FED: FUZZY EVENT DETECTION MODEL FOR WIRELESS SENSOR NETWORKS

ABSTRAC

SSEGR: Secure single-copy energy efficient geographical routing algorithm in wireless sensor networks

Geographical Routing Technique is a new trend in Wireless Sensor Networks in which the sensor nodes are enabled using Global Positioning Systems (GPS). This helps to easily detect the position of their neighboring nodes. The power consumption is more in the existing routing algorithms, since the nodes build the routing tables and the neighboring node IDs are determined by searching the routing table. In this paper, we have proposed Secure Single-Copy Energy Efficient Geographical Routing (SSEGR) algorithm in which the data traffic and energy consumption is minimized using single copy data transfer. In SSEGR, initially one copy is transmitted to the next node using greedy approach and another copy is preserved in the sending station. If acknowledgment is not received even after timeout then the second copy is transmitted. This dynamic single copy scheme reduces the data traffic in Wireless Sensor Networks. Security algorithms are incorporated in every sensor node to prevent any malicious node attack that disturb the normal functioning of the network. Simulation result shows that the performance of the proposed algorithm is better interms of packet delivery probability and energy consumption in comparision with existing algorithm

A middleware framework for wireless sensor network

Advances in wireless and Micro-Electro-Mechanical Systems (MEMS) technology has given birth to a new technology field sensor networks. These new technologies along with pervasive computing have made the dream of a smart environment come true. Sensors being small and capable of sensing, processing and communicating data has opened a whole new era of applications from medicine to military and from indoors to outdoors. Sensor networks although exciting have very limited resources, for example, memory, processing power and bandwidth, with energy being the most precious resource as they are battery operated. However, these amazing devices can collaborate in order to perform a task. Due to these limitations and specific characteristics being application specific and heterogeneous there is a need to devise techniques and software which would utilize the meager resources efficiently keeping in view the unique characteristics of this network. This thesis presents a lightweight, flexible and energy-efficient middleware framework called MidWSeN which combines aspects of queries, events and context of WSN in a single system. It provides a combination of core and optional services which could be adjusted according to the resources available and specific requirements of the application. The availability of multiple copies of services distributed across the network helps in making the system robust. This middleware framework introduces a new Persistent Storage Service which saves data within the sensor network on the nodes for lifetime of the network to provide historical data. A Priority algorithm is being also presented in this thesis to ensure that enough memory is always available. A novel context enhanced aggregation has also been presented in this thesis which aggregates data with respect to context. Application management service (AMS) provides Service optimization within the network is another novel aspect of the proposed framework. To evaluate the functionality of the work presented, different parts of the framework have also been implemented. The tests and results are detailed to prove the ideas presented in the framework. The work has also been evaluated against a set of requirements and compared against existing works to indicate the novel aspects of framework. Finally some ideas are presented for the future works

Distributed spatial analysis in wireless sensor networks

Wireless sensor networks (WSNs) allow us to instrument the physical world in novel ways, providing detailed insight that has not been possible hitherto. Since WSNs provide an interface to the physical world, each sensor node has a location in physical space, thereby enabling us to associate spatial properties with data. Since WSNs can perform periodic sensing tasks, we can also associate temporal markers with data. In the environmental sciences, in particular, WSNs are on the way to becoming an important tool for the modelling of spatially and temporally extended physical phenomena. However, support for high-level and expressive spatial-analytic tasks that can be executed inside WSNs is still incipient. By spatial analysis we mean the ability to explore relationships between spatially-referenced entities (e.g., a vineyard, or a weather front) and to derive representations grounded on such relationships (e.g., the geometrical extent of that part of a vineyard that is covered by mist as the intersection of the geometries that characterize the vineyard and the weather front, respectively). The motivation for this endeavour stems primarily from applications where important decisions hinge on the detection of an event of interest (e.g., the presence, and spatio-temporal progression, of mist over a cultivated field may trigger a particular action) that can be characterized by an event-defining predicate (e.g., humidity greater than 98 and temperature less than 10). At present, in-network spatial analysis in WSN is not catered for by a comprehensive, expressive, well-founded framework. While there has been work on WSN event boundary detection and, in particular, on detecting topological change of WSN-represented spatial entities, this work has tended to be comparatively narrow in scope and aims. The contributions made in this research are constrained to WSNs where every node is tethered to one location in physical space. The research contributions reported here include (a) the definition of a framework for representing geometries; (b) the detailed characterization of an algebra of spatial operators closely inspired, in its scope and structure, by the Schneider-Guting ROSE algebra (i.e., one that is based on a discrete underlying geometry) over the geometries representable by the framework above; (c) distributed in-network algorithms for the operations in the spatial algebra over the representable geometries, thereby enabling (i) new geometries to be derived from induced and asserted ones, and (ii)topological relationships between geometries to be identified; (d) an algorithmic strategy for the evaluation of complex algebraic expressions that is divided into logically-cohesive components; (e) the development of a task processing system that each node is equipped with, thereby with allowing users to evaluate tasks on nodes; and (f) an empirical performance study of the resulting system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Distributed collaboration for event detection in wireless sensor networks

With the advancement of technology in micro-electronics and wireless communication, small miniature devices called sensor nodes can be used to perform various tasks by form-ing themselves in to wireless sensor networks. In Wireless Sensor Networks(WSN), event detection is one of the main requirements for most of the applications. An event can be a simple event or a combination of two or more sim-ple events (Composite Event). Detecting and reporting an event desired by the application (user) inspite of stringent constraints of sensor nodes like low energy, low bandwidth, frequent failures etc., is one of the main challenges in WSN. This can be achieved with less uncertainty and masking fail-ures by considering collaboration among sensor nodes. We propose a framework for distributed event detection using collaboration in WSN. The framework consists of two pro-tocols that build a tree by using a communication model similar to the Publish-Subscribe paradigm. This framework is a part of Component Oriented Middleware for Sensor networks (COMiS). In COMiS framework, components are loaded as and when required based on the application se-mantics. If collaboration is considered, the goal of the ap-plication can be easily accomplished even in case of failures of sensors and low energy of nodes