International Journal of Communication Systems最新文献

In wireless sensor networks (WSNs), trusted routing path needs to be determined for guaranteeing reliable data dissemination with maximized Quality of Service (QoS). But the sensor nodes may not exhibit a cooperative behavior for the objective of conserving energy and remaining active in the network. Trust management techniques are essential for alleviating the problem of packet dropping attacks of sensor nodes that intentionally deteriorates the performance of the network. In this paper, Hybrid ELECTRE and bipolar fuzzy PROMOTHEE-based trust management (HEBFPTM) scheme is proposed for addressing the impact of packet dropping attacks that targets on improving QoS in WSNs. This is proposed as a multi-criteria decision analysis solution for obtaining feasible number of parameters that could be derived from the sensor nodes of the network to determine its cooperation degree in the network. This HEBFPTM is proposed with the objective of integrating the ordinal evaluation of mobile nodes into a cardinal procedure using the method of PROMETHEE to attain quantitative and qualitative analysis that aides in identifying the weights of each criterion considered for cooperation determination using pairwise comparison. It adopted three preference models using partial, complete, and outranking through intervals. It handled the problem of uncertainty using the merits of bipolar fuzzy that helped in attaining the weight of the criteria and preference functions used for ranking the sensor nodes in the routing path. The experiments of the proposed HEBFPTM achieved using ns-2 simulator confirmed its efficacy in improving the attack detection rate by 21.38%, reduced false positive rate by 15.42%, maximized packet delivery rate of 18.94%, and reduced energy utilization of 19.84% better than the benchmarked approaches used for investigation.

The sensor networks are the primary and essential components on which the world of Internet of Things (IoT) is built. IoT empowers smart communication, computation, and sensing capabilities. In sensor networks, the data are collected by the sensor nodes and sent to the sink along a communication path. These communication paths are collaboratively established by the nodes and the sink. By incorporating energy-efficient data gathering techniques, the lifetime of these networks is improved. The major contribution of the study in this work is to provide a survey of various techniques for data aggregation (DA) and the employed algorithmic strategies that facilitate and influence network lifetime (NL) in these environments. DA in wireless sensor networks (WSN), IoTs, and cloud computing extend the lifetime of these networks since it enables efficient merging of traffic flows, thus reducing transmissions and energy consumption of devices. In sensor networks, data aggregation tree (DAT)-based routing facilitates energy-efficient routing that extends NL. NL maximization using DATs constructs DATs with optimal NL and is a known NP-complete problem. Subsequently, the study in this work surveys the various approaches employed by researchers to construct DATs and discusses techniques for DAT scheduling. This work further explores various sensor deployment techniques and discusses real world scenario in which NL is influenced by uncertainty in communication links. Finally, the study in this survey highlights the achievements in realizing NL improvement using DAT and identifies the limitations and research challenges.

The long-range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy-intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy-efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter-based ADR (KF-ADR) and moving median-based ADR (Median-ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio.

It is estimated that there will be over two dozen billion Internet of Things (IoT) connections in the future as the number of connected IoT devices grows rapidly. Due to characteristics like low power consumption and extensive coverage, low-power wide area networks (LPWANs) have become particularly relevant for the new paradigm. Long range wide area network (LoRaWAN) is one of the most alluring technological advances in these networks. Although it is one of the most developed LPWAN platforms, there are still unresolved issues, such as capacity limitations. Hence, this research introduces a novel resource scheduling technique for the LoRAWAN network using deep reinforcement learning. Here, the information on the LoRaWAN nodes is learned by the reinforcement technique, and the knowledge is utilized to allocate resources to improve the packet delivery ratio (PDR) performance through a proposed coati optimal Q-reinforcement learning (CO_QRL) model. Here, Q-reinforcement learning is utilized to learn the information about nodes, and the coati optimization algorithm (COA) helps to choose the optimal action for enhancing the reward. In the proposed scheduling algorithm, the weighted sum of successfully received packets is treated as a reward, and the server allocates resources to maximize this Q-reward. The evaluation of the proposed method based on PDR, packet success ratio (PSR), packet collision rate (PCR), time, delay, and energy accomplished the values of 0.917, 0.759, 0.253, 85, 0.029, 7.89, and 10.08, respectively.

Wireless sensor networks (WSNs) consist of numerous sensor nodes with limited battery life, computational power, and network capabilities. These sensors are deployed in specific areas to monitor environmental physical parameters. Once the data are collected, it is processed and transmitted to a base station (BS) via designated routes. The processes of sensing and transmitting consume significant energy, leading to rapid depletion of node batteries and the occurrence of hot spot problems. Consequently, relying on a single route for data transmission can result in network overhead issues. Enhancing the energy efficiency of WSNs is a persistent challenge. To address this, improvements in processes, such as routing and clustering are necessary. Implementing dynamic cluster head (CH) selection is a key approach for optimal path selection and energy conservation. Accordingly, in this work, a novel multiobjective CH selection and routing method for providing energy-aware data transmission in WSN is presented. Here, CH selection is carried out using the proposed chronological wild geese optimization (CWGO) technique based on multiple constraints, such as delay, intercluster distance, intracluster distance, Link Life Time (LLT), and predicted energy. Further, the nodes' energy is determined by the deep recurrent neural network (DRNN). Then, the ideal path from the node to the BS is identified by the CWGO considering constraints, like predicted energy, delay, distance, and trust. Moreover, the proposed CWGO is examined considering metrics, like energy, trust, distance, and delay and is found to have attained superior values of 0.963 J, 0.700, 19.468 m, and 0.252 s, respectively.

Node localization in wireless sensor networks (WSNs) ensures that the collected data is contextually accurate, enabling effective monitoring and management of various applications. Recently, there has been a surge in research focused on addressing node localization within WSNs. Emerging trends in this field involve the application of metaheuristic optimization techniques to refine node location determination accuracy. However, existing techniques often struggle with balancing accuracy, energy consumption, network lifetime, and computational efficiency, particularly in challenging WSN environments. Therefore, this research introduces a novel approach called efficient hybrid bat sand cat swarm optimization (EHBSCSO) to address node localization within WSNs. The hybrid method leverages the exploration capabilities of the bat optimization algorithm and the exploitation strengths of the sand cat swarm optimization algorithm. This combination allows for efficient determination of node positions, significantly improving localization accuracy while minimizing energy consumption. The EHBSCSO utilizes the received signal strength indicator (RSSI) and time of flight (ToF) approaches to assess distances among nodes accurately. Accurate node localization directly improves data quality by ensuring spatially precise data collection, reducing communication overhead, and enhancing the overall reliability of the collected data. Compared to conventional methods, the proposed EHBSCSO algorithm demonstrates superior performance, with a mean localization error of 0.18%, energy consumption of 7.2 J, computational time of 8.9 s, and localization time of 0.19 s. These metrics underscore its efficiency and precision. The research indicates that EHBSCSO not only optimizes localization accuracy but also contributes to energy efficiency and faster computational times, addressing key challenges in WSN node localization.

The base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well-known NP-hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above-mentioned problem is modeled by covering a new 2-D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI-SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI-SLOB scheme over the other state-of-the-art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management.

One of the advanced field in 5G cellular networks is the Massive Multiple-Input-Multiple-Output (MIMO), which creates a massive antenna array by offering numerous antennas at the destination. This grows as a hot research topic in the wireless sectors as it enhances the volume and spectrum usage of the channel. The spectral efficiency (SE) is maximized using the abundant antennas employed by MIMO using spatial multiplexing of consumers, which needs precise channel state information (CSI). The SE is affected by both pilot overhead and pilot contamination. To mitigate the contamination and to estimate the suitable channel for communication, an efficient strategy is introduced using the proposed Namib Beetle Aquila optimization (NBAO)_Deep Q network (DQN). Here, the optimal pilot location is identified by employing NBAO, which is an integration of Namib beetle optimization (NBO) and Aquila optimizer (AO). Moreover, DQN is introduced to determine the suitable channel and metrics, such as bit error rate (BER) and normalized mean square error (MSE) is used for evaluation. The normalized MSE channel estimation is utilized to mitigate the effects of pilot contamination. Additionally, designed NBAO + DQN have attained a value of 0.0006 and 0.0005 for BER and normalized MSE.

Deterministic communication plays a crucial role for real-time distributed embedded systems by guaranteeing a low end-to-end delay and minimal jitter. Conflict-aware iterated ILP-based scheduling (IIS) is an incremental approach to enhance the scalability of single-shot ILP computation to solve the computationally hard Qbv-compliant offline scheduling problem for IEEE 802.1 Time-Sensitive Networking (TSN). Taking into account the conflicting demands of the streams, IIS computes no-wait schedules by dividing the set of streams into disjoint partitions, each of which is incrementally scheduled by an ILP solver. In this work, defining the no-wait communication constraints for TSN scheduling, we propose a novel iterated scheduling approach, namely, O-IIS, with a backtracking mechanism and partial solution support extending the conventional IIS procedure. O-IIS relies on an ordering strategy to determine the order in which the constructed partitions will be processed by an ILP solver. We evaluate the performance of our approach in terms of schedulability success rate and schedule synthesis time using various network topologies and different partitioning schemes to construct the partitions. The evaluation results show that our approach provides significantly better scheduling performance compared to the existing work in the literature.