Search Results (15)

A wireless-powered communication network (WPCN) provides sustainable power solutions for energy-intensive Internet of Things (IoT) devices in remote or inaccessible locations. This technology is particularly beneficial for applications in smart transportation and smart cities. Nevertheless, WPCN experiences performance degradation due to severe path loss and inefficient long-range energy and information transmission. To address the limitation, this paper investigates an intelligent reflecting surface (IRS)-enhanced multi-cell WPCN integrated with non-orthogonal multiple access (NOMA). The emerging IRS technology mitigates propagation losses through precise phase shift adjustments with symmetric reflective components. Asymmetric resource utilization in symmetric downlink and uplink transmissions is crucial for optimal throughput and quality of service. Alternative iterations are employed to optimize time allocation and IRS phase shifts in both downlink and uplink transmissions. This approach allows for the attainment of maximum sum throughput. Specifically, the phase shifts are optimized using two algorithms called semidefinite relaxation (SDR) and block coordinate descent (BCD). Our simulations reveal that integrating the IRS into multi-cell NOMA-WPCN enhances user throughput. This surpasses the performance of traditional multi-cell WPCN. In addition, the coordinated deployment of multiple hybrid access points (HAPs) and IRS equipment can expand communications coverage and network capacity. Full article

In this paper, we explore intelligent reflecting surface (IRS)-assisted physical layer security (PLS) in a wireless-powered Internet of Things (IoT) network (WPIN) by combining an IRS, a friendly jammer, and energy harvesting (EH) to maximize sum secrecy throughput in the WPIN. Specifically, we propose a non-line-of-sight system where a hybrid access point (H-AP) has no direct link with the users, and a secure uplink transmission scheme utilizes the jammer to combat malicious eavesdroppers. The proposed scheme consists of two stages: wireless energy transfer (WET) on the downlink (DL) and wireless information transmission (WIT) on the uplink (UL). In the first phase, the H-AP sends energy to users and the jammer, and they then harvest energy with the help of the IRS. Consequently, during WIT, the user transmits information to the H-AP while the jammer emits signals to confuse the eavesdropper without interfering with the legitimate transmission. The phase-shift matrix of the IRS and the time allocation for DL and UL are jointly optimized to maximize the sum secrecy throughput of the network. To tackle the non-convex problem, an alternating optimization method is employed, and the problem is reformulated into two sub-problems. First, the IRS phase shift is solved using quantum particle swarm optimization (QPSO). Then, the time allocation for DL and UL are optimized using the bisection method. Simulation results demonstrate that the proposed method achieves significant performance improvements as compared to other baseline schemes. Specifically, for IRS elements N = 35, the proposed scheme achieves a throughput of 19.4 bps/Hz, which is 85% higher than the standard PSO approach and 143% higher than the fixed time, random phase (8 bps/Hz) approach. These results validate the proposed approach’s effectiveness in improving network security and overall performance. Full article

Non-orthogonal multiple access (NOMA) enables the parallel offloading of multiuser tasks, effectively enhancing throughput and reducing latency. Backscatter communication, which passively reflects radio frequency (RF) signals, improves energy efficiency and extends the operational lifespan of terminal devices. Both technologies are pivotal for the next generation of wireless networks. However, there is little research focusing on optimizing the transmit power in backscatter-assisted NOMA-MEC systems from a green IoT perspective. In this paper, we aim to minimize the transmit energy consumption of a Hybrid Access Point (HAP) while ensuring task deadlines are met. We consider the integration of Backscatter Communication (BackCom) and Active Transmission (AT), and leverage NOMA technology and user cooperation to mitigate the double near–far effect. Specifically, we formulate a transmit energy consumption minimization problem, accounting for task deadline constraints, task offloading decisions, transmit power allocation, and energy constraints. To tackle the non-convex optimization problem, we employ variable substitution and convex optimization theory to transform the original non-convex problem into a convex one, which is then efficiently solved. We deduce the semi-closed form expression of the optimal solution and propose an energy-efficient algorithm to minimize the transmit power of the entire wireless powered MEC. The extensive simulation results demonstrate that our proposed scheme significantly reduces the HAP transmit power by around 8% compared to existing schemes, validating the effectiveness of our approach. This study provides valuable insights for the design of green IoT systems by optimizing the transmit power in NOMA-MEC networks. Full article

The article addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a time allocation scheme, named DaTA, which is based on the Different Target Simultaneous Wireless Information and Power Transfer (DT-SWIPT) scheme such that the wireless station can share the remaining energy after transmission to the Hybrid Access Point (HAP) to those who have not transmitted to the HAP to minimize the energy consumption of the WPCN. In addition to proposing a new frame structure, the article also considers the Signal-to-Noise (SNR) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the article, the problem of minimization of energy consumption is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. The simulation results showed that the proposed scheme outperforms the related work in terms of energy consumption and energy efficiency. Full article

Underwater acoustic communication (UWAC) has a wide range of applications, including marine environment monitoring, disaster warning, seabed terrain exploration, and oil extraction. It plays an indispensable and increasingly important role in marine resource exploration and marine economic development. In current UWAC systems, the terminal nodes are usually powered by energy-limited batteries. Due to the harshness of the underwater environment, especially in the ocean environment, it is very costly and difficult, even impossible, to replace the batteries for the terminal nodes in UWACs, which results in the short lifetime and unreliability of the terminal nodes and the systems. In this paper, we present the application of a wireless powered communication network (WPCN) to the UWAC systems to provide an auxiliary and convenient energy supplement for solving the energy-limited problem of the terminal nodes, where the hybrid access point (H-AP) transfers energy to the terminal nodes in the downlink. In contrast, the terminal nodes use the harvested energy to transmit the information to the H-AP in the uplink. To evaluate the proposed WPCN-based UWAC systems, we investigate the performance of the average bit error rate (BER), outage probability, and achievable information rate for the systems in a frequency-selective sparse channel and non-white noise environment. We derive the closed-form expression for the probability density function (PDF) of the received signal-to-noise ratio (SNR). Based on this, we further derive novel closed-form expressions for the average BER and the outage probability of the systems. Numerical results confirm the validity of the proposed analytical results. It is shown that there exists an optimal signal frequency and time allocation factor for the systems to achieve optimal performance, and a larger optimal time allocation factor is preferred for a smaller hybrid access point (H-AP) transmit power or a larger transmission distance, while a smaller optimal signal frequency is required for a larger transmission distance. Full article

Water quality monitoring systems that are enabled by the Internet of Things (IoT) and used in water applications to collect and transmit water data to data processing centers are often resource-constrained in terms of power, bandwidth, and computation resources. These limitations typically impact their performance in practice and often result in forwarding their data to remote stations where the collected water data are processed to predict the status of water quality, because of their limited computation resources. This often negates the goal of effectively monitoring the changes in water quality in a real-time manner. Consequently, this study proposes a new resource allocation method to optimize the available power and time resources as well as dynamically allocate hybrid access points (HAPs) to water quality sensors to improve the energy efficiency and data throughput of the system. The proposed system is also integrated with edge computing to enable data processing at the water site to guarantee real-time monitoring of any changes in water quality and ensure timely access to clean water by the public. The proposed method is compared with a related method to validate the system performance. The proposed system outperforms the existing system and performs well in different simulation experiments. The proposed method improved the baseline method by approximately 12.65% and 16.49% for two different configurations, demonstrating its effectiveness in improving the energy efficiency of a water quality monitoring system. Full article

As an advanced technology, simultaneous wireless information and power transfer (SWIPT), combined with the internet of things (IoT) devices, can effectively extend the online cycle of the terminal. To cope with the fluctuation of energy harvesting by the hybrid access points (H-AP), the energy cooperation base station is introduced to realize the sharing of renewable energy. In this paper, we study the SWIPT-enabled IoT networks with cooperation. Our goal is to maximize the energy efficiency of the system, and at the same time, we need to meet the energy harvesting constraints, user quality of service (QoS) constraints and transmission power constraints. We jointly solve the power allocation, time switching and energy cooperation problems. Because this problem is a nonlinear programming problem, it is difficult to solve directly, so we use the alternating variable method, the iterative algorithm is used to solve the power allocation and time switching problem, and the matching algorithm is used to solve the energy cooperation problem. Simulation results show that the proposed algorithm has obvious advantages in energy efficiency performance compared with the comparison algorithm. At the same time, it is also proved that the introduction of energy cooperation technology can effectively reduce system energy consumption and improve system energy efficiency. Full article

This paper presents a multiple concurrent slotframe scheduling (MCSS) protocol for wireless power transfer (WPT)-enabled wireless sensor networks. The MCSS supports a cluster-tree network topology composed of heterogeneous devices, including hybrid access points (HAPs) serving as power transmitting units and sensor nodes serving as power receiving units as well as various types of traffic, such as power, data, and control messages (CMs). To this end, MCSS defines three types of time-slotted channel hopping (TSCH) concurrent slotframes: the CM slotframe, HAP slotframe, and WPT slotframe. These slotframes are used for CM traffic, inter-cluster traffic, and intra-cluster traffic, respectively. In MCSS, the length of each TSCH concurrent slotframe is set to be mutually prime to minimize the overlap between cells allocated in the slotframes, and its transmission priority is determined according to the characteristics of transmitted traffic. In addition, MCSS determines the WPT slotframe length, considering the minimum number of power and data cells required for energy harvesting and data transmission of sensor nodes and the number of overprovisioned cells needed to compensate for overlap between cells. The simulation results demonstrated that MCSS outperforms the legacy TSCH medium access control protocol and TSCH multiple slotframe scheduling (TMSS) for the average end-to-end delay, aggregate throughput, and average harvested energy. Full article

Wireless-powered communication networks (WPCNs) consist of wireless devices (WDs) that transmit information to the hybrid access point (HAP). In this situation, there is interference among WDs that is considered to be noise and causes information loss because of adjacent signals. Moreover, power is limited and can be lost if transmission distance is long. This paper studies sum-throughput maximization with sectored cells for WPCN. We designed a downlink (DL) energy beamforming by sector based on the hybrid space division multiple access (SDMA) and nonorthogonal multiple access (NOMA) approach to maximize the sum throughput. First, a cell is divided into several sectors, and signals from each sector are transmitted to each antenna of the HAP, so that the signals are not adjacent. Further, the HAP decodes the overlapping information of each sector. Next, power allocation is optimized by sector. To optimize power allocation, a constrained optimization problem is formulated and then converted into a nonconstraint optimization problem using the interior penalty method. The optimal solution derives the maximal value to the problem. Power for each sector is optimally allocated according to this optimal solution. Under this consideration, sum-throughput maximization is performed by optimally allocating DL energy beamforming by sector. We analyzed sum throughput and fairness, and then compared them according to the number of sectors. Performance results show that the proposed optimal power allocation by sector using hybrid SDMA/NOMA outperforms the existing equal power allocation by sector in terms of the sum throughput while fairness is also maintained. Moreover, the performance difference between the hybrid approach and SDMA, which optimally allocates power by sector, was about 1.4 times that of sum throughput on average, and the hybrid approach was dominant. There was also no difference in fairness performance. Full article

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes. Full article

In this paper, we study the transmit power minimization problem with optimal energy beamforming in a multi-antenna wireless powered communication network (WPCN). The considered network consists of one hybrid access point (H-AP) with multiple antennae and multiple users with a single antenna each. The H-AP broadcasts an energy signal on the downlink, using energy beamforming to enhance the efficiency of the transmit energy. In this paper, we jointly optimize the downlink time allocation for wireless energy transfer (WET), the uplink time allocation for each user to send a wireless information signal to the H-AP, the power allocation to each user on the uplink, and the downlink energy beamforming vectors while controlling the transmit power at the H-AP. It is challenging to solve this non-convex complex optimization problem because it is numerically intractable and involves high computational complexity. We exploit a sequential parametric convex approximation (SPCA)-based iterative method, and propose optimal and sub-optimal solutions for the transmit power minimization problem. All the proposed schemes are verified by numerical simulations. Through the simulation results, we present the performance of the proposed schemes based on the effect of the number of transmit antennae and the number of users in the proposed WPCN. Through the performance evaluation, we show that the SPCA-based joint optimization solution performance is superior to other solutions. Full article

This paper presents a time-slotted channel hopping (TSCH) multiple slotframe scheduling (TMSS) protocol to ensure the timeliness of energy harvesting and data transmission for sensor devices with different transmission periods in Internet of Things (IoT) networks enabled with time-switching simultaneous wireless information and power transfer (TS-SWIPT). The TMSS uses a modified three-step 6P transaction to allocate power and data cells within the slotframe. The sensor device sets the slotframe length equal to the transmission period and estimates the number of power and data cells for allocation in the configured slotframe and requests cell allocation to the hybrid access point (HAP). Upon request from a sensor device, the HAP executes a cell-overlapping prevention (COP) algorithm to resolve the cell-overlapping problem and responds to the sensor device with a candidate cell list. Upon receiving the response from HAP, the sensor device determines its power and data cells by referring to the cell list. We conducted experimental simulations and compared the TMSS performance to that of the legacy TSCH medium access control (MAC) with a single slotframe and the harvest-then-transmit-based modified enhanced distributed coordination function (EDCF) MAC protocol (HE-MAC). The results showed that TMSS outperforms legacy TSCH MAC and HE-MAC in terms of delay, effective throughput and energy utilization. Full article

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed. Full article

The IoT system has become a significant component of next generation networks, and drawn a lot of research interest in academia and industry. As the sensor nodes in the IoT system are always battery-limited devices, the power control problem is a serious problem in the IoT system which needs to be solved. In this paper, we research the resource allocation in the wireless powered IoT system, which includes one hybrid access point (HAP) and many wireless sensor nodes, to obtain the optimal power level for information transmission and energy transfer simultaneously. The relationship between the HAP and the sensor nodes are formulated as the Stackelberg game, and the dynamic variations of the energy for both the HAP and IoT devices are formulated through the dynamic game with mean field control. Then the power control in the wireless powered IoT system is formulated as a mean field Stackelberg game model. We aim to minimize the transmission cost for each sensor node based on optimally power resource allocation. Meanwhile, we attempt to minimize the energy transfer cost based on power control. As a result, the optimal solutions based on the mean field control of the sensor nodes and the HAP are achieved through dynamic programming theory and the law of large numbers, and $\epsilon$ -Nash equilibriums can be obtained. The energy variations for both the sensor nodes and HAP after the control of resource allocation based on the proposed approach are verified based on the simulation results. Full article

A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation. Full article