Internet

Abstract

Nowadays Internet of Things (IoT) is emerging and effective technology along with Wireless Sensor Networks (WSNs) in a few constant applications in which the human intervention significantly reduced along with better human life. In IoT enabled WSNs, the sensor nodes used to assemble the fragile data and communicate towards the sink hub and actuators for automotive remote monitoring process. However as the WSNs operating at free frequency band, it is powerless against different attacks at various layers of WSNs protocol stack in which attackers may try to hack and compromise the user’s personal information. There are different types of attacks in WSNs and several research works conducted to protect from these attacks in WSNs. Majority of security methods proposed are based layered technique. However, the layer approach is not enough to protect the WSNs effectively as the many attackers used the cross-layer information to perform the attacks. This paper presents the study over the layered security solutions and their research problems at first to justify the importance of cross-layer solutions. The review of different ways of designing the cross-layer security techniques for WSNs with their behaviour presented as well. The challenges of IoT enabled WSNs single layered security solutions presented and then the various cross-layer solutions reviewed in this paper. The comparative study of different cross-layer techniques to demonstrate the layers and their parameters involved to detect of security threats. The outcome of this review work is the research challenges noticed from the present cross-layer solutions.

Development of a comprehensive performance appraisal instrument using Behaviorally Anchored Rating Scales and Fuzzy TOPSIS

“…Meanwhile, fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method used to rank alternatives when the decision criteria are uncertain or imprecise [10]. The decision criteria and the alternatives are represented by fuzzy numbers, a set of real numbers that represents a range of possible values, e.g., the fuzzy number "very good" could represent the range of real numbers from 0.7 to 1.0 (in 0 to 1 scale).…”

“…The decision criteria and the alternatives are represented by fuzzy numbers, a set of real numbers that represents a range of possible values, e.g., the fuzzy number "very good" could represent the range of real numbers from 0.7 to 1.0 (in 0 to 1 scale). Fuzzy TOPSIS works by first calculating the similarity between each alternative and the ideal solution, then select the alternative with the highest similarity to the ideal solution as the most preferred alternative [10].…”