In this work, we present a method to streamline underwater object detection, environmental monitoring, and surveillance. The proposed system employs an underwater acoustic sensor (UAS) network to detect and analyses objects within a three-dimensional underwater space, considering constraints imposed by acoustic path loss. The approach combines three key techniques–Delaunay’s convex hull-based reconstruction, the laws of magnetic equilibrium, and the Doppler effect–to improve the identification of object type, shape, location, and motion. Sensors are arranged in an optimized grid, with each grid containing eight sensors and a central insulated magnetometer to measure net magnetic field intensity. Data collected by the sensors is transmitted to a surface anchor or sink node for further processing and analysis. A prototypical deployment in an artificial pond further validates our approach, using a waterproof ultrasonic sensor, an electromagnetic coil, and a magnetometer synchronized via Arduino Uno. Real-time measurements confirm accurate detection and tracking of objects, demonstrating the effectiveness and feasibility of the proposed edge-enabled underwater monitoring framework.

Wireless sensor networks (WSNs) are primarily used for real time data collection and monitoring, especially in environments where direct human involvement is challenging due to harsh conditions. Optimized deployment of WSN nodes is a long standing issue and several ideas have been proposed to address this. Existing deployment strategies are mostly based on the assumption that the terrain for deployment of nodes is perfectly regular. This is an impractical assumption and in this paper we address this gap by proposing a deployment strategy for WSN nodes over irregular terrains. Such terrains comprise uneven elevations, morphology and vegetation based obstacles, rocky obstacles, and so on. Our approach comprises extraction of satellite images of the region of interest (RoI) from Google Earth and generating a KML file (Keyhole Markup Language) for the RoI containing the latitude, longitude, and elevation values of each and every point in the RoI. These points are used to generate a contour map of the RoI containing detailed terrain morphology. A radio frequency path loss model in combination with an advanced inverse distance weighted (IDW)-interpolation technique is proposed to ensure connectivity and coverage in such irregular terrains with varying nature of obstacles. The technique effectively detects occlusions and enables effective deployment. This edge computing approach involves real-time decision-making at the network edge (the sensor nodes) leading to a deterministic deployment of motes in diverse terrain conditions with various obstacles. The approach is compared with existing deployment techniques and the results validate its efficacy. To demonstrate the practicality of our approach, we have also implemented a deployment in real-world environmental conditions, validating our approach in challenging terrains.

A Wireless Sensor Network (WSN) is a network of sensor nodes using low-power wireless technology to collect data in a region of interest (ROI). Due to their low energy, locating sensor nodes in large outdoor areas is challenging, which precludes GPS integration. WSNs typically comprise a small number of beacon nodes (BN) whose locations are known in advance, with most nodes deployed at unknown coordinates within the ROI. Endeavours to determine the locations of such unknown WSN nodes are largely based on the impractical assumption that every unknown node (UN) is within the communication range of BNs. Subsequently, these approaches utilise at least two BNs to determine the position of one UN. The Received Signal Strength Indicator (RSSI) or Angle of Arrival (AoA) values of the signals from the BNs form the basis for such localisation. This article suggests an iterative hybrid approach incorporating AoA and RSSI techniques, achieving accurate localisation with just one BN. The iterative method gradually covers the region, avoiding the unrealistic assumption of having all UNs within range. It also presents an innovative use of a unipolar stepper motor for AoA measurements. Experiments in a simulated environment and a real-world prototype validate the approach’s effectiveness.

A Wireless Sensor Network (WSN) comprises an ad-hoc network of nodes laden with sensors that are used to monitor a region mostly in the outdoors and often not easily accessible. Despite exceptions, several deployments of WSN continue to grapple with the limitation of finite energy derived through batteries. Thus, it is imperative that the energy of a WSN be conserved and its life prolonged. An important direction of work to this end is towards the transmission of data between nodes in a manner that minimum energy is expended. One approach to doing this is cluster-based routing, wherein nodes in a WSN are organised into clusters, and transmission of data from the node is through a representative node called a cluster-head. Forming optimal clusters and choosing an optimal cluster-head is an NP-Hard problem. Significant work is done towards devising mechanisms to form clusters and choosing cluster heads to reduce the transmission overhead to a minimum. In this article, an approach is proposed to create clusters and identify cluster heads that are near optimal. The approach involves two-stage clustering, with the clustering algorithm for each stage chosen through an exhaustive search. Furthermore, unlike existing approaches that choose a cluster-head on the basis of the residual energy of nodes, the proposed approach utilises three factors in addition to the residual energy, namely the distance of a node from the cluster centroid, the distance of a node from the final destination (base-station), and the connectivity of the node. The approach is shown to be effective and economical through extensive validation via simulations and through a real-world prototypical implementation.

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

Edge computing technology has gained popularity due to its ability to process data near the source or collection device, benefiting from low bandwidth utilization and enhanced security. Edge devices are typically equipped with multiple devices that employ asymmetric multi-cores for efficient data processing. To ensure optimal performance, it is crucial to carefully assign tasks to the appropriate cores in asymmetric multi-core processors. However, the current Linux scheduler needs to consider the capabilities of individual cores when assigning tasks. Consequently, high-priority tasks may be assigned to energy-efficient cores, while low-priority tasks end up on high-performance cores. This sub-optimal task assignment negatively impacts the overall system performance. To address this issue, a new algorithm has been proposed. This algorithm considers both the core’s capabilities and the task’s priority. However, due to the asymmetric nature of the cores, prior knowledge of each core’s speed is necessary. The algorithm fetches the priorities of the tasks and classifies them into high, medium, and low-priority categories. High-priority tasks are scheduled on high-performance cores, while medium and low-priority tasks are allocated to energy-efficient cores. The proposed algorithm demonstrates superior performance for high-priority tasks compared to the existing Linux task scheduling algorithm. It significantly improves task scheduling time by up to 16%, thereby enhancing the system’s overall efficiency.

The problem of space layout planning, constrained by a number of functional and non-functional requirements, not only challenges architects in coming up with a good solution, but is more difficult to give an alternative. Genetic algorithms (GAs) have been found suitable for solving the problem of providing alternative solutions. However, GAs have been found to be susceptible to the problem of local maxima and plateau conditions. To overcome these problems, the multi-population genetic algorithm (MPGA) improves the diversity of the population, thereby improving the quality of the solution. Algorithms are employed to automatically generate layout designs in best-connected ways, either rectangular or square. The area of the floor plans is optimized to minimize the extra area in the layout. The layouts are divided into four groups and these groups are related to each other based on highest proximity. Layout designs have been simulated using GA and MPGA algorithms and MPGA has shown significant improvement in computation time as well as quality over alternative solutions. In addition, the algorithm also provides the architect with the facility to interactively modify the dimensions and adjacent criteria during the design phase. The system works on clouds and shows the result for inputs passed by an architect.

Wireless Body Area Networks (WBANs) comprise a network of sensors subcutaneously implanted or placed near the body surface and facilitate continuous monitoring of health parameters of a patient. Research endeavours involving WBAN are directed towards effective transmission of detected parameters to a Local Processing Unit (LPU, usually a mobile device) and analysis of the parameters at the LPU or a back-end cloud. An important concern in WBAN is the lightweight nature of WBAN nodes and the need to conserve their energy. This is especially true for subcutaneously implanted nodes that cannot be recharged or regularly replaced. Work in energy conservation is mostly aimed at optimising the routing of signals to minimise energy expended. In this article, a simple yet innovative approach to energy conservation and detection of alarming health status is proposed. Energy conservation is ensured through a two-tier approach wherein the first tier eliminates “uninteresting” health parameter readings at the site of a sensing node and prevents these from being transmitted across the WBAN to the LPU. The second tier of assessment includes a proposed anomaly detection model at the LPU that is capable of identifying anomalies from streaming health parameter readings and indicates an adverse medical condition. In addition to being able to handle streaming data, the model works within the resource-constrained environments of an LPU and eliminates the need of transmitting the data to a back-end cloud, ensuring further energy savings. The anomaly detection capability of the model is validated using data available from the critical care units of hospitals and is shown to be superior to other anomaly detection techniques.

Residential fire is a proven hazard for human life and property. Vision based approaches for fire detection are superior to sensor based ones in terms of accuracy and alleviating false positives. Several frameworks that utilize vision-based monitoring in combination with convolutional neural network and other machine learning algorithms, such as support vector machine, K-mean clustering, logistic regression, neural network, and decision rules are available in literature for fire detection. While such frameworks are effective, they cannot be used in private spaces such as inside homes and offices as the privacy of occupants is compromised. In this article, a vision based fire detection framework for monitoring private spaces while preserving the privacy of the occupant is proposed. This is a novel endeavor as no other approach has looked at the issue of privacy preservation in fire detection with vision sensors. The framework utilizes a near infra-red camera to capture images in a manner that the privacy of occupants is preserved. To confirm that images captured with this camera do preserve occupants’ privacy, two random user surveys were conducted. For effective fire detection using these images, a novel system incorporating both spatial and temporal properties of fire is employed. Experiments were conducted and confirm the superiority of the proposed framework when compared with existing techniques in literature both in terms of performance and model size. In addition to this, the lightweight nature of the proposed system enables its effective use over resource-constrained environments as well. This is validated through a real-world prototypical implementation.

Internet of Things’ (IoT) deployments are increasingly dependent upon learning algorithms to analyse collected data, draw conclusions, and take decisions. The norm is to deploy such learning algorithms on the cloud and have IoT nodes interact with the cloud. While this is effective, it is rather wasteful in terms of energy expended and temporal latency. In this article, the endeavour is to develop a technique that facilitates classification, an important learning algorithm, within the extremely resource constrained environments of IoT nodes. The approach comprises selecting a small number of representative data points, called prototypes, from a large dataset and deploying these prototypes over IoT nodes. The prototypes are selected in a manner that they appropriately represent the complete dataset and are able to correctly classify new, incoming data. The novelty lies in the manner of prototype selection for a cluster that not only considers the location of datapoints of its own cluster but also that of datapoints in neighboring clusters. The efficacy of the approach is validated using standard datasets and compared with state-of-the-art classification techniques used in constrained environments. A real world deployment of the technique is done over an Arduino Uno-based IoT node and shown to be effective.

Power and energy optimization of applications running on a High Performance Computing infrastructure is an important research area in today's HPC driven world. Considerable amount of investments are being made for the same, emphasizing on the importance of such a research. The above mentioned optimization is a two step process. First, one must measure and analyze the power consumption accurately and then optimize the application by modifying the relevant sections of the application software responsible for it. In this paper we propose an API, called the Unified Power Profiling API (UPPAPI), which allows a parallel application developer to measure the power and energy consumed by the heterogeneous system during the application execution. The first important feature of this API is the abstraction created over different co-processors. Second, we have incorporated a corrective power model to remove the discrepancy arising out of the on-board GPU sensors. We illustrate the working of the above mentioned tool using Rodinia and SHOC Benchmarks. We further verify the correctness of the API generated measurement by comparing the results returned by the API with the actual power measured with a Krykard ALM10 power analyzer. This paper is an early attempt to develop a well rounded software tool, which enables the developer to optimize the resource usage and create efficient applications.

Numerous service providers in the cloud market provide diverse services, at distinct price and level of competence. However, the concept of cloud computing is yet to reach many organizations and to the end users as well. With perspective of these organizations and end users, it is very difficult to choose one among many service providers. Further, adhering to only one provider will deprive these users of benefits from market diversity and fluctuating rates. Instead, having a broker be aware of the provider's service offerings and consumer's requirements, and managing the assignment under economic and technical constraints will benefit both ends of the service viz consumers and providers. In this work, a cloud resource broker is proposed that will govern the assignment of providers' resources to consumer dynamically. The proposed broker uses various requirements and constraints specified by the consumer in the requirement description template as input, to calculate aggregated requirements, using an aggregation algorithm. Further, the service scheduling algorithm is defined to find an optimized match between the aggregated requirements with the providers offerings. Thereafter, this algorithm is executed frequently, based on a strategy for dynamic scheduling to benefit consumers on account of introduction of new provider or some good offerings. Results show that the solution provided by broker proves to be a win-win situation for the consumer with respect to cost as well as performance.

Web service composition is the art of combining multiple platform independent and modular pieces of software with varied configurations, to achieve an efficient solution for a complex business process. Though, web service composition has been an important area of research in the last decade, however the issue is expected to exacerbate in the 'Future Internet', as it is expected to house billions of services, with thousands offering the same functionality. In this context, maintaining an operational and solid set of Web service configurations will be a challenging task. Moreover, a centralized solution for Web service composition in the Future Internet scenario would be another issue. In this paper, we present a technique inspired from ElectroMagnetism in physics to create an environment which facilitates the selection of a service from a set of similar services. The proposed model achieves service composition in a decentralized environment without involving a centralized orchestrator. To validate the proposed technique in-silico experiments were conducted whose results demonstrate good performance in terms of completion time and load balancing. Further, the proposed technique is validated in-house (within our Institute's Intranet and with real users) by deploying real Web services on decentralized nodes. The results obtained via simulation are verified through a prototype based on the proposed model. We present, discuss and compare the effectiveness of the proposed work in the results section.

Mobile devices are evolving as a new computing platform and a common means to provide access and process digital information. In the move to achieve ubiquitous computing, the role of mobile devices and web services cannot be understated. Mobile devices are predominantly in use for accessing web services. The same mobile devices are now becoming a feasible option for small and medium size enterprises to host and provide personalized web services to clients. The motivation being the minimal infrastructure requirements and configuration costs. Technology today enables the provision of web services over hand-held mobile devices, realizing a web based service-oriented architecture in a mobile environment. For this, an efficient service discovery mechanism is required. It is difficult to adapt traditional approaches of managing web service directory for mobile environments, this is mainly due to the dynamic arrivals/departures of mobile devices in network zones. In this paper, we propose a model for web services in mobile environments to maintain a service directory using the XMPP (eXtensible Messaging and Presence Protocol). The proposed model enables mobile devices to manage web service directory without requiring high-end computers and high management cost. This paper presents proposed architecture, design concept, system components and workflow of the framework. Moreover, a comparative study of the proposed approach and the traditional UDDI (Universal Description, Discovery, and Integration) registry is presented.

Dynamic Service composition is the art of composing a composite application from a set of service offerings from various providers at run time. Service composition is a complex task and the problem is with the construction of hierarchies of service providers where each service provider processes only a part of the application. This is further exacerbated by the fact that for every part of the application there are several potential service providers. Choosing the most appropriate service from functionally equivalent services is the issue being addressed here. In this paper, we focus on the waiting time of a service request as the criterion for selection. We present a technique borrowed and customized from the concept of friction in physics to select the best potential candidate service from a set of functionally equivalent ones, so that the waiting time for each service request is minimized. The technique not only facilitates the selection of the most appropriate service but also minimizes the waiting time of service requests. The model is validated through simulation and is shown to be able to equitably balance the load among similar service offering nodes.

Selecting the optimal service from a set of functionally equivalent services is non-trivial. Previous research has addressed this issue making use of Quality of Service (QoS) attributes of the candidate services. In doing this, researchers have however assumed that the customers' preference of the various QoS attributes varies linearly with the actual attribute values. In this work, we put forward a technique that overcomes this restriction and compares functionally equivalent services on the basis of the customers' perception of the QoS attributes rather than the actual attribute values. We utilize the 'mid-level splitting' method to track the customer's preference vis-a-vis the actual attribute values. Further, we utilize the 'Hypothetical Equivalents and In equivalents Method' to assign weights, reflecting the importance, to the attributes on the basis of the customer preference. The whole procedure is demonstrated using a simple running example.

Feature space conversion for classifiers is the process by which the data that is to be fed into the classifier is transformed from one form to another. The motivation behind doing this is to enhance the "discriminative power" of the data together with preserving its "information content". In this paper, a new method of feature space conversion is explored, wherein "enrichment" of the feature space is carried out by the augmentation of the existing features with new "implicit" features. The modus operandi involves generation of association rules in one case and closed frequent patterns in another and the extraction of the new features from these. This new feature space is first made use of independently to feed the classifier and then it is used in unison with the original feature space. The effectiveness of these methods is subsequently verified experimentally and expressed in terms of the classification accuracy achieved by the classifier.