The distributed deep learning architecture between front-deployed sensors and edge-deployed gateways attracts increasing interest. However, the inference performance of distributed deep models is also impacted by the delivery loss of intermediate representation in the wireless link, especially in the harsh industrial fading environments. Traditional communication systems usually focus on transmission errors at bit level, which treat all bits in the packets equally and fail to suit the varying importance in distributed deep models, which urges the essential evolution of the communication method to form a joint co-design paradigm for distributed deep models. This article then proposes to optimize the Mean Time To First Failure (MTTFF) of wireless link instead of traditional bit error rate, which enables a guaranteed transmission window. This paper first derives the analytical model of MTTFF under MIMO systems, then utilizes the kernel mixture distribution to obtain a closed-form solution of MTTFF, which forms a optimization algorithm minimizing the transmitted power while achieving the aiming MTTFF. Extensive reallife experiments show more than 70% satisfaction rate of MTTFF, which leads to more than 10 times higher inference accuracy than the original deep model.

In industrial environments, the wireless link of IoT systems often experiences complex channel fading effects, making accurate online estimation of link quality crucial for improving system performance. Using Gaussian Mixture Model (GMM) to fit I/Q symbols allows estimation of Rician channel parameters, but traditional GMMs typically rely on prior knowledge of the number of Gaussian components to ensure clustering accuracy, posing challenges for adaptive channel modulation schemes in industrial settings. This paper proposes an adaptive Gaussian mixture model based on Kullback-Leibler divergence (KLD), which autonomously determines the optimal number of clusters through iterative evaluation, achieving optimal clustering performance. Firstly, this study proposes the utilization of forward KLD as an optimization target, leveraging its known optimal prior of zero to avoid local optima. Secondly, the redundancy in the number of clusters is assessed using reverse KLD constructed with the single Gaussian distribution. These improvements ensure that the GMM converges correctly to the global optimum regardless of the initial cluster count settings.

Accurate online link quality metrics represented by the Rician parameter are critical to enhancing the reliability of industrial wireless networks subject to temporal fading channels. The Rician parameters can be estimated by fitting the received I/Q symbols with GMM (Gaussian Mixture Model). However, the classical Expectation-Maximization estimations of GMM rely on the preset hyper-parameter of kernel numbers to guarantee the convergence, making it hard to work under adaptive modulation schemes. To address this challenge, we first reveal that the derivative of likelihood is less capable of representing the global optimal, which leads to the well-known local optimal problem and the failure to recognize the false convergence caused by incorrectly configured kernel numbers. A new empirical metric derived from KLD (Kullback-Leibler divergence) has been proposed to identify the local optimal convergence, as well as a new metric tuple to discriminate redundant kernels. A novel estimation algorithm has then been designed to shift the number of kernels from the preset hyper-parameter to the adjustable parameter. This improvement guarantees the global optimal convergence of the GMM with any initial number of kernels. Extensive experiments demonstrate that the proposed method achieves over ten times better accuracy, while requires less than half the iterations.

As one of data sources in cloud–edge–terminal collaboration enabled artificial intelligence of things (CETC-AIoT), the integrity and confidentiality of sensed information in the terminal side directly impact on the modeling and decision-making for CETC-AIoT. However, due to openness of transmission media among cloud, edge and terminal, it could be vulnerable to pollution attack and eavesdropping attack. Additionally, the constrained resources of some terminals make it difficult to deploy strong security schemes. In this context, how to make a tradeoff between the requirement of security and the limitation of resources needs to be explored. Therefore, toward secure and lightweight data transmission for CETC-AIoT, we propose a novel Gold sequence-based secure network coding (GS-SNC) scheme in this article. Specifically, the Gold sequence is introduced to generate the pseudo-random sequence, which is used to scramble and descramble the original information. The precoding matrix is constructed to encode and decode the scrambled information. The intermediate nodes perform the random linear network coding. The simulation results show that GS-SNC has advantages compared with double prime numbers-based secure network coding (DP-SNC) and secure practical network coding (SPOC), in terms of security, computational complexity, encryption capacity, and space overhead.

The distributed deep learning architecture can support the front-deployment of deep learning systems in resource constrained IoT devices and is attracting increasing interest. However, most ready-to-use deep models are designed for centralized deployment without considering the transmission loss of the intermediate representation inside the distributed architecture. This oversight significantly affects the inference performance of distributed deployed deep models. To alleviate this problem, a state-of-the-art work chooses to retrain the original model to form an intermediate representation with ordered importance and yields better inference accuracy under constrained transmission bandwidth. This paper first reveals that this solution is essentially a pruning-like solution, where unimportant information is adaptively pruned to fit within the limited bandwidth. With this understanding, a novel scheme named Naturally Aggregated Intermediate Representation (NAIR) has been proposed, which aims to naturally amplify the difference of importance embedded in the intermediate representation from a mature deep model and reassemble the intermediate representation into a hierarchy of importance from high-to-low to accommodate the transmission loss. As a result, this method shows further improved performance in various scenarios, avoids compromising the overall inference performance of the system, and saves astronomical retraining and storage costs. The effectiveness of NAIR has been validated through extensive experiments, achieving a 112% improvement in performance compared to the state-of-the-art work.

Over-The-Air (OTA) measurement is considered the preferred method for measuring the antenna system and end-to-end performance of Multiple-Input-Multiple-Output (MIMO) devices under test. Spatial correlation has been widely utilized as a key metric for evaluating the accuracy of MIMO OTA measurements. However, there is no guarantee that the standard signal streams convoluted with specified impulse responses will be ideally independent of each other in the implementation of the MIMO OTA testing system. Thus, it is envisaged that the spatial correlation in practical MIMO OTA testing systems may not be exactly equivalent to the expected value of the ideal theoretical model. In this paper, we propose a new evaluation framework for evaluating the spatial correlation performance of MIMO OTA testing system. This evaluation framework provides a novel observation method for spatial correlation, which reflects the non-ideal configuration of the MIMO OTA testing system and can be utilized to predict or cross-validate spatial correlation errors that deviate from the theoretical model. The experimental and simulation results have been verified against the theoretical model, demonstrating good consistency between the theoretical model and the proposed evaluation framework. Furthermore, several test scenarios have been verified with the different varying factors.

Toward green Big Data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

The front deployed deep learning is a promising technology of the next generation industrial applications, which can extract essential information from high dimension sensors. However, part of these heavy computation tasks at resource constrained front devices have to be offloaded to the edge or cloud devices, which forms the cooperative deep learning system through the exchange of intermediate data. The inference efficiency of cooperative deep learning system will then be highly correlated with the communication latency caused by the non-stationary industrial multipath-rich fading channel. This paper proposes a novel method to control the distribution of communications latency, which is able to support efficient cooperative deep learning architecture in the harsh industrial environment. The proposed method is essentially an inverse process of Gaussian Mixture Model (GMM), which adjusts latency samples to approach the given arbitrary shape function. To achieve this objective, a new variation of Expectation-Maximization (EM) algorithm in analytical domain is derived to decompose arbitrary distribution shape with multiple Gaussian kernels and an optimized stochastic resource allocation algorithm is proposed to approximate each Gaussian kernels. The performance of proposed method is verified by both classical Rician channel model and field measured industrial fading channel responses.

The internal attack launched by compromised nodes is a serious security risk in distributed networks. It is due to the openness of wireless channel and the lack of trust relationships between nodes. The trust model is an interesting approach to detect and remove these compromised nodes from the trusted routing table and maintain trust relationships in distributed networks. However, how to construct the secure dominating node to aggregate and forward the information is an enormous challenge in distributed networks. In this paper, a distributed construction algorithm of multi-layer connected dominating set is proposed for secure routing with trust models. The probabilities of nodes being compromised, combined with the trust value of the trust model, are used to construct a trusted multi-layer connected dominating set. Moreover, the impact of a node being compromised on the distributed network is quantified as loss expectation. The simulation results show that the proposed algorithm can effectively reduce the impact of nodes being compromised on the distributed network, and enhance the security of the network.

Establishing an effective situation prediction model for cyber security can know the active situation of future network malicious events in advance, which plays a vital role in cyber security protection. However, traditional models cannot achieve sufficient prediction accuracy when predicting cyber situations. To solve this problem, the initial location information of the sparrow population is optimized, and a sparrow search algorithm based on the Tent map is proposed. Then, the BP neural network is optimized using the improved sparrow search algorithm. Finally, a situation prediction model based on the sparrow search algorithm and BP neural network is proposed, namely T-SSA-BPNN. The simulation results show that the convergence speed and global search ability of the prediction model are improved. It can effectively predict the network security situation with high accuracy.

The wireless-enabled Industrial Internet of Things (IIoT) system is promising due to its flexibility and cable-free deployment. The varying fading channel will lead to the random transmission delays and jitters, which are the major challenges hindering the adoption of wireless communication in mission-critical industrial systems. The performance or even the stability of closed-loop feedback control system will degrade severely with such delays and jitters. As a result, the maximum delay margin should be met to guarantee the performance of the IIoT system. Aiming to better satisfy this requirement, a novel concept to shape the delay distribution under the industrial multipath fading channel is proposed in this article. Consequently, a two-layer closed feedback control algorithm, referred as TACAN in this article, is designed through the decoupling of original optimization function, by which the variance of the delay distribution is minimized to improve the reliability and stability of the IIoT systems. The performance of proposed delay shape control method is verified by both the classical Rician channel model and the field measured industrial fading channel responses.

Currently, increasing attention is devoted to artificial intelligence (AI) enabled software defined industrial networks (AI-SDINs). Toward energy-efficient and secure data transmission in AI-SDINs, a metric called energy-cost-per-useful-bit (ECPUB), which means energy cost of transmitting per useful bit is presented to evaluate energy efficiency and a novel energy-efficiency based secure multipath routing scheme is then put forward. Specifically, the ECPUB incorporates the utility and the law of diminishing marginal utility, for revealing the relationship among energy consumption, residual energy, and useful bits required. Moreover, in this article, an energy-efficiency based secure multipath routing scheme (E2SMR) is proposed by adopting the ECPUB and (t,n) threshold secret sharing scheme, for enhancing the security under the premise of guaranteeing energy efficiency. Extensive simulation results show that ECPUB can evaluate the energy efficiency and facilitate the balance of network load, while E2SMR can prolong the lifetime of the network and simultaneously ensure the network functionality securely.

Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requiresa prioriaiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.

It has been observed that the Internet of Medical Things (IoMT) is being deployed to construct varieties of intelligent platforms in medical and healthcare field, in order to comprehensively improve the quality of medical services. However, the cyber security of IoMT is facing enormous threat. Although many trust schemes are proposed to address the issue, the ignorance of aging weight in trust increases the risk of long-term attacks before being detected. In this paper, we design a dynamic aging weight scheme for trust model in IoMT. Essentially, when there are cooperative behaviors between two nodes, the aging weight can be set as large as possible to slow down the increase in the trust value of normal nodes. Once noncooperative behaviors appear, the smaller aging weight could mitigate the danger of compromised nodes. The simulation results indicate that our proposed scheme could better meet the principle of “Easy to lose” for trust.

For mMTC and uRLLC coexistence system, traditional random access techniques cannot meet the different requirements of the mixed traffic. To tackle this issue, in this paper, a novel priority based distributed queuing random access (PDQRA) mechanism is proposed for mMTC and uRLLC coexistence system. To meet the access delay requirement of uRLLC devices (UTDs), the preambles are divided into two groups. The first group is completely selected by partial UTDs, while the second group is shared by the mMTC devices (MTDs) and the rest of the UTDs. To evaluate the performance of the proposed mechanism, the access delay of devices is mathematically analyzed. Moreover, an optimization problem is formulated via jointly optimizing the preamble division ratio and selection probability of the preamble groups to minimize the access delay of MTDs while guaranteeing the delay requirement of the UTDs. Then, a low-complexity algorithm developed based on particle swarm optimization (PSO) is proposed to obtain the suboptimal solutions. Finally, numerical simulations are presented to verify the effectiveness of the proposed mechanism.

The authenticity and integrity of sensed data in the data collection stage is a very critical aspect for the smart city industrial environment. They impact the accuracy of data analysis and the objectivity of making decisions. However, how to identify attack behaviors from environmental interference and establish a secure route to transmit data for resource-constrained terminals are challenging problems. To address these problems, in this article, we propose a trust-based security system (TSS). In TSS, we first design a trust model using binomial distribution for calculating the node's trust value and a third-party recommendation scheme for improving the objectivity of trust value. Then, we propose a trust management scheme for preventing theon–offattack. After that, we design a secure routing protocol, which is used to balance the security, transmission performance, and energy efficiency. Finally, the analytical results of the TSS are evaluated with extensive simulation experiment.

As a key component of the information sensing and aggregating for big data, cloud computing, and Internet of Things (IoT), the information security in wireless sensor network (WSN) is critical. Due to constrained resources of sensor node, WSN is becoming a vulnerable target to many security attacks. Compared to external attacks, it is more difficult to defend against internal attacks. The former can be defended by using encryption and authentication schemes. However, this is invalid for the latter, which can obtain all keys of the network. The studies have proved that the trust management technology is one of effective approaches for detecting and defending against internal attacks. Hence, it is necessary to investigate and review the attack and defense with trust management. In this paper, the state-of-the-art trust management schemes are deeply investigated for WSN. Moreover, their advantages and disadvantages are symmetrically compared and analyzed in defending against internal attacks. The future directions of trust management are further provided. Finally, the conclusions and prospects are given.

Motion tracking technologies have been widely used in mobile interaction applications, such as Virtual Reality (VR), healthy monitoring, and virtual touch control. Compared with dedicated hardware devices, mobile phones use reliable speakers and microphones, and can serve as ubiquitous devices for cheap acoustic-based motion tracking solutions. However, for complex indoor environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter named Multipath Effect Ratio (MER) is defined to indicate the multipath fading effect on received signals at different frequencies. Based on MER, a novel multipath effect mitigating technique is developed to calculate the phase change of acoustic signals and track the corresponding moving distance by using multiple speakers. A Phase-based Acoustic Motion Tracking (PAMT) method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, PAMT can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2mm and 4mm in one-dimensional and two-dimensional scenarios, respectively.

Motion tracking is attractive in what concerns a smart city environment, where citizens have to interact with Internet of Things (IoT) infrastructures spread all around one particular city. Motion tracking is important for smart services and location-based services in smart cities, since it provides natural ways for users to interact with the IoT infrastructures, such as the ability to recognize of a wide range of hand motion in real-time. Compared with dedicated hardware devices, ubiquitous devices with reliable speakers and microphones can be developed to achieve cheap acoustic-based motion tracking, which is appropriate for low-power and low-cost IoT applications. However, for complex urban environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter called multipath dispersion vector (MDV) is proposed to estimate and mitigate the impact of multipath fading on received signals using extreme learning machine. Based on MDV, a robust acoustic motion tracking (RAMTEL) method is proposed to calculate the moving distance based on the phase change of acoustic signals, and track the corresponding motion in 2-D plane by using multiple speakers. The method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, RAMTEL can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2 and 4 mm in 1-D and 2-D scenarios, respectively.

Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects. Such effects are not only caused by massive metal surfaces but also moving operators and logistical vehicles, which will lead to temporal fading effects. A three-layer impulse response framework is proposed to characterize such effects, in which both the specular and scattered components vary with the spacial movement of nearby objects. In this context, a received signal strength indicator will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power. Consequently, an augmented Kalman-filter-based link quality estimator has been designed to track both the specular and scattered power in the distribution parameter space with constant noise covariance matrices. Experiments from industrial sites show significantly increased accuracy.

Healthcare-oriented wireless sensor network (HWSN) is one of the applications of wireless sensor networks in e-health. It not only can better achieve the physiological information of people, but also more efficiently reduce the Iatency regarding information collection and transmission. However, similar to other distributed networks, it also faces enormous security challenges, especially from internal attacks. It is difficult to distinguish many attack behaviors from interference in the complex healthcare scenarios, such as On-Off attack. In this paper, we propose a Binomial Distribution-based Trust Management Scheme (BDTMS) for HWSN. The proposed method can rapidly detect and effectively defend against On-Off attacks. In addition, the proposed method is also applicable to defending against bad mouthing attacks. Simulation results show that, compared with the Time-window-based Resilient Trust Management Scheme (TRTMS), our proposed BDTMS achieves better performance in defending against On-Off attack under obstacle movement, especially with higher detection accuracy.

Motion tracking, as a mechanism of mobile interaction, allows devices to get fine-gained user input by locating the real-time position of target devices (e.g., smart phones, smart watches) in the air. With the proliferation of mobile devices and smart multimedia devices (e.g., smart TV, home audio system), the ubiquitous speakers and microphones in the devices provide more diverse ways of acoustic-based mobile interaction. In this demonstration, we propose a fine-gained motion tracking system, which can be developed on commercial mobile devices and track the devices with millimeter level (mm-level) accuracy. We first compensate the phase offset between receiver and audio source at each frequency. We then use the acoustic phase change at receiver to achieve accurate distance measurement. Finally, we implement our system on off-the-shelf devices, and achieve a fine-gained motion tracking in two-dimensional space. Our experiments show that our system achieves high accuracy as well as high sensitivity: our system could detect the sight and slow movement caused by human breathing for example.

Vehicular ad hoc networks, wireless sensor networks, Internet of Things, and mobile device-to-device communications can be modeled as different homogeneous fog networks, wherein similar terminals/things/devices/nodes are sharing their computation, communication, and storage resources in the neighborhood for achieving better system performance through effective collaborations. It is very desirable, but quite challenging, to simultaneously reduce service delay and energy consumption in such networks for delay-sensitive and energyconstraint applications, e.g., virtual reality and online 3-D gaming on mobile devices. In this paper, a cross-layer analytical framework is developed to formulate and study the balance between service delay and energy consumption. An effective control parameter V is derived to characterize their tradeoff relationship during dynamic task scheduling processes in fog networks. Combining this analysis with Lyapunov optimization techniques, a novel delay energy balanced tasking scheduling (DEBTS) algorithm is proposed to minimize the overall energy consumption while reducing average service delay and delay jitter. It is proved that DEBTS can achieve the theoretical [O(1/V), O(V)] tradeoff between these two performance metrics. Further, extensive simulation results show that DEBTS can offer much better delay-energy performance in task scheduling challenges. Specifically, for a typical V value of 4 × 104, DEBTS can save 26% and 29% more energy, and at the same time, reduce average service delay by 29% and 32%, than traditional random scheduling and least busy scheduling algorithms, respectively.

Nowadays the activity recognition based on multiple wearable sensors is still a challenging task due to the diversity of human activities. The application of unsupervised classification is helpful to discovery new activity classes and improve the activity classification model. Therefore, a new multi-sensor activity recognition scheme using the two-dimensional principal component analysis (2DPCA) and the k-means clustering with dual-measure distance (DMk-means) is proposed in this paper. Multiple activity signals are firstly decomposed by the wavelet packet decomposition. Then the 2DPCA is applied to wavelet feature matrices of the activity samples without changing the inherent data structure. In the DMk-means, different activities are grouped into clusters through measuring their feature vectors with both Euclidean distance and Pearson correlation distance. The recognition performance of proposed scheme is verified by the public dataset WARD. Clustering results show that more useful wavelet features can be captured by the 2DPCA than by the PCA. The dual-measure distance can calculate both the shape variance and the magnitude difference of feature vectors. The clustering indices of 2DPCA_DMk-means are superior than those of 2DPCA_k-means for activity recognition.

Dual-band and multi-band power amplifiers (PAs) becomes popular as they can effectively reduce the equipment cost and operation cost. On the other hand, the nonlinear interference among signals in different bands needs special attention. Previous modeling results [1] suffer performance degradation when the intermodulation terms among bands fall into bands of the signals. In this paper, we start from the passband Volterra model and derive a general baseband Volterra model considering all possible intermodulation terms. Existing model in [1] is a special case of the proposed baseband Volterra model. Simulation results show that the proposed dual-band Volterra DPD model can provide additional 2 dB performance improvement comparing to the existing dual-band Volterra DPD model.

Frequency band propagation characteristics measurement and modeling is a prerequisite for cellular network plan and infrastructure deployment. However, the generally assumed Okumura-Hata, and COST 231 large-scale channel model are based on transmission data collected in some specifical areas, which has application limitations for the other different cities. With the rapid development of 4G and study on 5G cellular systems, there are multiple radio access networks covering the same areas, and thus the transmission characteristics are more complicated and still unknown to all. Different from prior work focusing on channel characteristics for only one specifical frequency band in one specifical scenario, in this work, we investigate and compare Radio Frequency (RF) transmission characteristics for eleven different frequency bands varying from 700 MHz to 3500 MHz under various terrains and landscapes, including high-density area, urban city area and rural area. Moreover, three different fitting and modeling methods on testing data are compared, and under high-density scenario, the effects of different antenna height on the transmission characteristics is studied. It is found that the standard deviation of path loss indicates in the range of 9dB which is available for network requirement. With the increase of frequency, the standard deviation decreases. Moreover, the transmit antenna height is defined as above 30m to obtain best coverage. Our work will facilitate networks planners and researchers on future 5G networks.

Traffic flow modeling is important to the performance analysis/evaluation of services provided by Vehicular Adhoc Networks (VANET), and is also a useful guidance to the deployment of VANET. Different from prior work based on empirical data collected decades ago, in this work, we collect a large amount of empirical traffic flow data from five typical overhead road segments during two different time periods recently in Shanghai. Statistical results in a short time scale (i.e., within half an hour) show that the lane-level traffic volumes/vehicles' velocities in the monitored road segments followed truncated Gaussian distribution (with a match rate of roughly 90%) better than Poisson distribution (with a match rate of roughly 80%) which is normally assumed in the literature. Traffic flow characteristic in a long time scale (i.e., in a day or a week) is also presented, which shows that the traffic density in the night and daytime are very different, however, the traffic flow density in the daytime stays high. With the obtained traffic flow characteristics, we discuss the possibility of the coexistence of WAVE and WLAN in 5.9G band according to the Federal Communications Commission's intention, and point out that for areas in the vicinity of a overhead road inside Outer Ring road in Shanghai, it is not practical for WLAN devices to operate on 5.9G WAVE band.

Network Coding based Epidemic Routing (NECR) has been proposed to improve the data delivery efficiency in Delay Tolerant Networks (DTNs). With NCER, data packets are not only replicated but also encoded while being forwarded to their destination. To better understand how NCER facilitates the transmission, in this paper, we present an analytical framework for modeling the data transmission delay performance of NCER in DTNs. Unlike previous work, our framework can accommodate the inaccuracy of a widely adopted assumption that each node carrying one or more encoded packets is able to transmit an innovative encoded packet to the node it encounters. Numerical results are presented to validate the accuracy of our analytical framework.

Heterogeneous network (HetNet) [1] is considered as an energy efficient system structure to alleviate the problem of rapidly increasing power consumption in the wireless communication system. Significant research on HetNet energy efficiency has been conducted. However, most of them only consider power consumption of Base Stations (BSs) while ignoring influence on energy efficiency of Mobile Devices (MDs) brought by new BSs deployment. In this work, we propose a novel power saving metric for HetNet. Under the coexisting scenario of a single macrocell and a single microcell, we analyze the changes in power consumption at both the BSs side and the MDs side with the deployment of a micro BS. Optimum microcell radii for maximum power saving at the MDs sides and for highest network energy efficiency are obtained through analytical studies. It is found that total power saving for microcell MDs is close to 18% with a proper deployment of a microcell. Finally, extensive simulations have been provided to establish the accuracy of our theoretical analyses.

The random distribution of sensors and the irregularity of routing paths lead to unordered sensory data which are difficult to deal with in Wireless Sensor Networks (WSNs). However, for simplicity, most existing researches ignore those characteristics in the designs of Compressive Sensing based Data Aggregation Schemes (CSDAS). Since conventional sparsification bases (e.g., DCT, Wavelets) are inefficient to deal with unordered data, performances of CSDAS with conventional bases are inevitably constrained. In this work, a novel CSDAS which adopts Treelet transform as a sparse transformation tool is proposed. Our CSDAS is capable to exploit both spatial relevance and temporal smoothness of sensory data. Moreover, our CSDAS contains a novel correlation based clustering strategy which is realized with the localized correlation structure of sensory data returned by Treelets and facilitates energy saving of CSDAS in WSNs. Comparative results show the reconstruction error rate with adopting Treelet transform in CSDAS is about 18% lower than that of conventional ones when the normalized energy consumption is 0.3. Even larger performance gain will be obtained at higher energy consumption level. Meanwhile, simulations results further show that our novel correlation based clustering strategy is of great potential. Specially, there is a gain of roughly 35% for total energy savings with our proposed clustering strategy.

The power consumption of mobile devices (MDs) has been increasing dramatically since more functions and bigger screens are adopted in the latest MDs. To extend the usage time of MDs and improve customer satisfaction levels, this research study, for the first time, studies the influence of different deployment strategies of a few low‐power base stations (BSs) to reduce the power consumption of a large number of MDs. For both microcell and picocell scenarios, the authors’ analyse the power reductions at the MD side with the deployment of a low‐power BS. The optimum low‐power cell radius is derived for achieving the maximum MDs power reduction. The tradeoff relationships between MD power reduction and the key parameters of low‐power BSs are investigated and discussed. It is shown that authors’ proposed method can achieve about 18% power reduction for microcell MDs.

As smart phones usually have large high-resolution screens, many fancy functions, multiple communication modes and interfaces, and powerful processing capabilities, their power consumption is unsurprisingly much higher than traditional voice-centric phones. Unfortunately, the growth of battery capacity is far below the needs of smart phones and new applications, hence it is of great importance and strong interest to mobile users and service providers to prolong the usage time of smart phones without affecting quality of service (QoS) and user experience. To address this challenging issue, we develop a cross-disciplinary approach in this paper by jointly considering battery discharge and recovery characteristics with service/user requirements in energy efficient protocol design. Specifically, based on extensive experiments, we study and model the characteristics of battery recovery effect in a smart phone, under different discharge currents, discharge time and recovery time. A practical power consumption model for smart phone is then derived from real measurement data and empirical formulae. Further, this new model is applied to develop and implement a battery-aware protocol for online continuous music streaming service. Experimental results show the proposed protocol can effectively exploit the battery recovery effect in a smart phone and achieve a 10% gain in usage time while satisfying standard service and user requirements.

In this work, we implement an ARMv8 function and performance simulator based on gem5 infrastructure, which is the first open source ARMv8 simulator. All the ARMv8 A64 instructions other than SIMD are implemented using gem5 ISA description language. The ARMv8 simulator supports multiple CPU models, multiple memory systems, and McPAT power model.

Traffic flow models are essential to performance analysis/evaluation for applications/services provided by Intelligent Transportation Systems (ITS)/Vehicular Ad-hoc networks (VANET). They are also useful guidance for the deployment of ITS/VANET. Many macroscopic traffic flow models have been proposed in the past few decades on the basis of empirical data collected in the US, Canada, Turkey and etc. However these models may not be accurate for traffic flows in cities in China due to the differences in population, transportation infrastructure, and driving culture. In this paper, we collected a large amount of empirical traffic flow data in Shanghai overhead road during three different time periods. Statistical results showed that the lane-level traffic volumes in Shanghai followed Gaussian distribution rather than Poisson distribution which was normally assumed in literature. Regarding lane-level vehicles' velocities, they matched well with Gaussian distribution. The empirical probability mass functions (PMF) for both the traffic volumes and vehicles' velocities were presented. In addition, how these models would impact performance analysis in VANETs was discussed.

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

IEEE 802.11p and 1609 standards are currently under development to support Vehicle-to-Vehicle and Vehicle-to-Infrastructure communications in vehicular networks. For infrastructure-based vehicular relay networks, access probability is an important measure which indicates how well an arbitrary vehicle can access the infrastructure, i.e. a base station (BS). On the other hand, connectivity probability, i.e. the probability that all the vehicles are connected to the infrastructure, indicates the service coverage performance of a vehicular relay network. In this paper, we develop an analytical model with a generic radio channel model to fully characterize the access probability and connectivity probability performance in a vehicular relay network considering both one-hop (direct access) and two-hop (via a relay) communications between a vehicle and the infrastructure. Specifically, we derive close-form equations for calculating these two probabilities. Our analytical results, validated by simulations, reveal the tradeoffs between key system parameters, such as inter-BS distance, vehicle density, transmission ranges of a BS and a vehicle, and their collective impact on access probability and connectivity probability under different communication channel models. These results and new knowledge about vehicular relay networks will enable network designers and operators to effectively improve network planning, deployment and resource management.

Coverage is an important problem in wireless networks. Together with the access probability, which measures how well an arbitrary user can access a wireless network, in particular VANET, they are often used as major indicators of the quality of the network. In this paper, we investigate the coverage and access probability of the vehicular networks with roadside infrastructure, i.e. base stations. Specifically, we analyze the relation between these key parameters, i.e. the coverage range of base stations, coverage range of vehicles, vehicle density and distance between adjacent base stations, and how these parameters interact with each other to collectively determine the coverage and the access probability. We use the connectivity probability, the probability that all nodes in the network are connected to at least one base station within a designated number of hops, as a measure of the coverage. We derived close-form formulas for the connectivity probability and the access probability for a 1D vehicular network bounded by two adjacent base stations. The analytical results have been validated by simulations. The results in the paper can be used by network operators to design networks with specific service coverage guarantees.