Design of filter coefficients for optimal factored truncated cascade FIR filter using optimization algorithm

Signal filtering acts as one of the basic requirement of communication networks for the removal of unwanted features from the signal. The design of appropriate digital filter requires the selection of optimal filter coefficients for the generation of desired frequency response with reduced hardware complexity. This paper proposes a hybrid optimization algorithm named as Brain Storm- Grey Wolf Optimizer (BSGWO) algorithm for the selection of filter coefficients in the design of factored truncated cascade FIR filter. The proposed algorithm is the hybridization of the optimization algorithms, namely Brain Storm Optimization (BSO) and Grey Wolf Optimizer (GWO). The input signal is interpolated initially for the formation of an intermediate signal using the FIR filter. Then, the factored truncated cascade filter is developed for the interpolation of the signal. After designing the filter coefficients, the optimal selection of the filter coefficients is performed using the proposed BSGWO algorithm. The original filter is developed with the use of the least square estimation and the new filter is developed using the proposed algorithm that tunes the filter coefficients. The performance of the proposed system is analyzed using the metrics, such as fitness, Mean Absolute Error (MAE), magnitude, and the number of components. The proposed method produces minimum fitness, MAE, magnitude and number of components of 0.05, 0.0155, − 96.0 dB and 3372, respectively that shows the effectiveness of the proposed method.

Opportunities of hybrid random access protocols for M2M communications in LTE/LTE-A networks

Machine-to-machine (M2M) communications on Long-term evolution (LTE) networks form a substantial part for the Internet-of-things (IoT). The random access procedure is the first step for M2M devices to access network resources. Many researchers have attempted to improve the efficiency of the random access procedure. This work revisits the performance of the hybrid random access protocols which combine congestion control techniques with collision resolution techniques. In particular, we investigate two hybrid protocols. The first one combines the pre-backoff (PBO) with tree random access (TRA), and the second one combines dynamic access barring (DAB) with TRA. The probability analysis is presented for both protocols. The performance is evaluated based on the access success rate, the mean throughput, the mean delay, the collision rate and the mean retransmissions. The simulation results show that the hybrid protocols achieve the highest success rate and throughput with moderate delay and low collision rates with a lower mean number of retransmissions compared to three benchmarks that apply either a congestion control or a collision resolution. The opportunities of future developments of hybrid protocols are listed at the end of this paper to highlight the issues that could be investigated to improve the performance of hybrid random access protocols.

Adaptive Knn-based algorithm for network selection in next-generation networks

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users’ experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods.

A review of the technology and application of deposit and traceability based on blockchain

With the vigorous development of blockchain technology represented by bitcoin, blockchain technology has gradually entered the stage of blockchain 3.0 characterized by “programmable society”. And the application of blockchain technology in all walks of life has achieved actual results. Blockchain technology has typical characteristics of decentralization, Tamper-resistant data, information openness and transparency, and natural fit with the application requirements in the field of certificate tracing, which makes the development of the applications of blockchain deposit and traceability in full swing. First, this paper describes the concept, application process, key technology of blockchain deposit and traceability, the three application architectures of blockchain deposit and traceability, and the overall architecture of its system. Then, it introduces the application of scenarios and the blockchain deposit and traceability in various fields. Next, it discusses the issues existing in the development of the application of blockchain deposit and traceability. Finally, the paper also expresses the best wishes for the future of its application.

A comparison study of chi-square and uniform distributions of mesh clients for different router replacement methods using WMN-PSODGA hybrid intelligent simulation system

Wireless Mesh Networks (WMNs) are gaining a lot of attention from researchers due to their advantages such as easy maintenance, low upfront cost and high robustness. Connectivity and stability directly affect the performance of WMNs. However, WMNs have some problems such as node placement problem, hidden terminal problem and so on. In our previous work, we implemented a simulation system to solve the node placement problem in WMNs considering Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA), called WMN-PSODGA. In this paper, we compare chi-square and uniform distributions of mesh clients for different router replacement methods. The router replacement methods considered are Constriction Method (CM), Random Inertia Weight Method (RIWM), Linearly Decreasing Inertia Weight Method (LDIWM), Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). The simulation results show that for chi-square distribution the mesh routers cover all mesh clients for all router replacement methods. In terms of load balancing, the method that achieves the best performance is RDVM. When using the uniform distribution, the mesh routers do not cover all mesh clients, but this distribution shows good load balancing for four router replacement methods, with RIWM showing the best performance. The only method that shows poor performance for this distribution is LDIWM. However, since not all mesh clients are covered when using uniform distribution, the best scenario is chi-square distribution of mesh clients with RDVM as a router replacement method.

HIBAF: A data security scheme for fog computing

Fog computing complemented cloud computing integration services in the Internet of Things (IoT) and the web of real-time interactivity. Fog offers faster computing and other services facilities sitting close to user applications. However, secure data transfer in the fog is still a challenging issue requiring attention and efficient deployment of a secure data security scheme. We present an Identity Based Encryption (IBE) scheme to secure data security and transmission in fog clouds and IoT ecosystems. We devise and develop a four-level Hierarchical Identity Based Architecture for Fog Computing (HIBAF) data security scheme to enhance data security. We also analyze the system’s performance regarding response time, CPU utilization, run-time encryption-decryption, and key generation time in the fog computing paradigm to an increasing number of users data-loads. Moreover, we evaluate our scheme and compare the outcomes with different cryptography structures to discern our scheme’s effectiveness. Furthermore, we also evaluate secret key updating time, re-encrypted key updating time, and file revoking time by launching DDoS attacks both in the cloud and fog computing environment to compare improvements of HIBAF in the fog computing paradigm. Finally, through this overall evaluation, we have found that the developed HIBAF scheme provides a 33% performance enhancement in a fog environment in terms of data processing, provision, and management compared to the cloud environment.

Energy-efficient routing sensing technology of wireless sensor networks based on Internet of Things

This research was conducted to enhance the energy performance of wireless sensor networks (WSN) and improve the performance of end-to-end delay and packet receiving rate of network operation. In this study, the low-energy data collection routing algorithm and adaptive environment sensing method in WSN were mainly examined. The node centrality, energy surplus, and node temperature were calculated for cluster head selection to reduce the energy consumption of nodes and improve the reliability of network data. The research results have shown that the parameter setting guided by the theoretical analysis makes each node selfishly achieve the maximum expected benefit while the whole network runs reliably, and the energy consumption is reduced by the selfishness of the node. As a result, the proposed algorithm can effectively reduce the network energy consumption and increase the network life cycle of wireless sensor networks. It can be seen that the machine learning methods such as support vector machine are used to model and analyze the state of the sensing node, and to obtain more accurate wireless channel availability judgment based on the historical state data, thereby adaptively adjusting the working duty ratio and reducing the invalidity data sent.

Single node repair algorithm for a multimedia cloud storage system based on network coding

Multimedia is inconvenient to use, difficult to maintain, and redundant in data storage. In order to solve the above problems and apply cloud storage to the integration of university teaching resources, this paper designs a virtualized cloud storage platform for university multimedia classrooms. The platform has many advantages, such as reducing the initial investment in multimedia classrooms, simplifying management tasks, making maximum use of actual resources and easy access to resources. Experiments and analysis show the feasibility and effectiveness of the platform. Aiming at the problems of the single-node repair algorithm of the existing multimedia cloud storage system, the limited domain is large, the codec complexity is high, the disk I/O (Input/Output) cost is high, the storage overhead and the repair bandwidth are unbalanced, and a network coding-based approach is proposed. Multimedia cloud storage. System single node repair algorithm. The algorithm stores the grouped multimedia file data in groups in the system, and performs XOR (exclusive OR) on the data in the group on the GF(2) finite field. When some nodes fail, the new node only needs to be connected. Two to three non-faulty nodes in the same group can accurately repair the data in the failed node. Theoretical analysis and simulation results show that the algorithm can reduce the complexity and repair of the codec, and reduce the disk I/O overhead. In this case, the storage cost of the algorithm is consistent with the storage cost based on the minimum storage regeneration code algorithm, and the repair bandwidth cost is close to the minimum bandwidth regeneration code algorithm.

Real-time on-site inspection system for power transmission based on heterogeneous computing

As the need of power supply is tremendously increasing in modern society, the stableness and reliability of the power delivery system are the two essential factors that ensure the power supply safety. With the quick expansion of electricity infrastructures, the failures of power transmission system are becoming more frequent, leading to economic loss and high risk of maintenance work under hazardous conditions. The existing automatic power line inspection utilizes advanced convolutional neural network (CNN) to improve the inspection efficiency, emerging as one promising solution. But the needed computational complexity is high since CNN inference demands large amount of multiplication-and-accumulation operations. In this paper, we alleviate this problem by utilizing the heterogeneous computing techniques to design a real-time on-site inspection system. Firstly, the required computational complexity of CNN inference is reduced using FFT-based convolution algorithms, speeding up the inference. Then we utilize the region of interest (ROI) extrapolation to predict the object detection bounding boxes without CNN inference, thus saving computing power. Finally, a heterogeneous computing architecture is presented to accommodate the requirements of proposed algorithms. According to the experiment results, the proposed design significantly improves the frame rate of CNN-based inspection visual system applied to power line inspection. The processing frame rate is also drastically improved. Moreover, the precision loss is negligible which means our proposed schemes are applicable for real application scenarios.