We propose a novel method to maximise the aggregate throughput of an enhanced IEEE 802.15.4 gateway. By exploiting the gateway's beamforming antenna array and successive interference cancellation (SIC) we simultaneously increase range and maximise throughput. Our algorithm organises nodes into clusters, with each cluster assigned a unique spreading code and target gateway receive power. Within a cluster, nodes share the medium via CSMA/CA and inter-cluster packet collisions at the gateway are resolved with SIC. Theoretical throughput gains are calculated and then verified with cross layer simulations encompassing ray trace based propagation modelling, IEEE 802.15.4 bit error rate (BER) models, and a discrete event based enhanced MAC simulator. A 4.3x improvement in aggregated throughput is shown in simulations, reflecting the theoretical analysis and providing a systematic approach for throughput optimization.

We propose an enhanced IEEE 802.15.4 gateway for use in a hierarchical network architecture. We perform a system comparison between it and a gateway using a normal sensor mote. This comparison includes a PHY and MAC simulation benefiting from ray trace propagation modeling of a large office building. The proposed enhanced gateway offers a 21.2 dB improvement in receiver sensitivity and an ideal channel equaliser. We analyze these benefits before performing a full system simulation. We report improvements over a classical sensor gateway in all key areas of packet delivery, latency and mote energy efficiency. Just under a six-fold increase in indoors range is also reported. We observe a reduction in performance due to channel distortion and combat it by proposing a fractionally spaced equaliser. We show that for buildings with large RMS delay spreads - greater than 100ns (e.g. conference centres), significant benefit may be derived.

Wireless Sensor Networks (WSNs) are being considered for a wide range of applications, however, they still face various challenges, e.g. short communication range and the hidden terminal problem, to name a couple. In this paper, we build up on our previous work in [1], present a WSN prototype that demonstrates this solution, and analyze its performance in different indoor conditions by measurement and simulation. The measurement study explores radio propagation, transmission efficiency and deployment of the proposed extended-range WSN. The simulations are performed for a large scale WSN, taking into account indoor propagation, enhanced 802.15.4 PHY and MAC models and sensor application. The measurements demonstrate the feasibility and performance of the first generation extended-range 802.15.4 WSN, while the simulation study shows the network performance and scalability in terms of transmission efficiency and energy cost.

There are performance deficiencies that hamper the deployment of Wireless Sensor Networks (WSNs) in critical monitoring applications. Such applications are characterized by considerable network load generated as a result of sensing some characteristics of the monitored system. Excessive packet collisions lead to packet losses and retransmissions, resulting in significant overhead costs and latency. In order to address this issue, we introduce a distributed and scalable scheduling access scheme that mitigates high data loss in data-intensive sensor networks and can also handle some mobility. Our approach alleviates transmission collisions by employing virtual grids that adopt Latin Squares characteristics to time slot assignments. We show that our algorithm derives conflict-free time slot allocation schedules without incurring global overhead in scheduling. Furthermore, we verify the effectiveness of our protocol by simulation experiments. The results demonstrate that our technique can efficiently handle sensor mobility with acceptable data loss, low packet delay, and low overhead.

Sensors are energy constrained, which leads to possible selfishness as the sensor may decline to forward packets for other nodes. Sensors also face the problem of unfair depletion of their energy if communication protocols were energy-unaware. To address the above concern, we propose the dynamic Bayesian game approach: our algorithm derives mutually acceptable path selection strategies between the sensor and its neighbor, thereby resulting in fairer use of their energy. We validate the feasibility of this technique via analysis and simulations. The results show that it can efficiently handle the sensor's changes in energy and mobility while improving the energy efficiency-network lifetime.

We introduce a distributed grid-based scheduling access scheme that mitigates high data loss in data intensive sensor networks. Our approach alleviates transmission collisions by applying virtual grids and adopting Latin Squares Characteristic to time slot assignments. We demonstrate that our technique efficiently handles sensor mobility with acceptable data loss and low overhead.