Architectural design of IoT-cloud computing integration platform

Message Queue Telemetry Transport (MQTT) is a protocol with publish/subscribe interactions schemes and have a lightweight feature that suited for a constrained environment such in Wireless Sensor Network. This Paper presents the result of MQTT Protocols implementation in a Wireless Sensor Network (WSN) where Arduino and NRF24L01 were acted as WSN nodes, while the gateway consists of Arduino, NRF24L01 and ESP8266 Wi-Fi module as the IP based interface to the MQTT Broker. Sensor node and gateway interaction schemes were built using MQTT-SN protocols with different quality of Service (QoS) types which are QoS 0, QoS 1, and QoS 2. This paper measured several parameters which are gateway discovery process, the total number of packet loss in QoS0, data duplication along with total number of re-tries and Round-Trip Time (RTT) delay in QoS1 & QoS2. Based on the experiment, this research found several results. First, the average time for gateway discovery process is 1123.40 ms. Second, The QoS 0 is the fastest publish type protocol. However, the QoS 0 is unreliable. The QoS 0 publish type is having the most packet loss message based our experiment. Third, The QoS1 packet loss is better than QoS0 but the data duplication and several numbers of re-tries are found. Last, The QoS2 is the slowest publish type but most reliable publish message transfer. The experiment result that no packet loss occurred and the duplication data can be ignored by the gateway.

Analisis Availabilitas dan Reliabilitas Multi-Master Database Server Dengan State Snapshot Transfers (SST) Jenis Rsync Pada MariaDB Galera Cluster

In the context of edge computing, a cluster of small single-board computers like Raspberry Pi could serve as robust servers. These clusters not only offer robust server capabilities but also exhibit a remarkable versatility in handling incoming data streams from a multitude of sensors within the Internet of Things (IoT) ecosystem, particularly in underserved rural areas. Simultaneously, they can seamlessly double up as servers for web-based applications tailored to the specific requirements of small businesses. However, the operational context of such an edge computing cluster can present challenges. For instance, dynamic load fluctuations, ranging from high to low demands, may lead to performance service degradation or underutilized services. This is a typical problem in distributed computing environments, where the heterogeneity of devices, dynamic conditions, and reliability of connections can create scalability issues. This paper addresses these challenges through a selective set of integrated software suites aiming to autoscale an edge computing cluster. The software suites consist of a lightweight Kubernetes distribution called K3s, with an automation framework executed through Ansible. Rigorous testing, primarily focused on web-based applications, has showcased the efficacy of this approach. A compelling comparison has been drawn between this optimized edge computing setup and conventional desktop-based servers, emphasizing superior power efficiency and commendable performance levels. The service scaling can reduce power consumption by up to 45%.

Recent developments in Wireless Sensor Networks (WSN) focus on scalability and reliability. This research addresses the challenge of improving reliability in WSNs through optimal relay placement and multipath topology design. A heuristic method with a Multi-Objective Optimization (MOO) approach is proposed to solve this problem. The proposed method integrates a modified Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) characteristics. The hybrid approach aims to minimize the number of relays and associated communication costs while maintaining network reliability. The method encodes relay positions and quantities into GA chromosomes that are updated by mutation, crossover, and PSO-inspired particle motion. Simulations are performed in a simplified square area with twenty randomly placed sensors, a hundred and thirty-two arranged relays, and a single sink node. As a result, the simulation generated two multipath topologies that offer unique advantages. The first emphasizes relay efficiency (61 relays, with 2096 costs), while the second ensures lower communication costs (64 relays, 1832 costs). Comparisons with alternative algorithms, including Dijkstra, A-star, GA, and PSO, prove the superiority of the proposed approach. The optimum results obtained with a composition of 95% GA and 5% PSO, outperform alternative algorithms in terms of relay efficiency and communication cost. This research contributes to the field by providing a robust solution for designing reliable multipath WSNs with a minimum number of relays.

Wireless sensor networks (WSNs) utilize relay nodes (RNs) to enable multi-hop data transmission between sensor nodes (SNs) and the sink. Optimal RNs placement is crucial for ensuring network connectivity, reliability, and efficiency. WSN like undirected graph, so to optimal the RNs placement, can use shortest path algorithm. This paper investigates RNs placement optimization in WSNs using the A* algorithm in dual home of multi-tier architecture. Dual home architecture will address data transmission issues so that data will always be sent without being affected by dead relays. Multi-tiered architecture will solve problems over a large area, because data will be passed from SNs to nearby RNs to sinks. Simulations compare random, rectangular, and triangular RNs distribution patterns over varying area sizes, SN counts, and RNs counts. Results demonstrate the significant impact of area size and RNs quantity on connectivity metrics. Larger areas and fewer RNs dramatically reduce connected SNs and RNs connectivity. To Triangular pattern provide efficient connectivity versus rectangular pattern in most scenarios. Random pattern provided efficient connectivity in small area. The A* algorithm successfully optimizes RNs positions for connectivity across patterns and scenarios. This research provides insights into factors influencing optimal RNs placement and demonstrates the suitability of A* optimization for enhancing WSN reliability and performance.