Improving quality-of-service in wireless sensor networks by mitigating “Hidden-Node Collisions”

Wireless sensor networks (WSNs) emerge as underlying infrastructures for new classes of large-scale networked embedded systems. However, WSNs system designers must fulfill the quality-of-service (QoS) requirements imposed by the applications (and users). Very harsh and dynamic physical environments and extremely limited energy/computing/memory/communication node resources are major obstacles for satisfying QoS metrics such as reliability, timeliness, and system lifetime. The limited communication range of WSN nodes, link asymmetry, and the characteristics of the physical environment lead to a major source of QoS degradation in WSNs-the ldquohidden node problem.rdquo In wireless contention-based medium access control (MAC) protocols, when two nodes that are not visible to each other transmit to a third node that is visible to the former, there will be a collision-called hidden-node or blind collision. This problem greatly impacts network throughput, energy-efficiency and message transfer delays, and the problem dramatically increases with the number of nodes. This paper proposes H-NAMe, a very simple yet extremely efficient hidden-node avoidance mechanism for WSNs. H-NAMe relies on a grouping strategy that splits each cluster of a WSN into disjoint groups of non-hidden nodes that scales to multiple clusters via a cluster grouping strategy that guarantees no interference between overlapping clusters. Importantly, H-NAMe is instantiated in IEEE 802.15.4/ZigBee, which currently are the most widespread communication technologies for WSNs, with only minor add-ons and ensuring backward compatibility with their protocols standards. H-NAMe was implemented and exhaustively tested using an experimental test-bed based on ldquooff-the-shelfrdquo technology, showing that it increases network throughput and transmission success probability up to twice the values obtained without H-NAMe. H-NAMe effectiveness was also demonstrated in a target tracking application with mobile robots - over a WSN deployment.

F-LQE: a Fuzzy Link Quality Estimator for wireless sensor networks

Radio Link Quality Estimation (LQE) is a fundamental building block for Wireless Sensor Networks, namely for a reliable deployment, resource management and routing. Existing LQEs (e.g. PRR, ETX, Fourbit, and LQI ) are based on a single link property, thus leading to inaccurate estimation. In this paper, we propose F-LQE, that estimates link quality on the basis of four link quality properties: packet delivery, asymmetry, stability, and channel quality. Each of these properties is defined in linguistic terms, the natural language of Fuzzy Logic. The overall quality of the link is specified as a fuzzy rule whose evaluation returns the membership of the link in the fuzzy subset of good links. Values of the membership function are smoothed using EWMA filter to improve stability. An extensive experimental analysis shows that F-LQE outperforms existing estimators.

Trace Elements in Ambient Air at Porto Metropolitan Area—Checking for Compliance with New European Union (Eu) Air Quality Standards

Because of the scientific evidence showing that arsenic (As), cadmium (Cd), and nickel (Ni) are human genotoxic carcinogens, the European Union (EU) recently set target values for metal concentration in ambient air (As: 6 ng/m3, Cd: 5 ng/m3, Ni: 20 ng/m3). The aim of our study was to determine the concentration levels of these trace elements in Porto Metropolitan Area (PMA) in order to assess whether compliance was occurring with these new EU air quality standards. Fine (PM2.5) and inhalable (PM10) air particles were collected from October 2011 to July 2012 at two different (urban and suburban) locations in PMA. Samples were analyzed for trace elements content by inductively coupled plasma–mass spectrometry (ICP-MS). The study focused on determination of differences in trace elements concentration between the two sites, and between PM2.5 and PM10, in order to gather information regarding emission sources. Except for chromium (Cr), the concentration of all trace elements was higher at the urban site. However, results for As, Cd, Ni, and lead (Pb) were well below the EU limit/target values (As: 1.49 ± 0.71 ng/m3; Cd: 1.67 ± 0.92 ng/m3; Ni: 3.43 ± 3.23 ng/m3; Pb: 17.1 ± 10.1 ng/m3) in the worst-case scenario. Arsenic, Cd, Ni, Pb, antimony (Sb), selenium (Se), vanadium (V), and zinc (Zn) were predominantly associated to PM2.5, indicating that anthropogenic sources such as industry and road traffic are the main source of these elements. High enrichment factors (EF > 100) were obtained for As, Cd, Pb, Sb, Se, and Zn, further confirming their anthropogenic origin.