Reduce Control Overhead Research Articles

A Decentralized Collaborative Learning Approach in 5G+ Core Networks

The 5G+ networks are going through extensive overhauling at both the radio access network (RAN) and core network (CN) to truly support the envisioned 5G+ use cases with ever so increasingly stringent quality of service requirements. Although many academic and industrial advancements have been realized, there are still needs for innovative changes vital for 5G+ connectivity, applications, and services. Consequently, the 3GPP is ushering in the next paradigm shift for CNs. CNs, which consist of several network functions (NFs) working cooperatively to service a RAN, are expected to exploit artificial intelligence (AI) in the form of an all-seeing-eye AI engine known as the Network Data Analytics Function (NWDAF). Its purpose is to optimize and automate network operation and management. However, accurate analytics require extensive machine learning (ML) training and as such, in this article, we present a distributed collaborative ML approach to facilitate said training. Accordingly, we present two variations of a distributed NWDAF architecture to enhance NF data localization, improve security, reduce control overhead during model training, shorten the training time, and finally, enhance the accuracy of the trained models by virtue of local testing on real-time network data. The first architecture distributes the global model training by exploiting the unique learning domain of each local model for each network function instance in a federated learning-based scheme while the second partitions the actual training network between multiple entities for each network function in a split learning-based scheme.

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs.

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.

Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT.

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

Z number improved reference ideal method‐based decision‐making process for enforcing cooperation during data dissemination in mobile ad hoc networks

SummaryIn mobile ad hoc networks (MANETs), cooperative communication and resource constraint are the two important core characteristics essential to guarantee trusted data dissemination. The cooperative communication between mobile nodes depends on the trust rendered by them towards the process of reliable data routing. However, stringent resource constraints of mobile nodes such as energy, memory, communications, and computations result in the introduction of selfish and malicious node that completely degrades the network performance in different dimensions. In this paper, Z number improved reference ideal method (RIM)‐based decision‐making process (NIRIMDMP) is proposed with the merits of maximizing deviation method (MDM) and best–worst method (BWM) to ensure reliable data routing by modeling the cooperation degree in terms of Z number. This NIRIMDMP adopted Z number to represent the information reliability and handle the problem of inherent uncertainty during the process of evaluating each mobile node in the routing process. In specific, MDM and BWM are included into the proposed NIRIMDMP to determine comprehensive attribute weights based on the calculated objective and subjective weights that could be possible derived in routing. It extended the merits of classical RIM using Z numbers to confirm reliable ranking of mobile nodes, even when the optimal solution exists amid extreme values taken into consideration for assessing the mobile nodes during decision making. Simulation investigations of the proposed NIRIMDMP confirmed improved throughput and network lifetime with reduced control overhead, energy consumptions, and delay independent of the amount of malicious and non‐cooperative nodes.

Editorial

Dear Readers,  It gives me great pleasure to announce the second regular issue of 2023. I would like to thank all the authors for their sound research and the editorial board for the extremely valuable reviews and suggestions for improvement. These contributions together with the generous support of the consortium members enable us to run our journal and maintain its quality.  Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends.  In this regular issue, I am very pleased to introduce 4 accepted papers involving 16 authors from 6 different countries.  In a collaboration between researchers from South Africa and the USA, Trienko Grobler, Manfred Habeck, Lynette van Zijl and Jaco Geldenhuys address improved algorithms for combinatorial generation of bordered box repetition-free words based on tree-based search space and graph-based search space. In another joint research between Algeria and France, Mehdi Rouissat, Mohammed Belkheir, Hicham Sid Ahmed Belkhira, Sofiane Boukli Hacen, Pascal Lorenz and Merahi Bouziani describe a new technique to mitigate the version number attack for IoT networks, reducing control overhead by 83% and energy consumption by 74%. Amira Samir, Huda Amin Maghawry and Nagwa Badr from Egypt aim in their research to increase the effectiveness of the graphical user interface testing process of mobile applications by proposing an enhanced combinatorial-based metaheuristic approach that has been compared with monkey, frequency, random and greedy approaches. Qusai Y. Shambour, Mosleh M. Abualhaj and Ahmad Adel Abu-Shareha from Jordan propose an effective multi-criteria recommender algorithm for personalized restaurant recommendations by exploiting users’ and items’ implicit similarities to eliminate the sparseness of rating information.   Enjoy Reading!  Cordially, 

An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks

The Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balance.

Machine Learning based Frame Size Optimization in WLAN Downlink MU-MIMO Channel with the Least Cost of Delay

The key enhancement in the medium access control (MAC) layer is frame aggregation introduced by the IEEE 802.11n/ac standard to accommodate the growing traffic demand in the WLAN by allowing multiple packets aggregated per transmission. Frame aggregation efficiently reduces control overhead in the MAC layer, such as the MAC header and thus it helps to enhance transmission efficiency and throughput performance of WLAN. However, heterogeneous traffic demand among streams in the WLAN downlink MU-MIMO channel creates a challenge to efficiently utilize the benefits of frame aggregation. Transmission efficiency is also compromised during frame size setting determination because when a frame size is larger, the impact of the overhead frame can be lower, but they are also more vulnerable to transmission errors. Thus, this trade-off between maximizing frame size and minimizing overhead frames should be addressed by employing an adaptive frame aggregation technique to derive the optimal frame size that would maximize the throughput in WLAN downlink MU-MIMO channel. Moreover, when frame aggregation approach is employed, more frames must wait before transmission in a buffer which causes a delay in the performance of WLAN. Thus, analysing the trade-off between maximizing throughput and minimizing delay is a critical issue that should also be addressed to enhance the performance of WLAN. However, the majority of the existing adaptive aggregation algorithms in the WLAN downlink MU-MIMO channel are focused to maximize the throughput or minimize the delay. The main contribution of this paper is to propose a machine learning-based frame size optimization algorithm by extending our earlier approach in considering the cost of delay to maximize the system throughput of WLAN. The effectiveness of the proposed scheme is evaluated over the FIFO Baseline Approach and earlier conventional approaches under the effects of various traffic patterns, channel conditions, and the number of STAs.

Design and implementation of energy-aware cross-layer routing protocol for wearable body area network

PurposeA wireless body area network (WBAN) plays a crucial role in the health-care domain. With the emergence of technologies like the internet of things, there is increased usage of WBAN for providing quality health services. With wearable devices and sensors associated with human body, patient’s vital signs are captured and sent to doctor. The WBAN has number of sensor nodes that are resource constrained. The communications among the nodes are very crucial as human health information is exchanged. The purpose of this paper aims to have Quality of Service (QoS) with energy aware and control overhead aware. Maximizing network lifetime is also essential for the improved quality in services. There are many existing studies on QoS communications in WBAN.Design/methodology/approachIn this paper, with the aim of energy-efficient WBAN for QoS, a cross-layer routing protocol is designed and implemented. A cross-layer routing protocol that is ad hoc on-demand distance vector (AODV)-based, energy and control overhead-aware (AODV-ECOA) is designed and implemented for energy-efficient routing in WBAN. The cross-layer design that involves multiple layers of open systems interconnection reference model, which will improve energy efficiency and thus QoS.FindingsImplementation is simulated using the network simulator tool, i.e. NS-2. The proposed cross-layer routing protocol AODV-ECOA shows least bandwidth requirement by control packets, leading to less control overhead, highest packet delivery ratio and energy efficiency. The experimental results revealed that AODV-ECOA shows better performance over existing protocols such as AODV and POLITIC.Originality/valueAn efficient control overhead reduction algorithm is proposed for reducing energy consumption further and improves performance of WBAN communications to realize desired QoS.

ICDRP-F-SDVN: An innovative cluster-based dual-phase routing protocol using fog computing and software-defined vehicular network

Over time, wireless technology is evolving impulsively. For the most part, studies presently focused on the telecommunication field. Vehicular Ad-hoc Network (VANET) is one of the leading genuine and appealing areas for Intelligent Transportation Systems (ITS) originators and organizations. Essentially, VANET is an ad-hoc network consisting of several vehicles that flow on the same road. The existence of such networks revolutionizes the wireless communication field. The immense increase in the number of smart vehicles throughout time is the reason behind the massive evolution in VANET technologies. VANETs requirements are evolving quickly, therefore, having more flexible, scalable, and well-connected routes in VANETs is becoming essential in any new routing protocol. Antiquated VANETs routing protocols and designs encounter very challenging technical, employment, and control problems with the evolution of VANETs requirements. This paper presents a new efficient routing protocol for VANETs, namely, Innovative Cluster-Based Dual-Phase Routing Protocol Using Fog Computing and Software-Defined Vehicular Network (ICDRP-F-SDVN) which utilizes state-of-the-art technologies to overcome the drawbacks of traditional VANETs routing protocols. The use of fog computing and Software-Defined Networks (SDN) combined in our protocol presents a robust new architecture that meets all the new requirements and overcomes challenges arising from technological development and rapid escalation in the number of smart vehicles. Additionally, we propose a novel clustering algorithm mainly for the reason of lessening the long-distance communications in each cluster. In conjunction with this clustering algorithm, we propose an efficient control overhead reduction mechanism, thereby lowering the messages' exchanges imposed. Interestingly, our proposed protocol is a dual-phase protocol with a 90% of SDN packet delivery ratio. In other words, when the SDN fails to deliver packets, traditional Ad-hoc On-Demand Distance Vector (AODV) handles them as a fallback mechanism. In this paper, the performance of ICDRP-F-SDVN is evaluated in terms of throughput and end-to-end delay and further compared with four closely related routing protocols, that are, Intersection Dynamic VANET Routing (IDVR), Vehicle Density and Load Aware (VDLA), Intelligent Routing Protocol Using Real-Time Traffic Information in Urban Vehicular Environment (IRTIV), and Greedy Perimeter Coordinator Routing (GPCR). In terms of control overhead, the comparison is conducted with Control Overhead Reduction Algorithm (CORA), Moving Zone-Based Routing Protocol (MoZo), Beaconless Routing Algorithm for Vehicular Environment (BRAVE), and Cluster-Based Directional Routing Protocol (CBDRP). Our proposed protocol has achieved impressive simulation results compared to previously mentioned protocols. In particular, the throughput is increased by 8277%, 64309%, 76235%, and 96053% while the end-to-end delay is reduced by 77.5%, 82.5%, 87.2%, 95.6% over IDVR, VDLA, IRTIV, and GPCR, respectively. Finally, the control overhead is lowered by 42.3%, 87%, 93.5%, 96.7% over that of CORA, MoZo, BRAVE, and CBDRP, respectively.

OF-FZ: An Optimized Objective Function for the Ipv6 Routing Protocol for LLNs

Internet of Things (IoT) has seen a lot of traction over the last few years and is expected to play a major role in controlling devices and communication. IoT network involves sensors with constrained memory, limited processing and power capabilities. The process of network formation largely decides the Quality of service in these networks. IPv6 Routing Protocol for LLNs (RPL) is specially designed by the IETF ROLL working group to cater to routing requirements in such networks. In RPL, an Objective function specifies a set of metrics or constraints which can be used as criteria for best parent and path selection, ensuring a faster route to the destination. In our work, we propose a novel objective function OF-FZ, which uses four metrics to make routing decisions. Fuzzy Logic is used to combine the metrics hop count (HC), ETX, delay and node residual energy (RE) to obtain a single decision metric termed as Quality Assurance (QA) score. During routing, a neighbor node having the highest QA score is chosen as the best parent. The proposed Objective Function leads to significant savings in energy consumption (EC) (9% lower), Packet Delivery Ratio (PDR) (3% higher), End-to-end Delay (8% lower) as compared to existing OFs. Controlling advertisement messages circulating in the network leads to a significant reduction in control overhead to the tune of 45%. Performance evaluation and deep dive of OF-FZ is done in three configurations: network scalability, increasing packet transmission rates and multi-sink scenarios using Contiki OS and COOJA network simulator.

Cluster-based energy-efficient joint user association and resource allocation for B5G ultra-dense network

Control and user plane split (CUPS) aided ultra-dense network (UDN) is considered as a promising technology in the fifth generation (5G) networks and will undoubtedly develop in beyond 5G (B5G), which can reduce control overhead and improve the system throughput of network. However, the serious interference and increased power consumption of ultra-dense networking due to densely deployed base stations are also inevitable. To mitigate the interference and improve the system energy efficiency (EE) in CUPS-based B5G UDN, we propose a cluster-based energy-efficient joint user association and resource allocation scheme. First, an improved clustering algorithm is introduced in base station clustering stage. We use orthogonal resource allocation within cluster to reduce co-tier interference and reuse the frequency resource in different clusters. Then, a joint user association, sub-channel allocation and power coordination problem is modeled as a mixed-integer programming problem, which is transformed into a convex optimization problem by using alternative optimization and variable relaxation methods. Finally, we analyze the complexity of our proposed algorithm. Simulation results show that our proposed algorithm can reduce the interference and approximately boost 88% on EE of CUPS-based B5G UDN.

Energy-Aware Routing for Software-Defined Multihop Wireless Sensor Networks

In this paper, we propose an energy-aware routing algorithm and a control overhead reduction technique for prolonging the network lifetime of software-defined multihop wireless sensor networks (SDWSNs). This is an effort to optimize the energy consumption of WSNs that provide services to the Industrial Internet of Things (IIoT). A centralized controller grants a global view of the sensor network by introducing extra control overhead in the network, but this leads to extra energy costs. However, our new algorithm takes advantage of this global view and balances the network energy by selecting paths with the highest remaining energy level among multiple paths for each sensor node. We also identify key functions draining energy from the SDWSN and minimize their impact by implementing a data packet aggregation function, and minimizing the control overhead by keeping track of the sensor nodes' routing tables using a simple checksum function. We show that the proposed approach prolongs the network lifetime of the WSN by 6.5% on average compared to the standard shortest-path algorithm, and that the control overhead is reduced by approximately 12% while still maintaining a very high packet delivery ratio.

RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks

Future generation Internet of Things (IoT) communication infrastructure is expected to pave the path for innovative applications like smart cities, smart grids, smart industries, and smart healthcare. To support these diverse applications, the communication protocols are required to be adaptive and intelligent. At the network layer, an efficient and lightweight algorithm known as trickle-timer is designed to perform the route updates and it utilizes control messages to share the updated route information between IoT nodes. Trickle-timer tends to generate higher control overhead ratio and achieves lower reliability. Therefore, this article aims to propose an RL-based Intelligent Adaptive Trickle-Timer Algorithm (RIATA). The proposed algorithm performs three-fold optimization of the trickle-timer algorithm. Firstly, the RIATA assigns higher probability to control message transmission to nodes that have received an inconsistent control message in the past intervals. Secondly, the RIATA utilizes RL to learn the optimal policy to transmit or suppress a control message in the current network environment. Lastly, the RIATA selects an adaptive redundancy constant value to avoid unnecessary transmissions of control messages. Simulation results show that RIATA outperforms the other state-of-the-art mechanisms in terms of reducing control overhead ratio by an average of 21%, decreasing the average total power consumption by 10%, and increasing the packet delivery ratio by 4% on an average.

WINDMILL: A Scalable Multicast Routing Protocol for Mobile Ad-Hoc Networks

Multicasting supports various applications that need a high collaboration and require data transmission to numerous destinations concurrently. In environments where nodes are continuously moving, as in mobile Ad-hoc networks, seeking efficient routes from a specific source leading to anticipated destinations became an important issue. In this paper a novel scalable multicast routing procedure for mobile Ad-hoc networks is proposed. Our new protocol, WINDMILL, tries to improve performance by introducing a hierarchal routing algorithm and dealing with the area as zones. Furthermore, WINDMILL tries to demonstrate better scalability, performance and robustness through applying the restricted directional flooding. WINDMILL utilizes the network partitioning to forward the route request and reply packets in an efficient way and avoid forwarding duplicate packets. A qualitative comparison between WINDMILL, multicast Ad-hoc on-demand distance vector (MAODV), on-demand multicast routing protocol (ODMRP) and location aware multicasting protocol (LAMP) protocols is presented in this paper. This comparison has considered the used routing category, main contribution, routing structure and network structure maintenance, request and reply packets sending mechanisms, route activation time, selected routes length, and data packet copies number. Our investigation reveals that WINDMILL will be able to achieve scalability by attaining reduced control overhead and low number of data packets copies even within large networks. Hence, WINDMILL can be a good choice for multicasting in Ad-hoc networks established for example among students on a campus or soldiers in a battlefield, where scalability is a key issue.

Low-Cost and High-Throughput Hardware Design for the HEVC 16x16 2-D DCT Transform

This article presents the hardware design of the 16x16 2-D DCT used in the new video coding standard, the HEVC – High Efficiency Video Coding. The transforms stage is one of the innovations proposed by HEVC, since a variable size transforms stage is available (from 4x4 to 32x32), allowing the use of transforms with larger dimensions than used in previous standards. The presented design explores the 2-D DCT separability property, using two instances of the one-dimension DCT. The architecture focuses on low hardware cost and high throughput, thus the HEVC 16-points DCT algorithm was simplified targeting a more efficient hardware implementation. Operations and hardware minimization strategies were used in order to achieve such simplifications: operation reordering, factoring, multiplications to shift-adds conversion, and sharing of common sub-expressions. The 1-D DCT architectures were designed in a fully combinational way in order to reduce control overhead. A transposition buffer is used to connect the two 1-D DCT architectures. The synthesis was directed to Stratix III FPGA and TSMC 65nm standard cells technologies. The complete 2-D DCT architecture is able to achieve real-time processing for high and ultra-high definition videos, such as Full HD, QFHD and UHD 8K. When compared with related works, the architectures designed in this work reached the highest throughput and the lowest hardware resources consumption.

L-RPL: RPL powered by laplacian energy for stable path selection during link failures in an Internet of Things network

The routing protocol RPL for low power and lossy networks (LLNs) suffers from reduced reliability, increase in control overhead, longer latency and higher energy utilization on experiencing high node density, link or node failures in the network. This paper aims to elaborately study the behaviour of RPL’s expected transmissions count (ETX) based path selection for lengthier routes and the impact of node density and link or node failures on them. Our study finds that the path selection along long routes suffers due to the lack of decisive information on the neighbourhood links. As an alternate, a laplacian energy based path selection is proposed to enrich RPL by bringing in additional neighbourhood connection information into the path selection process to provide stable paths. Laplacian energy drop, a two-walk neighbourhood connectivity measure that is incorporated in L-RPL helps a node to pick the most resilient path. By simulation and testbed experiment results, it is shown that the proposed L-RPL improves path selection and reduces control overhead (29.05% and 39.68% reduction in (Destination Advertisement Object) control data when 9% of nodes fail and during normal conditions respectively), latency and also improves energy utilization.

ELMP: Efficient location based multicast protocol for mobile ad hoc networks

Node Location information may reduce the overalloverhead of communication for packet forwarding in Mobile Ad- Hoc Networks (MANETs). This research aims in developing Efficient Location based Multicast Protocol (ELMP) to reduce the communication and computation requirement for selection of next node (hop) for packet forwarding. To diminish the impact due to inaccurate local topology knowledge, the topology information is updated at a node in a periodic manner based on network dynamics and traffic demand. On-demand routing mechanism is used in order to reduce control overhead compared to the proactive schemes which are normally adopted in current geographic routing protocols. Proposed method procures the node position detail using GPS and tracks the effective routing algorithm to ensure communication follows with least no’s of hops and computations. This aims to prove that the proposed ELMP outperforms in all aspects such as improved energy consumption, packet delivery ratio and decreased delay, overhead.

Genetic spider monkey‐based routing protocol to increase the lifetime of the network and energy management in WSN

SummaryWireless sensor networks (WSNs) include large distributed nodes in the sensing field. However, the sensor nodes may die due to energy deficiency as they are situated in a hostile environment. Therefore, an energy‐efficient WSN routing protocol is necessary in order to better accommodate the various environmental conditions. In this paper, we have proposed a new Energy‐Efficient Genetic Spider Monkey‐based Routing Protocol (EGSMRP) to improve the stability and lifetime of sensor nodes. The operation of EGSMRP is classified into two stages: (i) setup phase and (ii) steady‐state phase. In the setup phase, GSMO‐based cluster head selection procedure is done. In this phase, the base station utilizes the GSMO algorithm as a device to generate energy‐efficient clusters. Followed with this, the steady‐state phase solves the load balancing issue by utilizing the intracluster data broadcast and dual‐hop intercluster broadcasting algorithm. Thereby, the proposed EGSMRP protocol has shown the energy‐based opportunistic broadcasting with reduced control overhead. Simulation is performed in various conditions to evaluate the effectiveness of the proposed EGSMRP protocol using different metrics such as throughput, control overhead, energy consumption, end‐to‐end delay, and network lifetime. From the simulation results, it was evident that EGSMRP has achieved a higher performance compared to other traditional approaches such as EBAR, MCTRP, IEEMARP, HMCEER, and EFTETRP.

Analysis of Control Overhead Reduction by Semi-Persistent Scheduling

For the efficient use of radio resource, semi-persistent scheduling (SPS) has been adopted to reduce control overhead required for resource allocation. In this letter, for voice over Internet protocol traffic, a lower bound on blocking probability and a closed-form maximum achievable control overhead reduction ratio are derived through a novel Markov chain model. Also, the performance evaluation of SPS under various resource assignment algorithms is provided on the blocking probability and overhead reduction ratio. Analytical expressions are validated through simulations.

Adaptive repair algorithm for TORA routing protocol based on flood control strategy

Temporally Ordered Routing Algorithms (TORA) is one of the most representative on-demand routing protocols in Mobile Ad Hoc Networks (MANET). This protocol is widely used in high-speed mobile MANET because of its robust topology. In order to improve the shortcomings of TORA in routing maintenance, such as high overhead and delay, and make the routing more suitable for emergency and disaster relief network, an adaptive repair algorithm for TORA routing protocol based on flood control strategy (AR-TORA-FCS) is proposed. Firstly, according to the characteristics of the uniform deployment of the nodes in the network, the self-repair process of the self-repair nodes in the directed acyclic graph (DAG) of TORA is transformed into the optimal search problem for the optimal nodes, and the formula is established. According to Ray-Algorithm to search for the optimal node, it is proved that the search result is the optimal solution of the process of searching for the optimal node. Then, a conditional algorithm to determine the self-repair process is given, and the conditional threshold to initiate the self-repair process is determined. The mapping relationship between the repair process of self-repair nodes and the distance between the nodes is established, and the path repair is carried out before the path fails. In order to reduce control overhead, the definition of optimization region is given, and the algorithm to determine the optimization region is proposed. Simulation results show that the algorithm reduces control overhead, improves the packet delivery rate, and improves average end-to-end delay. The mobile device, which is subject to unified deployment, is used as a network node to test the proposed adaptive repair algorithm in the actual rescue and disaster relief environment. The results show that it is basically consistent with the simulation results, and the overall performance is improved significantly.