It is challenging and significant to explore the impacts of non-real-time services on real-time services from the perspective of jitter. Most of current researches on jitter made too many mathematical hypotheses on networks and traffic. This paper puts forward a tandem queuing model to characterize the real communication scenario where heterogeneous services are served by IEEE 802.15.4 wireless sensor networks (WSNs), and then the packets served successfully are fed to Internet protocol (IP) networks. By analyzing the contention access processes in IEEE 802.15.4 WSNs, the authors derive the departure processes of the two types of services, i.e., the arrival processes of IP networks. The IP network is modeled as a queuing system, in which the real-time service is forwarded accompanied by the non-real-time service. Investigating the jitter of real-time services is intractable. Therefore, this paper abstracts this problem as a dynamic queuing system evolving on a dynamic time interval. Referring the transient analysis method (TAM), this paper obtains the queue length in a random time interval which is scaled by the arrival of real-time services. Queue length evolution is closely connected with the jitter. Benefiting from the derivation in probability generation domain, the jitter of real-time services is obtained.

Echo cancellation plays an important role in current Internet protocol (IP) based voice interactive systems. Voice state detection is an essential part in echo cancellation. It mainly comprises two parts: double talk detection (DTD) and voice activity detection (VAD). DTD is used to detect doubletalk and prevent filter divergence in the presence of near-end speech, and VAD is used to determine the near-end voice activity and output silence indicator when near-end is silent. However, DTD straightforwardly proceeded may mistakenly declare double talk under double silent condition, coefficients update under the far-end silence condition may lead to filter divergence, and current VAD algorithms may misjudge the residual echo from the near end to be far-end voice. Therefore, a voice detection algorithm combining DTD and far-end VAD is proposed. DTD is implemented when VAD declares far-end speech, filtering and coefficients update will be halted when VAD declares far-end silence, and the far-end VAD adopted is multi-feature VAD based on short-time energy and correlation. The new algorithm can improve the accuracy of DTD, prevent filter divergence, and exclude the circumstance that far-end signal only contains residual echo from near end. Actual test results show that the voice state decision of the new algorithm is accurate, and the performance of echo cancellation is improved.

To address the problems of the present TeraHertz medium access control (MAC) protocols such as not updating the time slot requests numbers in time, unreasonable superframe structures and not merging time slot requests from the same pair of nodes, high throughput low delay medium access control (HLMAC), a novel MAC protocol for TeraHertz ultra-high data-rate wireless networks is proposed. It reduces the data access delay largely with a new superframe structure, from which nodes can get time slot allocation information immediately. The network throughput is also improved with the help of updating time slot requests number and merging time slot requests from the same pair of nodes. The theoretical analysis verifies the effectiveness of HLMAC, and the simulation results show that HLMAC improves the network throughput by 65.7% and decreases the access delay by 30%, as compared to energy and spectrum-aware medium access control (ES-MAC).

With the rapid development of location-based networks, point-of-interest (POI) recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity (GTSCP) to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

The traffic congestion occurs frequently in urban areas, while most existing solutions only take effects after congesting. In this paper, a congestion warning method is proposed based on the Internet of vehicles (IOV) and community discovery of complex networks. The communities in complex network model of traffic flow reflect the local aggregation of vehicles in the traffic system, and it is used to predict the upcoming congestion. The real-time information of vehicles on the roads is obtained from the IOV, which includes the locations, speeds and orientations of vehicles. Then the vehicles are mapped into nodes of network, the links between nodes are determined by the correlations between vehicles in terms of location and speed. The complex network model of traffic flow is hereby established. The communities in this complex network are discovered by fast Newman (FN) algorithm, and the congestion warnings are generated according to the communities selected by scale and density. This method can detect the tendency of traffic aggregation and provide warnings before congestion occurs. The simulations show that the method proposed in this paper is effective and practicable, and makes it possible to take action before traffic congestion.

Wireless sensor networks (WSNs) are emerging as essential and popular ways of providing pervasive computing environments for various applications. Unbalanced energy consumption is an inherent problem in WSNs, characterized by multi-hop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. In multi-hop sensor networks, information obtained by the monitoring nodes need to be routed to the sinks, the energy consumption rate per unit information transmission depends on the choice of the next hop node. In an energy-aware routing approach, most proposed algorithms aim at minimizing the total energy consumption or maximizing network lifetime. In this paper, we propose a novel energy aware hierarchical cluster-based (NEAHC) routing protocol with two goals: minimizing the total energy consumption and ensuring fairness of energy consumption between nodes. We model the relay node choosing problem as a nonlinear programming problem and use the property of convex function to find the optimal solution. We also evaluate the proposed algorithm via simulations at the end of this paper.

The fifth generation mobile communication (5G) systems can provide Gbit/s data rates from massive multiple-input multiple-output (MIMO) combined with the emerging use of millimeter wavelengths in small heterogeneous cells. This paper develops an energy-efficiency based multi-user hybrid beamforming for downlink millimeter wave (mmWave) massive MIMO systems. To make better use of directivity gains of the analog beamforming and flexible baseband processing of the digital beamforming, this paper proposes the analog beamforming to select the optimal beam which can maximize the power of the objective user and minimize the interference to all other users. In addition, the digital beamforming maximizes the energy efficiency of the objective user with zero-gradient-based approach. Simulation results show the proposed algorithm provide better bit error rate (BER) performance compared with the traditional hybrid beamforming and obviously improved the sum rate with the increase in the number of users. It is proved that multi-user MIMO (MU-MIMO) can be a perfect candidate for mmWave massive MIMO communication system. Furthermore, the analog beamforming can mitigate the inter-user interference more effectively with the selection of the optimal beam and the digital beamforming can greatly improve the system performance through flexible baseband processing.

From the perspective of compressed sensing (CS) theory, the channel estimation problem in large-scale multiple input multiple output (MIMO)-orthogonal frequency division multiplexing (OFDM) system is investigated. According to the theory, the smaller mutual coherence the reconstruction matrix has, the higher success probability the estimation can obtain. Aiming to design a pilot that can make the system reconstruction matrix having the smallest mutual coherence, this paper proposes a low complexity joint algorithm and obtains a kind of non-orthogonal pilot pattern. Simulation results show that compared with the conventional orthogonal pilot pattern, applying the proposed pattern in the CS channel estimation can obtain the better normalized mean square error performance. Moreover, the bit error rate performance of the large-scale MIMO-OFDM system is also improved.

Considering the shortcomings of the existing vehicle-to-vehicle (V2V) communication antennas, this paper proposes a regular hexagon broadband microstrip antenna. By loading shorting pins and etching V-shape slots with different size at each angle of the regular hexagon patch, it realizes impedance matching and obtains better impedance bandwidth. The simulated results show that the relative bandwidth of this antenna reaches 35.55%, covers the frequency band of 4.74 GHz to 6.79 GHz. The antenna acquires an omni-directional radiation pattern in the horizontal plane whose out of roundness is less than 0.5 dB. In addition, the antenna is manufactured and tested, whose tested results are basically consistent with simulated results. Because the height of antenna is 3 mm, it is easy to be hidden on roof of a vehicle for V2V communication.

Objective video quality assessment methods often evaluate all the frames regardless of their importance. For wireless distorted videos, not every frame has the same contribution to the final overall quality due to the channel fading and interference, which may lead to the capacity variation in temporal. Besides, with the content similarity and error propagation pattern in temporal domain, it is possible to evaluate the overall quality with only part of the frames. In this paper, a demonstration is performed to show that the video quality can be evaluated with reduced frames set (RFS), and a state transition model is proposed to extract the RFS. At last, a video quality assessment (VQA) method is carried out based on RFS. Compared with several state-of-the-art methods, our method can achieve a suitable accuracy with less frames to be processed.

Because of its wide application in anonymous authentication and attribute-based messaging, the attribute-based signature scheme has attracted the public attention since it was proposed in 2008. However, most of the existing attribute-based signature schemes are no longer secure in quantum era. Fortunately, lattice-based cryptography offers the hope of withstanding quantum computers. And lattices has elevated it to the status of a promising potential alternative to cryptography based on discrete log and factoring, owing to implementation simplicity, provable security reductions and quantum-immune. In this paper, the first lattice attribute-based signature scheme in random oracle model is proposed, which is proved existential unforgeability and perfect privacy. Compared with the current attribute-based signature schemes, our new attribute-based signature scheme can resist quantum attacks and has much shorter public-key size and signature size. Furthermore, this scheme is extended into an attribute-based signature scheme on number theory research unit (NTRU) lattice, which is also secure even in quantum era and has much higher efficiency than the former.

As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers’ fatigue driving by using the human eye tracking technology.