With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently utilize the ever increasingly crowded 2.4 GHz spectrum near the gateway. Specifically, to enable the communication from ZigBee to WiFi, we leverage WiFi's fine-grained channel state information to extract the concurrently transmitted ZigBee-to-WiFi message from time overlapped ZigBee and WiFi packet. We further leverage this unique feature and design a novel forwarding protocol that can simultaneously forward uplink (e.g., collecting sensing data) and downlink (e.g., disseminating control messages) data by using a single ZigBee data stream. Our extensive experimental results show that Amphista significantly improves throughput (by up to 400x) and reduces the latency.

Traditionally, the channelization structures of wireless technologies (802.11/ZigBee/BLE) have been fixed. Each node content for the spectrum is assigned one channel with a specific bandwidth. However, classical channel‐based spectrum sensing and sharing algorithms have great limitations to further optimize spectrum utilization when multiple IoT with different wireless technologies coexisting in the same environment. Therefore, exploring the fine‐grained spectrum sensing algorithm becomes an essential work to further improve the spectrum utilization efficiency, especially in the Industrial Scientific Medical (ISM) band. This paper proposes Subcarrier-Sniffer, a novel subcarrier‐level spectrum sensing and sharing method, which utilizes channel state information (CSI) to sense the fine‐grained status of each subcarrier of the traditional channel. To evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that the accuracy of subcarrier‐level spectrum sensing could achieve 100% in our settings that the distance between Subcarrier-Sniffer and the monitor is not greater than 7 m. Subcarrier-Sniffer could be applied in WiFi and ZigBee, WiFi and BLE, and WiFi and LTE‐U coexisted environments for better spectrum utilization.

With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of the IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently utilize the ever increasingly crowded 2.4 GHz spectrum near the gateway. Specifically, by using a single ZigBee data stream, Amphista enables a ZigBee device to send out two different pieces of information to both the WiFi gateway and another ZigBee device. We further leverage this unique feature and design a novel forwarding protocol that can simultaneously forward uplink (e.g., collecting sensing data) and downlink (e.g., disseminating software updates) data by using a single ZigBee data stream. Our extensive experimental results show that Amphista significantly improves throughput (by up to 400×) and reduces the latency.

The exponentially increasing number of Internet of Things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4-GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among Wi-Fi and Bluetooth low energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to Wi-Fi cross-technology communication while supporting the concurrent BLE to BLE and Wi-Fi to Wi-Fi communications. We conducted extensive experiments under different real-world settings, and results show that its throughput is more than 85X times higher than that of the most recently reported cross-technology communication system, which only supports one-way communication (i.e., broadcasting) at any specific time.

The exponentially increasing number of IoT devices makes the unlicensed industrial, scientific, and medical (ISM) radio bands (e.g., 2.4 GHz) extremely crowded. Currently, there is no efficient solution to coordinate the large amount heterogeneous IoT devices that have different communication technologies (e.g., WiFi and ZigBee). To fill this gap, in this paper, we introduce embedded multiple flows (EMF) communication method, which (i) embeds different pieces of information in existing traffic and (ii)concurrently sends out these information from one IoT sender to multiple IoT receivers that have a different communication technology from the sender. By doing this, our EMF method (i) enables cross-technology communication among heterogeneous IoT devices, (ii) does not introduce any extra control traffic, and (iii) is transparent to the higher layer applications. Our approach is implemented on USRPs and commercial off-the-shelf (COTS) ZigBee devices. We also conducted extensive experiments to evaluate our approach in real-world settings. The evaluation results show that EMF's throughput is more than 14 times higher than the latest cross-technology communication technique (i.e. FreeBee[1]).

The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.

With the development of WSN applications, there is increasing concern for the research of WSN. The routing algorithm is one of the important supporting technologies of WSN. However, the current studies of routing algorithms focus on the single performance. For instance, the aim of Energy Multipath Routing Algorithm is to maximize the network life cycle. And the aim of the Minimal-hop routing algorithm is to minimize the network transmission delay. This paper synthesizes the three performance indicators, namely network life cycle, network latency and network reliability to propose a Self-adaptive Routing Algorithm (SARA). SARA could satisfy different network requirements by adjusting the parameters. The experimental result shows that SARA can reduce the network latency, extend the network lifecycle and enhance the network reliability compared with the Multi-path energy efficient algorithm.