Integrated Toolset for WSN Application Planning, Development, Commissioning and Maintenance: The WSN-DPCM ARTEMIS-JU Project

In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance. The tools are integrated in a single platform which promotes software reuse by automatically selecting suitable library components for application synthesis and the abstraction of the underlying architecture through the use of a middleware layer. The second objective of the project was to test the effectiveness of the toolset for the development of two case studies in different domains, one for detecting the occupancy state of parking lots and one for monitoring air concentration of harmful gasses near an industrial site

A comprehensive evaluation of cache utilization characteristics in large scale WSN considering network driven cache replacement techniques

WSNs as adopted in current smart city deployments, must address demanding traffic factors and resilience in failures. Furthermore, caching of data in WSN can significantly benefit resource conservation and network performance. However, data sources generate data volumes that could not fit in the restricted data cache resources of the caching nodes. This unavoidably leads to data items been evicted and replaced. This paper aims to experimentally evaluate the prominent caching techniques in large scale networks that resemble the Smart city paradigm regarding network performance with respect to critical application and network parameters. Through respective result analysis valuable insights are provided concerning the behaviour of caching in typical large scale WSN scenarios

A comprehensive evaluation of cache utilization characteristics in large scale WSN considering network driven cache replacement techniques

WSNs as adopted in current smart city deployments, must address demanding traffic factors and resilience in failures. Furthermore, caching of data in WSN can significantly benefit resource conservation and network performance. However, data sources generate data volumes that could not fit in the restricted data cache resources of the caching nodes. This unavoidably leads to data items been evicted and replaced. This paper aims to experimentally evaluate the prominent caching techniques in large scale networks that resemble the Smart city paradigm regarding network performance with respect to critical application and network parameters. Through respective result analysis valuable insights are provided concerning the behaviour of caching in typical large scale WSN scenarios

TALK: Topology Aware LKH Key Management

Cryptographic key management for wireless sensor networks (WSNs) and mobile ad-hoc networks (MANETs) is a particularly challenging task, as they mostly consist of high counts of resource constrained nodes, especially when group communication and dynamic network membership capabilities are required. The logical key hierarchy (LKH) is a well-known class of protocols which aims to solve the key management problem and minimize rekeying overhead, using hierarchical structures and taking advantage of multicast communication. In this work we propose a method for obtaining LKH structures by taking into account the topology of the underlying network, leading to a further decrease in rekeying costs, in terms of packet transmissions

Tackling Failure through Discovery of Semantic Neighbors Nodes in WSNs

Wireless Sensor Networks (WSNs) are attracting active and increasing research interest in various application fields including industrial control and environmental applications. In such cases accurate event detection is of utmost importance, thus sensors' data need to be fused through a sophisticated process (i.e. data mining algorithms). In this context, semantic correlations between sensor nodes and formation of semantic clusters is critical as it enables the fusion of specific sensor data regardless of the proximity criteria. Traditional clustering schemes aim to extend sensors' network lifetime using criteria such as received signal strength, while the semantic correlation is frequently omitted. In this paper, two novel techniques for discovering semantic neighbors are proposed, Diffusion Algorithm for Discover Semantic Neighbors (DADSN) and Trace Route Algorithm for Discover Semantic Neighbors (TRA-DSN). Design and development efforts are analysed while the evaluation results offer a useful guideline on which technique fits better in different WSNs deployments

Towards a resilient health status assessment employing intelligence in cyber physical systems

Resilience is defined as the capacity of a system to cope with a hazardous event or disturbance, responding or reorganizing in ways that maintain its essential function, identity, and structure, while also maintaining the capacity for adaptation, learning and transformation. A resilient health system is one that is capable to anticipate, respond to, cope with, recover from and adapt to system-related shocks and stress, so as to bring sustained improvements in population health, despite the unstable circumstances. Nowadays, the emergency department (ED) of hospitals faces growing demands, rising acuteness and longer waiting times. An efficient, accurate and resilient triage system to improve the operation of the ED becomes crucial. In this work, a resilient system for automatic priority, sorting and monitoring of medical events -triage system- is developed, so that the patients in the ED are treated according to the severity of their condition and not by the order of attendance utilizing a Fuzzy Inference System (FIS) that aggregates, processes patients’ vital signs as well as determines their Health Status (HS), according to which the ED staff performs the appropriate classification