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In Distributed Hash Table (DHT)-based Mobile Ad Hoc Networks (MANETs), a logical structured network (i.e., follows a tree, ring, chord, 3D, etc., structure) is built over the ad hoc physical topology in a distributed manner. The logical structures guide routing processes and eliminate flooding at the control and the data plans, thus making the system scalable. However, limited radio range, mobility, and lack of infrastructure introduce frequent and unpredictable changes to network topology, i.e., connectivity/dis-connectivity, node/link failure, network partition, and frequent merging. Moreover, every single change in the physical topology has an associated impact on the logical structured network and results in unevenly distributed and disrupted logical structures. This completely halts communication in the logical network, even physically connected nodes would not remain reachable due to disrupted logical structure, and unavailability of index information maintained at anchor nodes (ANs) in DHT networks. Therefore, distributed solutions are needed to tolerate faults in the logical network and provide end-to-end connectivity in such an adversarial environment. This paper defines the scope of the problem in the context of DHT networks and contributes a Fault-Tolerant DHT-based routing protocol (FTDN). FTDN, using a cross-layer design approach, investigates network dynamics in the physical network and adaptively makes arrangements to tolerate faults in the logically structured DHT network. In particular, FTDN ensures network availability (i.e., maintains connected and evenly distributed logical structures and ensures access to index information) in the face of failures and significantly improves performance. Analysis and simulation results show the effectiveness of the proposed solutions.

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The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme’s effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity.

The main focus of the study was to analyze trends and variability of wheat crop in Pakistan. Semi-log trend model was used to find trends and growth rate in area, yield and production of wheat crop whereas the variability was measured by Cuddy-Della Valle index of variability. The findings of the study illustrate that wheat area in Punjab, Sindh and Baluchistan was increased over the time whereas cultivated area of wheat in Khyber Pakhtunkhwa province was marginally decreased during 1981-85 to 2011-15. The results show that there was substantial increase in wheat yield and production in all four provinces of Pakistan. The increase in wheat yield may due to the adoption of new varieties of wheat in the country over the time. It was also concluded from the results that area and yield of wheat in Baluchistan recorded the highest degree of variability whereas in Punjab province area and yield of wheat crop were noticed the lowest degree of variability. Similarly, the maximum variability in wheat production was recorded for Baluchistan province followed by Sindh, Khyber Pakhtunkhwa, and Punjab. Mostly the variability in wheat production was due to the variability in wheat area and their yield.

Twenty six yellow maize hybrids on the basis of stability analysis were evaluated in National Uniform Maize Hybrid Yield Trials conducted across eight diversified environments of Pakistan. Combined analysis of variance based AMMI analysis shown highly significant differences for environments, genotypes and their interactions. The environments explained about 78 percent of the total yield variation followed by genotype by environment interaction. Environment was the main aspect that influences the performance of maize yield in study area. The first two interaction principal component axes (IPCA1 and IPCA2) explained about 63 percent of the grain yield variation due to genotype and genotype by environment interaction (GGE). The GGE biplot analysis shown that entry-2 (Mex-YLHY2) was the most stable hybrid and can be considered as adaptable to all the environments.

The risk of malware has increased drastically in recent years due to advances in the IT industry but it also increased the need for malware analysis and prevention. Hackers inject malicious code using awful applications. In this research, a framework is proposed to identify malicious Android applications based on repacked malicious code. The sensitive features of android applications are extracted using source code. These extracted features are compared with existing malware signatures to identify repacked malicious android applications. Experiments are performed using 3490 android-based malware samples belonging to 21 different malware families. A threshold value for malware categorization is defined using fuzzy logic. If the fuzzy comparison match is greater than 40%, the application is malicious. Meanwhile, if the match is greater than 10% and less than 40%, the application is suspicious otherwise benign. Furthermore, the proposed framework presents around 74% of the repacked malware compared to other similar approaches.

Recently, electronics devices, cognitive computing, and sensing enable the deployment of internet-of-things (IoTs) with a huge application domain. However, resource constraints such as low computing powers or limited storage leave IoTs infrastructures vulnerable to a variety of cyber-attacks. In dark-net the address space developed as designated unrestricted internet address space anticipated to be used by trustworthy hosts anywhere in the world, therefore, any communication activity is presumed to be unwanted and particularly treated as a probe, backscatter, or miss-configuration. This chapter investigates and evaluates the operation of dark-net traffic detection systems in IoTs networks. Moreover, the most recent work done to ensure security in the IoTs network has been discussed. In particular, the areas of privacy provisioning, lightweight cryptographic framework, secure routing, robustness, and DoS attacks have been addressed. Moreover, based on the analysis of existing state-of-the-art protocols, the security requirements and challenges are highlighted along with identified open issues.