One of the first standards in the wireless sensor networks domain, WirelessHART (HART (Highway Addressable Remote Transducer)), was introduced to address industrial process automation and control requirements. This standard can be used as a reference point to evaluate other wireless protocols in the domain of industrial monitoring and control. This makes it worthwhile to set up a reliable WirelessHART simulator in order to achieve that reference point in a relatively easy manner. Moreover, it offers an alternative to expensive testbeds for testing and evaluating the performance of WirelessHART. This paper explains our implementation of WirelessHART in the NS-2 network simulator. According to our knowledge, this is the first implementation that supports the WirelessHART network manager, as well as the whole stack (all OSI (Open Systems Interconnection model) layers) of the WirelessHART standard. It also explains our effort to validate the correctness of our implementation, namely through the validation of the implementation of the WirelessHART stack protocol and of the network manager. We use sniffed traffic from a real WirelessHART testbed installed in the Idrolab plant for these validations. This confirms the validity of our simulator. Empirical analysis shows that the simulated results are nearly comparable to the results obtained from real networks. We also demonstrate the versatility and usability of our implementation by providing some further evaluation results in diverse scenarios. For example, we evaluate the performance of the WirelessHART network by applying incremental interference in a multi-hop network.

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A pedestrian counter has a lot of applications like effective resource utilization, planning of service activities and ensuring safety and convenience. The design and implementation of a new intelligent Pedestrian Counter is presented in this paper. The counter is publicly usable, low cost, easily deployable and scalable. We used off-the-shelf components for our design and the overall cost is less than 200 ¿. The counter works in distributed mode and has wireless communication facilities. The hardware platform consists of PIR sensor units that detect the pedestrian movements, the wireless sensor nodes that handle the sensor data acquisition and transmission and the base station computer that process the data. We have trained an Echo State Network and this Recurrent Neural Network functions as the brain for the the Pedestrian Counter. A system model was constructed using Simulink for generating the training data for the neural network. A modular layered software framework was designed for processing the pedestrian counter data. The Echo State Network successfully learned the various motion patterns and the pedestrian counter gave a reasonably good performance of 80.4%. To improve the performance further, redesigning the systems using low cost Active IR distance sensor is suggested.

Maintaining connectivity among a group of autonomous agents exploring an area is very important, as it promotes cooperation between the agents and also helps message exchanges which are very critical for their mission. Creating an underlying Ad-hoc Mobile Router Network (AMRoNet) using simple robotic routers is an approach that facilitates communication between the agents without restricting their movements. We address the following question in our paper: How to create an AMRoNet with local information and with minimum number of routers? We propose two new localized and distributed algorithms 1) agent-assisted router deployment and 2) a self-spreading for creating AMRoNet. The algorithms use a greedy deployment strategy for deploying routers effectively into the area maximizing coverage and a triangular deployment strategy to connect different connected component of routers from different base stations. Empirical analysis shows that the proposed algorithms are the two best localized approaches to create AMRoNets.

We have designed an inexpensive intelligent pedestrian counting system. The pedestrian counting system consists of several counters that can be connected together in a distributed fashion and communicate over the wireless channel. The motion pattern is recorded using a set of passive infrared (PIR) sensors. Each counter has one wireless sensor node that processes the PIR sensor data and transmits it to a base station. Then echo state network, a special kind of recurrent neural network, is used to predict the pedestrian count from the input pattern. The evaluation of the performance of such networks in a novel kind of application is one focus of this work. The counter gave a performance of 80.4% which is better than the commercially available low-priced pedestrian counters. The article reports the experiments we did for analyzing the counterperformance and lists the strengths and limitations of the current implementation. It will also report the preliminary test results obtained by substituting the PIR sensors with low-cost active IR distance sensors which can improve the counter performance further.