Intra-cluster Transmission Research Articles

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Interney of Things (IoT) enabled by Wireless Sensor Network (WSN) is the principal idea behind target tracking, environment survelance, and patients monitoring systems in which human attentions are very crucial for round the clock. Since the sensor nodes that constitute the IoT is power constrained, it is suffering energy related problems which further badly affect the lifetime of the core sensor network. A well-knows topology management and routing scheme called Clustering is widely used for WSNs in maximizing the network lifetime due to its intrinsic characteristics. Clustering solves the energy constrained issues of WSN by providing a local infrastructure like arrangement to manage the network and resources in suitable manner. Various clustering approaches have been proposed so far by scientific community to address energy issues of WSN. But these existing approaches fail to provide required clustering output to improve lifetime by balancing the energy consumption in efficient manner. In this work, we propose a Minimized Intra-cluster Transmission Distance Clustering Protocol (MITDCP) to improve lifetime of WSN by innovatively clustering and intelligently placing the Base Station (BS). Innovative clustering involves a FCM (Fuzzy C Means) with Cluster Balancing algorithm to create balanced clusters. Then the proposed work makes use of back off timer weighted with residual energy to select and rotate Cluster Head (CH). Simulations show that our proposed work has achieved significant improvement in lifetime of WSN beneath the IoT systems when compared with Improved Energy Efficiency Clustering Protocol (IEECP).

A two‐vectordata‐predictionmodel forenergy‐efficient data‐aggregationin wireless sensor network

AbstractMost ecological management applications use wireless sensor networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two‐vector data prediction model that is based on normalized quantile regression (NQR) is proposed to proficiently accomplish energy reduction in synchronous data collecting cycles. The introduced NQR algorithm provides high‐accuracy data prediction. With accurate estimates and reduced data transmission, energy usage is reduced. Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two‐vector data‐prediction algorithm to coordinate the estimated sensor's reading, and, as a result, it will minimize cumulative inefficiencies from uninterrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to state‐of‐art methods, the suggested NQR methodology is shown to have high energy efficiency, greater prediction accuracy, and more positive predictions with high data quality, which help the network to last longer.

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>

A cluster-based model of COVID-19 transmission dynamics.

Many countries have manifested COVID-19 trajectories where extended periods of constant and low daily case rate suddenly transition to epidemic waves of considerable severity with no correspondingly drastic relaxation in preventive measures. Such solutions are outside the scope of classical epidemiological models. Here, we construct a deterministic, discrete-time, discrete-population mathematical model called cluster seeding and transmission model, which can explain these non-classical phenomena. Our key hypothesis is that with partial preventive measures in place, viral transmission occurs primarily within small, closed groups of family members and friends, which we label as clusters. Inter-cluster transmission is infrequent compared with intra-cluster transmission but it is the key to determining the course of the epidemic. If inter-cluster transmission is low enough, we see stable plateau solutions. Above a cutoff level, however, such transmission can destabilize a plateau into a huge wave even though its contribution to the population-averaged spreading rate still remains small. We call this the cryptogenic instability. We also find that stochastic effects when case counts are very low may result in a temporary and artificial suppression of an instability; we call this the critical mass effect. Both these phenomena are absent from conventional infectious disease models and militate against the successful management of the epidemic.

Reducing Network Energy Consumption using Multidimensional Optimization of LEACH Protocol

The optimized LEACH-focused protocol is proposed to make equal network energy usage and outspread the life cycle. In the first place, the threshold function of the cluster head is adjusted and the technique of division of the network area is configured to change reasonably the size of the clusters, taking account of residual energy and node distance. A sleep mechanism is introduced for intra-cluster transmission to balance the power consumption of nodes. And a new Barycenter Node has been introduced to help the head of the cluster fulfill the transmission task and thus prolong the lifespan. We derived a new formula, considering the angle, energy and distance, in multiple-hop communication between clusters to determine the fitting factor of the next hop. The results of simulations display that the remaining total energy of the optimized LEACH is decreased by 32.6% and the network life cycle respectively increases by around 54%.

Research of Multidimensional Optimization of LEACH Protocol Based on Reducing Network Energy Consumption

In order to equalize network energy consumption, and extend the life cycle, the optimized protocol based on low-power adaptive cluster stratification (LEACH) is proposed. Firstly, considering residual energy and distance of nodes, the threshold function of the cluster head is modified, and the network region division strategy is optimized, to reasonably adjust the size of clusters. For intracluster transmission, a sleep mechanism is added to balance node energy consumption. And a new barycenter node is added to assist cluster head (CH) to complete the transmission task, thereby extending the service lifetime. In the multihop communication between clusters, in order to calculate the fitting factor of the next hop, we derived a new formula, which takes into account the angle, remaining energy, and distance. Simulation results show that the total remaining energy of the optimized LEACH protocol is reduced by 31.4%, and the network life cycle is increased by about 52%.

Fuzzy Based Sleep Scheduling Algorithm with Machine Learning Techniques to Enhance Energy Efficiency in Wireless Sensor Networks

Wireless sensor networks, generally, are grouped into clusters to collect information effectively. Such grouping of nodes helps immensely to elongate the life of Wireless Sensor Networks. Message exchanges between nodes for consecutive and periodic clustering overload the sensor nodes and cause a shortfall of energy. Additional overhead during cluster formation, instability in energy use and the difficulty of information sharing during clustering, uncertain network structure, etc. are the current clustering problems. There is also a need to enhance intra-cluster transmission and to find effective methods to extend the network's lifespan. This paper aims to reduce the energy loss of nodes by reducing the message transmission overhead and simplifying the creation and upgrading of clusters to improve the lifespan of the network. A clustering strategy where the cluster is regularly restructured to decrease the overhead on cluster head nodes is also proposed in the paper. The suggested approach reduces data transmission using machine learning by the cluster member nodes and reduces the energy consumption of individual sensor nodes by implementing a suitable active/sleep schedule. To calculate the cluster update cycle and sleep cycle, it also makes use of the advantages of fuzzy logic by selecting appropriate fuzzy descriptors such as average data rate, distance from the head node to the sink and the remaining energy. The proposed approach optimizes the energy utilization of cluster heads and node members thereby enhancing the lifespan of the sensor network.

An energy-efficient prediction model for data aggregation in sensor network

Most environmental monitoring application periodically senses and aggregated data by sensor networks which usually exhibits high temporal redundancies. An enormous amount of energy is depleted in transmitting this redundant information making it extremely difficult to achieve an acceptable network lifespan, which has become a bottleneck in scaling such applications. To efficiently manage the energy depletion in concurrent data collection rounds, a prediction model based on Extended Cosine Regression (ECR) for Data Aggregation is proposed. The proposed technique delivers prediction with high accuracy and the energy consumption is minimized with successful predictions and thereby increases the data cycles and network lifetime. ECR also uses a two-vector model in the intra-cluster transmissions to synchronize the predicted data values and therefore minimizes cumulative errors from continuous predictions. The proposed ECR technique is simulated using NS2-34 shows high-energy efficiency as compared with the existing schemes. Results demonstrate high prediction accuracy, a number of successful predictions and a lesser degree of prediction errors, which obviously improve the network’s lifetime.

Novel Energy-Efficient Data Gathering Scheme Exploiting Spatial-Temporal Correlation for Wireless Sensor Networks

A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.

Firefly algorithm inspired energy aware clustering protocol for wireless sensor network

Most of the existing cluster-based routing protocols focus either on cluster head election schemes or on refinement of routing process ignoring process of cluster formation. In this paper, firefly algorithm inspired energy aware clustering protocol for WSN is presented. The idea of the proposed protocol is to elect the cluster head nodes by assigning them intensity value defined through firefly algorithm. Another contribution of this paper is energy efficient cluster formation where cluster members join to cluster heads based on intensity parameter associated with cluster head. The presented protocol ensures that the elected cluster heads are usually located in densely populated area of the network resulting in low distance intra-cluster data transmissions and ultimately leading to low energy consumption. The firefly-based protocol is compared with popular routing protocols such as LEACH, LEACH-C and EOICHD and comparative results indicate that proposed protocol is highly energy efficient than other protocols.

Energy Efficient Intra Cluster Transmission in Grid Clustering Protocol for Wireless Sensor Networks

Clustering based routing protocols have a pivotal role in wireless sensor networks (WSN) due to efficient energy consumption of limited power sensor node that is the prime challenge in WSN. Intra-cluster transmission and cluster size have great impact on balancing energy consumption within cluster and network. However, they play minor role in traditional routing protocols. In this paper, authors will offer mathematical analysis to provide judging metric that define energy efficient intra-cluster transmission. This metric is called threshold distance which is exploited in determining optimum cluster size. Based on the optimum cluster size, centralized energy aware grid clustering protocol (CEAG) will be proposed. Mat lab simulator will be utilized for appreciating the proposed protocol. The simulation results manifested that CEAG attains maximum network life tie and throughput in comparison with the existing protocols such as LEACH, K-LEACH, Energy-LEACH, DCHS and MOD-LEACH.

The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks

In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difficult since it is complex to handle the interference caused by simultaneous transmission. To solve this problem, we, according to the inhomogeneity of topology, divide the transmission into intra-cluster transmission and inter-cluster transmission. For the intra-cluster transmission, a spheroidal percolation model is constructed. The spheroidal percolation model guarantees a constant rate when a power control strategy is adopted. We also propose a cube percolation mode for the inter-cluster transmission. Different from the spheroidal percolation model, a constant transmission rate can be achieved without power control. For both transmissions, we propose a routing scheme with five phases. By comparing the achievable rate of each phase, we get the rate bottleneck, which is the throughput capacity of the network.

Fuzzy clustering in radar sensor networks for target detection

Fuzzy clustering has been an efficient tool for data science. In this paper, we present two fuzzy clustering schemes in radar sensor networks (RSN) data processing for target detection. We design cluster-head (CH) election for both intra-cluster single-hop and multi-hop data transmission on a basis of constant false alarm rate (CFAR) under fading environment. Small scale fading is considered in our fuzzy logic system (FLS) design (FLS with three-antecedents, F3) to compute the likelihood to be a CH for each radar sensor (RS) at the first stage. In case of single-hop routing, fuzzy c-means with singular value decomposition-QR (FCMSVDQR) approach is proposed to decide the final CH. As for multi-hop routing, firstly the RS with the highest FLS likelihood will be elected a CH. Secondly a graphical optimal routing selection (GORS) algorithm is applied for multi-hop data transmission. We also propose CFAR fusion approaches for both single-hop and multi-hop transmission at low SNR channel regime. Performances of our designs are compared with CHEF, a commonly adopted scheme, in terms of target detection and the lifetime of RSN. Numerical results show that F3&FCMSVDQR provides superior detection performances and the longest lifetime at large amount of residual alive RSs, while F3&GORS takes the second place in improving the detection performances at moderate-to-high SNRs, and has the same lowest power loss as CHEF&GORS at small amount of residual alive RSs.

A Cluster-based Routing Scheme to Improvethe Network Lifetime in Wireless SensorNetworks with a Mobile Sink

In a wireless sensor network which consists of a base station as a data sink and a number of sensor nodes, the clustering has been adopted as a way of reducing the energy consumption of communication. The clustering, however, has suffered from the rapid burnout of cluster heads. We propose a 2-tier data collection architecture which requires single-hop intra-cluster transmission and direct communication between mobile sink and cluster heads. To maximize the network lifetime, cluster heads are reselected based on the remaining energy level. The clustering and the routing algorithm are processed by the base station and a mobile node navigates among sensor nodes periodically, collects power usage data from each sensor node, and deliver the next clustering and routing information. A clustering and mobile sink navigation path search algorithm based on Voronoi graphs is proposed.

Secrecy Throughput in Inhomogeneous Wireless Networks with Nonuniform Traffic

We investigate the secrecy throughput of inhomogeneous wireless network, especially in cases of independent and uniform eavesdropper with single antenna and multiple antennas. Towards the inhomogeneous distribution of legitimate nodes, we novelly construct a circular percolation model under the idea of transforming “inhomogeneous” to “homogeneous.” Correspondingly, the information is transmitted by two ways: intracluster transmission and intercluster transmission. For intracluster transmission, a per-node secrecy throughput of [Formula: see text] is derived by circular percolation model, where n and [Formula: see text] represent the number of nodes and clusters in the network, respectively. As for intercluster case, a connection called “information pipelines” is built. Then the per-node secrecy throughput of [Formula: see text] can be obtained, where [Formula: see text] denotes the minimum node density in the network. Moreover, when the eavesdropper is equipped with [Formula: see text] antennas, the per-node secrecy throughput of [Formula: see text] and [Formula: see text] is achieved for intracluster and intercluster transmission, respectively.

A Spectrum-Aware Clustering for Efficient Multimedia Routing in Cognitive Radio Sensor Networks

Multimedia applications are characterized as delay-sensitive and high-bandwidth stipulating traffic sources. Supporting such demanding applications on cognitive radio sensor networks (CRSNs) with energy and spectrum constraints is a highly daunting task. In this paper, we propose a spectrum-aware cluster-based energy-efficient multimedia (SCEEM) routing protocol for CRSNs that jointly overcomes the formidable limitations of energy and spectrum. Clustering is exploited to support the quality of service (QoS) and energy-efficient routing by limiting the participating nodes in route establishment. In SCEEM routing, the number of clusters is optimally determined to minimize the distortion in multimedia quality that occurs due to packet losses and latency. Moreover, the cluster-head selection is based on the energy and relative spectrum awareness such that noncontiguous available spectrum bands are clustered and scheduled to provide continuous transmission opportunity. Routing employs clustering with hybrid medium access by combining carrier-sense multiple access (CSMA) and time-division multiple access (TDMA). TDMA operates for intracluster transmission, whereas CSMA is used for intercluster routing. Thus, a cross-layer design of routing, i.e., of medium access control (MAC) and physical layers, provides efficient multimedia routing in CRSNs, which is revealed through simulation experiments.

An Efficient Cluster-Based Routing Protocol for WSNs Using Time Series Prediction-Based Data Reduction Scheme

Wireless Sensor Networks (WSNs) consists of number of tiny, low cost and low power sensor nodes to monitor some physical phenomenon like temperature, pressure, vibration, seismic events, landslide detection etc. The communication subsystem in WSNs is primarily responsible for energy consumption. This becomes a constraint in these N/Ws owing to the usage of non-rechargeable battery having a limited power supply. Hence, the present work incorporates a time series prediction based data reduction scheme to develop an energy efficient routing protocol for minimizing energy consumption of WSNs. In this paper, three approaches have been devised involving energy efficient cluster based routing protocol with residual energy and distance based cluster head selection method. The cluster formation in the proposed approaches is different from Low Energy Adaptive Clustering Hierarchy (LEACH) routing protocol as its clusters will be fixed throughout the life of WSN. This leads to a reduction in overheads during cluster formation. Further, inter-cluster and intra-cluster transmissions are also minimized which results in an overall reduction in energy consumption of WSNs.

A Constructive Capacity Lower Bound of the Inhomogeneous Wireless Networks

Many research results in the direction of wireless network capacity are based on the homogeneous Poisson node process and random homogeneous traffic. However, most of the realistic wireless networks are inhomogeneous. And for this kind of networks, this paper gives a constructive capacity lower bound, which may be effective on network designing. To ensure significant inhomogeneities, we select both inhomogeneous node process and traffic. We divide the transmission into two parts: intra-cluster transmission and inter-cluster transmission. Within each distinct cluster, a circular percolation model is proposed and the highway system is established. Different with regular rectangle percolation model, the highway in our model is in the radial direction or around the circle. Based on this model, we propose a routing strategy and get the intra-cluster per-node rate. In the following, among these clusters, we set many "information pipes" connecting them. By getting the results of per-node transmission rate of each part, we can find that the bottleneck of the throughput capacity is caused by the difference of the node density all over the network region. Specially, the lower bound interval of the capacity can be easily obtained when the traffic is inhomogeneous.

LAAP: A Learning Automata-based Adaptive Polling Scheme for Clustered Wireless Ad-Hoc Networks

In multi-hop ad hoc networks, besides collision-free transmissions, channel utilization should be also enhanced due to the scarce bandwidth. In this paper, we propose a learning automat-based adaptive polling scheme for medium access scheduling in clustered wireless ad-hoc networks to enhance the channel utilization. In this scheme, each cluster-head takes the responsibility of coordinating intra-cluster transmissions so that no collisions occur. Taking advantage of learning automaton, each cluster-head learns the traffic parameters of its own cluster members. Cluster members are prioritized based on these traffic parameters. Each cluster-head then takes the traffic parameters into consideration for finding an optimal channel access scheduling within its cluster. By the proposed polling scheme, each cluster member is assigned a portion of bandwidth proportional to its need (i.e., traffic load). The results show that the proposed channel assignment policy considerably improves the channel utilization. Simulation experiments also show the superiority of the proposed polling-based medium access scheme over the existing methods in terms of channel utilization, waiting time for packet transmission, and control overhead.