Multi-hop Transmission Research Articles

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery.

In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.

A Fair Channel Hopping Scheme for LoRa Networks with Multiple Single-Channel Gateways.

LoRa is one of the most prominent LPWAN technologies due to its suitable characteristics for supporting large-scale IoT networks, as it offers long-range communications at low power consumption. The latter is granted mainly because end-nodes transmit directly to the gateways and no energy is spent in multi-hop transmissions. LoRaWAN gateways can successfully receive simultaneous transmissions on multiple channels. However, such gateways can be costly when compared to simpler single-channel LoRa transceivers, and at the same time they are configured to operate with pure-ALOHA, the well-known and fragile channel access scheme used in LoRaWAN. This work presents a fair, control-based channel hopping-based medium access scheme for LoRa networks with multiple single-channel gateways. Compared with the pure-ALOHA used in LoRaWAN, the protocol proposed here achieves higher goodput and fairness levels because each device can choose its most appropriate channel to transmit at a higher rate and spending less energy. Several simulation results considering different network densities and different numbers of single-channel LoRa gateways show that our proposal is able to achieve a packet delivery ratio (PDR) of around 18% for a network size of 2000 end-nodes and one gateway, and a PDR of almost 50% when four LoRa gateways are considered, compared to 2% and 6%, respectively, achieved by the pure-ALOHA approach.

Data Collection Method of Energy Adaptive Distributed Wireless Sensor Networks Based on UAV

In sensor networks, UAVs are often introduced to assist data collection tasks. UAVs can operate as data ferry nodes, connecting distributed areas that are separated from each other. This paper proposes a data collection method for distributed wireless sensor networks based on UAV and introduces the idea of edge computing in it. In the single-hop transmission scenario, the K -means++ clustering method is used for sensor node clustering and cluster head election in the initial state. In the next rounds of data collection, UAV is used to assist in the election of new cluster heads and data collection tasks, taking into account the relative distance and the relative remaining energy relationship of the sensor nodes in their clusters. In addition, reasonable priorities are set for some nodes that have never been elected in the previous rounds and for the dead nodes. In the multihop transmission scenarios, for nodes that cannot deliver directly, the optimal relay node is selected for routing by comprehensively considering factors such as transmission angle, transmission distance, and remaining energy of the node in each cluster. The method proposed in this paper coordinates the overall energy consumption of sensor nodes in the environmental monitoring area, delays the death time of key sensor nodes, and extends the network lifetime. At the same time, an improved ACO is used to reasonably plan the data collection path of the UAV. Compared with the comparison scheme, the improved ACO can obtain a better shortest path length and has the fastest convergence speed when reaching the shortest path.

Probabilistic cooperative coded forwarding for broadcast transmissions in industrial mobile edge communications

Mobile edge computing (MEC) is considered as a key enabler for the industrial internet of things (IIoT) to cope with the ever-increasing communication and computing demands of nodes. In consideration of the limited resource of the IIoT nodes, it is necessary to design cost-effective multi-hop data transmission schemes for mobile edge IIoTs. However, most of the traditional schemes have to spend enormous cost to meet the reliability requirement, which cannot support timely information processing of MEC-based IIoT. In this article, the probabilistic cooperative coded forwarding (PCCF) scheme for multi-hop data transmission in mobile edge IIoTs is proposed to address the above problem. First, the data packets are encoded at a source IIoT node using the systematic sparse network coding (SSNC) mechanism, then the source broadcasts the coded packets to its one-hop neighbors. To minimize the required number of redundant coded packets, the sparsity of coded packets is optimized. Second, the nodes which received the packets will become volunteer relay nodes and forward the coded packets using the cooperative coded forwarding (CCF) mechanism. The volunteer relays first forward the received coded packets with a forwarding probability, and then re-encode a pair of received coded packets and broadcast the re-encoded packet with a re-encoding probability. To guarantee the broadcast performance while minimizing the transmission number at relay nodes, the feasible forwarding and re-encoding probability are provided. Third, the receiver nodes will try to decode the received coded and re-encoded packets and recover data packets without sending acknowledgments. Finally, through a series of experiments, we verify the accuracy of analytical approximations and also find out the optimal sparsity of coded packets and the existence of minimum transmission numbers. These provide insights for further optimization of multi-hop data transmission in mobile edge IIoTs.

Hybrid Sparrow Search Optimization technique for quality of service cooperative routing in underwater acoustic sensor networks

Underwater Acoustic networks comprise sensor nodes distributed randomly in a subsea environment. These nodes deployed at varying pressure levels forward the monitored information through multi-hop transmissions. Data transmission through acoustic links exhibits challenges like frequency-dependent attenuation, noise, multipath distortion, and low propagating speed of acoustic signal (about 1500 m/s) causing the Doppler effect. Various existing algorithms consider only the energy and pressure levels in the routing process. The consideration of channel quality in routing is necessary to enhance the overall network performance. Hence, it is envisaged that the drawbacks of meta-heuristic techniques in computing the optimal solution can be solved by constructing a hybrid optimization algorithm with the fitness function based on pressure levels, energy, and channel conditions. Hybrid-SSO integrates Sparrow Search Optimization with Grey Wolf Algorithm to detect optimal relay nodes for data transmission towards the destination node. Implementation of the proposed algorithm is done using Aquasim (NS2.30) and its performance is collated with the existing techniques. Simulation results show that the proposed Hybrid-SSO successfully reaches a maximum Packet Delivery Ratio of 99.2% for 180 nodes with a transmission range of 100 m while reducing the Average End to End Delay, Average Energy Consumption, and No. of Dead nodes.

Performance of a hybrid dual-hop and multi-hop network based on NOMA

ABSTRACT In this work, a novel network is proposed, which supports simultaneous dual-hop and multi-hop transmissions using the key concept of non-orthogonal multiple access (NOMA). The proposed network is designed in such a way that the existing infrastructure of a multi-hop network can be utilised efficiently for concurrent dual-hop transmissions. While one source transmits its signal to a remote destination via relay nodes in time slots, a maximum of another sources may transmit their information to the corresponding destinations using dual-hop transmissions via the same relay nodes over time slots. The performance of this hybrid dual-hop/multi-hop network is analysed and the impact of number of hops, transmit signal-to-noise ratio (SNR) and imperfection of successive interference cancellation (SIC) on the outage performance of each source is evaluated. Novel closed-form expressions for outage probability and ergodic capacity have been developed to assess the performance of the proposed network. In addition, an asymptotic performance analysis has also been also carried out for both outage probability and ergodic capacity of the proposed network.

Fast-converging chain-cluster-based routing protocols using the Red-Deer Algorithm in Wireless Sensor Networks

PurposeThe performance of Wireless Sensor Networks (WSNs) applications is bounded by the limited resources of battery-enabled Sensor Nodes (SNs), which include energy and computational power; the combination of which existing research seldom focuses on. Although bio-inspired algorithms provide a way to control energy usage by finding optimal routing paths, those which converge slower require even more computational power, which altogether degrades the overall lifetime of SNs.Design/methodology/approachHence, two novel routing protocols are proposed using the Red-Deer Algorithm (RDA) in a WSN scenario, namely Horizontal PEG-RDA Equal Clustering and Horizontal PEG-RDA Unequal Clustering, to address the limited computational power of SNs. Clustering, data aggregation and multi-hop transmission are also integrated to improve energy usage. Unequal clustering is applied in the second protocol to mitigate the hotspot problem in Horizontal PEG-RDA Equal Clustering.FindingsComparisons with the well-founded Ant Colony Optimisation (ACO) algorithm reveal that RDA converges faster by 85 and 80% on average when the network size and node density are varied, respectively. Furthermore, 33% fewer packets are lost using the unequal clustering approach which also makes the network resilient to node failures. Improvements in terms of residual energy and overall network lifetime are also observed.Originality/valueProposal of a bio-inspired algorithm, namely the RDA to find optimal routing paths in WSN and to enhance convergence rate and execution time against the well-established ACO algorithm. Creation of a novel chain cluster-based routing protocol using RDA, named Horizontal PEG-RDA Equal Clustering. Design of an unequal clustering equivalent of the proposed Horizontal PEG-RDA Equal Clustering protocol to tackle the hotspot problem, which enhances residual energy and overall network lifetime, as well as minimises packet loss.

TRW-MAC: A thermal-aware receiver-driven wake-up radio enabled duty cycle MAC protocol for multi-hop implantable wireless body area networks in Internet of Things

Implantable Wireless Body Area Network (IWBAN), a network of implantable medical sensors, is one of the emerging network paradigms due to the rapid proliferation of wireless technologies and growing demand of sophisticated healthcare. The wireless sensors in IWBAN is capable of communicating with each other through radio frequency (RF) link. However, recurring wireless communication inside the human body induces heat causing severe thermal damage to the human tissue which, if not controlled, may appear as a threat to human life. Moreover, higher propagation loss inside the human body as well as low-power requirement of the sensor nodes necessitate multi-hop communication for IWBAN. A IWBAN also requires meeting certain Quality of Service demands in terms of energy, delay, reliability etc. These pressing concerns engender the design of TRW-MAC: A thermal-aware receiver-driven wake-up radio enabled duty cycle MAC protocol for multi-hop IWBANs in Internet of Things. TRW-MAC introduces a thermal-aware duty cycle adjustment mechanism to reduce temperature inside the body and adopts wake-up radio (WuR) scheme for attaining higher energy efficiency. The protocol devises a wake-up estimation scheme to facilitate staggered wake-up schedule for multi-hop transmission. A superframe structure is introduced that utilizes both contention-based and contention free medium access operations. The performance of TRW-MAC is evaluated through simulations that exhibit its superior performance in attaining lower thermal-rise as well as satisfying other QoS metrics in terms of energy-efficiency, delay and reliability.

Multi-hop ESP-Mesh Network and MQTT Protocol for Smart Light Systems in High-Rise Buildings

When a high-rise building's lights are not required because they are frequently left on, electrical energy is wasted as a result of this. The smart light is one of the effective ways to energy saving in a building. This study aims to create a smart light system using the ESP-Mesh network and the MQTT protocol to control the turn on/off of all lights in all rooms of a high-rise building. The ESP-Mesh network is a mesh of ESP8266 devices that are designed for multi-hop transmission. The MQTT is one of the widely used IoT protocols that allows a smartphone to control the lights in every room in a mesh topology via the internet remotely. The performance evaluation shows that a multi-hop ESP-Mesh is better than that a Single-hop ESP8266 in signal strength. The signal strength of the ESP8266 single-hop is bad. Meanwhile, the signal strength of the multi-hop ESP-Mesh is good in all rooms. Furthermore, The functional tests of the multi-hop ESP-Mesh show that although there are various broken paths caused by several disconnected nodes, all lights can be turned on or off suitably through the command from the smartphone switch. Turning off the not-required lights by smart light systems can help save energy.

EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks

In Underwater Sensor Networks (UWSNs), the energy sources of sensor nodes are limited and difficult to recharge and solar energy cannot be used in that environment. The power issue is one of the most significant constraints in underwater sensor networks and energy balancing is essential to prolong the network lifetime. The MAC/routing protocols that are used in other types of networks may not be suitable for UWSNs due to their unique characteristics. This paper aims to overcome the energy problem by developing a new MAC/routing protocol for UWSNs called the Energy-Efficient protocol for UWSNs (EE-UWSNs). It is based on five principles to save sensor energy and to prolong the lifetime of UWSNs. These principles are using finite levels of power, applying the multi-hops transmission, narrowing the scope of transmission, applying inactivation mode, and balancing energy consumption. Using the AUVNetSim simulator, which is a Python project developed by the Massachusetts Institute of Technology (MIT), the proposed EE-UWSNs protocol was compared with well-known protocols. Simulation results proved that the proposed protocol reduces the average energy consumption of sensors by up to 68.49% compared with the other protocols. Furthermore, the average number of collisions and the end-to-end delay are enhanced.

Constructing a Search Mechanism for Dementia Patient Based on Multi-Hop Transmission Path Planning and Clustering Method

Constructing a Search Mechanism for Dementia Patient Based on Multi-Hop Transmission Path Planning and Clustering Method

Matching Learning-Based Relay Selection for Substation Power Internet of Things

Wireless sensor network (WSN) can effectively solve the problems of weak coverage, blind coverage, and low survivability of smart substation communication networks by deploying multiple relay nodes and adopting multihop transmission. However, there are still some challenges in the traditional relay selection strategy of WSN in substation, including incomplete information and the selection conflicts among multisource nodes. In this paper, we propose a matching learning-based relay selection mechanism for WSN-based substation power Internet of things (SPIoT). Firstly, considering the electromagnetic interference caused by the operation of high-voltage equipment, a multihop transmission model of SPIoT is built. Furthermore, based on the upper confidence bound (UCB) algorithm and matching theory, a matching learning-based relay selection (MLRS) algorithm is proposed to minimize the energy consumption of SPIoT devices. Simulation results demonstrate that MLRS outperforms existing algorithms in terms of energy consumption and optimal selection probability.

Performance of a multihop cognitive radio network with diversity under imperfect CSI

ABSTRACT This paper analyses the outage performance of a multihop relay-based cognitive radio network under imperfect channel state information. A backoff power control scheme is applied to ensure both the quality of service (QoS) and interference constraints of the primary network. To reduce the interference at the primary network, we have proposed a new multihop transmission method where a relay node forwards the signal to the next relay node only when the signal is decoded successfully and QoS of the primary network is satisfied. Unlike the conventional relay transmission scheme, in our system model, a relay node receives signals not only from the previous node but also from the previous-to-previous node. The selection combining (SC) and maximum ratio combining (MRC) diversity schemes are applied to enhance the outage performance of the secondary network. The Monte Carlo simulation is adopted to validate the analysis. It is observed from the simulation results that the outage probability of the secondary network and interference probability of the primary network are impacted by the correlation coefficient between real and estimated channels, the number of hops and the backoff power coefficient.

Hybrid Energy-based Secured Clustering technique for Wireless Sensor Networks

<p>The performance of the Wireless sensor networks (WSNs) identified as the efficient energy utilization and enhanced network lifetime. The multi-hop path routing techniques in WSNs have been observed that the applications with the data transmission within the cluster head and the base station, so that the intra-cluster transmission has been involved for improving the quality of service. This paper proposes a novel Hybrid Energy-based Secured Clustering (HESC) technique for providing the data transmission technique for WSNs to produce the solution for the energy and security problem for cluster based data transmission. The proposed technique involves the formation of clusters to perform the organization of sensor nodes with the multi-hop data transmission technique for finding the specific node to deliver the data packets to the cluster head node and the secured transmission technique is used to provide the privacy of the sensor nodes through the cluster. The residual energy of the sensor nodes is another parameter to select the forwarding node. The simulation results can show the efficiency of this proposed technique in spite of lifetime within the huge amount data packets. The security of this proposed technique is measured and increases the performance of the proposed technique.</p> <p> </p>

ICIC: A Dual Mode Intra-Cluster and Inter-Cluster Energy Minimization Approach for Multihop WSN

Energy minimization is one of the paramount issues in designing of the wireless sensor network and becomes more complex for multi-hop transmission. Furthermore, selection of multi hop route for the packets transmission is also a tedious task. In this paper, we propose an intra-cluster and inter-cluster (ICIC) energy minimization strategy for multihop wireless sensor network (WSN). In the proposed strategy, in intra cluster communication, a dynamic duty cycle allocation based on the cluster head (CH) node distance from the child nodes for the energy minimization is presented. In the inter cluster communication, minimum energy consumption route is chosen for the transmission of packets to base station (BS). The proposed strategy is compared with four state of the art techniques i.e., HEEMP, LEACH, MLEACH, and SEP protocols of clustering. The evaluation of the proposed and existing strategies is done in two different scenarios i.e., number of round and number of nodes. Results of the simulation show that the proposed strategy transmits more packets and has lower number of dead nodes than other strategies. Furthermore, proposed strategy utilizes more network energy and more energy for relaying.

Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing

Mobile edge computing is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Due to the high frame loss rate during multi-hop transmissions in wireless edge computing networks, the problem of reliability and system safety will be of great importance. Data caching is a necessity to provide reliable edge computing and, at the same time a major consumer of energy. So, we propose a reliable mobile edge computing system using active caching and robust resource allocation approaches. This paper presents an energy-efficient stochastic network calculus framework to control edge-computing data flows for active data-caching systems. In accordance with the entrance processes of different QoS-class data streams, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a reliable user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to improving system safety. In this energy-cooperative approach, the power can be shared among the cells using a grid network. The simulation results show that the proposed approach can effectively increase the network reliability and power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot user distribution models. The numerical results also exhibit considerable power saving for different traffic models in addition to increasing the total network throughput by up to 25%.

A realistic relay selection scheme for cooperative MIMO networks

Most IoT devices are battery powered and have limited power supplies. To ensure communication between two nodes, multi-hop transmissions over relays could be used. However, this communication cannot be achieved if the Signal-to-Noise Ratio (SNR) at the destination does not reach a reasonable threshold. Different relay selection approaches have been proposed in the past. They assume that the battery discharge is linear, but the effectiveness of these schemes is not guaranteed when realistic non-linear battery models are considered. We propose a relay selection scheme by considering a realistic battery discharge model. We present an analytical study which demonstrates that, using cooperative relays nodes, offers a longer lifetime than using only one relay for each transmission round. Then, we derive an assignment scheme that attributes the transmission power for each relay in order to satisfy the SNR condition. The numerical results obtained demonstrate the effectiveness of our proposed solution.

Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning

The study aims to conduct big data analysis (BDA) on the massive data generated in the smart city Internet of things (IoT), make the smart city change to the direction of fine governance and efficient and safe data processing. Aiming at the multi-source data collected in the smart city, the study introduces the deep learning (DL) algorithm while using BDA, and puts forward the distributed parallelism strategy of convolutional neural network (CNN). Meantime, the digital twins (DTs) and multi-hop transmission technology are introduced to construct the smart city DTs multi-hop transmission IoT-BDA system based on DL, and further simulate and analyze the performance of the system. The results reveal that in the energy efficiency analysis of model data transmission, the energy efficiency first increases and then decrease as the minimum energy collected α0 increases. But a more suitable power diversion factor ρ is crucial to the signal transmission energy efficiency of the IoT-BDA system. The prediction accuracy of the model is analyzed and it suggests that the accuracy of the constructed system reaches 97.80%, which is at least 2.24% higher than the DL algorithm adopted by other scholars. Regarding the data transmission performance of the constructed system, it is found that when the successful transmission probability is 100% and the exponential distribution parameters λ is valued 0.01∼0.05, it is the closest to the actual result, and the data delay is the smallest, which is maintained at the ms level. To sum up, improving the smart city’s IoT-BDA system using the DL approach can reduce data transmission delay, improve data forecasting accuracy, and offer actual efficacy, providing experimental references for the digital development of smart cities in the future.

Majority Decision Aggregation with Binarized Data in Wireless Sensor Networks

Wireless sensor networks (WSNs) are the cornerstone of the current Internet of Things era. They have limited resources and features, a smaller packet size than other types of networks, and dynamic multi-hop transmission. WSNs can monitor a particular area of interest and are used in many different applications. For example, during the COVID-19 pandemic, WSNs have been used to measure social distancing/contact tracing among people. However, the major challenge faced by WSN protocols is limited battery energy. Therefore, the whole WSN area is divided into odd clusters using k-means++ clustering to make a majority rule decision to reduce the amount of additional data sent to the base station (or sink) and achieve node energy-saving efficiency. This study proposes an energy-efficient binarized data aggregation (EEBDA) scheme, by which, through a threshold value, the collected sensing data are asserted with binary values. Subsequently, the corresponding cluster head (CH), according to the Hamming weight and the final majority decision, is calculated and sent to the base station (BS). The EEBDA is based on each cluster and divides the entire WSN area into four quadrants. All CHs construct a data-relay transmission link in the same quadrant; the binary value is transferred from the CHs to the sink. The EEBDA adopts a CH rotation scheme to aggregate the data based on the majority results in the cluster. The simulation results demonstrate that the EEBDA can reduce redundant data transmissions, average the energy consumption of nodes in the cluster, and obtain a better network lifetime when compared to the LEACH, LEACH-C, and DEEC algorithms.

Adaptive cooperative routing transmission for energy heterogeneous wireless sensor networks

The mass machine type communication (mMTC) in 5G requires the technical support of the wireless sensor network (WSN). Compared with homogeneous WSNs, heterogeneous WSNs provide more flexibility and are more in line with the reality. For heterogeneous WSNs, the use of proper routing methods can prolong the lifetime as well as the stability of the system. This paper studies an energy heterogeneous WSN, where nodes can cooperate with each other to improve the energy efficient of the network. We focus on the network routing within distributed energy-efficient clustering (DEEC) protocol, a typical networking/communication protocol for heterogeneous WSNs. First, we derive a proposition on the choice of cooperative single-hop or multi-hop modes, which gives a sufficient condition on cooperative multi-hop transmission. Furthermore, we present two lemmas, which are the conditions on the distance threshold for mode selection. Based on the above, a novel adaptive cooperative routing algorithm combined with DEEC is proposed. Simulation results show that the proposed routing algorithm outperforms the benchmark schemes and can effectively reduce the network energy consumption and prolong the network lifetime.