1,721,102 research outputs found
Radio Map Interpolation using Graph Signal Processing
Full text linkInterpolating a radio map is a problem of great relevance in many scenarios such as network planning, network optimization and localization. In this work such a problem is tackled by leveraging recent results from the emerging field of signal processing on graphs. A technique for interpolating graph structured data is adapted to the problem at hand by using different graph creation strategies, including ones that explicitly consider NLOS propagation conditions. Extensive experiments in a realistic large-scale urban scenario demonstrate that the proposed technique outperforms other traditional methods such as IDW, RBF and model-based interpolation
A Framework for Storage-Accuracy Optimization of IoT Forensic Analysis
No full textThe proliferation of Internet of Things (IoT) devices, coupled with the recent popularity of machine-learning and artificial intelligence has given birth to a new research field named IoT forensics. Such a new field considers network traffic from IoT devices as possible source of evidence for forensic investigations. However, the massive amount of IoT devices and traffic produced makes storage challenging, especially when this is performed on limited-resource edge devices such as e.g., WiFi access points.
This paper proposes a framework to optimize the storage-accuracy trade-offs of IoT forensic analysis tasks.
The goal of the framework is to find the optimal working point in terms of number of features to extract from network traffic and the number of bits used for quantizing each feature, in order to maximize the IoT forensic task accuracy under storage constraints. After presenting the framework, we validate it over two different IoT forensics tasks: IoT device identification and activity recognition from encrypted traffic of IoT cameras. Results show that with low effort it is possible to find the optimal settings to operate to maximize the analysis accuracy under given storage limitations
Energy-accuracy trade-offs for hybrid localization using RSS and inertial measurements in wireless sensor networks
No full textThis paper presents a framework for optimizing the trade-off between energy consumption and localization accuracy in hybrid localization systems combining Received Signal Strength (RSS) measurements with inertial ones. The proposed framework aims at finding the optimal operation point that minimizes the radio energy consumption for a desired target accuracy, or equivalently, the one that maximizes the localization accuracy for a given energy budget. To this end, the proposed approach considers the joint optimization of the localization frequency and number of RSS measurements used at each localization round and leverages practical models to predict the energy consumption and the localization accuracy for combined RSS-inertial localization systems. Simulations and real-field experiments are used to demonstrate that, for a given target accuracy, the proposed strategy entails a lower energy consumption than state-of-the-art methods available in the literature
Designing a Broker Extension for Seamless CoAP and MQTT Interoperability
Full text linkThe Internet of Things (IoT) is a transformative paradigm facilitating connectivity among everyday objects and devices via the internet, simplifying industrial processes, and enhancing quality of life for individuals, while also fostering the development of innovative applications. However, the creation of an interconnected network of heterogeneous 'things' presents unique adaptation challenges in hardware and software characteristics, notably communication protocols, leading to major interoperability issues. This work addresses these challenges by proposing a solution for seamless integration of two widely adopted protocols, MQTT and CoAP, through the development of an interoperability broker extension. By effectively mapping REST and publish/subscribe mechanisms, this broker extension enables secure and seamless interaction between heterogeneous clients, utilizing a single broker structure and eliminating the need for additional middleware. Comprehensive testing demonstrates comparable performance in latency and memory resource consumption, validating the system as a promising solution to resolve IoT interoperability issues
Optimizing the placement of anchor nodes in RSS-based indoor localization systems2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)
No full textWe address the problem of optimizing the placement of anchor nodes for an indoor localization system based on a Wireless Sensor Network (WSN) when the Received Signal Strength (RSS) indicator is used as input of the localization algorithm. We consider as objective function the Cramer Rao Lower Bound (CRLB) as well as a simpler surrogate function, and we focus on the case where anchor nodes are constrained to lie on the walls of the buildings to monitor. For the problem version with the surrogate objective function, we propose two alternative mathematical programming formulations that are based on a discrete or continuous solution space. The resulting mixed-integer nonlinear problems (MINLP) can be solved to optimality for small-sized instances. For larger instances arising in practical scenarios, we propose a Tabu Search heuristic that provides near-optimal solutions in short computing time, and that can also directly minimize the CRLB
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