Information dissemination in dynamic social networks enables fast and frequent access to social news. Thereinto, the coexistence of public and private information creates a hybrid dissemination dynamics process in social networks. However, most existing information dissemination models treat the hybrid information in isolation and fail to consider their interactions through shared nodes and temporal dependencies. Thus, we propose a novel hybrid information dissemination (HID) model that explicitly captures the interconnected dissemination mechanisms of both public and private information within dynamic social networks. Additionally, considering heterogeneity among individuals, we further design a decision-making algorithm for the proposed HID model, aiming at maximizing individuals’ initiative. Furthermore, we derive equilibrium points and analyze their stability for the proposed HID model. Numerous experiments are conducted, and results show that the proposed HID model can effectively describe the dissemination process of hybrid information.

With the rapid development of 6G technologies, extremely large-scale reconfigurable intelligent surfaces (XL-RIS) have been introduced to enhance spatial diversity and improve signal coverage. Meanwhile, physical-layer key generation (PKG) has emerged as a promising, quantum-resistant solution for securing wireless communications. However, the deployment of XL-RIS significantly expands the near-field region, making traditional far-field planar wave models inadequate for PKG. To address this limitation, this paper proposes an XL-RIS-assisted PKG model based on near-field spherical wave propagation theory. We further derive a expression for the legitimate key generation rate (KGR) under passive eavesdropping attacks. To maximize the achievable KGR, we propose a novel alternating optimization(AO) algorithm ADA that combines Dinkelbach and the Alternating Direction Method of Multipliers (ADMM) to jointly optimize the base station (BS) beamforming vector and RIS reflection coefficients in an iterative manner. Simulation results indicate that the spatial resolution gain provided by the near-field channel helps enhance the independence between the legitimate and eavesdropping channels. The ADA demonstrates robust convergence. Notably, it maintains superior performance even when the eavesdropper and the legitimate user share the same azimuth angle.

The vulnerability of wireless communication links to eavesdropping poses significant challenges in securing AAV-assisted networks. To enhance security, reconfigurable intelligent surfaces (RIS) and artificial noise (AN) have emerged as promising technologies for mitigating eavesdropping by controlling wireless propagation environments and introducing interference against eavesdroppers. However, existing works have rarely combined transmitter beamforming, RIS, and AN integratedly considered, and leveraging their complementary characteristics for efficient security enhancement remains challenging. Additionally, optimizing such system security performance is complicated by the nonconvexity of secrecy rate maximization and the highly time-varying communication links caused by the mobility of AAVs and users. To address these challenges, we propose a secure communication framework that integrates RIS and AN transmission devices on AAVs. To solve the resulting nonconvex optimization problem, we develop a dual-mode framework based on twin delayed deep deterministic policy gradient (TD3), employing two subenvironments that interact independently before updating a global environment. Extensive simulations demonstrate that the proposed approach significantly enhances secrecy rate performance compared to other methods.

This study finds and proves that strictly periodic scheduling is optimal in terms of energy efficiency within the context of remote state estimation. We model a problem where the sensor transmits local state estimates over an independent and identically distributed packet dropping channel to a remote estimator. From the angle of energy efficiency, we discover and explain that periodic scheduling can arbitrarily approach optimal scheduling in infinite horizon. Building upon this, we have derived explicit conclusions regarding the optimal periodic scheduling, underscoring that: 1) Under the condition of maximum energy efficiency, the optimal scheduling policy is to enforce a strict periodic scheduling; 2) A concrete expression for the identified strict scheduling period based on the system parameters has been established. The study culminates with a series of numerical simulations that showcase the efficacy of our theoretical findings.

Numerous studies have demonstrated, in-depth, the vulnerability of the deep reinforcement learning (DRL) model’s elements (e.g., reward), which is a factor limiting the widespread deployment of DRL in some crucial domains, including intelligent traffic signal control (ITSC). While partial poisoning attacks with insidious rewards are enabled undetectable by directly employing regularization or cumulative reward restrictions, these constraints are somewhat 1-D and fail to consider the time dependence of DRL. Moreover, the adversary should avoid injecting undesirable perturbations when agents’ policies are unstable, namely, effectively maximizing the attacking strategy’s benefit. It is thus a challenge to perturb the DRL model stealthily with as few disruption steps or modifications to the original sample as possible while ensuring the attack’s efficiency. In this work, two black-box reward space attack strategies are introduced, where we encourage the adversary to learn a malicious adversarial policy actively. The first is the Multiconstraint Stealthy Time Attack which is updated with the penalties earned by attacking crucial moments, and restricted through action confidence and perturbations’ total number, to ensure attack times’ stealthiness. The second technique is the multiobjective stealthy modification attack which is modeled as a multiobjective optimization problem, and the adversary balance attack performance and stealthy modification with weighting factor$\omega $. Extensive simulation results evaluated in SUMO, involving comparison assessment and attack distribution, exhibit a dramatic increase in average travel time, implying that our attacks impose pressure on the traffic flow, namely, the efficacy of proposed attack strategies.

Average consensus is a key component of multi-agent systems coordination, while data privacy becomes a serious concern. Through the information exchange process, the initial state of an agent may be disclosed to its neighbors. The existing privacy-preserving research mainly addressed the situation of single-neighbor eavesdropping and infinite-time consensus, and they cannot deal with the cases of multi-neighbors eavesdropping and collusion inference attack or ensuring finite-time consensus. In this paper, we prove that it is impossible to preserve a node’s data privacy if all of its neighbors collusively infer the data. Otherwise, we propose a privacy-preserving framework to support conventional average consensus, push-sum consensus, and finite-time average consensus, which integrates multiplying random variables, finite-time error compensation, and updating rule jump. In this paper, each agent exchanges data with its neighbors by multiplying a random variable to its real-time state at each iteration. To eliminate errors caused by the random multiplier, a finite-time error compensation term and updating rule jump are designed, which ensure the accuracy of consensus. We prove that the proposed framework can converge and preserve privacy facing collusion inference attacks in both finite-time and infinite-time consensus, while traditional adding-noise-based methods cannot solve the finite-time case. We also derive the analytical expressions of the maximum privacy disclosure probability for the initial state of each agent, and present the impact of multiplying random variables. Extensive case studies demonstrate the effectiveness of the proposed framework.

The research on combining cryptanalysis with deep learning has recently attracted increasing attention. As an ultra-lightweight cipher for IoT environments, SPECK has attracted much attention from researchers for its excellent performance, and there have been some attempts to introduce deep learning into differential cryptanalysis on SPECK. However, existing work often built differential distinguishers based on traditional residual network, whose accuracy and interpretability on other tasks is inferior to that of attention mechanisms. In order to improve model accuracy and to further utilise the deep learning model to analyse the security of SPECK, this paper introduces the attention mechanism into the differential cryptanalysis on SPECK. First of all, by introducing an attention mechanism in the output layer of the residual network, we achieve a higher accuracy than existing works, and confusion matrices prove that the enhancement brought by attention is effective. Furthermore, using the visualization algorithm, we demonstrate the effectiveness of the attention mechanism intuitively and further analyze the features extracted from the ciphertext by deep learning. In addtion, the bit transfers captured from the ciphertexts by the attention mechanism reflects the possible insecurity of the 5, 6 and 7 rounds of SPECK for specific input differentials, and our work again demonstrates the great potential of deep learning for applications in differential cryptanalysis.

Keeping sensor data fresh is desired for Industrial Internet of Things (IIoT), especially, in real-time monitoring applications. However, this may require sensors always in active mode and, thus, incur low energy efficiency. In this article, we consider that a wireless sensor monitors a dynamical system and reports real-time measurements to a processing center through an unreliable wireless channel. We study the problem of optimizing the sensor data freshness in terms of Age of Information (AoI) while saving energy by scheduling the sensor to sleep when needed. The problem is formulated as a Markov decision process that takes both AoI and energy consumption into account, to which we theoretically prove that the optimal scheduling policy forms a cyclic sleep–wake pattern. The optimal sleep period is also analyzed. Simulation results demonstrate that the proposed scheduling policy outperforms other existing policies.

The problem of multiple integrity attacks (attackers) detection and identification (MIADI) in cyber–physical systems (CPSs) is still a challenging problem to date. The goal of this article is to develop a knowledge-based method capable of simultaneously detecting and identifying multiple integrity attacks aiming at different sensors in a CPS. In this article, with the help of labeled random finite set (RFS) theory, a new solution to solve the MIADI problem is proposed. The main contributions of this article lie in the following two aspects, the first is the novel formulation of the MIADI problem, in which labeled RFSs are used to model the behaviors of multiple integrity attackers for the first time, and the second is the proposed labeled RFS-based solution, which provides an elegant framework to cope with the MIADI problem. Numerical experiments are conducted and experimental results demonstrate the effectiveness of the proposed solution. This proposed solution further extends the feasibility of the labeled RFS theory in the context of CPSs cybersecurity.

Energy efficiency, real-time response, and data transmission reliability are important objectives during networked systems design. This paper aims to develop an efficient task mapping scheme to balance these important but conflicting objectives. To achieve this goal, tasks are triplicated to enhance reliability and mapped on the wireless nodes of the networked systems with Dynamic Voltage and Frequency Scaling (DVFS) capabilities to reduce energy consumption while still meeting real-time constraints. Our contributions include the mathematical formulation of this task mapping problem as mixed-integer programming that balances node energy consumption, enhancing data reliability, under real-time and energy constraints. Compared with the State-of-the-Art (SoA) , a joint-design problem is considered in this paper, where DVFS, task triplication, task allocation, and task scheduling are optimized concurrently. To find the optimal solution, the original problem is linearized, and a decomposition-based method is proposed. The optimality of the proposed method is proved rigorously. Furthermore, a heuristic based on the greedy algorithm is designed to reduce the computation time. The proposed methods are evaluated and compared through a series of simulations. The results show that the proposed triplication-based task mapping method on average achieves 24.84% runtime reduction and 28.62% energy saving compared to the SoA methods.

This article is concerned with the problem of multiple attacks detection (MAD) for distributed sensor networks (SNs) under multiple malicious attacks. The goal of this article is to develop an effective method capable of simultaneously detecting multiple attacks in distributed SNs. By integrating the theories of random finite set (RFS), fusion rules, and consensus, a novel distributed filter named consensus arithmetic average Gaussian mixture probability hypothesis density (AA-GMPHD) filter is proposed in this article, which can achieve the simultaneous detection of multiple attacks in the context of distributed SNs. The main contribution of this article, lies in the proposed consensus AA-GMPHD filter that solves the MAD problem in distributed SNs for the first time. Simulation experiments confirm the effectiveness of the proposed filter for the distributed MAD problem in the context of distributed SNs.

In this paper, we study how to minimize the age of information (AoI) for remote monitoring over a WiFi network, where a tagged node under study needs to deliver the sampling messages to the monitoring application installed at the access point (AP). We consider a very challenging practical scenario where multiple background nodes might incorporate heterogeneous and generic traffic models; all the nodes contend for the transmission channel through the practical IEEE 802.11 based medium access control (MAC) protocol. The existing AoI analyses over distributed MAC protocol are not sufficient for our problem, which are limited to simplified MAC modeling or homogeneous traffic modeling. We propose an AoI optimization algorithm that integrates the AoI queueing analysis with the 802.11 MAC performance analysis. Specifically, we develop an innovative method to address the impact of the MAC channel attention on the message service time of the tagged node and compute the minimal AoI iteratively. Simulation results demonstrate that our algorithm is very accurate and robust crossing a variety of networking scenarios. Moreover, our methods require only local computation and slight probing of the conditional collision probability, making them suitable for practical use.

In this paper, we propose a residual-based Global Position Stystem (GPS) spoofing detection and positioning recovery algorithm that uses inertial navigation system (INS) measurements to detect spoofing attacks on GPS receivers. We first calculate the error vector of satellite pseudorange, and use cumulative statistics to determine the validity of satellite data. Then, we utilize the remaining accurate GPS data and inertial measurement unit (IMU) data to achieve positioning recovery. Furthermore, we perform simulation experiments for fixed pseudorange bias and incremental pseudorange bias attacks, the results show that GPS spoofing is easily detected by using the proposed algorithm, which validates the effectiveness of the proposed algorithm.

Ensuring data freshness is important for Industrial Internet of Things (IIoT). In this article, we consider a typical IIoT application where multiple sensors monitor some time-varying physical processes and report measurements to a central base station through an unreliable wireless channel. In order to save energy, each sensor may switch to sleep mode for a while after successfully transmitting a packet. Since only awake sensors are able to transmit data, we propose a novel function of Age of Information with penalty (or AoI-penalty) to capture the eagerness of the active sensors to provide fresh information. We formulate a new AoI-penalty minimization problem for scheduling the sensors’ transmissions. We theoretically derive a necessary condition for the system’s AoI-penalty to converge to a finite value, and further obtain a lower bound of the AoI-penalty. Moreover, we develop a max-weight-based scheduling policy and theoretically prove that it is the optimal policy when the network is symmetric and the channel is error-free. Simulation results demonstrate that the proposed policy achieves AoI performance near to the lower bound and that with such sleep–wake sensors, the achieved AoI performance is close to that with nonsleeping sensors but at a much lower energy cost.

Privacy is of great importance for microgrids and has gained much attention recently. By eavesdropping on the communications among the devices, attackers may infer sensitive system operation information and user behavior due the intimate interplay between communication and control in mircogrids. In this paper, in order to facilitate simulational evaluations of privacy preservation techniques for microgrids, we develop a versatile cyber-physical co-simulator which integrates both networked communication and power system control subsystems as a whole. The co-simulator is built upon MATLAB/Simulink and OMNeT++ along with a module that coordinates the two tools in real-time simulations. Based on the co-simulator, we evaluates three privacy-preserving algorithms proposed in the literature, and find that SFPA performs better than PEMA and REP-CoDEMS in aspect of protecting the privacy of controllable inputs, but REP-CoDEMS and PEMA has a better performance considering both controllable inputs and uncontrollable inputs.

Networked systems are useful for a wide range of applications, many of which require distributed and collaborative data processing to satisfy real-time requirements. On one hand, networked systems are usually resource constrained, mainly regarding the energy supply of the nodes and their computation and communication abilities. On the other hand, many real-time applications can be executed in an imprecise way, where an approximate result is acceptable as long as the baseline Quality of Service (QoS) is satisfied. Such applications can be modeled through imprecise computation (IC) tasks. To achieve a better tradeoff between QoS and limited system resources, while meeting application requirements, the IC-tasks must be efficiently mapped to the system nodes. To tackle this problem, we first construct an IC-task mapping problem that aims to maximize system QoS subject to real-time and energy constraints. Dynamic voltage and frequency scaling (DVFS) and multipath routing are explored to further enhance real-time performance and reduce energy consumption. Second, based on the problem structure, we propose an optimal approach to perform IC-task mapping and prove its optimality. Furthermore, to enhance the scalability of the proposed approach, we present a heuristic IC-task mapping method with low computation time. Finally, the simulation results demonstrate the effectiveness of the proposed methods in terms of the solution quality and the computation time.

The emergence of fog computing has brought unprecedented opportunities to the Internet-of-Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this article, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality, and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.

In this article, we investigate optimal transmission power allocation at a sensor equipped with the energy-harvesting technology for remote state estimation in wireless cyber-physical systems. The sensor has access to an energy harvester, which can collect energy from the external environment and is an everlasting but unreliable energy source compared with conventional batteries. For the wireless dropping communication channel, the packet dropout rates depend on both the signal-to-noise ratio and the transmission power used by the sensor. We formulate the problem of the optimal transmission power allocation to minimize the remote estimation error covariances as a Markov decision processes (MDPs) subject to energy constraint of the sensor. By analyzing the MDP algorithm, we show that an optimal deterministic and stationary transmission power policy exists. Moreover, we show that the optimal policy has a threshold-type structure. A numerical simulation is provided to illustrate the performance of the transmission power allocation algorithm.

Recently, collaborative distributed energy management systems (CoDEMS) have emerged as an effective solution to manage distributed energy resources in microgrid. In CoDEMS, devices collaborate in a distributive manner over communication networks to meet electrical loads and supply balance at minimum cost. However, mutual information exchanges among the devices in CoDEMS may leak important information about the devices states. In this paper, we investigate the challenging problem of how to achieve optimality while preserving the privacy of CoDEMS at relatively low cost. Unlike many previous works that preserve the privacy by using additive noises, we propose a novel random-weight privacy-preserving algorithm with error compensation, termed as REP-CoDEMS, for CoDEMS. In the proposal, each distributed device generates two random weights each time and it communicates with its neighbor conveying values based on the weights, incremental cost estimation and power imbalance estimation information along with a novel error compensation term to eliminate the error induced by the random weights. We theoretically prove that the proposed REP-CoDEMS algorithm converges and preserves the privacy of all devices. We also derive analytical expressions of the maximum privacy disclosure probability for initial and final states of the CoDEMS. In addition, we conduct extensive simulations and the results demonstrate the effectiveness of the proposed algorithm.

Remote state estimation over a wireless network is of significant importance in many industrial applications, such as condition monitoring. In these cases, sensors deliver their data to remote estimators through wireless channels, which makes communication reliability a core issue. In this article, we propose an error-aware design to carry out network scheduling and channel allocation according to estimation error covariance and channel quality, with the aim of minimizing the total estimation error covariance. We develop multidimensional conflict graphs to model the interference and conflicts, and on this basis, a two-phase heuristic algorithm is further proposed to adaptively assign slots and channels at each superframe. Theoretical analysis and extensive simulations are given to show the effectiveness of our error-aware design in preventing the estimation error covariance from diverging, and hence able to improve the accuracy of remote estimation and monitoring.

In this article, we investigate the privacy issue of the remote state estimation problem in cyber-physical systems. Specifically, in the presence of an eavesdropper, a sensor observes a discrete linear time-invariant process and then sends the measurements to a remote state estimator with arbitrary finite kinds of transmission options through an unreliable wireless channel. The transmission options of the sensor are in silence state or transmitting aided by injection noise with different energy levels. The eavesdropper wiretaps the channel when the sensor transmits packets to the estimator. Aiming at minimizing the remote estimation error and the cost of the sensors transmission energy while maximizing the eavesdropper state estimation error, we theoretically prove that there exist some structural properties for the optimal transmission schedule for both the known and the unknown eavesdropper's estimation errors. Numerical simulation results are provided to validate the theoretical analysis.

It is well known that the Proof-of-Work (PoW) based blockchain scheme, first introduced in Bitcoin system, is not scalable, where the PoW implementation limits the transaction processing rate. In recent years, parallel chain techniques have been proposed to overcome this issue. Chainweb is one of the parallel chains to be studied in this paper with a focus on security related issues. It is worth noting that existing blockchain security studies mainly focus on traditional single-chain based protocols. There are not many security related studies on parallel blockchains. This paper for the first time reveals that selfish mining attack is possible on Chainweb blockchain, to the best of our knowledge. Specifically, we propose a selfish mining attack that exclusively mines blocks on a subset of parallel chains with the same block height and achieves gain through a proper withholding strategy. We develop a mathematical model to quantitatively evaluate the performance and demonstrate the effectiveness of the proposed attack. Our results show that the attacker can gain extra mining reward when his computational power is at least 38% of the total power in Chainweb network. Under the 50% computational power restriction, the attacker's extra gain increases monotonically with its computational power.

The protocol design of device-to-device (D2D) networks have regained research interest in recent years, due to the increasing number of networking devices and the diverse deployment settings. Most of the network optimization tasks are fundamentally difficult NP-hard problems in wireless settings, because managing interference introduces combinatorial complexity. Existing approaches use general heuristic algorithms for the underlying graph problems. While efficient and simple, they are not adaptive to the changing requirement and priorities of the service providers, and make no use of the past data to recognize and exploit the information within. In this paper, we study a representative network optimization task of maximizing the throughput-based system utility through link scheduling in a single-radio, single-channel D2D networks, and propose a learning-based method to leverage past experience to generate a good scheduling policy. We combine the pattern matching capabilities provided from recurrent neural networks (RNN) and the flexibility in changing environment from reinforcement learning (RL). The algorithm is implemented with existing software frameworks and tested with numerical experiments. We find that its overall solution quality is comparable to existing heuristics with various network scales, and report an improved system throughput with significant lower computation time.

WirelessHART is one of the most widely used communication standards in industrial wireless networks. In order to meet the stringent real-time requirements in industrial applications, WirelessHART incorporates many designs including the time slotted channel hopping mechanism that enables dynamic time scheduling and channel allocation. In this paper, we study the problem of joint transmission scheduling and channel allocation aiming to minimize the end-to-end delay of multiple flows in multihop WirelessHART networks. We propose a new network model based on a multidimensional scheduling space spanned by flow-link-channel-slot tuples. A multidimensional conflict graph is then established to depict the conflict relationships among the tuples. Based on this, the original delay minimization problem is formulated as an integer program, which however is difficult to solve due to its significantly large scale. To this end, we develop an iterative hop-wise scheduling algorithm by transforming the original problem into a series of maximum weighted independent set problems. We derive theoretical analysis on the schedulability and the performance bound of the proposed algorithm. In addition, we show that our results can be easily extended to accommodate more general scenarios. Finally, extensive simulation results are provided to demonstrate the effectiveness of the algorithm.

The emerging ad hoc clouds form a new cloud computing paradigm by leveraging untapped local computation and storage resources. An important application of ad hoc clouds is to outsource computational intensive problems to nearby cloud agents. Specifically, for the problem of solving a linear algebraic equation (LAE), an outsourcing client assigns each cloud agent a subproblem, and then all involved agents apply a consensus-based algorithm to obtain the correct solution of the LAE in an iterative and distributed manner. However, such a distributed collaboration paradigm suffers from cyber security threats that undermine the confidentiality of the outsourced problem and the integrity of the returned results. In this paper, we identify a number of such security threats in this process, and propose a secure outsourcing scheme which not only preserves the privacy of the LAE parameters and the final solution from the participating agents, but also guarantees the correctness of the final solution. We prove that the proposed scheme has low computation complexity at each agent, and is robust against the identified security attacks. Numerical and simulation results are presented to demonstrate the effectiveness of the proposed method.

In cyber-physical systems, mobile actuators can enhance system's flexibility and scalability, but at the same time incurs complex couplings in the scheduling and controlling of the actuators. In this paper, we propose a novel event-driven method aiming at satisfying a required level of control accuracy and saving energy consumption of the actuators, while guaranteeing a bounded action delay. We formulate a joint-design problem of both actuator scheduling and output control. To solve this problem, we propose a two-step optimization method. In the first step, the problem of actuator scheduling and action time allocation is decomposed into two subproblems. They are solved iteratively by utilizing the solution of one in the other. The convergence of this iterative algorithm is proved. In the second step, an online method is proposed to estimate the error and adjust the outputs of the actuators accordingly. Through simulations and experiments, we demonstrate the effectiveness of the proposed method.

The energy efficiency of future networks is becoming a significant and urgent issue, calling for greener network designs. However, the increasing complexity in network structure and resource space lead to growing problem scales and coupled resource dimensions, which bring great challenges in obtaining a joint solution in optimizing the energy efficiency. In this paper, we develop a multi-dimensional network model on the basis of tuple-links associated with transmission patterns (TPs) and formulate the optimization problem as a TP based scheduling problem which jointly solves transmission scheduling, routing, power control, radio, and channel assignment. In order to tackle the complexity issues, we propose a novel algorithm by exploiting the delay column generation technique to decompose the coupled problem into recursively solving a master problem for scheduling and a sub-problem for power allocation. Further, we theoretically prove that the performance gap between the proposed algorithm and the optimum is upper bounded by that for the sub-problem solution, where the latter is derived by solving a relaxed version of the sub-problem. Numerical results demonstrate the effectiveness of the multi-dimensional framework and the benefit of the proposed joint optimization in improving network energy efficiency.

Multiagent systems (MASs) are usually applied with agents classified into leaders and followers, where selecting appropriate leaders is an important issue for formation control applications. In this paper, we investigate two leader selection problems in second-order MAS, namely, the problem of choosing up to a given number of leaders to minimize the formation error and the problem of choosing the minimum number of leaders to achieve a tolerated level of error. We propose a game theoretical method to address them. Specifically, we design a supermodular game for the leader selection problems and theoretically prove its supermodularity. In order to reach Nash equilibrium of the game, we propose strategies for the agents to learn to select leaders based on stochastic fictitious play. Extensive simulation results demonstrate that our method outperforms existing ones.

The problem of optimal resource allocation in wireless networks usually involves scheduling of the network independent sets (ISs), of which the number increases exponentially in the network scale. To deal with such large-scale optimization problems, traditional approaches often resort to some heuristics or iterative algorithms for obtaining a relatively small set of ISs to solve the problems, but at the cost of suboptimality or long convergence time. In this paper, we consider wireless network resource allocation in dynamic flow environments, aiming at maximizing the network capacity. We propose a learning-based approach to find ISs based on the dynamic flow demands. Specifically, instead of searching for individual ISs, we propose to learn groups of ISs by using a deep belief network (DBN). We present detailed design of the DBN-based learning method including details in the offline training and online running phases. Simulation results demonstrate that our DBN-based method outperforms existing ones in terms of achieved network capacity and computation time.

The performance of wireless remote state estimation depends on the wireless channel quality, and hence is vulnerable to wireless channel jamming attack. In this paper, we investigate the problem of optimal jamming attack schedule that causes the largest performance degradation to the remote state estimation system. Unlike many previous studies, we consider that the sensor transmit data to the remote estimator through one of multiple independent wireless channels. Due to radio constraint of the attacker, we assume that it can only launch jamming attack on one of the channels at each step. We propose a matrix game approach to model the interactions between the attacker and the sensor and theoretically prove the existence of an optimal attack strategy. We further design an online algorithm based on temporal-difference learning for the attacker to make attack decisions. Numerical examples are provided to demonstrate the effectiveness of the game theoretical method.

Wireless network resource allocation is an important issue for designing Internet of Things systems. In this paper, we consider the problem of wireless network capacity optimization that involves issues such as flow allocation, link scheduling, and power control. We show that it can be decomposed into a linear program and a nonlinear weighted sum-rate maximization problem for power allocation. Unlike most traditional methods that iteratively search the optimal solutions of the nonlinear subproblem, we propose to directly compute approximated solutions based on machine learning techniques. Specifically, the learning systems consist of both support vector machines (SVMs) and deep belief networks (DBNs) that are trained based on offline computed optimal solutions. In the running phase, the SVMs perform classification for each link to decide whether to use maximal transmit power or be turned off. At the same time, the DBNs compute an approximation of the optimal power allocation. The two results are combined to obtain an approximated solution of the nonlinear program. Simulation results demonstrate the effectiveness of the proposed machine learning-based algorithm.

Wireless sensor and actuator networks (WSANs) are emerging as a new generation of wireless sensor networks. Due to the coupling between the sensing areas of the sensors and the action areas of the actuators, the efficient coordination among the nodes is a great challenge. In this paper, we address the problem of distributed node coordination in WSANs aiming at meeting the user's requirements on the states of the points of interest (POIs) in a real-time and energy-efficient manner. The node coordination problem is formulated as a nonlinear program. To solve it efficiently, the problem is divided into two correlated subproblems: 1) the sensor-actuator (S-A) coordination and 2) the actuator-actuator (A-A) coordination. In the S-A coordination, a distributed federated Kalman filter-based estimation approach is applied for the actuators to collaborate with their ambient sensors to estimate the states of the POIs. In the A-A coordination, a distributed Lagrange-based control method is designed for the actuators to optimally adjust their outputs, based on the estimated results from the S-A coordination. The convergence of the proposed method is proved rigorously. As the proposed node coordination scheme is distributed, we find the optimal solution while avoiding high computational complexity. The simulation results also show that the proposed distributed approach is an efficient and practically applicable method with reasonable complexity.

With the explosive growth of mobile applications and high computation burden on each single device, more and more end users demand to offload expensive computing tasks to external sites via job offloading technologies. Due to the fluctuating nature of jobs from end users, traditional cloud computing paradigm, however, has difficulties in accommodating highly dynamic job requests and meeting heterogeneous user requirements. Locating close to mobile users, edge-clouds have the potential to complement the cloud computing platform by acting as an efficient spot to perform users' deadline-sensitive tasks. In this paper, we study the resource allocation problem for accommodating deadline-sensitive jobs in edge-cloud system. We formulate a revenue maximization problem that captures the SLA-oriented property of job execution, and propose an efficient online multi-resource allocation algorithm that achieves low competitive ratio with moderate resource augmentation.

Mobile crowd sensing presents a new sensing paradigm which allows an individual participant with mobile device perform sensing tasks and activities of professional organizations. Privacy is one major concern in the mobile crowd sensing application. In this paper, we first present a fog-assisted mobile crowd sensing architecture, then we propose two privacy-preserving crowd sensing schemes for two categories of crowd sensing applications. The first scheme is based on an additive homomorphic encryption algorithm and allows the service subscriber collect the statistical data without revealing the individual data from each participant. The second scheme is based on a bitwise-XOR homomorphic encryption algorithm and allows the service subscriber collect the accurate data without know which data is from which participant. The proposed schemes can achieve κ-anonymity privacy level for each participant. We also give the performance analysis of the proposed schemes.

In cyber-physical system (CPS) over IEEE 802.11e-based wireless local area networks (WLANs), a misbehaving node can gain significant advantage over other normal nodes in terms of resource sharing by deliberately manipulating its protocol parameters. Due to the random spectrum-access nature of the protocol, it is challenging to detect the misbehaving node accurately and in real-time. Moreover, many existing misbehavior detectors, primarily designed for traditional IEEE 802.11 networks, become inapplicable in IEEE 802.11e networks with heterogeneous network configurations. In this paper, we propose novel real-time and light-weight countermeasures including a hybrid-share misbehavior detector and a packet-dropping-based misbehavior mitigation mechanism for IEEE 802.11e-based CPS. We develop mathematical models for the performance of the proposed detector and mitigation mechanisms. Extensive simulation results show that the proposed mechanisms can achieve a high detection rate and punish a misbehaving node with a high packet dropping rate.

The proliferation of smart phones enables ubiquitous Mobile Ad-hoc Networks (MANETs) where mobile devices communicate with peers over a wireless channel in an ad hoc mode. In this paper, we introduce a novel method to achieve multi-hop communication among open-source, non-rooted Android devices using Wi-Fi Direct Technology, also known as Wi-Fi Peer-to-Peer (P2P). Then we implement a proactive routing protocol in an MANET using multiple off-the-shelf smart phones to enable efficient message delivery over a multi-hop MANET.

As wireless networks are gaining increasing popularity, the network energy efficiency has become a critical issue. In this paper, we focus on energy-efficient networking in a generic multi-radio multi-channel (MR-MC) wireless network where transmission scheduling, transmit power control, radio and channel assignment are coupled together in a multi-dimensional resource space, thus requiring joint optimization and low complexity algorithms. We propose a novel Decomposed Approach For energy-efficient (DAFEE) networking in MR-MC networks, with the objective to minimize network energy consumption while guaranteeing a certain level of performance. In particular, we leverage a multi-dimensional tuple-link based model and a concept of resource allocation pattern to transform the complex optimization problem into a linear programming (LP) problem. The LP problem however has a very large solution space due to the exponentially many possible resource allocation patterns. We then exploit delay column generation and distributed learning techniques to decompose the problem and solve it with an iterative process. Furthermore, we propose a sub-optimal algorithm to speed up the iteration with constant-bounded performance. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

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.

Wireless personal area network (WPAN) is small-ranged network centered at an individual for interconnecting personal devices. For such a network, the bootstrapping mechanism with which the devices establish a secure group key is of critical importance. Most existing bootstrapping mechanisms require out-of-band channels and involve human interactions for authentication. In this paper, we aim to develop a fully automated bootstrapping mechanism with only in-band channels with approvable security. Toward this end, we designed an integrity-guaranteed message (IGM) structure, a self-authenticated key agreement protocol, and a prescheduling mechanism in allusion to the IEEE 802.15.4 standard for WPANs. The IGM structure guarantees that an adversary cannot modify the IGM message without being detected, thus protects the message integrity without the requirement of shared secrets between the sender and the receiver devices. The proposed self-authenticated key agreement protocol utilizes the IGM's integrity guaranteed property, works together with the prescheduling mechanism to achieve message self-authentication, thus protecting the secure bootstrapping process from the node impersonation attack and the man-in-the-middle attack without leveraging any out-of-band channels. We analyze the security performance of the proposed schemes, and show that they can be seamless interoperative with the existing IEEE 802.15.4 standard.

For a spectrum sharing system using economic approaches, conventional models without geographic considerations are oversimplified. In this paper, we develop a model where geographic information, including licensed areas of primary users (PUs) and locations of secondary users (SUs), plays an important role in the spectrum sharing system. We consider a multi-price policy and the pricing power of non-cooperative PUs in multiple geographic areas. Meanwhile, the value assessment of a channel is price-related and the demand from the SUs is price-elastic. To maximize the payoffs of the PUs, we propose a unique quota transaction process. By applying an evolutionary procedure defined as replicator dynamics, we prove the existence and uniqueness of the evolutionary stable strategy quota vector of each PU, which leads to the optimal payoff for each PU selling channels without reserve. In the scenario of selling channels with reserve, we predict the channel prices for the PUs leading to the optimal supplies of the PUs and hence the optimal payoffs. Furthermore, we introduce a grouping mechanism to simplify the process. In our simulation, the effectiveness of the learning processes designed for the two scenarios is verified and our spectrum sharing scheme is shown efficient in utilizing the frequency resources.

In IEEE 802.11-based networks, it is well-known that selfish nodes can gain significant performance advantage over other normal nodes by manipulating the medium access control (MAC) protocol parameters. There have been many studies on the detection of such misbehavior, though most of them focus on centralized detection with the assistant of an access point in wireless local-area networks (WLANs). In ad hoc networks, due to the complexity introduced by the hidden terminal issues, many existing misbehavior detection schemes rely on the RTS/CTS (request-to-send/clear-to-send) messages to infer the details of MAC layer behavior of the monitored nodes. However, for networks without the RTS/CTS mechanism (e.g., using the basic access mode), those schemes become inapplicable. In this paper, we propose a novel two-step misbehavior detector for IEEE 802.11-based ad hoc networks, based on observations of successful transmissions and channel conditions, which does not require the RTS/CTS messages. The main idea is to check whether the measured performance matches the model-based expectations, in which neighbors exchange data and cooperate to make detection decisions. Our detector sets two barriers to catch the misbehaving nodes: the first step utilizes neighbor- broadcasted information to establish the relationship between channel availability and transmission rate and checks if the relationship matches the theoretical model, while the second step checks whether the model-based throughput meets the observations. We demonstrate the effectiveness of the our detector through simulations.

Device-to-device (D2D) communication is the need of the hour in the domain of next generation wireless networking and in the rapidly evolving smartphone network world. D2D technology facilitates mobile users to communicate with each other directly, bypassing the cellular base stations. As a popular D2D technique, WiFi-Direct is also a budding new technology that has the ability to set up wireless communications between a group of smartphones. While single-hop D2D based networks have been promising and energy efficient, multi-hop D2D based networks, though demanded in some emerging applications, are not well studied. In this paper, we elaborate the concept of multihop smartphone networks based on WiFi-Direct and propose an energy efficient cluster-based routing protocol, QGRP, to address the energy issue of increasing importance due to high energy costs of smartphones. Simulations demonstrate that QGRP can save significant amounts of energy compared to the cases without QGRP.

Exploiting multiple radio interfaces over multiple channels and using directional antennas are promising technologies to enhance the performance of wireless networks. However, in such a multi-dimensional network resource space, assignment of radios and channels and configuration of antenna directions are coupled, making the complexity for network capacity optimization dramatically increase. Existing work has considered either multi-radio multi-channel (MRMC) networks or networks with directional antennas (DA); however, there lacks a generic framework for such complex MRMC-DA wireless networks. In this paper, we employ the tuple concept to define Link- Radio-Antenna-Channel tuple links, which are then utilized as building blocks to construct a multi-dimensional conflict graph (MDCG) of the MRMC-DA network. The MDCG model facilitates mapping the original MRMC-DA network into a simple virtual single-radio single-channel network, on which the capacity optimization problem can be formulated as a linear program. To circumvent searching the exponentially many independent sets, we apply the delayed column generation method to design our algorithm. Simulations demonstrate the performance of the proposed method and analyze the different effects of the numbers of channels, radios and antenna choices.

Wireless charging is a promising technology for provisioning dynamic power supply in wireless rechargeable sensor networks (WRSNs). The charging equipment can be carried by some mobile nodes to enhance the charging flexibility. With such mobile chargers (MCs), the charging process should simultaneously address the MC scheduling, the moving and charging time allocation, while saving the total energy consumption of MCs. However, the efficient solutions that jointly solve those challenges are generally lacking in the literature. First, we investigate the multi-MC coordination problem that minimizing the energy expenditure of MCs while guaranteeing the perpetual operation of WRSNs, and formulate this problem as a mixed-integer linear program (MILP). Second, to solve this problem efficiently, we propose a novel decentralized method which is based on Benders decomposition. The multi-MC coordination problem is then decomposed into a master problem (MP) and a slave problem (SP), with the MP for MC scheduling and the SP for MC moving and charging time allocation. The MP is being solved by the base station (BS), while the SP is further decomposed into several sub-SPs and being solved by the MCs in parallel. The BS and MCs coordinate themselves to decide an optimal charging strategy. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method.

Exploiting direct transmissions between geographically close mobile users without passing through the base stations, device‐to‐device (D2D) communications contribute significant improvement to the spectral efficiency of cellular networks. In D2D‐assisted cellular networks, the social interaction of mobile users is an important property that will affect the practical performance and should be seriously accounted in the network resource allocation, which is yet to be fully explored. In this study, the authors investigate the social interactions for D2D transmissions and develop a contact time model to characterise the D2D links. A D2D link can be considered for resource allocation only when the two users encounter and their contact time is enough long to complete a meaningful transmission. They formulate and compare both sociality‐blind and sociality‐aware optimisation problems for resource allocation in D2D‐assisted cellular networks. Extensive numerical results are presented, validating that the sociality‐aware resource allocation can achieve higher performance than that of the sociality‐blind approach.

The greedy maximal scheduling (GMS) and maximal scheduling (MS) algorithms are well-known low-complexity scheduling policies with guaranteed capacity region in the context of single-radio single-channel (SR-SC) wireless networks. However, how to design maximal scheduling algorithms for multiradio multichannel (MR-MC) wireless networks and the associated capacity analysis are not well understood yet. In this paper, we develop a new model by transforming an MR-MC network node to multiple node-radio-channel (NRC) tuples. Such a framework facilitates the derivation of a tuple-based back-pressure algorithm for throughput-optimal control in MR-MC wireless networks and enables the tuple-based GMS and MS scheduling as low-complexity approximation algorithms with guaranteed performance. An important existing work on GMS and MS for MR-MC networks is that of Lin and Rasool (IEEE/ACM Trans. Networking, vol. 17, no. 6, 1874-1887, Dec. 2009), where link-based algorithms are developed. Compared to the link-based algorithms, the tuple-based modeling has significant advantages in enabling a fully decomposable cross-layer control framework. Another theoretical contribution in this paper is that we, for the first time, extend the local-pooling factor analysis to study the capacity efficiency ratio of the tuple-based GMS in MR-MC networks and obtain a lower bound that is much tighter than those known in the literature. Moreover, we analyze the communications and computation overhead in implementing the distributed MS algorithm and present simulation results to demonstrate the performance of the tuple-based maximal scheduling algorithms.

The IEEE 802.15.4 standard, which supports low-cost communications, has been applied in a variety of wireless networks. Developing accurate analytical models for the IEEE 802.15.4 medium access control (MAC) protocol is critical for the design and performance evaluation of such networks. Periodic traffic is a common traffic pattern generated in many practical application scenarios, for which most existing analytical models assuming either saturated or random network traffic patterns become inapplicable. In this paper, we develop an accurate and scalable analytical model to analyze the IEEE 802.15.4 MAC protocol with the periodic traffic. Our model can accurately capture the protocol stochastic behavior in each period in scenarios such as with or without retransmissions and with single clear channel assessment (CCA) or double CCAs. Extensive simulations are conducted to validate the proposed model by both transient and aggregate performance evaluations, and the results show that the model captures MAC behavior with periodic traffic accurately. We also discuss about extending the proposed model to account for heterogeneous scenarios and the hidden node problem.

The performance of remote estimation over wireless channels is strongly affected by sensor data losses due to interference. Although the impact of interference can be alleviated by applying cognitive radio technique which features in spectrum sensing and transmitting data only on clear channels, the introduction of spectrum sensing incurs extra energy expenditure. In this paper, we investigate the problem of energy-efficient spectrum sensing for remotely estimating the state of a general linear dynamic system, and formulate an optimization problem which minimizes the total sensor energy consumption while guaranteeing a desired level of estimation performance. We model the problem as a mixed integer nonlinear program and propose a simulated annealing based optimization algorithm which jointly addresses when to perform sensing, which channels to sense, in what order and how long to scan each channel. Simulation results demonstrate that the proposed algorithm well balances the sensing energy and transmission energy expenditure and can achieve the desired estimation performance.

The Enhanced Distributed Channel Access (EDCA) specification in the IEEE 802.11e standard supports heterogeneous backoff parameters and arbitration inter-frame space (AIFS), which makes a selfish node easy to manipulate these parameters and misbehave. In this case, the network-wide fairness cannot be achieved any longer. Many existing misbehavior detectors, primarily designed for legacy IEEE 802.11 networks, become inapplicable in such a heterogeneous network configuration. In this paper, we propose a novel real-time hybrid-share (HS) misbehavior detector for IEEE 802.11e based wireless local area networks (WLANs). The detector keeps updating its state based on every successful transmission and makes detection decisions by comparing its state with a threshold. We develop mathematical analysis of the detector performance in terms of both false positive rate and average detection rate. Numerical results show that the proposed detector can effectively detect both contention window based and AIFS based misbehavior with only a short detection window.

Multi-radio multi-channel (MR-MC) networks contribute significant enhancement in the network throughput by exploiting multiple radio interfaces and non-overlapping channels. While throughput optimization is one of the main targets in allocating resource in MR-MC networks, recently, the network energy efficiency is becoming a more and more important concern. Although turning on more radios and exploiting more channels for communication is always beneficial to network capacity, they may not be necessarily desirable from an energy efficiency perspective. The relationship between these two often conflicting objectives has not been well-studied in many existing works. In this paper, we investigate the problem of optimizing energy efficiency under full capacity operation in MR-MC networks and analyze the optimal choices of numbers of radios and channels. We provide detailed problem formulation and solution procedures. In particular, for homogeneous commodity networks, we derive a theoretical upper bound of the optimal energy efficiency and analyze the conditions under which such optimality can be achieved. Numerical results demonstrate that the achieved optimal energy efficiency is close to the theoretical upper bound.

With the rapid growth of smartphone and tablet users, Device-to-Device (D2D) communications have become an attractive solution for enhancing the performance of traditional cellular networks. However, relevant security issues involved in D2D communications have not been addressed yet. In this paper, we investigate the security requirements and challenges for D2D communications, and present a secure and efficient key agreement protocol, which enables two mobile devices to establish a shared secret key for D2D communications without prior knowledge. Our approach is based on the Diffie-Hellman key agreement protocol and commitment schemes. Compared to previous work, our proposed protocol introduces less communication and computation overhead. We present the design details and security analysis of the proposed protocol. We also integrate our proposed protocol into the existing Wi-Fi Direct protocol, and implement it using Android smartphones.

We study how to achieve optimal network capacity in the most energy-efficient manner over a general large-scale wireless network, say, a multi-hop multi-radio multi-channel (MR-MC) network. We develop a multi-objective optimization framework for computing the resource allocation that leads to optimal network capacity with minimal energy consumption. Our framework is based on a linear programming multi-commodity flow (MCF) formulation augmented with scheduling constraints over multi-dimensional conflict graph (MDCG). The optimization problem however involves finding all independent sets (ISs), which is NP-hard in general. Novel delayed column generation (DCG) based algorithms are developed to effectively solve the optimization problem. The DCG-based algorithms have significant advantages of low computation overhead and achieving high energy efficiency, compared to the common heuristic algorithm that randomly searches a large number of ISs to use. Extensive numerical results demonstrate the energy efficiency improvement by the proposed energy-efficient optimization techniques, over a wide range of networking scenarios.

The main-thread approach for optimizing the throughput capacity over a multihop wireless network is to apply a multi-commodity flow (MCF) formulation, augmented with a scheduling constraint derived from the conflict graph associated with the network. A fundamental issue with the conflict graph based MCF formulation is that finding all independent sets (ISs) for scheduling is NP-hard in general. If we express the MCF formulation in a matrix format, the constraint matrix will contain a very large number of columns, with each IS being associated with one column. According to the linear programming theorem, such a type of problem can be addressed with the delayed column generation (DCG) method. Unfortunately, applications of the DCG in wireless networks have not received much attention. To the best of knowledge, none of the existing work conducted theoretical studies of the performance of DCG in wireless networks. In this paper, we study the DCG method in the context of a general network flow problem. With a protocol interference model, we rigorously prove that searching an entering column in the DCG operation is equivalent to a maximum weighted independent set (MWIS) problem. A prominent theoretical contribution of this paper is the theorem that: if an MWIS approximation algorithm with the approximation ratio β (<1) is applied in the DCG method, the maximum flow solved will be at least β of the optimal solution. Furthermore, the DCG method is also applied to the multi-radio multi-channel (MR-MC) networks. With extensive numerical results comparing to the existing methods, we show that the DCG method achieves the most preferred tradeoff between computation complexity and network capacity and maintains good scalability when addressing large-scale networks, particularly in the complex MR-MC context.

A robust and resilient medium access control (MAC) protocol is crucial for numerous machine-type devices to concurrently access the channel in a machine-to-machine (M2M) network. Simplex (reservation- or contention-based) MAC protocols are studied in most literatures which may not be able to provide a scalable solution for M2M networks with large number of heterogeneous devices. In this paper, a scalable hybrid MAC protocol, which consists of a contention period and a transmission period, is designed for heterogeneous M2M networks. In this protocol, different devices with preset priorities (hierarchical contending probabilities) first contend the transmission opportunities following the convention-based$p$-persistent carrier sense multiple access (CSMA) mechanism. Only the successful devices will be assigned a time slot for transmission following the reservation-based time-division multiple access (TDMA) mechanism. If the devices failed in contention at previous frame, to ensure the fairness among all devices, their contending priorities will be raised by increasing their contending probabilities at the next frame. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the channel utility by finding the key design parameters: the contention duration, initial contending probability, and the incremental indicator. Analytical and simulation results demonstrate the effectiveness of the proposed hybrid MAC protocol.

We investigate the state estimation problem in cyber-physical systems (CPS) where the dynamical physical process is measured by a wireless sensor and the measurements are transmitted to a remote state estimator. It has been shown that the estimation performance strongly depends on the wireless communication quality. To enhance the estimation performance, we apply the cognitive radio technique to the system and propose a CHAnnel seNsing and switChing mEchanism (CHANCE) to explore opportunistic accessibility of multiple channels. We consider two types of wireless channels, i.e., one unlicensed channel which can be accessed freely and several licensed channels which have been pre-assigned to primary users. For the single-licensed-channel case, we develop a necessary condition for the estimation stability based on the physical process dynamics, channel quality and the channel sensing accuracy. This condition becomes also sufficient under certain conditions. We also derive the conditions under which the estimation performance is guaranteed to be improved by CHANCE. The above results are then extended to multi-licensed-channel cases. Simulations based on a particular linear system show that, the long-run mean estimation error covariance with CHANCE is at least 63% less than that without CHANCE. It is also shown that CHANCE outperforms the existing RANDOM mechanism in terms of estimation performance.

Time synchronization is a fundamental requirement for the wide spectrum of applications with wireless sensor networks (WSNs). However, most existing time synchronization protocols are likely to deteriorate or even to be destroyed when the WSNs are attacked by malicious intruders. This paper is concerned with secure time synchronization for WSNs under message manipulation attacks. Specifically, the theoretical analysis and simulation results are first provided to demonstrate that the maximum consensus based time synchronization (MTS) protocol would be invalid under message manipulation attacks. Then, a novel secured maximum consensus based time synchronization (SMTS) protocol is proposed to detect and invalidate message manipulation attacks. Furthermore, we prove that SMTS is guaranteed to converge with simultaneous compensation of both clock skew and offset. Extensive numerical results show the effectiveness of our proposed protocol.

In Machine to Machine (M2M) networks, a robust Medium Access Control (MAC) protocol is crucial to enable numerous machine-type devices to concurrently access the channel. Most literatures focus on developing simplex (reservation or contention based) MAC protocols which cannot provide a scalable solution for M2M networks with large number of devices. In this paper, a frame-based Hybrid MAC scheme, which consists of a contention period and a transmission period, is proposed for M2M networks. In the proposed scheme, the devices firstly contend the transmission opportunities during the contention period, only the successful devices will be assigned a time slot for transmission during the transmission period. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the system throughput by finding the optimal contending probability during contention period and optimal number of devices that can transmit during transmission period. A practical hybrid MAC protocol is designed to implement the proposed scheme. The analytical and simulation results demonstrate the effectiveness of the proposed Hybrid MAC protocol.

Networked cyber-physical systems (NCPS), where control and communication are closely integrated, have been envisioned to have a large number of high-impact applications. In this paper, a joint optimization framework is presented, which combines the objective of control as well as other relevant system objectives and constraints such as communication errors, delays and the limited capabilities (e.g., energy capacities) of devices. The problem is solved by an online optimization approach, which consists of a communication protocol and a simulated annealing based control algorithm. Meanwhile, by taking into account the communication cost, we optimize the control intervals by integrating two kinds of acceptances, i.e., cyber and physical acceptances, into the control algorithm. Numerical results show the effectiveness of the proposed approach.

In wireless sensor and actuator networks (WSAN), both sensory measurements and control signals transmitted through the wireless media are prone to packet losses. Moreover, the distance between the sender and receiver is a critical factor for the loss rate. Therefore, where to place the controller to ensure optimal control performance is an interesting problem. In this paper, we focus on WSAN with one sensor and one actuator residing at different geographic locations. If the controller is constrained at either the sensor side or actuator side, we derive the necessary and sufficient conditions under which the optimal controller location can be directly determined. For the more general case when the controller can be placed anywhere, with mild assumptions on the packet drop model, we also provide the conditions under which the optimal controller location is unique and can be determined. Numerical simulations based on a practical packet loss model verify our results.

Abstract Vehicular Ad hoc Network (VANET) emerges as a promising technology and has chances of very likely to be deployed in the coming years. The security of vehicular networks will be an important way to facilitate road safety. In this paper, we are concerned with the problem of effective and efficient distribution of the certificate revocation units (RSUs) in vehicular networks. We propose a novel distributed approach by introducing mobile nodes that have public safety. An optimal route for mobile nodes is designed to cover blind areas under both delay and cost constraints. The performances of the movement‐assisted approach are measured and evaluated by extensive experiments in large scale networks for Certificate Revocation List (CRL) distribution in VANET. The results show that the proposed movement‐assisted approach obviously improves the performance. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we study the maximum throughput of IEEE 802.15.4 enabled wireless sensor networks (WSNs). In general, deploying more sensor nodes would increase the throughput, but if excessive sensor nodes were involved to report data, the throughput might decrease due to packet collisions. In this paper, we analyze the CSMA/CA performance of the IEEE 802.15.4 enabled WSNs, and develop an analytical throughput model between the maximum throughput and the optimal number of deployed sensor nodes. Extensive simulations are conducted to verify our analytical throughput model.

This paper presents a theoretical model of control and communication over wireless sensor and actuator networks (WSANs). We propose two control schemes, a centralized control scheme (CC) in which decisions are made based on global information, and a distributed control scheme (DC) that enables distributed actuators to make decisions locally. Because of global information, CC can obtain optimal control at each step. However, when that information is delivered over lossy wireless channels, it could become unstable. It is demonstrated by simulations that DC could also stabilize the control system analogously with the CC, though with more steps.