Guaranteed greedy routing in overlay networks

Greedy routing can be used in mobile ad-hoc networks as geographic routing protocol. This paper proposes to use greedy routing also in overlay networks by positioning overlay nodes into a multi-dimensional Euclidean space. Greedy routing can only be applied when a routing decision makes progress towards the final destination. Our proposed overlay network is built such that there will be always progress at each forwarding node. This is achieved by constructing at each node a so-called nearest neighbor convex set (NNCS). NNCSs can be used for various applications such as multicast routing, service discovery and Quality-of-Service routing. NNCS has been compared with Pastry, another topology-aware overlay network. NNCS has superior relative path stretches indicating the optimality of a path.

Dynamic Languages and Applications. Report on the Workshop Dyla 07 at ECOOP 2007

Following last two years’ workshop on dynamic languages at the ECOOP conference, the Dyla 2007 workshop was a successful and popular event. As its name implies, the workshop’s focus was on dynamic languages and their applications. Topics and discussions at the workshop included macro expansion mechanisms, extension of the method lookup algorithm, language interpretation, reflexivity and languages for mobile ad hoc networks. The main goal of this workshop was to bring together different dynamic language communities and favouring cross communities interaction. Dyla 2007 was organised as a full day meeting, partly devoted to presentation of submitted position papers and partly devoted to tool demonstration. All accepted papers can be downloaded from the workshop’s web site. In this report, we provide an overview of the presentations and a summary of discussions.

Screening renal artery angiography in hypertensive patients undergoing coronary angiography and 6-month follow-up after ad hoc percutaneous revascularization

OBJECTIVE: To determine the prevalence and independent predictors of significant atherosclerotic renal artery stenosis (RAS) in unselected hypertensive patients undergoing coronary angiography and to assess the 6-month outcome of those patients with a significant RAS. METHODS: One thousand, four hundred and three consecutive hypertensive patients undergoing drive-by renal arteriography were analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of RAS. In patients with significant RAS (>or=50% luminal narrowing), 6-month follow-up was assessed and outcome was compared between patients with or without renal revascularization. RESULTS: The prevalence of significant RAS was 8%. After multivariate analysis, coronary [odds ratio 5.3; 95% confidence interval (CI) 2.7-10.3; P < 0.0001], peripheral (odds ratio 3.3; 95% CI 2.0-5.5; P < 0.0001), and cerebral artery (odds ratio 2.8; 95% CI 1.5-5.3; P = 0.001) diseases, and impaired renal function (odds ratio 2.9; 95% CI 1.8-4.5; P < 0.0001) were found as independent predictors. At least one of these predictors was present in 96% of patients with RAS. In 74 patients (66%) with significant RAS, an ad hoc revascularization was performed. At follow-up, creatinine clearance was significantly higher in revascularized than in nonrevascularized patients (69.2 vs. 55.5 ml/min per 1.73 m, P = 0.029). By contrast, blood pressure was comparable between both groups, but nonrevascularized patients were taking significantly more antihypertensive drugs as compared with baseline (2.7 vs. 2.1, follow-up vs. baseline; P = 0.0066). CONCLUSION: The prevalence of atherosclerotic RAS in unselected hypertensive patients undergoing coronary angiography was low. Coronary, peripheral, and cerebral artery diseases, and impaired renal function were independent predictors of RAS. Ad hoc renal revascularization was associated with better renal function and fewer intake of antihypertensive drugs at follow-up.

Information-Centric Networking in Mobile and Opportunistic Networks

Information Centric Networking (ICN) as an emerging paradigm for the Future Internet has initially been rather focusing on bandwidth savings in wired networks, but there might also be some significant potential to support communication in mobile wireless networks as well as opportunistic network scenarios, where end systems have spontaneous but time-limited contact to exchange data. This chapter addresses the reasoning why ICN has an important role in mobile and opportunistic networks by identifying several challenges in mobile and opportunistic Information-Centric Networks and discussing appropriate solutions for them. In particular, it discusses the issues of receiver and source mobility. Source mobility needs special attention. Solutions based on routing protocol extensions, indirection, and separation of name resolution and data transfer are discussed. Moreover, the chapter presents solutions for problems in opportunistic Information-Centric Networks. Among those are mechanisms for efficient content discovery in neighbour nodes, resume mechanisms to recover from intermittent connectivity disruptions, a novel agent delegation mechanisms to offload content discovery and delivery to mobile agent nodes, and the exploitation of overhearing to populate routing tables of mobile nodes. Some preliminary performance evaluation results of these developed mechanisms are provided.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

SCAD: Sensor Context-aware Adaptive Duty-cycled Beaconless Opportunistic Routing for WSNs

Low quality of wireless links leads to perpetual transmission failures in lossy wireless environments. To mitigate this problem, opportunistic routing (OR) has been proposed to improve the throughput of wireless multihop ad-hoc networks by taking advantage of the broadcast nature of wireless channels. However, OR can not be directly applied to wireless sensor networks (WSNs) due to some intrinsic design features of WSNs. In this paper, we present a new OR solution for WSNs with suitable adaptations to their characteristics. Our protocol, called SCAD-Sensor Context-aware Adaptive Duty-cycled beaconless opportunistic routing protocol is a cross-layer routing approach and it selects packet forwarders based on multiple sensor context information. To reach a balance between performance and energy-efficiency, SCAD adapts the duty-cycles of sensors according to real-time traffic loads and energy drain rates. We compare SCAD against other protocols through extensive simulations. Evaluation results show that SCAD outperforms other protocols in highly dynamic scenarios.