Energy-limited Devices Research Articles

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs' lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.

Active IRS Aided Multiple Access for Energy-Constrained IoT Systems

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

Communication-, Computation-, and Control-Enabled UAV Mobile Communication Networks

Unmanned-aerial-vehicles (UAVs)-enabled mobile-edge computing (MEC) networks have shown a huge advantage in providing on-demand communication and computation service for the ground users. To reap the benefit of these integrated networks, reducing their energy consumption becomes a key issue, since both UAVs and ground users are energy-limited devices. To address this problem, this article attempts to provide a novel method that optimizes communication, computation, and control (3C), i.e., user association, computational task offloading, and UAVs flight control, to reduce the communication and computation energy consumption. Specifically, in order to find out the appropriate deployment positions for UAV MEC servers to provide on-demand communication and computation service, we propose the concept of the virtual force field (VFF) based on the user statistical distribution model and then devise a coordinated flight control algorithm for UAV MEC servers. After that, the user association and computational task offloading are optimized alternately. Utilizing the optimal transport theory (OTT), we derive the boundary formulation of the optimal user association and develop an iterative algorithm to approach the optimal association boundary. Then, given the user association, the optimal computational task offloading scheme is investigated. The convergence of the proposed iterative algorithm and the alternating optimization algorithm is proved. The complexity of the 3C optimization method is also analyzed. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithm. Comparisons with the benchmark scheme show that the proposed scheme can reduce about 88% energy consumption and also improve energy efficiency performance greatly under the same simulation setups.

Energy-Efficient Multiuser Localization in the RIS-Assisted IoT Networks

In various location-based Internet of Things (IoT) services, it is required to localize a large number of energy-limited devices simultaneously and accurately. In order to achieve this goal, a reconfigurable intelligent surface (RIS)-assisted positioning method for multiple IoT devices is proposed, where the signals transmitted by the users reach the base station (BS) along the direct path and the reflection path via the RIS. The difference in the propagation delay of the two paths is essential in the proposed triangulation-based localization framework, which is estimated via the cross-correlation function of the received signals. Based on the orthogonality of the transmitted signals, the optimization of the multiantenna BS and the RIS, with the goal of minimizing the total transmission power of the IoT devices, is constructed. For the orthogonal signal case, the nonconvex optimization problem for the RIS is recast into a convex problem via the semidefinite relaxation (SDR). For the nonorthogonal signal case, the zero-forcing (ZF) combining vectors at the BS are adopted to eliminate interferences among multiple users, and the block coordinate descent (BCD) algorithm is used to decouple the combining vectors and the RIS phases. Numerical results show that by using the proposed optimization method, decimeter-level positioning accuracy can be achieved with low-power consumption, and significant power gain can be achieved compared to the unoptimized RIS-assisted localization.

One-shot pruning of gated recurrent unit neural network by sensitivity for time-series prediction

Although deep learning models have been successfully adopted in many applications, they are facing challenges to be deployed on energy-limited devices (e.g., some mobile devices, etc.) due to their high computation complexity. In this paper, we focus on reducing the costs of Gated Recurrent Units (GRUs) for time-series prediction tasks and we propose a new pruning method that can recognize and remove the neural connections that have little influence on the network loss, using a controllable threshold on the absolute value of the pre-trained GRU weights. This is different from existing approaches which usually try to find and preserve the connections with large weight values. We further propose a sparse-connection GRU model (SCGRU) that only needs a one-time pruning (with fine-tuning), rather than using multiple prune-retrain cycles. A large number of experimental results demonstrate that the proposed method is able to largely reduce the storage and computation costs while achieving the state-of-arts performance in two datasets. Code is available ( https://github.com/imLingo/SCGRU).

Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks.

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGB-colored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., <8:8>. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems.

Optimal Relay Coil Placement in Magnetic Resonant Coupling-Based Power Transfer

Wireless power transfer system based on magnetic resonant coupling (MRC-WPT) is widely used to prolong the lifetime of energy-limited devices for short-distance transmission. To maximize the harvested power for the three-coil MRC-WPT system, in this letter a novel optimization problem is proposed to determine the location of relay coil. Although this problem is non-convex, we still have been able to obtain the globally optimal solution in the form of roots of five degree polynomial. We also present a tight closed-form approximation for the optimal location of relay coil to gain analytical insights. Numerical results verify the global optimality claim and provide insights on the coil location for different system parameters. Lastly, it’s shown that the optimal relay location is closer to the destination coil.

Density Based Fuzzy C Means Clustering to prolong Network Lifetime in Smart Grids

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span.

Deep Reinforcement Learning Based Energy Efficient Multi-UAV Data Collection for IoT Networks

Unmanned aerial vehicles (UAVs) are regarded as an emerging technology, which can be effectively utilized to perform the data collection tasks in the Internet of Things (IoT) networks. However, both the UAVs and the sensors in these networks are energy-limited devices, which necessitates an energy-efficient data collection procedure to ensure the network lifetime. In this paper, we propose a multi-UAV-assisted network, where the UAVs fly to the ground sensors and control the sensor's transmit power during the data collection time. Our goal is to minimize the total energy consumption of the UAVs and the sensors, which is needed to accomplish the data collection mission. We formulate this problem into three sub-problems of single UAV navigation, sensor power control as well as multi-UAV scheduling and model each part as a finite-horizon Markov Decision Process (MDP). We deploy deep reinforcement learning (DRL)-based frameworks to solve each part. Specifically, we use deep deterministic policy gradient (DDPG) method to generate the best trajectory for the UAVs in an obstacle-constraint environment, given its starting position and the target sensor. We also deploy DDPG to control the sensor's transmit power during data collection. To schedule activity plans for each UAV to visit the sensors, we propose a multi-agent deep Q-learning (DQL) approach by taking the total energy consumption of the UAVs on each path into account. Our simulations show that the UAVs can find a safe and optimal path for each of their trips. Continuous power control of the sensors achieves better performance over the fixed power approaches in terms of the total energy consumption during data collection. In addition, compared to the two commonly used baselines, our scheduling framework achieves better and near-optimal results.

Beamforming Design for Secure MISO Visible Light Communication Networks With SLIPT

Visible light communication (VLC) is a promising technology for the next generation wireless communication systems due to its high spectral efficiency and energy efficiency. In this article, a secure multiple-input single-output (MISO) VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support multiple energy-limited devices tacking into account a practical non-linear energy harvesting model. The transmit power minimization and the minimum secrecy rate maximization problems are formulated under both perfect and imperfect channel state information, respectively. To further improve the user connectivity, those problems are also investigated in MISO-VLC networks with non-orthogonal multiple access (NOMA). To tackle these challenging non-convex problems, semidefinite program relaxation and $\mathcal {S}$ -Procedure are exploited. It is proved that optimal beamforming schemes can be obtained for the considered two types problems in MISO-VLC SLIPT networks, while a sub-optimal solution can be obtained for the transmit power minimization problem in those networks with NOMA. It is found that there is a non-trivial trade-off between the average harvested power and maximum-minimum secrecy rate. Moreover, it is shown that the performance achieved with NOMA outperforms that of conventional orthogonal multiple access in MISO-VLC SLIPT networks, despite the existence of imperfect channel state information.

Wireless Powered Mobile Edge Computing: Offloading Or Local Computation?

Mobile-edge computing (MEC) and wireless power transfer are technologies that can assist in the implementation of next generation wireless networks, which will deploy a large number of computational and energy limited devices. In this letter, we consider a point-to-point MEC system, where the device harvests energy from the access point's (AP's) transmitted signal to power the offloading and/or the local computation of a task. By taking into account the non-linearities of energy harvesting, we provide analytical expressions for the probability of successful computation and for the average number of successfully computed bits. Our results show that a hybrid scheme of partial offloading and local computation is not always efficient. In particular, the decision to offload and/or compute locally, depends on the system's parameters such as the distance to the AP and the number of bits that need to be computed.

UAV-Enabled SWIPT in IoT Networks for Emergency Communications

Energy-limited devices and connectivity in complicated environments are two main challenges for Internet of Things (IoT)-enabled mobile networks, especially when IoT devices are distributed in a disaster area. Unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) is emerging as a promising technique to tackle the above problems. In this article, we establish an emergency communications framework of UAV-enabled SWIPT for IoT networks, where the disaster scenarios are classified into three cases, namely, dense areas, wide areas and emergency areas. First, to realize wireless power transfer for IoT devices in dense areas, a UAV-enabled wireless power transfer system is considered where a UAV acts as a wireless charger and delivers energy to a set of energy receivers. Then, a joint trajectory planning and resource scheduling scheme for a multi-UAVs system is discussed to provide wireless services for IoT devices in wide areas. Furthermore, an intelligent prediction mechanism is designed to predict service requirements (i.e., data transmission and battery charging) of the devices in emergency areas, and accordingly, a dynamic path planning scheme is established to improve the energy efficiency (EE) of the system. Simulation results demonstrate the effectiveness of the above schemes. Finally, potential research directions and challenges are also discussed.

PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing

Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems. We use transfer learning to compensate for the lack of data, and data augmentation to tackle varying scale and orientation. To reduce network size, we use a novel unsupervised learning approach based on k-means clustering, applied to all parts of the network: most network reduction methods use computationally expensive supervised learning methods, and apply only to the convolutional or fully connected layers, but not both. In experiments, we set new standards in classification accuracy on four remote-sensing and two scene-recognition image datasets.

Energy-Optimal Multiple Access Computation Offloading: Signalling Structure and Efficient Communication Resource Allocation

Computation offloading enables energy-limited devices to expand the scope of the computational tasks that they can complete within specified latency requirements. However, when multiple devices seek to offload, effective allocation of resources becomes crucial. In this paper, we develop an energy-optimal signalling structure for a $K$ -user offloading system, and an efficient algorithm for allocating the communication resources provided by that structure. The signalling structure is designed to exploit the differences between the users’ latencies and the reduction in interference that arises when a device completes its offloading. That results in a time-slotted signalling structure, which is then optimized for a multiple access scheme that exploits the full capabilities of the channel (FullMA). The optimized signalling structure enables us to substantially reduce the dimension of the resource allocation problem, and to develop efficient algorithms to tackle that problem for both the binary and partial offloading cases. Our numerical experiments illustrate that the proposed time-slotted FullMA signalling structure significantly reduces the energy consumption of the devices compared to some existing methods that employ orthogonal multiple access schemes, such as TDMA, and to those with FullMA, but sub-optimal single-time-slot signalling structures.

An efficient routing protocol for cognitive radio networks of energy-limited devices

In the era of Internet-of-things (IoT), the future 5G networks are supposed to provide ubiquitous connectivity, high speed, as well as low latency and energy efficiency at low cost to billions of battery-powered wireless devices. The anticipated tremendous demand for wireless bandwidth in 5G networks calls for efficient usage of the underutilized licensed frequency spectrum that preserves the energy consumption of these energy-limited devices. This is feasible by embracing the cognitive radio concept and making use of its functionalities and capabilities to form 5G-CR incorporation. As a step towards this goal, an efficient routing protocol for cognitive radio (ERCR) networks is proposed in this paper. The proposed protocol is location-based and can fully operate over a single wireless channel using a channel access mechanism that follows the IEEE 802.11 distributed coordination function. It selects the route with the minimum number of forwarding nodes that have sufficient remaining energy. This, in turn, increases the per-node capacity to meet the operational requirements of different IoT applications. Meanwhile, it conserves the limited energy of battery-powered devices. The efficiency of the proposed protocol has been evaluated using extensive network simulator-2 computer simulations for a wide range of performance metrics under different activity levels of licensed users in terms of channel occupancy likelihood and duration. The simulation results reveal that ERCR is capable of providing reliable packet delivery at a low packet transfer latency while saving the energy of the cognitive radio network nodes with a fairly small overhead.

Enabling Non-Linear Energy Harvesting in Power Domain Based Multiple Access in Relaying Networks: Outage and Ergodic Capacity Performance Analysis

The Power Domain-based Multiple Access (PDMA) scheme is considered as one kind of Non-Orthogonal Multiple Access (NOMA) in green communications and can support energy-limited devices by employing wireless power transfer. Such a technique is known as a lifetime-expanding solution for operations in future access policy, especially in the deployment of power-constrained relays for a three-node dual-hop system. In particular, PDMA and energy harvesting are considered as two communication concepts, which are jointly investigated in this paper. However, the dual-hop relaying network system is a popular model assuming an ideal linear energy harvesting circuit, as in recent works, while the practical system situation motivates us to concentrate on another protocol, namely non-linear energy harvesting. As important results, a closed-form formula of outage probability and ergodic capacity is studied under a practical non-linear energy harvesting model. To explore the optimal system performance in terms of outage probability and ergodic capacity, several main parameters including the energy harvesting coefficients, position allocation of each node, power allocation factors, and transmit signal-to-noise ratio (SNR) are jointly considered. To provide insights into the performance, the approximate expressions for the ergodic capacity are given. By matching analytical and Monte Carlo simulations, the correctness of this framework can be examined. With the observation of the simulation results, the figures also show that the performance of energy harvesting-aware PDMA systems under the proposed model can satisfy the requirements in real PDMA applications.

EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee.

With the expansion of the Internet-of-Things, energy-efficient communication is becoming vital. The communication among energy-limited devices (e.g., powered by batteries or harvesting the energy from their environment) must be energy-efficient, prolonging their lifetime or increasing data throughput. This article aims at proposing energy-efficient periodic communication for devices over the ZigBee protocol and powered by a battery. We propose using timing channels for different data priorities, thus, more important data are sent more frequently. The priority is also considered in case of congested traffic, where a central device (coordinator) prioritizes more important communication. We have implemented a simulator, which serves for verification of the proposed solution, and conducted experiments comparing the proposed EEMIP method with the standard nonbeacon ZigBee communication. The experimental results show that the proposed method is more energy efficient.

Spectral and Energy-Efficient Wireless Powered IoT Networks: NOMA or TDMA?

Wireless powered communication networks (WPCNs), where multiple energy-limited devices first harvest energy in the downlink and then transmit information in the uplink, have been envisioned as a promising solution for the future Internet-of-Things (IoT). Meanwhile, non-orthogonal multiple access (NOMA) has been proposed to improve the system spectral efficiency (SE) of the fifth-generation (5G) networks by allowing concurrent transmissions of multiple users in the same spectrum. As such, NOMA has been recently considered for the uplink of WPCNs based IoT networks with a massive number of devices. However, simultaneous transmissions in NOMA may also incur more transmit energy consumption as well as circuit energy consumption in practice which is critical for energy constrained IoT devices. As a result, compared to orthogonal multiple access schemes such as time-division multiple access (TDMA), whether the SE can be improved and/or the total energy consumption can be reduced with NOMA in such a scenario still remains unknown. To answer this question, we first derive the optimal time allocations for maximizing the SE of a TDMA-based WPCN (T-WPCN) and a NOMA-based WPCN (N-WPCN), respectively. Subsequently, we analyze the total energy consumption as well as the maximum SE achieved by these two networks. Surprisingly, it is found that N-WPCN not only consumes more energy, but also is less spectral efficient than T-WPCN. Simulation results verify our theoretical findings and unveil the fundamental performance bottleneck, i.e., "worst user bottleneck problem", in multiuser NOMA systems.

Efficient Traceable Authorization Search System for Secure Cloud Storage

Secure search over encrypted remote data is crucial in cloud computing to guarantee the data privacy and usability. To prevent unauthorized data usage, fine-grained access control is necessary in multi-user system. However, authorized user may intentionally leak the secret key for financial benefit. Thus, tracing and revoking the malicious user who abuses secret key needs to be solved imminently. In this paper, we propose an e scrow f ree t raceable a ttribute based m ultiple k eywords s ubset s earch system with v erifiable o utsourced d ecryption (EF-TAMKS-VOD). The key escrow free mechanism could effectively prevent the key generation centre (KGC) from unscrupulously searching and decrypting all encrypted files of users. Also, the decryption process only requires ultra lightweight computation, which is a desirable feature for energy-limited devices. In addition, efficient user revocation is enabled after the malicious user is figured out. Moreover, the proposed system is able to support flexible number of attributes rather than polynomial bounded. Flexible multiple keyword subset search pattern is realized, and the change of the query keywords order does not affect the search result. Security analysis indicates that EF-TAMKS-VOD is provably secure. Efficiency analysis and experimental results show that EF-TAMKS-VOD improves the efficiency and greatly reduces the computation overhead of users’ terminals.

A new efficient lightweight and secure image cipher scheme

The protection of multimedia content has become a key area of research, since very often a user’s privacy and confidentiality can be at risk. Although a large number of image encryption algorithms have recently emerged, only a subset of these algorithms are suitable for real applications. These algorithms however use non-integer operations such as chaotic solutions that introduce a sizeable overhead in terms of latency and resources, in addition to floating-point hardware that is costly to implement. Designing an efficient, lightweight, and secure image encryption algorithm is still a hard challenge; yet, it is crucial to have in order to meet the demands of recent multimedia applications running on energy-limited devices. In this paper, an efficient image encryption scheme based on a dynamic structure is proposed. The structure of the proposed cipher consists of two different lightweight rounds (forward and backward chaining blocks) and a block permutation process. In addition, a key derivation function is proposed to produce a dynamic key based on a secret key and a nonce. This key, according to its configuration, can be changed for each validate time (session) or for each new input image. Then, based on this key, the cipher layers are produced, which are an integer or a binary diffusion matrix and a substitution table S-box, together with a permutation table P-box. The proposed dynamic cipher is designed to provide high robustness against contemporary powerful attacks, and permits reducing the required number of rounds for achieving the lightweight property. Experimental simulations demonstrate the efficiency and robustness levels of the proposed scheme.