One of the biggest risks that wireless IoT networks encounter is malware or botnet epidemics. Malware can propagate from one device to another device existing in its coverage range as long as there are no check points (firewalls) to protect that device. Firewalls can be hardware (special devices) or software licenses installed on a limited number of devices. Unfortunately, in both cases the number of firewalls in any network is usually limited due to cost constraints. Therefore, it is crucial to reduce the number of required firewalls and/or make efficient use of the available firewalls. In this paper, we consider two optimization problems to optimize the firewall placement in a massive wireless IoT network. The objective of the first optimization problem is to reduce the number of firewalls required to partition the network into isolated clusters/partitions of a given maximum size. The second problem aims at reducing the maximum size of the isolated partitions that can be achieved given an available number of firewalls. These two clustering problems are non-convex and are known to be NP-complete. However, we provide efficient algorithms, with different variations, to solve the two problems, and we compare their performance to the well known K-Means and Spectral Partitioning algorithms. Simulation results show that in both problems the average performance of the proposed algorithms outperforms the performance of both algorithms. Furthermore, we show that adding the proposed algorithm as a second stage after the Spectral Partitioning algorithm improves the performance of the Spectral Partitioning significantly.

One of the biggest risks that wireless IoT networks encounter is malware or botnet epidemics. Malware can propagate from one device to another device that exists in its coverage range as long as there are no check points (firewalls) to protect that device. Firewalls can be hardware (special devices) or software licensed to be activated on a limited number of devices. Unfortunately, in both cases the number of firewalls that can be installed in any network is limited due to cost constraints. Therefore, it is mandatory to make efficient use of that available number of firewalls. In this paper we consider optimization of the firewall placement in a massive IoT network. The objective of the optimization problem is to reduce the number of firewalls required to divide the network into a given number of virtually isolated clusters. This clustering problems is non-convex and is known to be NP- hard. However, we provide an efficient algorithm to solve it, and we compare its performance to the well known K-Means clustering algorithm. Simulation results show that the average performance of the proposed algorithms outperforms performance of the the K-Means algorithm. Although many network clustering algorithms have been considered in the literature with different objectives, to the best of our knowledge, the objective of the clustering considered in this paper has not been considered before. Furthermore, the proposed clustering algorithm does not contradict with any other clustering objective. Once the firewalls are placed, any other clustering algorithm can be used to satisfy a different objective.

The internet of things (IoT) enables heterogeneous devices to independently participate in global communications; however, it exposes the low power devices with minimum capabilities to vulnerability. This has led to the rise of attackers who leverage their attacks on low power devices to collect private data, without the user knowledge. In this paper, we look at the possibility of creating a supervised machine learning mechanism that autonomously detects packet being sent over the systems before it has reached the internet. The proposed model is trained to identify distributed denial of service (DDoS) attacks for outgoing packets, and subsequently inform and send the detected data to a monitoring node. Considering the low power devices, the proposed solution enables a rule-based system where packets can be detected with binary decisions. However, the rules and detection requires decision tree model training with appropriate datasets. Our evaluations show that the proposed mechanism can detect malicious packets without incurring additional delays in the communication by forwarding all packets to intermediate routers or fog nodes for inspection.

Indoor positioning systems (IPS) are gaining higher attention recently due to the increased demand for indoor location aware services. Visible light communication (VLC) is a promising technology to use for IPS. In particular, received signal strength (RSS) based visible light positioning (VLP) systems are gaining high attention due to their low complexity and cost, in addition to higher positioning accuracy compared to their radio frequency (RF) counterparts. One of the main challenges in RSS based VLP systems is encountered when the receiver (the target) is tilted and not placed in parallel with the transmitters (the anchors). RSS based trilateration techniques require a computationally expensive and time-consuming process to solve the nonlinear problem of tilted receivers. Fingerprint based systems generally provide high positioning accuracy with short positioning time, and maybe used to circumvent the need to deal with the high complexity associated with tilted receivers. However, the design of a fingerprinting VLP system for tilted receiver has not been explored yet as far as receivers with a single photodetector (PD) are concerned. In this work, a fingerprint based VLP system for tilted receivers using artificial neural networks (ANN) is proposed, where different types of input features for training the positioning algorithm are studied. We show that using the components of the normal vector to the PD's surface in addition to RSS values provides an excellent positioning accuracy with an average positioning error of 25.41 cm and a remarkably low average positioning time less than$\mathbf{5} \boldsymbol{\mu} \boldsymbol{s}$. In addition, important research directions for future work are discussed.

In the Internet of Things (IoT) era, data freshness is critical for real-time monitoring and control applications. Data freshness is quantified via the Age of Information (AoI), which tracks the age of the most recent received packet at the destination. This paper utilizes a spatiotemporal mathematical model to characterize the AoI of a target IoT link that exists within a large-scale IoT network. The large-scale IoT network is modeled by a heterogeneous Poisson field (HPF) of interferers. Then, the AoI of the target link, with a single packet storage capability, is characterized via an absorbing Markov chain that accounts for the interwoven effects of packet size, transmission rate, and interfering IoT devices. In particular, the proposed model investigates the impact of packet segmentation in order to operate at a reliable rate in the presence of IoT interference. To this end, the AoI of preemptive and non-preemptive transmission schemes are studied and compared. Comparing the AoIs of the preemptive and non-preemptive transmission schemes, the results show that no scheme always outperforms the other. In contrast, the number of segments and preemption scheme should be determined based on the packet size, arrival rate, and interference congestion level to minimize the AoI.

Wide area networks for surveying applications, such as seismic acquisition, have been witnessing a significant increase in node density and area, where large amounts of data have to be transferred in real-time. While cables can meet these requirements, they account for a majority of the equipment weight, maintenance, and labor costs. A novel wireless network architecture, compliant with the IEEE 802.11ad standard, is proposed for establishing scalable, energy-efficient, and gigabit-rate backhaul across very large areas. Statistical path-loss and line-of-sight models are derived using real-world topographic data in well-known seismic regions. Additionally, a cross-layer analytical model is derived for 802.11 systems that can characterize the overall latency and power consumption under the impact of co-channel interference. On the basis of these models, a Frame Aggregation Power-Saving Backhaul (FA-PSB) scheme is proposed for near-optimal power conservation under a latency constraint, through a duty-cycled approach. A performance evaluation with respect to the survey size and data generation rate reveals that the proposed architecture and the FA-PSB scheme can support real-time acquisition in large-scale high-density scenarios while operating with minimal power consumption, thereby enhancing the lifetime of wireless seismic surveys. The FA-PSB scheme can be applied to cellular backhaul and sensor networks as well.

Modern seismic surveys can obtain subsurface images of superior quality and depth, albeit requiring wireless acquisition systems to keep up with higher data rate requirements. Data communication protocols have been studied primarily for links between the geophones and the gateway nodes, leaving a dearth of analysis for the communication links between the gateway nodes and the sink node. In this paper, a novel wireless geophone network architecture based on the IEEE 802.11ad standard is proposed with the objective of providing gigabit rates in order to support real-time seismic acquisition. A performance analysis reveals that the latency and power consumption are dependent on the ratio of the data generation rate to the data transfer rate. An additional Power-Saving Geophone Relay (PSGR) scheme is described, which exploits idle periods of operation to conserve power at the cost of an increased latency. The trade-off between the latency and power consumption under the PSGR scheme is evaluated for a variety of scenarios in seismic acquisition.

Modern geophysics methods for oil and gas exploration are able to generate subsurface images of superior quality and depth, albeit making real-time data acquisition a more challenging task. While current literature addresses data transfer issues primarily between the geophones and the gateway nodes, a communication scheme for the transfer of data from the gateway nodes to the sink largely remains unsolved. A novel wireless geophone network architecture for seismic data acquisition is described, with the objective of eliminating cable-based systems and providing Gigabit rates in order to support real-time acquisition. A performance analysis is conducted for various mesh networks employing IEEE 802.11ac, IEEE 802.11ad, and free space optical communication, from the geophysics perspective. Our findings suggest that the bottleneck links can be shifted from the top to the bottom of existing architectures, and a scalable approach requiring minimal number of gateway devices can be designed for high-density seismic surveys.

Seismic surveys are conducted by oil exploration companies to keep up with the global demand for oil and gas. Although seismic cables offer fast and reliable data transfer, they account for a majority of the equipment weight, logistics, and labor costs. A novel wireless geophone network architecture, compliant with the IEEE 802.11af standard, is described. Operation in television white spaces can achieve long transmission ranges, allowing for scalable coverage of large seismic survey areas. Two methods for data collection are proposed: a Geophone-Polling (GP) scheme and an Adaptive Geophone Time Division Multiple Access Scheduling (AGTS) scheme. The two schemes are analyzed and evaluated in terms of the total time taken for data acquisition and the average power consumption, in comparison to the default IEEE 802.11 channel access schemes. The problem of hexagonal clustering for orthogonal deployment of geophones is also tackled, and the impact of co-channel interference is considered. The proposed schemes not only reduce data acquisition time, but also the average power consumption of the geophones, thereby improving the lifetime of wireless seismic surveys.

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

Traditional seismic data acquisition systems used for surveying during the exploration of oil and gas rely on cables between geophones and data collection center. Despite the fact that cable-based systems provide reliable seismic data transfer, their deployment and maintenance costs increase substantially as the survey area increases in scale. This paper investigates the aggregate data throughput and transmission time from wireless geophones to gateway node in a wireless geophone network architecture based on IEEE802.11af standard. The analytical expressions of throughput and transmission time are derived using Markov chain models. Two Markov models are considered for this purpose: one for modeling carrier sense multiple access method with collision avoidance (CSMA/CA) behavior and the other for representing a buffer in a wireless geophone. Furthermore, the physical layer path-loss models are taken into account.

This paper proposes a novel physical layer security algorithm in interference alignment (IA) based multiuser communication system with a single amplify-and-forward (AF) relay in the coexistence of an eavesdropper that tries to hide its existence. Thus, the relay and the users cannot estimate the eavesdroppers channel state information (CSI). In the proposed algorithm, the users broadcasts artificial noise in the null of the relay at the multiple access (MAC) phase, and the relay jams the eavesdropper by broadcasting the artificial noise in the null of users at the broadcast (BC) phase. Therefore, the proposed algorithm is formulated as a joint optimization problem consisting of two semidefinite programming (SDP) problems aiming at maximizing the power of the artificial noise and preserving the quality- of-services (QoS) of the users. An iterative optimization algorithm is proposed to solve the joint problem. Extensive simulation results are provided to show the effectiveness of the proposed algorithm for IA based relaying networks.

In this paper, we study the physical layer security in a two-way relay system consisting of two transceivers, one eavesdropper, and multiple relays. We study the system in case of the channel state information (CSI) of the eavesdropper is unknown, and hence artificial noise is used to degrade the signal to noise ratio at the eavesdropper. In order to reserve the maximum possible power for artificial noise, we consider the problem of minimizing the total power of the information signal transmitted by the relays, under quality of service constraints at the legitimate transceivers. This problem has been solved using semidefinite programming (SDP) and second order cone programming (SOCP) methods. Here, aiming to significantly decrease the complexity, we propose a novel approach to find the optimal solution using the generalized eigenvalue. We show that in most cases, we can provide a closed-form expression of the optimal solution. In addition, our proposed solution can be used for all quadratically constrained quadratic programs (QCQPs) with positive definite objective function and two constraints. Simulation results demonstrate the effectiveness of our algorithm in terms of optimality and low complexity compared to SDP.

Abstract In this paper, we consider the problem of joint coalition formation (CF) and bandwidth allocation in cognitive radio networks. We assume that the secondary links will be partitioned into disjoint coalitions, by which the available spectrum will be reused. On the other hand, we assume that the members of each coalition will transmit over orthogonal sub‐bands with the available spectrum being optimally allocated among them. We formulate the problem as a coordinated CF game, where the optimization is held at a secondary coordinator that has all the required channel information. Given these assumptions, we derive a closed‐form expression of the optimal bandwidth allocation for any given network partition, and we propose an efficient algorithm to reach a Nash‐stable partition (coalition structure), with the objective of improving the network throughput. Furthermore, we provide a probabilistic analysis of the stability of a grand structure and a singleton structure, and we employ this to obtain a lower bound on the stability of a general coalition structure (other than grand and singleton). Performance analysis shows that the proposed CF algorithm with optimal bandwidth allocation provides a substantial gain in the network throughput over existing CF techniques as well as the simple cases of singleton and grand coalitions. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, a novel GPS-assisted scheme to reduce operation costs of cognitive radio networks with femtocells is proposed. In this scheme, the secondary user (SU) network, represented by the cognitive base station (CBS), determines the minimum number of channels needed for its operation before it starts purchasing spectrum from primary user (PU) networks. This is achieved by grouping the femtocell secondary users (FSU's) based on the distances between them. Simulations show that grouping the femtocells will result in a reduction in the number of channels to be purchased and will consequently cut the operation cost of the SU network.

In this paper we analyze the problem of distributed coalition formation (CF) and bandwidth allocation in ad hoc cognitive radio networks. We develop a CF algorithm to self organize distributed secondary links into disjoint coalitions and apply the concept of frequency reuse over different coalitions, such that the members of each coalition will transmit over orthogonal sub-bands with the available spectrum being optimally allocated among them. We formulate the problem as a CF game in partition form with non-transferable utility and propose a distributed algorithm through which players may join/leave a coalition based on their individual preferences. We study the convergence properties of the proposed CF rule and present means to guarantee Nash-stability. In addition, we also describe graceful exit procedures if a CF process leads to oscillations. We show that a grand/singleton structure will emerge in the network at very low/high SNR and analytically derive the lower bound on the probability that a general network partition, other than grand/singleton structure, is stable. Performance analysis shows the effectiveness of the proposed coalition formation algorithm with optimal bandwidth allocation over a wide SNR range and indicates a substantial gain in terms of average payoff per link over existing coalition formation techniques.

In this paper we consider joint optimization of rate and power for communication systems that use multilayer source coding with successive information refinement, accompanied with a broadcast approach at the physical layer of the system. We analyze the problem under the assumption of Rayleigh fading channels where rates and power ratios of the source layers are jointly optimized based on channel statistics information, with the objective of maximizing the expected user satisfaction which is usually defined by a differentiable concave increasing utility function of the total decoded rate. As special cases, we consider two utility functions; namely, the expected total decoded rate at the receiver and the expected rate distortion of a Gaussian source. We show that the optimal solution can be obtained using a two-dimensional bisection search for any number of layers. The outer bisection search is over the Lagrangian dual variable and the inner bisection search is over the decoding threshold of the layer. Moreover, we show that with a small number of layers, we can approach the performance upper bound that is achieved by transmitting an infinite number of layers.

In this paper we consider joint optimization of rate and power for communication systems that use multilayer superposition source coding with successive refinement of information. We assume a Rayleigh fading channel, where rates and power are jointly and optimally allocated between the source layers based on channel statistics information, with the objective of maximizing the expected total received rate at the end user. We show that the optimization problem possesses a strong duality, and hence we use the dual form to show that the optimal solution can be obtained using a two-dimensional bisection search for any number of layers. The outer bisection search is over the Lagrangian dual variable and the inner bisection search is over the decoding SNR threshold of the last layer. Moreover, we show that with a small number of layers, we can approach the performance upper bound, that is achieved by transmitting an infinite number of layers.

We consider the problem of minimizing the expected distortion in the multilayer transmission of a Gaussian source using the broadcast approach with successive information enhancement. This minimization is contingent on the jointly optimal choice of the rates and power ratios of the different layers. This problem was tackled in the literature with the assumption that the fading channel has a finite number of states and the number of source layers matches the number of channel states. In this paper, we provide a more generic solution for a continuous Rayleigh fading channel, and for any predetermined number of layers. We prove that the primal optimization problem has a strong duality with the Lagrangian dual problem. Consequently, we propose a two-dimensional bisection search algorithm that can, for any number of layers, find the optimal solution of the dual problem which will be the same as the optimal solution of the primal problem. The complexity of the search algorithm has linear order with respect to the number of layers. We provide numerical results for the optimal rate and power allocation. Moreover, we show that with a small number of layers, we can approach the distortion lower bound that is achieved by transmitting an infinite number of layers.

We investigate the power allocation problem in layered transmission with successive enhancement using the broadcast approach. This problem is particularly important for multimedia streaming applications and multicasting services over slowly fading channels with partial channel information at the transmitter. We consider the practical scenario of limited and pre-specified source layers, and we propose a generic and efficient solution to optimize the transmission power allocated to each layer in order to maximize the average user satisfaction defined by a utility function of the decoded layers. We assume a block-fading Rayleigh distributed channel model. However, our solution can be extended to a number of other channel distributions. Although the direct formulation of the problem is not convex, we propose change of optimization variable, and we use the dual problem and the perturbation analysis in order to obtain the optimal solution. The solution is based on a one-dimensional bisection search irrespective of the total number of layers. Our optimality analysis indicates that in some cases it may not be optimal to transmit all the layers.

We consider multiuser MIMO systems with some of the users having target-rate requirements while the other users are working on best-effort data transmission. Both multiple-access channels (MAC) and broadcast channels (BC) are considered. We show that the best-effort users can achieve higher rates when the data streams of any user are not restricted to be decoded consecutively and when time-sharing between different decoding orders is allowed. For MIMO-MAC, we show that the problem can be formulated as a convex optimization problem, and hence efficient algorithms can be used to obtain the globally optimal solution. We also show that in some cases it is possible to reduce the transmission power of some users without affecting the optimal solution, and we show that the power minimization problem is convex. We also exploit the MAC-BC duality in order to solve the same problem for the MIMO-BC channel.

In many half-duplex cooperative systems, the direct formulation of the problem of finding the jointly optimal power and channel resource allocation that maximizes a weighted sum of the achievable rates can be difficult to solve. In this paper, we provide an efficient algorithm to solve this problem for a class of systems with convex achievable rate regions. For those systems, we show that the weighted-sum-rate problem can be solved using a bisection-based search in which a “target rate” problem is solved at each step. The target rate problem involves maximizing one of the achievable rates subject to target values for the other rates, and can be efficiently solved in a number of cases. We show that the proposed technique can be applied to orthogonal multiple access relay systems and that joint optimization can result in significantly larger weighted sum rates than optimization over the powers alone with a fixed channel resource allocation.

In this paper we consider multicast systems that use layered source coding with successive refinement in a Rayleigh fading environment. For this kind of systems, we optimally allocate the transmission power, and hence the decoding threshold of each layer, in order to maximize the expected satisfaction defined by a utility function of the decoded rates of the users. In particular, we use the Lagrangian dual problem in order to efficiently solve the primal non-convex problem for any number of layers. Furthermore, we show that for some channel statistics, it is not optimal to transmit all the layers, rather we should allocate zero power to some of the upper layers in order to maximize the expected user satisfaction.

We consider a multiple-input multiple-output (MIMO) wireless communication scenario in which the channel follows a general spatially-correlated complex Gaussian distribution with non-zero mean. We derive an explicit characterization of the optimal input covariance from an ergodic rate perspective for systems that operate at low SNRs. This characterization is in terms of the eigen decomposition of a matrix that depends on the mean and the covariance of the channel, and typically results in a beamforming strategy along the principal eigenvector of that matrix. Simulation results show the potential impact of (jointly) exploiting the mean and the covariance of the channel on the ergodic achievable rate at both low and moderate- to-high SNRs.

We consider the jointly optimal allocation of transmission power and channel resources (such as time and bandwidth) for an orthogonal amplify-and-forward (AF) pairwise cooperation scheme. In particular, we derive a simple efficient algorithm for determining the joint allocations required to operate at any point on the boundary of the achievable rate region. The algorithm is based on a closed-form solution, derived herein, for the optimal power allocation for a given channel resource allocation, and on showing that the channel resource allocation problem is quasi-convex.

We study the problem of jointly allocating power and the channel resource of an orthogonal multiple access relay systems in order to maximize the achievable rate region. Four relaying strategies are considered: regenerative decode-and- forward (RDF), non-regenerative decode-and-forward (NDF), amplify-and-forward (AF), and compress-and-forward (CF). For RDF and NDF we show that the problem can be formulated as a quasi-convex problem, while for AF and CF we show that the problem can be made quasi-convex if the signal to noise ratios of the direct channels are at least -3 dB. Therefore, efficient algorithms can be used to obtain the jointly optimal power and channel resource allocation. Furthermore, we show that the convex subproblems in those algorithms admit a closed-form solution. Our numerical results show that the joint allocation of power and the channel resource achieves significantly larger achievable rate regions than those achieved by power allocation alone with fixed channel resource allocation. We also demonstrate that assigning different relaying strategies to different users together with the joint allocation of power and the channel resources can further enlarge the achievable rate region.

Motivated by some observations regarding the structure of the optimal power allocation for an existing orthogonal amplify-and-forward (AF) pairwise cooperation scheme, we propose a modified orthogonal AF cooperation scheme that uses the channel resources more efficiently and hence provides a larger achievable rate region. We consider the jointly optimal allocation of the transmission power and the channel resource for that scheme. In particular, we derive a simple efficient algorithm for determining the allocations required to operate at any point on the boundary of the achievable rate region. The algorithm is based on a closed-form solution, derived herein, for the optimal power allocation for a given channel resource allocation, and on showing that the channel resource allocation problem is quasi-convex.

We consider the jointly optimal allocation of the radio resources for a two-user orthogonal amplify-and-forward (AF) cooperation scheme. In particular, we derive a simple efficient algorithm for determining the power and channel resource allocations required to operate at any point on the boundary of the achievable rate region. The algorithm is based on two results derived herein: a closed-form solution for the optimal power allocation for a given channel resource allocation; and the fact that the channel resource allocation problem is quasiconvex. The structure of the optimal power allocation reveals that at optimality at most one user acts as a relay, and hence a fraction of the channel resource will be idle. We propose a modified orthogonal AF cooperation scheme that uses the channel resources more efficiently and hence provides a larger achievable rate region.

Multiple access schemes in which the transmitting nodes are allowed to cooperate have the potential to provide higher quality of service than conventional schemes. In the class of pairwise cooperative multiple access schemes in which channel state information is available at the transmitters, the allocation of transmission power plays a key role in the realization of these quality of service gains. Unfortunately, the natural formulation of the power allocation problem for full-duplex cooperative schemes is not convex, but it is shown herein that this non-convex formulation can be simplified and re-cast in a convex form. In fact, in most scenarios a closed form expression for the optimal power allocation for each point on the boundary of an achievable rate region can be obtained

Multiple access schemes in which the transmitting nodes are allowed to cooperate have the potential to provide higher quality of service than conventional schemes. In the class of half-duplex pairwise cooperative multiple access schemes in which channel state information is available at the transmitters, the allocation of transmission power and communication resources plays a key role in the realization of these quality of service gains. In this paper we propose a half-duplex cooperative multiple access scheme and develop an efficient algorithm for the joint allocation of power and the communication resources. We demonstrate that the new scheme can attain a significantly larger fraction of the achievable rate region for the full duplex case than an existing scheme that does not employ resource allocation.