Energy Efficient Fuzzy Adaptive Verification Node Selection-Based Path Determination in Wireless Sensor Networks

Abstract

Abstract:Wireless sensor networks are supplied with limited energy resources and are usually installed in unattended and unfriendly environments. These networks are also highly exposed to security attacks aimed at draining the energy of the network to render it unresponsive. Adversaries launch counterfeit report injection attacks and false vote injection attacks through compromised sensor nodes. Several filtering solutions have been suggested for detecting and filtering false reports during the multi-hop forwarding process. However, almost all such schemes presuppose a conventional underlying protocol for data routing that do not consider the attack status or energy dissipation on the route. Each design provides approximately the equivalent resilience in terms of protection against compromised node. However, the energy consumption characteristics of each design differ. We propose a fuzzy adaptive path selection to save energy and avoid the emergence of favored paths. Fresh authentication keys are generated periodically, and these are shared with the filtering nodes to restrict compromised intermediate filtering nodes from the verification process. The scheme helps delay the emergence of hotspot problems near the base station and exhibits improved energy conserving behavior in wireless sensor networks. The proposed scheme provides an extended network lifetime and better false data filtering capacity.

Energy Efficiency Enhancement of TICK –based Fuzzy Logic for Selecting Forwarding Nodes in WSNs

“…In such a case Fuzzy Rule-based techniques become a natural choice as they provide us with the best approach for dealing with the uncertainty of measurements performed in WSNs, which are affected by errors in precision and accuracy [25,26]. Rule-based fuzzy systems are effective because of their utility for approximate reasoning when there is a degree of uncertainty in the reasoning process and imprecision in the data [27]. The inference engine makes use of fuzzy membership functions as inputs and fuzzy rules for its decision making.…”

“…The COA method for defuzzification is most commonly used for implementation in wireless sensor network [27]. The Center of Area defuzzification method effectively calculates the best compromise between multiple output linguistic terms.…”

Genetic Algorithm-based Optimized Fuzzy Adaptive Path Selection in Wireless Sensor Networks

“…In minimum cost forwarding-based (MCF) schemes [ [10], routing paths are chosen by only considering the distance and the energy efficiency of the routing paths. Constant uses of shortest or minimum cost paths leads to uneven work load across the network and causes network partitioning [11]. In [11], we recommended fuzzy based adaptive selection of data routing path to facilitate energy balancing across the network and avoid network partitioning.…”

“…Constant uses of shortest or minimum cost paths leads to uneven work load across the network and causes network partitioning [11]. In [11], we recommended fuzzy based adaptive selection of data routing path to facilitate energy balancing across the network and avoid network partitioning. Fuzzy adaptive choice of data routing paths feasibly balance the work burden between different available routes.…”

“…The adaptive filtering route selection achieves extra energy saving and results in network lifetime extension. In fuzzy adaptive path selection (shortly referred to as FAPS hereafter) [11], a fuzzy rule-based inferencing system chooses among the available data forwarding paths, considering the input parameters such as the verification nodes' average distance, the distance of the routing path, and the average residual energy resources on the data routing path. To exploit FAPS in a real-world WSN, the fuzzy membership functions in the fuzzy inferencing configuration are required to be optimally tuned for the actual network configuration.…”