Power Consumption Constraints Research Articles

An agile and effective network function virtualization infrastructure for the Internet of Things

The processing and power-consumption constraints of the Internet of Things devices hinder them to offer more complex network services than the simple data transmission in smart city scenarios. The lack of complex services, such as security and quality of service, can even foster disasters in urban centers. In this paper, we propose the integration of complex network services from the IoT devices till a cloud environment through an agile and effective network function virtualization infrastructure of isolated IoT domains. Therefore, our proposal develops a simple gateway access node that virtualizes the domains to which the devices connect. A prototype for services of security and quality of service has been implemented and its evaluation shows that virtualization of the access node does not impact the performance of virtual network functions. The results also show that the proposal provides security for IoT devices, identifying malicious traffic with 99.8% accuracy, avoiding denial of essential services, and ensuring the quality of service.

Eavesdropping and Jamming Selection Policy for Suspicious UAVs Based on Low Power Consumption over Fading Channels.

Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV’s eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.

Performance analysis and optimization for non-uniformly deployed mmWave cellular network

In this paper, we propose a multi-tier mmWave cellular framework where sub-6 GHz macro BSs (MBSs) are assumed as a Poisson point process (PPP) and small-cell BSs (SBSs), operating on either mmWave or sub-6 GHz, follows non-uniform Poisson cluster point (PCP) model. This paper proposes both centralized and distributed user association algorithms. For the centralized two-step algorithm, we aim to maximize the sum rate while satisfying quality of service (QoS) and power consumption constraints based on eigenvalue analysis. Then, we derive the association probability, the coverage probability, and the average achievable rate, cosidering directivity and blockage effect, by stochastic geometry. On this basis, a distributed user association algorithm is proposed. The simulation results demonstrate the accuracy of our theoretical analysis and also reveal the effect of some parameters on the network performance. In addition, the proposed centralized algorithm can achieve near-optimal sum rate with a low complexity.

Decoding methods for the flow shop scheduling with peak power consumption constraints

The permutation flow shop scheduling problem (PFSP) is of wide application backgrounds and plays an important role in the manufacturing systems. With the serious energy concerns in manufacturing enterprises, peak power consumption is considered one of the significant issues. For the PFSP with peak power consumption constraints (PFSPP), the real-time power consumption cannot exceed a given peak power at any time. Since the classical first-come first-serve scheduling method is not suitable for the PFSPP, this paper addresses the decoding methods to obtain feasible schedules based on the permutation encoding scheme. First, an earliest processing rule (EPR) is designed to determine the starting time of each operation, satisfying the power consumption constraints. Then, five decoding methods based on EPR are proposed to determine the suitable priority between the operations to yield feasible schedules with high quality. After analysing the complexity of the proposed decoding methods and comparing the performances via extensive numerical tests, some suggestions are provided for solving the PFSPP with different scales and power constraints.

A Secure Wireless Information and Energy Cooperation Transmission Strategy in Spectrum Sharing Networks With Untrusted Dual-Relay

Spectrum efficiency, energy efficiency, and physical layer security are three critical issues in designing wireless networks, such as an energy-constrained device-to-device (D2D) communication in secure spectrum sharing femtocell networks. In this paper, we investigate a problem of security cooperation transmission between a single-antenna primary system and a multi-antenna wireless-powered untrusted secondary system, where the untrusted secondary devices can be regarded as potential malicious decoders. A novel artificial noise-aided (AN-aided) joint time division- and power splitting-based three-phase secure wireless information and energy cooperation transmission strategy is proposed for spectrum sharing networks with untrusted cooperative dual-relay. Furthermore, we consider two scenarios that the untrusted secondary system knows perfect and imperfect channel state information (CSI). We focus on the design of time division ratios, power splitting ratios, and beamforming vectors, with the objective to maximize the data rate of the untrusted secondary system, subject to the target data rate requirement of the primary system and the untrusted secondary system's confidentiality constraints, energy harvesting requirements, and power consumption constraints. Simulation results demonstrate that our proposed strategy is the best and significantly improves the average data rate of the untrusted secondary system under different primary user target data rate requirements, primary transmitter rated transmit power, untrusted secondary user initial power, and primary transmitter power allocation ratios. Finally, a large number of simulations show that the time division ratios have a significant impact on the performance of the system, and these simulation results also verify the feasibility and effectiveness of the proposed strategy.

Energy Efficient Mobile Service Computing With Differential Spintronic-C-Elements: A Logic-in-Memory Asynchronous Computing Paradigm

During recent years, researchers throughout academia and industry have been advancing the theory, designing, and applications of mobile service computing through the Internet of Things (IoT). Research interest in mobile service computing stems from its performance, security, reliability, and power consumption. Hence, ultra-low power integrated circuits are essential for mobile service computing that can offer the advantages of low power for computational tasks in the IoT that is driven by the restricting constraints of power consumption and autonomy in both computation and idle phases. To attain the benefits of ultra-low power circuits, the energy-consuming and computational intense demands are imposed by the underlying processing and memory devices on which the conventional ultra-low power integrated circuit can benefit substantially from innovative hardware designs. Logic-in-Memory (LIM) architectures are considered as the potential approaches to attain goals within area and energy constraints starting with the lowest layers of the hardware stack. In this paper, we propose and implement the LIM asynchronous computing paradigm for energy-efficient mobile service computing. The results indicate that the proposed design achieves 38% leakage reduction and 30% accuracy improvement compared to the state-of-the-art non-volatile asynchronous circuits. At the system level, we compare our designs with various commercial microprocessors. The experimental results show that the asynchronous processors attain a four-fold throughput increase relative to their synchronous counterparts under these operating constraints. Therefore, the proposed design offers an approach toward tangible benefits of the battery-constrained embedded mobile service computing.

An Efficient Approach To Civil Structures Health Monitoring Using Fog Computing As Clusters Through 5G Network Environment

Structural Health Monitoring (SHM) is a process to trace real time information about the physical health of a civil structures at any given point of time. SHM systems can also approximate the life expectancy of the structure. This constant monitoring can help during emergency situations such as earthquakes. The increasing need for SHM is the result of deterioration of several set of structures over time. A proper maintenance plan is beneficial to not just keep the structure in a favorable state throughout its lifecycle, but to additionally minimize the relative costs required by the structures through resource optimization. Various forms of SHM have been implemented over the past three decades like traditional wired systems. Recent development has resulted in Wireless Sensor Networks (WSN) being designed for the same purpose. Few publications have also included IoT framework along with WSNs. The introduction of WSN has led to easy deployment of SHM compared to the traditional wired network. But WSN's have led to significant challenges. The main challenge that has been focused in this paper is the constraint of power consumption. The power consumption is most critical factor for the sensors deployed in high-rise structures, high density traffic bridges etc., since it is difficult to reach the places to replace the battery drained sensors and also involved high installation cost. This paper proposes a concept of a framework for SHM with FOG computing which hasn't been explored in the field of SHM yet. In this model, the WSN is comprised of a piconet which transmits data through Bluetooth Low energy (LE) and the FOG transmits data to cloud by exploiting 5G network features in order to reduce the power consumption furthermore. Thus, saving power at every level possible in the SHM architecture. It also introduced a head sensor algorithm within clusters which maximizes the lifetime of the WSN. A mathematical model has been developed in support to evaluate how the power of the system can be minimized for the FOG node and cloud servers in accordance with the 5G network environment Experiments have been carried out to validate proposed framework and demonstrate the results.

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions.

Nowadays, there is an increasing number of cameras placed on mobile devices connected to the Internet. Since these cameras acquire and process sensitive and vulnerable data in applications such as surveillance or monitoring, security is essential to avoid cyberattacks. However, cameras on mobile devices have constraints in size, computation and power consumption, so that lightweight security techniques should be considered. Camera identification techniques guarantee the origin of the data. Among the camera identification techniques, Physically Unclonable Functions (PUFs) allow generating unique, distinctive and unpredictable identifiers from the hardware of a device. PUFs are also very suitable to obfuscate secret keys (by binding them to the hardware of the device) and generate random sequences (employed as nonces). In this work, we propose a trusted camera based on PUFs and standard cryptographic algorithms. In addition, a protocol is proposed to protect the communication with the trusted camera, which satisfies authentication, confidentiality, integrity and freshness in the data communication. This is particularly interesting to carry out camera control actions and firmware updates. PUFs from Static Random Access Memories (SRAMs) are selected because cameras typically include SRAMs in its hardware. Therefore, additional hardware is not required and security techniques can be implemented at low cost. Experimental results are shown to prove how the proposed solution can be implemented with the SRAM of commercial Bluetooth Low Energy (BLE) chips included in the communication module of the camera. A proof of concept shows that the proposed solution can be implemented in low-cost cameras.

Design space exploration of multi-core RTL via high level synthesis from OpenCL models

As more and more powerful integrated circuits are appearing on the market, more and more applications, with very different requirements and workloads, are making use of available computing power. Designing optimized accelerators that can meet particular requirements has always presented a tremendous challenge to hardware engineers. To do so, designers have to trade off performance for power consumption in a manner such that the final RTL consumes minimum energy to meet the required performance (e.g. FLOPS) target. Moreover, the growing trend towards heterogeneous platforms is crucial to meet time and power consumption constraints of high-performance computing (HPC) applications. The OpenCL parallel programming language and framework enables programming CPU, GPU and recently FPGAs using the high-level synthesis (HLS) methodology. This work presents a design space exploration flow based on execution time, resource utilization and power consumption of OpenCL kernels mapped on FPGAs using the Xilinx high-level synthesis tool chain. Our experiments suggest that the quality of generated solutions, in terms of performance-per-watt, can be determined using analytical formulas prior to implementation, thus enabling fast and accurate DSE by considering on-chip and off-chip sources of parallelism. Moreover, the automated flow suggests design hints to meet a given time constraint within available resources. The proposed technique is demonstrated by optimizing the well known bitonic sorting network from NVIDIA’s OpenCL benchmark. Our results report that FPGAs have at least 20% higher performance-per-watt with respect to two high-end GPUs manufactured in the same technology (28 nm). Additionally, FPGAs with more available resources and using a more modern process (20 nm) can outperform the tested GPUs while consuming at least 55% less power at the cost of more expensive devices.

New Method of Energy Efficient Subcarrier Allocation Based on Evolutionary Game Theory

Since there is a competition between subcarriers because FBMC (Filter Bank Multicarrier) modulation technology does not need subcarriers to be orthogonal to each other, we consider the evolutionary game method to optimize subcarrier allocation. Because the adjacent subcarriers do not need to be orthogonal to each other in FBMC, there is conflict and competition, thus the evolutionary game theory is used to optimize the subcarrier allocation problem. We innovatively introduced the channel state matrix to show the quality of subcarriers. Considering the height of secondary user and base station’s antenna, the total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier, a nonlinear fractional programming problem is established where maximum energy efficiency is the objective function, total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier are constraint conditions. The utility function for each secondary user is established when the evolutionary game operator is designed. When the utility function becomes optimal, the evolutionary game reaches Nash equilibrium, and the strategy combination is considered to be the energy efficient resource allocation scheme. Through experimental simulation, EESA-EG proposed in this paper gives the most reasonable subcarrier allocation scheme, allocates more subcarriers for the subcarriers with better channel state and the energy efficiency in EESA-EG is optimal.

DimRouter: A Multi-Mode Router Architecture for Higher Energy-Proportionality of On-Chip Networks

In the dark silicon era, many independent components of many-core processors are becoming voluntarily inactive due to the constraint of power consumption on a chip. However, to keep network connectivity, the on-chip interconnection must still be kept activated and wastes considerable energy to avoid the isolation of these inactive components, harming the energy-proportionality of the whole processor chip. In this paper, we propose a novel design to provide more energy-proportional on-chip connection without damaging the network connectivity. To achieve this goal, we redesign the router architecture. The new architecture, DimRouter, supports three modes: normal, dark and dim. In the dim mode, only part of the router is active and provides flexible connection while the dark mode puts all router elements in the asleep state. Moreover, to maximize the number of dark routers, we also propose a reconfiguration algorithm based on degree-constrained Steiner Tree. The evaluation result under synthetic traffic shows that the new design can reduce the energy consumption up to 85% compared with the common design. For real application traffic, the new design can also save average 46% energy consumption with 4% performance improvement.

TheSPoT: Thermal Stress-Aware Power and Temperature Management for Multiprocessor Systems-on-Chip

Thermal stress including temperature gradients in time and space, as well as thermal cycling, influences lifetime reliability and performance of modern multiprocessor systems-on-chip (MPSoCs). Conventional power and temperature management techniques considering the peak temperature/power consumption do not provide a comprehensive solution to avoid high spatial and temporal thermal variations. This work presents TheSPoT, a novel multilevel thermal stress-aware power and thermal management approach for MPSoCs. At the top level, core consolidation and deconsolidation is performed based on peak temperature, thermal stress, and power consumption constraints. These constraints are also used at the next level, where operating frequencies are determined. At this level, we obtain optimal core frequencies by solving a convex optimization problem. However, thereafter, to reduce the runtime overhead in large MPSoCs, we alternatively propose to use a fast heuristic algorithm. The efficacy of the proposed approaches in reducing the thermal cycles and temporal/spatial temperature gradients is evaluated by comparing the results with the state-of-the-art methods. The evaluation performed on 4-core, 8-core, and 16-core MPSoCs, using PARSEC benchmarks, reveals a considerable reduction in thermal stress. For the 8-core MPSoC case study, on average, for the proposed heuristic(optimal) approach, the mean time to failure improved by 47(35)% compared to the state-of-the-art techniques with only 6(4)% performance degradation. Also, our simulations show that TheSPoT is more efficient in thermal stress reduction when more heterogeneous workloads are used.

A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes.

Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.

Learning Automata-Based Data Aggregation Tree Construction Framework for Cyber-Physical Systems

A high degree of energy efficiency and real-time for data transmission is required in cyber-physical systems (CPSs). Data aggregation is an efficient technique to conserve energy by reducing the amount of transmission data. To optimize real-time communication under constraints of power consumption and data aggregation performance of each node in CPS, this paper presents a learning automata (LA)-based degree-bounded bottleneck data aggregation tree (DBBDAT) construction framework to minimize the maximum delay on data aggregation trees with bounded degree, which is an NP-hard problem. We model the network of CPS as a connected weighted and directed graph to form a network of LA. Degree-bounded data aggregation trees are constructed first by the action selection of each automaton. Then, the action vector of each automaton is updated by linear reward-inaction learning algorithm, and at last DBBDAT is constructed based on a threshold. Simulation results show that our approach significantly outperforms integer linear programming (ILP)-based method in terms of time complexity. Compared with ILP-based method, it can obtain an optimal solution or a suboptimal solution with guaranteed approximation ratios, and can control the tradeoff between accuracy and cost by choosing appropriate learning rate and threshold. Its distributed implementation is simple and it can efficiently solve the problem for the sparse graph in practice.

Joint User Pairing and Power Allocation in Virtual MIMO Systems

Virtual multiple-input multiple-output (V-MIMO) is an effective way to achieve a diversity gain in uplink channels. A well-designed user pairing scheme in V-MIMO systems can exploit multiuser diversity to yield a significant performance gain. This paper investigates joint user pairing and power allocation problems in V-MIMO systems to maximize energy efficiency (EE) while guaranteeing spectrum efficiency (SE) under a total power consumption constraint. We formulate such a joint problem as a complex nonlinear optimization problem, which can be solved in two steps in $M\times K$ V-MIMO systems, i.e., power allocation with known user group and joint user pairing and power allocation. A multi-level water-filling method is used, and an iterative algorithm based on the outputs from the first step is employed in the second step. Finally, we propose a joint user pairing and power allocation scheme to find a user group that can achieve the maximum EE with power allocation. Simulation results and performance comparisons illustrate improved performance of the proposed scheme. Compared to the existing user pairing approaches, the proposed scheme achieves a significantly high EE gain while guaranteeing SE of a V-MIMO system.

Low-Cost Massive MIMO: Pilot Length and ADC Resolution

When designing an energy efficient massive multiple-input multiple-output (MIMO) system where each receiver antenna is equipped with a low-resolution analog-to-digital converter (ADC), the number of base station (BS) antennas and quantization bits are generally two mutually conflicting system parameters. In this paper, we investigate the joint optimization of the number of BS antennas and ADC resolution in quantized massive MIMO systems, assuming imperfect channel state information (CSI). A tractable approximate expression for the uplink sum spectral efficiency (SE) using maximal ratio combining (MRC) receivers is derived, based on which the pilot length which maximizes the sum SE is put forward. Considering the effect of ADCs, a realistic model of total power consumption is given subsequently. Capitalizing on it, we formulate the optimization problem of selecting the number of BS antennas and ADC resolution to maximize the sum SE under a total power consumption constraint. Our results show that more pilot symbols should be assigned for massive MIMO systems with low-resolution ADCs, especially for the receivers with one-bit quantizers. Moreover, the results show the trade-off between the number of BS antennas and quantization bits. Numerical results suggest that there exists an optimal ADC resolution in massive MIMO systems, while lower quantization bits may cause a substantial degradation of the SE performance and higher one will consume more power.

Simplifying self-adaptive and power-aware computing with Nornir

Self-adaptation is an emerging requirement in parallel computing. It enables the dynamic selection of resources toallocate to the application in order to meet performance and power consumption requirements. This is particularly relevant in Fog Applications, where data is generated by a number of devices at a varying rate, according to users’ activity. By dynamically selecting the appropriate number of resources it is possible, for example, to use at each time step the minimum amount of resources needed to process the incoming data.Implementing such kind of algorithms may be a complex task, due to low-level interactions with the underlying hardware and to non-intrusive and low-overhead monitoring of the applications. For these reasons, in this paper we propose Nornir, a C++-based framework, which can be used to enforce performance and power consumption constraints on parallel applications running on shared memory multicores. The framework can be easily customized by algorithm designers to implement new self-adaptive policies. By instrumenting the applications in the PARSEC benchmark, we provide to strategy designers a wide set of applications already interfaced to Nornir. In addition to this, to prove its flexibility, we implemented and compared several state-of-the-art existing policies, showing that Nornir can also be used to easily analyze different algorithms and to provide useful insights on them.

Real-Time Spaceborne Synthetic Aperture Radar Float-Point Imaging System Using Optimized Mapping Methodology and a Multi-Node Parallel Accelerating Technique.

With the development of satellite load technology and very large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. A key goal of the on-board SAR imaging system design is to achieve high real-time processing performance under severe size, weight, and power consumption constraints. This paper presents a multi-node prototype system for real-time SAR imaging processing. We decompose the commonly used chirp scaling (CS) SAR imaging algorithm into two parts according to the computing features. The linearization and logic-memory optimum allocation methods are adopted to realize the nonlinear part in a reconfigurable structure, and the two-part bandwidth balance method is used to realize the linear part. Thus, float-point SAR imaging processing can be integrated into a single Field Programmable Gate Array (FPGA) chip instead of relying on distributed technologies. A single-processing node requires 10.6 s and consumes 17 W to focus on 25-km swath width, 5-m resolution stripmap SAR raw data with a granularity of 16,384 × 16,384. The design methodology of the multi-FPGA parallel accelerating system under the real-time principle is introduced. As a proof of concept, a prototype with four processing nodes and one master node is implemented using a Xilinx xc6vlx315t FPGA. The weight and volume of one single machine are 10 kg and 32 cm × 24 cm × 20 cm, respectively, and the power consumption is under 100 W. The real-time performance of the proposed design is demonstrated on Chinese Gaofen-3 stripmap continuous imaging.

Optimal Temporal Partitioning of a Multicore Server Processor for Virtual Machine Allocation

The problem of optimal temporal partitioning of a multicore server processor for virtual machine allocation in cloud computing is addressed as multivariable optimization problems and solved algorithmically and numerically. Analytical models for virtual machines are developed, i.e., partially available multi-server systems. The problem of optimal temporal partitioning of a multicore server processor for virtual machine allocation is formulated, where the overall performance (i.e., the average task response time) of a group of virtual machines is optimized. An algorithm is developed to solve the problem numerically. The problem of optimal temporal partitioning of a multicore server processor with power consumption constraint is also formulated and solved, where the overall performance of a group of virtual machines is optimized and the total power consumption of the virtual machines does not exceed certain available power. A virtual machine is treated as a queuing system with multiple servers, i.e., an M/M/m queuing system. The system performance measures are the average task response time and the average power consumption. Two core speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. Numerical examples are presented to demonstrate our methods.

Outage Constrained Robust Transmit Design for Secure Cognitive Radio With Practical Energy Harvesting

This paper studies the secure transmit design for a downlink multiple-input-single-output cognitive radio network (CRN) with a practical energy harvesting (EH) model. Due to the open architecture of a CRN, the confidential information intended for the secondary receiver is prone to be eavesdropped by external eavesdroppers (Eves). Specifically, considering imperfect channel state information, our aim is to jointly design the transmit beamforming vector and the power splitting ratio, such that the outage-constrained secrecy rate is maximized under the constraints of total power consumption, secrecy rate outage probability, EH and quality of service requirements. The original problem is not convex and intractable in general, in order to tackle this non-convexity issue, we utilize semidefinite relaxation and Bernstein-type inequality to transform the outage constraints into a deterministic form. We show that the optimal solution can be obtained by alternately optimizing two convex subproblems. Also, we prove that the optimal solution of the relaxed problem will always be rank one. Furthermore, we consider a large deviation inequality (LDI)-based approach to obtain a sub-optimal solution, which can significantly reduce the computational complexity. Finally, simulation results have been provided to demonstrate the performance of our proposed designs.