Resource Optimization in Wireless Sensor Networks for an Improved Field Coverage and Cooperative Target Tracking

There are various challenges that face a wireless sensor network (WSN) that mainly originate from the limited resources a sensor node usually has. A sensor node often relies on a battery as a power supply which, due to its limited capacity, tends to shorten the life-time of the node and the network as a whole. Other challenges arise from the limited capabilities of the sensors/actuators a node is equipped with, leading to complication like a poor coverage of the event, or limited mobility in the environment. This dissertation deals with the coverage problem as well as the limited power and capabilities of a sensor node. In some environments, a controlled deployment of the WSN may not be attainable. In such case, the only viable option would be a random deployment over the region of interest (ROI), leading to a great deal of uncovered areas as well as many cutoff nodes. Three different scenarios are presented, each addressing the coverage problem for a distinct purpose. First, a multi-objective optimization is considered with the purpose of relocating the sensor nodes after the initial random deployment, through maximizing the field coverage while minimizing the cost of mobility. Simulations reveal the improvements in coverage, while maintaining the mobility cost to a minimum. In the second scenario, tracking a mobile target with a high level of accuracy is of interest. The relocation process was based on learning the spatial mobility trends of the targets. Results show the improvement in tracking accuracy in terms of mean square position error. The last scenario involves the use of inverse reinforcement learning (IRL) to predict the destination of a given target. This lay the ground for future exploration of the relocation problem to achieve improved prediction accuracy. Experiments investigated the interaction between prediction accuracy and terrain severity. The other WSN limitation is dealt with by introducing the concept of sparse sensing to schedule the measurements of sensor nodes. A hybrid WSN setup of low and high precision nodes is examined. Simulations showed that the greedy algorithm used for scheduling the nodes, realized a network that is more resilient to individual node failure. Moreover, the use of more affordable nodes stroke a better trade-off between deployment feasibility and precision

Off-line handwritten signature recognition by wavelet entropy and neural network

Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%

Sensor relocation for improved target tracking

In many practical scenarios, targets tend to have certain mobility trends such as following a traverseable terrain, having a common starting/destination locations, or moving in a region with abundant resources. This work is interested in exploring the possible gain from sensor relocation in improving the localisation accuracy of targets that follow mobility trends similar to those previously observed. This objective is tackled using a three-phase approach. In the first phase, the wireless sensor network tracks the targets based on the initial deployment. The second phase uses the location estimates from phase 1 to form a region of interest (ROI). The last phase carries out the sensor relocation to the ROI. Two fitness functions are explored for optimising sensors\u27 locations in the ROI, namely geometric dilution of precision and K-coverage. K-coverage offered the best performance especially for sensors with a short-to-medium detection range. The uniform random relocation offered a comparable performance with a relatively low computational complexity. Results also revealed the degradation in coverage rate due to relocating sensors to the ROI, and how optimising sensor locations outside the ROI can help in mending coverage holes

Coverage optimization in a terrain-aware wireless sensor network

© 2016 IEEE. In hostile environments, random deployment of a Wireless ¡Sensor Network (WSN) may be the only viable approach. However, this leads to coverage holes in the Region of Interest (ROI) of the network, which degrades the WSN\u27s quality of service. Hence, there is a need for an algorithm that relocates the sensing nodes to maximize the coverage while minimizing the mobility cost. The cost of mobility is directly related to the traveled distance and the severity of the terrain. Since this problem is NP-complete, this work examines several evolutionary computation techniques in search for an optimal solution. Three algorithms are used to examine this problem: the Artificial Immune System (AIS) algorithm, the Normalized Genetic Algorithm (NGA) and the Particle Swarm Optimization (PSO) algorithm. Multiple experiments are carried out to assess the performance of the utilized algorithms, where depending on the scenario adopted for simulations, some algorithms perform better than the others. In the case where the execution time is not a critical issue, the AIS and NGA algorithms outperform the PSO algorithm in terms of coverage rate and mobility cost, especially for a lower count of sensors