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 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.

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.

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.

With the development of wireless networks, the scale of network optimization problems is growing correspondingly. While algorithms have been designed to reduce complexity in solving these problems under given size, the approach of directly reducing the size of problem has not received much attention. This motivates us to investigate an innovative approach to reduce problem scale while maintaining the optimality of solution. Through analysis on the optimization solutions, we discover that part of the elements may not be involved in the solution, such as unscheduled links in the flow constrained optimization problem. The observation indicates that it is possible to reduce problem scale without affecting the solution by excluding the unused links from problem formulation. In order to identify the link usage before solving the problem, we exploit deep learning to find the latent relationship between flow information and link usage in optimal solution. Based on this, we further predict whether a link will be scheduled through link evaluation and eliminate unused link from formulation to reduce problem size. Numerical results demonstrate that the proposed method can reduce computation cost by at least 50% without affecting optimality, thus greatly improve the efficiency of solving large scale network optimization problems.

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.

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.

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.

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) communications recently have attracted broad attention owing to its potential ability to improve spectrum and energy efficiency within the existing cellular infrastructure. Lacking sophisticated control, D2D user equipments (DUEs) themselves are not powerful enough to resist eavesdropping or fight against security attacks. This work investigates selection of jamming partners for D2D users to thwart reception by social outcasts in D2D overlay, by exploiting social relationship to improve secrecy rate. We aim to maximize the secrecy rate of worst case, eavesdropping by any outcast, through selecting jammer node while allocating transmit power for both source and jammer. We present a heuristic genetic algorithm based solution to evaluate the problem directly. In addition, we also propose approximated optimization solutions by considering power allocation of upper and lower bounds to simplify the problem, by leveraging the fractional programming (GFP) oriented Dinkelbach-type algorithm. Numerical results show that the proposed schemes can achieve better performance through finding an appropriate partner.

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.

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 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.

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.