Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA’s random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by real-world link quality fluctuations and DRP’s passive acknowledgments. Guided by this analysis, we design EDRP, which integrates two novel components: (i) Link-Quality Aware CSMA (LQ-CSMA) and (ii) a Machine Learning-based Block Size Selection (ML-BSS) algorithm for rateless codes. LQ-CSMA dynamically restricts the back-off delay range based on real-time link quality estimates, ensuring that nodes with stronger connectivity experience shorter delays. ML-BSS algorithm predicts future link quality conditions and optimally adjusts the block size for rateless coding, reducing overhead and enhancing goodput. In-field evaluations of EDRP demonstrate an average goodput improvement of 39.43% than the competing protocols.

As the number of wireless devices supporting multiple communication interfaces increases, the connection redundancy is being considered for efficient bandwidth utilization and QoS improvement. Accordingly, network technologies must adapt to emerging multi-interface devices to improve network performance. Multipath TCP (MPTCP) is default multipath transport protocol desired for networks with multi-interface devices and has achieved success in computer networks. However, it has not been well studied for wireless networks, especially for carrier sense multiple access (CSMA) based wireless networks, which present great challenges to round trip time (RTT) computation and multipath scheduling. This paper introduces MPTCP techniques for heterogeneous WiFi and 5G networks. We first model a proposed 5-state congestion control algorithm and WiFi CSMA function. We then present an innovative RTT computation method and a novel loss-ware multipath scheduling mechanism. We evaluated the proposed MPTCP techniques under varying network configurations. Our MPTCP can significantly outperform conventional MPTCP.

This paper explores the transformative potential of the IoT paradigm in promoting smart agriculture. Key challenges lie in how to connect agriculture sensors to remote cloud servers in the absence of feasible communication infrastructure and the unreliable wireless links in rural areas. To address these issues, we propose an innovative two-tier smart agriculture architecture: an Unmanned Aerial Vehicle (UAV) aided agriculture network model, which leverages UAVs as intermediaries to collect and route data from agriculture sensors to cloud servers. This novel architecture leads to two particular problems, i.e., data packet scheduling in the first-tier networks and multi-hop routing in the second-tier UAV mesh network. To that end, we present formal Markov decision process (MDP) based problem formulations for both tiers, with a primary focus on the more challenging multi-hop routing problem in the second-tier network. This problem is approached as a multi-agent reinforcement learning (MARL) framework, for which we introduce a novel distributed algorithm - Focus Coordination: attention-guided Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG). This algorithm reduces communication overhead and mitigates the risks associated with single-node failures. We evaluated the performance of the proposed FC-MADDPG algorithm, demonstrating its efficacy in enhancing data transmission reliability and efficiency.

Sub-1 GHz (920 MHz) frequency bands for LPWAN (Low Power Wide Area Network) wireless communications systems are attracting attention from various IoT applications. Environmental and infrastructure monitoring systems, such as smart meter, ground inclinometer, and bridge sensor, are widely deployed. AsLPWAN systems operating on Sub-1 GHz bands can provide long distance communications,, a large number of network devices can connect to networks. Although these networks can be configured in a star configuration for a relatively small area, the mesh configuration has been emerging recently. IEEE 802.15.4g-FSK PHY/OFDM PHY is a typical PHY technology in mesh networks for the purpose of transferring IoT application data over a wider area. To distribute the same data such as firmware to LPWAN devices during network operation, improving distribution efficiency becomes critical. On the one hand, using broadcast transmission, the delivery confirmation cannot be performed. On the other hand, unicast transmission is very time consuming if the number of IoT devices is large. Therefore, we proposed a novel firmware distribution method using erasure code for large scale IoT networks. Our ns-3 simulations demonstrate that the proposed method can improve the distribution efficiency by up to 1.8 times compared to conventional methods and achieve higher spectrum efficiency for IEEE 802.15.4g-OFDM PHY.

Existing methods for diagnosing middle ear diseases using temporal bone computed tomography (CT) imaging primarily focus on adult datasets and require labor-intensive manual input from radiologists to label and select regions of interest (ROIs). These methods rely on prior knowledge and introduce inter- and intra-observer variability. Additionally, the selected ROIs typically consist of a few 2D slices, underutilizing the 3D capabilities of CT imaging. Moreover, these methods are not reproducible on pediatric datasets, where rapidly developing heads exhibit greater morphological variability. To address these challenges, we developed a fully automated framework for precise diagnosis of pediatric chronic suppurative otitis media (CSOM) and cholesteatoma (MEC) using temporal bone CT imaging. Our method automatically detects the most informative 3D ROIs by calculating entropy changes in the 3D anatomical structures of the middle ear, eliminating the need for annotations or prior knowledge. We also introduce a multi-scale classification network that incorporates a global-local feature interaction strategy and uses a wide and deep multi-layer transformer for feature extraction, effectively learning feature dependencies. Experimental results on a dataset of CT images from 603 pediatric patients show that our method achieves a classification accuracy of 93.17% and an AUC-ROC of 96.84%, outperforming state-of-the-art methods. This innovative approach reduces radiologists’ workload, aids automated surgical navigation, and has the potential to transform diagnostic workflows in otolaryngology.

With the advent of 5G and beyond communication technologies, the consumer IoT devices are evolving from current generation to next generation. Next generation IoT devices can support multiple communication interfaces and perform more functions. Accordingly, IoT network technologies must adapt to the emerging multi-link devices to improve network performance. Multipath TCP (MPTCP) is desired for networks with multi-link devices and has achieved success in computer networks. However, MPTCP has not been well studied for wireless networks. To that end, this paper presents MPTCP techniques for heterogeneous wireless IoT networks consisting of IEEE 802.15.4 nodes and 5G nodes. We propose a path builder, an adaptive congestion controller and an innovative path scheduler. We evaluated our MPTCP techniques under varying network configurations. Compared with conventional MPTCP, the proposed MPTCP can significantly reduce the number of packet transmissions, shorten packet delivery time, improve network throughput and packet delivery rate.

Sub-1 GHz Wireless Communications of LPWAN (Low Power Wide Area Network) are attracting attention in IoT applications. In addition to battery-powered devices, the number of grid-powered and solar-powered sensor devices using LPWAN are also rapidly increasing for various IoT applications. We aim to improve reliability and efficiency of IEEE 802.15.4 CSMA/CA mechanism in the network consisting of devices without power constraint. We propose Suspendable Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithms for IEEE 802.15.4 to mitigate packet loss by channel access failure while maintaining compatibility with conventional IEEE 802.15.4 CSMA/CA. We have performed extensive simulations to valid the Suspendable CSMA/CA mechanism. Simulation results show that the proposed Suspendable CSMA/CA improves Packet Delivery Rate (PDR) by 9.7 points (89.9 % to 99.6 %) compared to the conventional IEEE 802.15.4g CSMA/CA and therefore, can lead higher spectrum efficiency for IoT applications operate in the limited Sub-l G Hz wireless bandwidth. The proposed Suspendable CSMA/CA mechanism has been also approved and adopted for the next IEEE 802.15.4 amendment by IEEE 802.15 Working Group.

Along with the fourth industrial revolution, industrial automation systems are evolving into a multi-tier end-edge computing architecture. Edge controllers, which are equipped with a larger computing capacity compared to local controllers, can communicate with local plants over mainstream wireless networks such as WirelessHART, Wi-Fi, and cellular networks. Well-known challenges induced by networks, such as uncertain time delays and packet drops, have been intensively investigated from various perspectives: control synthesis, network design, or control and network co-design. The status quo is that the industry remains hesitant to close the loop between the edge controller and the actuation side due to safety concerns. This work offers an alternative perspective to address the safety concern, by exploiting the design freedom of an end-edge computing architecture. Specifically, we present a smart actuation framework, which deploys (1) an edge controller, which communicates with physical plant via wireless network, accounting for optimality, adaptation, and constraints by conducting computationally expensive operations; (2) a smart actuator, which is co-located with the physical plant on the end tier and executes a local control policy, accounting for system safety in the view of network imperfections, (3) the end-edge control co-design strategies and cooperation logic for both performance and stability. For certain classes of plants, semi-globally asymptotic stability of the resulting end-edge control systems is established when the edge controller is the model predictive control (MPC), or policy iteration-based learning control. We also provide an adaptation strategy for the end-edge control systems facing model parameter mismatches when the edge controller employs reinforcement learning. Extensive simulations demonstrate the advantages of the proposed end-edge co-design and cooperation procedures. Note to Practitioners—Edge computing is gaining momentum in areas that require low latency and high efficiency, i.e., mobile computing, video analytics, and autonomous driving. Industrial automation systems are also evolving into a multi-tier end-edge computing architecture. It pays obvious dividends to leverage the cooperation between end and edge, benefiting from fast and reliable communication on the end side, and powerful computation capacity on the edge side. The current end-edge cooperation focuses on how to partition tasks and offload computation resources in order to minimize delay and energy consumption, as well as how to balance the tradeoff between them. However, the impacts of end-edge cooperation on the safety, optimality, and cost of industrial automation have not been systematically studied. This paper aims to tailor end-edge cooperation in a smart actuation framework, for industrial automation to reconcile the above aspects by leveraging co-design of end and edge controllers and their switching logic. Extensive pure and semi-physical simulations demonstrate the advantages in performance and system stability of the proposed end-edge co-design and cooperation procedures.

With the advent of 5G and beyond communication technologies, the consumer IoT devices are evolving from current generation to next generation. The next generation IoT devices are capable of supporting multiple communication modes and performing more functions. During the migration phase, it is impractical to completely remove the deployed current generation devices. Accordingly, the next generation IoT networks will consist of the mixed current and next generation devices. To that end, how to efficiently route diverse data in next generation IoT networks needs to be addressed. This paper presents a two-topology routing architecture for next generation IoT networks, one topology for regular data delivery and another topology for priority data delivery. The priority routes are discovered to minimize route overlap. We evaluated our route discovery algorithms under varying network configurations. Compared with standard RPL baseline, the proposed routing algorithms can simultaneously reduce route overlap, route transmission time and route length.

The software distribution in advanced IoT networks is inevitable. However, distributing software in multi-hop wireless networks consumes enormous communication bandwidth and can also suffer from reliability challenge. This paper proposes an innovative dynamic relay point (DRP) protocol to reduce the number of software packet transmissions. It also introduces a network condition based rateless coding scheme to improve the packet transmission reliability. The NS3 simulator is employed for performance evaluation. The proposed DRP protocol outperforms multi-point relay (MPR) baseline by reducing the software packet transmissions and improving the effective throughput.

While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is urgently necessary to make it a reality. Motivated by this, we propose a novel mobility, communication and computation aware online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories, while the on-board processors can train machine learning models using the collected data. To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold. To satisfy this threshold, the central server accepts partially trained models, the distributed roadside units (a) perform downlink multicast beamforming to minimize global model distribution delay and (b) allocate optimal uplink radio resources to minimize local model offloading delay, and the vehicle agents conduct heterogeneous local model training. Using real-world vehicle trace datasets, we validate our FL solutions. Simulation shows that the proposed integrated FL platform is robust and outperforms baseline models. With reasonable local training episodes, it can effectively satisfy all constraints and deliver near ground truth multi-horizon velocity and vehicle-specific power predictions.

With the explosive proliferation of wireless devices, our lives are improved by various applications supported by heterogeneous wireless technologies, such as WiFi and ZigBee. However, the coexistence of WiFi and ZigBee also results in the degradation of the network performance, which cannot be avoided if the WiFi devices are even unaware of the ambient ZigBee devices. To better accommodate the heterogeneous wireless devices, this paper presents X-Disco, the first cross-technology neighbor discovery mechanism, for a WiFi device to detect ZigBee neighbors, without modification to hardware or firmware. With the help of the recently proposed cross-technology communication, X-Disco enables a commodity WiFi device to trigger responses, containing ZigBee neighbor information, from the ambient ZigBee coordinators (including routers). Through exploring the WiFi PHY-layer information accessible by WiFi driver, X-Disco decodes the responded ZigBee messages and obtains the ZigBee neighbor information. To improve X-Disco's reliability, we also propose ZigBee neighbor validation and interruption mitigation to exclude hidden node terminals and mitigate the interference caused by the ambient WiFi traffic respectively. The evaluation of X-Disco is performed on the commodity devices (TP-Link WDR 4300 WiFi router, TelosB motes) and USRP B210. The results demonstrate X-Disco successfully detects nine ZigBee neighbors within 70ms in the office.

In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted to reduce the transmission distance to users thereby increasing data-rates and reducing latency. To further increase the spectral efficiency and achieve flexible spatial degrees of freedom, we consider that a distributed remote radio unit system (dRRUS) is installed in each of the clusters. A key characteristic of deploying the dRRUS in private networks is the associated multipath-rich and asynchronous delay propagation environment. Therefore, we consider asynchronous multiple signal reception at the remote radio units and propose an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve the full transmit diversity gain without requiring full channel state information of the private network. The spectral efficiency of the proposed dCDD-based JT is analyzed by deriving a new closed-form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire network. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server and cluster master (CM), which is the main backhaul connection, and between the CM to remote radio units, which are the secondary backhaul connections. Thus, it is important for us to investigate the impact of reliability of main and secondary backhaul connections on the system. Our results show that the resulting composite backhaul connections can be accurately modeled by our proposed product of independent Bernoulli processes.

This paper proposes an anomaly detection algorithm for a factory automation system, which jointly performs data pre-processing and time-delay autoencoder (TDAE) with a hybrid loss function. The source data are pre-processed by digital filters before feeding into a TDAE for anomaly detection. The digital filters extract analog signals from a variety of frequency bands to facilitate identifying anomalies. The pre-processed data then takes time-delay reform to explore temporal relationship of data signals. In addition, two anomaly diagnosis algorithms, a statistical based method and an autoencoder based method, are presented. Numerical results show that time-delay reform can improve the anomaly detection accuracy compared to the conventional autoencoder. Data pre-processing can further improve the anomaly detection accuracy. Moreover, we confirm that our anomaly diagnosis algorithms outperform traditional method that does not perform data pre-processing and time-delay reform.

In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks, in which the use of multiple clusters or small cells is preferable to increase transmission speeds, reduce latency, and bring transmissions closer to the users. To increase the spectral efficiency and coverage, and to achieve flexible spatial degrees of freedom, a distributed remote radio unit system (dRRUS) is installed in each of the clusters. When the dRRUS is disposed in the private environments, it will be associated with multipath-rich and asynchronous delay propagation. Taking into account of this unique environment of private networks, asynchronous multiple signal reception is considered in the development of operation at the remote radio units to make an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve a full transmit diversity gain without full channel state information. A spectral efficiency of the proposed dCDD-based JT is analyzed by deriving the closed- form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire private network.

A novel multi-task federated learning (FL) framework is proposed in this paper to optimize the traffic prediction models without sharing the collected data among traffic stations. In particular, a divisive hierarchical clustering is first introduced to partition the collected traffic data at each station into different clusters. The FL is then implemented to collaboratively train the learning model for each cluster of local data distributed across the stations. Using the multi-task FL framework, the route planning is studied where the road map is modeled as a time-dependent graph and a modified A * algorithm is used to determine the route with the shortest traveling time. Simulation results showcase the prediction accuracy improvement of the proposed multi-task FL framework over two baseline schemes. The simulation results also show that, when using the multi-task FL framework in the route planning, an accurate traveling time can be estimated and an effective route can be selected.

To improve the traffic flow in the interconnected intersections, the vehicles and infrastructure such as road side units (RSUs) need to collaboratively determine vehicle scheduling while exchanging information via vehicle-to-everything (V2X) communications. However, due to a large number of vehicles and their mobility, scheduling in the interconnected intersection is a challenging problem. Moreover, since low-latency information exchange and real-time decision making process are required, it becomes more challenging to design a holistic framework incorporating traffic control and V2X communications. In this paper, an edge computing framework is proposed to solve a travel time minimization problem at the interconnected intersections. The proposed framework enables each RSU to decide intersection scheduling while the vehicles individually determine travel trajectory by controlling their dynamics. To this end, a V2X communications protocol is designed to exchange information among vehicles and RSUs. Then, the road segments around intersection are partitioned into sequence, control, and crossing zones. In the sequence zone, optimal time is scheduled for vehicles to pass the intersection with a minimum delay. In the control zone, the location and velocity of each vehicle are controlled to arrive the crossing zone at the scheduled time by using a control algorithm designed to effectively increase driving comfort and reduce fuel consumption. Thus, the proposed framework enables the vehicles to safely pass the crossing zone without collision. Simulation results show that the proposed edge computing can successfully reduce the total travel time by up to 14.3% based on optimal scheduling for the interconnected intersections.

Motivated by an increasing interest in wireless networking in mission-critical applications, and a recent amendment of the time slotted channel hopping to IEEE 802.15.4, the multichannel delay sensitive scheduling is investigated in the many-to-one network, which is also known as the convergecast network. In such a network, each node has data to be transmitted to a gateway through multi-hop communications. As a realistic setting, packet release time at each node is not assumed to be uniform. Under this assumption, the goal of this work is to design a scheduling scheme that minimizes the schedule length and maximum end-to-end delay, in which the former is essential for repetitive data acquisition, whereas the later improves the freshness of the acquired data. To achieve the scheduling goal, the problem is formulated as a multi-objective integer programming. To obtain a feasible solution and gain an insight into the problem, a lower bound on the schedule length is derived. Based on that, a new scheduling scheme is designed to minimize the two objectives simultaneously. Link level simulations verify the performance improvement of the proposed scheme over the existing schemes.

In this paper, a distributed cyclic delay diversity (dCDD) is proposed to the cooperative infrastructure-to-vehicle (I2V) system comprising one road side unit (RSU). At a particular time, to connect a RSU and a target vehicle outside each other's transmission range, a multiple number of vehicles located within the transmission ranges of both the RSU and the target vehicle are configured to operate as the dCDD based decode-and-forward (DF) cooperative relays. By using dCDD in the I2V system, the transmission range of the RSU is extended without the need of full channel state information of the vehicles at the RSU, and the transmit diversity gain can also be achieved. For this new preliminary setting of the I2V system, we conduct performance analysis. The simulations are conducted to verify the outage probability. The asymptotic outage diversity gain is also investigated and justified by the link level simulations.

The coordinated on-ramp merging is one of the most common but critical vehicular applications that require complex data transmission and low-latency communication in the Connected and Automated Vehicles (CAVs) environment. An effective way to address on-ramp merging is to leverage the edge computing to optimize the coordination among vehicles to achieve overall minimum vehicle travel time and energy consumption. In this study, we propose an Bi-level Optimal Edge Computing (BOEC) model for on-ramp merging in the CAVs environment to optimize both merge time and vehicle trajectory. The simulation results show that the proposed BOEC model achieves great benefits in vehicle mobility, energy saving and air pollutant emission reduction by providing an energy-efficient trajectory following the optimal merge time without compromising safety.

IEEE 802.11ah and IEEE 802.15.4g are two wireless technologies designed for outdoor IoT applications. Both technologies have communication range up to 1000 meters. Therefore, 802.11ah network and 802.15.4g network are likely to coexist. Our simulation results show that using standard defined coexistence mechanisms, 802.11ah network can severely interfere with 802.15.4g network and lead to significant packet loss in 802.15.4g network. As a result, additional coexistence control mechanisms are needed. Due to asymmetrical features such as modulation scheme and frame structure, 802.11ah devices and 802.15.4g devices cannot perform automatic cooperation. Thus, self-coexistence control techniques are preferred. This paper proposes learning based self-coexistence control techniques for 802.11ah devices to mitigate the interference impact of 802.11ah network on 802.15.4g network. We first present a α-Fairness based energy detection clear channel assessment (ED-CCA) method that enables 802.11ah devices to detect more ongoing 802.15.4g packet transmissions. We then introduce a Q-Learning based backoff mechanism for 802.11ah devices to avoid interfering with 802.15.4g packet transmission process. The proposed coexistence techniques can achieve fair spectrum sharing between 802.11ah network and 802.15.4g network.

This paper analyze the difference of LTE FDD and LTE TDD, including specification evolution, protocol stack, frame structure, synchronous signal, HARQ, beamforming, frequency band and so on. Mainly focuses on the network performance analysis of LTE-R in FDD and TDD, including coverage, capacity, network redundancy, service characteristic and high speed adaptability. Industry chain is also be compared, in the end the duplex mode selection advice of railway next generation wireless communication is proposed.

Wireless machine-to-machine (M2M) communications are widely considered as part of the Internet of Things (IoT) infrastructure. Resource management is crucial for heterogeneous M2M networks. In this paper, we present new distributed sleep management techniques to prolong network lifetime for multi-hop heterogeneous wireless M2M networks, which consist of battery-powered nodes and mains-powered nodes. We also propose two novel battery energy aware routing metrics to efficiently select routes that satisfy performance guarantees. Finally, we present an extensive performance evaluation of our sleep management techniques and routing metrics. Simulation results show that our schemes achieve high packet delivery rate, long network lifetime and low energy consumption even in very low percentage of mains-powered nodes.

Routing algorithm can significantly impact network performance. Routing in a network containing heterogeneous nodes differs from routing in a network with homogeneous nodes. If the routing algorithm is designed to fit less powerful nodes, the resources of more powerful nodes are wasted and network performance can be degraded. If the routing algorithm is developed to suit more powerful nodes, less powerful nodes may not have sufficient resources to run the algorithm and network may break down. Routing algorithms developed for homogeneous networks do not work well for heterogeneous networks. The IETF designed the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) by taking into account resource heterogeneity and defined four modes of operation. However, RPL only allows one mode of operation for all routers in a network. This paper proposes a resource-aware adaptive mode RPL (RAM-RPL) to achieve adaptive mode of operation in heterogeneous wireless machine-to-machine (M2M) networks. RAM-RPL not only allows routers to have mixed modes of operation in a network but also allows routers to adaptively adjust their modes of operation during network operation. Acting parent and acting root techniques are introduced to realize adaptive mode of operation and route compression. RAM-RPL exploits resource heterogeneity and shifts routing workload from less powerful nodes to more powerful nodes. Simulation results show that RAM-RPL can improve data packet delivery rate by 26% and reduce control message overhead by 53% while maintaining similar packet latency.

The IETF standardized the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) to meet routing requirements of the emerging applications. RPL is a distributed routing protocol and shows good scalability and fast network setup. However, RPL does not support sleep operation well. To provide efficient energy management and enhance RPL for sleep operation support, this paper presents battery energy management solutions for heterogeneous wireless machine-to-machine networks containing both battery powered nodes and mains powered nodes. We introduce a distributed sleep model for battery powered nodes to manage their own sleep schedules based on their internal parameters and observed network conditions. We propose two broadcast message delivery methods for battery operated networks that use distributed sleep control. Two battery node aware routing metrics are introduced to discover more battery energy efficient routes. We also present a battery energy efficient routing protocol called B-RPL to leverage distributed sleep model and introduced routing metrics. A battery energy efficient data packet transmission and forwarding method is provided to select the most battery energy efficient route among multiple active routes to transmit and forward data packets. Simulation results show that compared with standard RPL, the proposed B-RPL can extend network lifetime by two times and improve data packet delivery rate by 75%.

To design a routing protocol for applications over low-power and lossy networks (LLNs), the IETF ROLL Working Group standardized the IPv6 Routing Protocol for LLNs (RPL), which organizes nodes in a LLN into a tree-like topology called Destination Oriented Directed Acyclic Graph (DODAG). RPL shows good scalability and fast network setup. However, it may suffer from severe unreliability due to the selection of suboptimal routes with low quality links. To optimize the reliability of RPL routes, this paper proposes a stability metric based routing protocol named sRPL for reliable routing and data collection in LLNs. We introduce a new routing metric for RPL called stability index (SI), which exploits stability characteristics of RPL nodes to select more stable routes. In addition, we present a passive and lightweight network layer technique to measure the bi-directional expected transmission count (ETX) for wireless links in LLNs. As a use case of SI, we combine SI metric with ETX metric to make routing decisions. Simulation results show that sRPL can improve packet delivery rate of RPL routing protocol by 20%.

A voltage/current transient typically caused by islanding and switching operations is treated as an adverse phenomenon that degrades power quality, and it may cause damage to electrical equipment. Therefore, a reliable system should effectively detect and monitor a transient disturbance. In this paper, the transient detection problem is formulated as a binary hypothesis test: normal signal (null) vs. transient (alternative). The sampled data is described by a sinusoid under the null hypothesis, while a sum of damped sinusoids is utilized to model the alternative one. As no prior knowledge is imposed on complex amplitudes, frequencies, or damping factors in signal modeling, the general likelihood ratio test (GLRT) is employed to fulfill the task. To reduce computational complexity, the maximum likelihood estimator is replaced by ESPRIT for parameter estimation. Probability of detection of 0.98 is achieved at a SNR of 27dB and probability of false alarm of 0.0005.

The RPL routing protocol published in RFC 6550 was designed for efficient and reliable data collection in low-power and lossy networks. Specifically, it constructs a Destination Oriented Directed Acyclic Graph (DODAG) for data forwarding. However, due to the uneven deployment of sensor nodes in large areas, and the heterogeneous traffic patterns in the network, some sensor nodes may have much heavier workload in terms of packets forwarded than others. Such unbalanced workload distribution will result in these sensor nodes quickly exhausting their energy, and therefore shorten the overall network lifetime. In this paper, we propose a load balanced routing protocol based on the RPL protocol, named LB-RPL, to achieve balanced workload distribution in the network. Targeted at the low-power and lossy network environments, LB-RPL detects workload imbalance in a distributed and non-intrusive fashion. In addition, it optimizes the data forwarding path by jointly considering both workload distribution and link-layer communication qualities. We demonstrate the performance superiority of our LB-RPL protocol over original RPL through extensive simulations.