OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing

IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.

Unicellular-Multicellular Evolutionary Branching Driven by Resource Limitations

Multicellular life forms have evolved many times in our planet, suggesting that this is a common evolutionary innovation. Multiple advantages have been proposed for multicellularity (MC) to emerge. In this paper we address the problem of how the first precondition for multicellularity, namely "stay together" might have occurred under spatially limited resources exploited by a population of unicellular agents. Using a minimal model of evolved cell-cell adhesion among growing and dividing cells that exploit a localised resource with a given size, we show that a transition occurs at a critical resource size separating a phase of evolved multicellular aggregates from a phase where unicellularity (UC) is favoured. The two phases are separated by an intermediate domain where where both UC and MC can be selected by evolution. This model provides a minimal approach to the early stages that were required to transition from Darwinian individuality to cohesive groups of cells associated with a physical cooperative effect: when resources are present only in a localised portion of the habitat, MC is a desirable property as it helps cells to keep close to the available local nutrients.

A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network

In recent years, Internet of Things (IoT for short) research has become one of the top ten most popular research topics. IoT devices also embed many sensing chips for detecting physical signals from the outside environment. In the wireless sensing network (WSN for short), a human can wear several IoT devices around her/his body such as a smart watch, smart band, smart glasses, etc. These IoT devices can collect analog environment data around the user’s body and store these data into memory after data processing. Thus far, we have discovered that some IoT devices have resource limitations such as power shortages or insufficient memory for data computation and preservation. An IoT device such as a smart band attempts to upload a user’s body information to the cloud server by adopting the public-key crypto-system to generate the corresponding cipher-text and related signature for concrete data security; in this situation, the computation time increases linearly and the device can run out of memory, which is inconvenient for users. For this reason, we consider that, if the smart IoT device can perform encryption and signature simultaneously, it can save significant resources for the execution of other applications. As a result, our approach is to design an efficient, practical, and lightweight, blind sign-cryption (SC for short) scheme for IoT device usage. Not only can our methodology offer the sensed data privacy protection efficiently, but it is also fit for the above application scenario with limited resource conditions such as battery shortage or less memory space in the IoT device network.

Benchmark Evaluation of Protein–Protein Interaction Prediction Algorithms

Protein–protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on ‘illogical’ and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source.

Can Formal Security Verification Really Be Optional? Scrutinizing the Security of IMD Authentication Protocols

The need for continuous monitoring of physiological information of critical organs of the human body, combined with the ever-growing field of electronics and sensor technologies and the vast opportunities brought by 5G connectivity, have made implantable medical devices (IMDs) the most necessitated devices in the health arena. IMDs are very sensitive since they are implanted in the human body, and the patients depend on them for the proper functioning of their vital organs. Simultaneously, they are intrinsically vulnerable to several attacks mainly due to their resource limitations and the wireless channel utilized for data transmission. Hence, failing to secure them would put the patient’s life in jeopardy and damage the reputations of the manufacturers. To date, various researchers have proposed different countermeasures to keep the confidentiality, integrity, and availability of IMD systems with privacy and safety specifications. Despite the appreciated efforts made by the research community, there are issues with these proposed solutions. Principally, there are at least three critical problems. (1) Inadequate essential capabilities (such as emergency authentication, key update mechanism, anonymity, and adaptability); (2) heavy computational and communication overheads; and (3) lack of rigorous formal security verification. Motivated by this, we have thoroughly analyzed the current IMD authentication protocols by utilizing two formal approaches: the Burrows–Abadi–Needham logic (BAN logic) and the Automated Validation of Internet Security Protocols and Applications (AVISPA). In addition, we compared these schemes against their security strengths, computational overheads, latency, and other vital features, such as emergency authentications, key update mechanisms, and adaptabilities.

Effective Deoxidation Process of Titanium Scrap Using MgCl2 Molten Salt Electrolytic

To overcome the scarcity and resource limitations of Ti metal, deoxidation of Ti scrap was conducted through electrolytic refining and chemical reaction with MgCl2 molten salt electrolysis. The oxygen concentration in Ti scraps was decreased by the electrochemical and chemical reactions generated by the applied voltages. The optimized conditions for the process were derived by controlling the conditions and parameters by decreasing the thermodynamic activity of the reactants. The correlation between the deoxidation efficiency and the behavior of the voltage and current was confirmed by setting the conditions of the electrolysis process in various voltage ranges. In addition, the correlation between the presence of impurities and the measured oxygen concentration was evaluated. The surface element analysis result indicated that the salt that was not removed contained a certain amount of oxygen. Thus, the removal efficiencies of impurities and particles by deriving various post-treatment process conditions were analyzed. The results confirmed that the most stable and efficient current was formed at a specific higher voltage. Moreover, the best deoxidation result was 2425 ppm, which was 50% lower than that of the initial Ti scrap.

Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving Applications

Research about deep learning applied in object detection tasks in LiDAR data has been massively widespread in recent years, achieving notable developments, namely in improving precision and inference speed performances. These improvements have been facilitated by powerful GPU servers, taking advantage of their capacity to train the networks in reasonable periods and their parallel architecture that allows for high performance and real-time inference. However, these features are limited in autonomous driving due to space, power capacity, and inference time constraints, and onboard devices are not as powerful as their counterparts used for training. This paper investigates the use of a deep learning-based method in edge devices for onboard real-time inference that is power-effective and low in terms of space-constrained demand. A methodology is proposed for deploying high-end GPU-specific models in edge devices for onboard inference, consisting of a two-folder flow: study model hyperparameters’ implications in meeting application requirements; and compression of the network for meeting the board resource limitations. A hybrid FPGA-CPU board is proposed as an effective onboard inference solution by comparing its performance in the KITTI dataset with computer performances. The achieved accuracy is comparable to the PC-based deep learning method with a plus that it is more effective for real-time inference, power limited and space-constrained purposes.

A review of the most impactful published pharmacotherapy-pertinent literature of 2019 and 2020 for clinicians caring for patients with thermal or inhalation injury

Keeping abreast with current literature can be challenging, especially for practitioners caring for patients sustaining thermal or inhalation injury. Practitioners caring for patients with thermal injuries publish in a wide variety of journals, which further increases the complexity for those with resource limitations. Pharmacotherapy research continues to be a minority focus in primary literature. This review is a renewal of previous years’ work to facilitate extraction and review of the most recent pharmacotherapy-centric studies in patients with thermal and inhalation injury. Sixteen geographically dispersed, board-certified pharmacists participated in the review. A MeSH-based, filtered search returned 1,536 manuscripts over the previous 2-year period. After manual review and exclusions, only 98 (6.4%) manuscripts were determined to have a potential impact on current pharmacotherapy practices and included in the review. A summary of the 10 articles that scored highest are included in the review. Nearly half of the reviewed manuscripts were assessed to lack a significant impact on current practice. Despite an increase in published literature over the previous 2-year review, the focus and quality remain unchanged. There remains a need for investment in well-designed, high impact, pharmacotherapy-pertinent research for patients sustaining thermal or inhalation injuries.

A synthetic distributed genetic multi-bit counter

A design for genetically-encoded counters is proposed via repressor-based circuits. An N-bit counter reads sequences of input pulses and displays the total number of pulses, modulo 2^N. The design is based on distributed computation, with specialized cell types allocated to specific tasks. This allows scalability and bypasses constraints on the maximal number of circuit genes per cell due to toxicity or failures due to resource limitations. The design starts with a single-bit counter. The N-bit counter is then obtained by interconnecting (using diffusible chemicals) a set of N single-bit counters and connector modules. An optimization framework is used to determine appropriate gate parameters and to compute bounds on admissible pulse widths and relaxation (inter-pulse) times, as well as to guide the construction of novel gates. This work can be viewed as a step toward obtaining circuits that are capable of finite-automaton computation, in analogy to digital central processing units.