Sustainability analysis and resource management for wireless mesh networks with renewable energy supplies

 There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.

Development and characterization of the 2,4,6-trichlorophenol (2,4,6-TCP) aerobic degrading granules in sequencing batch airlift reactor

2,4,6-trichlorophenol (2,4,6-TCP) aerobic degrading granules were successfully developed in the sequencing batch airlift reactor. The key strategy used in cultivation of the granules was dosing glucose and acetate as co-substrates. After granulation, average concentrations of 2,4,6-TCP and COD in the effluent were less than 8mgL-1 and 59mgL-1, respectively. The removal efficiencies of 2,4,6-TCP and COD were above 93% and 90%, respectively. The specific degradation rate of 2,4,6-TCP peaked at 61mg 2,4,6-TCP gVSS-1h-1 when inoculated at the concentration of 400mgL-1. The extracellular polymeric substance (EPS) contents of the 2,4,6-TCP aerobic degrading granules were decreased compared with the contents in seed sludge. Two peaks attributed to the protein-like fluorophores were identified by three-dimensional excitation emission matrix (EEM) fluorescence spectra. The decrease of fluorescence parameters, e.g., peak locations, intensities, indicated quenching effect of 2,4,6-TCP on the EPS fluorescence. Meanwhile, the shift of peak position indicated chemical changes of the EPS.

A block-aware hybrid data dissemination with hotspot elimination in wireless sensor network

As a significant milestone in the data dissemination of wireless sensor networks (WSNs), the comb-needle (CN) model was developed to dynamically balance the sensor data pushing and pulling during hybrid data dissemination. Unfortunately, the hybrid push-pull data dissemination strategy may overload some sensor nodes and form the hotspots that consume energy significantly. This usually leads to the collapse of the network at a very early stage. In the past decade, although many energy-aware dynamic data dissemination methods have been proposed to alleviate the hotspots issue, the block characteristic of sensor nodes has been overlooked and how to offload traffic from hot blocks with low energy through long-distance hybrid dissemination remains an open problem. In this paper, we developed a block-aware data dissemination model to balance the inter-block energy and eliminate the spreading of intra-block hotspots. Through the clustering mechanism based on geography and energy, "similar" large-scale sensor nodes can be efficiently grouped into specific blocks to form the global block information (GBI). Based on GBI, the long-distance block-cross hybrid algorithms are further developed by effectively aggregating inter-block and intra-block data disseminations. Extensive experimental results demonstrate the capability and the efficiency of the proposed approach. © 2014 Elsevier Ltd.