Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

Abstract

Large number of embedded devices, massive volumes of data, users and applications are driving the digital world to move faster than ever. To be competitive in today's digital economy companies have to process large volumes of dynamically changing data at real-time. There are many industries from health-care, e-commerce, insurance and telecommunications with various use cases such as DNA sequencing, capturing customer insights, real-time offers, high-frequency trading, and real-time intrusion detection that have taken the use of Big Data analytics into account to make critical decisions that impact their business [1]. On the other hand, the Internet of Things (IoT) is becoming the primary grounds for data mining and Big Data analytics [2]. With the rapid growth of IoT and its use cases in different domains such as Smart City, Mobile e-Health and Smart Grid, streaming applications are driving a new wave of data revolutions. In most IoT applications the resulting analytics give some feedbacks to the system to improve it [3]. Compared to the other Big Data domains, there is a low-latency cycle between system

DSPBench: A Suite of Benchmark Applications for Distributed Data Stream Processing Systems

“…The main areas identified from the set of 50 resulting papers were: sensor networks, advertising, finance, telecommunication, social networks, synthetic applications, and network monitoring. The percentage of papers in our set belonging to the different areas is depicted in Figure 2 and the complete list can be found in Table 2 Area Papers Finance [25], [26], [27], [28] Network Monitoring [29], [30], [31], [32] Synthetic [33], [34], [35], [36], [37], [38] Traffic Monitoring [39], [40], [41], [42], [43] Advertising [29], [30], [15], [44], [45], [46], [47] Sensor Network [48], [49], [50], [51], [52], [53], [11], [54] Social Network [55], [56], [57], [58], [59], [60], [61], [62], [63], [18] Telecom…”

Big data actionable intelligence architecture

“…First, we set up a streaming interface connection for each data source. We utilized Apache Storm's topology [33] and Apache Kafka's [34] interprocess communication mechanism to implement our Data Pipelines because they are known to achieve a high level of scalability, low latency, fault-tolerant, and the data is guaranteed [35,36]. A general workflow of our data pipeline is depicted in Fig.…”

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