While smart metering applications have initially focused on energy and gas utility markets, water consumption has recently become the subject of increasing attention. Unfortunately, despite the large number of solutions available on the market, the lack of an open and widely accepted communication standard means that vendors typically propose proprietary data collection solutions whose adoption causes non-trivial problems to water utility companies in term of costs, vendor lock-in, and lack of control on the data collection infrastructure. There is the need for open and interoperable smart water metering solutions, capable of collecting data from the wide range of water meters on the market. This paper reports our experience in the development and field testing of a highly interoperable smart water metering solution, which we designed in collaboration with several water utility companies and which we deployed in Gorino Ferrarese, Italy, in collaboration with CADF (Consorzio Acque Delta Ferrarese), the water utility serving the city. At the core of our solution is SWaMM (Smart Water Metering Middleware), an interoperable wireless IoT middleware based on the Edge computing paradigm, which proved extremely effective in interfacing with several types of smart water meters operating with different protocols.

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The management of fog computing applications is a challenging task that requires dealing with a dynamic and resource-scarce environment. We argue that approaches leveraging Value-of-Information (VoI) concepts and tools are particularly interesting to support the realization of that objective. This paper describes innovative methodologies and reference models for the service fabric management of fog computing services. First, we formalize the VoI concept and discuss its adoption in fog computing environments. Then, we propose a model that aims at maximizing the allocation of fog services from a valuebased perspective. To overcome the complexity of this model, we present two possible solutions (a simplified user-specific VoI model and a distance-based heuristic), and we compare them by adopting meta-heuristics as optimization techniques. Then, we investigate the adoption of a hybrid model, which combines the distance-based heuristic with the simplified user-specific VoI model. Experimental results prove the validity of all presented approaches and highlight the soundness of the distance-based heuristic, which is capable of reaching the 91% performance of the simplified user-specific VoI model in a very short amount of time. This makes it a suitable approach for online optimization of resources in fog computing.

Fog Computing is a recent and compelling paradigm that proposes to run informationprocessing services at the edge of the network. While interesting standardization efforts in Fog Computing are being currently pursued by many organizations, most of them focus on management and orchestration functions, and primarily propose the adoption and adaptation of programming models designed for Cloud applications. Instead, Fog Computing applications would significantly benefit from innovative solutions that, on the one hand, adopt an ''acceptable lossiness'' perspective and manage information processing/dissemination in a dynamic and integrated way and, on the other hand, support a Multi Layer Routing (MLR) approach to exploit multiple routing options at different abstraction levels at the same time. This paper presents an overview of the opportunities and challenges of Fog Computing-based Internet of Things (IoT) applications by jointly exploiting acceptable lossiness and MLR. In addition, the paper proposes the innovative Holistic pRocessing and NETworking (HORNET) Software Defined Networking (SDN) solution which leverages an information-centric and value-based service model and the MLR approach to support IoT applications. The reported preliminary experimental results show the feasibility and effectiveness of the proposed approach. INDEX TERMS Fog computing, IoT, network softwarization, value-of-information (VoI).