Self-organization Of Sensor Networks Research Articles

An autonomic bio-inspired algorithm for wireless sensor network self-organization and efficient routing

Self-configuration and autonomy are key features required for the next generation of gadgets and networks, since regular users are willing to have computationally enabled devices pervasively spread into their environment. Sensors are among the most promising devices into this new scenario. However, their low battery and processing power raise several issues to these autonomy requirements. This paper presents BiO4SeL (Bio-Inspired Optimization for Sensor Network Lifetime), a swarm intelligence-based algorithm to perform self-organization and optimization of lifetime by means of routing into a Wireless Sensor Network. Results show that BiO4SeL achieves its objectives when compared to similar approaches: ARAMA (Ant-based Routing Algorithm for MANETs), EAR (Energy-Aware Routing) and AODV (Ad-hoc On-demand Distance Vector).

Sensor Network Self-Organization Using Random Graphs

Sensor networks are deployed in, and react with, chaotic environments. Self-organizing peer-to-peer networks have admirable survivability characteristics. This chapter discusses random network formalisms for designing, modeling, and analyzing survivable sensor networks. Techniques are given for determining critical values. Applications in system security and surveillance networks are given.

Self organization of sensor networks for energy-efficient border coverage

Networking together hundreds or thousands of cheap sensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. As sensor nodes are typically battery operated, it is important to efficiently use the limited energy of the nodes to extend the lifetime of the wireless sensor network (WSN). One of the fundamental issues in WSNs is the coverage problem. In this paper, the border coverage problem in WSNs is rigorously analyzed. Most existing results related to the coverage problem in wireless sensor networks focused on planar networks; however, three dimensional (3D) modeling of the sensor network would reflect more accurately real-life situations. Unlike previous works in this area, we provide distributed algorithms that allow the selection and activation of an optimal border cover for both 2D and 3D regions of interest. We also provide self-healing algorithms as an optimization to our border coverage algorithms which allow the sensor network to adaptively reconfigure and repair itself in order to improve its own performance. Border coverage is crucial for optimizing sensor placement for intrusion detection and a number of other practical applications.

Self-organization of sensor networks for detection of pervasive faults

Resource aware operation of sensor networks requires adaptive re-organization to dynamically adapt to the operational environment. A complex dynamical system of interacting components (e.g., computer network and social network) is represented as a graph, component states as spins, and interactions as ferromagnetic couplings. Using an Ising-like model, the sensor network is shown to adaptively self-organize based on partial observation, and real-time monitoring and detection is enabled by adaptive redistribution of limited resources. The algorithm is validated on a test-bed that simulates the operations of a sensor network for detection of percolating faults (e.g. computer viruses, infectious disease, chemical weapons, and pollution) in an interacting multi-component complex system.

Connected sensor cover: self-organization of sensor networks for efficient query execution

Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology. The self-organization technique is beneficial for queries that run sufficiently long to amortize the communication cost incurred in self-organization. In this paper, we design and analyze algorithms for suchself-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a near-optimal connected sensor cover. We prove that the size of the constructed topology is within an O(logn) factor of the optimal size, where n is the network size. We develop a distributed self-organization version of the approximation algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. We also design another distributed algorithm based on node priorities that has a further lower communication overhead, but does not provide any guarantee on the size of the connected sensor cover constructed. Finally, we evaluate the distributed algorithms using simulations and show that our approaches results in significant communication cost reductions.

Self-organization in sensor networks

In an effort to better guide research into self-configuring wireless sensor networks, we discuss a technical definition of the term self-organization. We define a self-organizing system as one where a collection of units coordinate with each other to form a system that adapts to achieve a goal more efficiently. We then lay out some conditions that must hold for a system to meet this definition and discuss some examples of self-organizing systems. Finally, we explore some of the ways this definition applies to wireless sensor networks.