In this paper, we present results of a study of the data rate fairness among nodes within a LoRaWAN cell. Since LoRa LoRaWAN supports various data rates, we firstly derive the fairest ratios of deploying each data rate within a cell for a fair collision probability. LoRa LoRaWAN, like other frequency modulation based radio interfaces, exhibits the in which only the stronger signal of colliding signals will be extracted. This leads to unfairness, where far nodes or nodes experiencing higher attenuation are less likely to see their packets received correctly. Therefore, we secondly develop a transmission power control algorithm to balance the received signal powers from all nodes regardless of their distances from the gateway for a fair data extraction. Simulations show that our approach achieves higher fairness in data rate than the state-of-art in almost all network configurations.
Authors in @cite_23 proposed two @math allocation approaches, namely EXP-SF and EXP-AT, to help LoRaWAN achieve a high overall data rate. EXP-SF equally allocates @math to @math nodes based on the Received Signal Strength Indicator (RSSI), where the first @math nodes with the highest RSSI get @math assigned and then the next @math nodes @math and so on. EXP-AT is more dynamic than EXP-SF, where the @math allocation theoretically equalizes the airtime of nodes. The two aforementioned works @cite_6 and @cite_23 assumed perfectly orthogonal @math , which leads to a higher overall data rate than in reality.
@cite_6 @cite_23
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LoRaWAN is emerging as an attractive network infrastructure for ultra low power Internet of Thing devices. Albeit the technology itself is quite mature and specified, how to effectively allocate wireless resources so as to support a large amount of devices in a same terrestrial area is an open challenge. This paper contributes by proposing two algorithms (of incremental complexity) which are shown to outperform the basic Adaptive Rate Strategy (ADR) so far considered. A first approach, named EXPLoRa-SF, shows the benefits of a simple strategy which does not limit to use (as per ADR) distance RSSI measurements, but also selects Spreading Factors (SF) based on the total number of connected devices. The advantages attained with EXPLoRa-SF further lead us to propose a more sophisticated algorithm, named EXPLoRa-AT, which employs an innovative “ordered waterfilling” approach which attempts to allocate the spreading factors so as to equalize the Time on Air of the packets transmitted by the system's end devices in each spreading factor's group. Simulation results show that the proposed algorithms significantly outperform the basic ADR strategy, and particularly the EXPLoRa-AT algorithm appears very robust to different operating conditions and consistently guarantees high bit rates in the case of high traffic loads. In this paper, we propose a joint subchannel and power allocation algorithm for the downlink of an orthogonal frequency-division multiple access (OFDMA) mixed femtocell macrocell network deployment. Specifically, the total throughput of all femtocell user equipments (FUEs) is maximized while the network capacity of an existing macrocell is always protected. Towards this end, we employ an iterative approach in which OFDM subchannels and transmit powers of base stations (BS) are alternatively assigned and optimized at every step. For a fixed power allocation, we prove that the optimal policy in each cell is to give each subchannel to the user with the highest signal-to-interference-plus-noise ratio (SINR) on that subchannel. For a given subchannel assignment, we adopt the successive convex approximation (SCA) approach and transform the highly nonconvex power allocation problem into a sequence of convex subproblems. In the arithmetic-geometric mean (AGM) approximation, we apply geometric programming to find optimal solutions after condensing a posynomial into a monomial. On the other hand, logarithmic and d ifference-of-two- c oncave-functions (D.C.) approximations lead us to solving a series of convex relaxation programs. With the three proposed SCA-based power optimization solutions, we show that the overall joint subchannel and power allocation algorithm converges to some local maximum of the original design problem. While a central processing unit is required to implement the AGM approximation-based solution, each BS locally computes the optimal subchannel and power allocation for its own servicing cell in the logarithmic and D.C. approximation-based solutions. Numerical examples confirm the merits of the proposed algorithm. Background: We investigate direct use of 802.15.4 radio signal strength indication (RSSI) for human activity recognition when 1) a user carries a wireless node (device-bound) and when 2) a user moves in the wireless sensor net (WSN) without a WSN node (device-free). We investigate recognition feasibility in respect to network topology, subject and room geometry (door open, half, closed). Methods: In a 2 person office room 8 wireless nodes are installed in a 3D topology. Two subjects are outfitted with a sensor node on the hip. Acceleration and RSSI are recorded while subject performs 6 different activities or room is empty. We apply machine learning for analysis and compare our results to acceleration data. Results: 10-fold cross-validation with all nodes gives accuracies of 0.896 (device-bound), 0.894 (device-free) and 0.88 (accelerometer). Topology investigation reveals that similar accuracies may be reached with only 5 (device-bound) or 4 (device-free) selected nodes. Applying trained data from one subject to the other and vice-versa shows higher recognition difference on RSSI than on acceleration. Changing of door state has smaller effect on both systems than subject change; with least impact when door is closed. Conclusion: 802.15.4 RSSI suited for activity recognition. 3D topology is helpful in respect to type of activities. Discrimination of subjects seems possible. Practical systems must adapt no only to long-term environmental dispersion but consider typical geometric changes. Adaptable, robust recognition models must be developed. Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.