Many industrial wireless sensor network (IWSN) applications require real-time communications in which bounded delay requirements need to be satisfied. IWSN lossy links and limited resources of sensor nodes pose significant challenges for supporting real-time applications. Many IWSN routing algorithms focus on being energy efficient to extend the network lifetime, but the delay wasn't the main concern. However, these algorithms are unable to deal with real-time applications in which data packets need to be delivered to the sink node within a predefined real-time information. On the other hand, the most existing real-time routing schemes are often based on the desired deadline time (required delivery time) and end-to-end distance in the selection of forwarding node while the reliability of on-time data delivery, the effects of a collision, energy balance, and a number of a hop count to the sink node have largely been ignored. These issues can dramatically impact real-time performance. Therefore, the paper proposes a routing algorithm that achieves a balance between energy efficiency and reliability while being suitable for real-time applications as well. In addition, it reduces the effects of congestion by sufficiently utilizing the underloaded nodes to improve network throughput. Finally, the hop count to the sink is considered. This paper formulates the real-time routing problem into 0/1 Integer Linear Programming (ILP) problem and then proposes a Realtime Energy-Efficient Traffic-Aware approach (RTERTA) to solve the optimization problem for a large-scale IWSN. From the obtained simulation results, the proposed solution has proved to be able to enhance the network performance in terms of packets miss ratio, average end-to-end delay, packets delivery rate, as well as network lifetime.

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms.

Object tracking is one of the most important applications in wireless sensor networks (WSNs). Many recent articles have been dedicated to localization of objects; however, few of these articles were concentrated on the reliability of network data reporting along with objects localization. In this work, the authors propose an efficient data reporting method for object tracking in WSNs. This paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in the authors' model where the sensed data can reach the sink node in a more reliable way. This work first formulates the problem as 0/1 Integer Linear Programming (ILP) problem, and then proposes a SWARM intelligence for solving the optimization problem. Through simulation, the performance of proposed method to report information about the detected objects to the sink is compared with the previous works related to the authors' topic, such as LR-based object tracking algorithm, SEB, EPWSN, and ACO.