Earticle

The constrained application protocol (CoAP) can be used for remotely controlling various sensor devices in Internet of Things (IoT) networks. In CoAP, to support the handover of a mobile sensor device, service discovery and message transmission needs to be repeated, although doing so would increase the handover delay significantly. To address this limitation of CoAP, a centralized CoAP scheme has been proposed. However, it tends to result in performance degradation for an inter-domain handover case. In this letter, we propose a distributed CoAP handover scheme to support the inter-domain handover. In the proposed scheme, a distributed mobility agent (DMA) is used for managing the location of mobile sensors in a domain and performing handover control operations with its neighboring DMAs in a distributed manner. A performance comparison reveals that the proposed scheme offers a performance improvement of up to 29.5% in terms of the handover delay.

클라우드 뿐 아니라 모바일 기기들이 함께 학습과 추론을 위한 연산들을 나누어 수행하고 이를 다시 종합하는 distributed learning은 주목 받고 있는 learning 기법들 중 한 형태이다. 이러한 형태의 learning의 경우, 모바일 기기들을 활용하여 학습하고 추론 연산을 수행할 때, 응용의 성능뿐 아니라 통신 상황과 모바일 기기들에서 소모되는 전력을 고려하는 것은 매우 중요하다. 따라서 본 논문에서는 엣지 클라우드와 연결된 멀티 홉 기반의 네트워크에서 distributed learning을 수행할 때, 통신 상황과 소모 전력 관점에서 학습 및 추론 연산 분배에 따른 주요 case들에 대한 분석을 수행한다.

MANET은 노드들의 이동성으로 인한 동적 토폴로지와 hop-by-hop 데이터 전달 방식의 특징으로 인해 많은 공격들 의 대상이 된다. 그리고 MANET에서는 침입탐지시스템의 위치 설정이 어렵고, 지역적으로 수집된 정보로는 공격 탐지 가 더욱 어렵다. 또한 트래픽 양이 많아지면 침입탐지 성능이 현저히 떨어지게 된다. 따라서 본 논문에서는 MANET을 zone 형태로 구성한 후 대용량의 트래픽에도 안정된 침입탐지를 수행할 수 있도록 하기 위하여 정보 손실 없이 차원을 축소할 수 있는 random projection 기법을 사용하였다. 그리고 지역적인 정보만으로 탐지가 어려운 공격 탐지를 위해서 전역 탐지 노드를 이용하였다. 전역 탐지 노드에서는 IDS 에이전트들로부터 수신한 정보와 노드들의 패턴을 이용하여 공격 탐지를 수행하게 된다. 본 논문에서 제안한 기법의 성능 평가를 위하여 k-NN 기법과 ZBIDS 기법과 비교 실험하 였으며, 실험을 통해 성능의 우수성을 확인하였다.

MANET(Mobile Ad-hoc Network) is target of many attacks because of dynamic topology and hop-by-hop data transmission method. In MANET, location setting of intrusion detection system is difficult and attack detection using information collected locally is more difficult. The amount of traffic grow, intrusion detection performance will be decreased. In this paper, MANET is composed of zone form and we used random projection technique which reduces dimension without loss of information in order to perform stable intrusion detection in even massive traffic. Global detection node is used to detect attacks which are difficult to detect using only local information. In the global detection node, attack detection is performed using received information from IDS agent and pattern of nodes. k-NN and ZBIDS were experimented to evaluate performance of the proposed technique in this paper. The superiority of performance was confirmed through the experience.

Currently, most Internet of Things (IoT) resource-allocation solutions are based on centralized management, rendering it difficult to successfully establish diverse and dynamic IoT networks. Consequently, the fields of reinforcement learning and distributed computing require additional technological advancements. We propose an innovative approach for slot scheduling in IoT networks. This approach focuses on the utilization of distributed resource blocks. The purpose of this work is to demonstrate, via simulations, the influence of distributed slot assignment on the signal-to-interference ratio (SIR) and the probability of accidents occurring. The results of this research suggest that the proposed approach, in which each device in the IoT network is provided with an appropriate slot that possesses acceptable SIR levels, was successful. As the process of distributed slot allocation progresses, it is beneficial to build network convergence by utilizing the learning capabilities of each device. This was accomplished using a distributed slot-allocation process. Therefore, the existing bandwidth can be utilized more efficiently.

In this paper, we design and implement a distributed, moving objects management system for processing locations and sensor data from smart black boxes. The proposed system is designed and implemented based on Apache Kafka, Apache Spark & Spark Streaming, Hbase, HDFS. Apache Kafka is used to collect the data from smart black boxes and queries from users. Received location data from smart black boxes and queries from users becomes input of Apache Spark Streaming. Apache Spark Streaming preprocesses the input data for indexing. Recent location data and indexes are stored in-memory managed by Apache Spark. Old data and indexes are flushed into HBase later. We perform experiments to show the throughput of the index manager. Finally, we describe the implementation detail in Scala function level.

We consider a distributed estimation where many nodes remotely placed at known locations collect the measurements of the parameter of interest, quantize these measurements, and transmit the quantized data to a fusion node; this fusion node performs the parameter estimation. Noting that quantizers at nodes should operate in a non-regular framework where multiple codewords or quantization partitions can be mapped from a single measurement to improve the system performance, we propose a low-weight estimation algorithm that finds the most feasible combination of codewords. This combination is found by computing the weighted sum of the possible combinations whose weights are obtained by counting their occurrence in a learning process. Otherwise, tremendous complexity will be inevitable due to multiple codewords or partitions interpreted from non-regular quantized data. We conduct extensive experiments to demonstrate that the proposed algorithm provides a statistically significant performance gain with low complexity as compared to typical estimation techniques.

Generally, existing parallel main-memory spatial index structures to avoid the trade-off between query freshness and CPU cost uses light-weight locking techniques. However, still, the lock based methods have some limits such as thrashing which is a well-known problem in lock based methods. In this paper, we propose a distributed index structure for moving objects exploiting the parallelism in multiple machines. The proposed index is a lock free multi-version concurrency technique based on the D-Stream model of Spark Stream. The proposed method exploits the multiversion nature of D-Stream of Spark Streaming.

With the dramatic increase in mobile traffic in recent years, some of the limitations of mobility management frameworks have magnified. The current centralized mobility management (CMM) strategy has various problems, such as a suboptimal routing path, low scalability, signaling overhead, and a single point of failure. To overcome these weaknesses in the CMM strategy, the Internet Engineering Task Force has been discussing distributed mobility management (DMM) strategies. The fundamental concept of a DMM strategy is to distribute the mobility anchors closer to the users. While the distribution of mobility anchors results in low-cost traffic delivery, it increases the signaling cost. To reduce this higher signaling cost, we propose a new DMM strategy applying the pointer forwarding technique. The proposed strategy keeps the existing tunnels and extends the traffic path as much as possible. In this paper, we analyze the performance of the pointer forwarding-DMM strategy and discuss its pros and cons.

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

The use of computers in architectural professions has grown with the power of easy data management, increased sophistication of standalone applications, inexpensive hardware, improved speed of processing, use of standard library and tools for communication and collaboration. Recently, there has been a growing interest in distributed CAAD (Computer-Aided Architectural Design) integration due to the needs of direct collaboration among project participants in different locations, and Internet is becoming the optimal tool for collaboration among participants in architectural design and construction projects. The aim of this research is to provide a new paradigm for a CAAD system by combining research on integrated CAAD applications with recent collaboration technologies. To accomplish this research objective, interactive three-dimensional (3D) design tools and applications running on the Web have been developed for an Internet-based distributed CAAD application system, specifically designed to meet the requirements of the architectural design process. To this end, two different scopes of implementation are evaluated: first, global architecture and the functionality of a distributed CAAD system; and, second, the association of an architectural application to the system.

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems.

To effectively manage the escalating traffic, attention is drawn to the coexistence mechanism of cellular and Wi-Fi networks which offload the cellular traffic from the licensed band to the unlicensed band. For the coexistence of 5G new radio-unlicensed and Wi-Fi, we tackle the energy efficiency maximization problem by sequentially determining the duty cycle and transmit power through the use of distributed deep Q-network (DQN) techniques. When utilizing the existing unlicensed band, the proposed method minimizes the impact on Wi-Fi networks while achieving optimal energy efficiency. Simulations validate the superior energy efficiency of the proposed coexistence mechanism over various benchmark methods.

In this paper, we propose a novel successive cancellation flip (SCF) decoding to reduce the computational complexity compared to the conventional SCF decoding by using distributed CRC bits. The proposed decoding reduces the number of estimations for information bits by early termination of decodings for failed frames of the first SC decoding, while trying to minimize the additional sorting operations. Simulation results show that the proposed SCF decoding reduces the computational complexity of repeated SC decoding at least 27% compared to the conventional SCF decoding.

The urban aerial mobility (UAM) system, such as drone taxi or air taxi, is one of future on-demand transportation networks. Among them, electric vertical takeoff and landing (eVTOL) is one of UAM systems that is for identifying the locations of passengers, flying to the positions where the passengers are located, loading the passengers, and delivering the passengers to their destinations. In this paper, we propose a distributed deep reinforcement learning where the agents are formulated as eVTOL vehicles that can compute the optimal passenger transportation routes under the consideration of passenger behaviors, collisions among eVTOL, and eVTOL battery status.

This paper presents a method for clustering short text documents, such as news headlines, social media statuses, or instant messages. Due to the characteristics of these documents, which are usually short and sparse, an appropriate technique is required to discover hidden knowledge. The objective of this paper is to identify the combination of document representation, document distance, and document clustering that yields the best clustering quality. Document representations are expanded by external knowledge sources represented by a Distributed Representation. To cluster documents, a K-means partitioning-based clustering technique is applied, where the similarities of documents are measured by word mover's distance. To validate the effectiveness of the proposed method, experiments were conducted to compare the clustering quality against several leading methods. The proposed method produced clusters of documents that resulted in higher precision, recall, F1-score, and adjusted Rand index for both real-world and standard data sets. Furthermore, manual inspection of the clustering results was conducted to observe the efficacy of the proposed method. The topics of each document cluster are undoubtedly reflected by members in the cluster.

Since rapidly disseminating of Internet of Things (IoT) as the new communication paradigm, a number of studies for various applications is being carried out. Especially, interest in the smart medical system is rising. In the smart medical system, a number of medical devices are distributed in popular area such as station and medical center, and this high density of medical device distribution can cause serious performance degradation of communication, referred to as the coexistence problem. When coexistence problem occurs in smart medical system, reliable transmitting of patient's biological information may not be guaranteed and patient's life can be jeopardized. Therefore, coexistence problem in smart medical system should be resolved. In this paper, we propose a distributed coexistence mitigation scheme for IoT-based smart medical system which can dynamically avoid interference in coexistence situation and can guarantee reliable communication. To evaluate the performance of the proposed scheme, we perform extensive simulations by comparing with IEEE 802.15.4 MAC protocol which is a traditional low-power communication technology.

Mobile Ad hoc network is a self configuring network of mobile nodes. It allows mobile nodes to configure network spontaneously and share their services. In these networks, service discovery is very important because all nodes do not have same resources in term of memory and computing power. Nodes need to use different services offered by different servers. Some service discovery protocols have been proposed in last couple of years but they include significant traffic overhead and for small scale MANETS. In this paper, we present extensible lookup service scheme based on distributed mechanism. In our scheme neighboring nodes of service provider monitor service provider and send notification to lookup server when the service provider terminates its services unexpectedly. Lookup server can find other service provider or other alternative services in advance because of advance notification method and can provide consistent lookup services. In our scheme neighboring nodes also monitor lookup server and send notification to network when lookup server terminates unexpectedly. Simulation results show that our scheme can reduce up to 70% and 30% lookup failure as compare to centralize and simple distributed mechanism respectively.

As the cornerstone of IoT-based systems, WSN connecting a wide range of intelligent sensor nodes is expected to bring significant changes in the near future. Due to the limited battery capacity of the sensor node, WSN considers maximizing network lifetime by minimizing the power consumption to be the most important challenge. To this end, we propose a Distributed Adaptive Communication with On/Off switching and Dual queuing for Energy efficiency (DACODE) as a novel asynchronous duty cycling mechanism. The performance evaluation shows that the proposed mechanism significantly reduces power consumption while maintaining network throughput and guaranteeing data urgency and queue stability.