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Cognitive radio networking allows the unlicensed secondary users to opportunistically access the licensed spectrum as long as the performance of the licensed primary users does not degrade. This dynamic spectrum access strategy is enabled by cognitive radio coupled with spectrum sensing technologies. Due to the imperfection of wireless transmissions, collaborative spectrum sensing (CSS) has been proposed to significantly improve the probability of detecting the transmissions of primary users. Nevertheless, current CSS techniques are sensitive to malicious secondary users, leading to a high false alarm rate and low detection accuracy on the presence of the primary users. In this paper, we present several robust collaborative spectrum sensing schemes that can calculate a trust value for each secondary user to reflect its suspicious level and mitigate its harmful effect on cooperative sensing. Our approach explores the spatial and temporal correlations among the reported information of the secondary users to determine the trust values. Extensive simulation study has been performed and our results demonstrate that the proposed schemes can guarantee the accuracy of the cooperative sensing system with a low false alarm rate when a considerable number of secondary users report false information. [ABSTRACT FROM PUBLISHER]

The Internet of Things (IoT) enabled Cyber–Physical System (CPS) is a promising technology applying in smart home, industrial manufacturing, intelligent transportation, etc. The IoT enabled CPS consists of two main components, i.e., IoT devices and cybers, which interact with each other. The IoT devices collect sensory data from physical environments and transmit them to the cybers, and the cybers make decisions to respond to the collected data and issue commands to control the IoT devices. It is generally known that energy is an important but limited resource in IoT devices. Data compression is an efficient way to reduce the energy consumption of data collection in sustainable IoT enabled CPSs, especially the Principal Component Analysis (PCA) based data compression. The trade-off between data compression ratio and data reconstruction error is one of the biggest challenges for PCA based data compression. In this paper, we investigate PCA based data compression to maximize the compression ratio with bounded reconstruction error for data collection in IoT enabled CPSs. Firstly, a similarity based clustering algorithm is proposed to cluster IoT devices in an IoT enabled CPS. Then, a PCA based data compression algorithm is proposed to compress the collected data to the greatest extent in each cluster with a bounded reconstruction error. Extensive simulations are conducted to verify the efficiency and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel vision for roadway safety warning based on sensor networks, aiming at providing user-friendly zero-delay safety warnings to motorists. Our idea leverages the advanced sensing, networking and storage technologies. Roadway sensors detect events and store event records at multiple designated locations along the against traffic direction, such that the passing-by drivers can be alerted to potential dangers or traffic delays through the wireless communication between roadway sensors and the vehicle. We design a location-centric storage (LCS) protocol, which manages the propagation and storage of event records based on the time needed to clear the road. In LCS, the density of the sensors storing an event record decreases logarithmically with respect to the distance to the event location. Thus, the closer to the event position, the more number of warnings a driver may obtain. LCS is further tailored for the case of “highway” sensor networks when all sensors are deployed along a straight line mimicking a highway, and the more complex case when two roads intersect at some place. We conduct both theoretic analysis and simulation study to verify the performance of LCS when applied to roadway sensor networks for safety warning. The results indicate that LCS is fair to all sensors. We conclude that roadway safety warning based on sensor networks is a promising idea for realizing ITS''s “Zero Fatality, Zero Delay” roadway safety philosophy. [Copyright &y& Elsevier]

Social networks have gained tremendous popularity recently. Millions of people use social network apps to share precious moments with friends and family. Users are often asked to provide personal information such as name, gender, and address when using social networks. However, as the social network data are collected, analyzed, and re-published at a large scale, personal information might be misused by unauthorized third parties and even attackers. Therefore, extensive research has been carried out to protect the data from privacy violations in social networks. The most popular technique is graph perturbation, which modifies the local topological structure of a social network user (a vertex) via various randomization techniques before the social graph data is published. Nevertheless, graph anonymization may affect the usability of the data as random noises are introduced, decreasing user experience. Therefore, a trade-off between privacy protection and data usability must be sought. In this paper, we employ various graph and application utility metrics to investigate this trade-off. More specifically, we conduct an empirical study by implementing five state-of-the-art anonymization algorithms to analyze the graph and application utilities on a Facebook and a Twitter dataset. Our results indicate that most anonymization algorithms can partially or conditionally preserve the graph and application utilities and any single anonymization algorithm may not always perform well on different datasets. Finally, drawing on the reviewed graph anonymization techniques, we provide a brief overview on future research directions and challenges involved therein. [ABSTRACT FROM AUTHOR]

Hypergraphs are instrumental in modeling complex relational systems that encompass a wide spectrum of high-order interactions among components. One prevalent analysis task is the properties estimation of large-scale hypergraphs, which involves selecting a subset of nodes and hyperedges while preserving the characteristics of the entire hypergraph. This paper aims to sample hypergraphs via random walks and is the first to perform unbiased random walks for sampling of nodes and hyperedges simultaneously in large-scale hypergraphs to the best of our knowledge. Initially, we analyze the stationary distributions of nodes and hyperedges for the simple random walk, and show that there is a high bias in both nodes and hyperedges. Subsequently, to eliminate the high bias of the simple random walk, we propose unbiased random walk strategies for nodes and hyperedges, respectively. Finally, a single joint walk schema is developed for sampling nodes and hyperedges simultaneously. To accelerate the convergence process, we employ delayed acceptance and history-aware techniques to assist our algorithm in achieving fast convergence. Extensive experimental results validate our theoretical findings, and the unbiased sampling algorithms for nodes and hyperedges have their complex hypergraph scenarios for which they are applicable. The joint random walk algorithm balanced the sampling applicable to both nodes and hyperedges. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports within the issue on topics including the problem of dynamic spectrum access, a study regarding the spectrum coexistence of secondary wireless networks, and the effect of oscillator phase noise on energy detection (ED) based spectrum sensing.

This paper studies batch processing of core maintenance in hypergraph streams. We focus on updating the coreness of each vertex after the hypergraph evolves. Unlike existing works that mainly focus on exact coreness updates for the single hyperedge dynamic or approximate update, we propose the first known batch processing algorithms for exact core maintenance with insertions or deletions of multiple hyperedges. By proposing a hyperedge structure Joint Hyperedge Set, we tackle the challenges of quantifying the range of coreness change and finding potential vertices whose coreness may update. In addition, we accelerate coreness updates even further by finding structures that enable parallel execution. Extensive experiments illustrate the efficiency, scalability, and effectiveness of our batch core maintenance algorithms on real-world hypergraphs. It shows that our algorithms can be faster than the single hyperedge processing approaches by a factor of nearly half the number of hyperedges processed, and our parallel algorithms achieve linear acceleration with the increasing number of threads. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including the Internet of Things (IoT), social computing, and auction mechanism for multi-radio wireless networks.

Certificate validation in public key infrastructure (PKI) is a vital phase of establishing secure connections on any network. There has been a great deal of speculation on how to efficiently validate digital certificates in PKI on which the security of network communications rests. Developing such a system is challenging because digital certificates need to be quickly and securely validated for a large number of clients in a short period of time at a low cost. On the other hand, our analysis on the TLS handshakes of the Alexa Top 1 Million domains dataset indicates that the current popular certificate validation systems cannot deliver certificate validation information to the clients in a timely fashion and suffer from high overhead at the client side, making them susceptible to a number of attacks. Motivated by these observations, we present SecureGuard, a certificate validation system that can effectively handle certificate validation during TLS handshakes. Our system utilizes Internet service providers (ISPs) as the primary entity for certificate validation exploiting the fact that any Internet access request must pass through the ISP proxy-cache servers. We provide an extensive evaluation on SecureGuard and illustrate its efficiency. Moreover, we introduce a quantitative analysis method that can investigate the costs incurred by our system and other certificate validation approaches under the same evaluation scenarios. Our implementation results demonstrate that SecureGuard is able to validate the digital certificates within a short period of time, in a secure manner, with less network overhead. [ABSTRACT FROM AUTHOR]

In this paper, we present a signcryption scheme called CP ABSC based on Ciphertext-Policy Attribute Based Encryption (CP ABE) [7] to secure the multicast communications in smart grids that require access control, data encryption, and authentication to ensure message integrity and confidentiality. CP ABSC provides algorithms for key management, signcryption, and designcryption. It can be used to signcrypt a message based on the access rights specified by the message itself. A user can designcrypt a ciphertext if and only if it possesses the attributes required by the access structure of the data. Thus CP ABSC effectively defines a multicast group based on the access rights of the data specified by the data itself, which differs significantly from the traditional Internet based multicast where the destination group is predetermined and must be known by the data source. CP ABSC provides collusion attack resistance, message authentication, forgery prevention, and confidentiality. It can be easily applied in smart grids to secure the instructions/commands broadcast from a utility company to multiple smart meters (push-based multicast) and the data retrieved from a smart meter to multiple destinations (pull-based multicast). Compared to CP ABE, CP ABSC combines encryption with signature at a lower computational cost for signcryption and a slightly higher cost in designcryption for signature verification. We also consider the adoption of attribute-based signature (ABS), and conclude that CP ABSC has a much lower computational cost than ABS. [ABSTRACT FROM AUTHOR]

In this paper, we propose a systematic approach to designing and deploying a RFID Assisted Navigation System (RFID-ANS) for VANETs. RFID-ANS consists of passive tags deployed on roads to provide navigation information while the RFID readers attached to the center of the vehicle bumper query the tag when passing by to obtain the data for navigation guidance. We analyze the design criteria of RFID-ANS and present the design of the RFID reader in detail to support vehicles at high speeds. We also jointly consider the scheduling of the read attempts and the deployment of RFID tags based on the navigation requirements to support seamless navigations. The estimation of the vehicle position and its accuracy are also investigated. [ABSTRACT FROM AUTHOR]

Lane-level navigation has received a lot of attention in recent years. It has played a great role in assisting route planning, as well as navigating automated vehicles. Aside from sticking to the planned route, abnormal traffic situations which result in blocking lanes could impact lane switching decisions. Unfortunately, there is currently no navigation system that can sense and track a vehicle's lane position and to advise the driver of lane switching decisions. Google Maps stores a priori the number of lanes and their directions at each highway exit and provides this information to drivers when navigating. However, even with this information, some drivers may not be able to make an informed decision regarding when and where to make a correct lane switch. This motivated us to develop a mechanism for the detection and tracking of real-time lane changes. In this paper, we propose a GPS-aiding system that can sense and track a vehicle's lane position. The system leverages smart phones’ computing capability, rear cameras, and inertial motion sensors. With little extra computational overhead, the system applies computer vision techniques to achieve lane-level positioning. We also design a machine learning-based algorithm to detect and track lane switching. We conduct a series of experiments, analyze our system in real-world environments, and achieve very promising results. We believe our system can be a great asset to current smart phone navigation systems. [ABSTRACT FROM AUTHOR]

This paper considers the shortest link scheduling problem in wireless networks under the signal-to-interference-plus-noise ratio (SINR) model. We propose an $O({}\log({}l_{\max}/l_{\min}{}){})-approximation algorithm called shortest link scheduling with power control (SLSPC) with oblivious power assignment and an O(\log1+\varphi(l\max/l\min)-approximation algorithm called shortest link scheduling with uniform or mean power assignment (SLSUM) with uniform or mean power control, where \varphi > 0 is a constant serving as a regulatory factor for slight transmit power adjustment, and where l\max and l\min denote the lengths of the longest and shortest links, respectively. We conduct a rigorous theoretical performance analysis to analyze the feasibility and approximation factors of the proposed algorithms. We also carry out an extensive comparison-based simulation study, whose results indicate that the performances of SLSPC and SLSUM are superior over the state of the art as the set of the so-called “black and gray” links, which are difficult to schedule and should be sequentially scheduled, is completely removed by adjusting the transmit power appropriately via $\varphi$. Our numerical analysis demonstrates that the approximation ratios of our algorithms are tighter than the best known ratios. [ABSTRACT FROM PUBLISHER]

In widely deployed wireless 'hot-spot' networks, nodes frequently join or leave, and inelastic/elastic and saturated/nonsaturated flows coexist. In such dynamic and diverse environments, it is challenging to maximize the channel utilization while providing satisfactory user experiences. In this paper, we propose a novel contention-on-demand (CoD) MAC scheme to address this problem. The CoD scheme consists of a fixed-CW algorithm, a dynamic-CW algorithm, and an admission control rule. The fixed-CW algorithm allows elastic flows to access limited system bandwidth; the dynamic-CW algorithm enables inelastic flows to contend for channel on demand and quickly adapt to network change; and the admission control rule rejects overloaded traffic for providing good user experiences. We then perform an asymptotic analysis to develop a simple and practical admission control rule for homogeneous and heterogeneous traffic; our rule can not only adapt to the change in offered loads and node number, but also maximize the system utilization . Finally, extensive simulations verify that our scheme is very effective and our theoretical result is very accurate. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) are drawing more and more attention along with the increasingly scarce spectrum resource. A CRN can be easily invalid due to stochastic activities of primary users. How to sustain the connectivity of CRNs and prolong the lifetime of CRNs become challenging issues. Inspired by the success of constructing a connected dominating set (CDS) as a virtual backbone in traditional wireless networks to prolong the lifetime of the network, we study the CDS construction in CRNs in this paper. We propose a three-phase centralized algorithm and a distributed algorithm. Theoretical analysis shows that our algorithms have better performance than that of existing results. [ABSTRACT FROM AUTHOR]

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

• Structural similarity metrics and learning techniques are introduced. • A method for classifying algorithms by network structure is proposed. • General applications and research directions of network embedding are promoted. Social networks have a plethora of applications, and analysis of these applications has been gaining much interest from the research community. The high dimensionality of social network data poses a significant obstacle in its analysis, leading to the curse of dimensionality. The mushrooming of representation learning in various research fields facilitates network representation learning (also called network embedding), which will help us address the above-mentioned issue. Structural Representation Learning aims to learn low-dimensional vector representations of high-dimensional network data, allowing maximal preservation of network structural information. This representation can then serve as a backbone for various network-based applications. First, we investigate the techniques used in network representation learning and similarity indices. We then categorize the representative algorithms into three types based on the network structural level used in their learning process. We also introduce algorithms for representation learning of edges, subgraphs, and the whole network. Finally, we introduce the evaluation metrics and the applications of network representation learning and promising future research directions. [ABSTRACT FROM AUTHOR]

Underwater sensor networks are envisioned to enable a wide range of underwater applications such as pollution monitoring, offshore exploration, and oil/gas spill monitoring. Such applications require precise location information as otherwise the sensed data might be meaningless. On the other hand, security and privacy are critical issues as underwater sensor networks are typically deployed in harsh environments. Nevertheless, most underwater localization schemes are vulnerable to many attacks and suffer from potential privacy violations as they are designed for benign environments. However, a localization scheme that does not consider security and privacy could lead to serious consequences, especially in critical applications such as military monitoring. In this article, we discuss the security and privacy issues in underwater localization, and investigate the techniques that can provide security and preserve node privacy in underwater sensor networks. [ABSTRACT FROM PUBLISHER]

Security plays a crucial role in payment systems; however, some implementations of payment card security rely on weak cardholder verification methods, such as card and a signature, or use the card without having any cardholder verification process at all. Other vulnerable implementations of cardholder verification methods suffer from many security attacks, such as relay attacks and cloning attacks. In addition, the impact of these security attacks is high since they cause monetary losses for banks and consumers. In this paper, we introduce a new cardholder verification method using a multi-possession factor authentication with a distance bounding technique. It adds an extra level of security to the verification process and utilizes the idea of distance bounding which prevents many different security attacks. The proposed method gives the user the flexibility to add one or more extra devices and select the appropriate security level. This paper argues that the proposed method mitigates or removes many popular security attacks that are claimed to be effective in current card based payment systems, and that it can help to reduce fraud on payment cards. Furthermore, the proposed method provides an alternative verification technique and enables cardholders with special needs to use the payment cards and make the payment system more accessible. [ABSTRACT FROM AUTHOR]

In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal , spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs. [ABSTRACT FROM AUTHOR]

In wireless local area networks, adjusting the coverage of access points (APs) may force the clients near the coverage boundaries of congested APs to associate with lightly-loaded ones, thus realizing load-balancing. Such an approach has the advantage of requiring no modification on the client software/hardware compared to other load-balancing techniques. However, its applicability is undermined by the problems of AP service cheating and AP service loophole resulted from coverage adjustment, which significantly affect the AP service availability. Nevertheless, these two problems are largely ignored by the existent research. To tackle this challenge, a variable polyhedron genetic algorithm (GA) is proposed, which not only provides an AP service availability guarantee but also yields a near-optimal beacon range for each AP when the number of evolutions is large enough. Simulation study indicates that our algorithm is superior over the default 802.11 AP association model in terms of load-balancing and network throughput enhancement. In addition, the variable polyhedron GA outperforms the traditional GA in terms of fitness value and convergence speed. [ABSTRACT FROM AUTHOR]

Most existing studies on cognitive-radio networks assume that cognitive users (CUs) can switch to any available channel, regardless of the frequency gap between a target channel and the current channel. However, due to hardware limitations, CUs can actually jump only so far from where the operating frequency of their current channel is. This paper studies the performance of cognitive-radio networks while considering realistic channel handoff agility, where CUs can only switch to their neighboring channels. We use a continuous-time Markov process to derive and analyze the forced termination and blocking probabilities of CUs. Using these derived probabilities, we then study and analyze the impact of limited spectrum handoff agility on cognitive spectrum access efficiency. We show that accounting for realistic spectrum handoff agility reduces performance of cognitive-radio networks in terms of spectrum access capability and efficiency. [ABSTRACT FROM PUBLISHER]

The RFID System on Roads (RSR), which includes RFID tags deployed on roads and RFID readers installed on vehicles, is an essential platform for future transportation systems. It can provide unique features that are missing from the current systems, including lane level position, road traffic control information, vehicle distance estimation, real time driving behavior analysis, and so on. Based on these features, several novel vehicular applications can be implemented, which can significantly improve the transportation safety and efficiency. Specifically, the proposed applications on RSR include Assisted Navigation Systems, Electrical Traffic Control, Unmanned Patrol Systems, Vehicle Distance Estimation, Parking Assistant System, Route Tracing and Access Control, Unmanned Ground Vehicles. We also investigate the corresponding engineering/system and research challenges for implementing RSR and its applications in this article. [ABSTRACT FROM PUBLISHER]

Unbalanced traffic demands of different data center applications are an important issue in designing data center networks (DCN). In this paper, we present our exploratory investigation on a hybrid DCN solution of utilizing wireless transmissions in DCNs. Our work aims to solve the congestion problem caused by a few hot nodes to improve the global performance. We model the wireless transmissions in DCN by considering both the wireless interference and the adaptive transmission rate. Besides, both throughput and job completion time are considered to measure the impact of wireless transmissions on the global performance. Based on the model, we formulate the problem of channel allocation as an optimization problem. We also design an approximation algorithm with an approximation bound of 1/2 and a genetic algorithm to address the scheduling problem. A series of simulations are performed to evaluate and demonstrate the effectiveness of our wireless DCN scheme. [ABSTRACT FROM PUBLISHER]

Abstract: In verifiable multi-secret sharing schemes (VMSSs), many secrets can be shared but only one share is kept by each user and this share is verifiable by others. In this paper, we propose two secure, efficient, and verifiable multi-secret sharing schemes, namely Scheme-I and Scheme-II. Scheme-I is based on the Lagrange interpolating polynomial and the LFSR-based public key cryptosystem. The Lagrange interpolating polynomial is used to split and reconstruct the secrets and the LFSR-based public key cryptosystem is employed to verify the validity of the data. Scheme-II is designed according to the LFSR sequence and the LFSR-based public key cryptosystem. We compare our schemes with the state-of-the-art in terms of attack resistance, computation complexity, and so on, and conclude that our schemes have better performance and incur less computation overhead. Our schemes can effectively detect a variety of forgery or cheating actions to ensure that the recovery of the secrets is secure and creditable, and the length of the private key is only one third of that of others for the same security level. [Copyright &y& Elsevier]

Abstract: Many authors have discussed the Tricomi problem for some second order equations of mixed type, which has important applications in gas dynamics. In particular, Bers proposed the Tricomi problem for Chaplygin equations in multiply connected domains [L. Bers, Mathematical Aspects of Subsonic and Transonic Gas Dynamics, Wiley, New York, 1958]. And Rassias proposed the exterior Tricomi problem for mixed equations in a doubly connected domain and proved the uniqueness of solutions for the problem [J.M. Rassias, Lecture Notes on Mixed Type Partial Differential Equations, World Scientific, Singapore, 1990]. In the present paper, we discuss the general Tricomi–Rassias problem for generalized Chaplygin equations. This is one general oblique derivative problem that includes the exterior Tricomi problem as a special case. We first give the representation of solutions of the general Tricomi–Rassias problem, and then prove the uniqueness and existence of solutions for the problem by a new method. In this paper, we shall also discuss another general oblique derivative problem for generalized Chaplygin equations. [Copyright &y& Elsevier]

Abstract: In this paper, we present time-based positioning scheme (iTPS), a purely localized location detection scheme for sensor networks with long-range beacons. iTPS relies on time difference of arrival (TDoA) of radio frequency (RF) signals measured locally at each sensor to detect range differences from the sensor to four base stations. These range differences are combined to estimate the sensor location through trilateration. iTPS is an improvement over TPS (Cheng et al., IEEE INFOCOM, 2004), which produces two ambiguous position estimates when sensors are close to any base station. iTPS substantially reduces the number of ambiguous estimates and can improve accuracy. Features of iTPS include low communication overhead for sensors, no requirements for time synchronization, low computational overhead due to simple algebraic operations, and high scalability. We conduct extensive simulation to test iTPS and compare it with TPS. The obtained results show that iTPS is an efficient and effective scheme for location discovery in sensor networks with long-range beacon stations. [Copyright &y& Elsevier]

With the prosperity of vehicular networks and intelligent transport systems, vast amount of data can be easily collected by vehicular devices from their users and widely spread in the vehicular networks for the purpose of solving large-scale machine learning problems. Hence how to preserve the data privacy of users during the learning process has become a public concern. To address this concern, under the celebrated framework of differential privacy (DP), we present in this paper a decentralized parallel stochastic gradient descent (D-PSGD) algorithm, called DP2-SGD, which can offer protection for privacy of users in vehicular networks. With thorough analysis we show that DP2-SGD satisfies (ε, δ)-DP while the learning efficiency is the same as D-PSGD without privacy preservation. We also propose a refined algorithm called EC-SGD by introducing an error-compensate strategy. Extensive experiments show that EC-SGD can further improve the convergence efficiency over DP2-SGD in reality. [ABSTRACT FROM AUTHOR]

Quality control in crowdsourcing is challenging due to the heterogeneous nature of the workers. The state-of-the-art solutions attempt to address the issue from the technical perspective, which may be costly because they function as an additional procedure in crowdsourcing. In this paper, an economics based idea is adopted to embed quality control into the crowdsourcing process, where the requestor can take advantage of the market power to stimulate the workers for submitting high-quality jobs. Specifically, we employ two sequential games to model the interactions between the requestor and the workers, with one considering binary strategies while the other taking continuous strategies. Accordingly, two incentive algorithms for improving the job quality are proposed to tackle the sequential crowdsourcing dilemma problem. Both algorithms are based on a sequential zero-determinant (ZD) strategy modified from the classical ZD strategy. Such a revision not only provides a theoretical basis for designing our incentive algorithms, but also enlarges the application space of the classical ZD strategy itself. Our incentive algorithms have the following desired features: 1) they do not depend on any specific crowdsourcing scenario; 2) they leverage economics theory to train the workers to behave nicely for better job quality instead of filtering out the unprofessional workers; 3) no extra costs are incurred in a long run of crowdsourcing; and 4) fairness is realized as even the requestor (the ZD player), who dominates the game, cannot increase her utility by arbitrarily penalizing any innocent worker. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) services such as mobile P2P transmissions and resource sharing, provide efficient methods to deliver data without the deployment of any central server. Nevertheless, the free-riding phenomenon inherit in such services presses a need for incentive mechanisms to stimulate contributions of data transmissions or sharing. As a result, it is imperative to answer the following questions: whether, and if so to what extent, an incentive mechanism can invoke such contributions? To answer these questions, we employ an $n$ n -player continuous quantum game model to analyze the general extrinsic incentive mechanisms as well as the reputation-based incentive mechanisms, a typical class of extrinsic incentive mechanisms. We focus on studying the extrinsic incentive mechanisms in this paper due to their wide scope of applications stemming from the fact that they promote cooperative behaviors by offering rewards rather than depending on the internal bounds (e.g., social ties) among peers, which may not always exist between any pair of peers. To the best of our knowledge, we are the first to analyze the extrinsic incentive mechanisms for P2P services from a quantum game perspective. Such a perspective is adopted because the extended strategy space in the quantum game broadens the range for searching optimal strategies and the introduction of entanglement makes the proposed analytical frameworks more practical due to the consideration of the peers’ relationships imposed by the rewards in extrinsic incentive mechanisms. Our quantum game-based analytical framework is generic because it is compatible with classic game-based schemes. The analytical results can provide a straightforward insight on evaluating the potential of the extrinsic incentive mechanisms and can serve as important references for designing new extrinsic incentive mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust communication-efficient decentralized learning algorithm, named RCEDL , to address data heterogeneity , communication heterogeneity and communication efficiency simultaneously in real-world scenarios. To the best of our knowledge, this is the first work to address the above challenges in a united framework. In detail, we design a compressed cross-gradient aggregation mechanism with delay to resolve the Non-IID issues, a blocking-resilient mechanism which allows receiving delayed parameters and gradients, and a communication-efficient mechanism including parameters compression and adaptive neighbors selection methods to reduce the communication cost as much as possible. In addition, we also provide convergence analysis of RCEDL and prove its convergence rate O (1 N K ) same with the state-of-the-art decentralized learning algorithms. Finally, we conduct extensive experiments to evaluate RCEDL algorithm on two widely used datasets CIFAR-10 and MNIST under different experimental settings. Compared with the state-of-the-art baseline methods, the proposed RCEDL is much more robust with higher accuracy and at least 3.4 × communication cost reduction under the heterogeneous environment. [ABSTRACT FROM AUTHOR]

Location-based services (LBSs) are becoming an increasingly important component in our social and business life. All existing LBS providers support the nearest place searching via a single point of interest (POI) query. That is, in one query, a user is allowed to search for only one type of service. However, in real life, people usually need to search multiple different types of services and hope that their locations are as close as possible for convenience. For example, one user would like to search for a restaurant with a KTV nearby. To support this application scenario, we propose a novel LBS termed “ $N$ -in-One,” which is the first scheme to extend the function of single-POI LBS to multiple-POI LBS such that a single query can be employed to request multiple POIs that are geographically close. Providing “ $N$ -in-One” is challenging because: 1) serving a “ $N$ -in-One” query is not equivalent to serving $N$ queries independently due to the distance correlation among the $N$ POIs; and 2) the cask effect is getting worse in the service area mode of “ $N$ -in-one” as most of the returned results may be rendered useless when some hot POIs are blocked. To overcome these challenges, we propose several algorithms using computational geometry techniques to identify the best $K$ POIs that are geographically close and the service area (denoted by a given-sized rectangle) that can cover as many the best $Q$ clusters as possible while reducing the cask effect in the service area mode. Extensive simulations based on both synthetic and real world data demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel networking paradigm called Small-world SSDNet, servicing applications such as public safety, proximity based services, and fog computing based on device-todevice multi-hop wireless communications. The "small-world" feature is determined by the service area, whose size is usually within a community level, and the well known small-world properties existing in SSDNets; the "super-dense" feature comes from the fact that the increased direct communication range and the popularity of 5G and IoT devices jointly result in a large number of devices within a single-hop communication range. This article first formally defines SSDNet. Then the challenges and the opportunities brought by the design and the implementation of the SSDNet protocols and applications are addressed. Finally, the broader discussions on issues relevant to modeling, engineering, and dissemination are provided. [ABSTRACT FROM AUTHOR]

In a cognitive radio network, cooperative communications between a primary user (PU) and a second user (SU) may be able to significantly improve the spectrum utilization, and thus, the network performance. To be specific, the PU can select a number of SUs as its relays to cooperatively transmit its data. In turn, these relays can be granted access to the licensed channel of the PU to transmit their data. In this paper, an effective cooperation strategy for SUs is presented. We formulate the problem of cooperative relay selection as a coalition formation game, and develop a utility function based on the game. The utility function considers various factors such as transmission power and noise level. With the utility function, a distributed coalition formation algorithm is proposed, which can be used by SUs to decide whether to join or leave a coalition. Such a decision is based on whether it can increase the maximal coalition utility value. We rigorously prove that our proposed coalition formation algorithm can terminate and reach a stable state. Finally, this paper demonstrates that the proposed scheme is able to enhance the network throughput via a simulation study. [ABSTRACT FROM AUTHOR]

In this paper, we propose a green spectrum and infrastructure leasing framework for sharing unused TV spectrum, in which the ownership of the network infrastructure and spectrum are decoupled, and each can be leased as a service on demand. An incumbent TV spectrum owner (TSO) can lease a share of the infrastructure from a network infrastructure owner (NIO) with a pay-per-use model, to provide new services to its end users. On the other hand, a TSO can rent a portion of its unused spectrum to the NIO for revenue such that the NIO can obtain access to the spectrum to serve its customers. This proposed framework achieves green communications through both resource sharing and energy saving. Our paper aims to analyze the interplay between TSO and NIO, and create a win–win situation to provide sufficient incentives for them to cooperate on this green communications. A multistage Stackelberg game is formulated, where TSO and NIO sequentially determine the total bandwidth supply for sharing, the infrastructure leasing price, the amount of infrastructure to lease, the spectrum leasing price, and the amount of spectrum to lease to maximize their utilities. The best strategies that will be taken by the TSO and NIO under various deployment scenarios are analyzed. Evaluation results show that the proposed green spectrum and infrastructure leasing framework is a promising scheme under which both TSO and NIO can benefit, and their utilities can be maximized in terms of user data throughput and revenue/payment, which provides sufficient incentives for them to cooperate. [ABSTRACT FROM PUBLISHER]

The huge amount of sensory data collected from mobile devices has offered great potentials to promote more significant services based on user data extracted from sensor readings. However, releasing user data could also seriously threaten user privacy. It is possible to directly collect sensitive information from released user data without user permissions. Furthermore, third party users can also infer sensitive information contained in released data in a latent manner by utilizing data mining techniques. In this paper, we formally define these two types of threats as inherent data privacy and latent data privacy and construct a data-sanitization strategy that can optimize the tradeoff between data utility and customized two types of privacy. The key novel idea lies that the developed strategy can combat against powerful third party users with broad knowledge about users and launching optimal inference attacks. We show that our strategy does not reduce the benefit brought by user data much, while sensitive information can still be protected. To the best of our knowledge, this is the first work that preserves both inherent data privacy and latent data privacy. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network (CRN) when there exist multiple primary channels. Our work focuses on a scenario in which each secondary user (SU) has the freedom to decide whether to participate in cooperative spectrum sensing; if not, the SU becomes a free rider. Such a mechanism can conserve the energy for spectrum sensing at a risk of sacrificing the spectrum sensing performance. To overcome this problem, we address the following two questions: “Which action (contributing to spectrum sensing or not) should be taken?” and “which channel should be sensed?” We model our framework as an evolutionary game in which each SU makes its decision based on its utility history and takes an action more frequently if it brings a relatively higher utility. We also develop a coalition formation algorithm based on the channel status, where each SU always chooses the coalition that brings the most information regarding the status of the corresponding channel. The simulation results demonstrate that the proposed scheme can guarantee the detection probability at a low false alarm rate. The results also indicate that our algorithm can satisfy different requirements by carefully tuning the system parameters. [ABSTRACT FROM PUBLISHER]

Wearable computing has great potential to revolutionize the future of everyday lives. Nonetheless, communications among wearable devices may lead to significant security concerns, given that wearable devices often capture sensitive personal information and may have to operate in hostile environments. In this article, we systematically study the security issues in wearable communications. Specifically, we start with an overview of security concerns for typical wearable applications. Then we review the state-of-the-art research and state-of-the-practice development for security in wearable computing in both industry and academia. At last, we propose a layered adaptive security architecture, with the aim of preventing adversaries from "breaking through" all layers of security by simply compromising one particular security measure, and discuss possible mechanisms to enhance the security of each layer. [ABSTRACT FROM PUBLISHER]

Radio-frequency identification (RFID) systems on roads (RSR) is a recently developed framework that focuses on enhancing transportation safety. In RSR, large-scale passive RFID tags with road-related information are deployed on road surfaces or roadside units. A vehicle with an onboard RFID reader can acquire road information via reading from these RFID tags. As a result, it is critical to update the tags with the latest road information (particularly emergent alerts) in a timely manner in RSR. In this paper, we design a novel cluster-based information diffusion scheme that aims to quickly and accurately update the contents of the RFID tags. To the best of our knowledge, our work is the first to focus on the problem of updating RFID tags in RSR. Our proposed scheme organizes RFID tags into clusters. The emergent information and update status of these clustered tags can be read by vehicles passing by. These vehicles can exchange such information with each other via vehicle-to-vehicle (or vehicle-to-infrastructure) (V2V or V2I) communications. After synthesizing the received information, these vehicles can update the nearby tags and spread the information accordingly. Our extensive simulation results demonstrate that our proposed information diffusion scheme can effectively update the tags within a certain time constraint under various scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we study the clustering problem for crossroads in Vehicular Ad hoc Networks (VANETs). Considering the load balancing of both the whole network and each cluster based on the multiple metrics, an Enhanced Low Overhead and Stable Clustering (EnLOSC) scheme is presented to ensure the stability and security of clusters and to reduce the communication overhead in this case. The proposed capability metric, designed to find the vehicles with similar direction and better channel quality, is exploited in the processes of formation and maintenance to determine which node is suitable for a cluster head. Based on this, a Cluster Head Electing in Advance Mechanism (CHEAM) is developed in order to fairly select a new head for 'isolated' vehicles that may not belong to a cluster. Meanwhile, other metrics are related to the node density and cluster size, which are exploited in the Cluster Merging and Splitting Mechanisms to keep the system load balancing and to improve the communication quality. Furthermore, the proposed Discovery and Elimination Scheme (DES) is designed to tackle the malicious nodes that may hurt the cluster communication. Accordingly, an enhanced cluster maintenance strategy with multi-metrics and a secure scheme is proposed so as to reduce the number of isolated vehicles, keep appropriate loading for each cluster head, and protect the whole link over cluster communication. Numerical results and discussion indicate that the cluster stability, communication overhead, load balance, and security can be significantly enhanced by our proposed scheme. [ABSTRACT FROM AUTHOR]

Mobile social networking becomes increasingly popular with the explosive growth of mobile devices. By allowing mobile users to interact with potential friends around the real world, it enables new social interactions as a complement to Web-based online social networks. In this paper, we introduce a secure friend discovery mechanism based on encounter history in mobile social networks. By exploring the fact that sharing encounters indicate common activities and interests, our scheme can help people make friends with like-minded strangers nearby. To the best of our knowledge, this is the first work to take advantage of the encounter history in friend discovery. We provide peer-to-peer confidential communications with the location privacy and encounter privacy being strictly preserved. Unlike most existing works that either rely on a trusted centralized server or existing social relationships, our algorithm is designed in an ad hoc model with no such limitation. As a result, our design is more suitable and more general for mobile social scenarios. Extensive theoretical analysis and experimental study are conducted, and the results indicate that our scheme is feasible and effective for privacy-preserving friend discovery in mobile social networks. [ABSTRACT FROM AUTHOR]

The advantages brought by opportunistic data delivery make mobile opportunistic networking a promising technology for big data computing. Thus, it is important to study the potential of mobile opportunistic networks in supporting big data applications by analyzing their fundamental data dissemination properties. However, such an analysis is nontrivial as a large dimension of elements may affect the data spreading behavior in mobile opportunistic networks. In this article, we first outline the challenges of analyzing the potential of mobile opportunistic networks for big data applications. Then we survey the state-of-the-art analytical results on data dissemination properties in mobile opportunistic networks. Finally, we summarize our analytical results and present a few open research issues that may foster future research endeavors on mobile opportunistic networks to support big data applications. [ABSTRACT FROM PUBLISHER]

The benefits of cognitive radio networking (CRN) have been well recognized with the emerging wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interferences to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the PUs. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem is how to select a relay efficiently. Due to the potentially large number of SUs, it is infeasible for a PU to first scan all the SUs and then pick the best. Basically, the PU transmitter intends to observe the SUs sequentially. After observing an SU, the PU needs to make a decision regarding whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory and derive the optimal stopping rule. We also discuss the optimal observation order of the SUs and analyze the collision probability. To evaluate the performance of our proposed scheme, we compare our optimal stopping policy with the random selection policy through simulation study, and the results demonstrate the superiority of our policy. Extensive simulation study is conducted to investigate the impact of different parameters on the system performance, and the results indicate that our algorithm can satisfy different system requirements by carefully tuning the corresponding system parameters. [ABSTRACT FROM PUBLISHER]

This paper focuses on designing a distributed medium access control algorithm for fairly sharing network resources among contending stations in an 802.11 wireless network. Because the notion of fairness is not universal and there lacks a rigorous analysis on the relationships among the four types of most popular fairness criteria, we first mathematically prove that there exist certain connections between these types of fairness criteria. We then propose an efficient medium access algorithm that aims at achieving time fairness and throughput enhancement in a fully distributed manner. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is addressed rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead. [ABSTRACT FROM AUTHOR]