Geographic routing has been considered as an attractive approach for wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, it often suffers from the routing hole in various environments during data delivery, resulting in route failure. Currently, existing geographic routing tends to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, they cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we propose an energy-aware dual-path geographic routing (EDGR) to address these issues. EDGR utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into 3D WSNs to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR is superior to existing geographic routing in terms of energy efficiency, network lifetime, packet deliver ratio and delivery delay in WSNs over a variety of communication sessions passing through routing holes.

The contemporary Internet has evolved from an academic infrastructure to a tremendous commercial network, serving as an indispensable information platform for human communications, but with inherent limitations such as complicated management and manual configuration. To overcome these limitations, the basic Internet architecture has unleashed an unprecedented wave of innovation over the past decade, and introduced a promising networking paradigm, software-defined networking (SDN), often referred to as a "radical new idea in networking." SDN offers numerous potential benefits such as enhanced configuration, improved performance, and encouraged innovation in network architectures and operations, while undergoing unprecedented challenges due to the lack of global network-wide information. Nowadays, the emerging data-driven thought opens the era of the fourth paradigm for science research and transfers the design philosophy of future networks, based on the large-scale data rather than the small-scale data. We argue that the existence of a data-driven information plane in SDN can address these challenges. In this article, we explicitly propose a data-driven information plane and extend the SDN architecture specifically. We first overview the evolution of SDN, and then illustrate a novel SDN paradigm by introducing a data-driven information plane into the commonly accepted SDN reference architecture. Finally, we outline emerging challenges and discuss future directions for further research triggered by a data-driven information plane.

Continuous respiratory monitoring is an important tool for clinical monitoring. The most widely used flow measure device is nasal cannulae connected to a pressure transducer. However, most of these devices are not easy to carry and continue working in uncontrolled environments which is also a problem. For portable breathing equipment, due to the volume limit, the pressure signals acquired by using the airway tube may be too weak and contain some noise, leading to huge errors in respiratory flow measures. In this paper, a cost-effective portable pressure sensor-based respiratory measure device is designed. This device has a new airway tube design, which enables the pressure drop efficiently after the air flowing through the airway tube. Also, a new back propagation (BP) neural network-based algorithm is proposed to stabilize the device calibration and remove pressure signal noise. For improving the reliability and accuracy of proposed respiratory device, a through experimental evaluation and a case study of the proposed BP neural network algorithm have been carried out. The results show that giving proper parameters setting, the proposed BP neural network algorithm is capable of efficiently improving the reliability of newly designed respiratory device.

Energy Harvesting Wireless Sensor Networks (EHWSNs) form the foundation of Internet of Things (IoTs) systems. Energy harvesting nodes can be deployed strategically to monitor one or more targets such as a valuable asset. However, as these nodes rely on ambient energy sources such as solar, they experience random energy arrivals. Consequently, they may exhaust their harvested energy whilst monitoring a target. To this end, network operators require a robust solution that ensures all targets are monitored continuously over some time period with a given probability. In this paper, we consider three novel robust coverage requirements; each must hold with probability ( $1-\epsilon$ ), where $\epsilon$ is the probability of failures. First, sensor nodes must not expend more than their total harvested energy over $T$ time slots. Second, the energy expenditure of each sensor node must not exceed the energy harvested in each slot. We formulate chance-constrained stochastic programs that incorporate these requirements and solve them using the Sample Average Approximation (SAA) method. We confirm via extensive simulation studies that our programs are capable of computing sensor nodes activation times that meet a given coverage failure probability.

This paper considers energy harvesting sensor nodes monitoring multiple fixed targets. The goal is to monitor these targets for the longest time period. A key challenge, however, is that sensor nodes have varying energy harvesting rates, meaning the coverage lifetime of targets is limited by sensor nodes with low energy. To this end, we consider energy sharing between sensor nodes. We model the problem as a Mixed Integer Linear Program (MILP). Advantageously, our MILP models non-linear energy conversion rates. We also outline a heuristic for largescale networks. Our results show energy sharing and non-linear energy conversion rates prolong coverage lifetime. Lastly, our heuristic achieves 90% of the optimal coverage lifetime.