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Reliability optimization for industrial WSNs with FD relays and multiple parallel connections
Abstract
This article investigates the ultra-reliable communication for industrial wireless sensor networks (IWSNs), where frequency diversity, path diversity and cooperative diversity are jointly investigated. We propose a novel IWSN equipped with the full-duplex (FD) relays and carrier aggregation (CA) technique, to improve the reliability of all sensors. We derive a closed-form expression for the reliability characterization based on signal-to-interference-plus-noise (SINR) model for sensors connecting to sink node either via FD relays or direct communications. We formulate a joint resource allocation problem for reliability maximization, considering sub-carrier assignment, relay selection and power control. We propose to apply the distributed decision making (DDM) framework to decouple the problem into two sub-problems and find the joint optimal solution in an iterative manner. We further propose an improved artificial bee colony algorithm to obtain the optimal results for each sub-problem. Our simulation results showcase the reliability increases with increasing the number of FD-relays and sub-carriers. We also show that the FD and CA techniques along with our proposed algorithm can be an effective way for improving the reliability of IWSNs.
... Since FD transmission allows a wireless device to transmit and receive simultaneously, it can achieve as twice the spectral efficiency as the half-duplex (HD) counterpart. [9][10][11][12][13] However, the main disadvantage of FD transmission is the strong loopback interference or self-interference (SI) from FD device's transmitter to its receiver. Fortunately, this problem has been solved by many algorithms and solutions in literature. ...
... Herein, R is the predefined data rate of the considered IRS-FD-AFR system † . Based on the definition of OP in (12) and by doing the required mathematical analysis, we come up with the following Theorem 1. ...
It has been confirmed by many scientists and industrial experts that intelligent reflecting surface (IRS) should be one of the key enabling technologies for sixth‐generation (6G) wireless networks. This paper analyzes the performance of an IRS‐aided full‐duplex (FD) amplify‐and‐forward wireless relay (AFR) network, where the IRS is exploited to enhance the quality of the channel from the source to the relay. We successfully derive the analytical expressions of outage probability (OP), achievable data rate (ADR), and symbol error probability (SEP) of the considered IRS‐FD‐AFR network over Rayleigh fading channel. The corresponding expressions of the IRS‐aided half‐duplex (HD) AFR (IRS‐HD‐AFR) system are also derived as a benchmark to compare. The results demonstrate that the performance of both IRS‐FD‐AFR and IRS‐HD‐AFR systems are greatly improved by increasing the number of reflecting elements ( L L ) in the IRS. All derived expressions are validated by Monte Carlo simulations.
... Jia et al. [20] proposed an industrial WSN with FD relays and the CA technique to increase the reliability of sensor nodes. This work further maximises reliability by decomposing the resource allocation problem into sub-problems through the distributed decision-making (DDM) process. ...
Reliability is a crucial performance metric for wireless sensor networks (WSNs) because it ensures network functionality even with degraded sensor nodes and wireless connections. Due to the limited availability of power resources associated with the sensor nodes, their efficacy is reduced. In addition, parameters such as the number of neighbours, packet success rate, packet size, and node capacity have a significant impact on how sensor nodes function. Similarly, inter-node distance and signal-to-noise ratio (SNR) have the greatest influence on the functioning of wireless communications in WSNs. Before evaluating the reliability of a WSN, therefore, the node and link reliability must be computed. This paper evaluates the reliability of sensor nodes by taking into account all of the parameters previously mentioned. In addition, the lifetime of each sensor node is estimated in terms of the fractional energy consumed for each wake-up operation and the transmission of packets, respectively. Using the inter-node distance and SNR values, each wireless link’s reliability is determined. Following this, a proposed algorithm evaluates the reliability of WSN by first enumerating all minimal paths between the sensor node and the base station and then converting these minimal paths into their sum-of-disjoint-product terms. Making use of a suitable example network, every step of the proposed method is illustrated. For evaluating the reliability of WSN, the proposed method is simulated in different environments. In addition, the influence of parameters such as inter-node distance, SNR, number of neighbours, packet success ratio, and time on the reliability values of WSN is portrayed and analysed.
... Industrial applications typically rate reliability, fault tolerance, and security at the top of the list while adopting any IWSN-based solutions. Industrial Wireless Sensor Networks (IWSN) based solution faces various challenges such as real-time data communication [15], robustness [16], energy [11], reliability [12], and fault tolerance [13] due to the inherent limitations of WSN. The modus operandi of IWSN is that the sensors collect data from the indoor or outdoor environment and deliver it to a central/sink or controller for processing, decision-making, and control [14]. ...
Node failure in the Wireless Sensor Networks (WSN) topology may lead to economic loss, endanger people, and cause environmental damage. Node reliability can be achieved by adequately managing network topology using structural approaches, where the critical nodes are precisely detected and protected. This paper addresses the problem of critical node detection and presents two-phase algorithms (ABCND). Phase-I, a 2D Critical Node (C-N) detection algorithm, is proposed, which uses only the neighbor’s Received Signal Strength Indicator (RSSI) information. In Phase II, a correlation-based reliable RSSI approach is proposed to increase the node resilience against the adversary. The proposed algorithms (ABCND) require O(log(N)) time for convergence and O(δ(logN)) for Critical Node detection, N represents the number of IoT devices, and δ is the cost required to forward the message. We compare our algorithm (ABCND) with the current state-of-the-art on C-N detection algorithms. The simulation result shows that the proposed ABCND algorithm consumes 50% less energy to detect C-N with 90% to 95% accurate Critical Nodes (C-N).
... Unlike consumer applications, where cost is often the most critical system attribute, industrial applications typically rate reliability and security at the top of the list. Hence, Industrial Wireless Sensor Networks (IW SN ) face various challenges such as real-time data communication [13], robustness [14], energy [10], reliability [11], and fault tolerance [12]. The modus operandi of IWSN is that the sensors collect data from the indoor or outdoor environment and deliver it to a central node or controller for processing, decision making, and control. ...
Node failure in the Wireless Sensor Networks (WSN) topology may lead to economic loss, endanger people, and cause environmental damage. It is found that reliability and security are the two most essential concerns in IWSN. Reliability can be achieved by adequately managing network topology using the structural approaches, where the critical wireless sensor nodes are precisely detected and protected. In this paper, we address the problem of critical node detection and present two-phase algorithms. In Phase-I, a 2D critical node ( C-N ) detection algorithm is proposed, which uses only RSSI information of the neighbor. This algorithm is also extended to work in 3D topology. In Phase II, a correlation-based reliable approach is proposed to increase the node resilience against node failure to achieve low overhead. The proposed algorithms ( ABCND ) requires mathcalO ( log ( N )) time for convergence and mathcalO ( delta ( log N )) for critical node detection, N represents the number of IoT devices, and delta is the cost required to forward and backward message. We compare our algorithm with the current state-of-the-art on C-N detection algorithms. The simulation results show that the proposed ABCND consumes 50% less energy to detect C-N and 90% to 95% reliable C-N .
This article investigates the high-reliable data transmission for multihop and multichannel wireless sensor networks (WSNs), which jointly optimizes the channel allocation and channel access mechanisms. We propose a novel wireless paradigm empowered by mobile-edge computing (MEC) and deep reinforcement learning (DRL) to improve the data process ability of WSNs and formulate the joint resource allocation problem for reliability maximization as a partially observable Markov decision process (POMDP). Meanwhile, we introduce the distributed decision-making (DDM) framework to decouple channel optimization into two subproblems: 1) channel allocation and 2) channel access. Correspondingly, we present an asynchronous channel allocation algorithm for multiagent scenario and enable the neighbor cooperation to tackle the nonstationary problem, which can significantly improve the network convergence speed. Besides, we present a collision-free channel access algorithm including three submodules that can simultaneously eliminate vanishing nodes, hidden terminal, and exposed terminal problems in large-scale WSNs. Simulation results demonstrate that the proposed algorithm significantly improves network performance in terms of convergence, throughput, collision, and packet delivery ratio (PDR).
Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches , such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based approaches. Whale optimization algorithm (WOA) has recently gained the attention of the research community as an efficient method to solve a variety of optimization problems. As an alternative to the existing methods, our main goal in this article is to study the applicability of WOA to solve resource allocation problems in wireless networks. First, we present the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints. Then, we demonstrate three examples of WOA to resource allocation in wireless networks, including power allocation for energy-and-spectral efficiency tradeoff in wireless interference networks, power allocation for secure throughput maximization, and mobile edge computation offloading. Lastly, we present the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.
Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10 −9 packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon's capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.
The Industrial Internet of Things is expected to enable the Industry 4.0 through the large deployment of low-power devices. However, industrial applications require most of the time high reliability close to 100\%, and low end-to-end delays. Thus, most industrial wireless networks rely on a strict schedule of the transmissions to avoid collisions, and implement frequency hopping to combat external interference. In multihop topologies, the network has to decide both when the transmissions have to be scheduled, and which router can forward the packets. To be fault-tolerant, multipath routing consists in exploiting several paths in parallel. We exploit here a braided path routing structure, where each router has several possible next hops. Thus, we can cope with any fault along the path, while still providing a remaining operational path. We propose also a scheduling algorithm, where multiple transmitters are attached to a single cell, to the same receiver. The schedule is constructed such that only one transmitter is active at a time, and is consequently collision-free. Mutualizing the same cell for several transmitters reduces the energy consumption and increases the network capacity. Our approach is still fully compliant with the standard while minimizing idle listening. Our simulation results show the relevance of such solution to provide high-reliability and fault-tolerance. While the single and disjoint paths solutions achieve a very low reliability (20\%) when two nodes crash, we keep on providing a packet delivery ratio above 80\%, whatever the conditions. Besides, our scheduling algorithm is particularly energy efficient since it presents the same energy consumption as the classical single path routing scheme.
In industrial zones like an underground tunnel or an enclosed space, the quality of data transmission using wireless sensor networks (WSNs) can be reduced significantly due to multipath fading. Thus, we propose a new receiver that can remove the effect of multipath fading completely in low complexity. The proposed receiver combines a coherent detection scheme, which is good for a slow fading environment, and a direct conversion synthesizer that satisfies the requirements of WSNs: low cost, low bit error rate, and low power consumption. Since the coherent detection scheme requires high complexity in phase and frequency recovery, we devised a simple adaptive phase adjustor. We prove by both analysis and simulation that the new approach can achieve a bit error rate of 0.1% at power strength 18dB which is less than that of the recent best approach by 8 dB.
The exponential growth in data traffic and dramatic capacity demand in fifth generation (5G) have inspired the move from traditional single-tier cellular networks towards heterogeneous cellular networks (HCNs). To face the coming trend in 5G, the high availability requirement in new applications, needs to be satisfied to achieve low latency service. Usually, these applications require an availability of six nines or even higher. In this work, we present a tractable multi-tier multi-band availability model for spectrum aggregation-based HCNs. We first derive a closed-form expression for the availability of spectrum aggregation-based HCNs using the signal-to-interference-plus-noise (SINR) model. By doing so, we formulate two optimization problems, one is to maximize the average availability, and the other one is to minimize the average power consumption. These two optimization problems are both non-convex problems, which are challenging to solve. To cope with them, we propose to apply genetic algorithm (GA) for the joint user equipment (UE) association, subcarrier assignment and power allocation problem. Our results show that the average availability in spectrum aggregation-based HCNs improves with decreasing the number of UEs, as well as increasing the power budget ratio. We also show that increasing the maximum number of aggregated subcarriers decreases the average power consumption, but can not guarantee the substantial improvement of average availability.
This paper presents a low-power battery-less energy harvesting system for Internet of Things (IoT) applications. A dual-mode DC-DC converter is used to harvest the energy of a micro-scale photovoltaic (PV) transducer and provides energy, in the boost mode, to a super capacitor for storage. In the buck mode, the DC-DC converter provides energy to the load on demand. A piezoelectric transducer is used as a secondary input source to guarantee system’s self start-up. The proposed system includes a smart control to adaptively adjust the number of sensors at the load, leading to better usage of the available energy at any given time. The implementation also includes a programmable switch sizing to optimize the overall systemˆas efficiency for different load conditions. A maximum power point tracking is used to extract the maximum power of the PV transducer under various illumination conditions. The system has been implemented in a CMOS 130nm technology and tested in various modes of operation. Self start-up has been verified and a peak efficiency of 90.5% has been measured.
Traditional cellular networks are moving towards heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all UEs, where CoMP improves the single-path availability, and CA enhances availability via multi carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a maxmin optimization problem. To solve it, we then propose a twostep optimization algorithm and a joint two-step optimization algorithm. The two-step optimization algorithm (TSOA) is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on Lagrangian dual method for the power allocation. The joint two-step optimization algorithm (JTOA) is based on genetic algorithm (GA) to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of CoMP in availability improvement in HetNets.
The Industry 4.0 concept targets the interconnection and computerization of traditional industries to improve their adaptability and utilize efficiently their resources. Industrial wireless networks will play a key role within the Industry 4.0 as they will facilitate the deployment of novel industrial applications thanks to the flexible and reconfigurable wireless connection of industrial devices. Significant advances are still necessary to enable the deployment of reliable industrial wireless networks capable to guarantee the strict QoS (Quality of Service) requirements of industrial applications in harsh propagation conditions. This paper contributes towards this objective with a novel multipath routing protocol that identifies and establishes the necessary redundant routes between any pair of nodes of a wireless network in order to satisfy the reliability and delay QoS levels demanded by industrial applications. The proposed protocol is here presented and analyzed under the framework of the WirelessHART standard given its important industrial adoption. However, it can also be adapted to other centralized TDMA-based multi-hop wireless networks.
Artificial bee colony (ABC) is a recent swarm intelligence algorithm. There have been some greedy ABC variants developed to enhance the exploitation capability, but greedy variants are usually less reliable and may cause premature convergence, especially without proper control on the greediness degree. In this paper, we propose an adaptive ABC algorithm (AABC), which is characterized by a novel greedy position update strategy and an adaptive control scheme for adjusting the greediness degree. The greedy position update strategy incorporates the information of top t solutions into the search process of the onlooker bees. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the greediness degree to fit for different optimization scenarios, the proposed adaptive control scheme further adjusts the size of top solutions for selection in each iteration of the algorithm. The adjustment is based on considering the current search tendency of the bees. This way, by combining the greedy position update process and the adaptive control scheme, the convergence performance and the robustness of the algorithm can be improved at the same time. A set of benchmark functions is used to test the proposed AABC algorithm. Experimental results show that the components of AABC can significantly improve the performance of the classic ABC algorithm. Moreover, the AABC performs better than, or at least comparably to, some existing ABC variants as well as other state-of-the-art evolutionary algorithms.
In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of K-tier heterogeneous cellular networks (HCNs) with randomly located base stations (BSs) and mobile terminals (MTs). In the DL and UL, each energy-constrained MT pairs up with its corresponding BS, which provides the maximum received power at MT. Due to the densely located BSs and universal frequency
reuse between all tiers in HCNs, the typical MT is allowed to harvest energy from the serving BS by direct beamforming, as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we first derive exact expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT.As the number of BS antennas goes to infinity, we further derive asymptotic expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. Our results show that the UL outage probability per MT first decreases and then increases with increasing the time allocation factor (the fraction of time allocated to the DL), and the UL
outage probability and the UL average ergodic rate per MT can be largely improved by using the massive antenna arrays at the BSs.
As a relatively new optimization technique, in recent years, artificial bee colony (ABC) algorithm has attracted much attention for its good performance. However, its performance has also been challenged in solving complex optimization problems. This insufficiency is mainly caused by its solution search equation, which does well in exploration but badly in exploitation. Inspired by the concept of neighborhood search, in this paper, we introduce a global neighborhood search operator into ABC for balancing its explorative and exploitative capabilities. Extensive experiments are conducted on 22 benchmark functions, and six different algorithms are included in the comparison studies, including four ABC variants and two related evolutionary algorithms. The compared results demonstrate that in most cases our approach is able to provide better performance in terms of solution accuracy and convergence speed.
This paper develops a tractable framework for exploiting the potential
benefits of physical layer security in three-tier wireless sensor networks
using stochastic geometry. In such networks, the sensing data from the remote
sensors are collected by sinks with the help of access points, and the external
eavesdroppers intercept the data transmissions.We focus on the secure
transmission in two scenarios: i) the active sensors transmit their sensing
data to the access points, and ii) the active access points forward the data to
the sinks. We derive new compact expressions for the average secrecy rate in
these two scenarios. We also derive a new compact expression for the overall
average secrecy rate. Numerical results corroborate our analysis and show that
multiple antennas at the access points can enhance the security of three-tier
wireless sensor networks. Our results show that increasing the number of access
points decreases the average secrecy rate between the access point and its
associated sink. However, we find that increasing the number of access points
first increases the overall average secrecy rate, with a critical value beyond
which the overall average secrecy rate then decreases. When increasing the
number of active sensors, both the average secrecy rate between the sensor and
its associated access point and the overall average secrecy rate decrease. In
contrast, increasing the number of sinks improves both the average secrecy rate
between the access point and its associated sink, as well as the overall
average secrecy rate.
The signal-to-interference-and-noise ratio (SINR) is of key importance for
the analysis and design of wireless networks. For addressing new requirements
imposed on wireless communication, in particular high availability, a highly
accurate modeling of the SINR is needed. We propose a stochastic model of the
SINR distribution where shadow fading is characterized by random variables.
Therein, the impact of shadow fading on the user association is incorporated by
modification of the distributions involved. The SINR model is capable to
describe all parts of the SINR distribution in detail, especially the left tail
which is of interest for studies of high availability.
Recent advances in self-interference cancellation techniques enable in-band full-duplex relaying (FDR) systems,which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Meanwhile,wireless virtualization enables abstraction and sharing of infrastructure and radio spectrum resources in wireless networks. Both in-band FDR and wireless virtualization are promising technologies for 5G cellular networks. In this paper, we first give a brief survey for them to present their basic ideas, enabling technologies, characteristics, etc. Then, we propose a virtual resource management architecture for in-band FDR networks, where multiple service providers (SPs) and multiple mobile network operators (MNOs) co-exist. Simulation results are presented to show the effectiveness of the proposed scheme.
QoS-based service selection is one of the important requirements in Service Oriented Computing (SOC). A challenging task towards this purpose is the selection of the best combination of services that fulfils user’s requirements while meeting quality of service (QoS) constraints. This challenge becomes more complex when dealing with time-dependent QoS values and temporal properties. Indeed, during the selection,
mutual dependencies between the different temporal constraints may arise so that the selection of each service may influence or be influenced by the selection of other services. On other side, to find the best solution, all potential combinations must be compared. However, the number of these combinations may
be very high, which can present a barrier for enabling effective service selection. In this paper, we present a heuristic based timeaware service selection approach to efficiently select a close-tooptimal combination of services. First, pruning techniques are adopted to reduce the search space. Second, a novel heuristic
approach is proposed based on service clustering, constraints decomposition and local selection while considering both QoS and temporal constraints. Finally, experiments which confirm the feasibility and effectiveness of the proposed approach in terms of its timeliness and optimality, are conducted.
Ubiquitous monitoring over wireless sensor networks (WSNs) is of increasing interest in industrial Cyber-Physical Systems (CPSs). Question of how to understand a situation of physical system by estimating process parameters is largely unexplored. This paper is concerned with the distributed estimation problem for industrial automation over relay assisted wireless sensor networks (WSNs). Different from most existing works on WSN with homogeneous sensor nodes, the network considered in this paper consists of two types of nodes, i.e., sensing nodes (SNs) which is capable of sensing and computing, and relay nodes (RNs) which is only capable of simple data aggregation. We first adopt a Kalman filtering (KF) approach to estimate the unknown physical parameters. In order to facilitate the decentralized implementation of the KF algorithm in relay assisted WSNs, a tree-based broadcasting strategy is provided for distributed sensor fusion. With the fused information, consensus-based estimation algorithms are proposed for SNs and RNs, respectively. The proposed method is applied to estimate the slab temperature distribution in a hot rolling process monitoring system which is a typical industrial CPS. It is demonstrated that the introduction of RNs improves temperature estimation efficiency and accuracy compared with the homogeneous WSN with SNs only.
Energy savings optimization becomes one of the major concerns in the wireless sensor network (WSN) routing protocol design, due to the fact that most sensor nodes are equipped with the limited nonrechargeable battery power. In this paper, we focus on minimizing energy consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue network. Following the principle of opportunistic routing theory, multihop relay decision to optimize the network energy efficiency is made based on the differences among sensor nodes, in terms of both their distance to sink and the residual energy of each other. Specifically, an Energy Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power cost during data relay and protect the nodes with relatively low residual energy. Extensive simulations and real testbed results show that the proposed solution ENS_OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.
Collecting sensor data in industrial environments from up to some tenth of
battery powered sensor nodes with sampling rates up to 100Hz requires energy
aware protocols, which avoid collisions and long listening phases. The IEEE
802.15.4 standard focuses on energy aware wireless sensor networks (WSNs) and
the Task Group 4e has published an amendment to fulfill up to 100 sensor value
transmissions per second per sensor node (Low Latency Deterministic Network
(LLDN) mode) to satisfy demands of factory automation. To improve the
reliability of the data collection in the star topology of the LLDN mode, we
propose a relay strategy, which can be performed within the LLDN schedule.
Furthermore we propose an extension of the star topology to collect data from
two-hop sensor nodes. The proposed Retransmission Mode enables power savings in
the sensor node of more than 33%, while reducing the packet loss by up to 50%.
To reach this performance, an optimum spatial distribution is necessary, which
is discussed in detail.
In-band full-duplex (IBFD) operation has emerged as an attractive solution
for increasing the throughput of wireless communication systems and networks.
With IBFD, a wireless terminal is allowed to transmit and receive
simultaneously in the same frequency band. This tutorial paper reviews the main
concepts about in-band full-duplex wireless .A key challenge behind IBFD
operation is mitigation of self-interference, i.e., the interference caused by
an IBFD node's own transmissions to its desired receptions. A wide array of
techniques proposed in the literature for IBFD self-interference suppression
are surveyed in this tutorial. Also discussed are numerous research challenges
and opportunities in the design and analysis of IBFD wireless systems
Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.
An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive
is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the
intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the
results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm
(PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.
This paper introduces a wireless communication protocol for industrial control systems that uses channel quality awareness to dynamically create network-device cooperation and assist the nodes in momentary poor channel conditions. To that point, channel state information is used to identify nodes with strong and weak channel conditions. We show that strong nodes in the network are best to be served in a single-hop transmission with transmission rate adapted to their instantaneous channel conditions. Meanwhile, the remainder of time-frequency resources is used to serve the nodes with weak channel condition using a two-hop transmission with cooperative communication among all the nodes to meet the target reliability in their communication with the controller. We formulate the achievable multi-user and multi-antenna diversity gain in the low-latency regime, and propose a new scheme for exploiting those ondemand, in favor of reliability and efficiency. The proposed transmission scheme is therefore dubbed adaptive network-device cooperation (ANDCoop), since it is able to adaptively allocate cooperation resources while enjoying the multi-user diversity gain of the network. We formulate the optimization problem of associating nodes to each group and dividing resources between the two groups. Numerical solutions show significant improvement in spectral efficiency and system reliability compared to the existing schemes in the literature. System design incorporating the proposed transmission strategy can thus reduce infrastructure cost for future private wireless networks.
Aiming at the problems of low mobility, low positioning accuracy, high communication overhead and high energy consumption of sensor nodes in underwater acoustic sensor networks, MPL algorithm is proposed in this paper. The algorithm is divided into two stages: mobile prediction and node location. In the node location phase, firstly, in order to reduce the communication overhead and energy consumption, a TOA-based ranging strategy is proposed. Then, after dimension reduction processing, the gray wolf algorithm is used to find the optimal location of the secondary nodes with low location accuracy. Finally, the node location is obtained and the node movement prediction stage is entered. In the coastal area, tidal phenomenon is the main factor leading to node movement, so combining the tidal model with the node stress, a more practical node movement model is constructed. Therefore, in the movement prediction stage, the velocity and position of each time point in the prediction window are predicted according to the node movement model, and then the underwater positioning is completed. Finally, the proposed MPL algorithm is simulated and analyzed; the simulation results show that the proposed MPL algorithm has higher localization performance. Compared with LSLS, SLMP, GA-SLMP algorithm, the proposed MPL algorithm not only effectively reduces the network communication overhead and energy consumption, but also improves the network location coverage and node location accuracy.
Routing solutions for multi-hop underwater wireless sensor networks suffer significant performance degradation as they fail to adapt to the overwhelming dynamics of underwater environments. To respond to this challenge, we propose a new data forwarding scheme where relay selection swiftly adapts to the varying conditions of the underwater channel. Our protocol, termed CARMA for Channel-aware Reinforcement learning-based Multi-path Adaptive routing, adaptively switches between single-path and multi-path routing guided by a distributed reinforcement learning framework that jointly optimizes route-long energy consumption and packet delivery ratio. We compare the performance of CARMA with that of three other routing solutions, namely, CARP, QELAR and EFlood, through SUNSET-based simulations and experiments at sea. Our results show that CARMA obtains a packet delivery ratio that is up to 40% higher than that of all other protocols. CARMA also delivers packets significantly faster than CARP, QELAR and EFlood, while keeping network energy consumption at bay.
Wireless sensor networks (WSNs) are an important core of the Industrial Internet of Things (IIOT). Wireless rechargeable sensor networks (WRSNs) are sensor networks that are charged by mobile chargers (MCs), and can achieve self-sufficiency. Therefore, the development of WRSNs has begun to attract widespread attention in recent years. Most of the existing energy replenishment algorithms for MCs use one or more MCs to serve the whole network in WRSNs. However, a single MC is not suitable for large-scale network environments, and multiple MCs make the network cost too high. Thus, this paper proposes a collaborative charging algorithm based on network density clustering (CCA-NDC) in WRSNs. This algorithm uses the mean-shift algorithm based on density to cluster, and then the mother wireless charger vehicle (MWCV) carries multiple sub wireless charger vehicles (SWCVs) to charge the nodes in each cluster by using a gradient descent optimization algorithm. The experimental results confirm that the proposed algorithm can effectively replenish the energy of the network and make the network more stable.
Wireless sensor-actuator networks (WSANs) technology is appealing for use in the industrial Internet of Things (IoT) applications because it does not require wired infrastructure. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in the industrial facilities without running cabling for communication and power. The IEEE 802.15.4-based WSANs operate at low-power and can be manufactured inexpensively, which makes them ideal where battery lifetime and costs are important. Almost, a decade of real-world deployments of WirelessHART standard has demonstrated the feasibility of using its core techniques including reliable graph routing and time slotted channel hopping (TSCH) to achieve reliable low-power wireless communication in the industrial facilities. Today, we are facing the fourth Industrial Revolution as proclaimed by political statements related to the Industry 4.0 Initiative of the German Government. There exists an emerging demand for deploying a large number of field devices in an industrial facility and connecting them through the WSAN. However, a major limitation of current WSAN standards is their limited scalability due to their centralized routing and scheduling that enhance the predictability and visibility of network operations at the cost of scalability. This paper decentralizes the network management in WirelessHART and presents the first Distributed Graph routing and autonomous Scheduling (DiGS) solution that allows the field devices to compute their own graph routes and transmission schedules. The experimental results from two physical testbeds and a simulation study shows our approaches can significantly improve the network reliability, latency, and energy efficiency under dynamics.
This paper proposes a novel protocol for Industrial Wireless Sensor Networks (IWSN), which is called Adaptive and Beacon-based Multi-Channel Protocol (ABMP), and combines multi-channel communication, real-time link quality estimation, and dynamic channel allocation, to deal with the problems that affect the link quality in industrial environments. A hybrid channel diversity mechanism is employed, in which the beacon frames are transmitted using channel hopping, and the unicast data packets are transmitted using channel adaptation. The network dynamically allocates the channels to deal with temporal and spatial variations in the channel quality. Referring to ABMP, the end-nodes do not need to receive all the beacons to maintain the communication. As demonstrated in this paper, the proposed approach makes the network more robust against problems related to the beacon reception. The ABMP has been compared to the protocols TSCH, and CSMA/CA for networks with the star and the tree topologies, using theoretical and simulation studies, with a realistic channel model for the IWSN. The results indicate that the proposed protocol presents a better performance in comparison to the MAC protocols defined by the new standards for IWSN, in terms of packet delivery rate, delay, and determinism.
Due to overshadow and channel fading, many mobile users are unable to receive the signal transmitted from satellite directly. Hence, some relay stations should be set to help this type of users to receive signals reliably. In this paper, we present a novel cognitive hybrid satellite-terrestrial model, where two cognitive relays forward their received signal for a mobile user successively. Furthermore, we address its achievable rate maximization. We first convert the co-channel interference threshold into transmit power constraints, and then formulate the maximization of the achievable rate as an optimization problem. Based on Karush-Kuhn-Tucker conditions, the optimization problem is decomposed into four cases, each of which is solved in closed form. Simulation study with different system settings is presented, and the efficiency of the proposed power allocation scheme is shown.
This paper presents the design and implementation of the first in-band full duplex WiFi radios that can simultaneously transmit and receive on the same channel using standard WiFi 802.11ac PHYs and achieves close to the theoretical doubling of throughput in all practical deployment scenarios. Our design uses a single antenna for simultaneous TX/RX (i.e., the same resources as a standard half duplex system). We also propose novel analog and digital cancellation techniques that cancel the self interference to the receiver noise floor, and therefore ensure that there is no degradation to the received signal. We prototype our design by building our own analog circuit boards and integrating them with a fully WiFi-PHY compatible software radio implementation. We show experimentally that our design works robustly in noisy indoor environments, and provides close to the expected theoretical doubling of throughput in practice.
How to make efficient data routing in energy constrained wireless sensor networks (WSNs) is one of the key points. In order to find the optimal path of data transmission in the WSNs, a new routing algorithm based on ant colony algorithm is proposed. Using the improved heuristic function and considering the node communication transmission distance, transmission direction and residual energy, an optimal path from the source node to the destination node can be found. Thus the network energy consumption is reduced and the network lifetime is prolonged. Simulation results show that new ant algorithm can effectively save the energy of nodes and prolong the network lifetime.
Service selection is crucial to service composition in determining the composite Quality of Service (QoS). The proliferation of composable services on the Internet and the practical need for timely delivering optimized composite solutions motivate the adoption of population-based algorithms for QoS-aware service selection. However, existing population-based algorithms are generally complicated to use, and often used as a general approach to solving different optimization problems. We propose to develop specialized algorithms for QoS-aware service selection, based on the artificial bee colony algorithm (ABC). ABC is a new and simpler implementation of swarm intelligence, which has proven to be successful in solving many real-world problems, especially the numerical optimization problems. We develop an approximate approach for the neighborhood search of ABC, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. We present three algorithms based on the approach. All the three algorithms are designed to improve the performance and meanwhile preserve the simplicity of ABC. Each algorithm applies a different technique to leverage the unique characteristics of the service selection problem. Experimental results show higher accuracy and convergence speed of the proposed algorithms over the state of the art algorithms
The biggest challenge in adopting industrial wireless sensor networks (IWSNs) for factory automation applications is to provide low latency and highly reliable communication in harsh factory environments. IEEE 802.15.4e low latency deterministic network (LLDN) mode attempts to address this requirement at the medium access control (MAC) layer. However, the measures offered by this mode are inadequate considering realistic factory environments, suffering from noise, interference, multipath fading, and resulting in frequent packet losses. Cooperative diversity using relay nodes and incorporation of forward error correction (FEC) techniques are the two conventional ways to enhance communication reliability. However, the challenge lies in the placement of relay nodes considering a realistic three-dimensional (3-D) factory space and satisfying various physical, performance, and energy-related constraints. Moreover, the versatile and dynamic behavior of factory environment demand that the solutions offered to enhance communication reliability must be generic and adaptive, thereby eliminating the need for unnecessary redesigns. This paper proposes a twofold solution to enhance the communication reliability offered by 802.15.4e LLDN. First, an efficient and pragmatic relay-placement strategy based on rainbow product ranking algorithm for a 3-D factory space. Second, an adaptive transmission scheme (ATS) inspired from reinforcement learning (RL) technique called Q-learning is proposed, which incorporates cooperative diversity and Reed Solomon (RS) block codes. The effectiveness of the proposed solution is established and demonstrated using a real-world case study.
The interest in wireless communication systems for industrial applications has grown significantly over the last years. More flexible, easier to install and maintain, wireless networks present a promising alternative to the currently used wired systems. However, reliability and timeliness requirements at present met by wired networks also need to be fulfilled by wireless solutions. Packet errors introduced when packets travel through wireless channels imply a significant challenge to fulfill these requirements. Relaying has been recognized to improve the reliability in industrial wireless networks without causing additional delay. Furthermore, the recent results have shown that relaying combined with packet aggregation significantly outperforms simple relaying. However, it is not always cost efficient to introduce additional relay nodes into an industrial network and hence, in this paper, we propose using a combination of relaying and packet aggregation at the source nodes. The results show that when relaying and aggregation are used at the source nodes, the transmission schedule plays a crucial role. A schedule adapting to the varying channel conditions improves performance substantially. By carefully choosing which packet to aggregate, even further improvements can be achieved.
The adoption of wireless communications and, in particular, Wi-Fi, at the lowest level of the factory automation hierarchy has not increased as fast as expected so far, mainly because of serious issues concerning determinism. Actually, besides the random access scheme, disturbance and interference prevent reliable communication over the air and, as a matter of fact, make wireless networks unable to support distributed real-time control applications properly. Several papers recently appearing in literature suggest that diversity could be leveraged to overcome this limitation effectively. In this paper, a reference architecture is introduced, which describes how seamless link-level redundancy can be applied to Wi-Fi. The framework is general enough to serve as a basis for future protocol enhancements, and also includes two optimizations aimed at improving the quality of wireless communication by avoiding unnecessary replicated transmissions. Some relevant solutions have been analyzed by means of a thorough simulation campaign, in order to highlight their benefits when compared with conventional Wi-Fi. Results show that both packet losses and network latencies improve noticeably.
High resilience is expected to be a key component of next generation wireless networks enabling new services and applications in, e.g., vehicular communication, smart grids, and industrial automation. In this work, we analyze diversity concepts with a focus on the joint availability of power-controlled Nakagami-m fading links. For various fading environments, we investigate whether an optimal number of combined links in terms of total power consumption can be identified. Results show that, indeed, optimal operating points exist and huge power savings are possible when multiple lower power links instead of a single powerful link are used. The savings decrease with increasing fading parameter and decreasing outage probability. Furthermore, we present an optimization method based on min-plus convolution for determining the optimal power allocation among several selection combined Nakagami-m fading links with unequal fading parameters.
Future cellular networks have to meet enormous, unprecedented, and multifaceted requirements, such as high availability and low latency, in order to provide service to new applications in, e.g., vehicular communication, smart grids, and industrial automation. Such applications often demand a temporal availability of six nines or higher. In this work, we investigate how high availability can be achieved in wireless networks. To elaborate, we focus on the joint availability of power-controlled Rayleigh-fading links while using selection combining. By applying a basic availability model for uncorrelated links, we determine whether it is more efficient in terms of power to utilize multiple links in parallel rather than boosting the power of a stand-alone link. The results reveal that, for high availability, it can actually be beneficial to use multiple links in parallel. For instance, an availability of 1 - 10 12 is achieved with 100 dB less power when power is shared among multiple links. Depending upon the availability desired, an optimal number of parallel links in terms of power consumption exists. Additionally, we extend the availability model to correlated links and investigate the performance degradation due to correlation.
Industrial wireless sensor networks (IWSNs) have the potential to contribute significantly in areas such as cable replacement, mobility, flexibility, and cost reduction. Nevertheless, the industrial environment that the IWSNs operate in is very challenging because of dust, heat, water, electromagnetic interference, and interference from other wireless devices, which make it difficult for current IWSNs to guarantee reliable real-time communication. In this paper, we present a novel method based on the segmented slot assignment, fast slot competition, and free node concept that will improve the reliability and real-time communication significantly so that more advanced applications can be enabled. The main purpose of the algorithms is to improve the retransmission efficiency for time-division-multiple-access-based multihop IWSNs by using limited shared slot resources more efficiently. More importantly, the proposed algorithms support efficient slot rescheduling caused by link or node failure. We evaluate the proposed methods by using simulations and a real implementation targeting monitoring of welder machines. Our obtained results show that the proposed method outperforms the first published and most widely used IWSN standard called WirelessHART.
Multi-stream aggregation (MSA) allows users to receive data from multiple base stations simultaneously to increase their data rates. In this paper, we propose a joint user association and resource allocation algorithm for MSA systems to achieve energy efficiency (EE) balance among different base stations. The problem is formulated as a non-convex combinatorial sum-of-ratios optimization problem, which is very hard to solve directly. We first relax the combinatorial variables and then transform the problem into a convex optimization problem by the sum-of-ratios algorithm and the successive convex approximation (SCA) method. Based on this, a near-optimal algorithm is developed. Simulation results show that the proposed algorithm can achieve a good performance with a fast convergence speed.
Strict reliability and delay requirements of factory monitoring and control applications pose challenges for wireless communications in dynamic and cluttered industrial environments. To reduce outage in such fading-rich areas, cooperative relays can be used to overhear source-destination transmissions and forward data packets that a source fails to deliver. This article presents the results of an experimental study of selective cooperative relaying protocols that are implemented in off-the-shelf IEEE 802.15.4-compatible devices and evaluated in an industrial production plant. Three practical relay update schemes, which define when a new relay selection is triggered, are investigated: periodic, adaptive, and reactive relay selections. The results show that all relaying protocols outperform conventional time diversity retransmissions in delivery ratio and number of retransmissions for packet delivery. Reactive selection provides the best overall delivery ratio of nearly 99% over the tested network. There is a tradeoff, however, between achievable delivery ratio and required selection overhead. This tradeoff depends on protocol and network parameters, and is studied via protocol emulation using empirical channel values.
Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms' performances.
This paper presents the design and implementation of the first in-band full duplex WiFi radios that can simultaneously transmit and receive on the same channel using standard WiFi 802.11ac PHYs and achieves close to the theoretical doubling of throughput in all practical deployment scenarios. Our design uses a single antenna for simultaneous TX/RX (i.e., the same resources as a standard half duplex system). We also propose novel analog and digital cancellation techniques that cancel the self interference to the receiver noise floor, and therefore ensure that there is no degradation to the received signal. We prototype our design by building our own analog circuit boards and integrating them with a fully WiFi-PHY compatible software radio implementation. We show experimentally that our design works robustly in noisy indoor environments, and provides close to the expected theoretical doubling of throughput in practice.
The outage probability of a composite microscopic and macroscopic diversity system is evaluated over correlated shadowed fading channels. The correlations on both a microlevel and macrolevel are taken into account for the evaluations. The expression of the desired outage probability is explicitly presented, and two evaluation approaches, i.e. a compact Gaussian-Hermite quadrature method and an effective iterative algorithm, are proposed. The accuracy and efficiency of the proposed approaches are analysed, and a guideline is provided for their application. By employing the proposed evaluation approaches, results and demonstrations are presented, which display the implied effects of the corresponding parameters on the system outage performance, and reveal the potential to facilitate the design and analysis of such composite diversity systems.
In this letter, a radio-frequency interference (RFI) evaluation method for highly integrated mobile devices, such as smartphones and tablets, is proposed. A near-field specification for noise-emitting components in mobile devices is developed based on near-field emission measurements and RF sensitivity measurements of mobile devices with the bit-error-rate (BER) test. To devise the specification, a derivative relation between the near-field strength of noise sources and the system-level RF sensitivity is newly suggested using the theoretical relationship between the signal to interference and noise ratio and the probability of error in communication systems, and verified with empirical measurements. Using the proposed algorithm, an RFI evaluation specification for commercial camera modules widely used in modern smartphones was established at several RF communication bands.
The first decade of the new millennium has been a stage for the rapid development of wireless communication technologies for low-cost, low-power wireless solutions capable of robust and reliable communication [1]. IEEE Standard 802.15.4 for low-rate wireless personal area networks (WPANs) [2] has been the enabling technology for numerous applications within the field of wireless sensor networks (WSNs) [3], and more recently, wireless instrumentation. Although WSNs quickly found their way into a wide variety of applications, the adoption of wireless technology in the process automation and manufacturing industries has been slow.
Decision problems involving multiple agents invariably lead to conflict and gaming. In recent years, multi-agent systems have been analyzed using approaches that explicitly assign to each agent a unique objective function and set of decision variables; the system is defined by a set of common constraints that affect all agents. The decisions made by each agent in these approaches affect the decisions made by the others and their objectives. When strategies are selected simultaneously, in a noncooperative manner, solutions are defined as equilibrium points [13,51] so that at optimality no player can do better by unilaterally altering his choice. There are other types of noncooperative decision problems, though, where there is a hierarchical ordering of the agents, and one set has the authority to strongly influence the preferences of the other agents. Such situations are analyzed using a concept known as a Stackelberg strategy [13, 14,46]. The hierarchical optimization problem [11, 16, 23] conceptually extends the open-loop Stackelberg model toK players. In this paper, we provide a brief introduction and survey of recent work in the literature, and summarize the contributions of this volume. It should be noted that the survey is not meant to be exhaustive, but rather to place recent papers in context.
We study the problem of communication reliability of wireless networks in a fading environment based on the outage probability formulation. The exact expression for the disconnect probability, the probability that a transmission by a node is not received correctly by any other node in the network, is obtained for one and two dimensional random networks. We obtain the end-to-end reliability of multi-hop transmission using the outage probability metric and develop algorithms for finding the most reliable route subject to power constraints as well as the minimum energy route subject to a reliability constraint. Finally, we study the tradeoff between outage probability and transmission power, with and without route diversity.
Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.
n Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpreta-tion, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or de-sirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems re-late to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.
Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose an improved solution search equation, which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation. Then, in order to make full use of and balance the exploration of the solution search equation of ABC and the exploitation of the proposed solution search equation, we introduce a selective probability P and get the new search mechanism. In addition, to enhance the global convergence, when producing the initial population, both chaotic systems and opposition-based learning methods are employed. The new search mechanism together with the proposed initialization makes up the modified ABC (MABC for short), which excludes the probabilistic selection scheme and scout bee phase. Experiments are conducted on a set of 28 benchmark functions. The results demonstrate good performance of MABC in solving complex numerical optimization problems when compared with two ABC-based algorithms.
Focusing on two-antenna infrastructure relays em- ployed for coverage extension, we develop hybrid techniques that switch opportunistically between full-duplex and half-duplex relaying modes. To rationalize the system design, the classic three-node full-duplex relay link is first amended by explicitly modeling residual relay self-interference, i.e., a loopback signal from the transmit antenna to the receive antenna remaining after cancellation. The motivation for opportunistic mode selection stems then from the fundamental trade-off determining the spectral efficiency: The half-duplex mode avoids inherently the self-interference at the cost of halving the end-to-end symbol rate while the full-duplex mode achieves full symbol rate but, in practice, suffers from residual interference even after cancella- tion. We propose the combination of opportunistic mode selection and transmit power adaptation for maximizing instantaneous and average spectral efficiency after noting that the trade-off favors alternately the modes during operation. The analysis covers both common relaying protocols (amplify-and-forward and decode-and-forward) as well as reflects the difference of downlink and uplink systems. The results show that opportunistic mode selection offers significant performance gain over system design that is confined to either mode without rationalization. Index Terms—Full duplex, half duplex, infrastructure relays, power control, self-interference, spectral efficiency.