CAMP: cluster aided multi-path routing protocol for wireless sensor networks

Wireless Networks - In this article, we propose a novel routing algorithm for wireless sensor network, which achieves uniform energy depletion across all the nodes and thus leading to prolonged...     

Hyperspectral image classification using multiobjective optimization

Hyperspectral images constitute a substantial amount of data in the form of spectral bands. This information is used for land cover analysis, specifically in classifying a hyperspectral pixel, which is a popular domain in remote sensing. This paper proposed an efficient framework to classify spectral-spatial hyperspectral images by employing multiobjective optimization. Spectral-spatial features of hyperspectral images are passed for optimization. As hyperspectral images have a high dimensional feature set, many classifiers cannot perform well. Multiobjective optimization reduces the feature set without affecting the discrimination ability of the classifier. The proposed work is validated on a standard hyperspectral image set, Pavia University and Kennedy Space Centre.

     

Perfluoroalkyl sulfonates and perfluorocarboxylates in two wastewater treatment facilities in Kentucky and Georgia

Discharge of effluents from municipal wastewater treatment plants (WWTPs) is a route for the introduction of certain organic contaminants into aquatic environments. Earlier studies have reported the occurrence of perfluorochemicals in effluents from WWTPs. In this study, contamination profiles of perfluorinated compounds (PFCs), including perfluoroalkyl sulfonates (PFASs; PFOS, PFOSA, PFHxS) and perfluoroalkyl carboxylates (PFACs; PFOA, PFNA, PFDA, PFDoDA, PFUnDA), were determined in samples collected at various stages of wastewater treatment during different seasons. The two WWTPs selected for this study represent rural (Plant A, Kentucky) and urban (Plant B, Georgia) areas. PFOS was a major contaminant in samples from Plant A (8.2–990 ng/g dry wt in solid samples and 7.0–149 ng/L in aqueous samples), followed by PFOA (8.3–219 ng/g dry wt in solid samples and 22–334 ng/L in aqueous samples). PFOA was the predominant contaminant in samples from Plant B (7.0–130 ng/g dry wt in solid samples and 1–227 ng/L in aqueous samples), followed by PFOS (<2.5–77 ng/g dry wt in solid samples and 1.8–22 ng/L in aqueous samples). PFHxS, PFNA, PFDA, and PFOSA were detected in most of the samples, whereas PFUnDA and PFDoDA were detected in very few samples. Concentrations of some PFCs, particularly PFOA, were slightly higher in effluent than in influent, suggesting that biodegradation of some precursors contributes to the increase in PFOA concentrations in wastewater treatment processes. No large-magnitude seasonal variations in concentrations were found, although mass flow of PFCs was higher in winter than in summer. In general, samples from the rural plant in Kentucky contained greater concentrations of PFCs than did those from the urban plant in Georgia. Incineration of sludge reduced the PFC levels significantly. The mass flows of PFCs in these two plants were several hundreds of mg/day, comparable to flow values reported earlier.     

DSERR: Delay Sensitive Energy Efficient Reliable Routing Algorithm

Routing strategies need to strike a balance between responsiveness and energy efficiency. Achieving this balance poses new challenges that do not coalesce either with infrastructure or ad hoc wireless networks performance requirements. Multiple strategies have emerged as a workable solution to the routing problem. Either of these solutions however cater to but one of the many constraints posed by the networks. To address this issue we propose a delay sensitive energy efficient reliable routing (DSERR) algorithm which achieves application specified soft delays with energy scavenging being the foremost concern. We try to provide soft end-to-end (e2e) delay guarantee that is proportional to the distance between the source and destination. Our algorithm uses the per hop greedy selection for soft real-time guarantees as it is impossible to provide hard guarantees in a dynamic network. DSERR presents a stateless architecture providing hop by hop reliability of data delivery to support desired delivery reliability across the sensor network.