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Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called ``$t$-LocPred". It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods (``ConvAoI"); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model ("mem-attLSTM") to capture long-term behavior patterns. Experimental results show that t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and reduced latency, the heterogeneous network present challenges to the synchronisation methods, or barrier control methods, used in training, regarding system progress and model convergence etc. The design of these barrier mechanisms is critical for the performance and scalability of federated learning systems. We propose a new barrier control technique called Probabilistic Synchronous Parallel (PSP). In contrast to existing mechanisms, it introduces a sampling primitive that composes with existing barrier control mechanisms to produce a family of mechanisms with improved convergence speed and scalability. Our proposal is supported with a convergence analysis of PSP-based SGD algorithm. In practice, we also propose heuristic techniques that further improve the efficiency of PSP. We evaluate the performance of proposed methods using the federated learning specific FEMNSIT dataset. The evaluation results show that PSP can effectively achieve good balance between system efficiency and model accuracy, mitigating the challenge of heterogeneity in federated learning.

With the exponential growth of data created at the network edge, decentralized and Gossip-based training of deep learning (DL) models on edge computing (EC) gains tremendous research momentum, owing to its capability to learn from resource-strenuous edge nodes with limited network connectivity. Today’s edge devices are extremely heterogeneous, e.g., hardware and software stacks, and result in high performance variation of training time and inducing extra delay to synchronize and converge. The large body of prior art accelerates DL, being data or model parallelization, via a centralized server, e.g., parameter server scheme, which may easily turn into the system bottleneck or single point of failure. In this artice, we propose EdgeGossip, a framework specifically designed to accelerate the training process of decentralized and Gossip-based DL training for heterogeneous EC platforms. EdgeGossip features on: (i) low performance variation among multiple EC platforms during iterative training, and (ii) accuracy-aware training to fastly obtain best possible model accuracy. We implement EdgeGossip based on popular Gossip algorithms and demonstrate its effectiveness using real-world DL workloads, i.e., considerably reducing model training time by an average of 2.70 times while only incurring accuracy losses of 0.78 percent.

Vehicular crowdsensing (VCS) takes the advantage of vehicles’ mobility and exploits both the crowd wisdom and sensing abilities offered by vehicle drivers’ carried smart mobile devices and on-board sensors to accomplish challenging sensing tasks. The daily roadway commutes of vehicle drivers may form “virtual” mobile communities, called Vehicular Social Networks (VSNs). It offers an opportunity to include social network effect into incentive mechanism design where a driver can benefit from others’ sensing strategy in one VSN. In this paper, we consider a non-cooperative VCS campaign where multiple vehicles are incentivized by dynamically priced tasks and social network effect. In order to maximize the overall utility of vehicle drivers, we propose a social-aware incentive mechanism by deep reinforcement learning (called DRL-SIM), to derive the optimal long term sensing strategy for all vehicles. Finally, numerical results are supplemented to show both the convergence and the effectiveness of DRL-SIM when compared with other baselines.

Mobile crowdsensing (MCS) represents a new sensing paradigm that utilizes the smart mobile devices to collect and share data. Traditional MCS systems mainly leverages the people carried smartphones and other wearable devices which are constrained by the limited sensing capability and battery power. With the popularity of unmanned vehicles like unmanned aerial vehicles (UAVs) and driverless cars, they can provide much more reliable, accurate and cost-efficient sensing services due to to their equipped more powerful sensors. In this paper, we propose a distributed control framework for e nergy-efficient and DI stributed VE hicle navigation with cha R ging s T ations, called “e-Divert”. It is a distributed multi-agent deep reinforcement learning (DRL) solution, which uses a convolutional neural network (CNN) to extract useful spatial features as the input to the actor-critic network to produce a real-time action. Also, e-Divert incorporates a distributed prioritized experience replay for better exploration and exploitation, and a long short-term memory (LSTM) enabled N-step temporal sequence modeling module. The solution fully explores the spatiotemporal nature of the considered scenario for better vehicle cooperation and competition between themselves and charging stations, to maximize the energy efficiency, data collection ratio, geographic fairness, and minimize the energy consumption simultaneously. Through extensive simulations, we find an appropriate set of hyperparameters that achieve the best performance, i.e., 5 actors in Ape-X architecture, priority exponent 0.5, and LSTM sequence length 3. Finally, we compare with four baselines including one state-of-the-art approach MADDPG. Results show that our proposed e-Divert significantly improves the energy efficiency, as compared to MADDPG, by 3.62 and 2.36 times on average when varying different numbers of vehicles and charging stations, respectively.

In this paper, we aim to design a fully-distributed control solution to navigate a group of unmanned aerial vehicles (UAVs), as the mobile Base Stations (BSs) to fly around a target area, to provide long-term communication coverage for the ground mobile users. Different from existing solutions that mainly solve the problem from optimization perspectives, we proposed a decentralized deep reinforcement learning (DRL) based framework to control each UAV in a distributed manner. Our goal is to maximize the temporal average coverage score achieved by all UAVs in a task, maximize the geographical fairness of all considered point-of-interests (PoIs), and minimize the total energy consumptions, while keeping them connected and not flying out of the area border. We designed the state, observation, action space, and reward in an explicit manner, and model each UAV by deep neural networks (DNNs). We conducted extensive simulations and found the appropriate set of hyperparameters, including experience replay buffer size, number of neural units for two fully-connected hidden layers of actor, critic, and their target networks, and the discount factor for remembering the future reward. The simulation results justified the superiority of the proposed model over the state-of-the-art DRL-EC $^3$ 3 approach based on deep deterministic policy gradient (DDPG), and three other baselines.

Deep neural networks (DNNs) have been showing significant success in various anomaly detection applications such as smart surveillance and industrial quality control. It is increasingly important to detect anomalies directly on edge devices, because of high responsiveness requirements and tight latency constraints. The accuracy of DNN-based solutions rely on large model capacity and thus long training and inference time, making them inapplicable on resource strenuous edge devices. It is hence imperative to scale DNN model sizes in correspondence to the run-time system requirements, i.e., meeting deadlines with minimal accuracy losses, which are highly dependent on the platforms and real-time system status. Existing scaling techniques either take long training time to pre-generate scaling options or disturb the unsteady training process of anomaly detection DNNs, lacking the adaptability to heterogeneous edge systems and incurring low inference accuracies. In this article, we present LightDNN to scale DNN models for anomaly detection applications at edge, featuring high detection accuracies with lightweight training and inference time. To this end, LightDNN quickly extracts and compresses blocks in a DNN, and provides large scaling space (e.g., 1 million options) by dynamically combining these compressed blocks online. At run-time, LightDNN predicts the DNN's inference latency according to the monitored system status, and optimizes the combination of blocks to maximize its accuracy under deadline constraints. We implement and extensively evaluate LightDNN on both CPU and GPU edge platforms using 8 popular anomaly detection workloads. Comparative experiments with state-of-the-art methods show that our approach provides 145.8 to 0.56 trillion times more scaling options without increasing training and inference overheads, thus achieving as much as 15.05% increase in accuracy under the same deadlines.

In this paper, we propose to leverage the emerging deep learning techniques for sequential modeling of user interests based on big social data, which takes into account influence of their social circles. First, we present a preliminary analysis for two popular big datasets from Yelp and Epinions. We show statistically sequential actions of all users and their friends, and discover both temporal autocorrelation and social influence on decision making, which motivates our design. Then, we present a novel hybrid deep learning model, Social-Aware Long Short-Term Memory (SA-LSTM), for predicting the types of item/PoIs that a user will likely buy/visit next, which features stacked LSTMs for sequential modeling and an autoencoder-based deep model for social influence modeling. Moreover, we show that SA-LSTM supports end-to-end training. We conducted extensive experiments for performance evaluation using the two real datasets from Yelp and Epinions. The experimental results show that (1) the proposed deep model significantly improves prediction accuracy compared to widely used baseline methods; (2) the proposed social influence model works effectively; and (3) going deep does help improve prediction accuracy but a not-so-deep deep structure leads to the best performance.

Mobile crowd sensing is a new paradigm that enables smart mobile devices to collect and share various types of sensing data in urban environments. However, new challenges arise: one is how to evaluate the quality of data each mobile user potentially is capable of providing; another is how to allocate a satisfactory yet profitable amount of reward to mobile users in order to keep them participating in crowd sensing tasks. In this paper, we first introduce a mathematical model for characterizing quality of sensing data to be contributed by mobile users. Then, we present a utility function and formulate an optimization problem for the platform, who recruits participants to contribute sensing data, to maximize the amount of high quality sensing data under a limited task budget. We next present an effective and quality-aware incentive mechanism to solve this problem for online scenarios where participants may arrive or leave at any random time. Moreover, the proposed incentive mechanism allows the platform to provide selected participants with an extra bonus according to task completion level and their previous performance to motivate them further. We formally show the proposed mechanism has the desirable properties of truthfulness, individual rationality, budgetary feasibility, and computational efficiency. We compare the proposed scheme with existing methods via simulation using a real dataset. Extensive simulation results well justify the effectiveness and robustness of the proposed approach, e.g, compared with another online method “OMG”, the gap to the optimum for our proposed Online-QIM approach is reduce by 33.3 percent when budget $B = 1000$B=1000.

With the rapid development of smart mobile terminals (MTs), various industrial Internet of things (IIoT) applications can fully leverage them to collect and share data for providing certain services. However, two key challenges still remain. One is how to achieve high-quality data collection with limited MT energy resource and sensing range. Another is how to ensure security when sharing and exchanging data among MTs, to prevent possible device failure, network communication failure, malicious users or attackers, etc. To this end, we propose a blockchain-enabled efficient data collection and secure sharing scheme combining Ethereum blockchain and deep reinforcement learning (DRL) to create a reliable and safe environment. In this scheme, DRL is used to achieve the maximum amount of collected data, and the blockchain technology is used to ensure security and reliability of data sharing. Extensive simulation results demonstrate that the proposed scheme can provide higher security level and stronger resistance to attack than a traditional database based data sharing scheme for different levels/types of attacks.

High-quality data collection is crucial for mobile crowd sensing (MCS) with various applications like smart cities and emergency rescues, where various unmanned mobile terminals (MTs), e.g., driverless cars and unmanned aerial vehicles (UAVs), are equipped with different sensors that aid to collect data. However, they are limited with fixed carrying capacity, and thus, MT’s energy resource and sensing range are constrained. It is quite challenging to navigate a group of MTs to move around a target area to maximize their total amount of collected data with the limited energy reserve, while geographical fairness among those point-of-interests (PoIs) should also be maximized. It is even more challenging if fully distributed execution is enforced, where no central control is allowed at the backend. To this end, we propose to leverage emerging deep reinforcement learning (DRL) techniques for directing MT’s sensing and movement and to present a novel and highly efficient control algorithm, called energy-efficient distributed MCS (Edics). The proposed neural network integrates convolutional neural network (CNN) for feature extraction and then makes decision under the guidance of multi-agent deep deterministic policy gradient (DDPG) method in a fully distributed manner. We also propose two enhancements into Edics with $N$ -step return and prioritized experienced replay buffer. Finally, we evaluate Edics through extensive simulations and found the appropriate set of hyperparameters in terms of number of CNN hidden layers and neural units for all the fully connected layers. Compared with three commonly used baselines, results have shown its benefits.

Current avoidance systems mainly focus on the safety of the car occupants. The surrounding entities including the pedestrians, the cyclists are assumed to use a different avoidance system for their safety. Vehicle speed is reported as one of the major factors that causes such severe road accidents that affect other entities on the road. In response, several solutions have been implemented to control the causalities of over speeding ranging from speed camera, speed detectors to car avoidance systems. However, those solutions have not significantly improved the rate of traffic accidents and their impact. Additionally, the current solutions do not ensure timely notification of all the road users (surrounding vehicle drivers, pedestrians or others) that can alleviate crash causalities in case of fatal traffic accidents. The fifth generation (5G) cellular network is predicted to overcome the current limitations of Internet-of-Vehicles (IoV) by offering fast, low latency and reliable connections to enable IoV based applications. Fog computing has also been proposed to complement IoV by bringing computational entities in nearby proximity of the vehicles. 5G based fog vehicular networks is a new paradigm that empower real-time and low latency services for Intelligent Transportation System (ITS). In this paper we proposed a secure and privacy-preserving collision avoidance system in 5G fog based IoV. The fog devices are used to collect speed violation report (TVR) sent by the vehicles’ speed sensors. The fog nodes aggregate multiple TVRs, verify the signatures on the TVRs and broadcast anonymous notifications to other entities in the vicinity. The protocol makes use of certificateless aggregate signcryption coupled with pseudonymous technique as the building blocks to ensure authentication, integrity, confidentiality and privacy preservation respectively. The batch verification technique is used by the fog devices to allow simultaneous TVRs signature verification for a timely response. The authorization of reporting speed sensors is both guaranteed by the location-based information along with the digital signature to discard all the bogus TVRs. The analysis of the protocol confirms its lightweightness and efficiency.

Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

Internet of Things (IoT) require ubiquitous and seamless network connectivity. Meanwhile, it also asks for effective service management, data transmission and analysis. Towards this end, software defined networks (SDN) technology is introduced as a key solution to enable IoT network management. When IoT requires an efficient forwarding policy reconfiguration as the response to the alteration of system requirement (e.g., network environment or user demand), SDN is able to adjust the configurations easily to meet its demand. Previous research efforts tried to complete the reconfiguration as quickly as possible, since the update speed is critical to the performance of network management. However, update time can be further reduced. In this paper, we propose a novel update mechanism, which is referred to as rule co-existence update. It is able to enlarge the solution space and obtain more individual solutions, without any negative effect to the packet headers or throughput of flows. Then, we propose a scheme called USA (i.e., Update Speed Accelerator), as a novel algorithm for network updates based on the above-mentioned rule co-existence, that accelerates the update of forwarding policy with spare rule space on current network switches for general IoT environments. Based on the obtained suboptimal greedy solution, USA shows a novel way for rule co-existence to accelerate the update of forwarding policies. It also proposes a simplified algorithm to ensure the forwarding correctness during the update process. Extensive simulation results show that USA can clearly reduce the update time for nearly half of policies in our experiments with few rule space overhead, which is less than 8% of all policies, and maximum update time is less than previous works when spare rule space is limited.

Crowd saliency prediction refers to predicting where people look at in crowd scene. Humans have remarkable ability to rapidly direct their gaze to select visual information of interest when looking at a visual scene. Until now, research efforts are still focused on what type of feature is representative for crowd saliency, and which type of learning model is robust for crowd saliency prediction. In this paper, we propose a Random Forest (RF) based crowd saliency prediction approach with optimal feature combination, i.e., the Feature Combination Selection for Crowd Saliency (FCSCS) framework. More specifically, we first define three representative crowd saliency features, namely, FaceSizeDiff, FacePoseDiff and FaceWhrDiff. Next, we adopt the Random Forest (RF) algorithm to construct our saliency learning model. Then, we evaluate the performance of FCSCS framework with different feature combinations (fifteen combinations in our experiments). Those selected features include low-level features (i.e., color, intensity, orientation), four crowd features (i.e., face size, face density, frontal face, profile face) and three new defined features (i.e., FaceSizeDiff, FacePoseDiff and FaceWhrDiff). We use FCSCS framework to obtain the optimal feature combination that is most suitable for crowd saliency prediction and further train the saliency model based on the optimal feature combination. After that, we evaluate the performance of the crowd saliency prediction classifiers. Finally, we conduct extensive experiments and empirical evaluation to demonstrate the satisfactory performance of our approach.

Mobile crowd sensing systems have been widely used in various domains but are currently facing new challenges. On one hand, the increasingly complex services need a large number of participants to satisfy their demand for sensory data with multidimensional high quality-of-information (QoI) requirements. On the other hand, the willingness of their participation is not always at a high level due to the energy consumption and its impacts on their regular activities. In this paper, we introduce a new metric, called “QoI satisfaction ratio,” to quantify how much collected sensory data can satisfy a multidimensional task's QoI requirements in terms of data granularity and quantity. Furthermore, we propose a participant sampling behavior model to quantify the relationship between the initial energy and the participation of participants. Finally, we present a QoI-aware energy-efficient participant selection approach to provide a suboptimal solution to the defined optimization problem. Finally, we have compared our proposed scheme with existing methods via extensive simulations based on the real movement traces of ordinary citizens in Beijing. Extensive simulation results well justify the effectiveness and robustness of our approach.

Vehicular Ad Hoc Network (VANET) is envisaged to play an important role in the safety of drivers and passengers when moving on the roads. However, VANET still faces many challenges before it could be deployed. One such challenge is shortage of radio frequency spectrum channels. VANET has been allocated 7 channels for dedicated short range communication at 5.9GHz band. The 7 channels are likely to get congested in high vehicle densities when many vehicles are contending for the same medium. Consequently, affecting the transmission of safety and emergency messages. To alleviate the problem of scarcity channels, dynamic spectrum access (DSA) through cognitive radio (CR) technology has been proposed. One of the core functions of a CR is to identify spectrum holes in licensed frequency bands that can be accessed by unlicensed users through spectrum sensing. In VANET, spectrum sensing is challenging because of the mobility nature of vehicles, dynamic topological changes as well as other unique characteristics not found in other networks. However, these challenges have not been fully studied and how they affect spectrum sensing in cognitive vehicular network (CVN). In this paper, we discuss challenges associated with spectrum sensing in CVN. We describe the primary system activity model used by many schemes proposed in literature. Furthermore, we present an in depth analysis of state-of-art cooperative spectrum sensing techniques for CVN from 2010 to May 2016. In addition, we present some of the open issues in spectrum sensing for CVN.

Recognizing and retrieving multimedia content with movie/TV series actors, especially querying actor-specific videos in large scale video datasets, has attracted much attention in both the video processing and computer vision research field. However, many existing methods have low efficiency both in training and testing processes and also a less than satisfactory performance. Considering these challenges, in this paper, we propose an efficient cloud-based actor identification approach with batch-orthogonal local-sensitive hashing (BOLSH) and multi-task joint sparse representation classification. Our approach is featured by the following: 1) videos from movie/TV series are segmented into shots with the cloud-based shot boundary detection; 2) while faces in each shot are detected and tracked, the cloud-based BOLSH is then implemented on these faces for feature description; 3) the sparse representation is then adopted for actor identification in each shot; and 4) finally, a simple application, actor-specific shots retrieval is realized to verify our approach. We conduct extensive experiments and empirical evaluations on a large scale dataset, to demonstrate the satisfying performance of our approach considering both accuracy and efficiency.

Mobile crowdsourcing systems are important sources of information for the Internet of Things (IoT) such as gathering location related sensing data for various applications by employing ordinary citizens to participate in data collection. In order to improve the Quality of Information (QoI) of the collected data, the system server needs to coordinate participants with different data collection capabilities and various incentive requirements. However, existing participant coordination methods require the participants to reveal their trajectories to the system server which causes privacy leakage. But, with the improvement of ordinary citizens’ consciousness to protect their rights, the risk of privacy leakage may reduce their enthusiasm for data collection. In this paper, we propose a participant coordination framework, which allows the system server to provide optimal QoI for sensing tasks without knowing the trajectories of participants. The participants work cooperatively to coordinate their sensing tasks instead of relying on the traditional centralized server. A cooperative data aggregation, an incentive distribution method, and a punishment mechanism are further proposed to both protect participant privacy and ensure the QoI of the collected data. Simulation results show that our proposed method can efficiently select appropriate participants to achieve better QoI than other methods, and can protect each participant’s privacy effectively.

The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991–2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.

Although the robots integrated with communication module can provide various functions, there is intrinsic limitation because of the instable wireless connection, restricted bandwidth and limited coverage of network. Fortunately, assisted by LTE (Long Term Evolution) techniques, the robots can be deployed more widely to support bandwidth-intensive applications. Hence, this paper proposes a LTE-based robotics system integrated with cloud computing to enhance the capability of data transmissions and intelligence for providing higher quality and more friendly services. Furthermore, we develop a robot with emotional recognition and feedback for improving Quality of Service (QoS) and Quality of Experience (QoE), and design a testbed for verifying system's feasibility and performance.

Roadside units (RSUs) are public and personal wireless access points that can provide communications with infrastructure in ad hoc vehicular networks. We present CLOCS (Counting and Localization using Online Compressive Sensing), a novel system to retrieve both the number and locations of RSUs through wardriving. CLOCS employs online compressive sensing (CS), where received signal strength (RSS) values are recorded at runtime, and the number and location of RSUs are recovered immediately based on limited RSS readings. CLOCS also uses fine retrieval based on an expectation maximization method along the driving route. Extensive simulation results and experiments in a real testbed deployed in the campus of the University of California, Irvine, confirm that CLOCS can successfully reduce the number of measurements for RSU recovery, while maintaining satisfactory counting and localization accuracy. In addition, data dissemination, time cost, and effects of different mobile scenarios using CLOCS are analyzed, and the impact of CLOCS on network connectivity is studied using Microsoft VanLan traces.

Today's smartphones are also fundamentally transforming the traditional understanding of “crowdsourcing” to an emerging type of participatory task-oriented application. It aims to support the so-called “Citizen Science efforts” for knowledge discovery to understand human behavior and measure/evaluate their opinions. To facilitate these scenarios, in this paper, we propose a novel efficient network management framework for participatory crowdsourcing. Specifically, we first formulate the optimization problem and propose a closed-form optimal solution to meet the quality-of-information (QoI) requirements of the task while minimizing the energy consumption variance among participants. We then largely extend the traditional framework of Gur Game for distributed decision making to recommend different levels of information contribution for each participant by merging multiple automaton chains into a single chain with multiple steady states. By modeling the user bidding behaviors, we propose a few incentive-based participant selection schemes to maximize the platform's benefits and meet participants' expectations. We extensively evaluate the proposed schemes under the Massachusetts Institute of Technology (MIT) Social Evolution data set, where both QoI requirements of the request and credit saving are successfully achieved, with a satisfactory level of energy consumption fairness among participants.

New-generation mobile devices will inevitably be employed within the realm of ubiquitous sensing. In particular, smartphones have been increasingly used for human activity recognition (HAR)-based studies. It is believed that recognizing human-centric activity patterns could accurately enough give a better understanding of human behaviors. Further, such an ability could have a chance to assist individuals to enhance the quality of their lives. However, the integration and realization of HAR-based mobile services stand as a significant challenge on resource-constrained mobile-embedded platforms. In this manner, this paper proposes a novel discrete-time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to address a better realization of HAR-based mobile context awareness. In addition, we utilize power-efficient sensor management strategies by providing three intuitive methods and constrained Markov decision process (CMDP), as well as partially observable Markov decision process (POMDP)-based optimal methods. Moreover, a feedback control mechanism is integrated to balance the tradeoff between accuracy in context inference and power consumption. In conclusion, the proposed sensor management methods achieve a 40% overall enhancement in the power consumption caused by the physical sensor with respect to the overall 85–90% accuracy ratio due to the provided adaptive context inference framework.

In existing wireless networks, supporting multimedia services are becoming more popular and important. In general, wireless multimedia networks should require energy efficiency and reliable transmission while keeping satisfactory quality of services. In this respect, cooperative communications have been considered as an efficient approach to address these demands by offering significant diversity gains over single-antenna systems without increasing requirements on radio resources. In this paper, we propose a power-allocation method to optimize the decode-and-forward (DF) cooperative transmission for source and relay nodes as a means to reduce the total power consumption, while maintaining the required quality of services, and investigate fundamental characteristics of cooperative transmission in terms of power efficiency. Moreover, for a network with multiple cooperative nodes, we also propose an energy-efficient relay selection rule to offer fairness at each node and implement it into a practical routing protocol. Our performance analysis is supplemented by simulation results to illustrate significant energy savings of the proposed methods.

Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004–2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.

By using increasingly popular smartphones, participatory sensing systems can collect comprehensive sensory data to retrieve context-aware information for different applications (or sensing tasks). However, new challenges arise when selecting the most appropriate participants when considering their different incentive requirements, associated sensing capabilities, and uncontrollable mobility, to best satisfy the quality-of-information (QoI) requirements of multiple concurrent tasks with different budget constraints. This paper proposes a multitask-oriented participant selection strategy called “DPS,” which is used to tackle the aforementioned challenges, where three key design elements are proposed. First is the QoI satisfaction metric, where the required QoI metrics of the collected data are quantified in terms of data granularity and quantity. Second is the multitask-orientated QoI optimization problem for participant selection, where task budgets are treated as the constraint, and the goal is to select a minimum subset of participants to best provide the QoI satisfaction metrics for all tasks. The optimization problem is then converted to a nonlinear knapsack problem and is solved by our proposed dynamic participant selection (DPS) strategy. Third is how to compute the expected amount of collected data by all (candidate) participants, where a probability-based movement model is proposed to facilitate such computation. Real and extensive trace-based simulations show that, given the same budget, the proposed participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.

In opportunistic mobile networks (OppNets), a large amount of energy is consumed by idle listening, instead of infrequent data exchange. This makes energy saving a challenging and fundamental problem in OppNets since nodes are typically battery powered. Asynchronous duty-cycle operation is a promising approach for energy saving in OppNets; however, if its working schedule is not effectively designed, it may also cause significant network performance degradation. Therefore, it is pressing to design an energy-efficient working schedule for duty-cycle OppNets. In this paper, we first analyze the contact process in duty-cycle OppNets and then propose an adaptive working schedule for duty-cycle OppNets. The proposed adaptive working schedule uses the past recorded contact histories to predict future contact information to adaptively configure the working schedule of each node in the network. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed adaptive working schedule. Extensive real trace-driven simulation results demonstrate that our proposed adaptive working schedule is superior to the random working schedule and the periodical working schedule algorithms in terms of the number of effect contacts, delivery ratio, and delivery delay.

The ever increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor designs and deployment, have enabled smart devices to recognize daily human-based actions, activities, and interactions. Therefore, inferring a vast variety of user-device-based activities from diverse contexts obtained by a series of sensory observations has drawn much interest to the research area of ubiquitous sensing. The existence and awareness of context provides the capability of being conscious of physical environments or situations around users, and this allows network services to respond proactively and intelligently based on such awareness. Hence, with the evolution of smartphones, software developers have been empowered to create context-aware applications for recognizing human-centric or community based innovative social and cognitive activities in any situation and at any location. However, the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth compared to the capabilities of PCs and servers. Also, power is a major restriction on implementation of context-aware applications. Mobile device batteries do not last a long time while operating sensor(s) constantly. To this end, this article extensively surveys the emerging concepts of context awareness in mobile platforms by providing up-to-date literature and future research directives. We also point out the challenges faced in this regard and enlighten them by offering possible solutions.

Admission control (AC) is highly important in packet networks with quality-of-service (QoS) requirements since the uncontrolled admission of new data connections can jeopardize the QoS of existing connections and degrade the overall network performance. It requires a priory knowledge of the network capacity, the estimation of which is intricate and complex due to the operational characteristics of various communication protocols, complex network topologies, and dynamic traffic behavior with QoS requirements. In this work, we propose a generic admission control (GAC) methodology where the network conditions between any given ingress-egress node pair are summarized into a single parameter, referred to as the "QoS index". It accounts for various traffic volumes and QoS requirements, like throughput, delay, and packet error rate. Using this QoS index we track the network performance by predicting the potential impact of a new connection admission on the index. The decision depends on whether the predicted value lies below 1. We discuss its wide applicability and feasibility to many types of packet networks, wired or wireless, independent of what communication protocols in use at various layers. We finally validate the proposed GAC methodology by extensive simulations, confirming a significant improvement in terms of network goodput and QoS outage probability compared to other existing statistics-based AC methodologies.

In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a phenomenal increase in the usage and deployment of smartphone platforms and applications worldwide. This paper discusses the current state of the art in the merger of these two popular technologies, that we refer to as Mobile Cloud Computing (MCC). We illustrate the applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias. We further identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security and privacy.

Wireless communication channels are a scarce resource shared among multiple users in either scheduled or randomized fashions. We challenge a few design aspects of the widely used IEEE 802.11 MAC in wireless sensor networks (WSNs), such as the use of RTS, CTS, and ACK handshaking and the binary exponential backoff mechanisms, and argue that these key mechanisms incur high channel overhead and cannot effectively eliminate hidden terminal problems in multi-hop scenarios. Instead, we propose a set of efficient grid-based channel allocation and access scheduling algorithms using Latin squares, called as GAALS, for scalable WSNs with single-radio multi-channel communication capabilities. Using nodal location information and forming grids over the WSN deployment area, GAALS maps Latin squares to the grids, and dynamically assigns multiple channels to the WSN grids for channel access scheduling purposes. The fairness and scalability of GAALS are analyzed and evaluated in multiflow multihop WSNs with multi-channel capabilities. The results show that GAALS achieves much better performance than other multichannel protocols.

Cross-layer design for quality of service (QoS) in wireless mesh networks (WMNs) has attracted much research interest recently. Such networks are expected to support various types of applications with different and multiple QoS and grade-of-service (GoS) requirements. In order to achieve this, several key technologies spanning all layers, from physical up to network layer, have to be exploited and novel algorithms for harmonic and efficient layer interaction must be designed. Unfortunately most of the existing works on cross-layer design focus on the interaction of up to two layers while the GoS concept in WMNs has been overlooked. In this paper, we propose a unified framework that exploits the physical channel properties and multi-user diversity gain of WMNs and by performing intelligent route selection and connection admission control provides both QoS and GoS to a variety of underlying applications. Extensive simulation results show that our proposed framework can successfully satisfy multiple QoS requirements while it achieves higher network throughput and lower outage as compared to other scheduling, routing and admission control schemes.

Person identification plays an important role in semantic analysis of video content. This paper presents a novel method to automatically label persons in video sequence captured from fixed camera. Instead of leveraging traditional face recognition approaches, we deal with the task of person identification by fusing information from motion sensor platforms, like smart phones, carried on human bodies and extracted from camera video. More specifically, a sequence of motion features extracted from camera video are compared with each of those collected from accelerometers of smart phones. When strong correlation is detected, identity information transmitted from the corresponding smart phone is used to identify the phone wearer. To test the feasibility and efficiency of the proposed method, extensive experiments are conducted which achieved impressive performance.

Although Wireless Sensor Networks (WSN) have emerged with a great potential to change our life, especially with ubiquitous sensing and sensory data access through Sensing-as-a-service ( $\text{S}^{2}$ aaS) models, there are still many fundamental technical challenges that need to be addressed to realize this potential. These challenges include the complexity and diversity of applications, the needs for highly flexible and secure configuration management mechanisms, the need to design programmable and novel sensory techniques, and the needs to balance local intelligence and scalable cloud-based intelligence to satisfy real-time service objectives.

An interleaved resonant converter for high voltage application is presented in this paper. There are two half-bridge legs connected in series and two split capacitors in the adopted circuit to limit the voltage stress of each active switch at one-half of input voltage. Thus MOSFETs with 600V voltage rating can be adopted for 1200V input voltage application. For each half-bridge leg, two series resonant circuits are operated with interleaved one-half of switching period. The secondary windings of two transformers are connected in series in order to balance the transformer primary currents and to share the input current. Thus the sizes of the transformer core and bobbin are reduced. The centre-tapped rectifier is used on the output side to reduce the conduction loss with one diode voltage drop. Based on the resonant behaviour, MOSFETs are turned on at ZVS, and rectifier diodes can be turned off at ZCS if the switching frequency is less than the series resonant frequency. Two half-bridge legs are operated with one-fourth of switching period such that the ripple currents at input and output sides arc reduced. Finally, experiments with a 960W prototype, verifying the effectiveness of the proposed converter, are described.