Selection Of Intermediate Nodes Research Articles

Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network

With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm for the provision of flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, a heavy-workload IoT device may not respond on time. EC has sparked a popular wave of offloading user requests to edge servers at the edge of the network. The orchestration of user-requested offloading schemes creates a remarkable challenge regarding the delay in user requests and the energy consumption of IoT devices in edge networks. To solve this challenge, we propose a dynamic computation offloading strategy consisting of the following: (i) we propose the concept of intermediate nodes, which can minimize the delay in user requests and the energy consumption of the current tasks handled by IoT devices by dynamically combining task-offloading and service migration strategies; (ii) based on the workload of the current network, the intermediate node selection problem is modeled as a multi-dimensional Markov Decision Process (MDP) space, and a deep reinforcement learning algorithm is implemented to reduce the large MDP space and make a fast decision. Experimental results show that this strategy is superior to the existing baseline methods to reduce delays in user requests and the energy consumption of IoT devices.

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

SummaryNowadays, the wireless sensor network (WSN) with IoT is intended to monitor real‐world physical or environmental phenomena in a number of applications, including foreign areas such as health and habitat monitoring. The WSN‐IoT network generates huge volume of data, which has to be processed and accessed by the remote users. Due to this large volume of data generation and resource constraint ability make achieving optimal cluster based routing in WSN‐IoT. The location of the sensor nodes significantly affects the accuracy of the information collected, which determines the quality of service provided by the application system. WSN can have multiple conflicts, which can create different coverage holes. These holes will break the existing overlap or connection and affect the required operation of the networks. Therefore, it is essential to find and repair the coverage holes to ensure the full functioning of the WSN as a motivation of this study. In this paper, we suggest a novel Coverage Hole aware Optimal Cluster based Routing (CHOCR) scheme for WSN‐IoT. First, we propose Modified Lichtenberg optimization (MLO) algorithm for balanced clustering which improve the performance of coverage hole. Second, we develop a linear equilibrium optimization based decision making (LEO‐DM) technique to subtract trust value of each IoT node using multiple restraints in cluster and consider the highest trusted node is act as cluster head (CH). After that, a hybrid deep recurrent neural network (HD‐RNN) is developed for intermediate node selection to frame the routing between two nodes. Finally, we simulate our proposed CHOCR scheme on the NS3.26 simulator. According to energy consumption, network longevity, number of nodes that are still alive and packet delivery ratio, packet loss ratio and throughput, end‐to‐end latency and delay of our proposed CHOCR routing system, we compare it to other current routing schemes.

Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed spectrum. However, these cognitive radio devices (secondary users) are greatly affected by the original users (primary users) of licensed channels. Cognitive users have to adjust operation parameters frequently to adapt to the dynamic network environment, which causes extra energy consumption. Energy consumption can be reduced by predicting the future activity of primary users. However, the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory space. Moreover, many of these schemes lack methods to deal with the very busy network environments. In this paper, one semi-supervised learning algorithm, i.e., tri-training, has been employed to investigate the prediction of primary activity. Based on the prediction results of tri-training, a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy efficiency. Simulation results show the effectiveness of the proposed algorithm.

Multi Agent Assisted Safety Information Dissemination Scheme for V2V Communication in VANETs: Intelligent Agent Approach

The safety information dissemination plays a vital role in the VANET communication. It is a technique of transmitting the information at scheduled intervals or during road hazards by detecting the events using onboard system and interfaces. Information is shared between vehicles and road side units which are further used to predict vehicle collisions, road line crossings, environmental warnings, traffic data and road hazards. Interestingly the risk of lateral collisions and dense traffic for vehicles can be avoided by accomplishing fast data dissemination i.e. warning alerts by event detection. Vehicular technology which supports the safe mode of transportation is growing faster due to the deployment of new automated technology in the intelligent transportation system (ITS). The different scenarios used in vehicular communication are Vehicle to Vehicle (V-V), Vehicle to Infrastructure (V-I) and Vehicle to Internet. Some of the important characteristics of vehicular communications are the mobility, frequent changes in topology, varying transmission power of antennas, intermittent connectivity. ITS providing the solutions for most critical transportation issues and inspiring the researchers for the betterment of road safety. In this paper, we propose a multi agent based safety information dissemination scheme for vehicle to vehicle communication. The proposed algorithm performs the safety information dissemination with help of intelligent agents by optimizing the channel access techniques, message encoding and selection of intermediate nodes. Here the communication between source and destination is achieved with fever number of intermediate links by selecting the nodes in the special zone. Short interval codes which represent safety information are effectively transmitted in the intermittent nature of wireless connectivity. This proposed work describes the details of algorithm with associated network environment, multi agent functions and dissemination mechanism to illustrate the improvement in end to end delay, PDR, energy constraints etc. This method reduces the problem of broadcast storm by delivering the information to intended node. Simulation of the proposed work gives the improved results on PDR, latency and connection overhead.

A dynamic resource assignment scheme with aggregation node selection and power conservation for WBAN based applications

For real-time health status monitoring and treatment, the development of an efficient resource management scheme is an important research issue for wireless body area network (WBAN) based medical applications. To cope with the different application requirements, the coordination of cluster head selection, dynamic medium access control mechanism (MAC) considering both critical and normal data transfer, end-to-end connectivity, data transfer to multiple processing and destination device, intermediate node selection, and proper path selection, is mandatory. In this paper, a dynamic resource assignment scheme is proposed that assigns bandwidth and medical server resources to WBAN users for data transfer without any backoff delay and selects both the cluster head node and aggregation sensor node for the aid of WBAN sensors. The evaluation results show that the proposed dynamic resource assignment scheme performs better results than the compared random assignment scheme in terms of the delay overhead, energy overhead, goodput, and network lifetime.

Energy Efficiency Optimization for Intermediate Node Selection Using MhSA-LEACH: Multi-hop Simulated Annealing in Wireless Sensor Network

Energy usage on nodes is still a hot topic among researchers on wireless sensor networks. This is due to the increasing technological development increasing information requirements and caused the occurrence of information exchange continuously without stopping and impact the decline of lifetime nodes. It takes more effort to manually change the energy source on nodes in the wireless sensor network. The solution to such problems is to use routing protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH). The LEACH protocol works by grouping nodes and selecting the Cluster Head (CH) in charge of delivering data to the Base Station (BS). One of the disadvantage LEACH protocols, when nodes are far from the CH, will require a lot of energy for sending data to CH. One way to reduce the energy consumption of each node-far is to use multi-hop communication. In this research, we propose a multi-hop simulated annealing (MhSA-LEACH) with an algorithm developed from the LEACH protocol based on intra-cluster multi-hop communication. The selection of intermediate nodes in multi-hop protocol is done using Simulated Annealing (SA) algorithm on Traveling Salesman Problem (TSP). Therefore, the multi-hop nodes are selected based on the shortest distance and can only be skipped once by utilizing the probability theory, resulting in a more optimal node path. The proposed algorithm has been compared to the conventional LEACH protocol and the Multi-Hop Advance Heterogeneity-aware Energy Efficient (MAHEE) clustering algorithm using OMNeT++. The test results show the optimization of MhSA-LEACH on the number of packets received by BS or CH and the number of dead or alive nodes from LEACH and MAHEE protocols.

Intermediate node selection for Scatter-Gather VM migration in cloud data center

Virtual machine migration is used in cloud computing for server consolidation, system maintenance, opportunistic power savings, and load balancing. Scatter-Gather VM live migration is a new type of virtual machine migration. It is proposed to decouple the source quickly and reduce the time to evict the state of a migrating virtual machine from the host. In Scatter-Gather live migration, the memory pages of the VM are transferred not just to the destination but also to several pre-selected intermediaries. This open problem of selection of the intermediate nodes is not addressed in the literature. In this paper, we propose to define the problem of intermediate node selection in Scatter-Gather migration and prove that it is NP-complete. The problem is mathematically modelled as an integer programming problem based on two optimality criteria – minimizing eviction time and minimizing energy. Two heuristic algorithms – maximum-decrease-in-eviction-time and least-increase-in-energy are proposed to solve the problem and their performance is analyzed with respect to three parameters – eviction time, energy and total migration.

MCLMR: A Multicriteria Based Multipath Routing in the Mobile Ad Hoc Networks

In Mobile Ad hoc Networks (MANETs), nodes’ mobility, traffic congestion, and link quality estimation of the intermediate nodes are very crucial factors for establishing a reliable forwarding path between a source and destination node pairs. The unpredictable movement of nodes and random data traffic flow at a single node can cause congestion and network topology instability, which significantly lowers the performance of the ad hoc network. Indeed, the above-highlighted issues can be mitigated by implementing a more reliable mobility-centric, contention, and link quality-aware routing protocol for efficient data transmissions in a mobile network. This paper proposes a routing strategy called Mobility, Contention window, and Link quality sensitive multipath Routing (MCLMR) in MANETs, which considers the nodes mobility, contention window size, and link quality estimated value of the intermediate nodes in the optimal route selection. Also, Technique for Order of Preference by Similarity to Ideal Solution; a multicriteria decision-making technique, which provides weights according to node mobility, contention window size, and link quality estimated values, is also employed for the selection of intermediate nodes, whereas the Expected Number of Transmissions metric is used to minimize the effect of control message storm. The extensive simulations results prove that the proposed MCLMR routing scheme outperforms the conventional Multipath Optimized Link State Routing (MP-OLSR) and MP-OLSRv2 routing schemes in terms of network throughput, end-to-end delay, energy consumption, and packets loss ratio.

A Two Hop Relay Battery Aware Mote Scheme for Energy Redeemable and Network Lifespan Improvement in WSN

The nodes available in the market are now of miniaturized nature, also have the characteristics of low cost and power values. Wireless Sensor Network (WSN) will be a sparse network with independent points acting as energy sources. The application in WSN includes temperature sensing, sound sensing, and pressure session. The data is sent from one point to other using multi intermediate nodes. The selection of intermediate nodes will be done based on computation of trust. As the number of hops increases energy consumption will become high and this can be improved with the help of relay node which can store the data and deliver the data once destination is in its range. The selection of relay node can be done in multiple ways like random, based on meeting probability and in the proposed two hop relay battery energy aware algorithm makes use of multiple factors namely residual energy, virtual currency based data, meeting probability, security computation so that the communication can be optimized. The proposed method is also compared with several existing methods with respect to delay, energy consumption, alive nodes, dead nodes, lifetime ratio and residual energy.

Improved Compressed Network Coding Scheme for Energy-Efficient Data Communication in Wireless Sensor Networks

An improved energy-efficient compressed network coding method is proposed for the data communication in the wireless sensor networks (WSNs). In the method, the compressed sensing and network coding are jointly used to improve the energy efficiency, and the two-hop neighbor information is employed to choose the next hop to further reduce the number of the transmissions. Moreover, a new packet format is designed to facilitate the intermediate node selection. To theoretically verify the efficiency of the proposed method, the expressions for the number of the transmissions and receptions are derived. Simulation results show that, the proposed method has higher energy efficiency compared with the available schemes, and it only requires a few packets to reconstruct measurements with reasonable quality.

Clustering algorithm of hierarchical structures in large-scale wireless sensor and actuator networks

In this study, we propose a clustering algorithm to enhance the performance of wireless sensor and actuator networks (WSANs). In each cluster, a multi-level hierarchical structure can be applied to reduce energy consumption. In addition to the cluster head, some nodes can be selected as intermediate nodes (INs). Each IN manages a subcluster that includes its neighbors. INs aggregate data from members in its subcluster, then send them to the cluster head. The selection of intermediate nodes aiming to optimize energy consumption can be considered high computational complexity mixed-integer linear programming. Therefore, a heuristic lowest energy path searching algorithm is proposed to reduce computational time. Moreover, a channel assignment scheme for sub-clusters is proposed to minimize interference between neighboring subclusters, thereby increasing aggregated throughput. Simulation results confirm that the proposed scheme can prolong network lifetime in WSANs.

Resource Utilization of Multi-hop CDMA Wireless Sensor Networks with Efficient Forwarding Protocols

In this paper, we propose a new forwarding scheme for a multi-hop CDMA wireless sensor network (WSN). Selection of intermediate nodes considers detection probability along with maximum forwarding distance by taking into account the wireless channel condition, like path loss, and shadowing. Energy and latency performance using infinite ARQ between source and final destination of proposed scheme is studied. Performance of this scheme is compared with nearest neighbor based routing scheme, where an intermediate node in the route selects the nearest node within a sector angle, considered as search angle, towards the direction of the destination as the next hop. Further a frame work for packet size optimization based on resource utilization, capturing both energy consumption and delay, is presented.

An Adaptable Destination-Based Dissemination Algorithm Using a Publish/Subscribe Model in Vehicular Networks

Vehicular Ad Hoc Networks (VANETs) are highly dynamic and unstable due to the heterogeneous nature of the communications, intermittent links, high mobility and constant changes in network topology. Currently, some of the most important challenges of VANETs are the scalability problem, congestion, unnecessary duplication of data, low delivery rate, communication delay and temporary fragmentation. Many recent studies have focused on a hybrid mechanism to disseminate information implementing the store and forward technique in sparse vehicular networks, as well as clustering techniques to avoid the scalability problem in dense vehicular networks. However, the selection of intermediate nodes in the store and forward technique, the stability of the clusters and the unnecessary duplication of data remain as central challenges. Therefore, we propose an adaptable destination-based dissemination algorithm (DBDA) using the publish/subscribe model. DBDA considers the destination of the vehicles as an important parameter to form the clusters and select the intermediate nodes, contrary to other proposed solutions. Additionally, DBDA implements a publish/subscribe model. This model provides a context-aware service to select the intermediate nodes according to the importance of the message, destination, current location and speed of the vehicles; as a result, it avoids delay, congestion, unnecessary duplications and low delivery rate.

Mobile Anonymous Trust Based Routing Using Ant Colony Optimization

Problem statement: Ad hoc networks are susceptible to malicious attacks through denial of services, traffic analysis and spoofing. The security of the ad hoc routing protocol depends upon encryption, authentication, anonymity and trust factors. End-to-end security of data is provided by encryption and authentication, topology information of the nodes can be obtained by studying traffic and routing data. This security problem of ad hoc network is addressed by the use of anonymity mechanisms and trust levels. Identification information like traffic flow, network topology, paths from malicious attackers is hidden in anonymous networks. Similarly, trust plays a very important role in the intermediate node selection in ad hoc networks. Trust is essential as selfish and malicious nodes not only pose a security issue but also decreases the Quality of Service. Approach: In this study, a routing to address anonymous routing with a trust which improves the overall security of the ad hoc network was proposed. A new approach for an on demand ad-hoc routing algorithm, which was based on swarm intelligence. Ant colony algorithms were a subset of swarm intelligence and considered the ability of simple ants to solve complex problems by cooperation. The interesting point was, that the ants do not need any direct communication for the solution process, instead they communicate by stigmergy. The notion of stigmergy means the indirect communication of individuals through modifying their environment. Several algorithms which were based on ant colony problems were introduced in recent years to solve different problems, e.g., optimization problems. Results and Conclusion: It is observed that the overall security in the network improves when the trust factor is considered. It is seen that non performing nodes are not considered due to the proposed ACO technique.