REDUNDANCY MANAGEMENT OF MULTIPATH ROUTING FOR INTRUSION TOLERANCE IN HETEROGENEOUS WIRELESS SENSOR NETWORKS

ABSTRACT In this paper we propose redundancy management of heterogeneous wireless sensor networks (HWSNs), utilizing multipath routing to answer user queries in the presence ofunreliable and malicious nodes. The key concept of our redundancy management is to exploit the tradeoff between energy consumption vs. the gain in reliability, timeliness, and security to maximize the system useful lifetime. We formulate the tradeoff as an optimization problem for dynamically determining the best redundancy level to apply to multipath routing for intrusion tolerance so that the query response success probability is maximized while prolonging the useful lifetime. Furthermore, we consider this optimization problem for the case in which a voting-based distributed intrusion detection algorithm is applied to detect and evict malicious nodes in a HWSN. We develop a novel probability model to analyze the best redundancy level in terms of path redundancy and source redundancy, as well as the best intrusion detection settings in terms of the number of voters and the intrusion invocation interval under which the lifetime of a HWSN is maximized. We then apply the analysis results obtained to the design of a dynamic redundancy management algorithm to identify and apply the best design parameter settings at runtime in response to environment changes, to maximize the HWSN lifetime

ENERGY EFFICIENCY IN FILE TRANSFER ACROSS WIRELESS COMMUNICATION

Abstract: The key idea of our Energy Efficiency management is to use the exchange between energy consumption vs the gain in responsibility, timeliness, and security to maximize the system helpful time period. we tend to formulate the exchange as Associate in Nursing optimization downside for dynamically crucial the most effective redundancy level to use to multipath routing for intrusion tolerance so the question response success likelihood is maximized whereas prolonging the helpful time period. Moreover, we think about this optimization downside for the case during which a voting-based distributed intrusion detection formula is applied to sight and evict malicious nodes during a HWSN. we over see to develop a novel likelihood model to investigate the most effective redundancy level in terms of path redundancy and supply redundancy, further because the best intrusion detection settings in terms of the amount of voters and the intrusion invocation interval below that the time period of a HWSN is maximized. we over see to then apply the analysis results obtained to the planning of a dynamic redundancy management formula to identify and apply the most effective style parameter settings at runtime in response to environmental changes, to maximize the HWSN lifetime

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort