Fuzzy-logic theory  has been mainly applied to industrial problems including production systems. There has been significant attention given to modeling scheduling problems within a fuzzy framework. Several fuzzylogicbased scheduling systems have been developed, although direct comparisons between them are difficult due to their different implementations and objectives. In general, a FuzzyLogicSystem (FLS) is a nonlinear mapping of an input data vector into a scalar output. Figure 7 depicts a FLS that is widely used in fuzzylogic controllers. A FLS maps crisp inputs into crisp outputs, and this mapping can be expressed quantitatively as y = f(x). It contains four components: fuzzifier, fuzzy rules, inference engine, and defuzzifier.
This Project discussed the security and privacy issues in healthcare applications using medical sensor networks. In this respect, we have found many important challenges in implementing a secure healthcare monitoring systemusing medical sensors, which reflects the fact that if a technology is safe, then people will trust it. Our proposed system will overcome all the security issues obtained from the previous research schemes. This project fully based on the encryption and decryption data for the user and then server to existing systemusing the universal key in my proposed systemusing the generating the key for an elliptic curve cryptography.
This research uses a wireless sensor networkbased on ZigBee specification, which make use of IEEE 802.15.4 protocol. It mainly operates on a 2.4 GHz radio frequency, with a data rate of 250 Kbit/s. It is much simpler and energy efficient than conventional wireless personal area networks (WPANs) such as Wi-Fi or Bluetooth. The physical range (line of sight) of each sensor node varies between 10-100m. The model is based on a modified mesh network. ZigBee supports tree, star and mesh networks. Out of these a mesh network is the most energy efficient and provides alternate routes between nodes (so that a backup route of Wireless sensor network structure is always available if Wireless sensor network structure particular route fails). This is specifically useful for areas floods due to the likelihood of central node failing. The proposed system consists of two main components: Wireless Sensor Network (WSN) and a Server as presented on Figure 1. The wireless sensor network is used to constantly monitor the flood levels in the area of interest while the server will receive, analyze, store the data, and sends the alerts when level thresholds are reached.
Compared to the Tier-2-Comm‟s design, Tier-3- Commdesign is intended for use in metropolitan areas. Inorder to bridge the two networks for inter- BAN andbeyond-BAN communications, a gateway device, suchas a PDA can be employed to create a wireless linkbetween these two networks.As shown in Fig. 2, the beyond-BAN tier communicationscan enhance the application and coverage rangeof an E- healthare system a step further by enablingauthorized healthcare personnel (e.g., doctor or nurse)to remotely access a patient‟s medical information bymeans of cellular network or the Internet.A database is also an important component of the“beyond-BAN” tier. This database maintains the user‟s profile and medical history. According to user‟s servicepriority and/or doctor‟s availability, the doctormay access the user‟s information as needed. At thesame time, automated notifications can be issued tohis/her relatives based on this data via various meansof telecommunications.The design of beyond-BAN communication isapplication-specific, and should adapt to the requirementsof user-specific services. For example, if anyabnormalities are found based on the up-to-date bodysignal transmitted to the database, an alarm can benotified to the patient or the doctor through emailor short message service (SMS). If necessary, doctorsor other care-givers can communicate with patientsdirectly by video conference via the Internet. In fact, itmight be possible for the doctor to remotely diagnosea problem by relying on both video communicationswith the patient and the patient‟s physiological datainformation stored in the database or retrieved by aBAN worn by the patient.An ambulatory patient travelling to a location outside his/her hometown might experience a
This paper demonstrates the use of WSNs as a key infrastructure enabling unobtrusive, continual, ambulatory health monitoring. This new technology has potential to offer a wide range of benefits to patients, medical personnel, and society through continuous monitoring in the ambulatory setting, early detection of abnormal conditions, supervised rehabilitation, and potential knowledge discovery through data mining of all gathered information. We have described a general WWBAN architecture, important implementation issues, and our prototype WWBAN based on off-the-shelf wireless sensor platforms and custom-designed ECG and motion sensors. We have addressed several key technical issues such as sensor node hardware architecture, software architecture, network time synchronization, and energy conservation. Further efforts are necessary to improve QoS of wireless communication, reliability of sensor nodes, security, and standardization of interfaces and interoperability. In addition, further studies of different medical conditions in clinical and ambulatory settings are necessary to determine specific limitations and possible new applications of this technology.
can be used to develop a patient monitoring system which offers flexibility and mobility to patients. Use of a WBAN will also allow the flexibility of setting up a remote monitoring system via either the internet or an intranet. For such medical systems it is very important that a WBAN can collect and transmit data reliably, and in a timely manner to the monitoring entity. In this paper we examine the performance of an IEEE802.15.4/Zigbee MAC based WBAN operating in different patient monitoring environment. We study the performance of a remote patient monitoring systemusing an OPNET based simulation model.A WirelessBodyAreaNetwork (WBAN) WBAN based on a low cost wireless sensor network technology could greatly benefit patient monitoring systems in hospitals, residential and work environments . A WBAN system allows easy internetworking with other devices and networks, thus offering healthcare worker easy access to patient's critical and non-critical data. One of the main advantages of a WBAN is to monitor patients remotely using an intranet or the internet. A WBAN could be seen as a special purpose wireless sensor network with a number of additional system design requirements. A WBAN is mostly likely to incorporate wearable and implantable node operating in two different frequencies. An implantable node is most likely
s healthcare costs are rapidly increasing with the world’s population, there has been a need to monitor a patient health status anywhere both in and out of the hospital. This demand and the advancement in technology in mobile electronic devices, wireless communication, portable batters, and sensors as led to the development of wirelessbodyareanetwork (WBANS). A wirelessbodyareanetwork (WBAN) is a network with a special purpose design to operate automatically can autonomously connect and interact with various medical appliances and sensors, which is located inside or outside the human body. Apart from cost reduction and flexibility applications of WBAN in healthcare will offer significant advantages such as mobility of patients since portable monitoring devices and sensors are being used and secondly WBAN uses location independent monitoring devices which are not there in the contemporary electronic monitoring systems furthermore WBAN can connect itself to the internet and transmit data to a remote database or server and WBAN application can also be extended into military and sport areas where the soldier or player health status can be monitored. The main purpose of this paper is to present a very comprehensive and concise survey on WBAN and it various applications within the healthcare industries. Section (2) presents WBAN sensing and monitoring application in various medical scenarios, Section (3) examines the WBAN system architectures, WBANS network designs techniques such as the power reliability and efficiencies of WBAN is presented in Section (4). Section (5) explores various approaches to routing in WBAN, security techniques and protocols of WBAN are being presented in Section (6). While Section (7) presents the future scope of WBAN and its conclusion.
2. REVIEW OF LITERATURE Afridi et al. “HEAT: Horizontal Moveable Energy- efficient Adaptive Threshold-Based Routing Protocol for WirelessBodyArea Networks” Approaches of usingWirelessBodyArea Sensor Network (WBASN) in healthcare applications are getting much popularity. WBASN is a hot topic among the research community. Our proposed Horizontal moveable Energy-efficient Adaptive Threshold-based (HEAT) protocol is well suited for horizontally moving (walking) human body. The on body nodes attached at arms and legs of human body move forward and backward to sink during horizontal movement. We use direct communication for emergency data and multi-hop communication strategy for normal data transmission. Simulation results show that our proposed protocol performs better in terms of stability period and network lifetime.
Recently, with the technological advancement in sensors, low power integrated circuits and wireless communication, WirelessBodyAreaNetwork (WBAN) has emerged as one of the promising techniques. As a subgroup of wireless sensor networks, it is mainly designed to monitor the health conditions of the patients for early risk detection. A WBAN makes use of wireless sensor nodes that can either be implanted inside the human body or worn externally. These intelligent sensors monitor various vital signs such as temperature, pressure and ECG and provide feedback to the user. Data collected by the various sensors are analyzed and then transmitted to the medical servers or Application providers (APs). The reliable message transmission between the sensors and the application providers is achieved using a Portable Personal Device (PPD).Fig.1 shows the architecture of WBAN in which the information from the body sensor is transmitted to the portable personal device which in turn is transmitted to the medical servers through the internet.
Thus the sensor attached in the body will transmit the data for 1 to 40 times per hour where the BAN will provide a real time feedback to the users this can be made possible with the help of the specific applications in the personal device. The sensor nodes that are of deployed in the human body will have a contact with the neighbor nodes where the nodes will establish a key generation process to provide a group key identity to the healthcare server, these groupkey is classified based on the identity that is provided by the sensor nodes when they are of installed in the patient body ,the fig(1.2) represents the users of the home healthcare. The server will also have separate identity based on the type of the diseases they are of divided when these sensor node identity matches with that of the identity in the server then the group key identity is valid or else the key is mismatched .The nodes will inform the emergency to the other nodes in case of the critical situation where these notification is taken to the personal device which will in turn inform to the healthcare server
14. This mechanism is compared with present DSDV basedsystem where only admission control is employed for QoS. The proposed system initially looks for a viable network configuration changes locally in case of a link failure around the faulty area, depending on the current radio. Then the system generates number of reconfiguration plans to automatically recover from this link failure. The main advantage of this system is that it does minimum amount of changes in the network configuration of the surrounding healthy network. Next Automated Reconfiguration system also has a monitoring module that continuously monitors the state of the network. The planning algorithm uses this link quality information to determine the network changes to be done, in-order to meet the required QoS demands. Suppose we are watching a YouTube video on laptop, there are might multiple interface's to internet available such as modem, Bluetooth, wifi etc. whenever one of the currently used internet connection goes down, so in this case it has to automatically select the other available connection and keep the connection active without any of the human intervention.
Today many researches emerged on WirelessBodyAreaNetwork (WBAN) technology, cloud computing technology, telemedicine,health assessment system and home-care model both in native and abroad which have taken forward a numerous count of innovative theories and their applications. In this research design, we used a Raspberry Pi that takes the role of a Central Processing Unit (CPU) of an embedded system. In contradiction to that, apersonal computer that has a large domain and variety of applications which can be implemented always depending upon the programming. This research aims at monitoring the various health related parameters of the patient by using various sensors. The sensors used in this research were attached to the patient’s body and collect data in the analog data form.
Abstract: With the wireless communication, the ways of communication in present era of technology has changed which helps in fastest and efficient way of communication in each and every domain. In the field of medical science, to sense the human body activities such as heartbeat, blood pressure and other activities performed by internal body parts of the human, Wireless Sensor Network is employed. Then this sensed data is transmitted to the centralized server. The information that is collected is made to transfer to the destination through a dedicated route created by routing protocols in form of data packets. Thus, the network sometimes faces the issue of congestion due to increased data traffic to the nodes. The present paper defines an enhanced congestion handling concept for WirelessBodyAreaNetwork. For this purpose, the cost function of the nodes is evaluated on the basis of major factors such as distance, residual energy and delay. Additionally, by applying the Fuzzy Inference System, the congestion control model is executed. It also improves the routing strategy by introducing the firefly algorithm based forward-looking node selection approach. For evaluation, the proposed work is simulated in MATLAB and compared with the traditional congestion technique. The simulation results show that the lifetime of the network increases by 30%. The efficiency of packet received at sink improves by 18%. Path loss in the present study is 33% less as compared to traditional approach. And, also consumes near about 8% less energy.
One of the many applications of WirelessBodyAreaNetwork is in medial environment where conditions of patients are continuously monitored in real time. In order to deploy a complete wireless senor network in healthcare systems, wireless monitoring of physiological data from a large number of patients is one of the current needs. A wirelessnetwork containing small interdependent sensor nodes is called WSN (wireless sensor network). Such a wireless sensor networksystem is very suitable to be used in hospital environments to reduce human errors, to reduce healthcare cost, to provide more time to health professionals to deal with other important issues.Physiological data are to be measured and monitored with the help of this proposed system. The data that is measured by these sensor nodes is sent to a base station using RF (radio frequency) communication. The communication between the nodes and the base station can be a single hop communication or it can be a multi hop communication depending on the remoteness of the sensor node. The base station also controls the whole network. On each sensor node there are various hardware components. Some of those are Microcontroller, Sensor or Transducer, Radio Frequency Transceiver, Battery or some other power source. Several other components are used for signal processing purpose to bring the sensor output signal in proper form and for proper power supply required for main components. The components required for this purpose are voltage regulators, Amplifiers, resistors and capacitors. The main purpose of this system is to achieve the communication between different sensor nodes and a single receiver simultaneously.
DESIGN Wirelessbody sensor node serves a basic element in a WBSN system , , . Fig. 4 depicted the block diagram of a wirelessbody sensor node. It includes an adaptive controller, a phase lock loop (PLL), two down-sample circuits, an ADC, a sensor, a RF transmitter, and an antenna. The details of each component in the wirelessbody sensor node are described as follows: FIGURE 4. Block diagram of a wirelessbody sensor node. 1) Adaptive Fuzzy Resolution Controller: The adaptive fuzzy resolution controller was implemented by combinational logic circuits and finite state machines (FSMs). It is used to select the sampling clock of the ADC from three different clocks. First, it compares the body values with the windowconditions, as shown in Fig. 1. Second, it obtains the variance between the current and the previous values which is defined in Eq. 2. After getting the variance, the second input parameter ‘‘g’’ can be acquired by comparing with the slope conditions. Finally, the adaptive fuzzy resolution controller can select the sampling
Figure-4 contains the proposed work where there are nine body nodes, a BNC, Base network and world network. The whole network assumes that each node has their own battery and communicated to the BNC. This second scenario is implemented by BNC algorithm using the Probabilistic energy-aware routing protocol. In Figure 5 The human body acts as the node and it sends the sensed data to the BS which acts as the BNC and also the BNC is further connected to PDA or any mobile system, in our system sink acts as the mobile device which receives the data from BNC where it gets the sensed data from the human nodes. Initially the BNC is placed in a position and the position is changed by computing the MPBP algorithm. The entire data is communicated to Base World Utility which acts as the home network and sensed data can be retrieved anywhere within the network.
Bodyarea networking is enabled by the rapid development of wireless sensor networks and biomedical engineering. The healthcaresystem has a specific communicating technology from transferring the data to the patient monitoring system to the doctors. There is several methods have been proposed for the efficient transfer of health recorded data. Even though every method has some of the disadvantages in it. We assume that the healthcaresystem is a bodyareanetworksystem which is based on performance evaluation of UMTS (Universal Mobile Telecommunication system) and Ultra wide Band technology . Wiring system for sensor node connections providing power and signal lines to each of the sensor nodes must be realized with the minimum number of wires. This is because wires are hard to implement in stretchable textiles, they are most fragile and expensive part of such a smart textile requiring that their number should be kept to a minimum . This method should provide data rates of at least several full frames per second for the system to be usable in real time, this includes gathering data of every sensor in the system in each frame and transmitting the gathered data for processing . Active functionality could include power generation or storage, human interface element, radio functionality or assistive technology. Power generation can be achieved through piezoelectric elements that harvest energy from motion or photo voltaic elements . A self test to detect a heart attack using wearable sensors and mobile phones uses sensor nodes placed over the human body for pickup the signal from the human parts which are powered by the external battery, which shows the non reliability of the data recording system . A smart phone can be used to diagnose the rate of the signals from the wireless sensors which should provide a necessary action by the hospital through it. An android application is developed for communicating the data from the sensors by cloud storage of the internet thus the required action is taken by the controller .
One of the greatest challenges for software developers is forecasting the development effort for a software system for the last decades. The capability to provide a good estimation on software development efforts is necessitated by the project managers. Software effort estimation model divided into two main categories: algorithmic and non- algorithmic. These models too have difficulty in modeling the inherent complex relationships between the contributing factors, are unable to handle categorical data as well as lack of reasoning capabilities. The limitations of these models led to the exploration of the techniques which are soft computing based. In This paper we have compared neural network and fuzzylogic model for software development effort estimation. It will help us to make accurate software effort estimation by these estimation techniques
DejanDinevski, Peter Kokol, GregorStiglic, Petra Povalej  elaborates the use of self-organization to combine different specialist opinions generated by different intelligent classifier systems with a purpose to raise the classification accuracy. Early and accurate diagnosing of various diseases has proved to be of vital importance in many healthcare processes. In recent years intelligent systems have been often used for decision support & classification in many scientific and engineering disciplines including healthcare. However, in many cases the proposed treatment or the prediction or diagnose can vary from one intelligent system to another system similar to the real world where various specialists may have different opinions. The main aim here is to imitate this situation in the manner to combine different opinions generated by diverse intelligent systems using the self- organizing abilities of cellular automata because most ensembles are construct using definite machine learning method or a combination of that method, but the drawback being this is that the selection of the appropriate method or the combination of that method for a specific issue must be made by the user. So, to overcome this issue an ensemble of classifiers is constructed by a self-organizing system applying cellular automata (CA).
The intruder identifies Cluster Head as the nearest possible node. The intruder tries to communicate with one of the Cluster Head with hidden MAC address in listen mode only. Finally the intruder identifies Cluster Head in the first cluster to be the nearest Cluster Head. it tries to communicate with the Cluster Head with hidden MAC address in listen mode only. It successfully bonds with Cluster Head in listen mode keeping its identity hidden. The intruder has the capability of interpreting the packets being sent and received by Cluster Head. the Cluster Head is instructed to block the receiving and sending of data to ensure that the intruder can no longer infect the functioning of the wireless Sensor network