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In the past few years, the development of demand response (DR) programs for smart grid systems has provided residential customers with a real opportunity to participate in DR-driven projects. One typical DR-related application now available is automated energy scheduling for residential building, which can be used to help reduce energy costs. Residential energy scheduling focuses on saving cost by managing the operation time and energy consumption level of different appliances. Demand Response programs present as NP-hard problems, the equations are non-convex mixed integer non-linear problems (MINLP), for which it is difficult to obtain an optimal solution. In relation to residential DR, we propose a hybrid approach here that is able to solve an MINLP. The problem is decomposed into discrete and continuous variables. The discrete variables are optimized by using a particle swarm optimization (PSO) algorithm, whilst the continuous variables are determined by using a gradient-based deterministic algorithm. The superiority of the proposed algorithm was demonstrated by compared with commercial optimization software and heuristic based algorithm. Furthermore, the effectiveness of the proposed method in residential DR was tested and the results were presented.

China-05 method is the standard method for bone age assessment (BAA) in China. BAA is a typical target detection research project. In this study, a target detection algorithm was proposed combining image processing and deep learning based on the regions of interest (ROI) of 13 hand bones concerned by RUS-CHN method in China-05 method for bone age assessment. We sorted 13 actual ROIs blocks into 4 ROI collection regions (ROI-C), and detected ROI-C through image processing methods such as image equalization, binarization, rotation and positioning. After that, the YOLO V3 model is used to train the new data set composed of ROI-C to detect the actual ROI. This detection algorithm solves the problems of poor robustness, inaccurate detection area positioning, target detection error or detection duplication when we use traditional single image processing or deep learning algorithm. This method greatly improves the detection accuracy of ROI and has great significance for accurate assessment of bone age.

With the rapid development of environmental technologies such as renewable energy, smart grid, and electric power transportation, future power generation and power supply will show new features. The design of energy consumption characteristics of modern power system is more flexible and easy to control, which will also affect the scale of power generation system. This paper presents a combined capacity optimization method for a typical independent microgrid including solar photovoltaic, wind turbines, diesel generators and battery energy storage system. The mathematical models of photovoltaic, wind turbines, diesel generation system, battery energy storage system and electric vehicle charging loads are developed to improve the capacity optimization method. Research objectives include: 1) minimizing costs; 2) reducing greenhouse gas emissions; 3) reducing waste energy. This research can provide strong support for decision-making, analysis and strategy-making of multi-agent joint microgrid capacity.

Mobile edge and vehicle-based depth sending and real-time point cloud communication is an essential subtask enabling autonomous driving. In this paper, we propose a framework for point cloud multicast in VANETs using vehicle to infrastructure (V2I) communication. We employ a scalable Binary Tree embedded Quad Tree (BTQT) point cloud source encoder with bitrate elasticity to match with an adaptive random network coding (ARNC) to multicast different layers to the vehicles. The scalability of our BTQT encoded point cloud provides a trade-off in the received voxel size/quality vs channel condition whereas the ARNC helps maximize the throughput under a hard delay constraint. The solution is tested with the outdoor 3D point cloud dataset from MERL for autonomous driving. The users with good channel conditions receive a near lossless point cloud whereas users with bad channel conditions are still able to receive at least the base layer point cloud.

For grid-connected devices, synchronization scheme is adopted to track the phase of grid voltage, and it has also been reported that synchronization schemes can shape the impedance characteristics of grid-connected devices and lead to frequency coupling phenomenon, thus the system stability will be affected. Recently, a novel type-1 frequency-locked loop (FLL) is proposed to resolve the contradiction of fast dynamic responses and disturbance rejection capability that the widely applied synchronous reference frame phase-locked loop (SRF-PLL) suffers. Therefore, this paper tries to fill in the blanks of analytical modeling and analyzing the frequency coupling characteristic caused by type-1 FLL. In this paper, the analytical model of type-1 FLL will be developed and validated against time-domain simulations. The derived model of type-1 FLL is also analyzed to provide insights into its impedance shaping effect and frequency coupling characteristics. Applying the type-1 FLL to the doubly fed induction generator (DFIG) system, an analytical model that describes the frequency coupling characteristics of type-1 FLL based DFIG system will be developed and validated. Based on the frequency coupling model of DFIG, the influence of type-1 FLL on system stability will also be studied.

Cognitive radio networks, a.k.a. dynamic spectrum access networks, offer a promising solution to the problems of spectrum scarcity and under-utilization. In this paper, we consider two single-user links: primary and secondary links. To increase secondary user (SU) transmission opportunities and increase primary user (PU) throughput, we consider a cognitive relay network where a SU relays PU packets that are unsuccessfully received at the primary receiver (PR). At the PR side, two protocols are suggested: i) energy accumulation (EA), and ii) mutual-information accumulation (MIA). The average stable throughput of the secondary link is derived under these protocols for a specific throughput selected by the primary link. Results show that EA and MIA can significantly improve the secondary throughput compared with the no accumulation scenario, especially under extreme environment.

In underwater sensor networks (UWSNs), acoustic communication is commonly used unlike that in terrestrial wireless networks. The long propagation delay of acoustic signals causes spatio-temporal uncertainty, which makes the link scheduling in UWSNs a challenging problem. To describe the propagation delays of the transmission links and deal with the spatio-temporal uncertainty, we construct a so called slotted spatio-temporal conflict graph. We propose efficient scheduling algorithms with constant approximation ratios to the optimum solutions. We consider both unified and weighted traffic load scenarios when designing the scheduling algorithms. In the weighted traffic load scenario, we consider the scheduling with and without the consecutive constraint. Simulations validate our theoretical results, and show the efficiency of our proposed algorithms.

Broadcast is an essential and widely-used operation in multi-hop wireless networks. Minimum latency broadcast scheduling (MLBS) aims to provide a collision-free scheduling for broadcast with the minimum latency. Previous work on MLBS mostly assumes that nodes are always active, and thus is not suitable for duty-cycle-aware scenarios. In this paper, we investigate the duty-cycle-aware minimum latency broadcast scheduling (DCA-MLBS) problem in multi-hop wireless networks. We prove both the one-to-all and the all-to-all DCA-MLBS problems to be NP-hard. We propose a novel approximation algorithm called OTAB for the one-to-all DCA-MLBS problem, and two approximation algorithms called UTB and UNB for the all-to-all DCA-MLBS problem under the unit-size and the unbounded-size message models respectively. The OTAB algorithm achieves a constant approximation ratio of 17|T|, where |T| denotes the number of time-slots in a scheduling period. The UTB and UNB algorithms achieve the approximation ratios of 17|T|+20 and (Δ+22)|T| respectively, where Δ denotes the maximum node degree of the network. Extensive simulations are conducted to evaluate the performance of our algorithms.

Building a “Straightforward” path in wireless ad hoc sensor networks (WASNs) not only avoids wasting energy in detours, but also incurs less interference in other transmissions when fewer nodes are involved in the transmission. This plays an important role in recent WASN applications that require a streaming service to deliver large amount of data. In this paper, we extend our early work on the straightforward path routing in WASNs in the presence of the “local minima”, where the routing is blocked due to the lack of available forwarding successors. We bring some new insights of the safety information model for a forwarding routing that is limited in the request zone. A new routing is proposed to make a more intelligent decision in greedy advance and achieve more straightforward paths. The experimental results show substantial improvements of our approach compared with the best result known to date.

Sleep scheduling is a widely used mechanism in wireless sensor networks (WSNs) to reduce the energy consumption since it can save the energy wastage caused by the idle listening state. In a traditional sleep scheduling, however, sensors have to start up numerous times in a period, and thus consume extra energy due to the state transitions. The objective of this paper is to design an energy efficient sleep scheduling for low data-rate WSNs, where sensors not only consume different amounts of energy in different states (transmit, receive, idle and sleep), but also consume energy for state transitions. We use TDMA as the MAC layer protocol, because it has the advantages of avoiding collisions, idle listening and overhearing. We first propose a novel interference-free TDMA sleep scheduling problem called contiguous link scheduling, which assigns sensors with consecutive time slots to reduce the frequency of state transitions. To tackle this problem, we then present efficient centralized and distributed algorithms that use time slots at most a constant factor of the optimum. The simulation studies corroborate the theoretical results, and show the efficiency of our proposed algorithms.

In wireless ad-hoc sensor networks, an important issue often faced in geographic greedy forwarding routing is the "local minimum phenomenon" which is caused by deployment holes and blocks the forwarding process. In this paper, we provide a new information model for the geographic greedy forwarding routing that only forwards the packet within the so-called request zone. Under this new information model, the hole and its affected area are identified easily and quickly in an unsafe area with a labeling process. The greedy forwarding will be blocked if and only if a node inside the unsafe area is used. Due to the shape of the request zone, an unsafe area can be estimated as a rectangular region in the local view of unsafe nodes. With such estimate information, the new routing method proposed in this paper will avoid blocking by holes and achieve better performance in routing time while the cost of information construction is greatly reduced compared with the best results known to date.

Recent advances in wireless networking technologies, along with ubiquitous sensing and computing, have brought significant convenience for location service. The localization issue in wireless sensor networks under the non-adversarial scenario has already been well studied. However, most existing localization schemes cannot provide satisfied location service under the adversarial scenario. In this paper, we propose an attack-resistant localization scheme, called TSCD secure localization, to overcome the distance-consistent spoofing attack in wireless sensor networks. The main idea of the TSCD scheme is to firstly apply the temporal and spatial properties of locators to detect some attacked locators, and then utilize the consistent property of the detected attacked locators to find out other attacked locators. An enhanced TSCD scheme is also presented and analyzed. Simulation results demonstrate that both proposed schemes achieve better performance than existing approaches under the same network parameters.