In this paper a reliable, efficient in terms of power consumption and high stable network is proposed for Wireless Body Area Sensor Networks. Eight sensor nodes are used from which two are recording critical data. These two sensors are not apart of multi-hopping but send data direct to the sink. Remaining six sensors are computed to become a forwarder node. Forwarder nodes gathers data from sensors and after aggregating sends ti the sink. Two parameters are set for cost function so that a forwarder node is selected. If a sensor is having minimum distance and maximum energy as compared to the entire nodes then it will be selected as forwarder node. Multi-hopping is used to reduce the distance of data communication and to save energy consumption. Simulation is carried out and shows stable results.

An Energy Efficient Routing Protocol for Wireless Body Area Sensor Networks

Routing is one of the important topics in wireless sensor networks (WSNs), and has been attracting the research community in the last decade. Routing based on ant colony optimization (ACO) is a type of transmission method rich in characteristics. There are several survey papers that present and compare routing protocols from various perspectives, but the survey on ACO-based routing is still missing. This paper makes a first attempt to provide a comprehensive review on ACO-based routing protocols in WSNs. First, we offer a classification of these routing algorithms. Second, the most representative ACO-based routing protocols are described, discussed, and qualitatively compared. Finally, we put forward some open issues concerning the design of WSNs. This survey aims to provide useful guidance for system designers on how to evaluate and select appropriate routing schemes for specific applications.

Routing Protocols Based on Ant Colony Optimization in Wireless Sensor Networks: A Survey

Game theory is a field of applied mathematics that studies strategic behavior of rational factors. In other words, game theory is a collection of analytical tools that can be used to make optimal choices in interactional and decision making problems. Optimization in mathematics and computer science is the choice of the best member of an existing collection for a specific purpose. Several optimization methods have been used in many problems to minimize costs or maximize profits. From a particular point of view, it can be said that the game theory is in fact a kind of optimization. In this paper, a combined use of game theory and optimization algorithms has been reviewed and a new categorization is presented for researches which have been conducted in this area. In some of these combinations, game theory has been used to improve the performance of optimization algorithms, and in some others, optimizations methods help to solve game theory problems. Game theory and optimization algorithms are also used together to solve some other problems.

A Survey on the Combined Use of Optimization Methods and Game Theory

With the coming of the fifth-generation (5G) mobile communications, in mobile edge computing (MEC), the growth of user services and the personalization of QoS requirements have posed great challenges for heterogeneous wireless networks (HWNs) access selection. Based on the multiattribute decision theory and the fuzzy logic theory, we propose a novel network selection scheme for multiservice QoS requirements in MEC. The main procedures of the scheme include dynamic adaptive process, fuzzy process, hierarchical analysis, and integrated attributes assessment. The scheme proposed contributes to efficiently reduce the ping-pong effect and effectively select accurate network in a dynamic environment. Simulation results show that our scheme can select network access according to the type of user services and whether to switch networks. In addition, compared with commonly used simple additive weighting (SAW), random access selection (RAS), and price-based and QoS-based network selection scheme, our scheme has better performance in improving average user satisfaction and reducing access failures.

Adaptive Multiservice Heterogeneous Network Selection Scheme in Mobile Edge Computing

Device-to-device (D2D) communication enables the user equipments (UEs) located in close proximity to bypass the cellular base stations (BSs) and directly connect to each other, and thereby, offload traffic from the cellular infrastructure. D2D communication can improve spatial frequency reuse and energy efficiency in cellular networks. This paper presents a comprehensive and tractable analytical framework for D2D-enabled uplink cellular networks with a flexible mode selection scheme along with truncated channel inversion power control. Different from the existing mode selection schemes where the decision on mode selection is made based only on the D2D link distance (i.e., distance between two UEs using D2D mode of communication), the proposed mode selection scheme for a UE accounts for both the D2D link distance and cellular link distance (i.e., distance between the UE and the BS). The developed framework is used to analyze and understand how the underlaying D2D communication affects the cellular network performance. Through comprehensive numerical analysis, we investigate the expected performance gains and provide guidelines for selecting the network parameters.

Analytical Modeling of Mode Selection and Power Control for Underlay D2D Communication in Cellular Networks

Balancing emergency message dissemination and network lifetime in wireless body area network using ant colony optimization and Bayesian game formulation

In this paper, heart disease prediction modeled using partially observable markov decision process (POMDP) is proposed. In emergency, the patient is alerted through the doctor by fog computing. Ambulance sent to the location of patient at critical situations. The doctor gets the data through fog computing iFogSim. Fog computing in healthcare is a new area, which gains more attraction in research community. Many researches focus on cardiovascular disease i.e. heart disease. The important risk factor for cardiovascular disease is increase in blood viscosity. The highly viscous nature of blood does not allow the blood to flow creating a resistance in the blood flow. Heart disease risk factors are high blood pressure, obesity, diabetes, increased blood viscosity, etc. With the help of POMDP’s states, observations, beliefs, probability transitions the patient health is noted. The POMDP model for heart disease prediction computes the policy approximation using states and timeslots. Rewards are tabulated using policy approximations over different iterations.

Blood Viscosity based Heart Disease Risk Prediction Model in Edge/Fog Computing

A Mobile Ad hoc Network (MANET) is a network composed of numerous autonomous mobile nodes. In recent times, the opportunistic network, a type of MANET is gaining a lot of significance among the researchers, as it is capable of communicating with the sink node through an efficient selection of relay nodes. In the opportunistic networks, the node does not seek any knowledge about the network topology as it selects the efficacious relay node for transmission of packets. However, MANET requires nodal information about network topology. In the opportunistic network, the data stockpiled in the packets are transmitted from a source node to a sink node by utilizing relay node opportunistically for every hop. However, this type of communication leads to delayed data delivery with increased hops as a consequence of the unsystematic selection of relay nodes. To overcome these constraints, this article focuses on the selection of optimal relay nodes for attaining faster data delivery, by unveiling the location and by predicting the mobility pattern of the neighbor nodes. Hence, this research paper proposes Particle Swarm Optimization algorithm for the selection of optimal relay nodes by locating the neighbor nodes within an established Inter-Communication Range employing Cartesian based localization technique and by analyzing their mobility pattern using recurrent neural network-long short-term memory prediction model. The results of the proposed methodology are compared with four other existing methods, namely, MaxProp, Spray and Wait, and Epidemic. The comparative results infer that the proposed method is efficient in terms of performance, reduced hops, reduced delay with enhanced packet delivery ratio, and improved overhead ratio.

Heuristic Relay-Node Selection in Opportunistic Network Using RNN-LSTM Based Mobility Prediction

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.

https://www.mdpi.com/1424-8220/20/7/2153/pdf

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care.

The energy efficiency is considered as a major issue due to large power consumption of eNBs in heterogeneous cellular networks. In this paper, a novel relay station (RS) deployment scheme a...

/pdf/energy-efficiency-based-on-relay-station-deployment-and-3ws60ra78u.pdf

P. Vetrivelan

Papers

Balancing emergency message dissemination and network lifetime in wireless body area network using ant colony optimization and Bayesian game formulation

Blood Viscosity based Heart Disease Risk Prediction Model in Edge/Fog Computing

Heuristic Relay-Node Selection in Opportunistic Network Using RNN-LSTM Based Mobility Prediction

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care.

Energy efficiency based on relay station deployment and sleep mode activation of eNBs for 4G LTE-A network