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The fifth-generation (5G) communication systems will include very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, high speed data transfer and ubiquitous connectivity among various types of devices will be provided by integration of LTE and Wi-Fi.

According to World Urbanization Prospects published by the United Nations, globally, more people live in urban areas than in rural areas, with 54 per cent of the world’s population residing in urban areas in 2014. By 2050, 66 per cent of the world’s population is projected to be urban, compared with 30 per cent of the world’s population was urban in 1950. The development of “Smart Cities” and “Smart and Connected Communities” is becoming a major focus in the U.S. and around the world.

Limited memory bandwidth is considered as the major bottleneck in multimedia cloud computing for more and more virtual machines (VMs) of multimedia processing requiring high memory bandwidth simultaneously. Moreover, contending memory bandwidth among parallel running VMs leads to poor quality of service (QoS) of the multimedia applications, missing the deadlines of these soft real-time multimedia applications. In this paper, we present an adaptive framework, Service Maximization Optimization (SMO), which is designed to improve the QoS of the soft real-time multimedia applications in multimedia cloud computing. The framework consists of an automatic detection mechanism and an adaptive memory bandwidth control mechanism. With the automatic detection mechanism, the critical section to the multimedia application performance in the VMs is detected. Then, our adaptive memory bandwidth control mechanism adjusts the memory access rates of all the parallel running VMs to protect the QoS of the soft real-time multimedia applications. From the case studies with real-world multimedia applications, our SMO significantly improves the QoS of the soft real-time multimedia applications with a negligible penalty on system throughput.

With rapid economic growth, the management of employees working outdoors gradually becomes more intensive. The traditional attendance systems typically focus on collecting employee attendance information and are not suited to the quantification of work performance. As a remedy to this issue, a smart work performance measurement system is proposed in this paper. The proposed system consists of three components: 1) a smartphone-based APP to collect employee attendance, work, and location information; 2) a data warehouse to preprocess and store the data; and 3) a smart data analysis center to make a comprehensive and systematic evaluation of employee work performance. Management can obtain the key information quickly and adjust work assignments based on performance. New solutions and algorithms for indoor and outdoor location, GPS deviation improvement, and work performance measurement are put forward. This system is being used by over 1100 police officers in one city traffic management bureau, and the observed results are encouraging, demonstrating the efficiency and accuracy of our system, and helping governments to better regulate the traffic operation and reduce associated costs.

Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.

Underwater Acoustic Sensor Networks (UASNs) have drawn great attention for their potential value in ocean monitoring and offshore exploration. In order to make the underwater application possible, the unique characteristics of underwater acoustic channels and continuous node movement inspired the emergence of routing protocols for underwater environment. In this paper, we introduce and compare four prominent routing protocols proposed for UASNs, namely, H2-DAB, GEDAR, E-PULRP, and PER. Performances of the routing protocols are evaluated in terms of the average number of control packets, end-to-end delay, data delivery ratio, and total energy consumption. The impact of water currents on the routing algorithms is also analyzed in our simulation. Experimental results demonstrate that E-PULRP provides high data delivery ratio at the cost of end-to-end delay. H2-DAB has better real-time performance for minimal delay transmission. GEDAR efficiently addresses the problem of void region without introducing extra energy. PER requires the most control packets in the process of routing establishment. Our work aims to provide useful insights to select appropriate routing protocols to fulfil different application requirements in UASNs.

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

The sensing data of nodes is generally correlated in dense wireless sensor networks, and the active node selection problem aims at selecting a minimum number of nodes to provide required data services within error threshold so as to efficiently extend the network lifetime. In this paper, we firstly propose a new Cover Sets Balance (CSB) algorithm to choose a set of active nodes with the partially ordered tuple (data coverage range, residual energy). Then, we introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. Finally, we propose a High Residual Energy First (HREF) node selection algorithm to further reduce the number of active nodes. Extensive experiments demonstrate that HREF significantly reduces the number of active nodes, and CSB and HREF effectively increase the lifetime of wireless sensor networks compared with related works.