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Wireless body-area sensor networks (WBSNs)are key components of e-health solutions.Wearable wireless sensors can monitor and collect many different physiological parameters accurately, economically and efficiently. In this work we focus on WBSN for fall detection applications,where the real-time nature of I/O data streams is of critical importance. Additionally, this generation of alarms promises to maximize system life. Through put and energy efficiency of the communication protocol must also be carefully optimized. In this article we investigate ZigBee’s ability to meet WBSN requirements, with higher communication efficiency and lower power consumption than a Bluetooth serial portprofile(SPP) based solution. As a case study we implemented an accelerometer-based fall detection algorithm,able to detect eight different fall typologies by means of a single sensor worn on the subjects’waist. This algorithm has a low computational complexity and can be processed on an embedded platform. Fall simulations were performed by three voluntary subjects. Preliminary results are promising and show excellent values for both sensitivity and specificity. This case study showed how aZigBee-based network can be used for high through put WBSN scenarios.

Wireless sensor networks are characterized by very tight code size and power constraints and by a lack of well-established standard software development platforms such as Posix. In this article, we present a comparative study between a few fairly different such platforms, namely MANTIS, TinyOS, and ZigBee, when considering them from the application developer's perspective, that is, by focusing mostly on functional aspects, rather than on performance or code size. In other words, we compare both the tasking model used by these platforms and the API libraries they offer. Sensor network applications are basically event based, so most of the software platforms are also built on considering event handling mechanism, however some use a more traditional thread based model. In this article, we consider implementations of a simple generic application in MANTIS, TinyOS, and the Ember ZigBee development framework, with the goal of depicting major differences between these platforms, and suggesting a programming style aimed at maximizing portability between them.

Hardware and software platforms for Wireless Sensor Networks (WSNs) are almost as diverse as their application areas, with very limited standardization. Moreover, heterogeneous programming abstractions put high barrier in application development and there is hardly any support for application debugging, except for a few blinking LEDs. Similar problems have been solved in application domains that have similar cost constraints, such as automotive, by the use of model-based design. We address the lack of model-based design tools for the WSN domain by providing a framework (based on Simulink, Stateflow and Stateflow Coder) in which an application developer can model a WSN application by using Stateflow constructs and then use a single model to perform multi-platform Hardware-In-the-Loop (HIL) simulation and platform-specific application code generation.

— Wireless Body-area Sensor Networks (WBSN) are a key component of e-Health solutions. Many different physiological parameters can be monitored and collected continuously by wearable wireless sensors with high accuracy, low power consumption and limited cost. In this work we focus on WBSN for real-time biofeedback applications. In this domain the real-time nature of I/O data streams is of critical importance for providing an useful and efficient sensorial feedback for the user while system lifetime should be maximized. Thus, bandwidth, throughput and energy efficiency of the communication protocol must be carefully optimized. The research community is still divided on the choice of the protocol to use (proprietary, ZigBee, Bluetooth). In this article we investigate how ZigBee meets WBSN requirements showing a higher communication efficiency and lower power consumption, with respect to Bluetooth Serial Port Profile, (SPP) based solution.

Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energyefficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation framework for functional analysis of WSN applications taking our proposed algorithm as an example.

Application domains of wireless sensor networks are emerging day-by-day, so developers are creating software components at various layers by using platforms such as MANTIS, TinyOS or ZigBee. Since these platforms are quite different in terms of programming paradigm and provided APIs, porting applications between them is difficult and expensive. In this paper, we show techniques that might be used to dramatically ease the porting application code between different software platforms for WSNs.

Showing functional correctness by simulation before implementation, and preserving it by automated code generation, is extremely useful to reduce the development time for an embedded application. This is even more true for wireless sensor networks, since their nodes often provide very rudimentary debugging facilities, and sufficiently large networks for realistic analysis may be expensive to deploy. While this approach, also known as model-based design, is becoming quite standard for several domains that have similar constraints as wireless sensor networks, such as automotive electronics, there is a lack of tools for this purpose in the WSN world. In order to fill this gap, in this paper we present a framework (based on Simulink, Stateflow and Embedded Coder) in which an engineer can create sensor network components (both at the application and at the protocol level) that can be used as building blocks to model, simulate and automatically generate code for different underlying platforms and operating systems.

Data aggregation is a basic mechanism that is used to reduce the traffic flow in sensor networks. In this mechanism, one node basically the cluster leader collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm also reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader.

The paper presents E2RINA, an aggregation algorithm for wireless sensor network applications characterized by clustered topologies, such as building automation and manufacturing plants. Thank to an efficient use of the wireless channel, E2RINA offers the robustness of the gossip-based algorithms and, at the same time, the energy performance of the faster cluster head-based algorithms. A mathematical model to predict the performance of the algorithm with respect to the free variables without the need of extensive simulations was also developed. The model was validated and the robustness of E2RINA by running a simulation model of a test case consisting of a cluster of MICA nodes.

This paper describes the design flow used for an asynchronous 8-bit processor implementing the Atmel AVR instruction set architecture. The goal is to show dramatic reductions in power and energy with respect to the synchronous case, while retaining essentially a traditional design flow. The processor was implemented in a 130nm technology using desynchronization, starting from an initial design downloaded from OpenCores.org. It consumes 14 pJ per instruction to deliver 170 MIPS at 1.2 V, and 2.7 pJ per instruction to deliver 48 MIPS at 0.54 V. It thus dramatically improves the energy consumed per instruction with respect to previous results from the literature.