Intelligent Antenna Selection Decision in IEEE 802.15.4 Wireless Sensor Networks: An Experimental Analysis

International audienceThe goal of this paper is to study the feasibility of making intelligent antenna selection decision in IEEE 802.15.4 Wireless Sensor Networks (WSNs). This study provides us the basis to design and implement software defined intelligent antenna switching capability to wireless sensor nodes based on Received Signal Strength Indicator (RSSI) link quality metric. First, we discuss the results of our newly designed radio module (Inverted-F Antenna) for 2.4 GHz bandwidth (WSNs). Second, we propose an intelligent antenna selection strategy to exploit antenna diversity. Third, we propose the prototype of our diversity antenna for the TelosB mote and the intelligent switch design. Finally, we compare the performance of the built-in TelosB antenna with our proposed external antenna in both laboratory and realistic environments. Experimental results confirm the gain of 6 to 10 dB of the proposed radio module over the built-in radio module of the TelosB motes

A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted

Flexible dual-diversity wearable wireless node integrated on a dual-polarised textile patch antenna

A new textile wearable wireless node, for operation in the 2.45 GHz industrial, scientific and medical (ISM) band, is proposed. It consists of a dual-polarised textile patch antenna with integrated microcontroller, sensor, memory and transceiver with receive diversity. Integrated into a garment, the flexible unit may serve for fall detection, as well as for patient or rescue-worker monitoring. Fragile and lossy interconnections are eliminated. They are replaced by very short radiofrequency signal paths in the antenna feed plane, reducing electromagnetic compatibility and signal integrity problems. The compact and flexible module combines sensing and wireless channel monitoring functionality with reliable and energy-efficient off-body wireless communication capability, by fully exploiting dual polarisation diversity. By integrating a battery, a fully autonomous and flexible system is obtained. This novel textile wireless node was validated, both in flat and bent state, in the anechoic chamber, assessing the characteristics of the integrated system in free-space conditions. Moreover, its performance was verified in various real-world conditions, integrated into a firefighter garment, and used as an autonomous body-centric measurement device

Implementing and Evaluating a Wireless Body Sensor System for Automated Physiological Data Acquisition at Home

Advances in embedded devices and wireless sensor networks have resulted in new and inexpensive health care solutions. This paper describes the implementation and the evaluation of a wireless body sensor system that monitors human physiological data at home. Specifically, a waist-mounted triaxial accelerometer unit is used to record human movements. Sampled data are transmitted using an IEEE 802.15.4 wireless transceiver to a data logger unit. The wearable sensor unit is light, small, and consumes low energy, which allows for inexpensive and unobtrusive monitoring during normal daily activities at home. The acceleration measurement tests show that it is possible to classify different human motion through the acceleration reading. The 802.15.4 wireless signal quality is also tested in typical home scenarios. Measurement results show that even with interference from nearby IEEE 802.11 signals and microwave ovens, the data delivery performance is satisfactory and can be improved by selecting an appropriate channel. Moreover, we found that the wireless signal can be attenuated by housing materials, home appliances, and even plants. Therefore, the deployment of wireless body sensor systems at home needs to take all these factors into consideration.Comment: 15 page

Application priority framework for fixed mobile converged communication networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The current prospects in wired and wireless access networks, it is becoming increasingly important to address potential convergence in order to offer integrated broadband services. These systems will need to offer higher data transmission capacities and long battery life, which is the catalyst for an everincreasing variety of air interface technologies targeting local area to wide area connectivity. Current integrated industrial networks do not offer application aware context delivery and enhanced services for optimised networks. Application aware services provide value-added functionality to business applications by capturing, integrating, and consolidating intelligence about users and their endpoint devices from various points in the network. This thesis mainly intends to resolve the issues related to ubiquitous application aware service, fair allocation of radio access, reduced energy consumption and improved capacity. A technique that measures and evaluates the data rate demand to reduce application response time and queuing delay for multi radio interfaces is proposed. The technique overcomes the challenges of network integration, requiring no user intervention, saving battery life and selecting the radio access connection for the application requested by the end user. This study is split in two parts. The first contribution identifies some constraints of the services towards the application layer in terms of e.g. data rate and signal strength. The objectives are achieved by application controlled handover (ACH) mechanism in order to maintain acceptable data rate for real-time application services. It also looks into the impact of the radio link on the application and identifies elements and parameters like wireless link quality and handover that will influence the application type. It also identifies some enhanced traditional mechanisms such as distance controlled multihop and mesh topology required in order to support energy efficient multimedia applications. The second contribution unfolds an intelligent application priority assignment mechanism (IAPAM) for medical applications using wireless sensor networks. IAPAM proposes and evaluates a technique based on prioritising multiple virtual queues for the critical nature of medical data to improve instant transmission. Various mobility patterns (directed, controlled and random waypoint) has been investigated and compared by simulating IAPAM enabled mobile BWSN. The following topics have been studied, modelled, simulated and discussed in this thesis: 1. Application Controlled Handover (ACH) for multi radios over fibre 2. Power Controlled Scheme for mesh multi radios over fibre using ACH 3. IAPAM for Biomedical Wireless Sensor Networks (BWSN) and impact of mobility over IAPAM enabled BWSN. Extensive simulation studies are performed to analyze and to evaluate the proposed techniques. Simulation results demonstrate significant improvements in multi radios over fibre performance in terms of application response delay and power consumption by upto 75% and 15 % respectively, reduction in traffic loss by upto 53% and reduction in delay for real time application by more than 25% in some cases

Low power wireless sensor network for structural health monitoring of buildings using MEMS strain sensors and accelerometers

Within the MEMSCON project, a wireless sensor network was developed for structural health monitoring of buildings to assess earthquake damage. The sensor modules use custom-developed capacitive MEMS strain and 3D acceleration sensors and a low power readout application-specific integrated circuit (ASIC). A low power network architecture was implemented on top of an 802.15.4 media access control (MAC) layer in the 900MHz band. A custom patch antenna was designed in this frequency for optimal integration into the sensor modules. The strain sensor modules measure periodically or on-demand from the base station and obtain a battery lifetime of 12 years. The accelerometer modules record during an earthquake event, which is detected using a combination of the local acceleration data and remote triggering from the base station, based on the acceleration data from multiple sensors across the building. They obtain a battery lifetime of 2 years. The MEMS strain sensor and its readout ASIC were packaged in a custom package suitable for mounting onto a reinforcing bar inside the concrete and without constraining the moving parts of the MEMS strain sensor. The wireless modules, including battery and antenna, were packaged in a robust housing compatible with mounting in a building and accessible for maintenance such as battery replacement

Low power wireless sensor network for building monitoring

A wireless sensor network is proposed for monitoring buildings to assess earthquake damage. The sensor nodes use custom-developed capacitive MEMS strain and 3D acceleration sensors and a low power readout ASIC for a battery life of up to 12 years. The strain sensors are mounted at the base of the building to measure the settlement and plastic hinge activation of the building after an earthquake. They measure periodically or on-demand from the base station. The accelerometers are mounted at every floor of the building to measure the seismic response of the building during an earthquake. They record during an earthquake event using a combination of the local acceleration data and remote triggering from the base station based on the acceleration data from multiple sensors across the building. A low power network architecture was implemented over an 802.15.4 MAC in the 900MHz band. A custom patch antenna was designed in this frequency band to obtain robust links in real-world conditions

A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities

The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data such as velocity of individual vehicles as well as vehicle type information can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%