On Network Lifetime Expectancy With Realistic Sensing and Traffic Generation Model in Wireless Sensor Networks

et al. 2016

IEEE Trans. Ind. Inf.

203

Network lifetime is a crucial performance metric to evaluate data-gathering wireless sensor networks (WSNs) where battery-powered sensor nodes periodically sense the environment and forward collected samples to a sink node. In this paper, we propose an analytic model to estimate the entire network lifetime from network initialization until it is completely disabled, and determine the boundary of energy hole in a data-gathering WSN. Specifically, we theoretically estimate the traffic load, energy consumption, and lifetime of sensor nodes during the entire network lifetime. Furthermore, we investigate the temporal and spatial evolution of energy hole, and apply our analytical results to WSN routing in order to balance the energy consumption and improve the network lifetime. Extensive simulation results are provided to demonstrate the validity of the proposed analytic model in estimating the network lifetime and energy hole evolution process.Index Terms-wireless sensor network, network lifetime, energy hole, energy efficiency, routing.

Section: A Network Modelmentioning

confidence: 99%

Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

Ren

et al. 2016

IEEE Trans. Ind. Inf.

203

“…There are a couple of software tools of network traffic generating and these tools can be divided into the following four categories [3][4][5][6] [8][9][10]:…”

Section: The Methods Of Traffic Generatingmentioning

confidence: 99%

“…About the task profile of network, the reliability profile usually includes work stress and environment stress [9]. For network reliability test, the work stress is performed on network traffic [15], in other words, the network traffic can reflect the users' behavior.…”

Section: The Profile Of Network Reliability Testmentioning

confidence: 99%

A new hybrid traffic generation model for Tactical Internet reliability test

Huang

2015 Annual Reliability and Maintainability Symposium (RAMS)

2015

Tactical Internet (TI) is the communication infrastructure of tactical level maneuver warfare forces; its reliability directly affects the military combat. Therefore, in the TI reliability test, how to generate the appropriate network traffic and get the most accurate evaluation results should be an important part of the TI reliability test research.This paper analyzes the characteristics of applications and traffic in TI, and proposes a new hybrid traffic model which can reflect TI mission profile, to generate traffic for TI reliability test. Firstly, we research the current popular traffic generation technologies of network reliability test area, and point out their advantages and disadvantages. Secondly, according to the research result of traffic generation technologies, we propose a method for application traffic generation and combine it with background traffic generation method. Then the completed hybrid traffic model which includes application traffic generation method and background traffic method is proposed. Based on this traffic model, application traffic and background traffic are generated for TI reliability test and can get the most accurate reliability evaluation result. Finally, all the theories and models are verified by a case study. In the same case, we get the reliability test results by using hybrid traffic generation method and reliability simulation results by using OPNET, according to comparing both results, the ranges of reliability curves are same and the effectiveness of the method is proven. The profile of network reliability testThe purpose of task profile researching is supposing to 978-1-4799-6703-2/15/$31.00©2015 IEEE find a method of generating suitable traffic to carry out the network reliability test.About the task profile of network, the reliability profile usually includes work stress and environment stress [9]. For network reliability test, the work stress is performed on network traffic [15], in other words, the network traffic can reflect the users' behavior. Thus, in order to implement TI reliability test, we have to establish the reliability test profile from TI, and then break down the profile to network traffic, establish the traffic model, the last step is generating the property network traffic for network reliability test.The definition of "profile" is: a description of event, state, process function and the environment, so this kind of description is a timing description [1]. Hence, the task profile's definition in reliability area should be: A timing description of all necessary events and states that the products have to experience for completing the specify tasks. The task profile usually includes: the working status of products, maintenance program, working time and order of products, the environment time and order for products, the failure definition of the tasks. Now the current profile modeling methods of reliability test are focus on temperature, pressure and voltage of system components. But for some complex systems, it is very difficult to establish the p...

“…We utilize the probabilistic sensing model in [26], [27] to evaluate the sensing qualities of UAVs. Specifically, the successful sensing probability (SSP) for a UAV is an exponential function of the distance between the UAV and its target.…”

Section: B Uav Sensingmentioning

confidence: 99%

Cellular UAV-to-Device Communications: Trajectory Design and Mode Selection by Multi-Agent Deep Reinforcement Learning

et al. 2020

IEEE Trans. Commun.

In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed spectrum.However, the interference from surrounding terminals is uncontrollable due to the opportunistic channel access. In this paper, we consider a cellular Internet of UAVs to guarantee the Quality-of-Service (QoS), where the sensory data can be transmitted to the mobile devices either by UAV-to-Device (U2D) communications over cellular networks, or directly through the base station (BS). Since UAVs' sensing and transmission may influence their trajectories, we study the trajectory design problem for UAVs in consideration of their sensing and transmission. This is a Markov decision problem (MDP) with a large state-action space, and thus, we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space, and then propose a multi-UAV trajectory design algorithm to solve this problem.Simulation results show that our proposed algorithm can achieve a higher total utility than policy gradient algorithm and single-agent algorithm. ). I. INTRODUCTIONWith high mobility and low operational cost, unmanned aerial vehicle (UAV) is recognized as a powerful facility to provide sensing services [2], [3], which has found use in a wide range of sensing applications including traffic monitoring [4], industrial inspection [5], precision agriculture [6], and fire surveillance [7]. In the current unmanned aircraft systems (UASs) for sensing services, UAVs transmit their sensory data to terrestrial mobile devices 1 over the unlicensed spectrum [8]. However, due to the opportunistic channel access at the media access control (MAC) layer, the interference from surrounding terminals is uncontrollable, and the Quality-of-Service (QoS) for sensing services cannot be guaranteed. Therefore, a more reliable network is necessary.As an effective solution, terrestrial cellular networks can be utilized to provide UAV sensing services with guaranteed QoS, which we refer to as the cellular Internet of UAVs [9], [10]. In the cellular Internet of UAVs, UAVs first transmit the sensory data to the BS, after which the BS sends the received data to mobile devices. When UAVs fly far from their sensing targets, they may suffer low sensing quality, while if UAVs are far from the BS, it is difficult to transmit their sensory data back to the BS. Therefore, to ensure the QoS, UAVs should jointly take sensing and transmission into consideration in the designing of their trajectories. However, when UAVs are close to their mobile devices, the data rate can be further improved if UAVs can directly transmit the sensory data to proximal mobile devices, namely the UAV-to-Device (U2D) communications, rather than transmit through cellular communications.In this paper, we propose to enable U2D communications in the cellular Internet of UAVs.We consider a orthogonal frequency division multiple access (OFDMA) cellular Internet of UAVs 2 , where the sensory data ca...