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Industrial robots are prototypical cyber-physical systems which operate according to the operation code and are monitored in real-time based on their movement data. However, industrial robots suffer from replay attacks, via which attackers can manipulate the robot operation without being observed by the monitoring system. To mitigate this vulnerability, we design a novel intrusion detection system for industrial robots using their power fingerprint, called PIDS (Power-based Intrusion Detection System), and deliver PIDS as a bump-in-the-wire module installed at the powerline of commodity robots. The foundation of PIDS is the physically-induced dependency between the robot movement and the concomitant power consumption, which PIDS captures via joint physical analysis and (cyber) data-driven modeling. PIDS then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information a mismatch thereof flags anomalies of the observed movements. We have evaluated PIDS using three models of robots from different vendors i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots with over 2,000 operation cycles. Experimental results show that PIDS detects replay attacks at an average rate of 96.5% (up to 99.9%) and a 0.1s latency.

Incentive mechanism design and quality control are two key challenges in data crowdsourcing, because of the need for recruitment of crowd users and their limited capabilities. Without considering users' social influences, existing mechanisms often result in low efficiency in terms of the platform's cost. In this paper, we exploit social influences among users as incentives to motivate users' participation, in order to reduce the cost of recruiting users. Based on social influences, we design incentive mechanisms with the goal of achieving high quality of crowdsourced data and low cost of incentivizing users' participation. Specifically, we consider three scenarios. In the full information scenario, we design task assignment and user recruitment mechanisms to optimize the data quality while reducing the incentive cost. In the partial information scenario, users' qualities and costs are unknown. We exploit the correlation between tasks to overcome the information asymmetry, for both cases of opportunistic crowdsourcing and participatory crowdsourcing. Further, in the dynamic social influence scenario, we investigate the dynamics of users' social influences and design extra rewards for users to make full use of the social influence and achieve maximum cost saving. We evaluate the incentive mechanisms using numerical results, which demonstrate their effectiveness.

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. However, as a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes’ operation states: working or recharging, to achieve a desirable network utility. In our design, we first consider a single-hop special case of small-scale networks. We transform the operation state scheduling problem into a linear programming problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. The multi-hop operation state scheduling problem is proved to be NP-hard. We show that the spatiotemporal coupling caused by time-varying network topology makes the problem quite challenging. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85 percent of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.

Recently, cloud computing has become a vital part that supports people’s normal lives and production. However, accompanied by the increasing complexity of the cloud network, failures constantly keep coming up and cause huge economic losses. Thus, to guarantee the cloud network performance and prevent execrable effects caused by failures, cloud network diagnostics has become of great interest for cloud service providers. Due to the characteristics of cloud network (e.g., virtualization and multi-tenancy), transplanting traditional network diagnostic tools to the cloud network face several difficulties. Additionally, many existing tools cannot solve problems in the cloud network. In this paper, we summarize and classify the state-of-the-art technologies of cloud diagnostics which can be used in the production cloud network according to their features. Moreover, we analyze the differences between cloud network diagnostics and traditional network diagnostics based on the characteristics of the cloud network. Considering the operation requirements of the cloud network, we propose the points that should be cared about when designing a cloud network diagnostic tool. Also, we discuss the challenges that cloud network diagnostics will face in future development.

Hybrid long-range (LoRa) network is a promising approach to overcome the half-duplex issue in traditional LoRa networks, increasing the network efficiency and confidence for today’s fast-developing smart city services. Gateways (GWs) in hybrid LoRa networks link the end devices (EDs) and the Netserver and have a great impact on the system performance. Previous results on GW planning cannot be directly applied to hybrid LoRa networks since the heterogeneous EDs require different redundancy of coverage. Furthermore, the spreading factors (SFs) which determine the system performance should be considered concurrently. In this article, in order to find the optimal planning scheme, i.e., deciding the number and locations of GWs in the hybrid LoRa network, we propose a heterogeneous redundant coverage solution to meet the requirements of the heterogeneous EDs using the same or different frequencies for uplink and downlink. Specifically, we formulate this problem as a point coverage problem that meets the requirements of EDs. The deleted greedy algorithm (DGA) and the nondeleted greedy algorithm (NDGA) are designed to solve this problem, in which the DGA shows better performance when compared to NDGA. Furthermore, we build models of system performance and analyze the system throughput and energy efficiency based on SFs. The simulation results show that our solution gains more system throughput and energy efficiency than a one-coverage solution.

The papers in this special section focus on security and privacy of smart network systems. The explosion in the availability of smart devices and the extensive development of network infrastructure bring a new paradigm, called Smart Network Systems. Rich information can be transferred among the connected devices in Smart Network Systems, which enable a large variety of real-world applications, such as smart cities, smart grid, internet of vehicles, and intelligent medication. Even though Smart Network Systems have enormous values in terms of tackling the computational and communicational overheads and resource management problems, there are still many challenging issues that hinder their further developments. Specifically, distributed computing protocols play a significant role in Smart Network Systems where a large number of smart devices are geographically distributed and logically scattered. However, such distributed techniques are vulnerable to malicious codes and false data injections, internal and/or external attacks, and private information disclosures. Furthermore, designing secure and privacy-preserving protocols in Smart Network Systems is also challenging, since extra communicational and computational costs can be created due to encryption/decryption and the network utility may be degraded under certain security and privacy protection strategies.

The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants’ involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones.

The drone has been applied in various areas due to its small size, high mobility and low price. However, illegal uses of drones have posed huge threats to both public safety and personal privacy. There is an urgent demand for technologies that can timely detect and counter the drones. In this paper, we propose an adaptive switching spatial-temporal fusion detection method for remote flying drones in the airspace using electrical-optical cameras, which can enhance the contrast between the target and background as well as suppressing the noises and clutters simultaneously. For each incoming video frame, a dark-attentive interframe difference method and a row-column separate black-hat method are proposed to generate temporal feature maps (TFM) and spatial feature maps (SFM), respectively, in parallel. Inspired by the phenomenon that the features in TFMs and SFMs both go strong at the regions of the intended target while they do not at other regions where noises and clutters locate, we design an adaptive switching spatial-temporal fusion mechanism to fuse the SFMs and TFMs, generating adaptive switching spatial-temporal feature maps (ASSTFM). Finally, an adaptive local threshold mechanism is used in ASSTFMs to segment the targets from backgrounds. In order to validate the effectiveness of our method, we conduct both offline experiments and field tests. The experiment results manifest that our method is superior to the other seven baseline methods and works more stably for different backgrounds and various types of drones.

Offloading cellular traffic through WiFi Access Points (APs) has been a promising way to relieve the overload of cellular networks. However, data offloading process consumes a lot of resources (e.g., energy, bandwidth, etc.). Given that the owners of APs are rational and selfish, they will not participate in the data offloading process without receiving the proper reward. Hence, there is an urgent need to develop an effective incentive mechanism to stimulate APs to take part in the data offloading process. This paper proposes a novel Delay-constraint and Reverse Auction-based Incentive Mechanism, named DRAIM. In DRAIM, we model the reverse auction-based incentive problem as a nonlinear integer problem from the business perspective, aiming to maximize the revenue of the Mobile Network Operator (MNO), and jointly consider the delay constraint of different applications in the optimization problem. Then, two low-complexity methods: Greedy Winner Selection Method (GWSM), and Dynamic Programming Winner Selection Method (DPWSM) are proposed to solve the optimization problem. Furthermore, an innovative standard Vickrey-Clarke-Groves scheme-based payment rule is proposed to guarantee the individual rationality and truthfulness properties of DPWSM. At last, extensive simulation results show that the proposed DPWSM is superior to the proposed GWSM and the Random Winner Selection Method in terms of the MNO’s utility and traffic load under different scenarios.

Recent studies have exploited moving target defense (MTD) for thwarting false data injection (FDI) attacks against the state estimation (SE) by actively perturbing branch parameters (i.e., impedance or admittance) in power grids. To hide the activation of MTD from attackers, a new strategy named hidden MTD has been proposed by the latest literature. A hidden MTD can increase the defender’s chance to detect FDI attacks and avoid the attacker from inferring new branch parameters. However, by using an MTD-confirming detector like the bad data detection (BDD) checker in SE, we observe that it is still possible for the attacker to detect this hidden MTD when the power flows change with time. To uncover the insight of MTD’s hiddenness, we study the conditions needed for achieving a hidable MTD. We find that the hiddenness of MTD is closely related to the branch perturbations, system topology, and attacker’s knowledge. From the attacker’s perspective, we prove that an MTD can be detected by the attacker only if he/she knows the previous parameters of a set of branches that forms a circle and the measurements corresponding to those branches after MTD. But once the attacker has full knowledge of branch parameters before MTD and has obtained all measurements after MTD, it is proved that we can never achieve a hidable and effective MTD. From the defender’s perspective, since it is impossible to know the attacker’s capability, we cannot determine whether a constructed MTD is hidable or not by purely depending on the MTD design. To address this issue, we propose that, by protecting a basic set of measurements, we always can achieve a hidable and effective MTD regardless of the changes of power flows, the attacker’s knowledge, and the branch perturbations. Furthermore, we validate our findings with the IEEE standard test power systems.

As key sensors of the ADAS, mmWave radars have been widely used for fulfilling tasks including adaptive cruise control, lane-changing assistance and collision avoidance. However, the interference generated by mmWave radars mounted on close-by vehicles, if not managed well, could seriously degrade the radars’ performance, giving rise to a safety hazard. In this work, we study interference mitigation for the off-the-shelf 77 GHz FMCW mmWave radars. We review several state-of-the-art ideas for suppressing co-channel interference, based on which we introduce a VANET-assisted radar interference mitigation scheme, which is shown to be effective in an environment with dense traffic. Specifically, a TDMA based MAC protocol is devised that enables the coordination among vehicles on efficient multiple access of radar spectrum. Numerical results corroborate the performance gain of the proposed approach compared to the existing random frequency hopping approaches.

Customer churn as a common but extremely harmful phenomenon haunts service operators in telecommunications industry for a long time. Discriminating customers prone to churning in the early stage and taking precaution measures can mitigate customer churn effectively. Unlike previous works which mainly considered the inter-operator customer churn, we focus on a new problem of intra-operator customer churn, that is, customers abandon their fourth generation (4G) mobile communication services and switch to third generation/second generation (3G/2G). Due to quite different mechanisms and causes, previous studies cannot tackle this new problem well. In this paper, we propose a novel profit maximizing framework to solve this special customer churn problem. Specifically, we first build a profit maximizing classifier to predict whether a customer is prone to churn or not, in which profitable customers have more probability to be classified correctly. Then, we describe and quantify the switching behaviors of 4G customers by introducing a notion of switching score. Finally, we investigate relations between operators and customers, finding that some features of 4G service plans provided by operators indeed affect switching behaviors of 4G customers remarkably. This provides insight into reasonable design of 4G service plans for operators in the future. Our framework uncovers the root causes of intra-operator customer churn and solve the problem well. Experimental results based on real data demonstrate the effectiveness of our framework.

Electric vehicles (EVs) have experienced a sensational growth in the past few years, due to the potential of mitigating global warming and energy scarcity problems. However, the high manufacturing cost of battery packs and limited battery lifetime hinder EVs from further development. Especially, electric bus, as one of the most important means of public transportation, suffers from long daily operation time and peak-hour passenger overload, which aggravate its battery degradation. To address this issue, we propose a novel data-driven battery-lifetime-aware electric bus scheduling system. Leveraging practical bus GPS and transaction datasets, we conduct a detailed analysis of passenger behaviors and design a reliable prediction model for passenger arrival rate at each station. By taking passenger waiting queue at each bus station analogous to data buffer in network systems, we apply Lyapunov optimization and obtain an electric bus scheduling strategy with reliable performance guarantee on both battery degradation rate and passengers' service quality. To verify the effectiveness of the system, we evaluate our design on a 12-month electric bus operation datasets from the city of Shenzhen. The experimental results show that, compared with two baseline methods, our system reduces the battery degradation rate by 14.3% and 21.7% under the same passenger arrival rate, while preserving good passenger service quality.

Recently, full-view coverage has been introduced to capture intruders from multiple directions in the camera sensor networks. It is more efficient than traditional coverage in identifying the intruders. However, full-view coverage typically calls for a large number of camera sensors. Hence, we exploit limited mobility or orientation to improve the performance of full-view coverage since camera sensors typically can rotate to cover more areas without being relocated after installation. Observing that target points may not be full-view covered constantly due to the sensor rotation, we emphasize the importance of the fairness-based coverage maximization problem, i.e., how to schedule the orientations of camera sensors to maximize the minimum cumulative full-view coverage time of target points. To solve this issue, we first try to reduce the dimension space of orientations by dividing the orientation space into a set of discrete directions. We then study how to select the minimum number of sensing regions that camera sensors should rotate to cover in order to ensure the full-view coverage of all target points. Next, we unveil the relationship between the full-view coverage and target points, which are spatially correlated. Based on these results, we devise a centralized algorithm to solve the problem based on “largest demand first serve” principle, by which the target points with less cumulative full-view coverage time will be preferentially selected to be full-view covered with a higher probability. We further design a distributed solution as a counterpart of the centralized algorithm. Extensive simulations are presented to show the performances of the proposed algorithms. Results show that exploiting limited mobility of sensor rotation has good potential in promoting the efficiency and reducing the cost of ensuring full-view coverage.

Urban freeway traffic control is of great importance for traffic management and intelligent transportation systems. Various approaches have been proposed to relieve urban freeway traffic jam, among which, ALINEA, a ramp metering strategy, is commonly implemented with fixed triggering threshold and static controller parameter. However, such a strategy may not be able to effectively alleviate the traffic congestion while maintaining certain ramp throughput due to two reasons: i). the congestion threshold can be time-varying due to different factors, such as segment ID, weather condition, time, and etc. ii). The congestion evolution patterns are time-varying even for the same segment. In this paper, based on over 890 million records of vehicles collected on ramps in Hangzhou, China, we established dynamic congestion threshold for each road segment with external factors. Based on such dynamic congestion threshold, we further clustered the congestion evolution patterns, and designed adaptive ramp controller which could switch the controller parameter according to the predicted congestion evolution pattern. Finally, in order to show the performance among different strategies, we introduced three baseline groups, which are ‘without Controller’, ‘ALINEA controller’, and ‘Direct RBF(radial basis function)-neural network controller’, respectively.The evaluation of proposed controller design over real large-scale data indicated that our method achieves 8.4%(7.2%), 4.62%(9.48%) efficiency improvement in terms of average speed in km/h (average traffic flow in veh/h) than the performance with normal ALINEA controller and RBF-neural network controller respectively.

In network systems, calculating maximum element plays a fundamental role for distributed data analysis. Maximum consensus algorithm can accomplish the calculation in a fully distributed way. However, the data for communication are privacy-sensitive in many scenarios, making nodes unwilling to participate in the distributed calculation process. How to ensure privacy and accuracy for distributed calculation of maximum element is essential while challenging. In this paper, we first prove that exact maximum consensus and differential privacy cannot be guaranteed simultaneously. Then, to provide sufficient privacy preservation for nodes, we propose a novel differentially private maximum consensus (DPMC) algorithm where nodes perturb their initial states with Laplacian noises. We prove that DPMC algorithm preserves differential privacy and give the analytical expression of privacy preserving degree. Furthermore, the proposed algorithm also guarantees finite-time convergence and achieves high convergence accuracy. The performance tradeoff is finally analyzed and evaluated by extensive numerical simulations.

MCS is an emerging technology that exploits the enormous sensing power of widely used mobile devices to complete sensing tasks in a cost-efficient manner. Among all outstanding issues of current MCS systems, the concern about a lack of privacy protection for the sensing data of participants has drawn increasing attention recently. Various privacy-preserving MCS mechanisms have been proposed for the static scenario where users' privacy protection requirements remain unchanged. In practice, however, users' requirements for privacy protection can be time-varying, which further complicates the design of privacy-preserving MCS. In this article, we first give an overview of multiple promising approaches for privacy-preserving MCS, based on which we make a first attempt to explore privacy-preserving MCS in a dynamic scenario, which is cast as a Markov Decision Process. Specifically, we develop a reinforcement learning based approach, by which the platform can dynamically adapt its pricing policy catering to the varying privacy-preserving levels of participating users. We further use a case study to evaluate the performance of our proposed approach.

Imagine when you line up in a store, the person in front of you can make you pay her bill by using a passive wearable device that forces a scan of your credit card without your awareness. An important assumption of today's Near-field Communication (NFC) enabled cards is the limited communication range between the commercial reader and the NFC cards -- a distance below 5~cm. Previous approaches to attacking this assumption effectively use mobile phones and active relays to enlarge the communication range, in order to attack the NFC cards. However, these approaches require a power supply at the adversary side, and can be easily localized when mobile phones or active relays transmit NFC signals. We propose ReCoil, a system that uses wearable passive relays to attack NFC cards by expanding the communication range to 49.6 centimeters, a ten-fold improvement over its intended commercial distance. ReCoil is a magnetically coupled resonant wireless power transfer system, which optimizes the energy transfer by searching the optimal geometry parameters. Specifically, we first narrow down the feasible area reasonably and design the ReCoil-Ant Colony Algorithm such that the relays absorb the maximum energy from the reader. In order to reroute the signal to pass over the surface of human body, we then design a half waist band by carefully analyzing the impact of the distance and orientation between two coils on the mutual inductance. Then, three more coils are added to the system to keep enlarging the communication range. Finally, extensive experiment results validate our analysis, showing that our passive relays composed of common copper wires and tunable capacitors expand the range of NFC attacks to 49.6 centimeters.

UAV aided communication technology holds tremendous potential to upgrade outdoor link throughput and provide on-demand wireless services. The flexible deployment characteristic makes UAV-aided networks competent at emergency situations, including natural disasters and sudden traffic hotspots. In this backdrop, UAVs are required to be densely deployed to accommodate the huge volume of data traffic, where interference amid the neighboring cells turns out to be extremely challenging. To break this stalemate, this article systematically investigates spectrum sharing technology for ultra-dense UAV-aided networks from the architecture level down to the physical layer. We shed light on design principles and key challenges in utilizing overlapped spectrum for interference-enabled concurrent transmissions. With these principles in mind, we introduce SpecShare, which utilizes coding redundancy at the PHY layer for UAV spectrum sharing. We explore the optimal UAV placement strategy in the network layer to fully unleash the potential of such spectrum sharing capacity. We discuss the feasibility of SpecShare, and demonstrate its effectiveness in terms of network throughput.

In recent years, drones have undergone tremendous development. Due to the low price and ease of use, drones have been widely utilized in many application scenarios, which potentially pose great threats to public security and personal privacy. To mitigate these threats, it is necessary to deploy anti-drone systems in sensitive areas to detect, localize, and defend against the intruding drones. In this article, we provide a comprehensive overview of the technologies utilized for drone surveillance and the existing anti-drone systems. Then we develop an anti-drone system at Zhejiang University, named ADS-ZJU, which combines multiple passive surveillance technologies to realize drone detection, localization, and radio frequency jamming. Furthermore, we discuss the challenges and open research issues in such a system.

This paper investigates the problem of resilient consensus under malicious attacks for multiagent systems. Compared with most of existing works, a more general attack model is considered, where malicious agents can neighbor and collude with each other and the number of tolerable attacks is not limited by the network connectivity, which makes the problem more challenging. To solve this problem, we exploit the mobile agents as the detectors, and design the resilient consensus algorithm with mobile detectors (MRCA). MRCA includes basic average iteration process and the detection algorithms of both static and mobile agents. We prove that under MRCA, malicious agents will be detected by mobile agents and isolated by normal agents in finite time in expectation and thus the convergence can be achieved. We also show that MRCA can tolerate neighboring malicious attacks and the number of tolerable attacks is not limited by the network connectivity. Simulations are conducted to demonstrate the effectiveness of MRCA and validate the correctness of the theoretical results.

Data collection by multiple sinks is a fundamental problem in wireless-sensor networks. Existing works focused on designing the optimal offline methods provided that the number and positions of sensors and sinks (or trajectories of mobile sinks) are predetermined. This may not be practical, because although sensors are cheap, sinks are quite expensive in reality. A more practical scenario is that sinks are deployed step by step during the network operation due budget constraints, and we do not know the number, positions, and capacities of sinks a priori. In this paper, we investigate the problem of data collection with multiple sinks, and design a suboptimal online algorithm via a primal-dual approach, requiring very little priori knowledge. We theoretically derive the competitive ratio of the online algorithm, and further improve it by finding the optimal sink location with an approximation ratio. We also analyze the computational complexity of the improved approach. Extensive simulations are conducted to demonstrate the performance of the proposed online algorithm and performance-complexity tradeoff.

Recently, narrowband Internet of Things (NB-IoT), one of the most promising low power wide area (LPWA) technologies, has attracted much attention from both academia and industry. It has great potential to meet the huge demand for machine-type communications in the era of IoT. To facilitate research on and application of NB-IoT, in this paper, we design a system that includes NB devices, an IoT cloud platform, an application server, and a user app. The core component of the system is to build a development board that integrates an NB-IoT communication module and a subscriber identification module, a micro-controller unit and power management modules. We also provide a firmware design for NB device wake-up, data sensing, computing and communication, and the IoT cloud configuration for data storage and analysis. We further introduce a framework on how to apply the proposed system to specific applications. The proposed system provides an easy approach to academic research as well as commercial applications.

Wireless charging technologies have attracted much attention from both academia and industry due to their great potential of freeing devices from wires and batteries, becoming one of the key research areas in recent years. Many platforms have been designed based on different charging technologies for a wide range of scenarios, including structural monitoring, habitat monitoring, wearable devices, volcano monitoring, and so on. However, these existing platforms cannot be deployed quickly and flexibly in many harsh environments, resulting in high deployment cost. In this article, we propose Q-Charge, a wireless charging platform integrated with a quadcopter, which is able to provide desirable energy for large-scale sensing applications. Q-Charge is composed of three components: an energy transfer module, a programmable quadcopter unit, and a cloud server. A wireless charger is installed on a quadcopter to achieve remote energy delivery, and a sink node retrieves data packets from sensors and forwards them to the cloud via a service gateway. Our platform has been successfully implemented for building structure and farmland monitoring, which demonstrates Q-Charge to be effective and efficient.

We study data gathering problem in Rechargeable Sensor Networks (RSNs) with a mobile sink, where rechargeable sensors are deployed into a region of interest to monitor the environment and a mobile sink travels along a pre-defined path to collect data from sensors periodically. In such RSNs, the optimal data gathering is challenging because the required energy consumption for data transmission changes with the movement of the mobile sink and the available energy is time-varying. In this paper, we formulate data gathering problem as a network utility maximization problem, which aims at maximizing the total amount of data collected by the mobile sink while maintaining the fairness of network. Since the instantaneous optimal data gathering scheme changes with time, in order to obtain the globally optimal solution, we first transform the primal problem into an approximate network utility maximization problem by shifting the energy consumption conservation and analyzing necessary conditions for the optimal solution. As a result, each sensor does not need to estimate the amount of harvested energy and the problem dimension is reduced. Then, we propose a Distributed Data Gathering Approach (DDGA), which can be operated distributively by sensors, to obtain the optimal data gathering scheme. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

Recently, device-to-device (D2D) communication has emerged to offer opportunities for high-rate wireless transmission locally in cellular networks. Such proximity-based communication technology, with advantages in high data transmission rate, spectrum efficiency, and energy efficiency over traditional technologies, has attracted tremendous research interest. Since D2D communication highly relies on user participation, providing incentives is critical to enable D2D communication with desired quality of service (QoS). Existing popular approaches for designing incentive mechanisms (e.g., auction) may cause large overhead of both communication and computation, and thus is inefficient for D2D communication. In this article, we introduce contract theory to provide effective and distinctive incentive mechanisms, which can also reduce computation and communication costs and thus has great potential for practical implementation. To evaluate the performance of the proposed contract-based approach, we also provide a case study that shows the advantages of contract theory in designing efficient incentives and handling uncertainty.

Crowdsensing offers an efficient way to meet the demand in large-scale sensing applications. In crowdsensing, optimal task allocation is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations, and mobile users have time constraints. We show that the allocation problem is NP-hard. We then focus on approximation algorithms and devise an efficient local-ratio-based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We extend the results to the general scenario where mobile users are heterogeneous. A distributed version of LRBA, namely DLRBA, is designed, which can be iteratively executed at each mobile user without the need for the platform to collect all the information of mobile users. We prove that both centralized and distributed versions can output the same solution. Extensive simulation results are provided to demonstrate the advantages of our proposed algorithms.

Pricing and task allocation are vital to improving the efficiency in mobile crowdsensing , an emerging human-in-the-loop application paradigm. Previous studies focused on incentive mechanism design for specific sensing applications where one party (either task initiators or platform) can dominate the pricing and task allocation process. These results, however, are not applicable to a free crowdsensing market where multiple task initiators and task participants (mobile users), as peers, are engaged to maximize their own interests. New incentive mechanisms are pressingly needed to produce a solution, so that the interests of all participating parties can be considered. In this paper, appealing to exchange economy theory, we employ the notion of “Walrasian Equilibrium” as a comprehensive metric, at which there exists a price vector for mobile users and an allocation for task initiators such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). We consider a standard model where the utility function for sensing quality is monotonically increasing, differentiable, and concave, and the payoff function for a mobile user is linear. To address the problem, we first characterize the supply–demand pattern for a given price vector, which is the subset of mobile users selected by each task initiator to perform the task. We then devise methods for validating the existence of a Walrasian Equilibrium within each supply–demand pattern. One key step is to divide the space of prices into a collection of appropriate cells, based on the hyperplane arrangement, so that each cell has a unique supply–demand pattern. We devise an algorithm that can find a Walrasian Equilibrium in polynomial time, for a case of practical interest where the classes of mobile devices are bounded. Based on the insight, we further consider the general case and design an efficient pattern search (EPS) algorithm to reduce the search space, thus accelerating the search process accordingly. This is realized by choosing the supply–demand pattern which is closer to the “Walrasian Equilibrium” than the pattern in previous iteration in the search process. Our results show that EPS can find an $\epsilon $ -approximation Walrasian Equilibrium in polynomial time for the general case, given a constant $\epsilon $ .

An incentive mechanism is important for mobile crowdsensing to recruit sufficient participants to complete large-scale sensing tasks with high quality. Previous incentive mechanisms have focused on quantifying participants' contribution to the quality of sensing and provide incentives directly to them. In this article, we introduce a novel approach, called the social incentive mechanism, which, surprisingly, incentivizes the social friends of the participants who perform the sensing tasks. The basic idea is to leverage the social ties among participants to promote global cooperation. Since the incentive that a participant receives largely relies on the behaviors of his/her social friends, participants have the motivation to impact their friends' behaviors through their social relationships in order to gain a higher payoff. This approach is applicable to many scenarios where the contributions to the quality of sensing among participants are interdependent, such as data aggregation. We have provided a case study which shows that the social incentive mechanism is more cost-effective than traditional incentive mechanisms.

RFID technology has been widely adopted for real-world applications, such as warehouse management, logistic control, and object tracking. This paper focuses on a new angle of applying RFID technology—monitoring the temporal change of a tag set in a certain region, which is called churn estimation. This problem is to provide quick estimations on the number of new tags that have entered a monitored region, and the number of pre-existing tags that have departed from the region, within a predefined time interval. The traditional cardinality estimator for a single tag set cannot be applied here, and the conventional tag identification protocol that collects all tag IDs takes too much time, especially when the churn estimation needs to perform frequently to support real-time monitoring. This paper will take a new solution path, in which a reader periodically scans the tag set in a region to collect their compressed aggregate information in the form of empty/singleton/collision time slots. This protocol can reduce the time cost of attaining pre-set accuracy by at least 35%, when comparing with a previous work that uses only the information of idle/busy slots. Such a dramatic improvement is due to our awareness of collision slot state and the full utilization of slot state changes. Our proposed churn estimator, as shown by the extensive analysis and simulation studies, can be configured to meet any pre-set accuracy requirement with a statistical error bound that can be made arbitrarily small.

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches l in the power system is not less than twice that of the system states n (i.e., l \geq 2n , where n + 1 is the number of buses); (ii) the susceptances of more than n branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the stealthy attack space, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the stealthy attack space and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800204, in part by the National Natural Science Foundation of China under Grant 61833015, in part by the Singapore University of Technology and Design-Zhejiang University Innovation, Design and Entrepreneurship Alliance (SUTD-ZJU IDEA) under Award 201805, in part by the Nanyang Technological University (NTU) Internal Funding-Start-up Grant (SUG)- the College of Engineering (CoE) under Grant M4082287, and in part by the A*STAR- Nanyang Technological University-Singapore University of Technology and Design AI Partnership under Grant RGANS1906. This article was presented at the IEEE PES ISGT 2019 [1]. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Walid Saad.

ZigBee has been widely recognized as an important enabling technique for Internet of Things (IoT). However, the ZigBee nodes are normally resource-limited, making the network susceptible to a variety of security threats. This paper closely investigates a severe attack on ZigBee networks termed as ghost , which leverages the underlying vulnerabilities of the IEEE 802.15.4 security suites to deplete the energy of the nodes. We show that the impact of ghost is very large and that it can facilitate a variety of threats including denial of service and replay attacks. We highlight that merely deploying a standard suite of advanced security techniques does not necessarily guarantee improved security, but instead might be leveraged by adversaries to cause severe disruption in the network. We propose several recommendations on how to localize and withstand the ghost and other related attacks in ZigBee networks. Extensive simulations are provided to show the impact of the ghost and the performance of the proposed recommendations. Moreover, physical experiments also have been conducted and the observations confirm the severity of the impact by the ghost attack. We believe that the presented work will aid the researchers to improve the security of ZigBee further.

We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a given region. Full-view area coverage is challenging because the full-view coverage of a 2-D continuous domain has to be considered. To tackle this challenge, we first study the intrinsic geometric relationship between the full-view area coverage and the full-view point coverage and prove that the full-view area coverage can be guaranteed, as long as a selected full-view ensuring set of points is full-view covered. This leads to a significant dimension reduction for the full-view area coverage problem. Next, we prove that the minimum-number full-view point coverage is NP-hard and propose two approximation algorithms to solve it from two different perspectives, respectively: 1) By introducing a full-view coverage ratio function, we quantify the “contribution” of each camera sensor to the full-view coverage through which we transform the full-view point coverage into a submodular set cover problem and propose a greedy algorithm (GA); and 2) by studying the geometric relationship between the full-view coverage and the traditional coverage, we propose a set-cover-based algorithm (SCA). We analyze the performance of these two approximation algorithms and characterize their approximation ratios. Furthermore, we devise two distributed algorithms that obtain the same approximation ratios as GA and SCA, respectively. Finally, we provide extensive simulation results to validate our analysis.

Recent years have witnessed many new promising technologies to power wireless sensor networks, which motivate some fundamental topics to be revisited. Different from energy harvesting, which generates dynamic energy supply, the mobile charger is able to provide a stable and reliable energy supply for sensor nodes and, thus, enables sustainable system operations. While previous mobile charging protocols focus on either the charger travel distance or the charging delay of sensor nodes, in this work, we propose a novel energy synchronized mobile charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of traveling salesman problem (TSP)-based solutions, when the nodes' energy consumption is diverse, we construct a set of nested TSP tours based on their energy consumption, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging request sequence of nodes with their sequence on the TSP tours. Experimentation and simulation demonstrate that ESync can reduce charger travel distance and nodes' charging delay by about 30% and 40%, respectively.

Wireless sensor networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pairwise based, in which discovery is passively achieved when two nodes are prescheduled to wake up at the same time. In contrast, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Since proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pairwise one. Our testbed experiments using 40 sensor nodes and extensive simulations confirm the theoretical analysis, showing one order of magnitude reduction in discovery delay compared with legacy pairwise methods in dense, uniformly distributed sensor networks with at most 8.8 percent increase in energy consumption.

Limited energy in each node is the major design constraint in wireless sensor networks (WSNs). To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. In a typical WRSN, batteries in sensor nodes can be replenished by a mobile charger that periodically travels along a certain trajectory in the sensing area. To maximize the charged energy in sensor nodes, one fundamental question is how to control the traveling velocity of the charger. In this paper, we first identify the optimal velocity control as a key design objective of mobile wireless charging in WRSNs. We then formulate the optimal charger velocity control problem on arbitrarily-shaped irregular trajectories in a 2D space. The problem is proved to be NP-hard, and hence a heuristic solution with a provable upper bound is developed using novel spatial and temporal discretization. We also derive the optimal velocity control for moving the charger along a linear (1D) trajectory commonly seen in many WSN applications. Extensive simulations show that the network lifetime can be extended by $2.5 \times$ with the proposed velocity control mechanisms.

This paper studies the network utility maximization (NUM) problem in static-routing rechargeable sensor networks (RSNs) with the link and battery capacity constraints. The NUM problem is very challenging as these two constraints are typically coupling in RSNs, which cannot be directly tackled. Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally coupled part, both of which are not practical, and will also degrade the network performance. In this paper, we attempt to jointly optimize the sampling rate and battery level by carefully tackling the spatiotemporally coupled link and battery capacity constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm in the context of joint rate and battery control, called decouple spatiotemporally-coupled constraint (DSCC), which can converge to the globally optimal solution. Numerical results, based on the real solar data, demonstrate that the proposed algorithm always achieves higher network utility than existing approaches. In addition, the impact of link/battery capacity and initial battery level on the network utility is further investigated.

The increasing number of instances of privacy leakage in CPSs and the corresponding serious consequences have caused great worry in our society. In most privacy preserving mechanisms proposed to protect sensitive individual information, system performance is compromised at the same time. In this article, we consider the trade-off between individual privacy and system performance in CPSs. After introducing the CPS architecture and the basic definition of differential privacy, we formulate the performance optimization problem subject to a given differential privacy requirement. For a simplified system, we derive the closed-form optimal system performance under the desired privacy requirement. Simulation results are provided to verify the proposed mechanism, which balances the trade-off between system performance and privacy. We also identify future research topics on the privacy preserving problem in CPSs.