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This paper considers the centralized spectrum allocations in resource-constrained wireless sensor networks with the following goals: (1) allocate spectrum as fairly as possible, (2) utilize spectrum resource maximally, (3) reflect the priority among sensor data, and (4) reduce spectrum handoff. The problem is formulated into a multi-objective problem, where we propose a new approach to solve it using modified game theory (MGT). In addition, cooperative game theory is adopted to obtain approximated solutions for MGT in reasonable time. The results obtained from numerical experiments show that the proposed algorithm allocates spectrum bands fairly with well observing each sensor's priority and nearly minimal spectrum handoffs.

Biomedical sensor networks have been widely used in medical scenarios. Examples include patient monitoring, elderly assistance and disaster response. In medical applications, where data packets usually contain vital sign information and the network used for communications should guarantee that these packets can be delivered to the medical center within a given time and a certain packet delivery ratio. In other words, a set of Quality of Services (QoS) must be satisfied.In this paper, a cross-layer designed QoS-aware routing service framework is proposed. The main goal of the framework is to provide prioritized routing service and user specific QoS support. Routes are determined by user specific QoS metrics, wireless channel status, packet priority level, and sensor node's willingness to be a router. Furthermore, the routing service can send feedback on network conditions to the user application, so the medical application service level can be adjusted to obtain the highest adaptability and robustness.Simulation results have shown that the routing service framework performs well in respects of QoS metrics and energy efficiency in various medical scenarios. The routing service can provide guaranteed QoS for users of high priority level and acceptable network performances for 'best effort' required users.

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Considering that the uncertain linguistic variable (or interval linguistic term) has some limitations in calculation, we extend it to the continuous interval-valued linguistic term set (CIVLTS), which is equivalent to the virtual term set but has its own semantics. It has the advantages of both the uncertain linguistic variable and the virtual term set but overcomes their defenses. It not only can interpret more complex assessments by continuous terms, but also is effective in aggregating the group opinions. We propose some methods to aggregate the individual decision matrices represented by CIVLTSs to the collective matrix. The extended Gaussian-distribution-based weighting method is proposed to derive the weights for aggregating the large group opinions. Furthermore, the general ranking method ORESTE, is extended to the CIVL environment and is named as the CIVL-ORESTE method. The proposed method is excellent by no requirements of crisp criterion weights and the objective thresholds. A case study of selecting the optimal innovative sharing bike design for the "Mobike" sharing bikes is operated to show the practicability of the CIVL-ORESTE method. Finally, we compare the CIVL-ORESTE method with other ranking methods to illustrate the reliability of our method and its advantages.

A Biomedical Sensor Network (BSN) is a small-size sensor network for medical applications, that may contain tens of sensor nodes. In this paper, we present a formal model for BSNs using timed automata, where the sensor nodes communicate using the Chipcon CC2420 transceiver (developed by Texas Instruments) according to the IEEE 802.15.4 standard. Based on the model, we have used UPPAAL to validate and tune the temporal configuration parameters of a BSN in order to meet desired QoS requirements on network connectivity, packet delivery ratio and end-to-end delay. The network studied allows dynamic reconfigurations of the network topology due to the temporally switching of sensor nodes to power-down mode for energy-saving or their physical movements. Both the simulator and modelchecker of UPPAAL are used to analyze the average-case and worst-case behaviours. To enhance the scalability of the tool, we have implemented a (new text-based) version of the UPPAAL simulator optimized for exploring symbolic traces of automata containing large data structures such as matrices. Our experiments show that even though the main feature of the tool is model checking, it is also a promising and competitive tool for efficient simulation and parameter tuning. The simulator scales well; it can easily handle up to 50 nodes in our experiments. The model checker installed on a notebook can also deal with networks with 5 up to 16 nodes within minutes depending on the properties checked; these are BSNs of reasonable size for medical applications. Finally, to study the accuracy of our model and analysis results, we compare simulation results by UP-PAAL for two medical scenarios with traditional simulation techniques. The comparison shows that our analysis results coincide closely with simulation results by OMNeT++, a widely used simulation tool for wireless sensor networks.£ The work is supported by EC IST project CREDO.All models for the experiments of this work can be found at

In recent years, sustainable supply chains that balance economic development and the environment have become an inevitable focus for many businesses and industries. Supply chain finance as the core driving force for supply chain development, plays a vital role in resolving any financing difficulties that exist in many small and medium-sized enterprises (SMEs) in the upstream and downstream of the supply chain. However, most SME supply chain financing assessments currently use economic indicators as the sole measure of the evaluation system and rarely consider sustainability. While existing supply chain financing decision-making systems can resolve SME financing problems to some extent, the one-sided pursuit of maximum economic benefits is contrary to sustainable development and does not assist financial institutions in avoiding finance risks. Therefore, this paper, based on the theory of the triple bottom line (economy, environment, and society) from a sustainable development perspective, innovatively proposes an SME financing evaluation model for supply chain finance that applies a fuzzy multi-criteria evaluation method combined with Topsis. Additionally, at the end, an example is given to demonstrate model validity and evaluate the best possible SME financing model for financial institutions.