Probabilistic Delay Analysis of Internet of Things Based on LTE Network

An Long Term Evolution (LTE) network based mobile Internet of Things (IoT) is modeled and analyzed with the probabilistic delay distribution as the main interest. Stochastic network calculus is relied on to conduct the analysis. Two typical traffic models, i.e., Compound Poisson and Aggregated ON-OFF Source, are analyzed. The wireless fading channel is modeled as a Gilbert-Elliot channel. Numerical results are presented, where the probabilistic delay distribution and guaranteed capacity under certain delay constraint are shown and discussed.

Improved sample filtering method for measuring end-to-end path capacity

By analyzing the effect of cross traffic (CT) enforced on packet delay, an improved path capacity measurement method, pcapminp algorithm, was proposed. With this method, path capacity was measured by filtering probe samples based on measured minimum packet-pair delay. The measurability of minimum packet-pair delay was also analyzed by simulation. The results show that, when comparing with pathrate, if the CT load is light, both pcapminp and pathrate have similar accuracy; but in the case of heavy CT load, pcapminp is more accurate than Pathrate. When CT load reaches 90%, pcapminp algorithm has only 5% measurement error, which is 10% lower than that of pathrate algorithm. At any CT load levels, the probe cost of pcapminp algorithm is two magnitudes smaller than that of pathrate, and the measurement duration is one magnitude shorter than that of pathrate algorithm.

FAST CAPACITY ESTIMATION ALGORITHMS FOR MANETS USING DIRECTIONAL ANTENNAS

In this letter,capacity estimation for Mobile Ad hoc NETworks (MANETs) using direc- tional antennas are studied.Two Matrix-based Fast Calculation Algorithms (MFCAs) are proposed to estimate the network capacity in a network scenario in which there is no channel sharing among multiple sessions and traffic is sensitive to delay with an end-to-end delay constraint.The first algo- rithm MFCA-1 is used to estimate network capacity in a situation where all links have the same delay. It estimates the maximum number of k-hop sessions in a network based on the k-hop adjacency matrix of the network.The second algorithm MFCA-2 is used to estimate network capacity in a situation where different links may have different delays.It calculates the maximum number of sessions in a network with an end-to-end delay constraint based on the adjacency matrix and the link-delay matrix of the network.Numerical and simulation results show that both MFCA-1 and MFCA-2 can calculate network capacity much faster than the well-known Brute-Force Search Algorithm (BFSA) but with the same accuracy.

Delay and Capacity Trade-offs in Mobile Wireless Networks with Infrastructure Support

In this paper,we investigate the trade-offs between delay and capacity in mobile wireless networks with infrastructure support.We consider three different mobility models,independent and identically distributed （i.i.d） mobility model,random walk mobility model with constant speed and L＇evy flight mobility model.For i.i.d mobility model and random walk mobility model with the speed θ（1/n～（1/2））,,we get the theoretical results of the average packet delay when capacityis θ（1）,θ（1/n～（1/2）） individually,where n is the number of nodes.We find that the optimal average packet delay is achieved whencapacity λ（n） 〈（1/（2.n.log_2（1/（（1-e）-（k/n））＋1））,where K is the number of gateways.It is proved that average packet delay D（n） dividedby capacity λ（n） is bounded below by （n/（k·w））.When ω（n～（1/2））≤KO（n（（1-η）·（α＋1））/2）ln n） when K=o（n～η）（0≤η〈1）.We also provethat when ω（1/2）≤K）.

A dynamic network Qo S control mechanism based on traffic prediction

A dynamic network Qo S control mechanism was proposed based on traffic prediction. It first predicts network traffic flow and then dynamically distributes network resources, which makes full use of network flow self-similarity and chaos. So it can meet changing network needs very well. The simulation results show that the dynamic Qo S control mechanism based on prediction has better network performance than that based on measurement.

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.

DAWN: A Density Adaptive Routing for Deadline-Based Data Collection in Vehicular Delay Tolerant Networks

Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.

A novel repetition space-time coding scheme for mobile FSO systems

Considering the influence of more random atmospheric turbulence, worse pointing errors and highly dynamic link on the transmission performance of mobile multiple-input multiple-output （MIMO） free space optics （FSO） communica- tion systems, this paper establishes a channel model for the mobile platform. Based on the combination of Alamouti space-time code and time hopping ultra-wide band （TH-UWB） communications, a novel repetition space-time coding （RSTC） method for mobile 2x2 free-space optical communications with pulse position modulation （PPM） is devel- oped. In particular, two decoding methods of equal gain combining （EGC） maximum likelihood detection （MLD） and correlation matrix detection （CMD） are derived. When a quasi-static fading and weak turbulence channel model are considered, simulation results show that whether the channel state information （CSI） is known or not, the coding sys- tem demonstrates more significant performance of the symbol error rate （SER） than the uncoding. In other words, transmitting diversity can be achieved while conveying the information only through the time delays of the modulated signals transmitted from different antennas. CMD has almost the same effect of signal combining with maximal ratio combining （MRC）. However, when the channel correlation increases, SER performance of the coding 2×2 system de- grades significantly.