Information extraction from sensor networks using the Watershed transform algorithm

Wireless sensor networks are an effective tool to provide fine resolution monitoring of the physical environment. Sensors generate continuous streams of data, which leads to several computational challenges. As sensor nodes become increasingly active devices, with more processing and communication resources, various methods of distributed data processing and sharing become feasible. The challenge is to extract information from the gathered sensory data with a specified level of accuracy in a timely and power-efficient approach. This paper presents a new solution to distributed information extraction that makes use of the morphological Watershed algorithm. The Watershed algorithm dynamically groups sensor nodes into homogeneous network segments with respect to their topological relationships and their sensing-states. This setting allows network programmers to manipulate groups of spatially distributed data streams instead of individual nodes. This is achieved by using network segments as programming abstractions on which various query processes can be executed. Aiming at this purpose, we present a reformulation of the global Watershed algorithm. The modified Watershed algorithm is fully asynchronous, where sensor nodes can autonomously process their local data in parallel and in collaboration with neighbouring nodes. Experimental evaluation shows that the presented solution is able to considerably reduce query resolution cost without scarifying the quality of the returned results. When compared to similar purpose schemes, such as “Logical Neighborhood”, the proposed approach reduces the total query resolution overhead by up to 57.5%, reduces the number of nodes involved in query resolution by up to 59%, and reduces the setup convergence time by up to 65.1%

Extensible Context-aware Stream Processing on the Cloud

Rationale and Challenges for Massive Data Stream Processing on the CloudThe ubiquity of mobile devices, location services, and sensor pervasiveness, e.g., as in smart city initiatives, call for scalable computing platforms and massively parallel architectures to process the vast amounts of the generated streamed data. Cloud computing provides some of the features needed for these massive data streaming applications. For example, the dynamic allocation of resources on an as-needed basis addresses the variability in sensor and location data distributions over time. However, today’s cloud computing platforms lack very important features that are necessary in order to support the massive amounts of data streams envisioned by the massive and ubiquitous dissemination of sensors and mobile devices of all sorts in smart-city-scale applications

Inter-organizational Integration of Smart Objects: White Spots in the Solution Landscape

The vision of the Internet of Things (IoT) has sparked considerable efforts in research and development over the past decade.Much of these efforts were driven by applications of RFID technology for monitoring the flow of goods and prominent earlyadopters such as Wal-Mart and Metro Group. Also, the global standards organization GS1 provided a number of wellrecognized specifications that are tailored to monitor objects across organizations.Development of the IoT has certainly benefited from the strong demand for monitoring goods in business applications.However, the dominance of these application scenarios and corresponding standards comes at the risk of neglectingrequirements from other domains. In this paper, we review the focus of existing works. Our contribution is twofold. (1) Usinga systematic literature review, we analyze existing research contributions and identify underrepresented areas. (2) We discussselected approaches in detail and highlight open issues in the covered functionality. The aim of our work is to raise awarenessfor open potentials in the IoT service domain and to direct future research and developments

Programming Wireless Sensor Networks with Logical Neighborhoods

Wireless sensor network (WSN) architectures often feature a (single) base station in charge of coordinating the application functionality. Although this assumption simplified the path to adoption of WSN technology, researchers are now being attracted by more decentralized architectures with multiple sinks and heterogeneous nodes. These scenarios are brought to an extreme in Wireless Sensor and Actor Networks (WSANs), where sensing and acting nodes collaborate in a decentralized fashion to implement complex control loops. In these settings, new programming abstractions are required to manage complexity and heterogeneity without sacrificing efficiency. In this paper we propose and define a logical neighbor-hood programming abstraction. A logical neighborhood includes nearby nodes that satisfy predicates over their static (e.g., type) or dynamic (e.g., sensed values) characteristics. The span of the neighborhood and the definition of its predicates are specified declaratively, along with requirements about the cost of the communication involved. Logical neighborhoods enable the programmer to “illuminate” different areas of the network according to the application needs, effectively replacing the physical neighborhood provided by wireless broadcast with a higher-level, application-defined notion of proximity. This paper presents the definition of a declarative language for specifying logical neighborhoods, highlighting its expressiveness, flexibility and simplicity. Moreover, although the language con- structs are readily implemented using existing communication mechanisms, we briefly report about a novel routing scheme we expressly designed to support efficiently our abstractions

Secure Multi-Purpose Wireless Sensor Networks

Wireless sensor networks (WSNs) were made possible around the late 1990s by industry scale availability of small and energy efficient microcontrollers and radio interfaces. Application areas for WSNs range from agriculture to health care and emergency response scenarios. Depending on the scenario a sensor network can span from some rooms to an area of several square miles in size and so the number of sensor nodes can vary from a fistful of nodes to hundreds or thousands. Sensor nodes are composed from a set of building blocks: processing, communication, sensing/actuating and a power supply. The power supply is usually a battery pack. Especially these limited energy resources make it tremendously important to save resources to achieve a long lifetime. Today’s WSNs are usually planned and developed to satisfy only one application, and they are controlled by a single user. But, with the Internet of Things approaching, more and more sensor networks will be used for multiple tasks simultaneously and are reaching larger sizes. As sensor networks grow it becomes mandatory to localize traffic, both for energy conservation as well as security. Additionally, the broadcast medium of the wireless channel of WSNs allows an adversary all sorts of attacks, like eavesdropping, replaying messages, and denial of service attacks. In large or unattended networks it is even possible to physically attack the hardware of a sensor node to gain access to its firmware and cryptographic keys. In this work we propose the Scopes Framework and the security enhancement Sec- Scopes. The Scopes Framework introduces dynamic partitioning of a WSN with support for multiple in-network tasks. SecScopes enables secure access control, key exchange and communication. The partitioning is done by a scoping mechanism which allows the dynamic defini- tion of subsets of sensor nodes. The Scopes Framework supports in-network tasks by managing network connections for each task, and allowing the selection of efficient routing algorithms. To allows access control on a partition of the network we introduce attribute-based encryption in sensor networks. Secure key exchange is also based on this encryption scheme. To secure communication more efficient symmetric cryptography is employed. With the Scopes Framework we provide a modular and flexible architecture that can be adjusted to the needs of different scenarios. We present a detailed evaluation of the performance of the framework and compare and discuss the results for the different stages of the framework. The results of the evaluation show the general feasibility of the approach, in spite of the adverse resource constraints

The pragmatic proof: hypermedia API composition and execution

Machine clients are increasingly making use of the Web to perform tasks. While Web services traditionally mimic remote procedure calling interfaces, a new generation of so-called hypermedia APIs works through hyperlinks and forms, in a way similar to how people browse the Web. This means that existing composition techniques, which determine a procedural plan upfront, are not sufficient to consume hypermedia APIs, which need to be navigated at runtime. Clients instead need a more dynamic plan that allows them to follow hyperlinks and use forms with a preset goal. Therefore, in this paper, we show how compositions of hypermedia APIs can be created by generic Semantic Web reasoners. This is achieved through the generation of a proof based on semantic descriptions of the APIs' functionality. To pragmatically verify the applicability of compositions, we introduce the notion of pre-execution and post-execution proofs. The runtime interaction between a client and a server is guided by proofs but driven by hypermedia, allowing the client to react to the application's actual state indicated by the server's response. We describe how to generate compositions from descriptions, discuss a computer-assisted process to generate descriptions, and verify reasoner performance on various composition tasks using a benchmark suite. The experimental results lead to the conclusion that proof-based consumption of hypermedia APIs is a feasible strategy at Web scale.Peer ReviewedPostprint (author's final draft