Power Aware Routing in Mobile Ad Hoc Networks

The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.

https://www.mdpi.com/2079-9292/8/7/768/pdf

A Survey on Internet of Things and Cloud Computing for Healthcare

How do humans recognize the action "opening a book" ? We argue that there are two important cues: modeling temporal shape dynamics and modeling functional relationships between humans and objects. In this paper, we propose to represent videos as space-time region graphs which capture these two important cues. Our graph nodes are defined by the object region proposals from different frames in a long range video. These nodes are connected by two types of relations: (i) similarity relations capturing the long range dependencies between correlated objects and (ii) spatial-temporal relations capturing the interactions between nearby objects. We perform reasoning on this graph representation via Graph Convolutional Networks. We achieve state-of-the-art results on both Charades and Something-Something datasets. Especially for Charades, we obtain a huge 4.4% gain when our model is applied in complex environments.

Videos as Space-Time Region Graphs.

The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. This paper gives a near complete and up-to-date view of the IoT authentication field. It provides a summary of a large range of authentication protocols proposed in the literature. Using a multi-criteria classification previously introduced in our work, it compares and evaluates the proposed authentication protocols, showing their strengths and weaknesses, which constitutes a fundamental first step for researchers and developers addressing this domain.

A Survey of Internet of Things (IoT) Authentication Schemes

Clustering Objectives in Wireless Sensor Networks: A Survey and Research Direction Analysis

Wireless sensor networks(WSN) have wide range of application such as traffic analysis, environmental monitoring, industrial process monitoring, and tactical systems. Large-scale wireless sensor networks are expected to play increasingly important role in future civilian and military application. Designing of MAC layer protocol for wireless sensor network is a challenging task due to limited battery power and limited bandwidth. Time Division Multiple Access Protocol solves both problems at the level of MAC layer. Various scheduling method for TDMA protocol with different objective have been proposed for wireless sensor networks. In this paper, we first outline the sensor network properties that are crucial for the design of TDMA protocols and then, we describe several TDMA protocols which are proposed for sensor networks. Finally, we point out open research issue with regard to TDMA protocols.

https://www.ijcaonline.org/volume20/number5/pxc3873271.pdf

A Survey on Scheduling Algorithms for Wireless Sensor Networks

Mobile ad hoc network (MANET) is defined as the category of wireless network that is capable of operating without any fixed infrastructure. The main assumption considered in this network is that all nodes are trusted nodes but in real scenario, some nodes can be malicious node and therefore can perform selective dropping of data packets instead of forwarding the data packets to the destination node. These malicious nodes behave normally during route discovery phase and afterwards drop fractions of the data packets routed through them. Such type of attack is known as smart gray hole attack which is variation of sequence number based gray hole attack. In this paper, we have launched smart gray hole attack and proposed a new mechanism for mitigating the impact of smart gray hole attack. Mitigating Gray hole Attack Mechanism (MGAM) uses several special nodes called as G-IDS (gray hole-intrusion detection system) nodes which are deployed in MANETs for detecting and preventing smart gray hole attack. G-IDS nodes overhear the transmission of its neighbouring nodes and when it detects that the node is dropping the data packets which are greater than threshold value then it broadcast the ALERT message in the network notifying about the identity of malicious node. The identified malicious is then blocked from further its participation by dropping the request and reply packet. In order to validate the effectiveness of our proposed mechanism, NS-2.35 simulator is used. The simulation results show that the proposed mechanism performs slightly well as compared with the existing scheme under smart gray hole attack.

A novel approach for mitigating gray hole attack in MANET

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The recent development in wireless communication technology and portable devices has led to the development of Mobile Ad-Hoc Network which has unique features such as dynamic topology, open wireless medium, decentralized system etc but these network are susceptible to various denial of service attacks such as black hole, gray-hole, worm hole etc. In order to mitigate black-hole attack, various protocols have been proposed but these protocols have been not analyzed under gray-hole attack. In this paper, we have made classification of denial of service attack and highlighted the key differences among black hole, sequence number based gray-hole and smart gray-hole attack. We simulated two types of attacks namely Attack1 and Attack2 by modifying AODV protocol. The Attack1 (GAODV) is sequence number based gray-hole attack protocol whereas Attack2 (SGAODV) is smart gray-hole attack protocol. In order to observe the impact of gray-hole attacks on AODV, IDS-AODV and MBDP-AODV, NS-2.35 simulator is used. The simulation results show that MBDP-AODV performs well as compared with IDS-AODV and AODV under sequence number based gray-hole attack. Through simulation, it has also been found that the impact of the smart gray-hole attack is low as compared with the sequence number based gray-hole attack.

Siddhartha Chauhan

Papers

A Survey on Scheduling Algorithms for Wireless Sensor Networks

A novel approach for mitigating gray hole attack in MANET

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

Performance analysis of black-hole attack mitigation protocols under gray-hole attacks in MANET

Clustering protocols in wireless sensor network: A survey, classification, issues, and future directions