The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information-and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes.
Representing an algorithmic workflow as a state machine is a frequently used technique in distributed systems. Replicating a state machine in a fault tolerant way is one of the main application areas under this context. When implementing a replicated state machine, a crucial problem is to maintain consistency among replicas that might handle various different requests arriving at each different replica. This problem requires maintaining a single consistent ordering of the distributed requests handled separately by replicas. Basic consensus protocols such as two phase commit (2PC), can be used to maintain consistency between replicas whenever a request is to be processed. In this study we modify 2PC protocol to take advantage of basic properties of a state machine and detect possible write conflicts earlier. Our experiments on distributed cloud environments show that our modified 2PC protocol increases the throughput and decrease wasted write operations by a significant amount.
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