Accurately predicting traffic conditions is vital for smart city development, yet it remains challenging due to the intricate spatio-temporal dependencies in road networks. Existing works often propose intra-mixing deep learning-based prediction models for individual nodes and share parameters among them or spatial intermixing deep learning-based models for traffic predictions. However, these approaches may neglect essential principles of information exchange in traffic flow or capture useless or even erroneous spatio-temporal dependencies. To address these limitations, we propose a Mixing Spatio-Temporal neural network (MiST) for enhancing traffic predictions. In MiST, we propose (i) a temporal encoder that embeds the traffic data along with periodic features, (ii) a spatial encoder that embeds the positional information in graph and hypergraph spectral domains, as well as spatial node identities, and (iii) a mixing spatio-temporal encoder that merges the diverse features provided by the temporal and spatial encoders. Our empirical evaluations on real-world traffic prediction tasks, including flow and speed predictions, validate the superiority of MiST, underscoring its innovative contribution to traffic prediction methodologies.

The problem of traffic speed predictions is still very challenging due to the complex and dynamic urban traffic conditions. Many existing works have implied the importance of integrating spatial correlations into models to explore nonlinear spatio-temporal dependencies and make traffic predictions in near future. However, some of the works only consider pairwise correlations and cannot model the hidden information among multiple nodes well, and the others only consider hyper-correlations (that can be shared by more than two nodes) and discount the role of the pairwise ones for propagating spatial dependencies. Therefore, we propose a Spatio-Temporal neural nEtwork based on both Pairwise and Hyper-correlations (STEPH) for traffic speed predictions. It is distinguished primarily by incorporating both types of spatial correlations into temporal information and designing new hybrid spatio-temporal blocks in neural networks to effectively overcome the challenge. Experiments on two real-world traffic datasets demonstrate the effectiveness of the proposed model, and show its superiority performance compared to other state-of-the-art baselines.

This paper proposed a miniature neural interface system. A single chip neural recording SoC was fabricated in 40nm CMOS process with an area of 3mm×3mm. It integrated a 16-channel analog front end (AFE), and a low power constant envelope polar transmitter. The general form of continuous phase modulation was used as the modulation scheme. Algorithms for receiver including frequency offset calibration, frame synchronization, and symbol demodulation were proposed and implemented on a software-defined radio platform. Simulation results showed that a bit error rate of $10^{-4}$ is achieved at the signal to noise ratio of 19 dB at high data rate mode of 971.4 kbps. A graphic user interface was designed for channel decoding and real-time display. Experimental results showed that the input referred noise of the AFE is 2.87$\mu V_{rms}$, and the energy efficiency of the transmitter is 2. 8nJ/bit. The proposed chip consumes 5. 47mW power in total in its maximum workload. The neural signal can be correctly decoded at least at a RSSI (Received Signal Strength Indicator) of -95dBm, and a working distance of 8 m. In-vivo tests on rat have been conducted, showing a good usability of the proposed system.

This paper proposed a fully integrated MBAN (2360–2400 MHz) continuous phase modulated transmitter (TX) with tunable less than 0dBm output power for medical band. A duty-cycle tuning strategy was proposed for the power amplifier (PA) featuring adaptive optimized efficiency for different output powers. A fully on-chip transformer-based match network was proposed to suppress the 2nd harmonic using a series$LC$resonator and to suppress the 3rd harmonic by introducing a transformer inter-winding capacitor feedback path. A fractional-N all-digital phase locked loop (ADPLL) with a transformer-based digitally controlled oscillator (DCO) is employed to reduce power consumption as well as improve modulation quality. The transmitter was fabricated in 40-nm CMOS technology, occupying an active area of 0.48mm2. Experimental results show a 26% drain efficiency with −10dBm PA output and 4dB tunable range. A 2mW total power consumption was measured with a TX efficiency of 5% and an energy efficiency of 2nJ/bit. The measured 2nd and 3rd harmonic distortion of the output were −44.3dBm and −57.2dBm, respectively, with on-chip matching network. The measured FSK error of CPM was 2.3% with an M of 2 and 1.57% with an M of 4.

Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.

Integrating raw Global Position System (GPS) trajectories with a road network is often referred to as a map-matching problem. However, low-frequency trajectories (e.g., one GPS point for every 1-2 min) have raised many challenges to existing map-matching methods. In this paper, we propose a novel and global spatial-temporal map-matching method called spatial and temporal conditional random field (ST-CRF), which is based on insights relating to: 1) the spatial positioning accuracy of GPS points with the topological information of the underlying road network; 2) the spatial-temporal accessibility of a floating car; 3) the spatial distribution of the middle point between two consecutive GPS points; and 4) the consistency of the driving direction of a GPS trajectory. We construct a conditional random field model and identify the best matching path sequence from all candidate points. A series of experiments conducted for real environments using mass floating car data collected in Beijing and Shanghai shows that the ST-CRF method not only has better performance and robustness than other popular methods (e.g., point-line, ST-matching, and interactive voting-based map-matching methods) in low-frequency map matching but also solves the “label-bias” problem, which has long existed in the map matching of classical hidden Markov-based methods.

We examine the asymptotic behavior of degree correlation (i.e., the joint degree distribution of adjacent nodes) in several scale-free topology generators GED [13], PLRG [1], GLP [10], BA [3], AB [2]. We present a unifying analytical framework that allows tractable analysis of degree correlation in all studied models and derive asymptotic formulas of two degree correlation metrics - assortativity and clustering. Our results indicate that all studied generators become uncorrelated as graph size increases, which is inconsistent with time-invariance of these metrics in real networks such as the Internet [36], [48], [50]. Since the class of degree-based generators is incapable of reproducing evolving characteristics of the Internet, we study three other models that evolve graphs using different rules than preference of degree (e.g., based on random walks [50], optimization [17], and geometry [23]) and show using simulations that these models are much more viable alternatives for replicating the complex structure of Internet-like graphs.

In this paper, we explore multi-hop bandwidth estimation assuming arbitrary cross-traffic at each node and develop a new probing method called Envelope that can asymptotically estimate not only the available bandwidth but also the raw capacity of the tight link. Envelope is based on a multi-link recursive extension of unbiased single-hop estimators proposed in the past (e.g., [14]) and a variation of the packet-cartouche technique [6]. Through extensive simulations, we evaluate Envelope in various network settings and cross-traffic conditions and find that it can measure tight-link bandwidth characteristics with accuracy that significantly surpasses that of the existing methods. We also find that Envelope can measure non-tight links in certain path and cross-traffic configurations.

End-to-end packet-pair probing has been used as one of the primary mechanisms to measure bottleneck capacity, cross-traffic intensity, and available bandwidth of end-to-end Internet paths. However, there has been lacking an understanding of what types of information about the path are sampled by packet-pairs and how they are encoded in the corresponding output dispersions. In this paper, we answer these questions by deriving the expression of individual output spacings (i.e., dispersions) in the context of multihop paths and general bursty cross-traffic arrivals. Based on these results, we examine stochastic properties of the sampled signals and derive the statistical mean of the output interpacket spacings as a recursive function of the input spacing. We show that the result is different from what has been obtained in prior work using fluid cross-traffic models and that this discrepancy has a significant adverse impact on the accuracy of packet-pair bandwidth measurement techniques

Although packet-pair probing has been used as one of the primary mechanisms to measure bottleneck capacity, cross-traffic intensity, and available bandwidth of end-to-end Internet paths, there is still no conclusive answer as to what information about the path is contained in the output packet-pair dispersions and how it is encoded. In this paper, we address this issue by deriving closed-form expression of packet-pair dispersion in the context of a single-hop path and general bursty cross-traffic arrival. Under the assumptions of cross-traffic stationarity and ASTA sampling, we examine the statistical properties of the information encoded in inter-packet spacings and derive the asymptotic average of the output packet-pair dispersions as a closed-form function of the input dispersion. We show that this result is different from what was obtained in prior work using fluid cross-traffic models and that this discrepancy has a significant impact on the accuracy of packet-pair bandwidth estimation.

In this paper, we examine the problem of estimating the capacity of bottleneck links and available bandwidth of endto -end paths under non-negligible cross-traffic conditions. We present a simple stochastic analysis of the problem in the context of a single congested node and derive several results that allow the construction of asymptotically-accurate bandwidth estimators. We first develop a generic queuing model of an Internet router and solve the estimation problem assuming renewal cross-traffic at the bottleneck link. Noticing that the renewal assumption on Internet flows is too strong, we investigate an alternative filtering solution that asymptotically converges to the desired values of the bottleneck capacity and available bandwidth under arbitrary (including non-stationary) cross-traffic. This is one of the first methods that simultaneously estimates both types of bandwidth and is provably accurate. We finish the paper by discussing the impossibility of a similar estimator for paths with two or more congested routers.

In this paper, we take the sample-path approach in analyzing the asymptotic behavior of single-hop bandwidth estimation under bursty cross-traffic and show that these results are provably different from those observed under fluid models of prior work. This difference, which we call the probing bias, is one of the previously unknown factors that can cause measurement inaccuracies in available bandwidth estimation. We present an analytical formulation of "packet probing," based on which we derive several major properties of the probing bias. We then experimentally observe the probing bias and investigate its quantitative relationship to several deciding factors such as probing packet size, probing train length, and cross-traffic burstiness. Both our analytical and experimental results show that the probing bias vanishes as the packet-train length or packet size increases. The vanishing rate is decided by the burstiness of cross-traffic.

An infrastructure network often maintains nodes that participate in the distribution of video and/or content from a server to clients. To support these application sessions, the infrastructure network needs to maintain logical connectivity between the server and client entities, realized through transport-level path segments that interconnect one or more intermediate nodes. The connectivity can be viewed as an acyclic graph, i.e., a tree whose vertices include the nodes containing the client and server entities with edges set up over the intervening path segments. The paper deals with reconfiguration algorithms to handle the addition or removal of a vertex due to application-level actions like a client joining the distribution session or a server leaving. Dynamic network changes may also trigger the reconfiguration of a distribution path at the session level. With reconfiguration activities occurring asynchronously at the connectivity management level, coordination among these activities is required so that the reconfigured distribution path maintains the tree-structure (i.e., the connectivity graph remains acyclic). The paper provides an algorithmic framework that employs loosely synchronized time-stamp ordering to coordinate between multiple reconfiguration activities. Policy functions that prescribe constraints on the tree setups (e.g. minimum cost trees) can augment our algorithms to suit specific application domains. The usefulness of our framework in distribution path setups under resource constraints is also described. The framework is evaluated using discrete-event simulation.

Current works on QOS-routing allow flow and QOS control to be exercised on multicast paths after an underlying routing protocol (e.g., DVMRP and CBT) sets up these paths. The 'QOS-control-after-routing' paradigm limits the ability of routers to provide cost-efficient routing (in a network-wide sense) because they can explore only those paths already set up even if more cost-efficient alternate paths exist. This motivates the need to integrate routing with flow and QOS control, wherein alternate paths can be considered to enable cost-efficient path setups. The paper proposes scalable IntServ-style mechanisms at the flow management and routing protocol levels.