Energy harvesting (EH) from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many benefits to green communications. In this paper, we investigate the device-to-device (D2D) user equipments (DUEs) multiplexing cellular user equipments (CUEs) downlink spectrum resources problem for EH-based D2D communication heterogeneous networks (EH-DHNs). Our goal is to maximize the average energy efficiency of all D2D links, in the case of guaranteeing the quality of service of CUEs and the EH constraints of the D2D links. The resource allocation problems contain the EH time slot allocation of DUEs, power and spectrum resource block (RB) allocation. In order to tackle these issues, we formulate an average energy efficiency problem in EH-DHNs, taking into consideration EH time slot allocation, power and spectrum RB allocation for the D2D links, which is a nonconvex problem. Furthermore, we transform the original problem into a tractable convex optimization problem. We propose joint the EH time slot allocation, power and spectrum RB allocation iterative algorithm based on the Dinkelbach and Lagrangian constrained optimization. Numerical results demonstrate that the proposed iterative algorithm achieves higher energy efficiency for different network parameters settings.

Energy Efficient Resource Allocation Algorithm in Energy Harvesting-Based D2D Heterogeneous Networks

Quality of service approaches in IoT: A systematic mapping

The future wireless networks need to improve spectrum and energy efficiency to satisfy the increasing demand for high data rate. Device-to device (D2D) communications have the ability to address this problem. This paper focuses on the underlay D2D relay function to improve cellular coverage quality. Although there are a few relevant works in this aspect, there is room for further improvement. For example, there is the constraint on the number of relays in a transmission path, which hardly meets the requirement of the cell-edge devices to fully improve their cellular throughput. Also, there is little energy constraint for underlay D2D relay selection, which is difficult to guarantee the service life of underlay D2D relaying links. Furthermore, without careful regulation of transmission power in terms of cellular coverage improvement, it is not conducive to the improvement of spectrum and energy efficiency in this aspect. Therefore, this paper proposes the improved scheme to deal with these problems, which can: 1) improve spectrum efficiency by using underlay spectrum sharing mode and alleviating its weakness (e.g., co-channel interference); 2) enhance comprehensive performance of underlay D2D relaying links by jointly considering multiple Quality-of-Service (QoS) metrics; 3) reduce overhead of relay selection by proposing a greedy algorithm based on a distributed local search; and 4) improve both energy efficiency and convergence time by designing a new power adjustment scheme based on the improved potential game decision algorithm. The theoretical analysis proves the existence of Nash equilibrium, and the simulation results show that the proposed game decision algorithm accelerates convergence and the proposed whole scheme improves cellular coverage quality.

Multi-Hop Relay-Aided Underlay D2D Communications for Improving Cellular Coverage Quality

Matrix completion has emerged very recently and provides a new venue for low cost data gathering in Wireless Sensor Networks (WSNs). Existing schemes often assume that the data matrix has a known and fixed low-rank, which is unlikely to hold in a practical system for environment monitoring. Environmental data vary in temporal and spatial domains. By analyzing a large set of weather data collected from 196 sensors in ZhuZhou, China, we reveal that weather data have the features of low-rank, temporal stability, and relative rank stability. Taking advantage of these features, we propose an on-line data gathering scheme based on matrix completion theory, named MC-Weather, to adaptively sample different locations according to environmental and weather conditions. To better schedule sampling process while satisfying the required reconstruction accuracy, we propose several novel techniques, including three sample learning principles, an adaptive sampling algorithm based on matrix completion, and a uniform time slot and cross sample model. With these techniques, our MC-Weather scheme can collect the sensory data at required accuracy while largely reducing the cost for sensing, communication, and computation. We perform extensive simulations based on the data traces from weather monitoring and the simulation results validate the efficiency and efficacy of the proposed scheme.

Low Cost and High Accuracy Data Gathering in WSNs with Matrix Completion

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as capacity planning and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a 2-D matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel sequential tensor completion algorithm, which can efficiently exploit the tensor decomposition result based on the previous traffic data to derive the tensor decomposition upon arriving of new data. Furthermore, to better capture the changes of data correlation over time, we propose a dynamic sequential tensor completion algorithm. To the best of our knowledge, we are the first to propose sequential tensor completion algorithms to significantly speed up the traffic data recovery process. This facilitates the modeling of Internet traffic with the tensor to well exploit the hidden structures of traffic data for more accurate missing data inference. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithms can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high.

Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach

The bi-directional communication system is an indispensable component in smart grid (SG) for monitoring and exchanging essential information among the electrical devices. IEEE 802.11s based wireless mesh networks recently have been proposed as an important networking technology to deploy in SG for data collection and remote control purposes, as the cost of networking equipments decreases and performance increases. In this paper, we focus on analyzing the MAC layer performance for IEEE 802.11s wireless mesh networks in the smart grid based on Markov model, taking into account the impact of hidden nodes and different QoS requirements of smart grid applications. We first develop a new Markov chain model to analyze the back-off process for different applications with hidden nodes problem. Then based on the analytical model, we derive a few MAC layer performance metrics such as MAC layer packet dropping probability, the mean throughput and the mean packet delay which contains the queuing delay. Finally, the proposed analytical model is validated via comparing the analytical results with simulation results by ns-3 in NAN scenarios with various applications. We observe a good match between the analytical model and simulations which confirms the veracity of our model.

Performance Analysis for IEEE 802.11s Wireless Mesh Network in Smart Grid

Communication networks in smart grid collect and transfer various information between smart meters and control centre to support different quality of service (QoS) requirements of smart grid applications. However, there are many factors which could affect high reliability and QoS communication such as electromagnetic interference, equipment noise and multi-path effects. To address these challenges, a new routing metric is proposed in this study. The proposed metric takes into account the impacts caused by inter- and intra-flow interference, yielding reliable and high capacity links for neighbourhood area network communications in smart grid. In addition, it differentiates traffic flows into four different classes and makes packets compete for accessing a channel according to their priorities. Based on the metric, an interference aware QoS routing protocol, named interference-aware expected transmission time routing (IAEETR), is further proposed to provide reliable and real-time data transmission. Extensive simulation results demonstrate that high-priority traffic in the network is with small delay, and low-priority traffic can also have chance to compete for a wireless channel to transmit information. Furthermore, IAEETR protocol can improve end-to-end delay and packet delivery ratio with multiple simultaneous connections, even with a higher flow rate of mesh nodes.

Interference-aware QoS routing for neighbourhood area network in smart grid

The invention proposes an interference aware QoS wireless Mesh smart grid routing mechanism IAEETR. The routing mechanism includes four steps: firstly, creating a network interference model according to a condition that a node gets access to a channel to transmit data in a multi-radio multi-channel wireless Mesh network; secondly, in order to accurately analyze the influence of inter-stream interference on the delay in the neighbor area network of the smart grid, computing the attainable bandwidth under the multi-radio multi-channel path by acquiring the influence of the inter-stream interference on the link bandwidth; thirdly, designing an interference aware QoS routing criterion IAEET based on a WCETT criterion to accurately describe end-to-end delay in the multi-radio multi-channel path; and finally, in combination with the advantages of proactive routing and reactive routing, designing an IAEETR mechanism HWMP protocol routing selection and optimizing a routing maintenance process to ensure the grid traffic QoS while reducing network congestion, thereby enhancing network throughput and reliability.

Interference aware QoS routing mechanism oriented to smart grid NAN

IEEE 802.11s based wireless mesh networks (WMN) recently have been proposed as an important networking technology to deploy in Smart Grid (SG) for data collection and remote control purposes. In this paper, we focus on modeling and analyzing the MAC layer performance of EDCA for IEEE 802.11s wireless mesh networks in the smart grid, taking into account the impact of hidden nodes and different QoS requirements of smart grid applications. We first develop a new Markov chain model to analyze the back-off process for different applications with hidden nodes problem. Then based on the analytical model, we derive a few MAC layer performance metrics. Finally, the proposed analytical model is validated via comparing the analytical results with simulation results by ns3 in NAN scenarios with various applications.

Tingting He

Papers

Performance Analysis for IEEE 802.11s Wireless Mesh Network in Smart Grid

Interference-aware QoS routing for neighbourhood area network in smart grid

Interference aware QoS routing mechanism oriented to smart grid NAN

Modeling and Analysis MAC Layer Performance for IEEE 802.11s Wireless Mesh Network in Smart Grid