Transmission Energy Consumption Research Articles

Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments.

Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers’ interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed.

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication.

Industrial Internet of Things Model Driven by Particle Filter and Network Communication Technology.

In this paper, a better particle filter algorithm is put forth to address the issues of particle filter sample exhaustion and weight degradation. The algorithm frames the received signal and separates the signals in two steps based on the slow-varying properties of system parameters in practical applications, such as phase shift and transmission delay. In addition, the network model and energy consumption model are built while the sensor data is being collected and processed using the industrial IoT's communication mechanism and algorithm. The repeater is chosen as the node with the lowest transmission energy consumption, and the industrial field's sensor data is gathered via the fog server node. The simulation results demonstrate that the proposed algorithm's accuracy rate is 95.54 percent, higher than that of the comparison algorithm. The enhanced algorithm suggested in this paper can simultaneously achieve improved parameter estimation performance and achieve signal separation with low bit error rates. Additionally, the communication system and algorithm can efficiently gather the sensing information from the industrial field, and the indicators like energy consumption and the first dead node are better than other algorithms. It offers an innovative method for enhancing industrial field application.

Impact of mechanical ventilation control strategies based on non-steady-state and steady-state Wells-Riley models on airborne transmission and building energy consumption.

Ventilation is an effective solution for improving indoor air quality and reducing airborne transmission. Buildings need sufficient ventilation to maintain a low infection risk but also need to avoid an excessive ventilation rate, which may lead to high energy consumption. The Wells-Riley (WR) model is widely used to predict infection risk and control the ventilation rate. However, few studies compared the non-steady-state (NSS) and steady-state (SS) WR models that are used for ventilation control. To fill in this research gap, this study investigates the effects of the mechanical ventilation control strategies based on NSS/SS WR models on the required ventilation rates to prevent airborne transmission and related energy consumption. The modified NSS/SS WR models were proposed by considering many parameters that were ignored before, such as the initial quantum concentration. Based on the NSS/SS WR models, two new ventilation control strategies were proposed. A real building in Canada is used as the case study. The results indicate that under a high initial quantum concentration (e.g., 0.3 q/m3) and no protective measures, SS WR control underestimates the required ventilation rate. The ventilation energy consumption of NSS control is up to 2.5 times as high as that of the SS control.

Neural Network-Based Routing Energy-Saving Algorithm for Wireless Sensor Networks.

With the evolvement, standards have changed, mobile Internet technology has also been upgraded, and it has also driven the development of smart objects mobile. With the continuous development of smart objects mobile, the bottleneck of small node size and low battery energy storage has not been solved in the end, which makes the research of wireless sensor network energy-saving technology become the focus, and the improvement of routing technology is an effective way to improve energy-saving technology. From the data transmission energy consumption of smart objects mobile, the routing algorithm of smart objects mobile is discussed and analyzed and the classical representative LEACH is the object of in-depth research. Routing algorithms can easily and reliably process network data and make the network work well and are widely used in highly secure military systems and smaller commercial networks. Aiming at these deficiencies, a corresponding improved algorithm is proposed, and it is tested through simulation and specific experiments to verify the correctness and the system's reliability. The SMPSO-BP algorithm converges when the number of iterations is about 600, which is earlier than the LEACH algorithm and the improved LEACH algorithm, so the SMPSO-BP algorithm is due to the other two algorithms. In the wireless sensor network routing energy consumption experiment, in addition, the SMPSO-BP algorithm uses less energy than the other two methods. Therefore, the energy-saving algorithm under the neural network data fusion mechanism is still feasible.

Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption

PurposeMinimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.Design/methodology/approachIn this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.FindingsEnergy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.Originality/valueThis paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Energy consumption-based services composition optimization for internet of things

Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality.

Communication Efficient Decentralized Learning Over Bipartite Graphs

In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers. The proposed algorithm, Censored and Quantized Generalized GADMM (CQ-GGADMM), leverages the worker grouping and decentralized learning ideas of Group Alternating Direction Method of Multipliers (GADMM), and pushes the frontier in communication efficiency by extending its applicability to generalized network topologies, while incorporating link censoring for negligible updates after quantization. We theoretically prove that CQ-GGADMM achieves the linear convergence rate when the local objective functions are strongly convex under some mild assumptions. Numerical simulations corroborate that CQ-GGADMM exhibits higher communication efficiency in terms of the number of communication rounds and transmit energy consumption without compromising the accuracy and convergence speed, compared to the censored decentralized ADMM, and the worker grouping method of GADMM.

Efficient Event Inference and Context-Awareness in Internet of Things Edge Systems

Internet of Things (IoT) connects physical, cyber and human spaces. Event-based system is one of the cornerstones to help IoT achieve real-time monitoring, context-awareness and intelligent control. In the era of big data, the huge amount and high complexity of event inference rule pose a great challenge to traditional event-based system in its efficiency, especially resources-constrained IoT edge systems. This paper proposes a high-efficiency joint event inference model for real-time contextawareness and decision-making in IoT edge systems. We define different kinds of redundancy relations between event inference models and propose a description mechanism, named event containing graph, to support multi-pattern optimization. Three operations on single-pattern event inference models, Merge, Failure and Output are defined respectively. The joint inference model is established by merging sharing patterns, constructing failure transitions and conditional output to eliminate inter-model redundancies. Experimental results prove that the joint model consumes less computational resources and provides higher performance than other benchmarks. It also verifies and proves that joint model has better optimization effect when processing large number of complex events. Especially in edge computing environment, joint inference model improves the real-time performance and significantly reduces the energy consumption in data transmission from edges to data center.

An Improved Approach for Wireless Sensor Networks With Mobile Sink Using Dynamic Minimum Spanning Tree

In wireless sensor networks (WSNs), finding the best transmission route from each node to the mobile sink is not a simple task because of the dynamic characteristics of sink node and cluster transmission. In order to balance energy consumption and reduce collisions in wireless transmissions, this paper proposes a Dynamic Spanning Tree with Mobile Sink (DSTMS) routing algorithm. Firstly, based on the Low Energy Adaptive Clustering Hierarchy Protocol (LEACH), a multi-layer transmission framework with dynamic minimum spanning tree (MST) is constructed to optimize data transmission route. In this framework, the selected rendezvous points, according to the motion parameters of mobile sink, build the dynamic rendezvous layer to constraint the hierarchical transmission of DSTMS. In addition, considering some factors of transmission energy consumption and residual energy distribution,the weight factor based on energy efficient function is introduced to evaluate the cost of transmission path within the MST. Moreover, the proposed algorithm not only reduces network energy consumption caused by frequent location updates of mobile sink, but also reduces data transmission congestion by constructing dynamic hierarchical transmission strategy. The simulation results show that the DSTMS algorithm can effectively prolong the network life, balance the network load and reduce collisions in wireless transmissions.

Toward Energy-Efficient Multiple IRSs: Federated Learning-Based Configuration Optimization

Intelligent reflecting surface (IRS) can enhance the capacity and cost-effectiveness in future wireless networks substantially. However, the configuration optimization of IRS in an energy-efficient way is still a challenging work. In this paper, we propose a solution to the problem of maximizing the total throughput of a multiple IRSs assisted multi-user communication system. A federated deep learning (FDL) based algorithm is designed to obtain the optimal reflection configurations of all IRSs in parallel, where the model parameters are transmitted instead of the dataset itself as in deep learning (DL). Specifically, a deep neural network (DNN) is formulated to fit the coupling relationship between the coordinate information of users and the optimal reflecting vector of IRS. Meanwhile, the analysis of transmission and computation overhead is performed to establish an accurate energy consumption model. For performance evaluation, we conduct a series of simulations to verify the effectiveness of the FDL framework. The simulation results demonstrate that the test accuracy of the FDL framework is as high as 95.22% with only 1/36 of the transmission energy consumption compared with the DL. Moreover, the total throughput can achieve 93% of the theoretical performance.

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach

This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm’s feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

Study on Mechanical Properties and Energy Consumption of Fissured Sandstone with Different Dip Angles under Impact Load

To study the dynamic mechanical properties and energy consumption of fissured sandstone with different dip angles under impact load, impact compression tests were conducted on seven groups of and intact and fractured sandstone specimens with different dip angles using the split Hopkinson pressure bar (SHPB) device with 0.3 MPa air pressure. The influence of dip fissures on the crushing shape, dynamic compressive strength, dynamic elastic modulus, dynamic peak strain, dynamic average strain rate, dynamic stress-strain curve, and energy consumption of rock specimens was systematically analyzed. The results show that the 45° fissure angle is the best fragile angle according to the failure mode and dynamic compressive strength of the specimen and that difference in specimen failure modes specimens is attributed to the existence of fractures with different dip angles. The dynamic elastic modulus reaches the minimum when the fissure angle is 45° and the maximum when the fissure angle is 90°. The dynamic peak strain is the lowest and minimal influence of fissure angle on the average strain rate of the specimen is presented when the fissure angle is 45°. From the stress-strain curves, the two specimen ends are most vulnerable to the relative sliding and dislocation of the lateral fissure angle during impact compression when the fissure angle is 45°. With stable incident energy in the test, a prominent relationship exists between the reflected energy, transmission energy, and energy consumption and the fissure angles. In addition, the fissure angle exhibits a prominent influence on reflected energy and the energy consumption of the specimen when the fissure angle ranges from 45° to 60°.

Resource Scheduling and Energy Consumption Optimization Based on Lyapunov Optimization in Fog Computing.

Delay-sensitive tasks account for an increasing proportion of all tasks on the Internet of Things (IoT). How to solve such problems has become a hot research topic. Delay-sensitive tasks scenarios include intelligent vehicles, unmanned aerial vehicles, industrial IoT, intelligent transportation, etc. More and more scenarios have delay requirements for tasks and simply reducing the delay of tasks is not enough. However, speeding up the processing speed of a task means increasing energy consumption, so we try to find a way to complete tasks on time with the lowest energy consumption. Hence, we propose a heuristic particle swarm optimization (PSO) algorithm based on a Lyapunov framework (LPSO). Since task duration and queue stability are guaranteed, a balance is achieved between the computational energy consumption of the IoT nodes, the transmission energy consumption and the fog node computing energy consumption, so that tasks can be completed with minimum energy consumption. Compared with the original PSO algorithm and the greedy algorithm, the performance of our LPSO algorithm is significantly improved.

Power-Efficient Passive Beamforming and Resource Allocation for IRS-Aided WPCNs

This paper studies an intelligent reflecting surface (IRS)-assisted wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. In particular, we propose three types of IRS beamforming configurations, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully dynamic IRS beamforming (FDBF)</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially dynamic IRS beamforming (PDBF)</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static IRS beamforming (SBF)</i> , to strike a balance between the system performance and signaling overhead as well as implementation complexity. Moreover, we adopt a practical non-linear energy harvesting (EH) model, and leverage a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio frequency power to get stuck into the saturation regime. We aim to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problems, a successive convex approximation (SCA) based alternating optimization algorithm is proposed. Moreover, we study the case with the ideal linear EH model and two algorithms, namely SCA-based algorithm and semidefinite relaxation (SDR) algorithm, are proposed. Simulation results demonstrate the effectiveness of our proposed designs over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice.

From Dynamic Superwettability to Ionic/Molecular Superfluidity.

Life systems present ultralow energy consumption in high-efficiency energy conversion, information transmission, and biosynthesis. The total energy intake of the human body is about 2000 kcal/day to maintain all of our activities, which is comparable to a power of ∼100 W. The energy required for the brain to work is equivalent to ∼20 W, and the rest of the energy (∼80 W) is used for other activities. All in vivo biosyntheses take place only at body temperature, which is much lower than that of in vitro reactions. To achieve these ultralow energy-consumption processes, there should be a kind of ultralow-resistivity matter transport in nanochannels (e.g., ionic and molecular channels), in which the directional collective motion of ions or molecules is a necessary condition rather than traditional Newton diffusion. The directional collective motion of ions and molecules is considered to be ionic/molecular superfluidity. The driving force of ionic/molecular superfluidity formation requires two necessary conditions: (1) Ions or molecules are confined at a certain distance (e.g., approximately twice Debye length (2λD) for ions or twice the van der Waals equilibrium distance (2d0) for molecules). (2) When the attractive potential energy (E0) is stronger than the thermal noise (kBTc), ionic/molecular superfluidity can be formed. The concept of ionic/molecular superfluidity will promote the understanding of energy conversion with ultralow energy consumption in biological systems. The swing of an eel's body generating electricity and cardiac resuscitation denote the conversion from mechanical energy to electrical energy, and mechanical modulation might result in a coherent resonance of ionic motion. The coherent resonance of Ca2+ in myocardium cells can induce a heartbeat, realizing the conversion from the electrical energy to the mechanical energy of a biological system. The macroscopic quantum state of ion channels is considered to be a carrier of neural information, and the environment field might play a significant role in regulating the macroscopic quantum states of various ion channels. In the biological ion channels system, the coupling of ion channels and their released photons might induce an environment wave which in turn regulates the ion oscillations in the channels to a coherent state. The states of decoherence and coherence might correspond to the states of sleep and action. We also demonstrated the decomposition of ATP to ADP released photons with a frequency of ∼34 THz, which could further drive DNA polymerization in the nanocavity of DNA polymerase. The photochemical (mid- and far-IR) reaction might be the driving force in high-efficiency biosynthesis. Quantized syntheses resonantly driven by multiple mid- and far-IR photons could be further designed in a tubular reactor with membranes of different microporous structures to achieve a high-efficiency synthesis with a low energy consumption. Finally, we point out that the Bose-Einstein condensate potentially widely exists. We expect that this Account will provide new ideas for the key problem in life science: how can life systems present ultralow energy consumption in high-efficiency energy conversion, information transmission, and biosynthesis?

A energy-conserving predictive preemptive multipath routing protocol for adhoc networks: a lifetime improvement

Mobile device are widely used today in MANETs, due to their rich functionality. However, route failure may occur due to lower receive signal strength, mobility, congestion and device failures. Also, the battery life of these devices is very limited and deploying high resource consuming applications such as streaming on these mobile devices, is a challenging task. It is extremely important to efficiently use of the contained resources on these devices when they participate in a mobile ad hoc network. In this paper, we propose an optimization of the cross-layer networking mechanism. Our work focuses on MAC and routing layers of the OSI stack. We propose a cooperation of the routing layer with the MAC layer power-control technique to decrease the energy consumption in transmission within adhoc networks. We propose an energy-conserving multipath routing protocol for adhoc networks lifetime improvement protocol called E-PPAOMDV (Energy aware Predictive Preemptive AOMDV). This protocol is based on new metric to preserves the residual energy of nodes and balances the consumed energy to increase the network lifetime. Also, we propose a mechanism based on Newton interpolation, to distinguish between both situations, failures due to congestion or mobility, and consequently avoiding unnecessary route repair process. The E-PPAOMDV was implemented using NS-2. The simulation results demonstrate the merits of our proposed E-PPAOMDV. Our proposal improves the performance of mobile ad hoc networks by extending the lifetime of the network and decreasing the average consumed energy with approximately 1 to 3%, while the average end-to-end delay is reduced by 33%, normalized routing load by 20 to 27%; also, increasing the packet delivery ratio with approximately 2-10% and the throughput with 5% when compared with AOMR-LM.

Non-Cooperative Game Forwarding Leveraging User Trustworthiness in Mobile Edge Networks

Given frequent changes of network topology caused by limited computing power, strong mobility and weak reliability of most nodes in mobile edge computing (MEC) networks, a Non-Cooperative Game forwarding strategy based on user Trustworthiness (NCGT) is proposed to deal with low security and efficiency of data transmission. NCGT firstly considers device residual energy ratio, contact probability, service degree and link stability between devices to measure the reliability of nodes. Then, leverages Entropy Weight (EW) method and Golden Section Ratio (GSR) to develop a security optimal neighbors screening model based on multi-attribute decision-making, which ensures that high-performance security nodes are selected as forwarding game objects. Third, NCGT takes forwarding and non-forwarding as the policy set, designs the benefit function, and gets forwarding probability of nodes through Nash equilibrium, to reduce a large number of redundancy, competition and conflict in forwarding requests and improve its broadcasting efficiency. The simulation results show, NCGT is more effective against black hole and witch attacks than S-MODEST and AODV+FDG when there exists malicious nodes. Meanwhile, with the increasing of network load, NCGT with or without GSR always performs best in the terms of data delivery rate, delay, transmission energy consumption and system throughput in MEC environment.

Multi-Armed Bandits Learning for Task Offloading in Maritime Edge Intelligence Networks

In the context of complex and dynamic marine environment, the offloading of computing tasks for ships of Internet of Things (IoT) users is a very challenging problem considering the different quality of service (QoS) requirements of maritime applications. Mobile edge computing driven by powerful computing capability and edge intelligence is taken as a promising solution, especially for the resource-constrained and delay-sensitive maritime IoT users. In this paper, we study the optimal edge server selection problem for ship IoT users to jointly minimize the latency and energy consumption for task offloading. Specifically, we first propose a novel space-air-ground-edge (SAGE) integrated maritime network architecture to offload computation-intensive IoT services at sea. Then, the latency and energy consumption of data transmission and processing during offloading are modelled. Based on the models, the edge server selection problem is formulated into a Multi-Armed Bandits learning problem, with considering the task latency requirement and energy budget. To achieve the optimal solution, a novel algorithm, referred to as UCB1-ESSS, is developed, which links the latency, energy consumption, and network constraints by introducing both reward and cost. The simulation results show that the proposed algorithm can achieve considerably lower offloading latency and weighted latency-energy cost compared with the traditional algorithms under different QoS requirements, which proves the efficacy of theproposed algorithm.

A buffer-aware dynamic UAV trajectory design for data collection in resource-constrained IoT frameworks

The emergence of unmanned aerial vehicle (UAV)-enabled technology in the Internet of Things (IoT) era leads to a significant reduction in data collection delays when accumulating sensory data from ground IoT nodes (INs). As a flying data collector, the UAV hovers at a limited number of Access Points (APs) to collect data, outperforming ground data collectors in terms of transmission energy consumption, data delivery reliability, and timeliness. However, the INs have a finite amount of buffer capacity to store the data that must be collected before they overflow. As a result, the data gathering route for UAVs should be adaptable to INs’ buffer deadline in order to minimize data loss. In this paper, a buffer-aware dynamic UAV trajectory design protocol is proposed for data collection from resource-constrained INs. A distributed AP nomination strategy is proposed in order to reduce UAV hovering latency. Furthermore, using machine learning approaches, a modified ant colony optimization algorithm is constructed to minimize the data loss penalty due to buffer overflow. Finally, the performance of the proposed scheme is evaluated against several state-of-the-art protocols with regards to parameters such as data loss penalty, packet delivery ratio, and network lifetime.