Wireless Networks最新文献

The efficient management of energy in wireless sensor networks (WSNs) is a primary concern among researchers. Clustering algorithms serve as a crucial technique to address this issue. However, the initial uncertainties in both measuring the WSN’s values and in node localization due to GPS lead to secondary uncertainties like residual energy of nodes, cluster centrality, and distance from the cluster to the base station in the higher layers of WSNs. In this study, we have incorporated five improvements to our previous algorithm “FSCVG: A Fuzzy Semi‑Distributed Clustering Using Virtual Grids in WSN”. Firstly, we have discussed and classified uncertainties into two categories: primary uncertainties and secondary uncertainties. Secondly, we have applied a Type-3 fuzzy system to handle secondary uncertainties. Thirdly, we have used an adaptive imaginary grid to generate uneven clusters and balance the load according to the base station location. Fourthly, both decentralized and centralized clustering have applied based on new adaptive imaginary grid updates. Finally, we have determined the threshold level of each cluster proportionally, based on the energy of nodes within the same cluster. The findings of these improvements indicate an increased lifetime of the network concerning comparable methods.

The proliferation of technological advancement in the Internet of Things (IoT) and mobile devices accelerates the need for latency-sensitive multimedia applications. The network operator may charge application vendors to cache their content. Vendors risk losing their user base if users don’t receive the expected Quality of Service (QoS), which includes meeting specific delay thresholds for services like video on demand. While multiple content copies can enhance QoS by delivering content quickly, maintaining redundant copies is costly. Therefore, deploying edge servers at edge nodes and caching content is a prominent solution for application vendors. This approach guarantees lower latency, fewer content replicas, and reduced strain on backhaul links. However, the limited cache capabilities of individual edge nodes present challenges, such as efficiently allocating scarce resources to meet user demands. This work addresses the earlier challenges by formulating a cooperative content caching and forwarding problem by placing the various latency-sensitive content to minimize the total cost with resource and deadline constraints. The problem is formulated as an integer linear programming problem for cooperative caching in mobile edge networks to mitigate the total cost to the application vendors. In the absence of content popularity information, an online cooperative caching mechanism is presented to solve the dynamic content request and forwarding problem. Extensive simulations have been performed to illustrate that the proposed online algorithm significantly enhances the performance in terms of the total cost, load on the cloud, hit ratio, time utility, and cache utilization.

Data aggregation in the Wireless Body Area Networks (WBAN) is a multidimensional problem. It can be addressed at different levels of the network. The proposed work identifies the scheduling problem for the slots in a Time Division Multiple Access Medium Access Control (TDMA-MAC) superframe. A resource-constrained single channel WBAN is considered, and the proposed work models the set of nodes and slots of one subframe as a bipartite graph and aims to obtain a globally optimal matching solution for one subframe with end-to-end latency and temperature rise minimization as prime goals. Later, it extends the solution to the entire superframe, which consists of several subframes. The proposed Learning to Allocate (LTA) framework uses a Multi-Agent Reinforcement Learning (MARL)-based dynamic bipartite weight update. The proposed Reinforcement Learning-Optimized Delay and Temperature Aware Scheduling (RL-ODTAS) algorithm deployed on a WBAN co-ordinator was tested on a custom-made simulation testbed with heterogeneous nodes that handle two categories of data. The simulation results indicate an average of 14.15% end-to-end delay improvement for emergency data. Also, at the end of 1500 superframes, a temperature rise reduction of up to 0.43 °C is seen compared to Hungarian Matching without a learning component.

Nowadays, secured data transmission has become the most desired need for all networks. Ensuring secure authentication and data transmission in the network is a significant and important task in a heterogeneous wireless sensor network (HWSN). Even though several models are adopted in the network, offering secure authentication and sustaining the energy along with key management is still considered a major problem in the research community. Thus, in this paper, a novel energy efficient clustering and routing algorithm is proposed based on the Hierarchical enhanced identity based digital signature (Hierarchical-EIDS) algorithm in HWSN. Initially, an efficient clustering and cluster head (CH) selection are performed using a fuzzy transient search algorithm (FTSA) in order to play a significant role in energy consumption. In the next stage, adaptive Aquila optimization (AAO) has been introduced to define the optimal route for efficient data transmission. To overcome the mutual authentication, bilinear pairings, and public key generation, a Hierarchical-EIDS scheme is applied. Here, the homomorphic equilibrium (HE) algorithm is employed to generate a key for a particular sensor node (SN). The proposed method is implemented in MATLAB, and a comparative analysis has been carried out to determine the efficacy of the model. As a result, the proposed method accomplishes better performance in terms of security strength (0.08 per packet), average energy (AE) consumption (8.861 J), average end-to-end (AEE) delay (0.344 ms), and key length (1900 character). The acquired results outperform the existing models and prove their effectiveness.

Unmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.

For data collection systems, it is important to rationally allocate link capacity to each source node so that the base station can receive fresh data. In recent years, a metric called age of information (AoI) has appeared to measure the freshness of the received information. In this paper, we build a model in which multiple heterogeneous source nodes with energy constraints transmit samples to a base station via a shared capacity-constrained channel. Then, with the objective of minimizing average weighted AoI in data collection systems, we propose a strategy where each source node has two buffers to store its latest partially transmitted sample and its complete latest collected sample, respectively. We establish the AoI model, sample transmission model and energy consumption model for the two buffers, and design a scheduling algorithm two-buffers strategy algorithm in this strategy. Finally, the proposed algorithm has been compared with the other four scheduling algorithms by simulation. The results show that the proposed algorithm performs better than them in terms of the average weighted AoI.

Medical Body Area Networks are emerging as remote healthcare solutions where many sensors monitor the physiological parameters of the human body and communicate the data to a central hub. In this work, the goal has been to improve the reliability of data transmission using the Carrier Sense Multiple Access with Collision Avoidance protocol used in the medium access control layer. The authors propose an algorithm for optimizing backoff counter value using a multi-parameter decision-making algorithm called Criteria Importance through Intercriteria Correlation (CRITIC). We use CRITIC for deciding the user priority and backoff counter values, to reduce collisions and improve the reliability of medical body area networks. The work has evolved into three parts, the first part being the use of CRITIC to determine the sensor node’s priority when multiple nodes with different packet sizes and data rates are ready for data transmission. The second part was to use CRITIC to compute the backoff counter value depending on node parameters. These new values have the advantage that they are dependent on the weights of various parameters associated with the sensor node and therefore minimize the waiting time. Thirdly, a Markov chain-based analytical model has been presented to obtain the expression for performance metrics such as reliability, probability of transmission, probability of channel access failure, and probability of packet drop due to maximum retransmission limit. Simulation have been done for 10 practical sensor nodes with various distinct parameters using MATLAB. The results validate that the CRITIC backoff counter value in IEEE 802.15.6 standard enhances the reliability as compared with the conventional IEEE 802.15.6 backoff counter value. The results in this work show that the probability of packet drop reduces from 0.93 to 0.49.

Energy resource limitations, data packet traffic congestion, mobility, and communication link failure of relay nodes are vital challenges in establishing a reliable and efficient path between end-to-end users in IoT Communication toward 6G. These factors can severely affect routing and network performance. Hence, it is imperative to design a routing approach to adapt to such heterogeneity to improve the quality of service (QoS) performance toward 6G networks. This paper proposes a multi-criteria-aware integrated decision-making (MCAIDeM) routing protocol in which relay nodes’ residual energy, queue length, mobility, and link quality are integrated into a single routing approach for efficient and reliable route selection. In particular, the MCAIDeM routing protocol utilizes an analytic hierarchy process (AHP) approach for multi-criteria decision-making, which determines appropriate weights according to the parameter values of the nodes to select suitable relay nodes during the route selection process. Extensive simulations are performed to evaluate the performance, and the obtained simulation results showed that the proposed MCAIDeM routing protocol outperforms conventional routing protocols in terms of network throughput, latency, and energy consumption with various network parameters. The end-to-end delay obtained by MCAIDeM was 9.03% and 11.26% lower than that of MBMQA, 11.21%, and 13.67% lower than that of MEQSA-OLSRv2, and 19.15% and 16.61% lower than that of the MP-OLSRv2 routing protocols when the node speed increased from 5 m/s to 30 m/s, respectively.

Vehicular ad-hoc network (VANET) is widely applied in transportation applications to ensure safety and security during road transportation. In VANET, all the information’s are interchanged among vehicles with the available infrastructure on the roadside to form an ad-hoc network. In VANET, due to its communication in an open medium achieving the major security goals like integrity, authenticity, anonymity and traceability are the major concern. Since, there are numerous vehicles in the network generation and verification of digital signature requires high storage along with and verification time. To overcome all these issues, in this paper an Efficient group signature based certificate less verification scheme (EGSCVS) has been proposed to provide lightweight end to end authentication in the network. The proposed EGSCVS employs certificate less aggregation signature mechanism which is based on Elliptical curve cryptography to optimize the storage and reduce the overhead in terms of communication and computation during authentication process. Moreover, the proposed protocol utilizes the Random oracle model (ROM) to provide the formal security analysis. The implementation of the proposed protocol has been implemented using NS3 simulator with realistic simulation parameters. The simulation result shows that the proposed protocol improves the transmission overhead by 53.16%, computational cost by 48.75%, communication cost by 42.81%, signing delay by 55.17%, verification delay by 28.38% and aggregate verification delay by 45.35%.

Unmanned Aerial Vehicles (UAVs) are an emerging technology with the potential to be used in various sectors for various applications and services. In wireless networking, UAVs can be used as a vital part of the supplementary infrastructure to improve coverage, principally during public safety emergencies. Because of their affordability and potential for widespread deployment, there has been a growing interest in exploring the ways in which UAVs can enhance the services offered to isolated ground devices. Large areas may lose cellular coverage following a public safety emergency that impacts critical communication infrastructure. This prompts the need for the employment of D2D communication frameworks as a complement. In such critical conditions, timely response and network connectivity are essential factors for reliable communication. This study focuses on the mathematical models of UAV-based wireless communication in the context of disaster recovery. Particularly, we aim to model a queuing framework comprising UAVs as mobile relay nodes between the stranded user devices and neighbouring operational base stations. We present an iterative solution with a novel method for generating initial conditions for the two-stage queuing model. The approximate approach presented is validated for its accuracy using discrete-event simulation. The effects of various factors on performance measures are also analysed in detail. The validation results show that the discrepancy between the analytical approach and the simulation is less than 5%, which is the confidence interval of the simulation.