Industrial applications of wireless sensor networks require timeliness in exchanging messages among nodes. Although IEEE 802.15.4 provides a superframe structure for real-time communication, a real-time message-scheduling algorithm is still required to schedule a large number of real-time messages to meet their timing constraints. We propose a distance-constrained real-time offline message-scheduling algorithm which generates the standard specific parameters such as beacon order, superframe order, and guaranteed-time-slot information and allocates each periodic real-time message to superframe slots for a given message set. The proposed scheduling algorithm is evaluated and analyzed extensively through simulations. In addition, a guaranteed time service is implemented in a typical industrial sensor node platform with a well-known IEEE 802.15.4-compliant transceiver CC2420 and ATmega128L to verify the feasibility of the guaranteed time service with the schedule generated by the proposed scheduling algorithm. Through experiments, we prove that the real system runs accurately according to the schedule calculated by the proposed algorithm.

ZigBee is the emerging industrial standard for ad hoc networks based on IEEE 802.15.4. Due to characteristics such as low data rate, low price, and low power consumption, ZigBee is expected to be used in wireless sensor networks for remote monitoring, home control, and industrial automation. Since one of the most important goals is to reduce the installation and running cost, ZigBee stack is embedded in small and cheap micro-controller units. Since tree routing does not require any routing tables to send the packet to the destination, it can be used in ZigBee end devices that have limited resources. However, tree routing has the problem that the packets follow the tree topology to the destination even if the destination is located nearby. We propose the shortcut tree routing protocol to reduce the routing cost of ZigBee tree routing by using the neighbor table that is originally defined in the ZigBee standard. While following the ZigBee tree routing algorithm, we suggest forwarding the packet to the neighbor node if it can reduce the routing cost to the destination. Simulation results show that the shortcut tree routing algorithm saves more than 30 percent of the hop count compared with ZigBee tree routing.

This paper describes the results of real deployment of A S which consists of WSN(Wireless Sensor Network) to monitor and control the environments and a management sub-system to manage the WSN and provide various and convenient services to consumers with hand-held devices such as a PDA living a farming village. The WSN were deployed in greenhouses with melon and cabbage in Dongbu Handong Seed Research Center. A S was used to monitor the growing process of them and control the environment of the greenhouses. We acquired valuable experiences and ideas from this real deployment and operation of A S and believe that they can be useful in consumer electronics field such as home network as well as automated agriculture field. .

The ZigBee tree routing is widely used in many resource-limited devices and applications, since it does not require any routing table and route discovery overhead to send a packet to the destination. However, the ZigBee tree routing has the fundamental limitation that a packet follows the tree topology; thus, it cannot provide the optimal routing path. In this paper, we propose the shortcut tree routing (STR) protocol that provides the near optimal routing path as well as maintains the advantages of the ZigBee tree routing such as no route discovery overhead and low memory consumption. The main idea of the shortcut tree routing is to calculate remaining hops from an arbitrary source to the destination using the hierarchical addressing scheme in ZigBee, and each source or intermediate node forwards a packet to the neighbor node with the smallest remaining hops in its neighbor table. The shortcut tree routing is fully distributed and compatible with ZigBee standard in that it only utilizes addressing scheme and neighbor table without any changes of the specification. The mathematical analysis proves that the 1-hop neighbor information improves overall network performances by providing an efficient routing path and distributing the traffic load concentrated on the tree links. In the performance evaluation, we show that the shortcut tree routing achieves the comparable performance to AODV with limited overhead of neighbor table maintenance as well as overwhelms the ZigBee tree routing in all the network conditions such as network density, network configurations, traffic type, and the network traffic.

As small-scale fading is a spatial phenomenon, the movement of objects in the environment around static sensor nodes can induce significant fades. However, there have not been many works characterizing small-scale fading due to environmental factors for ground-surface wireless communications. We first measure the temporal fading characteristics experienced by antennas located just 1.5 cm above the surface of the ground due to wind-blown foliage or human movement in the environment for a narrowband channel in the 400-MHz frequency band. We then compare the extracted data to existing distributions and show that fading due to wind-blown foliage can be modeled as a Nakagami-m distribution, with wind speed and excess path-loss-dependent m shape factors. The α-μ distribution best characterizes the small-scale fading of a single human pedestrian, which is shown to have a repeatable pattern and can be up to 40 dB below the no-fading mean, whereas the Rician distribution, with an excess path-loss-dependent K-factor, can be used to characterize fading from multiple human pedestrians. We also report the second-order statistics of the average fade duration and the level crossing rate for fading caused by wind-blown foliage and multiple human pedestrians. Finally, we discuss the significance of the results on wireless sensor network protocol design and applications.

The increasing volume of traffic has led to severe challenges, including traffic congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like traffic management, their primary focus has been on single-agent setups, limiting their applicability to real-world multi-agent systems. Managing agents and fostering collaboration in a multi-agent reinforcement learning scenario remains a challenging task. This paper introduces a cooperative multi-agent federated reinforcement learning algorithm named FLDQN to address the challenge of agent cooperation by solving travel time minimization challenges in dynamic multi-agent reinforcement learning (MARL) scenarios. FLDQN leverages federated learning to facilitate collaboration and knowledge sharing among intelligent agents, optimizing vehicle routing and reducing congestion in dynamic traffic environments. Using the SUMO simulator, multiple agents equipped with deep Q-learning models interact with their local environments, share model updates via a federated server, and collectively enhance their policies using unique local observations while benefiting from the collective experiences of other agents. Experimental evaluations demonstrate that FLDQN achieves a significant average reduction of over 34.6% in travel time compared to non-cooperative methods while simultaneously lowering the computational overhead through distributed learning. FLDQN underscores the vital impact of agent cooperation and provides an innovative solution for enabling agent cooperation in a multi-agent environment.