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Human body functions require brain activities. Neurons present in the brain control these activities. There are over 86 billion neurons in the brain, and these neuron’s interactions are responsible for the brain activities. Neurocomputing is a computational process to investigate the pattern of brain signals for various purposes. It is an exciting and interesting research topic for neuroscience and computer science field researchers to visualize human behavioral actions through neural activity. Neurocomputing has established its identity by analyzing brain signals for clinical and non-clinical applications using machine learning and deep learning. Here, brain signals are extracted, and relevant features are collected to classify. There are some brain imaging signals, such as Electroencephalogram (EEG), Positron Emission Tomography (PET)—obtrusive atomic imaging strategy based on gamma radiation, metabolic screen action, Magnetoencephalography (MEG)—gather the Magnetic fields created by neural movement, and Functional Magnetic Resonance Imaging (fMRI)—measures changes in the bloodstream related with neural activity to perceive brain activities. In non-clinical applications such as motor imaging, drowsiness detection and emotion detection, electroencephalography (EEG) signals are used more frequently than other brain signals. EEG signals have a good temporal resolution, versatile, lightweight, and easy to acquire and use. Moreover, these signals refer to nerve activity without surgery and can mobilize the mental activity. The main theme of this chapter is to understand how the EEG-based methods for detecting drowsiness were developed for sleep analysis and prevent drowsiness-related risks by combining neuroscience and computer science knowledge.

Sensor network and Internet of Things are the emerging paradigms for data collection and monitoring purpose today. It has significant application in industry and academic research. Body Sensor Network (BSN) has found a lot of research attention recently due to its demand in healthcare management and hospital automation. In this paper a health monitoring system has been proposed for assisting doctors and patients within a hospital. The key focus of the work is on the convenience, reliability, efficiency and scalability of the system. Real time data monitoring, doctor prescription management, data sourcing from hospitals for medical analysis are the typical contributions discussed in the paper. The work has been analyzed using network simulations to determine the performance of the proposed architecture. Two different communication protocols and their performance have been analyzed to determine the best choice for the proposed hospital management system.

A decade earlier, the basic guiding theory of the blockchain was implemented. It took a few years for the technology to be widely recognized outside the computer science sector in industry and academic communities. Since then, several scientific institutions have taken up the topic. Through this chapter, the authors focus not only on the working mechanism of the technology but also towards the use cases in varied industries. Blockchain implementations are an effective way to actively move business expertise to study goals that support both technical growth and testing through analysis, design, and research approach.

IoT is the administration systems which are worldview used and where all the interconnected, keen articles constantly create information and sends it over the Internet. A significant part of the IoT activities is equipped in the direction of assembling minimal effort and vitality proficient equipment for these articles, just as the correspondence innovations that give objects interconnectivity. Moreover, the answers and solutions for managing and using the monstrous volume of information created by these articles of IoT are yet to develop. Conventional database arrangements miss the mark in fulfilling the refined application demands for an IoT device that has a global scale. The recent answers for IoT information the board conveys halfway parts for IoT conditions along an extraordinary spotlight on sensor systems and interconnection. This chapter provides an overview of information and the arrangements which are opted for IoT and the subsystems of IoT. We feature the unmistakable and distinctive plan that we accept to be tended into IoT data management solutions and examine how they are drawn closer by the proposed arrangements. We, at last, suggest the information and the board structure for IoT that deliberates over the talked about plan components and goes about as a seed to an exhaustive IoT information arrangement procedure. The framework we work will adopt a federated, data- and the information-significant method to loop the diversified items along with the profusion of information for the devices that are coined for IoT.

In graph theory, definition of clique is given as sub-graph(complete) where each node is connected to each other . Identifying functional units in complex network, modelling evolution of social network and community detection are some of the applications of maximum clique problem under graph theory. This problems comprises of a NP-Hard problem where all cliques are enumerating in large scale complex network . However, by applying heuristic based optimisation algorithms it may be possible to identify maximal clique in complex network in reasonable amount of time. The problem can be solved in polynomial time whereas, other NP-hard problems like Travelling Salesman Problem(TSP), graph colouring problem, 3-SAT problem etc. could be diminished to maximal clique problem in order to process in polynomial amount of time. Maximal Cliques are not subset of any other cliques. Out of all the maximal cliques, the clique with maximum cardinality or maximum weight is treated as maximum cliques in the network. Detection of maximum clique problem is formulated by combining the optimal problem where the aim is to maximize sum of weights or the cardinality of the sub graph. A number of approaches have been presented in literature to identify cliques in the network. Most of them are computationally expensive in analyzing large scale network. In this paper, an efficient genetic algorithm has been proposed to enumerate all the cliques in large scale network.The effectiveness of the proposed algorithm has been verified by comparing computational complexity with other existing algorithms.

IoT solutions are based on low power and lossy networks (LLNs). LLN network consists of nodes having limited processing power, memory and battery capacity. The Routing Over Low Power And Lossy Networks (ROLL) group have developed an RPL protocol for LLN. To fulfil the QoS requirements for different applications, the RPL needs to be optimized and the resources have to be utilized efficiently. The RPL protocol uses an objective function that helps to calculate the rank of a node and select an optimized parent through which data is forwarded. The single routing metric used by RPL does not satisfy all the QoS requirements. So, we have proposed a new composite routing technique called EHA-RPL which combines three routing metrics, i.e. Expected Transmission Count (ETX), hop count and available energy using two approaches, namely lexicographic and additive approach. It is observed that our approach shows better performance in terms of energy consumption, network latency and packet delivery ratio as compared to the traditional approaches.

In this paper, we compare the performance of IT-SDN framework and Mininet-WiFi OpenFlow Network Emulator for Software-Defined Wireless Networks. The topology consists of a number of source nodes generating traffic towards a sink node. The controller sets up flow rules for the packets, following which the switches route the wireless traffic. The communication between the controller and the switches is governed by the underlying southbound protocol. The performance of IT-SDN and Mininet-WiFi for the stated network scenario was measured using the metrics: end-to-end delay, throughput, packet delivery ratio and control overhead. The results show that IT-SDN outperforms Mininet-WiFi in terms of the above mentioned metrics for the given wireless network scenario.

Applications of the Internet of Things (IoT) span from the industrial field to the agriculture field and from the smart city to the smart city healthcare. The wireless sensors play a major role in making these applications work as they are desired. These tiny, light-weight and low battery-powered sensors make the smallest of the smallest device communicate in an IoT environment. All of these applications require hundred to thousands of nodes to solve a purpose. Routing in such energy constrained network becomes a challenging task, so scalability in IoT is one of the major challenges that need to be solved. Routing protocol for low power and lossy networks (RPL) is one of the protocols developed by the Routing Over Low Power And Lossy Networks (ROLL) group to meet the QoS requirements for various IoT based applications. However, the existing versions of RPL fail to provide better results when the number of nodes in the network is increased. Our proposed protocol Clustered Additive RPL (CA-RPL) uses a weight based clustering technique to meet the efficiency of a scalable network. In addition to that, the path selection for data transmission is done by considering three parameters namely Expected transmission count (ETX), hop count and available energy. It is observed that the proposed approach outperforms other approaches in terms of packet delivery ratio, end to end delay and energy consumption in the network.

Wirelessly networked systems of sensors could enable revolutionary applications at the intersection of biomedical science, networking and control systems. It has a strong potential to take ahead the applications of wireless sensor networks. In this paper, a wireless brain computer interface (BCI) framework for drowsiness detection is proposed, which uses electroencephalogram (EEG) signals produced from the brain wave sensors. The proposed BCI framework comprises of a braincap containing EEG sensors, wireless signal acquisition unit and a signal processing unit. The signal processing unit continuously monitor the preprocessed EEG signals and to trigger a warning tone if a drowsy state happens. This experimental setup provides longer time EEG monitoring and drowsiness detection by incorporating the clustering mechanism into the wireless networks.

A mobile ad hoc network (MANET) is a network with no infrastructure, operating on wireless mobile nodes. MANET consists of quickly deployable, independent as well as self-configuring nodes with no centralized administration. There is no fixed topology in the network and the nodes have limited energy and computing resources. Jitter, a small random variation in timing, is widely used in between the periodic transmission of the control message in wireless communication protocols. It is an especially important technique during route discovery process when a process may cause a situation where adjacent nodes have to broadcast concurrently, then the use of jitter makes a protocol able to avoid concurrent packet transmissions over the same channel by neighboring nodes in the network. In AODV, a small delay during the flooding of a control message is used during route discovery process to avoid simultaneous packet transmission by neighboring nodes, which might result in the collision between these packets. The proposed energy and load-based protocol (ENL-AODV) introduces energy and load factor in the calculation of jitter while forwarding of route requests (RREQs), making it select the path with enough energy to transfer the data packet. As simulation results describe, ENL-AODV improves the efficiency of ad hoc networks, increases packet delivery ratio, throughput, and network lifetime, and also decreases average end-to-end delay.

Wireless sensor network swears an exceptional fine-grained interface between the virtual and physical worlds. The clustering algorithm is a kind of key technique used to reduce energy consumption. Many clustering, power management, and data dissemination protocols have been specifically designed for wireless sensor network (WSN) where energy awareness is an essential design issue. Each clustering algorithm is composed of three phases cluster head (CH) selection, the setup phase, and steady state phase. The hot point in these algorithms is the cluster head selection. The focus, however, has been given to the residual energy-based clustering protocols which might differ depending on the application and network architecture. In this paper, a survey of the state-of-the-art clustering techniques in WSNs has been compared to find the merits and demerits among themselves. It has been assumed that the sensor nodes are randomly distributed and are not mobile, the coordinates of the base station (BS) and the dimensions of the sensor field are known.

The growth of interest and research on multihop wireless network is exponential in recent years. In mobile ad hoc networks (MANET), the nodes play the role of routers to forward the packets of neighbor nodes as there is no fixed infrastructure available to do so. Clustering is a proven solution that maps the architecture of cellular networks into ad hoc networks. Here, selected nodes form the virtual backbone of the network and take part in packet routing. This achieves faster packet delivery as limited nodes are responsible for the same even though the network is not strongly connected. In this paper, a distributed topology adaptive clustering algorithm is designed that requires local information by the nodes for the formation of clusters. The role of cluster head is fairly distributed among the nodes to obtain a longer network lifetime. The change of cluster heads and the mobility of nodes disturb the node connectivity resulting in communication instability. To overcome such situations, a topology control protocol is developed that adjusts the transmission range of concerned mobile nodes to achieve local connectivity among nodes within the clusters even after the hand-off by the heads takes place.

Now-a-days power management plays a vital role in reducing the consumption and efficient utilization of the resource. In traditional system, manual operation of electrical devices in university gets unnoticed, that leads to maximum wastage of power i.e., with different device operating even when the classroom is abandoned. These extended hours of operation leads to maximum power wastage. In order to overcome this problem, we have designed a “smart and intelligent power saving system for Indian Universities”, where every classroom is equipped with passive infrared sensor (PIR) which responds to occupancy, and corresponding devices are switched ON/OFF automatically. The entire system is monitored and controlled by the central base station.

Energy has always been the main issue for wireless sensor networks because in many situations battery recharging or replenishment is not possible. Many solutions have been provided for energy conservation. Clustering protocols have been successful for solving this issue to an extent but are not perfect. In our proposed algorithm we utilize the ability of the sensor nodes to control their transmission power range. By utilizing this ability we are able to minimize their intra cluster energy. Although this is local energy saving but this leads us to minimization of overall network energy consumption. The other thing that can be considered is about the task of a cluster head in clustering algorithms where cluster-head is doing the task as transmitter and receiver simultaneously. Providing these tasks to a single node is not efficient. So we are introducing the notion of a special node called s-node where this s-node is working as a transmitter for a cluster and sending the aggregated data to the sink. We have simulated the proposed scheme with LEACH and LEACH-C protocol and simulation results show that the proposed scheme is better in terms of network life time than both protocols.

In this paper, we propose an authenticated data transmission technique for Clustered wireless sensor network (CWSN). The proposed scheme starts by assigning the identity, shared secret key and an encryption key to the sensor nodes by the base station (BS). Driven by the technology advances in micro-electronics, it is possible to run some key management techniques at each sensor level. This step helps in authentication of the sensors and secured communication in the network. Subsequently the sensor nodes are deployed in the hostile environments, due to the self healing nature of the sensors, they will form a network to transmit the data. The secured communicating protocol described in this paper guarantees that, when two sensor nodes are in communication, they must have gone through prior authentication and key pre-distribution process. The proposed scheme is invariant to the environment where the clusters are formed dynamically and periodically. According to the message communication point of view, we are restricting the network from receiving junk message from the adversaries or compromised nodes due to the authentication and confidentiality.

Mobile ad hoc networks (MANET) support multi hop routing in the absence of central base station. The change in network topology due to the node movement associated with the link failure and creation, scarce in radio resources and bandwidth, limited battery power and computing capability pose challenges in packet routing in MANET. The proposed Energy Saving Ad hoc Routing (ESAR) algorithm targets to achieve better energy efficient with a longer network life time. The algorithm selects a path for routing by considering the actual distance between the source and destination along with the minimum available energy of a node in the path. This selected path is chosen as the best path for packet transmission till any node in the path exhausts battery power beyond a threshold value. At this point of time, a backup path is selected as an alternate path for packet transmission. The process is repeated till all the paths from the same source to destination are exhausted with their battery power. The simulation result of the proposed algorithm ESAR indicates that the network life time is improved upon the existing routing algorithms.

Storaci-Wireless sensor networks are energy limited and imbalance networks, load balancing and energy efficiency is the most challenging task in these networks. The clustering algorithm is a kind of key technique used to reduce energy consumption. Many algorithms have been specifically designed for WSNs, where energy awareness is an essential design issue. The focus, however, has been given to the residual energy based clustering protocols, which might differ depending on the application and network architecture. In this paper, a staggered clustering protocol to prolong the stable region of wireless sensor networks is being proposed. Compared with classical clustering protocols, this protocol can maintain efficient load balancing of networks, and extremely prolong the network lifetime.

In this paper, we propose energy efficient mobility adaptive distributed clustering algorithm for mobile ad hoc network. Node mobility in the dynamic network has a remarkable affect on cluster stability. In order to reduce the initial cluster setup time of the dynamic network with frequently changing topology, we consider a single node parameter as the cluster head selection criteria. That is the average displacement of individual nodes in the network is computed by keeping track of its total displacement in last n time units. As the selected cluster heads form the routing backbone of the dynamic network, better stability is ensured by preferring low mobile nodes to act as cluster heads. A new energy consumption model has been proposed for the cluster heads that takes into account the network traffic, density of cluster members and the transmission power utilized to communicate the member nodes. A better cluster stability and a low maintenance overhead is aimed to achieve by electing volunteer and non-volunteer cluster heads.

Emergence of implantable and wearable medical sensors for patient monitoring, diagnosis as well as cure has helped in the development of body area networks (BANs). For networking of these implantable and wearable devices, an appropriate choice of radio technology and communication methodology has to be made. Ultra wideband (UWB) has been identified as a solution for wireless body area networks (WBANs) in IEEE 802.15.6, but there are issues to be addressed such as attenuation inside body for implantable devices, energy efficient networking with low heat dissipation and others. Ultrasonic communication although an emerging technology promises better propagation inside body in watery media. It is known to be used without much health hazard in medical applications. This paper compares and evaluates the above two technologies for WBAN. It provides an extensive survey of existing work on UWB and ultrasonic communication for body area networks and highlights the unaddressed research challenges in using UWB and ultrasonic for WBAN applications.