In recent years, gig platforms like Uber and DoorDash have implemented strategies to boost gig drivers' earnings during peak hours. Uber's 'back-to-back' feature allows drivers to accept new trips while still on route, and Uber Eats' 'Batch Order Route' initiative allows drivers to pick up multiple deliveries from different locations, which may result in multiple tops before one order is delivered. Despite revenue gains, these features lead to user complaints about extended waiting times. In response, platforms introduce features like Uber Eats' 'Priority Delivery' and Uber's 'Priority', where customers pay an extra subscription fee for guaranteed reduced waiting times. This paper focuses on designing matching policies to enhance system revenue while limiting customer waiting times. We present a hybrid model combining online matching and queue theory for quantitative analysis of users' waiting times. Additionally, we introduce an LP-based sampling framework and a unified queue-theory-based method for evaluating online performance. Comprehensive experiments on real datasets validate our theoretical findings, highlighting the efficiency of our matching framework in promoting profit and meeting committed waiting times.

Task complexity is widely regarded as a major barrier to cooperation in mobile crowdsensing (MCS), often leading to trust collapse and market failure. However, this view overlooks the constructive role of task complexity in shaping cooperative evolution. In this article, we propose a three-party evolutionary game framework involving workers, platforms, and task requesters, in which task complexity is explicitly modeled as an endogenous driver of trust dynamics and strategic interactions. We derive a set of anti-collapse conditions under which the marginal benefits of cooperative behavior overcompensate for the marginal costs induced by task complexity. Task complexity thereby propels an evolutionary phase transition from a low-trust trap to a stable cooperative equilibrium by reshaping the payoff structure of cooperative strategies. We further characterize the critical complexity thresholds that govern this phase transition through theoretical stability analysis. Extensive numerical simulations validate the theoretical predictions and demonstrate the robustness and effectiveness of the proposed mechanism in sustaining cooperative behavior across a wide range of task complexities.

While ridesharing provides substantial convenience, it also raises several security concerns, with location privacy being a primary issue. A common state-of-the-art solution is to add random noise to user locations to preserve privacy. However, this approach often degrades matching efficiency due to reduced location accuracy. In this paper, we study the real-time matching problem between ridesharing requests and drivers, aiming to maintain high matching efficiency despite obfuscated locations. We model the order dispatching process as an online bipartite matching problem, where drivers are offline and requests arrive sequentially following a known distribution. We construct benchmark linear programs (LPs) and propose an LP-based online matching algorithm with provable performance guarantees. To address privacy concerns, we further develop a privacy-aware LP-based method that mitigates the impact of Laplace noise. Experiments on real-world datasets demonstrate the effectiveness of our algorithms and support our theoretical findings.

Nowadays, the use of Domain Generation Algorithm (DGA) in botnets has made the detection of DGA domain names very important. Compared with blacklist and character-based detection, traffic analysis has the advantages of small datasets and vocabulary. However, the current research of traffic analysis mainly use a single type of traffic, and few features in the traffic has been considered. In this paper, a DGA domain name detection method based on spatiotemporal features of DNS and NetFlow traffic is proposed. Based on the difference between the traffic performance of DGA and benign domain name after the attack behavior, the method selects 8 effective features in time and space from DNS and NetFlow traffic, including the number of user IP visits, the suddenness of user access, and the standard deviation mean ratio of the occurrence of each resolved IP address. Using these spatiotemporal features, we can detect DGA domain names from the network. During the detection process, the features extracted from the training set were put into the C4.5 supervised machine learning classification model for training, and the DGA domain name detection system with classification ability was obtained. Experimental results in China Telecom network show that the model can detect DGA domain names with a high accuracy rate of 99.14% in the actual network, including anti-detection DGA domain names. This indicates that the model has achieved long-term effectiveness and stability, and copes well with adversarial attacks.

Zero-power communication, enabled by energy harvesting, backscattering, and low-power computing, is capable of fulfilling the requirements of emerging Internet of Things (IoT) communication scenarios that demand low cost, compact size, and minimal power consumption. Thus, it holds great potential as a transformative technology for the future of IoT. Trusted access and secure transmission remain essential in zero-power communication scenarios. Nevertheless, conventional complex security mechanisms become impractical due to limited power consumption and resources. This work presents a lightweight security protocol for authentication. Initially, a sliding window algorithm, utilizing the Hamming distance, is designed to generate the message digest. This algorithm leverages the remaining electric quantity of the transmitter as a secret parameter for authentication. Subsequently, a key distribution function based on the hash chain is employed to ensure the security of the session key. The protocol’s security attributes regarding transmitted data and its ability to withstand common attacks are demonstrated through formal security analysis and the utilization of the ProVerif analysis tool. Extensive simulations validate the efficacy of the proposed security algorithms, which are well suited for lightweight IoT devices with severely constrained resources and outperform benchmark algorithms.

We consider two versions of the (stochastic) budgeted Multi-Armed Bandit problem. The first one was introduced by Tran-Thanh et al. (AAAI, 2012): Pulling each arm incurs a fixed deterministic cost and yields a random reward i.i.d. sampled from an unknown distribution (prior free). We have a global budget B and aim to devise a strategy to maximize the expected total reward. The second one was introduced by Ding et al. (AAAI, 2013): It has the same setting as before except costs of each arm are i.i.d. samples from an unknown distribution (and independent from its rewards). We propose a new budget-based regret-analysis framework and design two simple algorithms to illustrate the power of our framework. Our regret bounds for both problems not only match the optimal bound of O(ln B) but also significantly reduce the dependence on other input parameters (assumed constants), compared with the two studies of Tran-Thanh et al. (AAAI, 2012) and Ding et al. (AAAI, 2013) where both utilized a time-based framework. Extensive experimental results show the effectiveness and computation efficiency of our proposed algorithms and confirm our theoretical predictions.

Real-world online matching markets (OMMs) often involve multiple objectives, such as maximizing relevance and diversity in online recommendation and crowdsourcing systems. In this paper, we propose a generic bi-objective maximization model for OMMs with the following features: (1) there are two types of agents—offline and online—with online agents arriving dynamically and stochastically; (2) upon each online agent’s arrival, an immediate and irrevocable decision must be made regarding which subset of relevant offline agents to assign; and (3) each offline and online agent has a specific matching capacity, i.e., an upper bound on the number of allowable matchings. Our model supports two general linear objective functions defined over all possible assignments to online agents. We formulate a bi-objective linear program (LP) and design an LP-based parameterized algorithm. Departing from prevalent non-adaptive attenuation methods, we introduce a time-adaptive attenuation framework that achieves an almost tight competitive ratio for each objective. To complement our theoretical analysis, we implement the proposed algorithm and evaluate it against several heuristics using two real-world datasets. Extensive experimental results demonstrate the flexibility and effectiveness of our approach, validating our theoretical predictions.

Utilizing mobile crowdsensing (MCS) for data collection and analysis has become a prominent paradigm in the Internet of Things (IoTs). However, the existing research predominantly focuses on platform-user interactions, often neglecting the potential for user collaboration, which is crucial for improving data quality and task efficiency. In practical applications, mobile users tend to cooperate with familiar individuals based on their preferences in sensing tasks. To tackle this issue, we introduce a novel MCS model that integrates user cooperation, significantly enhancing the system's overall effectiveness. Specifically, users’ capabilities and costs are synthesized and managed through a cooperation degree matrix. Additionally, cooperation is updated based on historical behaviors and user preferences. To incentivize user participation, currencies are employed for recruitment. Within this framework, we investigate the maximum collaborative user selection (MCUS) problem, which is dedicated to the problem of maximizing the amount of recruitment under user cooperation. The MCUS problem is proved to be an NP-hard problem and thus intractable. To address this, we propose the minimum weighted cost replacement (MWCR) algorithm. Experimental results demonstrate that the MWCR algorithm exhibits low complexity and high efficiency across various scales, making it an excellent solution for collaborative crowd recruitment.

Opportunistic Networks (OppNets) can provide a low-cost and reliable way for the message forwarding in urban areas, especially in which traffic jams occur frequently. However, in terms of the OppNet based on the bicycle-sharing system (BSS), how to predict bicycle trips and improve routing performance still remains unsolved. Moreover, the exchange of auxiliary information among OppNet nodes (bike stations) will compromise the privacy of nodes/users. Thus we design the Two-Tier Probability Model (TTPM), including the InteR-day pattern and the IntrA-day pattern, to predict the trips accurately. Then the Discrete Optimization Differential Privacy (DODP) method is utilized to disturb the estimated InteR-day and IntrA-day probabilities, which will further protect the privacy of nodes and users. With TTPM and DODP, we propose an efficient privacy-preserving routing scheme for OppNet, which transforms the relay selection problem into the shortest path problem approximately. Extensive simulations show that the proposed routing scheme (TTPM) outperforms the benchmarks with the delivery ratio of more than 0.75 when the time-to-live is 5 days and the message generation rate is 6 pkts/hour. Compared with TTPM+Lap and TTPM+GRR, the proposed TTPM+DODP improves the delivery ratio by 30% and 3%, respectively.

Graph kernels are a significant class of tools for measuring the similarity of graph data, which is the basis of a wide range of graph learning methods. However, graph kernels often suffer from high computing overhead. With the shining of cloud computing, it is desirable to transfer the computing burden to the server with abundant computing resources to reduce the cost of local machines. Nonetheless, under the honest-but-curious cloud assumption, the server may peek at the data, raising privacy concerns. To eliminate the risk of data privacy leakage, we propose CloudRGK to securely perform Random walk Graph Kernel(RGK), one of the most well-known graph kernels, on the cloud. We first prove that the edge- and vertex-labeled graphs could be transformed into an equivalent matrix representation. Afterward, we prove that the cloud could perform the core operations in RGK on the encrypted graphs without feature information loss. Evaluations of the real-world graph data demonstrate that our strategy significantly reduces the overhead of the local party to perform RGK without performance degradation. Meanwhile, it introduces only a small amount of extra computation cost. To the best of our knowledge, it is the first work towards private graph kernel computation on the cloud.

Selfishness detection offers an effective way to mitigate the routing performance degradation caused by selfish behaviors in Opportunistic Networks but leads to extra network traffic and computational burden. Most existing efforts focus on designing the selfishness detection scheme by exploiting the behavioral records of nodes. In this paper, we investigate the scheduling strategy of selfishness detection during the message lifespan with the game theory. Specifically, the Long-term Selfishness Detection Game (LSDG) is proposed based on the differential game and the payoff in the integral form. LSDG formulates the selfishness detection and the node’s selfishness with the Ordinary Differential Equations (ODEs). Then, we prove the existence of the Nash equilibrium in LSDG and deduce the necessary conditions of the equilibrium strategy based on Pontryagin’s maximum principle. The recursion-based algorithm is designed in this paper to compute the numerical solution of the equilibrium strategy via Euler’s method. Both the soundness of our modeling approach and solution properties are verified by extensive experiments. The simulations also show that the obtained solution can achieve the Nash equilibrium, where neither the source node nor relay nodes can benefit more by solely changing their own strategies.

As a promising network architecture, Mobile Edge Computing (MEC), has been proven that can effectively reduce the end-to-end latency and the energy consumption. The Unmanned Aerial Vehicle (UAV) assisted MEC network, where the UAV can provide the computation offloading services for the mobile users, can further alleviate the huge deployment cost of static edge servers. However, it remains unsolved how multiple cooperative flying UAVs serve the ground users, especially considering that these UAVs may share the same wireless channel and can communicate with the users while flying. In this paper, we first propose the Age of Task (AoT) metric to measure the quality of experience, and then formulate the joint optimization problem to minimize the worst AoT among all the users. Based on the block coordinate descent (BCD) method, this problem is transformed into three non-convex programming sub-problems (i.e., the UAV-user association sub-problem, the UAV trajectory planning sub-problem and the transmit power optimization sub-problem). Specifically, the successive convex approximation (SCA) technique is exploited iteratively to deal with the non-convexity in the UAV trajectory and transmit power optimization. Numerical results show that the proposed scheme outperforms the benchmark offloading schemes in terms of AoT.

Abstract Using deep learning models to deal with the classification tasks in network traffic offers a new approach to address the imbalanced Internet of Things malicious traffic classification problems. However, the employment difficulty of these models may be immense due to their high resource consumption and inadequate interpretability. Fortunately, the effectiveness of sampling methods based on the statistical principles in imbalance data distribution indicates the path. In this paper, we address these challenges by proposing a hybrid sampling method, termed HSS, which integrates undersampling and oversampling techniques. Our approach not only mitigates the imbalance in malicious traffic but also fine-tunes the sampling threshold to optimize performance, as substantiated through validation tests. Employed across three distinct classification tasks, this method furnishes simplified yet representative samples, enhancing the baseline models’ classification capabilities by a minimum of 6.02% and a maximum of 182.66%. Moreover, it notably reduces resource consumption, with sample numbers diminishing to a ratio of at least 83.53%. This investigation serves as a foundation, demonstrating the efficacy of HSS in bolstering security measures in IoT networks, potentially guiding the development of more adept and resource-efficient solutions.

This paper examines the income inequality among rideshare drivers resulting from discriminatory cancellations by riders, considering the impact of demographic factors such as gender, age, and race. We investigate the tradeoff between income inequality, referred to as the fairness objective, and system efficiency, known as the profit objective. To address this issue, we propose an online bipartite-matching model that captures the sequential arrival of riders according to a known distribution. The model incorporates the notion of acceptance rates between driver-rider types, which are defined based on demographic characteristics. Specifically, we analyze the probabilities of riders accepting or canceling their assigned drivers, reflecting the level of acceptance between different rider and driver types. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous analysis of online competitive ratios is conducted to illustrate the flexibility and efficiency of our algorithms in achieving a balance between fairness and profit. Furthermore, we present experimental results based on real-world and synthetic datasets, validating the theoretical predictions put forth in our study.

Exploiting mobile crowdsensing to conduct data collection and analysis brings unprecedented opportunities to promote the development of the Internet of Things(IoT). However, malicious platforms may provide untrusted data or illegally leak users’ information, which leads users in crowdsensing networks to be reluctant to participate in sensing activities. Besides, users are unwilling to report malicious platforms without sufficient incentives. To tackle the problem, a new incentive mechanism is proposed by modeling users’ preferences in this paper. Specifically, two scenarios are considered to detect malicious platforms when users join sensing activities according to the system grasps user’s information, i.e., complete information scenario and partial information scenario. Different incentive algorithms are designed for each scenario to optimize the systems incentive cost. In the complete information scenario, we minimize the total incentive cost by ranking users’ preferences. In the partial information scenario, uniform Distribution and Laplace Distribution are employed to model the distribution of users’ preferences to find the optimal cost. Specifically, we incorporate the concept of non-convexity into design the incentive mechanism, when user preferences obey the Laplace Distribution. By conducting an in-depth exploration the properties of Laplace Distribution, we can transform it into a convex problem to solve it efficiently. The analysis based on these mechanisms lays a theoretical foundation on the detection of malicious platforms. Furthermore, the soundness of modeling and the accuracy of analysis are verified through extensive simulation, which also guides the design of more sophisticated incentive schemes for the detection of malicious platforms.

Encrypted communications, implemented for the confidential information exchange, facilitate the preservation of individual privacy. Unfortunately, some criminals abuse encrypted communications to conduct illegal activities, leading to the proliferation of the Darknet. To curb malicious darknet activities, the accurate and effective classification of darknet traffic is imperative. Considerable endeavors have been devoted to identifying the darknet traffic. However, the classification of darknet applications has not yielded a satisfactory result. This deficiency arises from the limitations of current approaches, e.g., some traditional methods rely on hand-crafted features that consume labor, and other neural network-based methods disregard the graph structure of the traffic. To tackle these challenges, we propose the Darknet Traffic Graph (DTG), a graph structure that captures the interactions between local clients and remote servers in darknet traffic. Furthermore, based on DTG, we combine the GNN model and attention mechanism to create the Darknet Graph Neural Networks, i.e., DGNN, a powerful model that sufficiently exploits the benign and darknet traffic features. As a result, on the CIC-Darknet2020 dataset, the accuracy of DGNN in traffic classification and application classification is 98.52% and 99.06%, respectively, which outperforms other classifiers.

There are many news articles reporting the obstacles confronting poverty-stricken households in access to public transits. These barriers create a great deal of inconveniences for these impoverished families and more importantly, they contribute a lot of social inequalities. A typical approach addressing the issue is to build more transport infrastructure to offer more opportunities to access the public transits especially for those deprived communities. Examples include adding more bus lines connecting needy residents to railways systems and extending existing bus lines to areas with low socioeconomic status. Recently, a new strategy is proposed, which is to harness the ubiquitous ride-hailing services to connect disadvantaged households with the nearest public transportations. Compared with the former infrastructure-based solution, the ride-hailing-based strategy enjoys a few exclusive benefits such as higher effectiveness and more flexibility. In this paper, we propose an optimization model to study how to integrate the two approaches together for equity-promotion purposes. Specifically, we aim to design a strategy of allocating a given limited budget to different candidate programs such that the overall social equity is maximized, which is defined as the minimum covering ratio among all pre-specified protected groups of households (based on race, income, etc.). We have designed a linear-programming (LP) based rounding algorithm, which proves to achieve an optimal approximation ratio of 1-1/e. Additionally, we test our algorithm against a few baselines on real data assembled by outsourcing multiple public datasets collected in the city of Chicago. Experimental results confirm our theoretical predictions and demonstrate the effectiveness of our LP-based strategy in promoting social equity, especially when the budget is insufficient.

While ride-hailing brings great convenience to our daily life, it also poses a threat to the passengers' location privacy. Although many perturbation-based methods have been proposed to protect the passengers' location privacy, imprecise can still result in poor assignments of the ride-hailing platform. In addition, the platform often has to balance multiple conflicting objectives when dispatching drivers. Thus, trading these objectives in an appropriate way is critical to the long-term development of the platform. In this paper, we focus on the assignment strategy design in ride-hailing, aiming to promote the performance of the dispatching system while protecting the passengers' location privacy. We first model the online order dispatching as a bipartite matching problem, where drivers are assumed to be offline available and orders arrive sequentially following a known distribution. Then, we construct several linear programs (LPs) to obtain the obfuscation matrix (for privacy-preserving) and the guiding solutions of assignments (for online dispatching). Finally, a parameterized LP-based online dispatching algorithm is proposed to flexibly trade the two assignment objectives, i.e., dispatch efficiency and fairness. Experimental results on real-world datasets demonstrate the effectiveness of our algorithms.

Nowadays, the flourish of the internet of things incurs a great demand for progressive technologies to prolong the lifetime of Wireless Sensor Networks. Exploiting a fleet of Mobile Chargers (MCs) to replenish the energy-critical sensor nodes provides a new dimension to maintain long-term network operations, but may suffer from high charging delay due to MC’s limited mobility. Most existing studies focus on the reduction of server-oriented delay, i.e., the overall time taken by MCs (servers) to carry out sensor charging and travel inside the sensing field. However, these solutions may not be robust enough as some energy-critical sensor nodes will run out of the stored energy before the charger’s arrival. In this article, we address this challenge by reducing the client-oriented delay—referred to as the wait-for delay —which is defined as the “arrival times” at the to-be-charged sensor nodes (clients). To this end, we first formulate a novel wait-for charging delay minimization problem under the multi-node energy charging scheme. We then prove the NP-hardness of the proposed problem. Inspired by empirical observations, we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We have evaluated the proposed algorithm using real-life system settings. The experimental results suggest that the proposed algorithm certainly performs better than the existing benchmarks; it could reduce the wait-for delay by up to 87.4 percent.

We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government. We focus on the internal-equity by assuming that arriving requesters are homogeneous in terms of their external factors like demands but heterogeneous for their internal attributes like demographics. Specifically, we associate each arriving requester with one or several groups based on their demographics (i.e., race, gender, and age), and we aim to design an equitable distributing strategy such that every group of requesters can receive a fair share of resources proportional to a preset target ratio. We present two LP-based sampling algorithms and investigate them both theoretically (in terms of competitive-ratio analysis) and experimentally based on real COVID-19 vaccination data maintained by the Minnesota Department of Health. Both theoretical and numerical results show that our LP-based sampling strategies can effectively promote equity, especially when the arrival population is disproportionately represented, as observed in the early stage of the COVID-19 vaccine rollout.

Blackhole detection in the opportunistic networks offers an effective means to mitigate the routing performance degradation but faces many challenges from corrupted nodes due to their collusion behaviors. Most existing effort in the literature focuses on the blackhole feature extraction from the message exchange. However, the decay effect of features and the forged features from the corrupted node, which acts as the rational node in performing message exchange, degrade the performance of the detection. In this article, we investigate the evidence construction, i.e., the direct and indirect evidence with the statistical parameters in message exchange. Specifically, we construct behavior classifiers to distinguish the blackhole behaviors from rational ones and design the collusion filtering strategy to improve the detection accuracy by separating corrupted nodes from rational ones, laying a behavior identification foundation. The contact evidence-driven blackhole detection (CEBD) based on machine learning is proposed to improve the routing performance. The soundness of the proposed scheme is verified statistically and the detection accuracy is evaluated based on random waypoint model (RWP) trace and Shanghai taxi trace. Extensive simulations show that our scheme outperforms the benchmarks, including SDBG, Li, and MDS, in terms of the delivery ratio in various scenarios.

Routing is a fundamental problem and has been extensively studied in various networks. However, in highly dynamic networks (e.g., wireless ad hoc networks), nodes have limited transmission opportunities due to high mobility, noise and interference, where traditional routing is often not the best approach.Opportunistic routing (OR), on the other hand, can effectively minimize the routing cost (e.g., the number of hops) and improve the success of routing by utilizing link metrics. However, the link metrics are usually unknown in advance and changing. In this paper, we design an adaptive algorithm calledThompson sampling-based opportunistic routing (TSOR)motivated by the distributed Bellman-Ford algorithms. TSOR is able to learn the link metrics and route packets simultaneously to reduce the overall cost. Theoretically, we show a lower bound and an upper bound of the cumulative regret (i.e., performance gap) between TSOR and the optimal routing algorithm that knows all link metrics in advance. The regret increases sublinearly with respect to the number of packets, and has a lower order in terms of the network size than the best-known results. Furthermore, we compare TSOR with the state-of-the-art algorithms, and the evaluation results show that TSOR has a lower regret and a faster convergence rate to the optimal policy than the state-of-the-art algorithms.

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of 1/4 from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.

Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we study the income inequality among rideshare drivers due to discriminative cancellations from riders, and the tradeoff between the income inequality (called fairness objective) with the system efficiency (called profit objective). We proposed an online bipartite-matching model where riders are assumed to arrive sequentially following a distribution known in advance. The highlight of our model is the concept of acceptance rate between any pair of driver-rider types, where types are defined based on demographic factors. Specially, we assume each rider can accept or cancel the driver assigned to her, each occurs with a certain probability which reflects the acceptance degree from the rider type towards the driver type. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous online competitive ratio analysis is offered to demonstrate the flexibility and efficiency of our online algorithms in balancing the two conflicting goals, promotions of fairness and profit. Experimental results on a real-world dataset are provided as well, which confirm our theoretical predictions.

Localization has drawn much attention in the Internet of Things (IoT) era. Under traditional multilateration techniques, existing solutions usually need multiple anchor nodes to perform localization, which introduces more system complexity and cost. In this paper, the single anchor localization (SAL) is first modeled, where a multi-antenna anchor node is able to estimate the location of the target node using both angle and distance information. Then, according to SAL, we propose an accurate and distributed localization (ADL) algorithm, which can not only estimate the location of the target node with fewer anchor nodes but also be more accurate than the traditional multilateration method. Furthermore, we prove that the location estimate under ADL can converge towards the real location of the target node with probability 1. The lower and upper bounds of ADL are also derived under a bounded noise model. Extensive simulations are conducted to demonstrate the performance of ADL and the correctness of the theoretical results.

In order to alleviate the traffic congestion caused by the bike-sharing system, the bicycles should be parked in designated parking sites, particularly around the hot scenic spots. A proper route, which guides the cyclers to select a vacant place among the sites to park the bike, is required. In this paper, the Expected Travel Distance (ETD) and the Probability of Successful Parking (PSP) are formulated to evaluate the routes, which will guide the users to travel all the parking sites. We exploit the Poisson process to model the increment of the bicycle number in the parking site and construct the travel tree for the route plan problem. To provide a proper route, we propose the GOR algorithm and the F-M method based on the travel tree. Through extensive simulations, our algorithms are compared with TSP in terms of ETD, PSP and the execution time.

Worker selection for location-based crowdsensing can be described as the strategy of choosing the proper cooperative participants to complete the allocated tasks in specified regions. Due to the mobility pattern of vehicles and regular road networks, the Vehicular Ad-hoc Networks (VANETs) are expected to provide many opportunities for task execution in opportunistic crowdsensing, enabling some emerging applications. To fulfill tasks with the least execution time under the spatial-temporal restrictions, we propose a Location-Aware Worker Selection scheme (LAWS) for mobile opportunistic crowdsensing in urban areas. Different from the traditional worker selection schemes assigning a task to one designated worker, LAWS exploits the vehicles contacts provided by taxicabs and buses and makes full advantage of prior knowledge of vehicles to promote the performance of task execution. Real-world vehicle traces are introduced to construct the extensive simulations. The simulation results show that our scheme outperforms the well-known algorithms, e.g., Epidemic, Prophet, in terms of the task execution success ratio, the execution time and the network load.

Exploiting camera sensors to conduct intruder detection has attracted a lot of research attention. Different from the traditional sensor with omni-directional sensing model, a camera sensor usually has a specified direction with a fixed sensing angle of sensing area. The sensing quality of coverage is critical to the application of coverage scheme. In this paper, we first investigate the complete coverage issue with two alternative layouts of sensing area by 2 camera sensors. Considering the weighted image quality and the importance of sensing area, we propose a quality-enhancing coverage scheme for camera sensor networks, QCC, to improve the coverage performance. We then mathematically present a geometrical probability-based analysis to theoretically evaluate the performance of intruder detection approach. Furthermore, the extension of QCC scheme, QCC-D, is proposed to cover the sensing areas with differentiated importance. Through extensive simulations, our scheme is demonstrated to outperform the best known coverage algorithms, in terms of both overlap area ratio and total weighted quality.

Summary In Vehicular Ad hoc Networks (VANETs), because of the selfishness of the vehicles, the resource allocation in VANET has become one of the primary tasks. Exploiting the Road Side Units (RSUs), which constructs the Cloud Computing environment, provides more data access opportunities and stable communication time for the vehicles. We investigated the cloud resource allocation for data access with noncooperative game based on a Gauss–Seidel iteration method. We further proposed a repeated game scheme, which can approximately achieve the near Pareto‐optimal flow allocation among the vehicles. Considering the vehicles' irrational behavior, a punishment strategy was designed to prevent the vehicles from behavior deviation. The analysis based on these models lays a theoretical method foundation on cloud resource allocation process. The validity of the modeling and the accuracy of the analysis were verified through the extensive simulations, which also guide the future design of more sophisticated cloud resource allocation schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Exploiting social characteristics to make opportunistic forwarding decisions in mobile social networks offers a new approach to improve the forwarding performance and reduce the additional network workload. Actually, people in social networks will have more opportunities to contact with each other if they have more similar characteristics. In this paper, the data forwarding strategy is investigated through the weighted Characteristic-based Opportunistic Forwarding (COF) scheme, in which the weight for each characteristic is computed by exploring the contact frequency of the nodes. In our approach, the message is forwarded to the node which has more common characteristics with the destination. Furthermore, we compare the proposed COF scheme with several benchmark forwarding algorithms, including PeopleRank, Spray&Wait, Epidemic and Wait destination. The validity of the modeling and the accuracy of the analysis are verified through the extensive simulations with real traces, which also guide the design of more sophisticated data forwarding schemes.

In mobile opportunistic networks, the ad hoc nodes with high mobility are expected to have many opportunities of node contacts and serving message forwarding. Here we considering two typical mobility models, e.g., the RWP(Random WayPoint) model and Manhattan model, where the probabilistic characteristics of the node contact provided by the mobile nodes are explored. Furthermore we propose a Location-Aware Opportunistic Forwarding scheme, which exploits the location information of the messages and the node mobility for message forwarding. The message is forwarded to its destination regions or neighbor regions to improve the delivery ratio and reduce the network overhead. We construct a simulation environment, integrated with the RWP mobility trace and the city taxis' traces. Through extensive simulations, our scheme is demonstrated to outperform the best known opportunistic forwarding algorithms in terms of both delivery ratio/latency and transmission overhead.