The Internet of things (IoT) devices generate large volumes of data that require secure, reliable, and low-latency processing to support time-sensitive decision-making. However, the limited computational capabilities of IoT devices often lead to processing delays and performance bottlenecks. We propose a hierarchical queuing-based uncrewed aerial vehicle (UAV)-aided edge federated learning (FL) framework to address these challenges. In our proposed framework, UAVs are organized into multiple layers to provide on-demand computational resources for straggling IoT devices (i.e., devices that struggle to process their local dataset within the required time constraints) by processing offloaded data segments that cannot be handled locally within the time constraints. We formulate a system delay minimization problem that considers computation and communication capabilities of IoT devices, follower UAVs, and leader UAVs. Our model incorporates a queueing system at each node, including integration, offloading, and local processing queues, and enforces quality of service (QoS) and delay constraints. We apply a Lyapunov-based approach and decompose the problem into three subproblems to solve it efficiently. The sub-problems are then solved using the proposed solution based on sequential quadratic programming (SQP) method. Simulation results demonstrate that the proposed framework significantly reduces system delay and improves resource utilization compared to traditional FL and existing schemes.

Trustworthy communication is essential for the success of sixth-generation (6G) networks, enabling seamless and reliable interactions across diverse connected Internet of things (IoT) devices and systems. Uncrewed aerial vehicles (UAVs) are expected to play a pivotal role in this ecosystem by facilitating low-latency communication and promoting efficient resource utilization through flexible deployment strategies. In this context, we propose a trust-driven framework for UAV-assisted mobile edge computing (MEC). Initially, UAVs are deployed using the K-means clustering algorithm to maximize coverage. We then formulate an optimization problem that simultaneously targets several conflicting objectives: (i) maximize the trust level of serving UAVs; (ii) minimize computational latency for IoT devices tasks; (iii) maximize the number of served IoT devices; and (iv) balance the trade-off between maximizing trust and minimizing service provisioning cost. UAV trustworthiness is modeled as a composite metric encompassing success rate, security score, communication stability, computational resource availability, and energy sufficiency. To solve this problem, we develop a penalty-guided optimization (PGO) algorithm that guides relaxed binary decision variables toward integer solutions, followed by refinement using sequential quadratic programming. Simulations demonstrate that the PGO algorithm outperforms baseline relaxation methods and achieves performance close to the optimal branch-and-bound approach, while significantly reducing computational complexity. Additionally, unequally weighted trust metrics yield better UAV trust levels as compared to equal weighting, highlighting the advantage of adaptive trust modeling. The proposed UAV trust evaluation framework and deployment strategy thus collectively lead to enhanced network accessibility, reliability, and utility in aerial IoT networks.

The Internet of Things (IoT) generates large volumes of data that need secure, reliable, and timely processing to enable effective decision-making. However, the limited processing capabilities of IoT devices often create a bottleneck. To address this, we propose a queuing-based uncrewed aerial vehicle (UAV)-aided edge federated learning (FL) (QUAFL) framework. The proposed framework uses UAVs as mobile edge computing (MEC) servers to process portions of data from the queues of straggling IoT devices that are unable to meet processing deadlines, thereby reducing system delay. We formulate an optimization problem that minimizes system delay by considering UAV-MEC computation power, IoT device computation and communication power, and the capacities of various queues (incoming, offloading, and local processing at IoT devices; incoming and local at UAV-MEC), along with quality-of-service and delay constraints. The problem is reformulated as a Lyapunov-based formulation and then decomposed into three subproblems, which are solved iteratively using closed-form solutions and the sequential quadratic programming (SQP) algorithm. Simulation results demonstrate the impact of various system components on overall delay.

With technological advancements, Unmanned Aerial Vehicles (UAVs) are becoming prominent in next-generation wireless networks because they enable rapid deployment, enhance coverage, and provide advanced services to end users. End users benefit significantly from offloading complex and computationally demanding tasks to flying platforms made possible by UAVs outfitted with edge computing servers. However, attaining optimal and effective network performance depends on proper resource management. The capabilities of next-generation networks are increased through the integration of UAV-assisted Mobile Edge Computing with current wireless infrastructure. Internet of Things (IoT) devices are frequently used for real-time data monitoring, gathering, analysis, and transmission for decision-making. This paper presents an optimization problem to increase the number of IoT devices UAVs can serve while minimizing latency and resource costs related to communication, computing, caching, and energy harvesting. We use Digital Twin technology, allowing thorough network replication and monitoring to analyze latency. A complex mixed-integer nonlinear programming problem has been formulated. We provide a multi-stage offloading mechanism called the Integrality Gap Method with an Interior Point mechanism to tackle this complexity. Simulation findings show that the suggested approach performs better than the straightforward relaxation heuristic technique, confirming its effectiveness.

Unmanned aerial vehicles (UAVs) are emerging as key enablers for adaptive connectivity in future sixth-generation (6G) networks, offering high mobility, flexible deployment, and enhanced line-of-sight coverage. However, the open nature of wireless communication and the elevated positioning of UAVs as a relay expose them to severe security threats, including jamming and eavesdropping. In this work, we propose a secure downlink communication framework where a UAV relay flying at a fixed altitude serves multiple ground users while contending with an active jammer and a passive eavesdropper. To enhance physical layer security, we jointly optimize the UAV’s trajectory and transmit power over a discrete time horizon to maximize the cumulative secrecy rate. The resulting non-convex optimization problem is solved using both a successive convex approximation (SCA) method and deep reinforcement learning (DRL). Simulation results demonstrate the optimized trajectories in various user topologies and shed light on the relationship between secrecy energy efficiency and maximal transmit power of the UAV relay as well as the number of users.

Future wireless networks will play an essential role as the need for performance and feature availability grows. Most of the traffic in future wireless networks is due to increased Internet of things (IoT) devices, making resource optimization critical. Traditional optimization algorithms have limitations due to their high computational complexity, which restricts their use in modern applications. To address this, machine learning algorithms are now the preferred alternative to traditional optimization algorithms due to their improved runtime complexity. We present a comprehensive survey on the use of machine learning for resource optimization in future wireless networks. The use of machine learning is divided into three categories: (i) comprehensive solutions, where machine learning is the primary component of the solution approach; (ii) partial solutions, where machine learning is used alongside a traditional approach for optimization; and (iii) environment-only solutions, where optimization is performed in a machine-learning environment. We have further classified objective functions (e.g., energy, latency, data rate, etc.) within each category based on the pure objective function, variations on the objective function, and objective function tradeoffs with respect to other objective functions. We present objective functions and constraints used in the literature for optimization problem formulation. We provide an overview of frequently used machine learning algorithms for resource optimization, followed by a detailed survey of machine learning works in the literature in the three aforementioned categories. Finally, we discuss future research directions for utilizing machine learning to optimize resource management in future wireless networks.

Unmanned aerial vehicles (UAVs) enable a mobile edge computing (MEC) paradigm with reduced latency by bringing computational resources closer to the network edge. However, UAV-MEC servers have less computation and caching resources than ground base stations (BSs). The management of communication and control resources is crucial to coordinate communication, computing, and caching due to the involvement of aerial networks. Thus, managing joint caching, communication, computing, and control (4C) resources is vital in UAV-assisted MEC networks. To address these challenges, we developed a computational model for efficient resource management to reduce the linear combination of network cost and latency under constrained caching, computing, and offloading. We used binary decision variables for the allocation of computational and offloading resources. The formulated problem is a binary linear programming problem incorporating binary decision variables and linear constraints. We propose an interior point method-based heuristic to obtain a sub-optimal solution with low complexity. Simulation results demonstrate the effectiveness of our proposed approach compared to the branch and bound algorithm.

The sixth-generation (6G) wireless networks are expected to provide ubiquitous connectivity, high data rate, low latency, energy efficiency, and edge intelligence for Internet of Things (IoT) applications. Digital twin technology is a promising solution to enable multi-layer wireless networks that incorporate IoT devices on the ground, unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers, and cloud servers. Multi-layer processing can handle time-sensitive and computationally intensive tasks from IoT devices. This paper proposes a digital twin-assisted multi-layer network for low-latency and energy-efficient communication and computation. We mathematically formulate an optimization problem to minimize the latency and energy consumption of IoT devices by optimizing their association with the UAV-MECs, computation resources, communication resources, and offloading portions of tasks. We propose a two-stage scheme based on the K-means method and the deep neural network approach to solve the above optimization problem. We compare the proposed two-stage scheme with existing schemes to highlight the scalability of the proposed solution. Simulation results demonstrate that the proposed multi-layer network achieved optimization results comparable to existing schemes with less computational cost, highlighting its usefulness in achieving low latency and energy-efficient computation and communication.

Unmanned-aerial-vehicles (UAVs) have been gaining much attention in the next-generation wireless networks due to their ability to enhance coverage and provide advanced services, particularly for first responders. UAVs equipped with mobile-edge computing (MEC) capabilities can migrate computational resources to airborne platforms. However, it is crucial to manage resources efficiently to optimize overall network performance. Moreover, in public safety scenarios, UAVs can help charge low-power Internet of Things (IoT) devices to sustain system operations. A holistic approach to managing communication, computation, caching, and energy resources is necessary to leverage UAV-assisted MEC networks fully. We formulated an optimization problem to minimize latency and reduce resource costs associated with communication, computation, caching, and energy harvesting while maximizing the number of IoT devices served by UAVs. Therefore, we integrated digital twin technology to analyze the latency. The optimization problem is challenging as it involves a mixed-integer nonlinear programming problem. To address this complexity, we propose a multistage offloading algorithm named the penalty function method heuristic algorithm that combines a learning algorithm with an interior-point method, ultimately delivering a practical solution. Our simulation results validate the performance of the proposed algorithm, which yields superior results compared to the simple relaxation heuristic algorithm.

The Internet of things (IoT) applications are generating large volumes of data, and processing this data securely, reliably, and timely is required for effective decision-making. However, the limited processing capability of IoT devices is a significant bottleneck in processing these datasets. A potential solution to overcome this challenge is federated learning using unmanned aerial vehicle (UAV) as mobile edge computing (MEC) servers. In this paper, we propose a UAV-aided edge federated learning (UAFL) framework where we utilize UAV-MEC's computation capacity to process some portion of the datasets from the straggling devices (devices which are unable to process their dataset in reasonable time and are lagging, increasing delay in the whole system). We formulate an optimization problem to minimize system delay considering UAV-MEC's computation power, computation and communication power of IoT devices, and quality of service constraints. We transform the proposed problem by introducing auxiliary variables and epigraph form and then solve the problem using concurrent deterministic simplex with root relaxation algorithm. Simulation results show that UAFL outperforms the traditional federated learning and edge-based learning system by approximately 5%.

The sixth-generation (6G) is expected to offer ubiq-uitous connectivity, high data rate, low latency, energy efficiency, and edge intelligence for Internet of things (IoT) applications. To achieve this, the digital twin is considered as a potential technology in multi-layer wireless networks with IoT devices on the ground, unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers, and cloud server. Multi-layer processing is used to handle time-sensitive and computationally intensive tasks by IoT devices. This paper proposes a digital twin-assisted multi-layer network for low-latency and energy-efficient communication and computation. We mathematically formulate an optimization problem to minimize IoT devices' latency and energy consumption by optimizing their association with the UAV-MECs, computation resources, communication resources, and offloading portions of tasks. We propose a multi-stage solution based on a learning algorithm and interior point method to solve the problem. The simulation results demonstrate the usefulness of the proposed multi-layer network.

Recently, unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) networks are considered to provide effective and efficient solutions for disaster management. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption, and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. This article considers a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed-integer nonlinear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power-splitting architecture splits the source power for communication and EH. We jointly optimize user association, transmission power of user equipment (UE), task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt$k$-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of the interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading-EH, and no-offloading-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumption.

This paper proposes an${i}$terative${w}$ater-${f}$illing algorithm (IWF) for the${e}$nergy-${e}$fficiency (EE) maximization problem of the multi-user${m}$ultiple-${i}$nput and${m}$ultiple-${o}$utput (MIMO)${b}$roadcast${c}$hannel (BC). This algorithm is termed as IWF-EE-BC and has two levels of operations. The inner level computes solutions, by an algorithm, is named as${w}$ater-${f}$illing for the EE of the BC, which is implemented within a single iteration, with the short name: WF-EE-BC1. The solutions by WF-EE-BC1 are the optimal solutions of the auxiliary energy-efficiency maximization problems. Each term of the added throughput part in these auxiliary problems is decoupled in power variables for all users. Then the outer level determines when to output a good solution to the considered problem, based on the results obtained by the inner level. The considered problem has complex-valued matrix optimization variables, beyond the range of the optimization problems whose optimization variables are often real-valued variables. Particularly, it is a${s}$emi-${d}$efinite${o}$ptimization problem (SDO) with a more complicated form of the objective function, over the field of complex numbers. Since existing results on optimization algorithms, including SDO ones, cannot guarantee convergence of IWF-EE-BC, the novel fixed point method is designed and used. Overcoming these difficulties, this paper obtains convergence of IWF-EE-BC, with efficiency.

Abstract The rampant deployment of data distribution service (DDS) as middle‐ware service providers for industrial network platforms has been widely investigated. All DDS‐based node discovery protocols establish communication with its intended target systems by accessing matched endpoints/nodes information. These matched endpoints/nodes information is usually embedded with the system’s programmable control plane network which acts as the conveyor vehicle. The introduction of software defined networking (SDN) is to characterize the control plane from embedded data plane. The DDS implements the simple discovery protocol (SDP) as its inherent node discovery protocol. Deploying DDS for data packet exchange in server‐based collaborative distributed networked control (DNC) systems has gained traction. The current automatic discovery protocol (ADP) based on SDP is fraught with real‐time limitations such as high memory consumption and poor packet transmission. This work presents novel memory‐efficient automatic discovery data distribution service (MAD‐DDS) with enhanced threshold bloom filters (ETBF) where ETBF stores transmission packets at simulation end‐nodes. The packet is further adjusted using optimized binarization and decision thresholds inside ADP, hence guaranteeing memory reduction. The testbed computation recorded significantly improved quality of service (QoS) whereas numerical results depict significant decline in memory consumption with consistent packet transmission rates that produces increased computational capacity.

Major device nodes within the Internet of Things (IoT) system collects and store information in bit forms of 0’s and 1’s regardless of its repetition. The nodes do not possess the capability of processing redundant data information except for outright rejection/replacement of packets of similar sizes. This becomes a research problem since high volumes of packet redundancy are prevalent owing to repetitive information. Many optimal solutions have been provided to reprocess redundant packets and to store them in edge server for accessibility by other connected IoT systems and networks servers. To do so, major IoT platforms implements tier-based network layers which primarily aid seamless communication among nodes. These network layers perform near-similar tasks of guaranteeing packet sensing and exchange although at often-higher energy requirement. To mitigate the energy concerns, packets are clustered and compressed, allowing exchange of essential information only. But the approach continues to present heavy packet-losses and/or redundancies. In this article, two-tier layered network—where packet exchange is conducted at the top layer in order to lower energy consumption and promote system-reliability is investigated. All packets are first segregated into multiple clusters using the Voronoi cell-based correlation cluster formation (VC3F) technique. Cluster heads (CHs) are identified by their multipath (M-Score) value, thus, assuming sole responsibility of redundant packet removal within each cluster. The redundant packets are then moved to the edge-tier layer using optimized multiobjective flower pollination (MO-FPO) routing, and finally processed using hybrid models of novel fast-fully connected neural network (F2CNN) accelerator and Lempel–Ziv–Welch (LZW) data compression. The F2CNN and LZW models are harmonized to further collectively explore potential benefits of both models. These benefits include the neural capability to work on the sensitivity level of the packets determined by the packet classification and validation approaches. The system is evaluated with detailed experimental investigations where higher system throughput, packet delivery ratio (PDR), end-to-end delay, and system reliability are corroborated.

We propose an Iterative Water-Filling algorithm for Energy-Efficiency maximization problem of the Multi-User Multiple-Input Multiple-Output Multiple-Access-Channel (MU-MIMO-MAC) system, named as IWF-EE-MIMO. The algorithm can be regarded as two levels of operations. The inner level of IWF-EE-MIMO aims at computing the solution to each user of the family, while other users keeping their previous power allocation, in turn. The outer level of IWF-EE-MIMO aims at determining when to accept a good solution to the considered problem based on the results obtained by the inner level. For our designed algorithms in this paper, WF-EE-MIMO1, as the subordinate algorithm, is only used for the inner level. IWF-EE-MIMO, as the main algorithm, computes the solution to the considered problem. Note that IWF-EE-MIMO includes WF-EE-MIMO1, to avoid confusion. The considered problem is of non-linear fractional semi-definite optimization in complex-valued matrix optimization variables, beyond the range of standard (semi-definite) optimization theory. Thus, the optimality condition of the considered maximization problem needs to be created. Under this creation, for the considered problem, convergence or optimality of IWF-EE-MIMO is obtained with its efficiency.

Cooperative communication by employing unmanned aerial vehicle (UAV)-based relays with radio-frequency (RF) energy harvesting (EH) has been emerged as a prominent solution to provide extended coverage, connectivity, capacity, energy efficiency, and reliability in the future Internet-of-Things (IoT) systems. For successful integration of UAV relays in IoT networks, efficient radio resource management (RRM) is critical. We developed a multicriterion framework for energy-efficient RRM in a cooperative IoT network. We considered UAVs as relays, onboard EH facilities and deployed to relay the messages from a satellite terminal to the network IoT devices. We adopted a power splitting (PS)-based EH scheme, i.e., PS relaying protocol, for RF-EH at UAV relays. We formulate a joint optimization problem for IoT device selection, UAV relay assignment, source power allocation, and PS ratio selection. In our multicriterion framework, we consider three conflicting objectives by applying a weighted-sum method: 1) maximizing the network sum rate; 2) maximizing the number of IoT devices to be served; and 3) minimizing the carbon dioxide emissions. We propose an outer approximation algorithm (OAA) to solve the formulated problem, which is a mixed-integer nonlinear programming (MINLP) problem. Simulation results of the proposed algorithm are compared with two existing solutions, namely, the nonlinear optimization by mesh adaptive direct search (NOMAD) algorithm and an evolutionary algorithm (EA). The performance of the NOMAD algorithm is better in terms of computational complexity. However, the simulation results reveal the supremacy of the proposed OAA in terms of network sum rate, the number of selected IoT devices, and network utility.

Device-to-device (D2D) communication has emerged as a potential candidate for 5G and beyond networks to optimize spectrum utilization, reduce power consumption, and support higher data rates. The energy and spectral efficiency of D2D communication can be enhanced further by adapting cognitive radio (CR), nonorthogonal multiple access (NOMA), and radio-frequency energy harvesting (RF-EH) technologies. Therefore, this article proposes a sum-rate maximization problem for a D2D-assisted CR network with NOMA, considering the RF-EH mechanism. A joint optimization problem is modeled to maximize the sum throughput of cellular and D2D nodes by considering power assignments, radio resource allocation, user pairing, and transmission time ratio assignment. The problem is then converted into a standard convex optimization problem subject to power allocations at individual nodes, interference temperature limits, individual data rate, and secrecy capacity. The duality theory is adopted to decompose the problem into multiple subproblems, and Karush–Kuhn–Tucker (KKT) conditions are applied to provide the solution. Finally, the proposed schemes are validated through simulation results. The proposed schemes outperform the existing schemes in terms of sum throughput and energy harvesting under different quality of service requirements.

On-demand data sensing and wireless power transfer (WPT) can provide sustainability and robust operations in large-scale industrial Internet of Things (IoT) networks. The efficiency of on-demand data collection and WPT can be increased by efficient scheduling of IoT nodes and dedicated energy transmitters respectively. In this article, we propose an energy-aware mode switching strategy to enable IoT nodes to perform either on-demand sensing or dedicated WPT. For on-demand sensing, we propose an IoT node scheduling scheme to maximize the utility of the IoT nodes comprising residual energy and energy required for sensing operation while considering the reliability of sensing tasks. For WPT, we propose an energy transmitter scheduling scheme for IoT nodes to minimize the cost of charging while keeping IoT nodes sufficiently charged. The simulation results for IoT node scheduling demonstrate that less than 50% IoT nodes need to be activated in all scenarios to complete the tasks. The proposed energy transmitter scheduling scheme shows that less than 60% energy transmitters should be scheduled in all the scenarios which results in significant energy reduction in the overall system.

Cloud radio access network (CRAN) is a promising approach to provide ubiquitous and on demand access to future Internet of Things (IoT) networks. The existing CRANs assume a single cloud which suffers from computational complexity and signaling latency to support massive number of IoT devices in large scale network deployments. This paper focuses on the scheduling of IoT devices in a multicloud IoT network scenario. This paper considers the downlink of an IoT network consisting of multiple clouds, each coordinates a cluster of several base stations (BSs) allowing joint signal processing. The transmit frame of each BS is composed of several resource blocks (RBs). The multiple clouds are linked to the central cloud which performs scheduling of IoT devices and synchronization of transmit frames. The work models the IoT devices to RBs assignment problem considering the intercloud and intracloud interference. The optimization problem maximizes the overall network utilization under practical network constraints. Further, this paper also proposes a low complexity heuristic algorithm to solve the constraint resource allocation problem in linear time. Complexity analysis of proposed algorithm is carried out and simulations results for a number of IoT network scenarios demonstrate that proposed solution is numerically accurate and performs close to the optimal solution.

Emerging device centric communication technologies such as device to device (D2D) communication, devices to device (Ds2D) communication and multi- homing (MH) D2D have been considered as essential part of future 5G networks as well as internet of things (IoT). The device centric communication offers enhanced cellular data rates, high spectral efficiency, reduced latency, improved fairness, better energy efficiency and extended coverage; however, the battery life of end devices is crucial to fully reap benefits of this technology. In this article we propose a new method for device centric communication in IoT system, where multiple source IoT devices can send data to multiple destination IoT devices using multiple interfaces. This method is called devices to devices (Ds2Ds) communication. A tree search algorithm is proposed to select the optimal source IoT devices, destination IoT devices and radio interfaces. The results of proposed Ds2Ds communication are benchmarked against Ds2D and MH- D2D. Extensive simulation has been carried out to compare energy efficiency per source device. The simulation results show the superiority of Ds2Ds over Ds2D in terms of energy efficiency, which, in turn implies better throughput. Ds2DS is superior to MH-D2D in terms of energy consumption per source device, a very good and promising requirement for green communication.

Multi-band cooperative spectrum sensing can provide access to a wide range of spectrum in cognitive radio networks (CRNs). The design of multi-band spectrum sensing is very challenging mainly due to scheduling of secondary users (SUs) to sense a subset of channels. In this paper, we propose a distributed learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme to select most appropriate SUs to sense channels. The proposed scheme allows SUs to sense multiple channels, and consists of two stages: 1) leader selection for each channel, and 2) selection of corresponding cooperative SUs to sense these channels. We formulate an optimization problem to select leaders that can effectively communicate with other SUs subject to the constraint that each SU can act as a leader for only one channel, and there will be only one leader for each channel. We then formulate another optimization problem to select corresponding cooperative SUs for each channel. After this stage, selected cooperative SUs sense channels, and use consensus learning to determine the availability of channels in a distributed manner. Simulation results show that the proposed M2CSS scheme can enhance detection performance, avoid the choice of redundant cooperative SUs, owning similar sensed information, and provide fair energy consumption for all channels compared to the existing schemes.

Device to device (D2D) communications are emerging as an essential part of technological solutions to boost data rates in the next generation networks. Cognitive radio (CR) opportunistically utilises spectrum to boost spectral efficiency. CR‐assisted D2D networks will bring the benefits of both D2D as well as CR together in futuristic cellular networks. This study proposes to opportunistically use TV spectrum white spaces. A joint user selection, mode assignment, and power allocation in CR‐assisted D2D networks can definitely yield higher data rates. The proposed study maximises data rate together with users' selection fulfilling various users' power, base station's transmit power, quality of service, and interference related thresholds. This problem is mixed integer non‐linear programming and considered non‐deterministic polynomial time (NP)‐complete. Due to the discrete variables in the problem, finding an optimal solution with the help of an exhaustive search algorithm (ESA) becomes very challenging. The problem gets exponentially complex with the increasing number of user pairs. Thus, the need of another method becomes imperative that yields near optimal solution. Mesh adaptive direct search (MADS) algorithm is considered for solution in the CR‐assisted D2D network resource management problem. Simulation results using MADS yield near optimal solution confirming the suitability of MADS for CR‐assisted D2D networks.

Energy and spectral efficient solutions are indispensable to the success of Internet of Things (IoT). The design and development of energy and spectral efficient solutions for IoT are very challenging mainly because of the large-scale deployment of a massive number of sensors and devices. Energy harvesting and cognitive radios (CRs) are considered as promising technologies for energy and spectral efficiency, respectively. In this paper, we propose an energy and spectrum efficient scheme for CR sensor networks (CRSNs). We present an architecture of CRSNs for IoT, in which sensor nodes can access the spectrum opportunistically and harvest energy from ambient radio-frequency sources. We then propose an energy management scheme that consists of: (1) energy-aware mode switching strategy which allows sensor nodes to perform dedicated energy harvesting based on their current energy level and (2) cluster head selection algorithm which considers current and average of past energy levels of sensor nodes to achieve a balance between network performance and lifetime. Furthermore, for reliable intracluster reporting, we propose a channel management strategy to assign the best quality channel to the sensor nodes in terms of stability and reliability. Extensive simulation results demonstrate the effectiveness of the proposed energy and spectrum efficient scheme and show superiority over existing schemes.

The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive fifth generation (5G) network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multiband cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multiband approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multiband approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality-of-service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for crosslayer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.

Lloret, J.; Ahmed, SH.; Rawat, DB.; Ejaz, W.; Yu, W. (2018). Editorial on Wireless Networking Technologies for Smart Cities. Wireless Communications and Mobile Computing (Online). 2018. doi:10.1155/2018/1865908

A novel framework for software defined Wireless Sensor Networks (SDWSNs) is presented that draws on Software Defined Networking (SDN) concepts and capabilities to enhance control, management, and security, whilst reducing device complexity. These inherent complexities pose significant challenges toward the advancement of ubiquitous sensing and sensory data access through Sensing-as-a- Service (S2aaS) model. Therefore, it is advantageous to utilize SDN to decouple the control and the data forwarding planes and incorporate greater control over dynamic virtualization and approaches to improve the quality of experience. Enhanced algorithms can be applied on improved knowledge of the network conditions that is attainable when SDN is employed. We run simulations based on sensor flow model and provide a comprehensive analysis of the SDWSN framework, architecture and implementation constraints.

Device-to-Device (D2D) communications underlaying cellular communications has been explored in the literature for a while since the benefits of enhanced sum throughput and more efficient spectrum usage have been proven very promising through the activation of direct transmissions between a pair of devices. To achieve better performance in terms of energy preservation, we consider introducing energy harvesting (EH) mechanism into the traditional D2D model. Our aim is to maximize sum throughput for D2D users without compromising the QoS performance of cellular users (CUs) in an EH-aided communications model. D2D transmissions will only be activated at the beginning of a time slot if there remains enough energy, which is set as a lower threshold, for one-slot data transmission in the batteries of D2D users. Otherwise, it will switch into energy harvesting mode until the energy level in the batteries rises back to an upper threshold. The formulated optimization problem is a nonlinear mixed integer problem. Since it is mathematically challenging to get an optimal solution, we aim for a suboptimal solution with an iterative joint resource block and power resource allocation algorithm. Then we compare this heuristic algorithm with a simplified version where the constraints to make sure that every D2D user has at least one RB for communications are slighted. Numerical simulation results show that energy harvesting mechanism can efficiently power D2D communications underlaying cellular networks. They also corroborate higher sum throughput under different parameter settings of our first proposed approach.

This study examines subcarrier and optimal power allocation in orthogonal frequency division multiple access based 5G device‐to‐device (D2D) networks. To improve spectrum efficiency, D2D users share same subcarriers with the legacy users using underlay approach. In this approach, it is challenging to design an efficient subcarrier and power allocation method for D2D networks which guarantees the quality of service requirements of legacy users. Therefore, the key constraint is to check the interference condition among D2D and legacy users while allocating the same resources to D2D users. In this study, the authors propose a throughput efficient subcarrier allocation (TESA) and geometric water‐filling based optimal power allocation (GWFOPA) method for multi‐class cellular D2D systems. First, the TESA method selects subcarriers and allocates power equally for D2D users according to their service classes while maintaining interference and data rate constraints. Then, the GWFOPA method is applied to optimise power in a computationally effective way. The objective of TESA and GWFOPA method is to maximise the data rate of each class while maintaining interference constraint and fairness among the D2D users. Finally, the authors present simulation results to evaluate performance of TESA and GWFOPA in terms of throughput, user data rate, and fairness.

In order to meet requirements of local services in next generation cellular networks, Device to device (D2D) communication is gaining popularity as strategy to maximize overall system throughput. This paper presents a joint resource allocation (JRA) technique with the aim to maximize system throughput while observing the limitations of power and interference. The optimization problem presented is mixed integer, non linear and NP- hard. Increasing discrete variables in the problems, enhances the computational complexity exponentially and exhaustive search becomes formidable task. The problem is solved using mesh adaptive search (MADS) algorithm. It is shown that proposed strategy is suitable for solution of such kind of combinatorial problem. Computational convergence towards solution with adequate iterations shows efficacy of the proposed strategy. System throughput maximization results of simulations show suitability of the suggested scheme in this paper compared to other techniques.

The rapid growth of modern wireless applications results in spectrum and energy scarcity. Cognitive radio (CR) technology is pivotal to resolve spectrum shortage. However, energy consumption is a critical issue in CR networks (CRNs) due to the unique functionality of spectrum sensing. Besides RF energy harvesting has come up as a potential solution to provide energy to CR devices. In this paper, we first provide an overview of spectrum sensing in CRNs with RF energy harvesting. Then, we propose a framework for multi-band spectrum sensing in CRNs with RF energy harvesting. We formulate a problem to optimize sensing time for throughput maximization while protecting primary users and keeping a minimum level of residual energy. Simulation results show the performance of multi-band cooperative spectrum sensing in terms of average throughput and energy harvested.

In machine-type communication (MTC) over LTE cellular network, resource allocation problem becomes a challenging issue as MTC devices compete with LTE users for the same radio resources. Compared to the LTE users, MTC devices generate more uplink traffic requests and signalling which results in congestion arises in uplink transmission when a large number of devices send connection requests simultaneously. In this paper, we consider the resource allocation problem for MTC over LTE networks in which LTE users, MTC devices, and relay nodes co-exist. Firstly, we derive an analytical model which detects overload condition in the base station (eNB) and estimates available resources for MTC devices. We propose a relay-assisted radio resource allocation (R3A) scheme for MTC devices which utilize dynamic access class barring method in overload situations when the number of resource blocks are less than the MTC devices. In the case when the number of MTC devices is less than the available resources than we use relay nodes to maximize the throughput of MTC system. Numerical results demonstrate the significance of proposed R3A method. The results are evaluated in terms of access success probability, access drop percentage, and MTC channel capacity.

It is our pleasure to write the Editorial for the Special Issue on Evolution and Development of 5G Wireless Communication Systems. Upon conclusion of fourth generation (4G) cellular network standardization tasks a few years ago, the direction of research has started to shift systematically towards fifth generation (5G) communication systems. The difference between 4G and 5G is not limited to the increased throughput and performance. 5G systems are supposed to be flexible to accommodate heterogeneous traffic and devices, and various applications with different quality-of-service (QoS) requirements. Particularly, the goal is to take full benefit of advances in technology including cloud computing, Internet of Things (IoT), ultra-dense networks, massive MIMO, device-to-device communication, pervasive and social computing. In order to meet stringent goals, 5G communication systems build upon the evolution of the existing technologies and the development of the new technologies mentioned above. The Special Issue contains 11 papers, each paper covers the subject from different prospective, and thus, offer readers a holistic view of different research challenges currently under investigation by research communities. The papers can be grouped under following topics: C. Hua et al. present a paper entitled “Wireless backhaul resource allocation and user-centric clustering in ultra-dense wireless networks”. It considers optimization of resource allocation in wireless backhaul links and user-centric clustering in the access links. The objective is to maximise the weighted sum rate of all users under the backhaul resource constraints. An iterative algorithm is proposed to solve the transformed problem based on its special property. Simulation results show that the proposed algorithm outperforms other existing schemes under different network settings. Z. Wang et al. present a paper entitled “Interference pricing in 5G ultra-dense small cell networks: a Stackelberg game approach” which models the scenario as a Stackelberg game, where the macrocell base stations (MBS) act as the leader and all small cell base stations (SCBSs) as followers. Simulation results show the correctness of the analysis and the significant benefits when the power control and channel allocation are jointly considered in the proposed schemes. Z. Kaleem et al. present “Public safety users’ priority-based energy and time-efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long-term evolution-advanced systems”, which proposes a time and energy-efficient contention-resolving device discovery resource allocation (TEECR-DDRA) scheme that has the capability to enhance the success ratio for discovery of D2D users by reducing collisions among users. Moreover, the proposed TEECR-DDRA scheme has the ability to prioritise PS users to meet their QoS and latency requirements. System-level simulations show that the proposed TEECR-DDRA scheme performs remarkably well under D2D network. M. T. Gul et al. present a paper entitled “Merge-and-forward: a cooperative multimedia transmissions protocol using RaptorQ codes”, proposing a cooperative multimedia transmission protocol based on a novel merge-and-forward relaying and the best relay selection (RS) schemes. Moreover, to combat the packet loss for enhanced and reliable video delivery, they adopt application layer forward error correction scheme which is based on the most improved and advanced version of fountain codes (i.e., RaptorQ codes). They evaluate the performance of the proposed scheme in terms of decoding failure probability, decoding overhead, peak signal-to-noise ratio, and mean opinion score. K. Yang et al.'s paper “Edge aware cross-tier base station cooperation in heterogeneous wireless networks with non-uniformly-distributed nodes” investigates the cross-tier base station (BS) cooperation in non-uniform heterogeneous networks where the distribution of pico BSs (PBSs) is modelled as Neyman–Scott cluster process. The authors propose an edge aware cross-tier cooperation scheme to improve the performance of edge hotspot users that have weaker signal-to-interference-plus noise ratio (SINR). Stochastic geometry is utilised to derive the SINR and energy efficiency performance of the proposed scheme, which is compared with other classical schemes such as full cooperation (FC) and traditional non-cooperation scheme. Y. Cai et al. present “Secure transmission in the random cognitive radio networks with secrecy guard zone and artificial noise” which proposes a simple and decentralised secure transmission scheme by jointly incorporating the secrecy guard zone and artificial noise in cognitive radio networks. Numerical results show how the system parameters affect the achievable maximum secrecy throughput, the optimal transmission power and the optimal power allocation between the information-bearing signal and the artificial noise. Y. Sun et al. present “Local altruistic coalition formation game for spectrum sharing and interference management in hyper-dense cloud-RANs” and investigate the spectrum sharing and interference management in hyper-dense cloud radio access networks (C-RANs). The authors formulate this problem as a local altruistic coalition formation game (LACF) with externalities. The authors propose a distributed coalitional formation algorithm based on modified recursive core to obtain the final stable coalition partition. Furthermore, the system stability, convergence and complexity of the proposed algorithm are analysed. W. Chang et al. paper “Effects of non-uniform quantisation on the interference mitigation using multi-cell multiple-input and multiple-output coordinated beamforming” proposes a low complexity cumulative distribution function (CDF)-based non-uniform quantisation method with a limited number of feedback bits for applying more quantisation levels to represent feedback CSI, which occurs with higher probability. The simulation results proved the higher transmission rate, particularly in cases with fewer feedback bits. D. Liu et al.'s paper “Self-organising multiuser matching in cellular networks: a score-based mutually beneficial approach” studies the self-organising user assignment problem for the multi-user cooperation network. Furthermore, the multi-user assignment problem is formulated as a one-to-one matching game, in which idle users and active users rank one another individually based on their own preference. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results. D. C. Araújo et al.'s paper “Massive MIMO: survey and future research topics” presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research. R. Sun et al. present the paper “Transceiver design for cooperative nonorthogonal multiple access systems with wireless energy transfer”. The paper considers an energy harvesting-based cooperative non-orthogonal multiple access (NOMA) system. Transmitter beamforming, power splitter and receiver filter are jointly designed to maximise rate with the predefined QoS constraint of weaker node and the power constraint of node which simultaneously sends independent signals to a stronger node and weaker node. Since the problem is non-convex, they propose an iterative approach to solve it. Moreover, a zero-forcing based low-complexity solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission. All of the papers in Special Issue show that 5G systems can support the specialized use cases which are not supported by the current access systems. In addition, authors investigated the issues related to backhaul for 5G systems and latency reduction. The integration of new technologies with the evolved current systems bring tremendous improvement in 5G systems. Alagan Anpalagan received the B.A.Sc., M.A.Sc., and Ph.D. degrees in electrical engineering from the University of Toronto, Toronto, ON, Canada. In 2001, he joined the Department of Electrical and Computer Engineering, Ryerson University, Toronto, where he was promoted to Full Professor in 2010. He served the department as the Graduate Program Director (2004–2009) and the Interim Electrical Engineering Program Director (2009–2010). He directs a research group working on radio resource management and radio access and networking areas within the WINCORE Lab. During his sabbatical (2010–2011), he was a Visiting Professor with Asian Institute of Technology and a Visiting Researcher with Kyoto University, Kyoto, Japan. His industrial experience includes working at Bell Mobility, Nortel Networks, and IBM Canada. He has coauthored three edited books, namely, Design and Deployment of Small Cell Networks (Cambridge University Press, 2014), Routing in Opportunistic Networks (Springer, 2013), and Handbook on Green Information and Communication Systems (Academic Press, 2012). His current research interests include cognitive radio resource allocation and management, wireless cross-layer design and optimization, cooperative communication, machine-to-machine communication, small cell networks, and green communications technologies. Dr. Anpalagan has served as an Associate Editor of the IEEE Communications Surveys & Tutorials since 2012 and Springer Wireless Personal Communications since 2009. Adnan Shahid received the B.Eng. and the M.Eng. degrees in computer engineering with communication specialization from the University of Engineering and Technology, Taxila, Pakistan in 2006 and 2010, respectively, and the Ph.D degree in information and communication engineering from the Sejong University, South Korea in 2015. He is currently working as a Postdoctoral Researcher at iMinds/IBCN, Department of Information Technology, University of Ghent, Belgium. From Sep 2015 – Jun 2016, he was with the Department of Computer Engineering, Taif University, Saudi Arabia. From Mar 2015 – Aug 2015, he worked as a Postdoc Researcher at Yonsei University, South Korea. From Aug 2012 – Feb 2015, he worked as a PhD research assistant in Sejong University, South Korea. From Mar 2007 – Aug 2012, he served as a Lecturer in electrical engineering department of National University of Computer and Emerging Sciences (NUCES-FAST), Pakistan. He was also the recipient of the prestigious BK 21 plus Postdoc program at Yonsei University, South Korea. He is a member of IEEE and actively involved in various research activities. He is also serving as an Associate Editor at IEEE Access Journal and Annals of Telecommunication Journal. His research interests includes the next generation wireless communication and networks with prime focus on resource management, interference management, cross-layer optimization, self-organizing networks, small cell networks, device to device communications, machine to machine communications, 5G wireless communications, etc. Waleed Ejaz (S’12, M’14, SM‱16) is a Senior Research Associate at the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada. Prior to this, he was a Post-doctoral fellow at Queen's University, Kingston, Canada. He received his Ph.D. degree in Information and Communication Engineering from Sejong University, Republic of Korea in 2014. He earned his M.Sc. and B.Sc. degrees in Computer Engineering from National University of Sciences & Technology, Islamabad, Pakistan and University of Engineering & Technology, Taxila, Pakistan, respectively. He worked in top engineering universities in Pakistan and Saudi Arabia as a Faculty Member.His current research interests include Internet of Things (IoT), energy harvesting, 5G cellular networks, and mobile cloud computing. He is currently serving as an Associate Editor of the Canadian Journal of Electrical and Computer Engineering and the IEEE ACCESS. In addition, he is handling the special issues in IET Communications, the IEEE ACCESS, and the Journal of Internet Technology. He also completed certificate courses on Teaching and Learning in Higher Education from the Chang School at Ryerson University. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in the Centre for Communication Systems Research (CCSR) at the University of Surrey, UK. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has lead role in a number of multimillion international research projects including the new physical layer work area for 5G innovation centre at Surrey. He has supervised 17 successful PhD graduates and published over 150 peer-reviewed research papers including more than 20 IEEE Journals. His research interests include the derivation of information theoretic performance limits, energy efficient design of cellular system and learning/self-organizing techniques for optimization of cellular system operation. He is a senior member of IEEE and a Fellow of Higher Education Academy (FHEA), UK. Kandeepan Sithamparanathan has a PhD from the University of Technology, Sydney and is currently with the School of Electrical and Computer Engineering at RMIT University. He is also a NICTA Researcher at the NICTA Victoria Research Laboratory (VRL, Melbourne). In the past he had worked with the National ICT Australia (Canberra Research Laboratory) and CREATE-NET (Trento). Kandeepan served as one of the Vice Chairs for the IEEE Technical Committee on Cognitive Networks (TCCN) and has published a book together with Dr Andrea Giorgetti from the University of Bologna, Italy, titled ‘Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation and Localization’, published by Artech House (Boston). He currently Chairs the IEEE VIC Communication Society Chapter and is a Senior Member of the IEEE. He was awarded as one of the best IEEE Reviewers by the IEEE Communications Society. Kandeepan has published around ninety peer reviewed journal and conference papers. He has chaired several IEEE workshops and other conferences. His research interests are in 5G communications, cognitive radios and signal processing techniques. Yuhua Xu received his B.S. degree in Communications Engineering, and Ph.D. degree in Communications and Information Systems from College of Communications Engineering, PLA University of Science and Technology, in 2006 and 2014 respectively. He has been with College of Communications Engineering, PLA University of Science and Technology since 2012, and currently as an Assistant Professor. His research interests focus on opportunistic spectrum access, learning theory, game theory, and distributed optimization techniques for wireless communications. He has published several papers in international conferences and reputed journals in his research area. He served as Associate Editor for Wiley Transactions on Emerging Telecommunications Technologies and KSII Transactions on Internet and Information Systems. In 2011 and 2012, he was awarded Certificate of Appreciation as Exemplary Reviewer for the IEEE Communications Letters. He was selected to receive the IEEE Signal Processing Society's (SPS) 2015 Young Author Best Paper Award, and the Funds for Distinguished Young Scholars of Jiangsu Province in 2015.

Energy efficiency is one of the most critical design issues in wireless sensor networks (WSNs). Recently, RF energy transfer emerges as a promising solution to enhance energy efficiency. In RF energy transfer, energy is supplied to wireless networks through dedicated energy transmitters. WSNs can be equipped with the RF energy charging capabilities such that the WSNs are referred as wireless rechargeable sensor networks. This paper aims to 1) optimally place the energy transmitters and 2) determine optimal number of energy transmitters in WSNs with RF energy transfer. For optimal placement of energy transmitters, a trade-off between maximum energy charged in the network and fair distribution of energy is studied. We present a mechanism by defining a utility function to maximize both total energy charged and fairness. For optimal number of energy transmitters, an optimization problem is formulated and solved while satisfying the constraint on minimum energy charged by each sensor node. Simulation results illustrate the performance of WSNs with RF energy transfer in terms of average energy charged, fairness, and optimal number of energy transmitters.

Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging, requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration, and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

Being able to understand human behaviour and monitoring daily life activities is seen as a significant approach for alleviating functional decline among elderly people. The aim of the research work presented in this paper is to investigate a mechanism that can recognise high level activities and behaviour of low entropy people in order to help them improve their health related daily life activities by using wireless proximity data (e.g. Bluetooth, Wi-Fi). A number of scenarios and experiments are designed to prove the validity of the proposed methodology. Using wireless proximity data for activity recognition enhances the intrusion into personal privacy and helps exploiting important structures in human behaviour.

A cognitive radio (CR) is the most promising candidate for the successful deployment of dynamic spectrum access (DSA). To embed DSA in a wireless sensor network, a CR is required to be installed on each sensor node. Such a sensor network is known as a cognitive radio sensor network (CRSN). Spectrum sensing is a prerequisite for a CR. Therefore every node in the CRSN consumes energy for spectrum sensing. To achieve a high-sensing accuracy, the nodes share sensing results among themselves, which is known as cooperative spectrum sensing (CSS). CSS improves sensing; however it increases energy consumption and shortens the lifetime of the network. As a CRSN is characterised as an energy constraint network, to prolong the lifetime of the network, the number of cooperating nodes should be minimum. This study presents a user selection scheme to minimise the overhead energy consumed by CSS in a CRSN. On the basis of the binary knapsack problem and its dynamic programming solution, the proposed technique selects the best nodes among the potential nodes subject to the energy constraint of the CRSN. The simulation results indicate the advantages of employing the proposed method, depending on the desired performance-energy consumption tradeoff.