S×C4IoT: A Security-by-contract Framework for Dynamic Evolving IoT Devices

The Internet of Things (IoT) revolutionised the way devices, and human beings, cooperate and interact. The interconnectivity and mobility brought by IoT devices led to extremely variable networks, as well as unpredictable information flows. In turn, security proved to be a serious issue for the IoT, far more serious than it has been in the past for other technologies. We claim that IoT devices need detailed descriptions of their behaviour to achieve secure default configurations, sufficient security configurability, and self-configurability. In this article, we propose S×C4IoT, a framework that addresses these issues by combining two paradigms: Security by Contract (S×C) and Fog computing. First, we summarise the necessary background such as the basic S×C definitions. Then, we describe how devices interact within S×C4IoT and how our framework manages the dynamic evolution that naturally result from IoT devices life-cycles. Furthermore, we show that S×C4IoT can allow legacy S×C-noncompliant devices to participate with an S×C network, we illustrate two different integration approaches, and we show how they fit into S×C4IoT. Last, we implement the framework as a proof-of-concept. We show the feasibility of S×C4IoT and we run different experiments to evaluate its impact in terms of communication and storage space overhead.

A Highly Secure IoT Firmware Update Mechanism Using Blockchain

Internet of Things (IoT) device security is one of the crucial topics in the field of information security. IoT devices are often protected securely through firmware update. Traditional update methods have their shortcomings, such as bandwidth limitation and being attackers’ easy targets. Although many scholars proposed a variety of methods that are based on the blockchain technology to update the firmware, there are still demerits existing in their schemes, including large storage space and centralized stored firmware. In summary, this research proposes a highly secure and efficient protection mechanism that is based on the blockchain technology to improve the above disadvantages. Therefore, this study can reduce the need of storage space and improve system security. The proposed system has good performance in some events, including firmware integrity, security of IoT device connection, system security, and device anonymity. Furthermore, we confirm the high security and practical feasibility of the proposed system by comparing with the existing methods.

Unbalanced Big Data-Compatible Cloud Storage Method Based on Redundancy Elimination Technology

In order to meet the requirements of users in terms of speed, capacity, storage efficiency, and security, with the goal of improving data redundancy and reducing data storage space, an unbalanced big data compatible cloud storage method based on redundancy elimination technology is proposed. A new big data acquisition platform is designed based on Hadoop and NoSQL technologies. Through this platform, efficient unbalanced data acquisition is realized. The collected data are classified and processed by classifier. The classified unbalanced big data are compressed by Huffman algorithm, and the data security is improved by data encryption. Based on the data processing results, the big data redundancy processing is carried out by using the data deduplication algorithm. The cloud platform is designed to store redundant data in the cloud. The results show that the method in this paper has high data deduplication rate and data deduplication speed rate and low data storage space and effectively reduces the burden of data storage.

Dual regulation of lipid droplet-triacylglycerol metabolism and ERG9 expression for improved β-carotene production in Saccharomyces cerevisiae

Background The limitation of storage space, product cytotoxicity and the competition for precursor are the major challenges for efficiently overproducing carotenoid in engineered non-carotenogenic microorganisms. In this work, to improve β-carotene accumulation in Saccharomyces cerevisiae, a strategy that simultaneous increases cell storage capability and strengthens metabolic flux to carotenoid pathway was developed using exogenous oleic acid (OA) combined with metabolic engineering approaches. Results The direct separation of lipid droplets (LDs), quantitative analysis and genes disruption trial indicated that LDs are major storage locations of β-carotene in S. cerevisiae. However, due to the competition for precursor between β-carotene and LDs-triacylglycerol biosynthesis, enlarging storage space by engineering LDs related genes has minor promotion on β-carotene accumulation. Adding 2 mM OA significantly improved LDs-triacylglycerol metabolism and resulted in 36.4% increase in β-carotene content. The transcriptome analysis was adopted to mine OA-repressible promoters and IZH1 promoter was used to replace native ERG9 promoter to dynamically down-regulate ERG9 expression, which diverted the metabolic flux to β-carotene pathway and achieved additional 31.7% increase in β-carotene content without adversely affecting cell growth. By inducing an extra constitutive β-carotene synthesis pathway for further conversion precursor farnesol to β-carotene, the final strain produced 11.4 mg/g DCW and 142 mg/L of β-carotene, which is 107.3% and 49.5% increase respectively over the parent strain. Conclusions This strategy can be applied in the overproduction of other heterogeneous FPP-derived hydrophobic compounds with similar synthesis and storage mechanisms in S. cerevisiae. Graphical Abstract

Palmprint Recognition System Based on Multi-Block Local Line Directional Pattern and Feature Selection

In this paper, we deal with multimodal biometric systems based on palmprint recognition. In this regard, several palmprint-based approaches have been already proposed. Although these approaches show interesting results, they have some limitations in terms of recognition rate, running time and storage space. To fill this gap, we propose a novel multimodal biometric system combining left and right palmprints. For building this multimodal system, two compact local descriptors for feature extraction are proposed, fusion of left and right palmprints is performed at feature-level, and feature selection using evolutionary algorithms is introduced. To validate our proposal, we conduct intensive experiments related to performance and running time aspects. The obtained results show that our proposal shows significant improvements in terms of recognition rate, running time and storage space. Also, the comparison with other works shows that the proposed system outperforms some literature approaches and comparable with others.

Penerapan Algoritma Cosine Similarity untuk Deteksi Kesamaan Konten pada Sistem Informasi Penelitian dan Pengabdian Kepada Masyarakat

Research and community service activities are the obligations of a lecturer that must be carried out from part of the Tri Dharma of Higher Education in addition to teaching, where research activities should have a level of innovation in the form of development or discovery of something new, but with a large number of lecturers, this results in research activities. and community service has many similarities with previous activities. At the Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Pamulang University, experiencing several problems in the management of research and community service activities, namely the absence of a system used to manage research and community service activities and data related to the track record of research and community service activities that have been carried out by 2,613 lecturers who impact on the difficulty in finding data, efficiency of storage space and more importantly is the number of similar proposals in the research itself. The research carried out aims to develop an information system that can process research and community service activities and detect similarities in content by applying the Cosine Similarity algorithm, so that it can overcome existing problems. The system development method uses a waterfall. From the results of making the system that has been carried out, it shows that the system is capable of processing activities in the field of research and community service carried out by lecturers, supporting storage, and facilitating screening of proposals for research and community service activities that will be approved.

Application of Risk Analysis in the Screening of Flood Disaster Hot Spots and Adaptation Strategies

In recent years, Taiwan has established a sound flood control foundation in terms of river management. Due to climate change and land development, surface runoff has increased. In addition, the functions of flood control engineering facilities have their limits. Surface runoff cannot be fully absorbed by rivers, and frequent floods still occur in some areas. According to the characteristics of water flowing along the terrain to low-lying land, the terrain features can be used to find out the hot areas prone to flooding and the appropriate location of flood storage space for improving flooding. On the basis of the natural terrain environment, the disaster risk framework is used to manage environmental complexity, and to carry out research on flood warning and governance decision-making systems, so that human beings can coexist with the uncertainty of flood risk. In this study, the Zhuoshuixi Basin was used as the sample area, the SCS-CN method was used to analyze the excess runoff, and the risk concept was used to establish a flood evaluation model. In addition, through the changes in land use, the SCS-CN method estimates the difference of potential maximum retention, quantifies the variation of excess rainfall in each watershed division, and uses the digital elevation model to calculate the depression site to analyze the relationship between the difference of potential maximum retention and the depression space of the watershed. The results show that the adaptation strategy for high-risk flooded areas should be strengthened, and areas with large water storage space and a small potential maximum retention difference can be the best location for offsite compensation.

Multi-Criteria Inventory Optimisation of University of Ibadan, Ibadan, Nigeria’s Bakery Using Goal Programming Approach and Flour as the Major Raw-Material

Asides inventory cost, which is being used as the only inventory performance measure at the University of Ibadan bakery, a number of other criteria, such as inventory cost, service level, inventory turnover and delivery lead time which influence the performance of an inventory system have surfaced over the years. Hence, there is the need to examine all these criteria-objectives altogether. Therefore, this research was centred towards optimising the inventory system of University of Ibadan bakery, putting multiple criteria into consideration. Data on 17 raw materials: their costs, suppliers, usage rate, lead time, storage space and available capital were collected by means of interviews, past records and observations. The weighted goal program algorithm was adopted to find the best compromise between fulfilling the four objectives by minimising the sum of the deviation from the target values of the goals. Subsequently, Lingo 17.0 and Tora 1.0 optimisation software packages were used to solve and compare the model generated, while putting into consideration storage space constraint and budgeted capital. The developed model from the goal programming algorithm exhibited four goals (combined into one objective function). Same solutions were obtained from Lingo 17.0 and Tora 1.0. While Lingo 17.0 gave a uniform service level of 100% , a turnover ratio greater than 1(>1) for all the materials at a negligible increase (of < 0.0001%) in total inventory cost of the raw materials and available lead time duration of zero days (< 24 hours) for each material, Tora 1.0 gave a uniform service level of 100% , a turnover ratio greater than 1 (> 1) for all the materials at a negligible increase (of < 0.0001%) in total inventory cost of the raw materials and available lead time duration of zero days (< 24 hours) for each material. Implementation of the developed model will eliminate unnecessary waiting time between production, thereby ensuring effective and efficient utilisation of raw materials in production which will lead to reduced cost of holding inventory, elimination of unnecessary overall cost and wastages, and also improvement of the productivity and profit on the long run.

Soft Compression for Lossless Image Coding Based on Shape Recognition

Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.