Amjad Mehmood

Energy-efficient multi-level and distance-aware clustering mechanism for WSNs

Summary Most sensor networks are deployed at hostile environments to sense and gather specific information. As sensor nodes have battery constraints, therefore, the research community is trying to propose energy-efficient solutions for wireless sensor networks (WSNs) to prolong the lifetime of the network. In this paper, we propose an energy-efficient multi-level and distance-aware clustering (EEMDC) mechanism for WSNs. In this mechanism, the area of the network is divided into three logical layers, which depends upon the hop-count-based distance from the base station. The simulation outcomes show that EEMDC is more energy efficient than other existing conventional approaches. Copyright

ICMDS: Secure inter-cluster multiple-key distribution scheme for wireless sensor networks

Abstract In wireless sensor networks (WSNs), a large number of nodes are densely deployed in an open environment to gather some useful required information. These nodes are small in size, operating on limited processing capabilities with scarce working memory and battery life and not very powerful radio transceivers. They can only communicate with each other through wireless media. Radio waves are insecure in nature; therefore, by using such waves for communication there are always opportunities for different attacks on the network. Most wireless techniques are founded on the cluster-based sensor network. Forwarding cluster heads (CHs) data in a secure manner is very important because CHs collect data from the cluster members and send it to the sink node or base station. For securing CHs data, we propose a mechanism termed ICMDS (Inter-Cluster Multiple Key Distribution Scheme for Wireless Sensor Networks), which enables the securing of the entire network. In ICMDS, we use two phases of security implementations for the sensor nodes authenticity while communicating with the CH. A recovery phenomena is also stated at the time when a CH ceases to function due to its high energy consumption.

Multi-Agent based Framework for Secure and Reliable Communication among Open Clouds

Cloud Computing (CC) is an emerging field of Information Technology. CC environment completely relies on the perception of utility, service-oriented, cluster and grid computing. The idea of virtualization discriminates CC from other fields . CC environment provides better, reliable, and scalable services. Since clouds are working independently smooth, but standalone, cloud operation is complex. Therefore the need of interoperability and portability with other clouds come into play which increases the scope of the cloud environment. Then, the security threats are increased in the cloud environments. In order to address the problem, a Secure Multi-Agent based framework for Communication among Open Clouds is proposed in this paper. In the framework, each cloud has a secure Mobile Agent which is responsible of the secure communication among clouds. Thus, authentication of Mobile Agents is performed by the Directory Agent. Directory agents are included in order to avoid the joining malicious or attacker mobile agents into the cloud. The theoretical and practical results show that Multi-agent based framework is more reliable and secure than other cloud environments.

Effect of Fast Moving Object on RSSI in WSN: An Experimental Approach

In this paper, we experimentally investigate the effect of fast moving object on the RSSI in the wireless sensor networks in presence of the ground effect and antenna orientation in elevation direction. In experimental setup, MICAz mote pair was placed on the ground, where one mote acts as a transmitter and the other as a receiver. The transmitter mote’s antenna was oriented in elevation direction with respect to the receiver mote’s antenna. The fast moving object i.e. car, was passed between the motes and the fluctuations in the RSSI are observed. The experimental results show some sequential pattern in RSSI fluctuations when car moves at some relatively slow speed. However, some irregularities were also observed when antenna was oriented at 45 and 90 in elevation direction.

ELDC: An Artificial Neural Network based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs

The range of applications of Wireless Sensor Networks (WSNs) is increasing continuously despite of their serious constraints of the sensor nodes’ resources such as storage, processing capacity, communication range and energy. The main issues in WSN are the energy consumption and the delay in relaying data to the Sink node. This becomes extremely important when deploying a big number of nodes, like the case of industry pollution monitoring. We propose an artificial neural network based energy-efficient and robust routing scheme for WSNs called ELDC. In this technique, the network is trained on huge data set containing almost all scenarios to make the network more reliable and adaptive to the environment. Additionally, it uses group based methodology to increase the life-span of the overall network, where groups may have different sizes. An artificial neural network provides an efficient threshold values for the selection of a groups CN and a cluster head based on back propagation technique and allows intelligent, efficient, and robust group organization. Thus, our proposed technique is highly energy-efficient capable to increase sensor nodes’ lifetime. Simulation results show that it outperforms LEACH protocol by 42 percent, and other state-of-the-art protocols by more than 30 percent.

A secure and low-energy zone-based wireless sensor networks routing protocol for pollution monitoring

Sensor networks can be used in many sorts of environments. The increase of pollution and carbon footprint are nowadays an important environmental problem. The use of sensors and sensor networks can help to make an early detection in order to mitigate their effect over the medium. The deployment of wireless sensor networks (WSNs) requires high-energy efficiency and secures mechanisms to ensure the data veracity. Moreover, when WSNs are deployed in harsh environments, it is very difficult to recharge or replace the sensors batteries. For this reason, the increase of network lifetime is highly desired. WSNs also work in unattended environments, which is vulnerable to different sort of attacks. Therefore, both energy efficiency and security must be considered in the development of routing protocols for WSNs. In this paper, we present a novel Secure and Low-energy Zone-based Routing Protocol (SeLeZoR) where the nodes of the WSN are split into zones and each zone is separated into clusters. Each cluster is controlled by a cluster head. Firstly, the information is securely sent to the zone-head using a secret key; then, the zone-head sends the data to the base station using the secure and energy efficient mechanism. This paper demonstrates that SeLeZoR achieves better energy efficiency and security levels than existing routing protocols for WSNs. Copyright

ANTSC: An Intelligent Naïve Bayesian Probabilistic Estimation Practice for Traffic Flow to Form Stable Clustering in VANET

The vehicular ad hoc network (VANET) is one of the promising and encouraging technologies, and it is going to attract great attention in the near future. VANET has turned into a main module of the intelligent transport system. It is a self-controlled, wheeled network (also called network on wheels), and a wider and stimulating class of mobile ad hoc network (MANET). VANETs raise many innovative challenges because of their high-class and unique features, such as high-node mobility, dynamic topology changes, wireless links breakage, network constancy, and network scalability. A well-organized routing protocol is one of the most challenging matters of such networks. In this paper, we propose an intelligent naïve Bayesian probabilistic estimation practice for traffic flow to form a stable clustering in VANET, briefly named ANTSC. The proposed scheme aims to improve routing by employing awareness of the current traffic flow as well as considering the blend of several factors, such as speed difference, direction, connectivity level, and node distance from its neighbors by using the intelligent technique. The proposed technique has proven to be more strong, stable, robust, and scalable than existing ones.

SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer’s attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption.

NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks

Internet of Things (IoT) makes physical objects and devices interact with each other through wireless technologies. IoT is expected to deliver a significant role in our lives in near future. However, at the current stage, IoT is vulnerable to various kinds of security threats just like other wired and wireless networks. Our work mainly focuses on protecting an IoT infrastructure from distributed denial-of-service attacks generated by the intruders. We present a new approach of using Naïve Bayes classification algorithm applied in intrusion detection systems (IDSs). IDSs are deployed in the form of multi-agents throughout the network to sense the misbehaving or irregular traffic and actions of nodes. In the paper, we also discuss the fundamental concepts related to our work and recent research done in similar area.

Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)

Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message.