first group landmarks with similar traffic attribute values using the entropy minimization clustering method, and choose better candidate landmarks from each group suitable for deployment. To determine the deployment locations from the candidate landmarks, we infer the expected vehicle residual energy at each landmark using a Kernel Density Estimator fed by the vehicles' mobility, and formulate and solve an optimization problem to minimize the total deployment cost while ensuring a certain level of expected residual energy of EVs at each landmark. Our trace-driven experiments demonstrate the superior performance of CatCharger over other methods.

Electric vehicles (EVs) will become a component of the future generation intelligent transportation system. Because of EVs' limited battery power, the wireless power transfer (WPT) system has drawn much attention in recent years. The WPT system charges EVs in motion when they pass the charging lanes installed in roads without requiring physical contact between utility power supply and vehicle battery. A charging lane has limited power that can be transferred to EVs on the charging lane. A challenge here is how to allocate the limited power to the EVs so that they have sufficient power to arrive at the next charging lane or their destinations (when there are no charging lanes ahead). In this paper, we study this power distribution scheduling problem.We provide solutions to handle this challenge and also achieve each of the following goals as much as possible: i) balancing the state of charge (SOC) of the EVs, ii) balancing the amount of stored power of the EVs, and iii) minimizing the total power charged. This paper is the first work that handles such a power distribution scheduling problem in WPT systems. Our extensive experiments on MatLab and Simulation for Urban MObility (SUMO) show the effectiveness of our scheduling solutions in achieving the different goals compared with other scheduling methods including first-come- first-serve and equal share.

Crowdsourcing allows many people to complete tasks of various difficulty with minimal recruitment and administration costs. However, the lack of participant accountability may entice people to complete as many tasks as possible without fully engaging in them, jeopardizing the quality of responses. In this paper, we present a dynamic and time efficient solution to the task assignment problem in crowdsourcing platforms. Our proposed approach, CrowdSelect, offers a theoretically proven algorithm to assign workers to tasks in a cost efficient manner, while ensuring high accuracy of the overall task. In contrast to existing works, our approach makes minimal assumptions on the probability of error for workers, and completely removes the assumptions that such probability is known apriori and that it remains consistent over time. Through experiments over real Amazon Mechanical Turk traces and synthetic data, we find that CrowdSelect has a significant gain in term of accuracy compared to state-of-the-art algorithms, and can provide a 17.5\% gain in answers' accuracy compared to previous methods, even when there are over 50\% malicious workers.

To minimize the fuel consumption for driving, several methods have been proposed to calculate vehicles' optimal velocity profiles on a remote cloud. Considering the traffic dynamism, each vehicle needs to keep updating the velocity profile, which requires low latency for information uploading and profile calculation. However, these proposed methods cannot satisfy this requirement due to (1) high queuing delay for information uploading caused by a large number of vehicles, and (2) the neglect of the traffic light and high computation delay for velocity profile. For (1), considering the driving features of close vehicles on a road, e.g., similar velocity and interdistances, we propose to group vehicles within a certain range and let the leader vehicle in each group to upload the group information to the cloud, which then derives the velocity of each vehicle in the group. For (2), we propose spatial-temporal DP (ST-DP) that additionally considers the traffic lights. We innovatively find that the DP process makes it well suited to run on Spark (a fast parallel cluster computing framework) and then present how to run ST-DP on Spark. Finally, we demonstrate the superiority of our method using both trace-driven simulation (NS-2.33 simulator and MATLAB) and real-world experiments.

Though intensive research efforts have been devoted to the study of the link scheduling problem in wireless networks, no previous work has discussed this problem for cooperative communication networks, in which receivers are allowed to combine messages from different senders to combat transmission errors. In this paper, we study the link scheduling problem in wireless cooperative communication networks, in which receivers are allowed to combine copies of a message to combat fading. We formulate two problems named cooperative link scheduling problem (CLS) and one-shot cooperative link scheduling problem (OCLS). The first problem aims to find a schedule of links that uses the minimum number of time slots to inform all the receivers. The second problem aims to find a set of links that can inform the maximum number of receivers in one time slot. As a solution, we propose an algorithm for both CLS and OCLS with g(K) approximation ratio, where g(K) is so called diversity of key links. In addition, we propose a greedy algorithm with O(1) approximation ratio for OCLS when the number of links for each receiver is upper bounded by a constant. Simulation results indicate that our cooperative link scheduling approaches outperform non-cooperative ones.

As an alternate form in the road transportationsystem, electric vehicle (EV) can help reduce the fossil-fuelconsumption. However, the usage of EVs is constrained by thelimited capacity of battery. Wireless Power Transfer (WPT) can increase the driving range of EVs by charging EVs inmotionwhen they drive through a wireless charging laneembedded in a road. The amount of power that can be suppliedby a charging lane at a time is limited. A problem here iswhen a large number of EVs pass a charging lane, how toefficiently distribute the power among different penetrationslevels of EVs? However, there has been no previous researchdevoted to tackling this challenge. To handle this challenge, wepropose a system to balance the State of Charge (called BSoC) among the EVs. It consists of three components: i) fog-basedpower distribution architecture, ii) power scheduling model, and iii) efficient vehicle-to-fog communication protocol. The fogcomputing center collects information from EVs and schedulesthe power distribution. We use fog closer to vehicles ratherthan cloud in order to reduce the communication latency. Thepower scheduling model schedules the power allocated to eachEV. In order to avoid network congestion between EVs and thefog, we let vehicles choose their own communication channelto communicate with local controllers. Finally, we evaluateour system using extensive simulation studies in NetworkSimulator-3, MatLab, and Simulation for Urban MObilitytools, and the experimental results confirm the efficiency ofour system.

Functioning as an intermediary between cloud tenants and providers, cloud service brokerages (CSBs) bring about great benefits to the cloud market. To maximize its own profit, a CSB is faced with a challenge: how to reserve servers and distribute tenant demands to the reserved servers such that the total reservation cost is minimized while the reserved servers can satisfy the tenant service level agreement (SLA)? Demand prediction and demand allocation are two steps to solve this problem. However, previous demand prediction methods cannot accurately predict tenant demands since they cannot accurately estimate prediction errors and also assume the existence of seasonal periods of demands. Previous demand allocation methods only aim to minimize the number of reserved servers rather than the server reservation cost, which is more challenging. To solve this challenge, we propose a Probabilistic Demand Allocation system (PDA). It predicts demands and more accurate prediction errors without the assumption of the existence of seasonal periods. It then formulates a nonlinear programming problem and has a decentralized method to find the problem solution. In addition to overcoming the shortcomings in previous methods, PDA is novel in that rather than separately conducting the prediction and demand allocation, it considers prediction errors in demand allocation in order to allocate demands with offsetting prediction errors (e.g., -1 and +1) to the same server, which helps find the problem solution. Both simulation and real-world experimental results demonstrate the superior performance of our system in reducing servers' reservation cost.

Always keeping a certain distance between vehicles in a platoon is important for collision avoidance. Centralized platoon systems let the leader vehicle determine and notify the velocities of all the vehicles in the platoon. Unfortunately, such a centralized method generates high packet drop rate and communication delay due to the leader vehicle's limited communication capability. Therefore, we propose a decentralized platoon network, in which each vehicle determines its own velocity by only communicating with the vehicles in a short range. However, the multiple simultaneous transmissions between different pairs of vehicles may interfere with each other. Directly applying current channel allocation methods for interference avoidance leads to high communication cost and delay in vehicle joins and departures (i.e., vehicle dynamics). As a result, a challenge is how to reduce the communication delay and cost for channel allocation in decentralized platoon networks? To handle this challenge, by leveraging a typical feature of a platoon, we devise a channel allocation algorithm, called the Fast and Lightweight Autonomous channel selection algorithm (FLA), in which each vehicle determines its own channel simply based on its distance to the leader vehicle. We conduct experiments on NS-3 and Matlab to evaluate the performance of our proposed methods. The experimental results demonstrate the superior performance of our decentralized platoon network over the previous centralized platoon networks and of FLA over previous channel allocation methods in platoons.

Though a cooperative broadcast scheme has been proposed for fading environments, it has two defects: First, it only handles a packet flow from a single source node in the network, but does not consider the scenario of multiple packet flows simultaneously broadcasted from different source nodes. Second, it only allows a single relay node to forward a packet in each time slot, though multiple relay nodes forwarding in a time slot can significantly reduce broadcast latency. In this paper, we aim achieve low-latency multi-flow broadcast in wireless multi-hop networks with fading channels. To describe the interference among the transmission in different flows, we incorporate the Rayleigh fading model to the signal to noise ratio (SNR) model. Then, we introduce a cooperative diversity scheme which allows multiple relays forwarding in a time slot to reduce broadcast latency. We then formulate an interesting problem: In a fading environment, what is the optimal relay allocation schedule to minimize the broadcast latency? We propose a warm up heuristic algorithm for single-flow cooperative broadcast, based on which, we further propose a heuristic algorithm for multi-flow cooperative broadcast. Simulation results demonstrate that the two algorithms achieve lower broadcast latency than a previous method.

Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.

Functioning as an intermediary between tenants and cloud providers, cloud service brokerages (CSBs) can bring about great benefits to the cloud market. CSBs buy the cloud resources, i.e., servers, with lower prices from cloud providers and sell the resources to the tenants with higher prices. To maximize its own profit, a CSB may distribute tenants' requests to the clouds that waste energy resources. However, as energy costs of cloud computing have been increasing rapidly, there is a need for cloud providers to optimize energy efficiency while maintain high service level performance to tenants, not only for their own benefit but also for social welfares (e.g., protecting environment). Thus, for green cloud companies, two questions have arisen: 1) under what pricing policies from the cloud providers to the CSB, a profit-driven CSB is willing to minimize the total cloud energy cost while satisfy tenant demands and 2) how should a CSB distribute tenants' demands to achieve this objective? To address question 1), we find a pricing policy for cloud providers such that maximizing CSB's profit is equivalent to minimizing cloud providers' energy cost. To address question 2), we first devise a greedy solution, and then propose an approximation algorithm with a constant approximation ratio. Both simulation and real-world Amazon EC2 experimental results demonstrate the effectiveness of our pricing policy to incentivize CSBs to save energy for cloud providers and the superior performance of our algorithms in energy efficiency and resource utilizations in comparison with the previous algorithms.

In this paper, we study the minimum energy broadcast problem in time-varying graphs (TVGs), which are a very useful high level abstraction for studying highly dynamic wireless networks. To this end, we first incorporate a channel model, called energy-demand functions, to the current TVGs, namely time-varying energy-demand graphs (TVEGs). Based on this model, we formulate the problem: given a TVEG, what is the optimal schedule (i.e., Which nodes should forward a packet in what times and at what power levels) to minimize the energy consumption of the broadcast? We prove the problem to be NP-hard and o(log N) in approximable. It is a challenge to find a solution for this problem on continuous time. Fortunately, we prove that the problem on continuous time is equivalent to the problem on certain discrete time points, called discrete time set (DTS). Based on this property, we propose polynomial time solutions for this problem with different channel models, and evaluate the performance of these methods from real-life contact traces.

In this paper, we study the link scheduling problem in wireless cooperative communication networks, in which receivers are allowed to combine copies of a message from different senders to combat fading. We formulate a problem called cooperative link scheduling problem (CLS), which aims to find a schedule of links that uses the minimum number of time slots to inform all the receivers. As a solution, we propose an algorithm for CLS with g(K) approximation ratio, where g(K) is so called diversity of key links. Simulation results indicate that our cooperative link scheduling approaches outperform noncooperative ones.

Present approaches to achieve k-coverage for Wireless Sensor Networks still rely on centralized techniques. In this paper, we devise a distributed method for this problem, namely Distributed VOronoi based Cooperation scheme (DVOC), where nodes cooperate in hole detection and recovery. In previous Voronoi based schemes, each node only monitors its own critical points. Such methods are inefficient for k-coverage because the critical points are far away from their generating nodes in k-order Voronoi diagram, causing high cost for transmission and computing. As a solution, DVOC enables nodes to monitor others' critical points around themselves by building local Voronoi diagrams (LVDs). Further, DVOC constrains the movement of every node to avoid generating new holes. If a node cannot reach its destination due to the constraint, its hole healing responsibility will fall to other cooperating nodes. The experimental results from the real world testbed demonstrate that DVOC outperforms the previous schemes.

Hybrid wireless networks combining the advantages of both mobile ad-hoc networks and infrastructure wireless networks have been receiving increased attention due to their ultra-high performance. An efficient data routing protocol is important in such networks for high network capacity and scalability. However, most routing protocols for these networks simply combine the ad-hoc transmission mode with the cellular transmission mode, which inherits the drawbacks of ad-hoc transmission. This paper presents a Distributed Three-hop Routing protocol (DTR) for hybrid wireless networks. To take full advantage of the widespread base stations, DTR divides a message data stream into segments and transmits the segments in a distributed manner. It makes full spatial reuse of a system via its high speed ad-hoc interface and alleviates mobile gateway congestion via its cellular interface. Furthermore, sending segments to a number of base stations simultaneously increases throughput and makes full use of widespread base stations. In addition, DTR significantly reduces overhead due to short path lengths and the elimination of route discovery and maintenance. DTR also has a congestion control algorithm to avoid overloading base stations. Theoretical analysis and simulation results show the superiority of DTR in comparison with other routing protocols in terms of throughput capacity, scalability, and mobility resilience. The results also show the effectiveness of the congestion control algorithm in balancing the load between base stations.

Underlying link-layer protocols of well-established wireless networks that use the conventional “store-and-forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and balancing en/decoding load among nodes. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in a packet's route. To identify the intermediate nodes for the decoding, we mathematically calculate the average packet delay and formalize the problem as a nonlinear integer programming problem. By minimizing the delays, we derive three propositions that: 1) can identify the intermediate nodes that minimize the propagation and transmission delay of a packet; and 2) and 3) can identify the intermediate nodes that simultaneously minimize the queuing delay and maximize the fairness of en/decoding load of all the nodes. Guided by the propositions, we then propose a scalable and distributed scheme in CEDAR to choose the intermediate en/decoding nodes in a route to achieve its objective. The results from real-world testbed “NESTbed” and simulation with MATLAB prove that CEDAR is superior to schemes using hop-by-hop decoding and destination decoding not only in packet delay and throughput but also in energy-consumption and load distribution balance.

Cooperative broadcast, in which receivers are allowed to combine received packet from different senders to combat transmission errors, has gained increasing attention. Previous studies showed that broadcast optimization solutions are sufficient in non-fading environments but may suffer a low delivery ratio under wireless channel fading. Though previous work analyzed the tradeoff between energy and delay in cooperative broadcast, no works investigated the tradeoff in a fading environment. Thus, in this paper, we study this tradeoff with the consideration of fading. We formulate this problem as a Fading-resistant Delay-constrained Minimum Energy Cooperative Broadcast (FDMECB) problem, and prove that it is NP-complete. We then propose an approximation algorithm for theoretical interests. We further propose a heuristic algorithm that makes approximately optimal local decision to achieve global optimization. Our experimental results show that our algorithms outperform a previous non-fading resistant algorithm.

Recently, many schemes have been proposed for detecting and healing coverage holes to achieve full coverage in wireless sensor networks (WSNs). However, none of these schemes aim to find the shortest node movement paths to heal the coverage holes, which could significantly reduce energy usage for node movement. Also, current hole healing schemes require accurate knowledge of sensor locations; obtaining this knowledge consumes high energy. In this paper, we propose a Delaunay-based coordinate-free mechanism (DECM) for full coverage. Based on rigorous mathematical analysis, DECM can detect coverage holes and find the locally shortest paths for healing holes in a distributed manner without requiring accurate node location information. Also, DECM incorporates a cooperative movement mechanism that can prevent generating new holes during node movements in healing holes. Simulation results and experimental results from the real-world GENI Orbit testbed show that DECM achieves superior performance in terms of the energy-efficiency, effectiveness of hole healing, energy consumption balance and lifetime compared to previous schemes.

Radio frequency identification (RFID) and wireless sensor networks (WSNs) have been popular in the industrial field, and both have undergone dramatic development. RFID and WSNs are well known for their abilities in identity identification and data transmission, respectively, and hence widely used in applications for environmental and health monitoring. Though the integration of a sensor and an RFID tag was proposed to gather both RFID tag and sensed information, few previous research efforts explore the integration of data transmission modes in the RFID and WSN systems to enhance the performance of the applications. In this paper, we propose a hybrid RFID and WSN system (HRW) that synergistically integrates the traditional RFID system and WSN system for efficient data collection. HRW has hybrid smart nodes that combine the function of RFID tags, the reduced function of RFID readers, and wireless sensors. Therefore, nodes can read each other's sensed data in tags, and all data can be quickly transmitted to an RFID reader through the node that first reaches it. The RFID readers transmit the collected data to the back-end servers for data processing and management. We also propose methods to improve data transmission efficiency and to protect data privacy and avoid malicious data selective forwarding in data transmission. Comprehensive simulation and trace-driven experimental results show the high performance of HRW in terms of the cost of deployment, transmission delay and capability, and tag capacity requirement.

Underlying link-layer protocols of wireless networks use the conventional “store and forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and high throughput. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically-sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in packet's route. To identify the intermediate nodes for the en/decoding for minimizing average packet latency, we mathematically analyze the average packet delay, using Finite State Markovian Channel model and priority queuing model, and then formalize the problem as a non-linear integer programming problem. Also, we propose a scalable and distributed scheme to solve this problem. The results from real-world testbed “NESTbed” and simulation with Matlab prove that CEDAR is superior to the schemes using hop-by-hop decoding and destination-decoding not only in packet delay but also in throughput. In addition, the simulation results show that CEDAR can achieve the optimal performance in most cases.

Cooperative broadcast, in which a packet receiver cooperatively combines received weak signal power from different senders to decode the original packet, has gained increasing attention. However, existing approaches are developed based on the assumption that there is a single flow in the network; thus, they are not suitable for multi-flow broadcasting in which broadcasts are initiated by different nodes and consist of more than one packet at any point in time. In this paper, we aim to achieve low-latency multi-flow broadcast in wireless multihop networks with fading channels. We formulate this problem as a Minimum Slotted Delay Cooperative Broadcast (MSDCB) problem, and prove that it is NP-complete and o(logN) inapproximable. We then propose two heuristic algorithms named PCBHS and PCBH-M to solve MSDCB. Our experimental results show that our algorithms outperform previous methods.

Recently, many schemes have been proposed for detecting and healing coverage holes to achieve full coverage in wireless sensor networks (WSNs). However, none of these schemes aim to find the shortest node movement paths to heal the coverage holes, which could significantly reduce energy usage for node movement. Also, current hole healing schemes require accurate knowledge of sensor locations, obtaining this knowledge consumes high energy. In this paper, we propose a DElaunay-based Coordinate-free Mechanism (DECM) for full coverage. Based on rigorous mathematical analysis, DECM can detect coverage holes and find the locally shortest paths for healing holes in a distributed manner without requiring accurate node location information. Simulation results and experimental results from the real-world GENI Orbit test bed show that DECM achieves superior performance in terms of the energy-efficiency and effectiveness of hole healing compared to previous schemes.