J. Sens. Actuator Netw.2014, 3(4), 297-330; doi:10.3390/jsan3040297 - published 5 December 2014 Show/Hide Abstract
Abstract: This paper explores the network performance and costs associated with the deployment, labor, and maintenance of a long-term outdoor multi-hop wireless sensor network (WSN) located at the Audubon Society of Western Pennsylvania (ASWP), which has been in operation for more than four years for environmental data collection. The WSN performance is studied over selected time periods during the network deployment time, based on two different TinyOS-based WSN routing protocols: commercial XMesh and the open-source Collection Tree Protocol (CTP). Empirical results show that the network performance is improved with CTP (i.e., 79% packet reception rate, 96% packet success rate and 0.2% duplicate packets), versus using XMesh (i.e., 36% packet reception rate and 46% packet success rate, with 3%–4% duplicate packets). The deployment cost of the 52-node, 253-sensor WSN is $31,500 with an additional $600 per month in labor and maintenance resulting in a cost of $184 m−2·y−1 of sensed area. Network maintenance during the first four years of operation was performed on average every 12 days, costing approximately $187 for each field visit.
J. Sens. Actuator Netw.2014, 3(4), 274-296; doi:10.3390/jsan3040274 - published 4 November 2014 Show/Hide Abstract
Abstract: Network coding techniques are usually applied upon network-layer protocols to improve throughput in wireless networks. In scenarios with multiple unicast sessions, fairness is also an important factor. Therefore, a network coding-aware packet-scheduling algorithm is required. A packet-scheduling algorithm determines which packet to send next from a node’s packet backlog. Existing protocols mostly employ a basic round-robin scheduling algorithm to give “equal” opportunities to different packet flows. In fact, this “equal”-opportunity scheduling is neither fair, nor efficient. This paper intends to accentuate the importance of a coding-aware scheduling scheme. With a good scheduling scheme, we can gain more control over the per-flow throughput and fairness. Specifically, we first formulate a static scheduling problem and propose an algorithm to find the optimal scheduling scheme. We then extend the technique to a dynamic setting and, later, to practical routing protocols. Results show that the algorithm is comparatively scalable, and it can improve the throughput gain when the network is not severely saturated. The fairness among flows is drastically improved as a result of this scheduling scheme.
J. Sens. Actuator Netw.2014, 3(4), 245-273; doi:10.3390/jsan3040245 - published 14 October 2014 Show/Hide Abstract
Abstract: In this paper, a novel concept of coupling the actuators of an automated order picking system for pouch packed goods with an embedded CCD camera sensor by means of image processing and machine learning is presented. The picking system mechanically combines the conveyance and singularization of a still-connected chain of pouch packed goods in a single machinery. The proposed algorithms perform a per-frame processing of the captured images in real-time to detect the sealed seams of the ongoing pouches. The detections are used to deduce cutting decisions in order to control the system’s actuators, namely the drive pulley for conveyance and the cutting device for the separation. Within this context, two controlling strategies are presented as well which specify the interaction of the sensor and the actuators. The detection is carried out by two different marker detection strategies: enhanced Template Matching as a heuristic and Support Vector Machines as a supervised classification based concept. Depending on the employed marker, detection rates of almost 100% with a calculation time of less than 40 ms are possible. From a logistic point of view, sealed seam widths of 20 mm prove feasible.
J. Sens. Actuator Netw.2014, 3(3), 224-244; doi:10.3390/jsan3030224 - published 3 September 2014 Show/Hide Abstract
Abstract: Designing secure authentication mechanisms in wireless sensor networks in order to associate a node to a secure network is not an easy task due to the limitations of this type of networks. In this paper, we propose different multi-hop node authentication protocols for wireless sensor networks. For each protocol, we provide a formal proof to verify the security of our proposals using Scyther, which is an automatic cryptographic protocols verification tool. We also provide implementation results in terms of execution time consumption obtained by real measurements on TelosB motes. These protocols offer different security mechanisms depending on the design of the protocol itself. Moreover, we evaluate the overhead of protection of each solution by studying the effect on execution time overhead of each protocol. Finally, we propose a mechanism to detect possible attack based on our evaluation results.
J. Sens. Actuator Netw.2014, 3(3), 207-223; doi:10.3390/jsan3030207 - published 4 August 2014 Show/Hide Abstract
Abstract: Solar energy harvesting allows for wireless sensor networks to be operated over extended periods of time. In order to select an appropriate harvesting architecture and dimension for its components, an effective method for the comparison of system implementations is required. System simulations have the capability to accomplish this in an accurate and efficient manner. In this paper, we evaluate the existing work on solar energy harvesting architectures and common methods for their modeling. An analysis of the existing approaches demonstrates a mismatch between the requirement of the task to be both accurate and efficient and the proposed modeling methods, which are either accurate or efficient. As a result, we propose a data-driven modeling method based on artificial neural networks for further evaluation by the research community. Preliminary results of an initial investigation demonstrate the capability of this method to accurately capture the behavior of a solar energy harvesting architecture, while providing a time-efficient model generation procedure based on system-level data.