Firmware Over-the-air Programming Techniques for IoT Networks - A Survey

The devices forming Internet of Things (IoT) networks need to be re-programmed over the air, so that new features are added, software bugs or security vulnerabilities are resolved, and their applications can be re-purposed. The limitations of IoT devices, such as installation in locations with limited physical access, resource-constrained nature, large scale, and high heterogeneity, should be taken into consideration for designing an efficient and reliable pipeline for over-the-air programming (OTAP). In this work, we present a survey of OTAP techniques, which can be applied to IoT networks. We highlight the main challenges and limitations of OTAP for IoT devices and analyze the essential steps of the firmware update process, along with different approaches and techniques that implement them. In addition, we discuss schemes that focus on securing the OTAP process. Finally, we present a collection of state-of-the-art open-source and commercial platforms that integrate secure and reliable OTAP.

New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

One of the mathematical programming techniques is data envelopment analysis (DEA), which is used for evaluating the efficiency of a set of similar decision-making units (DMUs). Fixed resource allocation and target setting with the help of DEA is a subject that has gained much attention from researchers. A new model was proposed by determining a common set of weights (CSW). All DMUs were involved with the aim of achieving higher efficiency in every DMU after the procedure. The minimum resources and targets allocated to each DMU were commensurate to the efficiency of that DMU and the share of DMU in the input resources and the output productions. To examine the proposed method, other methods in the DEA literature were examined as well, and then, the efficiency of the method was demonstrated through a numerical example.

Modeling of Motor Insurance Extreme Claims through Appropriate Statistical Distributions

In the area of insurance, probability modeling has a wide variety of applications. In life insurance, the compensation sum is calculated in advance and may often be estimated using actuarial techniques, while in motor insurance, the claim amount is generally not known in advance. In the insurance business, the improvement of actuarial risk control strategies is an essential technique for controlling insurance risk. Although an insurance company’s risk assessment about its solvency is a complex and detailed problem, its solution begins with statistical modeling of individual claims’ amounts. This article emphasizes the possible ways of obtaining a suitable probability distribution model that accurately explains insurance risks and how to use such a model for risk management purposes. For this reason, we have applied modern programming techniques and statistical software implemented the methods provided based on data on premium amounts of third-party motor insurance claims.

How to start a heuristic? Utilizing lower bounds for solving the quadratic assignment problem

The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimization problems and is known for its diverse applications. The QAP is an NP-hard optimization problem which attracts the use of heuristic or metaheuristic algorithms that can find quality solutions in an acceptable computation time. On the other hand, there is quite a broad spectrum of mathematical programming techniques that were developed for finding the lower bounds for the QAP. This paper presents a fusion of the two approaches whereby the solutions from the computations of the lower bounds are used as the starting points for a metaheuristic, called HC12, which is implemented on a GPU CUDA platform. We perform extensive computational experiments that demonstrate that the use of these lower bounding techniques for the construction of the starting points has a significant impact on the quality of the resulting solutions.

Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches

Mathematical programming techniques are widely used in the determination of optimal functional configuration of a wireless sensor network (WSN). But these techniques have usually high computational complexity and are often considered as Non Polynomial (NP) complete problems. Therefore, machine learning (ML) techniques can be utilized for the prediction of the WSN parameters with high accuracy and lesser computational complexity than the mathematical programming techniques. This paper focuses on developing the prediction model for determination of the node status to be included in the set cover based on the coverage probability and trust values of the nodes. The set covers are defined as the subset of nodes which are scheduled to monitor the region of interest with the desired coverage level. Several machine learning techniques have been used to determine the node activation status based on which the set covers are obtained. The results show that the random forest based prediction model yields the highest accuracy for the considered network setting.

Transformation of the Program Strategy of a Niche Entertainment TV Channel under Conditions of a Competitive Media Environment (on the Example of STS and TNT)

Under conditions of a competitive media environment, linear TV channels should pay special attention to broadcast planning. This article presents the results of a study that was an attempt to analyze the changes that have occurred since 2012 in the programming of niche entertainment channels STS and TNT. Created in the second half of the 1990s, both TV networks remain popular among the target audience today; it seemed relevant to find out how their programming strategies were transformed taking into account the increased competition, including the one from non-linear services. The study was based on the method of quantitative content analysis with further comparison of the results obtained. In the course of the work, the genre and thematic characteristics of broadcasters’ content were determined, the origin of telecasts was established, the quality of films and series was assessed, and the programming tactics used in the formation of broadcast grids were analyzed. The study found that the changed media environment had a significant impact on the broadcast planning of both TV channels: over the years, both TNT and STS have become more active in using programming techniques aimed at retaining the audience and ensuring its natural flow between time slots, as well as taking a more careful approach to selecting content for showing. Taking into account the audience ratings of STS and TNT, we can talk about the success of such an approach to broadcast planning, which becomes more effective in combination with the broadcaster’s active presence on the Internet.

Modeling of Mono-Commodity Multi-Location Linear Programming Techniques for Determining Optimum Transportation Network of a Manufacturing Industry

This research on Modeling and Application of Mono-Commodity Multi-Location Linear Programming Techniques For Determining Optimum Transportation Network was carried out at a Manufacturing Industry in Lagos, which comprises of two plants, three depots and twenty retailers axis. The model was analyzed using Micro Soft Excel Software. The analysis to determine the optimal transportation network was carried out in two phases by considering numbers of truckload transported and each commodity from plants to depots and depots to retailers and their optimals. It was discovered that the existing practices transportation cost for truckloads moving from plant to retailers is N3,544,000,000,000 and when optimized, cost is N1,932,650,000,000 while considering each product the optimized transportation cost is N1,871,065,369,000. This implies that the transportation network generated considering each product will yield 47.2% gain in profit than existing network. Hence, it is recommended that mono-commodity multi-location transportation network be used. Keywords: [EXCEL Software, Mono-Commodity Multi-Location Model, Transportation Cost, Transportation Model, Transportation Network.].

Simulations of the Concentration Fields of Rosette-Type Multiport Buoyant Discharges Using Combined CFD and Multigene Genetic Programming Techniques

Rosette-type diffusers are becoming popular nowadays for discharging wastewater effluents. Effluents are known as buoyant jets if they have a lower density than the receiving water, and they are often used for municipal and desalination purposes. These buoyant effluents discharged from rosette-type diffusers are known as rosette-type multiport buoyant discharges. Investigating the mixing properties of these effluents is important for environmental impact assessment and optimal design of the diffusers. Due to the complex mixing and interacting processes, most of the traditional simple methods for studying free single jets become invalid for rosette-type multiport buoyant discharges. Three-dimensional computational fluid dynamics (3D CFD) techniques can satisfactorily model the concentration fields of rosette-type multiport buoyant discharges, but these techniques are typically computationally expensive. In this study, a new technique of simulating rosette-type multiport buoyant discharges using combined 3D CFD and multigene genetic programming (MGGP) techniques is developed. Modeling the concentration fields of rosette-type multiport buoyant discharges using the proposed approach has rarely been reported previously. A validated numerical model is used to carry out extensive simulations, and the generated dataset is used to train and test MGGP-based models. The study demonstrates that the proposed method can provide reasonable predictions and can significantly improve the prediction efficiency.

Parametric Analysis of Linear Periodic Arrays Generating Flat-Top Beams

Several synthesis techniques are available to optimize amplitude and phase excitations of periodic linear arrays to generate flat-top beams. Clearly, the optimal tapering depends on design parameters such as the array length, the number of array elements, the beam flatness, the beam width, the side lobe levels, and others. In this paper, in order to derive useful guidelines and rule of thumb for the synthesis of periodic array antennas, relations between these parameters are derived employing linear programming techniques, which guarantee optimality of the solutions. Such relations are then plotted and used in some design examples.