Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things

In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices’ transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05).

Adaptive transmission power level algorithm according to random deployment of nodes in WSN: خوارزمية سوية طاقة الإرسال المتكيفة حسب النشر العشوائي للعقد في شبكات الحساسات اللاسلكية

Wireless sensor nodes are generally deployed randomly in hostile, harsh and inaccessible environments. For this reason, the sensor nodes are supposed to operate over long periods of time without human intervention in order to extend the life of the network as much as possible, and also, it is not possible to restore the nodes or change their positions after their deployment, but by changing the transmitting power level and redeploying a new nodes above the deployment Previously, the network performance improves and we guarantee that the deployed nodes are not lost, and we also guarantee the operation of the network as a whole. The researcher has developed an algorithm "Adaptive transmission power level according to random deployment (ATPLRD)", where the presented algorithm includes determining the power levels relative to random deployment and identifying possible paths in the network in order to reach high interconnection between nodes to achieve the least number of published nodes at the lowest energy levels for the nodes, and also determines the most important nodes in the network whose exit or failure leads to the collapse of the network, and determining The boundary nodes of the network, as well as the weakest coverage areas, which represent gaps in the network, and from it determines the number of nodes needed to deploy within these gaps as few as possible. The results of the study showed that the imposed algorithm is effective in all of the above, and we focus in this research on adaptively determining the transmission energy levels of the nodes and reducing the number of deployed nodes that make the network work effectively and improving the quality of deployment by deploying additional nodes within the Reigon of Interest. The results showed achieving the least number of deployed nodes at the lowest transmission power level and achieving high interconnection between nodes. An overall energy consumption improvement of 31.25% was achieved.

Unlocking the Hidden Dimension of the World’s Transmission and Distribution Power Losses through Fractal Statistical Inference

Various studies on the transmission and distribution power losses had been conducted but a global view of the transmission and distribution power loss rates had not been explored yet. This paper explained the fractal dimension of the global distribution and transmission power losses using fractal statistical analysis. Findings revealed that the computed fractal dimension of 1.08 means that power losses of countries across the globe deviated from uniformity by about 8 percent. The upper and bottom eight (8) percent of the dataset were analyzed and findings revealed that the ruggedness of the phenomenon was attributable to political and economic dimensions. The extreme high distribution and transmission power losses may be traced back to chaos, pilferage, and substandard materials. On the other hand, the economic stability and state control capacity contributed much to the extreme low distribution and transmission power losses. Keywords: fractal analysis, power loss, power transmission and distribution, xfractal.

Approach for Collision Minimization and Enhancement of Power Allocation in WSNs

Wireless sensor networks (WSNs) have attracted much more attention in recent years. Hence, nowadays, WSN is considered one of the most popular technologies in the networking field. The reason behind its increasing rate is only for its adaptability as it works through batteries which are energy efficient, and for these characteristics, it has covered a wide market worldwide. Transmission collision is one of the key reasons for the decrease in performance in WSNs which results in excessive delay and packet loss. The collision range should be minimized in order to mitigate the risk of these packet collisions. The WSNs that contribute to minimize the collision area and the statistics show that the collision area which exceeds equivalents transmission power has been significantly reduced by this technique. This proposed paper optimally reduced the power consumption and data loss through proper routing of packets and the method of congestion detection. WSNs typically require high data reliability to preserve identification and responsiveness capacity while also improving data reliability, transmission, and redundancy. Retransmission is determined by the probability of packet arrival as well as the average energy consumption.

Modelling of geomagnetically induced currents in the Czech transmission grid

We investigate the maximum expected magnitudes of the geomagnetically induced currents (GICs) in the Czech transmission power network. We compute a model utilising the Lehtinen–Pirjola method, considering the plane-wave model of the geoelectric field, and using the transmission network parameters kindly provided by the operator. We find that the maximum amplitudes expected in the nodes of the Czech transmission grid during the Halloween storm-like event are about 15 A. For the “extreme-storm” conditions with a 1-V/km geoelectric field, the expected maxima do not exceed 40 A. We speculate that the recently proven statistical correlation between the increased geomagnetic activity and anomaly rate in the power grid may be due to the repeated exposure of the devices to the low-amplitude GICs. Graphical Abstract

Optimum energy harvesting model for bidirectional cognitive radio networks

Wireless devices’ energy efficiency and spectrum shortage problem has become a key concern worldwide as the number of wireless devices increases at an unparalleled speed. Wireless energy harvesting technique from traditional radio frequency signals is suitable for extending mobile devices’ battery life. This paper investigates a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. Analytical expressions for considered network metrics, namely data rate, outage probability, and energy efficiency, are derived for uplink and downlink scenarios. In addition, optimal transmission power and energy harvesting power are derived for maximum energy efficiency in downlink and uplink scenarios. Numerical results show that outage probability improves high transmission power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result shows that energy efficiency improves using optimum transmission power and energy harvesting power for downlink and uplink scenarios.

Energy Efficiency Analysis in Modified GoF Spectrum Sensing-Based AF Relay Cooperative Cognitive Sensor Network with Energy Harvesting

In this paper, we propose a joint sensing duration and transmission power allocation scheme to maximize the energy efficiency (EE) of the secondary user (SU) in a cooperative cognitive sensor network (CSN). At the initial time slot of the frame, the secondary transmitter (ST) performs energy harvesting (EH) and spectrum sensing simultaneously using power splitting (PS) protocol. The modified goodness of fit (GoF) spectrum sensing algorithm is employed to detect the licensed spectrum, which is not sensitive to an inaccurate noise power estimate. Based on the imperfect sensing results, the ST will act as an amplify-and-forward (AF) relay and assist in transmission of the primary user (PU) or transmit its own data. The SU’s EE maximization problem is constructed under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement. Since the SU’s EE function is a nonconvex problem and difficult to solve, we transform the original problem into a tractable convex one with the aid of Dinkelbach’s method and convex optimization technique by applying a nonlinear fractional programming. The closed-form expression of the ST’s transmission power is also derived through Karush-Kuhn-Tucker (KKT) and gradient method. Simulation results show that our scheme is superior to the existing schemes.

Enhanced Dynamic Spectrum Access in UAV Wireless Networks for Post-Disaster Area Surveillance System: A Multi-Player Multi-Armed Bandit Approach

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.