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A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

Action recognition is essential for many human-centered applications in the Internet of Things (IoT). Especially, in the Internet of Medical Things (IoMT), action recognition shows great importance in surgical assistance, patient monitoring, etc. Recently, 3-D skeleton sequence-based action recognition draws broad attention. It is a challenging task that needs effective modeling on intraframe skeleton representations and interframe temporal dynamics. Standard long short-term memory (LSTM)-based models are widely used for sequence modeling due to its long-term memory, yet they are unable to fully model the relationship between different body joints or persons to extract crucial co-occurrence features from different levels. To handle this shortcoming, we propose an attention-based multilevel co-occurrence graph convolutional LSTM (AMCGC-LSTM). By integrating graph convolutional networks (GCNs) into LSTM, the proposed model is capable of leveraging body structural information from skeletons and strengthening the multilevel co-occurrence (MC) feature learning. Specifically, we first design the spatial attention module for feature enhancement of key joints from skeleton inputs. Second, we design MC memory units coupled with GCN to automatically model the spatial relationship between joints, and simultaneously capture the co-occurrence features from different joints, persons, and frames. Finally, we construct aggregated features of MCs (AFMCs) from MC memory units to better represent the intraframe action context encoding, and leverage a concurrent LSTM (Co-LSTM) to further model their temporal dynamics for action recognition. Our model significantly outperforms mainstream methods on NTU RGB+D 60/120 data set, mutual action subset of NTU RGB+D 60/120 data set, and Northewestern-UCLA data set.

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

As an industrial application of Internet of Things (IoT), Internet of Vehicles (IoV) is one of the most crucial techniques for Intelligent Transportation System (ITS), which is a basic element of smart cities. The primary issue for the deployment of ITS based on IoV is the security for both users and infrastructures. The Intrusion Detection System (IDS) is important for IoV users to keep them away from various attacks via the malware and ensure the security of users and infrastructures. In this paper, we design a data-driven IDS by analyzing the link load behaviors of the Road Side Unit (RSU) in the IoV against various attacks leading to the irregular fluctuations of traffic flows. A deep learning architecture based on the Convolutional Neural Network (CNN) is designed to extract the features of link loads, and detect the intrusion aiming at RSUs. The proposed architecture is composed of a traditional CNN and a fundamental error term in view of the convergence of the backpropagation algorithm. Meanwhile, a theoretical analysis of the convergence is provided by the probabilistic representation for the proposed CNN-based deep architecture. We finally evaluate the accuracy of our method by way of implementing it over the testbed.

Automation in smart manufacturing depends on many parties in the supply chain and distribution networks. A smart manufacturer needs to track the location and state of materials from its suppliers and products to its customers/distributors to manage the corresponding production schedule. The need for such information can be fulfilled by the use of Industrial Internet of Things (IIoT) devices installed at each respective party. However, even though all parties may trust one another, they have confidential data produced by the IIoT devices that are not allowed to be shared with other parties, or only allowed to be exchanged with certain parties. Even though the data are not confidential, they may still be required to share partial data based on the needs of each party. On the other hand, all parties have their independent systems that differ from each other, which leads to complex integration. Regarding those situations, a private-blockchain-based IIoT is promising to bridge the need for product and material tracking information exchange between parties while maintaining the respective confidentiality.

The rapid development of IoMT facilitates pervasive healthcare networks. However, numerous MUs lead to deficient wireless channel and computation resources. Edge computing based healthcare system is appreciated as a satisfactory paradigm to solve this obstacle. Based on the physical bound of WBANs, the IoMT is separated into two sub-networks, that is, intra- WBANs and beyond-WBANs. Considering the characteristics of healthcare systems, the cost of MUs relies on medical urgency, AoI and energy dissipation. In intra-WBANs, the wireless channel resource allocation problem is modeled by a cooperative game. The Nash bargaining solution is utilized to obtain the unique Pareto optimal point. In beyond-WBANs, MUs are considered to be rational and potentially selfish. Thus, a non-cooperative game is formulated to minimize the system-wide cost. To demonstrate the effectiveness of our proposed solution, performance evaluations are conducted with respect to the system-wide cost and the number of MUs benefiting from edge computing. Finally, several research challenges and open issues are discussed for further work.

This study aimed to construct a novel multimodal model by fusing different electroencephalogram (EEG) data sources, which were under neutral, negative and positive audio stimulation, to discriminate between depressed patients and normal controls. The EEG data of different modalities were fused using a feature-level fusion technique to construct a depression recognition model. The EEG signals of 86 depressed patients and 92 normal controls were recorded simultaneously while receiving different audio stimuli. Then, from the EEG signals of each modality, linear and nonlinear features were extracted and selected to obtain features of each modality. In addition, a linear combination technique was used to fuse the EEG features of different modalities to build a global feature vector and find several powerful features. Furthermore, genetic algorithms were used to perform feature weighting to improve the overall performance of the recognition framework. The classification accuracy of each classifier, namely the k-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM), was compared, and the results were encouraging. The highest classification accuracy of 86.98% was obtained by the KNN classifier in the fusion of positive and negative audio stimuli, demonstrating that the fusion modality could achieve higher depression recognition accuracy rate compared with the individual modality schemes. This study may provide an additional tool for identifying depression patients.

As an emerging communication platform in the Internet of Things, IoV is promising to pave the way for the establishment of smart cities and provide support for various kinds of applications and services. Energy management in IoV has been attracting an upsurge of interest in both academia and industry. Currently, green IoV mainly focuses on two aspects: energy management of battery- enabled RSUs and EVs. However, these two issues are always resolved separately while ignoring their interactions. This standalone design may cause energy underutilization, a mismatch between traffic demands and energy supplies, as well as high deployment and sustainable costs for RSUs. Therefore, the integration of energy management between battery-enabled RSUs and EVs calls for comprehensive investigation. This article first provides an overview of several promising research fields for energy management in green IoV systems. Given the significance of efficient communications and energy management, we construct an intelligent energy-harvesting framework based on V2I communications in green IoV communication systems. Specifically, we develop a three-stage Stackelberg game to maximize the utilities of both RSUs and EVs in V2I communications. After that, a real-world trajectory-based performance evaluation is provided to demonstrate the effectiveness of our scheme. Finally, we identify and discuss some research challenges and open issues for energy management in green IoV systems.

Mobile edge computing (MEC) has risen as a promising paradigm to provide high quality of experience via relocating the cloud server in close proximity to smart mobile devices (SMDs). In MEC networks, the MEC server with computation capability and storage resource can jointly execute the latency-sensitive offloading tasks and cache the contents requested by SMDs. In order to minimize the total latency consumption of the computation tasks, we jointly consider computation offloading, content caching, and resource allocation as an integrated model, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We design an asymmetric search tree and improve the branch and bound method to obtain a set of accurate decisions and resource allocation strategies. Furthermore, we introduce the auxiliary variables to reformulate the proposed model and apply the modified generalized benders decomposition method to solve the MINLP problem in polynomial computation complexity time. Simulation results demonstrate the superiority of the proposed schemes.

Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

Alarm sounds are acknowledged to play an important role in daily life; however, sometimes improper alarm sounds may mess things up. In this paper, we construct a smart alarm sound recommendation system working with smartphones to deliver smart alarm sounds by considering not only specified information, such as sleep patterns, but also context information such as weather, and social information. Our system aims to provide a smart life through orchestrating sensing data collected by multiple sensors on smartphones and collaborating with cloud computing to recommend preferable alarm sounds. To demonstrate the effectiveness and efficiency of our system, we conduct experiments of using our system to recommend smart alarm sounds to wake people up in the morning. Experimental results show that our system can improve people emotional states by about 10.73%, compared to traditional alarm sound delivery methods.

Vehicular social networks (VSNs), viewed as the integration of traditional vehicular networks and social networks, are promising communication platforms based on the development of intelligent vehicles and deployment of intelligent transportation systems. Passengers can obtain information by searching over Internet or querying vehicles in proximity through intra-vehicle equipment. Hence, the performance of content dissemination in VSNs heavily relies on inter-vehicle communication and human behaviors. However, privacy preservation always conflicts with the usability of individual information in VSNs. The highly dynamic topology and increasing kinds of participants lead to potential threats for communication security and individual privacy. Therefore, the privacy-preserving solutions for content dissemination in VSNs have become extremely challenging, and numerous researches have been conducted recently. Compared with related surveys, this article provides the unique characteristics of privacy-preserving requirements and solutions for content dissemination in VSNs. It focuses on: 1) a comprehensive overview of content dissemination in VSNs; 2) the privacy issues and potential attacks related to content dissemination; and 3) the corresponding solutions based on privacy consideration. First, the characteristics of VSNs, content dissemination and its solutions in VSNs are revealed. Second, the privacy issues for content dissemination in the current VSN architecture are analyzed and classified according to their features. Various privacy-preserving content dissemination schemes, attempting to resist distinct attacks, are also discussed. Finally, the research challenges and open issues are summarized.

In the big data era, thousands of hundreds of devices play the role of data producer as well as data consumer. However, wireless devices bear the power exhausted problem in various situations. How to balance the trade-off between data processing speed and power efficiency is meaningful to be researched. In this study, we propose a Sleep Data Fusion Networks module (SFDN) which has a star topology Bluetooth network to fuse data of sleep-aware applications basing on our designed application protocol. In the network, a center Bluetooth device fuses data generated from target node Bluetooth devices. Due to the low power consumption of Bluetooth as well as connection safety, our method is a better choice for a long lifetime and non-real time sleep data processing and fusion tasks then the Wi-Fi-based approach used in EAST and Smart-Alarm sleep-aware applications.

With the expansion of the IoT, it is important to optimize available bandwidth to reliably support edge to device communications. Thus we propose a wireless network where each edge server communicates with its end devices using its wireless band as a primary channel, assisted by a secondary edge server that can relay communications via its own wireless band as a secondary channel. The network can optimize capacity by balancing load between primary and secondary wireless bands, and we analyze the geometry of achievable rate regions, depending on the state of bands modeled as Rayleigh fading channels. The allocation of a connection to the primary or secondary band is formulated as an optimization problem which is then solved, and illustrated with numerical examples.

Generalized anxiety disorder (GAD), as one of the most common chronic anxiety disorders, faces difficulties in clinical diagnosis. With the rapid development and wide application of smartphones in recent years, smartphones have a vivid application prospect in the field of mental disease monitoring and diagnosis. Based on WeChat applet platform on smartphones, an APP that integrates scale testing and inertial sensor data collection is developed to study the detection of subjects with GAD in task state. A behavior patterns extraction method is proposed using sliding windows to split behavior data, and processing data segments for clustering. Distribution information are extracted from the subjects' behavior patterns and are combined with the descriptive statistical features of the sample to identify GAD. The results show that this method has an accuracy of 66.44% for female subjects and 71.43% for male subjects in GAD recognition.

As an emerging communication platform, opportunistic Cloud of Things (CoT) is promising for clients to exchange messages through opportunistic contacts in cloud computing-enabled Internet of Things. Recently, numerous socially aware schemes have been put forward, leveraging users’ social attributes and contact history to predict future contacts with the purpose of improving message forwarding efficiency and network throughput. However, individual privacy is generally overlooked in the prediction process and transmission stage of opportunistic CoT. In this paper, we construct a privacy-preserving message forwarding framework for opportunistic CoT to guarantee individual privacy and improve transmission efficiency. We first set up a two-layer architecture of a cloud server to improve communication efficiency for terminal clients. By integrating a security-based mobility prediction algorithm with a routing decision process, our scheme can effectively protect individual privacy. We integrate an attribute-based cryptographic algorithm with a message delivery process to enable our scheme to resist attacks, such as Sybil attack, drop for profit, and data tampered attack. Compared with some existing solutions, our scheme improves network security significantly at the cost of slightly increased communication overhead.

As an emerging platform based on ITS, SIoV is promising for applications of traffic management and road safety in smart cities. However, the end-to-end delay is large in store-carry-and-forward-based vehicular networks, which has become the main obstacle for the implementation of large-scale SIoV. With the extensive applications of mobile devices, crowdsensing is promising to enable real-time content dissemination in a city-wide traffic management system. This article first provides an overview of several promising research areas for traffic management in SIoV. Given the significance of traffic management in urban areas, we investigate a crowdsensing-based framework to provide timely response for traffic management in heterogeneous SIoV. The participant vehicles based on D2D communications integrate trajectory and topology information to dynamically regulate their social behaviors according to network conditions. A real-world taxi trajectory analysis-based performance evaluation is provided to demonstrate the effectiveness of the designed framework. Furthermore, we discuss several future research challenges before concluding our work.

Because of the enormous potential to guarantee road safety and improve driving experience, social Internet of Vehicle (SIoV) is becoming a hot research topic in both academic and industrial circles. As the ever-increasing variety, quantity, and intelligence of on-board equipment, along with the ever-growing demand for service quality of automobiles, the way to provide users with a range of security-related and user-oriented vehicular applications has become significant. This paper concentrates on the design of a service access system in SIoVs, which focuses on a reliability assurance strategy and quality optimization method. First, in lieu of the instability of vehicular devices, a dynamic access service evaluation scheme is investigated, which explores the potential relevance of vehicles by constructing their social relationships. Next, this work studies a trajectory-based interaction time prediction algorithm to cope with an unstable network topology and high rate of disconnection in SIoVs. At last, a cooperative quality-aware system model is proposed for service access in SIoVs. Simulation results demonstrate the effectiveness of the proposed scheme.

With the development of mCRAHNs, more emerging mobile applications and services could be foreseen and applied in our human society. In this article, we propose an emotion-aware cognitive system (ECS), which aims to provide a systematic approach that can facilitate deployment of different emotion-aware mobile applications in mCRAHNs. ECS is constructed to provide multidimensional emotional data collection and processing approaches for mobile applications working in mCRAHNs. Also, we present a flow of emotion-aware solution designed on ECS, and highlight the implementation and orchestration of several key technologies and schemes we apply in this system for different emotion-aware mobile applications in runtime. In addition, we demonstrate the feasibility of the proposed ECS by presenting a novel emotion-aware mobile application, iSmile, and evaluate the system performance based on this application.

Due to the dynamic characteristics of mobile wireless ad hoc networks, a node may join or leave/fail at any time, and links are usually intermittently unavailable. The destruction of the network connectivity would dramatically degrade the performance. To resolve this challenge, topology control is an efficient way to maintain network connectivity, which focuses on the adjustment of node transmit power in the past few decades. However, the adjustment of node movement has not been adequately addressed to restore the connectivity in mobile wireless ad hoc networks. In this paper, we propose three hybrid recovering algorithms, namely, cascade movement based on nearest nodes, cascade movement based on connected dominating set, and cascade movement based on critical nodes. With the adoption of Kalman filter, all these schemes can predict the movement of nodes so as to identify and maintain connectivity of mobile wireless ad hoc networks. Meanwhile, once a node fails or a link breaks down, they can also recover the connectivity immediately. We conduct extensive simulations on the metrics of the average number of participating nodes, the deviation from original direction, and the ratio of cascade movement for the three proposed algorithms, and the algorithm of cascade movement based on critical nodes achieves the best results.

Mobile cyber-physical systems (CPS) that take the advantages and extend the application domains of CPS have become increasingly popular in recent years. For example, mobile CPS could be a kind of foundational techniques to support the development of vehicular networking systems, thereby improving security and privacy of users in the dynamic environments of vehicular networks. In this paper, we first distinguish mobile CPS from traditional CPS. Then, we introduce their three emerging application areas, i.e., vehicular networking systems, healthcare systems, and mobile education . After that, we discuss four main research challenges of mobile CPS regarding security, energy consumption, mobile dynamic environment , and system stability . Also, we consider the corresponding techniques, which may address these challenges, and analyze the inter-relations among them. Finally, we outline the possible research directions and applications of mobile CPS in the future.

Vehicular transportation is an essential part of modern cities. However, the ever increasing number of road accidents, traffic congestion, and other such issues become obstacles for the realization of smart cities. As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSNs) are promising to solve the above-mentioned problems by enabling smart mobility in modern cities, which are likely to pave the way for sustainable development by promoting transportation efficiency. In this article, the definition of and a brief introduction to VSNs are presented first. Existing supporting communication technologies are then summarized. Furthermore, we introduce an application scenario on trajectory data-analysis-based traffic anomaly detection for VSNs. Finally, several research challenges and open issues are highlighted and discussed.

Stable and reliable wireless communication is one of the critical demands for smart cities to connect people and devices. Although intelligent terminals can be leveraged to deliver and exchange data through Internet, poor network coverage and expensive network access challenge the deployment of network infrastructure. In this paper, we propose a social-oriented smartphone-based adaptive transmission mechanism to improve the network connectivity and throughput in Internet of Things (IoTs) for smart cities. First, a social-oriented double-auction-based relay selection scheme is investigated to stimulate the relay smartphones to forward packets for others so that the network connectivity can be strengthened. Furthermore, for the sake of achieving high throughput in smartphone-based IoTs, the relay method selection is determined by integrating various kinds of transmission schemes in an optimal fashion to make full use of wireless spectrum resource. Due to its high computational complexity, a firefly-algorithm-based scheme is investigated, by which the formulated NP-complete problem can be solved effectively. Simulation results demonstrate the superiority of our proposed method.

This article analyzes security vulnerabilities in bandwidth allocation and request mechanisms that are specified in the IEEE 802.16 Standard, and introduces a bandwidth request adversary model called UL-BRA based on these vulnerabilities. The goal of UL-BRA is to abuse the bandwidth request/grant operation of a WiMAX network, resulting in a significant decrease of the overall uplink transmission performance of the network. To overcome the challenge caused by the vulnerabilities of the standard, we propose a security patch incorporating a real-time anomaly detection and restoration (RADR) mechanism, which combines an MCDR scheme with an ATD scheme in order to eliminate the impacts of UL-BRA in real-time. We verify the effectiveness of RADR under UL-BRA threats via simulations, and demonstrate the effectiveness of the proposed patch in mitigating the threats of UL-BRA.

To meet the demand of increasing mobile data traffic and provide better user experience, heterogeneous cellular networks (HCNs) have become a promising solution to improve both the system capacity and coverage. However, due to dense self-deployment of small cells in a limited area, serious interference from nearby base stations may occur, which results in severe performance degradation. To mitigate downlink interference and utilize spectrum resources more efficiently, we present a novel graph-based resource allocation and interference management approach in this paper. First, we divide small cells into cell clusters, considering their neighborhood relationships in the scenario. Then, we develop another graph clustering scheme to group user equipment (UE) in each cell cluster into UE clusters with minimum intracluster interference. Finally, we utilize a proportional fairness scheduling scheme to assign subchannels to each UE cluster and allocate power using water-filling method. To show the efficacy and effectiveness of our proposed approach, we propose a dual-based approach to search for optimal solutions as the baseline for comparisons. Furthermore, we compare the graph-based approach with the state of the art and a distributed approach without interference coordination. The simulation results show that our graph-based approach reaches more than 90% of the optimal performance and achieves a significant improvement in spectral efficiency compared with the state of the art and the distributed approach both under cochannel and orthogonal deployments. Moreover, the proposed graph-based approach has low computation complexity, making it feasible for real-time implementation.

It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSNs), will not be isolated but connected and integrated with computer networks in future Internet of Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful Web service (WS) approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6-based open standards for accessing wireless resource-constrained networks, we consider to implement IPv6 over low-power wireless personal area network (6LoWPAN)/routing protocol for low power and lossy network (RPL)/constrained application protocol (CoAP) protocols on sensor devices and propose a CoAP-based DM solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.

Mobile social networks (MSNs) have become increasingly popular in supporting many novel applications since emerging in the recent years. Their applications and services are of great interest to service providers, application developers, and users. This paper distinguishes MSNs from conventional social networks and provides a comprehensive survey of MSNs with regard to platforms, solutions, and designs of the overall system architecture. We review the popular MSN platforms and experimental solutions for existing MSN applications and services and present the dominant mobile operating systems on which MSNs are implemented. We then analyze and propose the overall architectural designs of conventional and future MSN systems. In particular, we present the architectural designs from two perspectives: from the client side to the server side, and from the wireless data transmission level to the terminal utilization level. We further introduce and compare the unique features, services, and key technologies of two generations of architectural designs of MSN systems. Then, we classify the existing MSN applications and propose one special form of MSN, i.e., vehicular social network, and demonstrate its unique features and challenges compared with common MSNs. Finally, we summarize the major challenges for on-going MSN research and outline possible future research directions.

People spend up to one-third of lives asleep, and healthy sleep habits can make a big difference in their quality of life. But in modern society, many people have unhealthy sleep diaries and suffer from various sleep disorders, which may result in irregular mood fluctuations or even mental health problems such as anxiety and depression. We propose the Emotion-Aware Smart Tips (EAST), a novel approach that could help to inform users about their irregular emotional states with smart tips to improve their sleep qualities. EAST aims at helping users keep healthy sleep schedules and emotional states by providing smart tips through a novel model that combines multivariate regression, random forest, and neural network to quantify the relations between sleep patterns and emotional states. Prototype implementation and initial experiments of EAST in mobile phones have demonstrated its desired functionality and practicality for real-world deployment.

In sustainable smart cities, power saving is a severe challenge in the energy-constrained Internet of Things (IoT). Efficient utilization of limited multiple non-overlap channels and time resources is a promising solution to reduce the network interference and save energy consumption. In this paper, we propose a joint channel allocation and time slot optimization solution for IoT. First, we propose a channel ranking algorithm which enables each node to rank its available channels based on the channel properties. Then, we propose a distributed channel allocation algorithm so that each node can choose a proper channel based on the channel ranking and its own residual energy. Finally, the sleeping duration and spectrum sensing duration are jointly optimized to maximize the normalized throughput and satisfy energy consumption constraints simultaneously. Different from the former approaches, our proposed solution requires no central coordination or any global information that each node can operate based on its own local information in a total distributed manner. Also, theoretical analysis and extensive simulations have validated that when applying our solution in the network of IoT: (i) each node can be allocated to a proper channel based on the residual energy to balance the lifetime; (ii) the network can rapidly converge to a collision-free transmission through each node’s learning ability in the process of the distributed channel allocation; and (iii) the network throughput is further improved via the dynamic time slot optimization.

This paper proposes a novel power management solution for resource-constrained devices in the context of Internet of Things (IoT). We focus on smartphones in the IoT, as they are getting increasingly popular and equipped with strong sensing capabilities. Smartphones have complex and asynchronous power consumption incurred by heterogeneous components including their on-board sensors. Their interaction with the cloud allows them to offload computation tasks and access remote data storage. In this work, we aim at monitoring the power consumption behaviours of the smartphones, profiling both individual applications and the system as a whole, to make better decisions in power management. We design a cloud orchestration architecture as an epic predictor of behaviours of smart devices by extracting their application characteristics and resource utilization. We design and implement this architecture to perform energy profiling and data analysis on massive data logs. This cloud orchestration architecture coordinates a number of cloud-based services and supports dynamic workflows between service components, which can reduce energy consumption in the energy profiling process itself. Experimental results showed that small portion of applications dominate the energy consumption of smartphones. Heuristic profiling can effectively reduce energy consumption in data logging and communications without scarifying the accuracy of power monitoring.

Music listening is an integral part of many adolescents? everyday lives, but it is also a time when adolescents are uniquely vulnerable. Emotional-oriented and avoidance through listening to unsuitable music may bring negative emotion to adolescents and increase the level of their depression. We propose MeDJ, a multidimensional emotion-aware music delivery application, which turns adolescents' music listening into a health and pleasure way. MeDJ aims at helping adolescents to improve emotional management and prevent depression. It is built on a cloud-based platform that enables adolescents and their peers collaboratively recommend suitable music to each other through smartphones. Prototype implementation and initial results of MeDJ have demonstrated its practicability for real-world deployment.

Software defined networking (SDN) has the potential to meet the requirements of the next generation traffic and service requirements. It is especially feasible and flexible when combining with small cell networks, which emerges into a software defined small cell networking (SDSCN) framework. SDSCN stands a chance to play a fundamental role in developing future 5G networks. It is particularly a challenging task to deploy dense small cell networks in the presence of dynamic traffic patterns and severe co-channel interference. Based on the SDSCN framework, in this paper, we propose a traffic clustering method to obtain all traffic patterns in a given area and an energy-efficient scheme to deploy and switch on/off small cell base stations (s-BSs) according to the prevailing traffic pattern. The simulation results indicate that our scheme can meet dynamic traffic demands with optimized deployment of small cells and enhance the energy efficiency of the system without compromising on the spectrum efficiency and quality-of-service (QoS) requirements. In addition, our scheme can achieve very close performance compared with the leading optimization solver CPLEX and find solutions in much less computational times than CPLEX.

In smart cities, cellular network plays a crucial role to support wireless access for numerous devices anywhere and anytime. The future 5G network aims to build the infrastructure from mobile internet to connected world. Small Cell is one of the most promising technologies of 5G to provide more connections and high data rate. In order to make the best use of small cell technology, smart cell planning should be implemented to guarantee connectivity and performance for all end nodes. It is particularly a challenging task to deploy dense small cells in the presence of dynamic traffic demands and severe co-channel interference. In this paper, we model various traffic patterns using stochastic geometry approach and propose an energy-efficient scheme to deploy and plan small cells according to the prevailing traffic pattern. The simulation results indicate that our scheme can meet dynamic traffic demands with optimized deployment of small cells and enhance the energy efficiency of the system without compromising on quality-of-service (QoS) requirements. In addition, our scheme can achieve very close performance compared with the leading optimization solver CPLEX and find solutions in much less computational times than CPLEX.

Mobile Edge Computing (MEC) is promising to alleviate the computation and storage burdens for terminals in wireless networks. The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries. In addition, Orthogonal Multiple Access (OMA) technique cannot utilize limited spectrum resources fully and efficiently. Therefore, Non-Orthogonal Multiple Access (NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important, especially in highly-dynamic vehicular edge computing networks. The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers. Self-Imitation Learning (SIL)-based Deep Reinforcement Learning (DRL) has emerged as a promising machine learning technique to break through obstacles in various research fields, especially in time-varying networks. In this paper, we first introduce related MEC technologies in vehicular networks. Then, we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL, with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers. Numerical results demonstrate that the proposed algorithm outperforms other methods.