Security applications of wireless sensor networks (such as surveillance systems) require highly accurate detection systems for distinguishing the events of vital importance from simultaneously occurring random events. In order to improve the accuracy of the detection of such events of critical importance, a distributed in-network inference scheme is proposed in this paper. The problem of detection of such events is formulated as a sparse event detection problem and the awake cycle of every node is partitioned into smaller time blocks. Such a division allows the distributed inference algorithm to precisely identify the events of importance and their exact location in the physical sensor field. In order to further minimize the false alarm rate (FAR), spatially correlated data, from targeted regions, are obtained via a compressed sensing based data gathering scheme. The proposed scheme is able to achieve a significantly high accuracy rate of 0.9875 and exceptionally low FAR of 0.01. The prodigious performance of the proposed scheme continues in the experiments performed in outdoor environment and simulations.

Simultaneously occurring random events are often reported by multiple nodes. However, the accuracy of the event detection at every node is dependent on the node’s relative position from the event, and hence, not reliable. Moreover, the factors influencing the event inference are so many, that the accuracy of such an event detection is compromised. Targeting the problem of accurate event inference in the detection of priority events, such as forest fire, a fuzzy rule-based method is proposed. Four parameters are identified for which fuzzyfied values are obtained by a membership function for every variable. A set of 256 rules are used to generate different permutations of the fire index with respect to the identified variables. Extensive analysis of the results proves the efficacy of the proposed scheme with a significantly reduced error rate of 2.01% for humidity and an error rate of 1.94% for temperature.

The coverage-lifetime tradeoff is a perennial research problem in sensor-based internet of things framework. This article proposes a centralized scheme to achieve energy efficient maximum area COverage in Sensor network Based IoT (COSBIoT) for detection of events in dense and randomly deployed network. The sensor nodes are scheduled to function in sleep–awake cycle such that network is virtually a set of contiguous anticomplementary triangles (ACT) of corresponding Miquel triangles (MT). The node activation is accomplished by randomly choosing the first Miquel point as reference point to form anticomplementary triangles. Extensive experiments and simulations have been performed to test the efficacy of the scheme and results have been shown in different possible scenarios. Comparison with other schemes shows that the proposed scheme outperforms the existing schemes like, MPSO, BBO, VF-BBO, and MIA in terms of area coverage of region of interest. Similarly, in terms of network lifetime and energy efficiency, an average performance increase is achieved compared to MCLCT, OCCH, CWGC, Greedy CSC, and GIECC.

Network intrusion detection systems work on huge data sets, with large feature sets dominated by noisy data and irrelevant features, resulting in steep degradation in detection accuracy and a steep proliferation in model training and computation time. This work presents a novel method to optimize the feature selection process in machine learning algorithms for accurate detection of intrusion attacks in communication networks. The proposed method targets features with a high impact on the target variable to optimize feature selection and reduction. The CICIDS-2017 data set is used to test the performance of the proposed approach. Results prove the dexterity of the proposed method as it is able to achieve almost 51% reduction in irrelevant features and increases the detection accuracy of the tuned random forest classifier to 99.9% with an almost 50% reduced model computation time.