This paper proposes a novel environmental monitoring strategy, incremental environmental monitoring, that enables scientists to reveal the ecology of wild animals in the field. We applied this strategy to the habitat of endangered freshwater fish. Specifically, we designed and implemented a network-based system using distributed sensors to continuously monitor and record the habitat of endangered fish. Moreover, we developed a set of analytical tools to exploit a variety of sensor data, including environmental time-series data such as amount of dissolved oxygen, as well as underwater video capturing the interaction of fish and their environment. We also describe the current state of monitoring the behavior and habitat of endangered fish and discuss solutions for making such environmental monitoring more efficient in the field.

As IoT services become more popular, mobile networks will have to accommodate a wide variety of devices that have different requirements such as different bandwidth limitations and latencies. This paper describes edge distributed mobile network architectures for the IoT era based on dedicated network technology and multi-access edge computing technology, which have been discussed in 3GPP and ETSI. Furthermore, it describes two context-aware control methods that will make mobile networks on the network architecture more efficient, reliable, and real-time: autonomous and distributed mobility management and bandwidth-guaranteed transmission rate control in a networked control system.

The introduction of a drone-based mobile sink into wireless sensor networks (WSNs), which has flexible mobility to move to each sensor node and gather data with a single-hop transmission, makes cumbersome multi-hop transmissions unnecessary, thereby facilitating data gathering from widely-spread sensor nodes. However, each sensor node spends significant amount of energy during their idle state where they wait for the mobile sink to come close to their vicinity for data gathering. In order to solve this problem, in this paper, we apply a wake-up receiver to each sensor node, which consumes much smaller power than the main radio used for data transmissions. The main radio interface is woken up only when the wake-up receiver attached to each node detects a wake-up signal transmitted by the mobile sink. For this mobile and on-demand data gathering, this paper proposes a route control framework that decides the mobility route for a drone-based mobile sink, considering the interactions between wake-up control and physical layer (PHY) and medium access control (MAC) layer operations. We investigate the optimality and effectiveness of the route obtained by the proposed framework with computer simulations. Furthermore, we present experimental results obtained with our test-bed of a WSN employing a drone-based mobile sink and wake-up receivers. All these results give us the insight on the role of wake-up receiver in mobile and on-demand sensing data gathering and its interactions with protocol/system designs.

Location information is essential to varieties of applications. It is one of the most important context to be detected by wireless distributed sensors, which is a key technology in Internet-of-Things. Fingerprint-based methods, which compare location unique fingerprints collected beforehand with the fingerprint measured from the target, have attracted much attention recently in both of academia and industry. They have been successfully used for many location-based applications. From the viewpoint of practical applications, in this paper, four different typical approaches of fingerprint-based radio emitter localization system are introduced with four different representative applications: localization of LTE smart phone used for anti-cheating in exams, indoor localization of Wi-Fi terminals, localized light control in BEMS using location information of occupants, and illegal radio localization in outdoor environments. Based on the different practical application scenarios, different solutions, which are designed to enhance the localization performance, are discussed in detail. To the best of the authors' knowledge, this is the first paper to give a guideline for readers about fingerprint-based localization system in terms of fingerprint selection, hardware architecture design and algorithm enhancement.

Densification of mmWave smallcells overlaid on the conventional macro cell is considered to be an essential technology for enhanced mobile broadband services and future IoT applications requiring high data rate e.g. automated driving in 5G communication networks. Taking into account actual measurement mobile traffic data which reveal dynamicity in both time and space, this paper proposes a joint optimization of user association and smallcell base station (BS)'s ON/OFF status. The target is to improve the system's energy efficiency while guaranteeing user's satisfaction measured through e.g. delay tolerance. Numerical analyses are conducted to show the effectiveness of the proposed algorithm against dynamic traffic variation.

In this paper, we propose an IEEE 802.15.10-based layer 2 routing (L2R) method with a load balancing algorithm; the proposal considers fairness in terms of the cumulative number of sending packets at each terminal to resolve the packet concentration problem for the IEEE 802.15.4-based low-power consumption wireless smart utility network (Wi-SUN) systems. The proposal uses the accumulated sending times of each terminal as a weight in calculating each path quality metric (PQM) to decide multi-hopping routes with load balancing in the network. Computer simulation of the mesh network with 256 terminals shows that the proposed routing method can improve the maximum sending ratio (MSR), defined as the ratio of the maximum sending times to the average number of sending times in the network, by 56% with no degradation of the end-to-end communication success ratio (E2E-SR). The proposed algorithm is also experimentally evaluated by using actual Wi-SUN modules. The proposed routing method also improves the MSR by 84% with 70 terminals. Computer simulations and experiments prove the effectiveness of the proposed method in terms of load balancing.

As one functionality of the wireless distributed network (WDN) enabling flexible wireless networks, it is supposed that a dynamic spectrum access is applied to OFDM systems for superior radio resource management. As a basic technology for such WDN, our study deals with the OFDM signal detection based on its cyclostationary feature. Previous relevant studies mainly relied on software simulations based on the Monte Carlo method. This paper analytically clarifies the relationship between the design parameters of the detector and its detection performance. The detection performance is formulated by using multiple design parameters including the transfer function of the receive filter. A hardware experiment with radio frequency (RF) signals is also carried out by using the detector consisting of an RF unit and FPGA. Thereby, it is verified that the detection characteristics represented by the false-alarm and non-detection probabilities calculated by the analytical formula agree well with those obtained by the hardware experiment. Our analysis and experiment results are useful for the parameter design of the signal detector to satisfy required performance criteria.

Spectrum databases are required to assist the process of radio propagation estimation for spectrum sharing. Especially, a measurement-based spectrum database achieves highly efficient spectrum sharing by storing the observed radio environment information such as the signal power transmitted from a primary user. However, when the average received signal power is calculated in a given square mesh, the bias of the observation locations within the mesh strongly degrades the accuracy of the statistics because of the influence of terrain and buildings. This paper proposes a method for determining the statistics by using mesh clustering. The proposed method clusters the feature vectors of the measured data by using the k-means and Gaussian mixture model methods. Simulation results show that the proposed method can decrease the error between the measured value and the statistically processed value even if only a small amount of data is available in the spectrum database.

IEEE 802.11ah is a specification being developed for sub-1GHz license-exempt operation and is intended to provide Low Power Wide Area (LPWA) communication services and support Internet of Things (IoT) features such as large-scale networks and extended transmission range. However, these features also make the 802.11ah networks highly susceptible to channel contention and hidden node problem (HNP). To address the problems, the 11ah Task Group proposed a Restricted Access Window (RAW) mechanism. It shows outstanding performance in alleviating channel contention, but its effect on solving HNP is unsatisfactory. In this paper, we propose a simple and effective hidden node grouping algorithm (HNGA) based on IEEE 802.11ah RAW. The algorithm collects hidden node information by taking advantage of the 802.11 association process and then performs two-stage uniform grouping to prevent hidden node collisions (HNCs). Performance of the proposed algorithm is evaluated in comparison with other existing schemes in a hidden node situation. The results show that our proposed algorithm eliminates most of hidden node pairs inside a RAW group with low overhead penalty, thereby improving the performance of the network. Moreover, the algorithm is immune to HNCs caused by cross slot boundary transmissions.

Large-scale virtualized data centers are increasingly becoming the norm in our data-intensive society. One pressing challenge is to reduce the energy consumption of servers while maintaining a high level of service agreement fulfillment. Due to the convenience of virtualization, virtual machine migration is an effective way to optimize the trade-off between energy and performance. However, there are obvious drawbacks in the current static threshold strategy for migration. This paper proposes a new decision strategy based on decision-theoretic rough sets. In the new strategy, the status of a server is determined by the Bayesian rough set model. The space is divided into positive, negative and boundary regions. According to this information, a migration decision with minimum risk will be made. This three-way decision framework in our strategy can reduce over-migration and delayed migration. The experiments in this paper show that this new strategy outperforms the benchmark examined. It is an efficient and flexible approach to the energy and performance trade-off in the cloud.

Data aggregation, which is the process of summarizing a large amount of data, is an effective method for saving limited communication resources, such as radio frequency and sensor-node energy. Packet aggregation in wireless LAN and sensed-data aggregation in wireless sensor networks are typical examples. We propose and analyze two queueing models of fundamental statistical data aggregation schemes: constant interval and constant aggregation number. We represent each aggregation scheme by a tandem queueing network model with a gate at the aggregation process and a single server queue at a transmission process. We analytically derive the stationary distribution and Laplace-Stieltjes transform of the system time for each aggregation and transmission process and of the total system time. We then numerically evaluate the stationary mean system time characteristics and clarify that each model has an optimal aggregation parameter (i.e., an optimal aggregation interval or optimal aggregation number), that minimizes the mean total system time. In addition, we derive the explicit optimal aggregation parameter for a D/M/1 transmission model with each aggregation scheme and clarify that it provides accurate approximation of that of each aggregation model. The optimal aggregation interval was determined by the transmission rate alone, while the optimal aggregation number was determined by the arrival and transmission rates alone with explicitly derived proportional constants. These results can provide a theoretical basis and a guideline for designing aggregation devices, such as IoT gateways.

Ensemble learning is widely used in the field of sensor network monitoring and target identification. To improve the generalization ability and classification precision of ensemble learning, we first propose an approximate attribute reduction algorithm based on rough sets in this paper. The reduction algorithm uses mutual information to measure attribute importance and introduces a correction coefficient and an approximation parameter. Based on a random sampling strategy, we use the approximate attribute reduction algorithm to implement the multi-modal sample space perturbation. To further reduce the ensemble size and realize a dynamic subset of base classifiers that best matches the test sample, we define a similarity parameter between the test samples and training sample sets that takes the similarity and number of the training samples into consideration. We then propose a k-means clustering-based dynamic ensemble selection algorithm. Simulations show that the multi-modal perturbation method effectively selects important attributes and reduces the influence of noise on the classification results. The classification precision and runtime of experiments demonstrate the effectiveness of the proposed dynamic ensemble selection algorithm.

Effective capacity (EC) is an important performance metric for a time-varying wireless channel in order to evaluate the communication rate in the physical layer (PHL) while satisfying the statistical delay quality of service (QoS) requirement in data-link layer (DLL). This paper analyzes EC of amplify-and-forward wireless relay network with different relay selection (RS) protocols. First, through the analysis of the probability density function (PDF) of received signal-to-noise ratio (SNR), the exact expressions of EC for direct transmission (DT), random relay (RR), random relay with direct transmission (RR-WDT), best relay (BR) protocols are derived. Then a novel best relay with direct transmission (BR-WDT) protocol is proposed to maximize EC and an exact expression of EC for BR-WDT protocol is developed. Simulations demonstrate that the derived analytical results well match those of Monte-Carlo simulations. The proposed BR-WDT protocol can always achieve larger EC than other protocols while guaranteeing the delay QoS requirement. Moreover, the influence of distance between source and relay on EC is discussed, and optimal relay position for different RS protocols is estimated. Furthermore, EC of all protocols becomes smaller while delay QoS exponent becomes larger, and EC of BR-WDT becomes better while the number of relays becomes larger.

The energy efficiency of Internet of Things (IoT) could be improved by RF energy transfer technologies.Aiming at IoT applications with a mobility-constrained mobile sink, a double-sourced energy transfer (D-ET) scheme is proposed. Based on the hierarchical routing information of network nodes, the Simultaneous Wireless Information and Power Transfer (SWIPT) method helps to improve the global data gathering performance. A genetic algorithm and graph theory are combined to analyze the node energy consumption distribution. Then dedicated charger nodes are deployed on the basis of the genetic algorithm's output. Experiments are conducted using Network Simulator-3 (NS-3) to evaluate the performance of the D-ET scheme. The simulation results show D-ET outperforms other schemes in terms of network lifetime and data gathering performance.

This paper investigates receive power control for multiuser inductive power transfer (IPT) systems with a single-frequency coil array. The primary task is to optimize the transmit coil currents to minimize the total input power, subject to the minimum receive powers required by individual users. Due to the complicated coupling mechanism among all transmit coils and user pickups, the optimization problem is a non-convex quadratically constrained quadratic program (QCQP), which is analytically intractable. This paper solves the problem by applying the semidefinite relaxation (SDR) technique and evaluates the performance by full-wave electromagnetic simulations. Our results show that a single-frequency coil array is capable of power control for various multiuser scenarios, assuming that the number of transmit coils is greater than or equal to the number of users and the transmission conditions for individual users are uncorrelated.

In many communications applications, maximum-likelihood decoding reduces to solving an integer least-squares problem, which is NP-hard in the worst case. It has recently been shown that over a wide range of dimensions and SNRs, the branch and bound (BB) algorithm can be used to find the exact solution with an expected complexity that is roughly cubic in the dimension of the problem. However, the computational complexity becomes prohibitive if the SNR is too low and/or the dimension of the problem is too large. The dichotomous coordinate descent (DCD) algorithm provides low complexity, but its detection performance is not as good as that of the BB detector. Two methods are developed to bound the optimal detector cost to reduce the complexity of BB in this paper. These methods are DCD-based detectors for MIMO and multiuser detection in the scenario of a large number of transmitting antennas/users. First, a combined detection technique based on the BB and DCD algorithms is proposed. The technique maintains the advantages of both algorithms and achieves a good trade-off between performance and complexity compared to using only the BB or DCD algorithm. Second, since the first feasible solution obtained from the BB search is the solution of the decorrelating decision feedback (DF) method and because DCD results in better accuracy than the decorrelating DF solution, we propose that the first feasible solution of the BB algorithm be obtained by the box-constrained DCD algorithm rather than the decorrelating DF detector. This method improves the precision of the initial solution and identifies more branches that can be eliminated from the search tree. The results show that the DCD-based BB detector provides optimal detection with reduced worst-case complexity compared to that of the decorrelating DF-based BB detector.

After large-scale disasters, information sharing among people becomes more important than usual. This, however, is extremely difficult to achieve in disaster zones due to serious damage to the existing network infrastructure, power outages, and high traffic congestion. For the quick provision of alternative networks to serve heavy communication demands after disasters, establishing local area networks (LANs) consisting of portable servers with data storage has been considered as one of the most promising solutions. Based on the established LAN and a data server in each area, people can share many kinds of disaster-related information such as emergency information and supply/demand information via deployed neighboring servers. However, due to the lack of stable Internet connection, these servers are isolated and cannot be synchronized in real time. To enable and guarantee more efficient information sharing across the whole disaster-hit area, data stored on each server should be synchronized without the Internet. Our solution is to propose an intermittent data synchronization scheme that uses moving vehicles as relays to exchange data between isolated servers after disasters. With the objective of maximizing the total number of synchronized high priority data under the capability constraints of mobile relays, we first propose a data allocation scheme (DAS) from a server to a mobile relay. After that, we propose a trajectory planning scheme for the relays which is formulated as a Mixed Integer Linear Fractional Programming (MILFP) problem, and an algorithm to solve it efficiently. Extensive simulations and comparisons with other methods show the superior performance of our proposals.