Distributed Query Processing Optimization in Wireless Sensor Network Using Artificial Immune System

A visual modelling approach and its computational technique were proposed to represent and simulate a kind of immune system, which is comprised of immune cells and immune molecules etc. To study natural immune system and artificial immune system according to information theories and computational methodologies, the hierarchical model of the immune system was proposed, more faithful and suitable for visual simulation than traditional models. The hierarchical immune system basically consisted of innate immune tier, adaptive immune tier and immune cell tier. Thus, the tri-tier model of the immune system was seamless and coherent with the architecture of the artificial immune system, so that the research on the natural immune system and the research on the artificial one could improve and synchronize each other. Though the structure and features of the natural immune system were difficult to measure and test, the tri-tier architecture and qualitative features of the artificial immune system were built, changed and verified. To validate the new approach to visualize and explore the natural immune system, many experiments were tested on the tri-tier artificial immune system. At last, the visual results of the simulations show that the visual modelling approach can provide an effective and better way of understanding the natural immune system.

Effective distribution outage cause identification can help expedite the restoration procedure and improve the system availability. The fuzzy classification E-algorithm and the immune system inspired classification algorithm, artificial immune recognition system (AIRS), have demonstrated good capabilities in outage cause identification, especially with the existence of imbalanced data. E-algorithm extracts inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, fuzzy artificial immune recognition system (FAIRS) has been proposed to take advantage of the strengths of E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major customer interruption causes (tree, animal, and lightning) as prototypes; and FAIRS achieves comparable fault diagnosis performance with two base algorithms while being able to extract linguistic rules to explain the inference within significantly reduced computing time than E-algorithm.

In this paper, a novel dynamic addressing scheme for wireless sensor networks (WSNs) is proposed by using variable length coding. A WSN is typically composed of numerous tiny energy-constrained sensor nodes with limited information processing and data storage capabilities; thus, the energy-efficient strategy is the key issue in designing protocols for WSN. Traditional addressing strategies adopt flat addressing (static and uniform addresses) for sensor nodes. However, the proposed variable length dynamic addressing (VLDA) for sensor nodes is based on the fact that different nodes in the network have uneven traffic loads. Therefore, nodes with more data to receive or send are allocated with shorter addresses. Whether a node is busy or not is determined by the network traffic distribution (NTD), which is defined as the number of data packets each node has received or sent in a period of time. Sensor nodes’ energy is saved by VLDA scheme; hence, the wireless sensor network’s lifetime is extended. In the simulation, a 20% improvement has been achieved through the addressing scheme compared to traditional flat addressing.

In this paper, we apply belief propagation algorithm for consensus building in distributed wireless sensor networks (WSN). The objective is to achieve convergence to a global consensus value at all sensor nodes in the network. In order to implement the proposed belief propagation algorithm in a WSN, we introduce virtual fusion center nodes, which serve as sensors as well as local fusion centers in the sensor network. This paper also investigates the structure of WSNs that achieves convergence asymptotically, and guarantees achieving global consensus at all sensor nodes. Our results suggest that introducing certain range of irregularity in the network structure results in an improvement in the rate of convergence.

In this paper, we are intend to formulate the problem of query plan generation in distributed database using a bio-inspired algorithm. The algorithm based on the antibody-antigen clonal selection scientific theory in Artificial Immune System. Later, in experimental section, Results and implications of the proposed approach has been discussed and compared with the genetic algorithm (GA). Results are graphically proved better as compared to GA.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Energy saving in wireless sensor networks (WSNs) is a critical problem for a diversity of applications. Data aggregation between sensor nodes is huge unless a suitable sensor data flow management is adopted. Clustering the sensor nodes is considered an effective solution to this problem. Each cluster should have a controller denoted as a Cluster Head (CH), and a number of nodes located within its supervision area. Clustering demonstrated an effective result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSN, to make efficient use of the available energy sources, and reducing traffic transmission can be achieved. In solving this problem, we need to find the optimal distribution of sensors and CHs; thus we can increase the network lifetime while minimizing the energy consumption. In this talk, we are proposing three hybrid clustering algorithms based K-Means clustering and Particle Swarm Optimization (PSO) to achieve efficient energy management of the WSNs. Our proposed algorithms are compared to the traditional clustering techniques such as the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol and K-means clustering.

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction for the topology has shown great impact on the performance of such services as location, routing, and path planning in wireless sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in wireless sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, are not immediately applicable for the discrete and distributed wireless sensor networks. In this paper, we present a novel Connectivity-bAsed Skeleton Extraction (CASE) algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, CASE is distributed as no centralized operation is required, and is scalable as both its time complexity and its message complexity are linearly proportional to the network size. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. We believe that CASE has broad applications and present a skeleton-assisted segmentation algorithm as an example. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms the state-of-the-art algorithms.

For location-aware applications in wireless sensor networks (WSNs), it is important to ensure that sensor nodes can get correct locations in a hostile WSNs. Sybil attacks, which are vital threats in WSNs, especially in the distributed WSNs. They can forge one or multiple identities to decrease the localization accuracy, or sometimes to collapse the whole localization systems. In this paper, a novel lightweight sybil-free (SF)-APIT algorithm is presented to solve the problem of sybil attacks in APIT localization scheme, which is a popular range-free method and performs at individual node in a purely distributed fashion. The proposed SF-APIT scheme requires minimal overhead for wireless devices and works well based on the received signal strength. Simulations demonstrate that SF-APIT is an effective scheme in detecting and defending against sybil attacks with a high detection rate in distributed wireless localization schemes.

Secure Distributed Wireless Sensor Network (DWSN) is empowered by using an efficient key management technique. Battery powered sensor nodes faces precarious aspects during key distribution in order to reduce the energy consumption for the components of sensor nodes. Thus, implementing a key distribution algorithm should possess minimum power consumption. The proposed work steps towards greater heights by integrating power module with pairwise key establishment using group configuration methodology. We present an investigation of power consumption using STEM (Sparse Topology and Energy Management) protocol on sensor nodes (SNs) during the pairwise key establishment using graphical tools. Further memory utilization is reduced due to less number of keys generated from group configuration methodology. Analytical results of this PEAK (Power Efficiency Analysis for Key Distribution) show that energy efficiency is increased with sensor nodes while establishing pairwise key using group configuration when compared to existing key predistribution techniques.

This pager surveys the use of wireless sensors and sensor networks in monitoring Dam safety. Engineers paid much attention to fucus on the research of Dam safety monitoring (DSM), Since it could increase the safety and reduce the maintenance costs of engineering structures. Recently, wireless sensor network (WSN) has been explored for adoption to improve the centralized cable-based DSM system performances. This paper presents a multi-agent design method and system evaluation for wireless sensor network based Dam safety monitoring to validate the efficiency of the multi-agent technology. Through the cooperation of four different agents for DSM applications, the distributed wireless sensor network can automatically allocate DSHM tasks; self-organize the sensor network and aggregate different sensor information.

Key distribution in wireless sensor networks (WSNs) is challenging. Symmetric cryptosystems can perform it efficiently, but they often do not provide a perfect trade-off between resilience and storage. Further, even though conventional public key and elliptic curve cryptosystem are computationally feasible on sensor nodes, protocols based on them are not. They require exchange and storage of large keys and certificates, which is expensive. Using pairing-based cryptography (PBC) protocols, conversely, parties can agree on keys without any interaction. In this work, we (i) show how security in WSNs can be bootstrapped using an authenticated identity based non-interactive protocol and (ii) present TinyPBC, to our knowledge, the most efficient implementation of PBC primitives for an 8-bit processor. TinyPBC is able to compute pairings in about 5.5s on an ATmega128L clocked at 7.3828-MHz (the MICA2 and MICAZ node microcontroller).

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.