A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical

Internet of Things (IoT) is expected to play a major role in our lives through pervasive systems of sensor networks encompassing our environment. These systems are designed to monitor vital physical phenomena generating data which can be transmitted and saved at cloud from where this information can be accessed through applications and further actions can be taken. This paper presents the implementation and results of an environmental monitoring system which employs sensors for temperature and humidity of the surrounding area. This data can be used to trigger short term actions such as remotely controlling heating or cooling devices or long term statistics. The sensed data is uploaded to cloud storage and an Android application accesses the cloud and presents the results to the end users. The system employs Arduino UNO board, DHT11 sensor, ESP8266 Wi-Fi module, which transmits data to open IoT API service ThingSpeak where it is analyzed and stored. An Android application is developed which accesses the cloud and displays results for end users via REST API Web service. The experimental results show the usefulness of the system.

Wireless sensor networks (WSNs) are one of the chief enabling technologies for the Internet of Things. These networks are severely resource-constrained which calls for designing energy-efficient and effective routing techniques. The hierarchical-or clustering-based routing approaches have shown to improve both energy-efficiency and scalability in WSNs. However, when clustering is implemented in mobile WSNs (MWSNs), the mobility of sensor nodes results in high data loss due to possible dis-association of nodes with their cluster heads which negatively affects the data rates and energy consumption. In order to mitigate the impact of node mobility on clustering, we propose two mobility-aware hierarchical clustering algorithms for MWSN based on three-layer clustering hierarchy: mobility-aware centralized clustering algorithm (MCCA) and mobility-aware hybrid clustering algorithm (MHCA). The MCCA algorithm employs centralized gridding at both layers of clustering hierarchy, and the MHCA algorithm employs centralized gridding at the upper layer and distributed clustering at the lower layer. The simulation results show that our proposed algorithms improve network lifetime, reduce energy consumption, stabilize cluster formation, and enhance data rates in mobile sensor networks. We also observe that the centralized clustering approach is superior to the hybrid clustering approach.INDEX TERMS Distributed clustering, hierarchical clustering, Internet of Things, mobile sensor networks, node mobility.

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A data center is a facility for housing computational and storage systems interconnected through a communication network called data center network (DCN). Due to a tremendous growth in the computational power, storage capacity and the number of inter-connected servers, the DCN faces challenges concerning efficiency, reliability and scalability. Although transmission control protocol (TCP) is a time-tested transport protocol in the Internet, DCN challenges such as inadequate buffer space in switches and bandwidth limitations have prompted the researchers to propose techniques to improve TCP performance or design new transport protocols for DCN. Data center TCP (DCTCP) emerge as one of the most promising solutions in this domain which employs the explicit congestion notification feature of TCP to enhance the TCP congestion control algorithm. While DCTCP has been analyzed for two-tier tree-based DCN topology for traffic between servers in the same rack which is common in cloud applications, it remains oblivious to the traffic patterns common in university and private enterprise networks which traverse the complete network interconnect spanning upper tier layers. We also recognize that DCTCP performance cannot remain unaffected by the underlying DCN architecture hence there is a need to test and compare DCTCP performance when implemented over diverse DCN architectures. Some of the most notable DCN architectures are the legacy three-tier, fat-tree, BCube, DCell, VL2, and CamCube. In this research, we simulate the two switch-centric DCN architectures; the widely deployed legacy three-tier architecture and the promising fat-tree architecture using network simulator and analyze the performance of DCTCP in terms of throughput and delay for realistic traffic patterns. We also examine how DCTCP prevents incast and outcast congestion when realistic DCN traffic patterns are employed in above mentioned topologies. Our results show that the underlying DCN architecture significantly impacts DCTCP performance. We find that DCTCP gives optimal performance in fat-tree topology and is most suitable for large networks.