A Smart Building Resource Prediction, Navigation and Management System Supported by Radio Tomography and Computational Intelligence

This article presents research results on a smart building prediction, navigation and asset management system. The main goal of this work was to combine all comfort subsystems, such as lighting, heating or air conditioning control, into one coherent management system supported by navigation using radio tomographic imaging techniques and computational intelligence in order to improve the building’s ability to track users and then maximize the energy efficiency of the building by analyzing their behavior. In addition, the data obtained in this way were used to increase the quality of navigation services, improve the safety and ergonomics of using the room access control system and create a centralized control panel enriched with records of the working time of individual people. The quality of the building’s user habit learning is ensured by a network of sensors collecting environmental data and thus the setting values of the comfort modules. The advantage of such a complex solution is an increase in the accuracy of navigation services provided, an improvement in the energy balance, an improvement in the level of safety and faster facility diagnostics. The solution uses proprietary small device assemblies with implementation of popular wireless transmission standards such as Bluetooth, Wi-Fi, ZigBee or Z-Wave. These PANs (personal area networks) are used to update and transmit environmental and navigation data (Bluetooth), to maintain the connection of other PANs to the master server (Wi-Fi) and to communicate with specific end devices (ZigBee and Z-Wave).

LC-DEX: Lightweight and Efficient Compressed Authentication Based Elliptic Curve Cryptography in Multi-Hop 6LoWPAN Wireless Sensor Networks in HIP-Based Internet of Things

The high level of security requirements and low capabilities of constrained devices that are connected to the Internet of Things (IoT) constitute a new challenge in terms of proposing an authentication solution that deals with the problem of energy constraints. The Host Identity Protocol Diet EXchange (HIP DEX) is primarily designed to be suitable for constrained devices and designed to be resistant to Denial of Service (DoS) and man-in-the-middle (MITM) attacks. In this paper, we propose an efficient saving energy solution to secure end-to-end (E2E) communications based on the compression of the IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) header for HIP DEX packets. We implement our solution in an IoT based-WSN over Constrained Application Protocol (CoAP) in the application layer and Routing Protocol for Low power and lossy networks (RPL) in the routing layer. We also propose a novel distribution model that minimizes the number of signaling messages. Both proposed compression and distribution models for HIP DEX combined with an original implementation of an opportunistic association establishment of the handshake, constitute an efficient security solution for IoT. We called our solution Lightweight Compressed HIP DEX in the IoT (LC-DEX).

Reinforcement-Learning-Based IDS for 6LoWPAN

<p>The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL) based IDS to detect various attacks on RPL in 6LoWPANs, including several unaddressed by current research. The proposed scheme can also detect previously unseen attacks and the presence of mobile intruders. The scheme is well suited to the resource constrained environments of our target networks.</p><br>

Reinforcement-Learning-Based IDS for 6LoWPAN

<p>The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL) based IDS to detect various attacks on RPL in 6LoWPANs, including several unaddressed by current research. The proposed scheme can also detect previously unseen attacks and the presence of mobile intruders. The scheme is well suited to the resource constrained environments of our target networks.</p><br>

Taguchi Approach for Recognizing the Most Influential Factor in Improving IEEE 802.15.4 Performance

WPAN using IEEE 802.15.4 protcol operated in non beaconing mode an attempt has made to find out the most significant factor of defacto parameters of IEEE 802.15.4 to enhance the performance by minimizing energy consumption and to enhance the network lifetime of the Wireless Personal Area Networks(WPAN). The factors include Buffer size, Beacon Interval, Back-off-transmission has an ideal impact on QoS metrics in IEEE 802.15.4 protocol. A Design of experiments have been simulated to an optimum level using the taguchi approach. The experimental results from the taguchi approach reveales that Back-of-transmission as the most significant factor for IEEE 802.15.4 in minimizing the power consumption in the WPAN.

Modified Adaptive Mechanism For Optimising IEEE 802.15.4 WPANs For Wireless Sensor Networks

Different applications of Wireless Sensor Networks (WSNs) have different expectations from the working of Medium Access Control (MAC) protocols. Some value reliability more than delay incurred while some demand a fair trade-off for the factors like: Throughput, Bit Error Rate (BER) etc. This paper evaluates the performance of Wireless Personal Area Networks (WPANs) from 802.15.4 group for WSNs with modified algorithm which helps in reducing the Medium Access Delay and Delay in reaching of the packet from one end to another end. In this paper certain modifications to existing algorithm have been proposed for reducing the Medium Access Delay and to reduce the number of packets dropped. The result comparisons on the performance parameters like: network output load, generated acknowledged traffic, media access delay, battery consumed and delay in packet transmission from one end to another end that the back-off number & exponent values used for transmission play vital role for improving the performance of WSNs as they directly affect the number of packets dropped, successfully acknowledged and Medium Access Delay.

Identifying Misbehaving Greedy Nodes in IoT Networks

One of the central communication infrastructures of the Internet of Things (IoT) is the IEEE 802.15.4 standard, which defines Low Rate Wireless Personal Area Networks (LR- WPAN). In order to share the medium fairly in a non-beacon-enabled mode, the standard uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). The nature of connected objects with respect to various resource constraints makes them vulnerable to cyber attacks. One of the most aggressive DoS attacks is the greedy behaviour attack which aims to deprive legitimate nodes to access to the communication medium. The greedy or selfish node may violate the proper use of the CSMA/CA protocol, by tampering its parameters, in order to take as much bandwidth as possible on the network, and then monopolize access to the medium by depriving legitimate nodes of communication. Based on the analysis of the difference between parameters of greedy and legitimate nodes, we propose a method based on the threshold mechanism to identify greedy nodes. The simulation results show that the proposed mechanism provides a detection efficiency of 99.5%.